571

This chapter should be cited as:

Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U.

Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and Aerosols. In: Climate

Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung,

A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and

New York, NY, USA.

Coordinating Lead Authors:

Olivier Boucher (France), David Randall (USA)

Lead Authors:

Paulo Artaxo (Brazil), Christopher Bretherton (USA), Graham Feingold (USA), Piers Forster

(UK), Veli-Matti Kerminen (Finland), Yutaka Kondo (Japan), Hong Liao (China), Ulrike Lohmann

(Switzerland), Philip Rasch (USA), S.K. Satheesh (India), Steven Sherwood (Australia), Bjorn

Stevens (Germany), Xiao-Ye Zhang (China)

Contributing Authors:

Govindasamy Bala (India), Nicolas Bellouin (UK), Angela Benedetti (UK), Sandrine Bony (France),

Ken Caldeira (USA), Anthony Del Genio (USA), Maria Cristina Facchini (Italy), Mark Flanner

(USA), Steven Ghan (USA), Claire Granier (France), Corinna Hoose (Germany), Andy Jones (UK),

Makoto Koike (Japan), Ben Kravitz (USA), Benjamin Laken (Spain), Matthew Lebsock (USA),

Natalie Mahowald (USA), Gunnar Myhre (Norway), Colin O’Dowd (Ireland), Alan Robock

(USA), Bjørn Samset (Norway), Hauke Schmidt (Germany), Michael Schulz (Norway), Graeme

Stephens (USA), Philip Stier (UK), Trude Storelvmo (USA), Dave Winker (USA), Matthew Wyant

(USA)

Review Editors:

Sandro Fuzzi (Italy), Joyce Penner (USA), Venkatachalam Ramaswamy (USA), Claudia

Stubenrauch (France)

Clouds and Aerosols

572

Table of Contents

Executive Summary ..................................................................... 573

7.1 Introduction ...................................................................... 576

7.1.1 Clouds and Aerosols in the Atmosphere .................... 576

7.1.2 Rationale for Assessing Clouds, Aerosols and

Their Interactions ...................................................... 576

7.1.3 Forcing, Rapid Adjustments and Feedbacks............... 576

7.1.4 Chapter Roadmap ..................................................... 578

7.2 Clouds ................................................................................. 578

7.2.1 Clouds in the Present-Day Climate System................ 578

7.2.2 Cloud Process Modelling ........................................... 582

7.2.3 Parameterization of Clouds in Climate Models ......... 584

7.2.4 Water Vapour and Lapse Rate Feedbacks .................. 586

7.2.5 Cloud Feedbacks and Rapid Adjustments to

Carbon Dioxide ......................................................... 587

7.2.6 Feedback Synthesis ................................................... 591

7.2.7 Anthropogenic Sources of Moisture

and Cloudiness .......................................................... 592

7.3 Aerosols ............................................................................. 595

7.3.1 Aerosols in the Present-Day Climate System ............. 595

7.3.2 Aerosol Sources and Processes ................................. 599

7.3.3 Progress and Gaps in Understanding Climate

Relevant Aerosol Properties ...................................... 602

7.3.4 Aerosol–Radiation Interactions ................................. 604

7.3.5 Aerosol Responses to Climate Change

and Feedback ............................................................ 605

7.4 Aerosol–Cloud Interactions ......................................... 606

7.4.1 Introduction and Overview of Progress Since AR4 .... 606

7.4.2 Microphysical Underpinnings of Aerosol–Cloud

Interactions ............................................................... 609

7.4.3 Forcing Associated with Adjustments in

Liquid Clouds ............................................................ 609

7.4.4 Adjustments in Cold Clouds ...................................... 611

7.4.5 Synthesis on Aerosol–Cloud Interactions .................. 612

7.4.6 Impact of Cosmic Rays on Aerosols and Clouds ........ 613

7.5 Radiative Forcing and Effective Radiative

Forcing by Anthropogenic Aerosols ........................... 614

7.5.1 Introduction and Summary of AR4 ............................ 614

7.5.2 Estimates of Radiative Forcing and Effective Radiative

Forcing from Aerosol–Radiation Interactions ............ 614

7.5.3 Estimate of Effective Radiative Forcing from

Combined Aerosol–Radiation and Aerosol–Cloud

Interactions ............................................................... 618

7.5.4 Estimate of Effective Radiative Forcing from Aerosol–

Cloud Interactions Alone ........................................... 620

7.6 Processes Underlying Precipitation Changes ......... 624

7.6.1 Introduction .............................................................. 624

7.6.2 The Effects of Global Warming on Large-Scale

Precipitation Trends ................................................... 624

7.6.3 Radiative Forcing of the Hydrological Cycle .............. 624

7.6.4 Effects of Aerosol–Cloud Interactions on

Precipitation .............................................................. 625

7.6.5 The Physical Basis for Changes in

Precipitation Extremes .............................................. 626

7.7 Solar Radiation Management and Related

Methods ............................................................................. 627

7.7.1 Introduction .............................................................. 627

7.7.2 Assessment of Proposed Solar Radiation

Management Methods .............................................. 627

7.7.3 Climate Response to Solar Radiation

Management Methods .............................................. 629

7.7.4 Synthesis on Solar Radiation Management

Methods .................................................................... 635

References .................................................................................. 636

Frequently Asked Questions

FAQ 7.1 How Do Clouds Affect Climate and Climate

Change? ................................................................... 593

FAQ 7.2 How Do Aerosols Affect Climate and Climate

Change? ................................................................... 622

FAQ 7.3 Could Geoengineering Counteract Climate

Change and What Side Effects Might Occur? ..... 632

Supplementary Material

Supplementary Material is available in online versions of the report.

573

Clouds and Aerosols Chapter 7

In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.

A level of conﬁdence is expressed using ﬁve qualiﬁers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium conﬁdence. For a given evidence and

agreement statement, different conﬁdence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing conﬁdence (see

Section 1.4 and Box TS.1 for more details).

Executive Summary

Clouds and aerosols continue to contribute the largest uncertainty to

estimates and interpretations of the Earth’s changing energy budget.

This chapter focuses on process understanding and considers observa-

tions, theory and models to assess how clouds and aerosols contribute

and respond to climate change. The following conclusions are drawn.

Progress in Understanding

Many of the cloudiness and humidity changes simulated

by climate models in warmer climates are now understood

as responses to large-scale circulation changes that do not

appear to depend strongly on sub-grid scale model processes,

increasingconﬁdence in these changes.For example,multiplelines

of evidence now indicate positivefeedback contributions from circula-

tion-driven changes in both the height of high clouds and thelatitudi-

nal distribution of clouds (medium to high conﬁdence

).However, some

aspects of the overall cloud response vary substantially among models,

and these appear to depend strongly on sub-grid scale processes in

which there is less conﬁdence. {7.2.4, 7.2.5, 7.2.6, Figure 7.11}

Climate-relevant aerosol processes are better understood, and

climate-relevant aerosol properties better observed, than at the

time of AR4. However, the representation of relevant processes varies

greatly in global aerosol and climate models and it remains unclear

what level of sophistication is required to model their effect on climate.

Globally, between 20 and 40% of aerosol optical depth (medium conﬁ-

dence) and between one quarter and two thirds of cloud condensation

nucleus concentrations (low conﬁdence) are of anthropogenic origin.

{7.3, Figures 7.12 to 7.15}

Cosmic rays enhance new particle formation in the free tropo-

sphere, but the effect on the concentration of cloud condensa-

tion nuclei is too weak to have any detectable climatic inﬂuence

during a solar cycle or over the last century (medium evidence,

high agreement). No robust association between changes in cosmic

rays and cloudiness has been identiﬁed. In the event that such an asso-

ciation existed, a mechanism other than cosmic ray-induced nucleation

of new aerosol particles would be needed to explain it. {7.4.6}

Recent research has clariﬁed the importance of distinguishing

forcing (instantaneous change in the radiative budget) and

rapid adjustments (which modify the radiative budget indirectly

through fast atmospheric and surface changes) from feedbacks

(which operate through changes in climate variables that are

mediated by a change in surface temperature). Furthermore, one

can distinguish between the traditional concept of radiative forcing

(RF) and the relatively new concept of effective radiative forcing (ERF)

that also includes rapid adjustments. For aerosols one can further dis-

tinguish forcing processes arising from aerosol–radiation interactions

(ari) and aerosol–cloud interactions (aci). {7.1, Figures 7.1 to 7.3}

The quantiﬁcation of cloud and convective effects in models,

and of aerosol–cloud interactions, continues to be a challenge.

Climate models are incorporating more of the relevant process-

es than at the time of AR4, but conﬁdence in the representation

of these processes remains weak.Cloud and aerosol properties vary

at scales signiﬁcantly smaller than those resolved in climate models,

and cloud-scale processes respond to aerosol in nuanced ways at these

scales. Until sub-grid scale parameterizations of clouds and aerosol–

cloud interactions are able to address these issues, modelestimates of

aerosol–cloud interactions and their radiative effectswill carry large

uncertainties. Satellite-based estimates of aerosol–cloud interactions

remain sensitive to the treatment of meteorological inﬂuences on

clouds and assumptions on what constitutes pre-industrial conditions.

{7.3, 7.4, 7.5.3, 7.5.4, 7.6.4, Figures 7.8, 7.12, 7.16}

Precipitation andevaporation are expected to increase on aver-

age in a warmer climate, but also undergo global and regional

adjustments to carbon dioxide (CO

) and other forcings that

differ from their warming responses. Moreover, there is high

conﬁdence that, as climate warms, extreme precipitation rates

on for example, daily time scales will increase faster than the

time average.Changesin average precipitation must remain consis-

tent with changesinthe net rate of cooling of the troposphere, which

is affected by its temperature but also by greenhouse gases (GHGs)

and aerosols. Consequently, while the increase in global mean pre-

cipitation would be 1.5 to 3.5% °C

–1

due to surface temperature

change alone, warming caused by CO

or absorbing aerosols results

in a smaller sensitivity, even more so if it is partially offset by albedo

increases. The complexity of land surface and atmospheric process-

es limits conﬁdence in regional projectionsof precipitation change,

especially over land, although there is a component of a ‘wet-get-wet-

ter’ and ‘dry-get-drier’ response over oceans at the large scale. Chang-

es inlocalextremes on daily and sub-daily time scales are strongly

inﬂuenced by lower-tropospheric water vapour concentrations,and on

average will increase by roughly 5 to 10% per degree Celsius of warm-

ing (medium conﬁdence).Aerosol–cloudinteractions can inﬂuence the

character of individual storms, butevidence fora systematic aerosol

effect on storm or precipitation intensity ismore limited and ambigu-

ous. {7.2.4,7.4, 7.6, Figures 7.20, 7.21}

574

Chapter 7 Clouds and Aerosols

Water Vapour, Cloud and Aerosol Feedbacks

The net feedback from water vapour and lapse rate changes

combined, as traditionally deﬁned, is extremely likely

positive

(amplifying global climate changes). The sign of the net radia-

tive feedback due to all cloud types is less certain but likely

positive. Uncertainty in the sign and magnitude of the cloud

feedback is due primarily to continuing uncertainty in the

impact of warming on low clouds. We estimate the water vapour

plus lapse rate feedback

to be +1.1 (+0.9 to +1.3) W m

−2

°C

−1

and

the cloud feedback from all cloud types to be +0.6 (−0.2 to +2.0) W

–2

°C

–1

. These ranges are broader than those of climate models to

account for additional uncertainty associated with processes that may

not have been accounted for in those models. The mean values and

ranges in climate models are essentially unchanged since AR4, but are

now supported by stronger indirect observational evidence and better

process understanding, especially for water vapour. Low clouds con-

tribute positive feedback in most models, but that behaviour is not well

understood, nor effectively constrained by observations, so we are not

conﬁdent that it is realistic. {7.2.4, 7.2.5, 7.2.6, Figures 7.9 to 7.11}.

Aerosol–climate feedbacks occur mainly through changes in the

source strength of natural aerosols or changes in the sink efﬁ-

ciency of natural and anthropogenic aerosols; a limited number

of modelling studies have bracketed the feedback parameter

within ±0.2 W m

–2

°C

–1

with low conﬁdence. There is medium con-

ﬁdence for a weak dimethylsulphide–cloud condensation nuclei–cloud

albedo feedback due to a weak sensitivity of cloud condensation nuclei

population to changes in dimethylsulphide emissions. {7.3.5}

Quantiﬁcation of climate forcings

due to aerosols

and clouds

The ERF due to aerosol–radiation interactions that takes rapid

adjustments into account (ERFari) is assessed to be –0.45 (–0.95

to +0.05) W m

–2

. The RF from absorbing aerosol on snow and

ice is assessed separately to be +0.04 (+0.02 to +0.09) W m

–2

Prior to adjustments taking place, the RF due to aerosol–radiation

interactions (RFari) is assessed to be –0.35 (–0.85 to +0.15) W m

–2

. The

assessment for RFari is less negative than reported in AR4 because of a

re-evaluation of aerosol absorption. The uncertainty estimate is wider

but more robust, based on multiple lines of evidence from models,

remotely sensed data, and ground-based measurements. Fossil fuel

and biofuel emissions

contribute to RFari via sulphate aerosol: –0.4

(–0.6 to –0.2) W m

–2

, black carbon (BC) aerosol: +0.4 (+0.05 to +0.8)

W m

–2

, and primary and secondary organic aerosol: –0.12 (–0.4 to

+0.1) W m

–2

. Additional RFari contributions occur via biomass burning

In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,

Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely

than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1

for more details).

This and all subsequent ranges given with this format are 90% uncertainty ranges unless otherwise speciﬁed.

All climate forcings (RFs and ERFs) are anthropogenic and relate to the period 1750–2010 unless otherwise speciﬁed.

This species breakdown is less certain than the total RFari and does not sum to the total exactly.

emissions

: +0.0 (–0.2 to +0.2) W m

–2

, nitrate aerosol: –0.11 (–0.3 to

–0.03) W m

–2

, and mineral dust: –0.1 (–0.3 to +0.1) W m

–2

although

the latter may not be entirely of anthropogenic origin. While there

is robust evidence for the existence of rapid adjustment of clouds in

response to aerosol absorption, these effects are multiple and not well

represented in climate models, leading to large uncertainty. Unlike in

the last IPCC assessment, the RF from BC on snow and ice includes the

effects on sea ice, accounts for more physical processes and incorpo-

rates evidence from both models and observations. This RF has a 2 to 4

times larger global mean surface temperature change per unit forcing

than a change in CO

. {7.3.4, 7.5.2, Figures 7.17, 7.18}

The total ERF due to aerosols (ERFari+aci, excluding the effect

of absorbing aerosol on snow and ice) is assessed to be –0.9

(–1.9 to –0.1) W m

–2

with medium conﬁdence. The ERFari+aci esti-

mate includes rapid adjustments, such as changes to the cloud lifetime

and aerosol microphysical effects on mixed-phase, ice and convective

clouds. This range was obtained from expert judgement guided by cli-

mate models that include aerosol effects on mixed-phase and convec-

tive clouds in addition to liquid clouds, satellite studies and models

that allow cloud-scale responses. This forcing can be much larger

regionally but the global mean value is consistent with several new

lines of evidence suggesting less negative estimates for the ERF due to

aerosol–cloud interactions than in AR4. {7.4, 7.5.3, 7.5.4, Figure 7.19}

Persistent contrails from aviation contribute a RF of +0.01

(+0.005 to +0.03) W m

–2

for year 2011, and the combined con-

trail and contrail-cirrus ERF from aviation is assessed to be

+0.05 (+0.02 to +0.15) W m

–2

. This forcing can be much larger

regionally but there is now medium conﬁdence that it does not pro-

duce observable regional effects on either the mean or diurnal range

of surface temperature. {7.2.7}

Geoengineering Using Solar Radiation Management

Methods

Theory, model studies and observations suggest that some Solar

Radiation Management (SRM) methods, if practicable, could sub-

stantially offset a global temperature rise and partially offset

some other impacts of global warming, but the compensation

for the climate change caused by GHGs would be imprecise

(high conﬁdence). SRM methods are unimplemented and untested.

Research on SRM is in its infancy, though it leverages understanding

of how the climate responds to forcing more generally. The efﬁcacy of

a number of SRM strategies was assessed, and there is medium con-

ﬁdence that stratospheric aerosol SRM is scalable to counter the RF

from increasing GHGs at least up to approximately 4 W m

–2

; however,

575

Clouds and Aerosols Chapter 7

the required injection rate of aerosol precursors remains very uncertain.

There is no consensus on whether a similarly large RF could be achieved

from cloud brightening SRM owing to uncertainties in understanding

and representation of aerosol–cloud interactions. It does not appear

that land albedo change SRM can produce a large RF. Limited literature

on other SRM methods precludes their assessment. Models consistently

suggest that SRM would generally reduce climate differences compared

to a world with elevated GHG concentrations and no SRM; however,

there would also be residual regional differences in climate (e.g., tem-

perature and rainfall) when compared to a climate without elevated

GHGs. {7.4.3, 7.7}

Numerous side effects, risks and shortcomings from SRM have

beenidentiﬁed.Several lines of evidenceindicatethat SRM would

produce a small but signiﬁcant decrease in globalprecipitation (with

larger differences on regional scales) if the global surface tempera-

turewere maintained.A number of side effects have been identiﬁed.

One that is relatively well characterized is the likelihood of modest

polar stratospheric ozone depletion associated with stratospheric

aerosol SRM. There could also be other as yet unanticipated conse-

quences. As long as GHG concentrations continued to increase, the

SRM would require commensurate increase, exacerbating side effects.

In addition, scaling SRM to substantial levels would carry the risk that

if the SRM were terminated for any reason, there is high conﬁdence

that surface temperatures wouldincrease rapidly(within a decade or

two)to values consistent with the GHGforcing, which would stress

systems sensitiveto the rate of climatechange.Finally,SRM would not

compensate for ocean acidiﬁcation from increasing CO

. {7.6.3, 7.7,

Figures 7.22 to 7.24}

576

Chapter 7 Clouds and Aerosols

7.1 Introduction

7.1.1 Clouds and Aerosols in the Atmosphere

The atmosphere is composed mostly of gases, but also contains liquid

and solid matter in the form of particles. It is usual to distinguish these

particles according to their size, chemical composition, water content

and fall velocity into atmospheric aerosol particles, cloud particles and

falling hydrometeors. Despite their small mass or volume fraction,

particles in the atmosphere strongly inﬂuence the transfer of radi-

ant energy and the spatial distribution of latent heating through the

atmosphere, thereby inﬂuencing the weather and climate.

Cloud formation usually takes place in rising air, which expands and

cools, thus permitting the activation of aerosol particles into cloud

droplets and ice crystals in supersaturated air. Cloud particles are gen-

erally larger than aerosol particles and composed mostly of liquid water

or ice. The evolution of a cloud is governed by the balance between

a number of dynamical, radiative and microphysical processes. Cloud

particles of sufﬁcient size become falling hydrometeors, which are cat-

egorized as drizzle drops, raindrops, snow crystals, graupel and hail-

stones. Precipitation is an important and complex climate variable that

is inﬂuenced by the distribution of moisture and cloudiness, and to a

lesser extent by the concentrations and properties of aerosol particles.

Aerosol particles interact with solar radiation through absorption and

scattering and, to a lesser extent with terrestrial radiation through

absorption, scattering and emission. Aerosols

can serve as cloud

condensation nuclei (CCN) and ice nuclei (IN) upon which cloud drop-

lets and ice crystals form. They also play a wider role in atmospheric

chemistry and biogeochemical cycles in the Earth system, for instance,

by carrying nutrients to ocean ecosystems. They can be of natural or

anthropogenic origin.

Cloud and aerosol amounts

and properties are extremely variable in

space and time. The short lifetime of cloud particles in subsaturated air

creates relatively sharp cloud edges and ﬁne-scale variations in cloud

properties, which is less typical of aerosol layers. While the distinction

between aerosols and clouds is generally appropriate and useful, it is

not always unambiguous, which can cause interpretational difﬁculties

(e.g., Charlson et al., 2007; Koren et al., 2007).

7.1.2 Rationale for Assessing Clouds, Aerosols and

Their Interactions

The representation of cloud processes in climate models has been rec-

ognized for decades as a dominant source of uncertainty in our under-

standing of changes in the climate system (e.g., Arakawa, 1975, 2004;

Charney et al., 1979; Cess et al., 1989; Randall et al., 2003; Bony et al.,

2006), but has never been systematically assessed by the IPCC before.

Clouds respond to climate forcing mechanisms in multiple ways, and

For convenience the term ‘aerosol’, which includes both the particles and the suspending gas, is often used in its plural form to mean ‘aerosol particles’ both in this chapter and

the rest of this Report.

In this chapter, we use ‘cloud amount’ as an inexact term to refer to the quantity of clouds, both in the horizontal and vertical directions. The term ‘cloud cover’ is used in its

usual sense and refers to the horizontal cloud cover.

inter-model differences in cloud feedbacks constitute by far the prima-

ry source of spread of both equilibrium and transient climate responses

simulated by climate models (Dufresne and Bony, 2008) despite the

fact that most models agree that the feedback is positive (Randall et

al., 2007; Section 7.2). Thus conﬁdence in climate projections requires a

thorough assessment of how cloud processes have been accounted for.

Aerosols of anthropogenic origin are responsible for a radiative forcing

(RF) of climate change through their interaction with radiation, and

also as a result of their interaction with clouds. Quantiﬁcation of this

forcing is fraught with uncertainties (Haywood and Boucher, 2000;

Lohmann and Feichter, 2005) and aerosols dominate the uncertain-

ty in the total anthropogenic RF (Forster et al., 2007; Haywood and

Schulz, 2007; Chapter 8). Furthermore, our inability to better quantify

non-greenhouse gas RFs, and primarily those that result from aerosol–

cloud interactions, underlie difﬁculties in constraining climate sensitiv-

ity from observations even if we had a perfect knowledge of the tem-

perature record (Andreae et al., 2005). Thus a complete understanding

of past and future climate change requires a thorough assessment of

aerosol–cloud–radiation interactions.

7.1.3 Forcing, Rapid Adjustments and Feedbacks

Figure 7.1 illustrates key aspects of how clouds and aerosols contribute

to climate change, and provides an overview of important terminolog-

ical distinctions. Forcings associated with agents such as greenhouse

gases (GHGs) and aerosols act on global mean surface temperature

through the global radiative (energy) budget. Rapid adjustments

(sometimes called rapid responses) arise when forcing agents, by alter-

ing ﬂows of energy internal to the system, affect cloud cover or other

components of the climate system and thereby alter the global budget

indirectly. Because these adjustments do not operate through changes

in the global mean surface temperature (DT), which are slowed by the

massive heat capacity of the oceans, they are generally rapid and most

are thought to occur within a few weeks. Feedbacks are associated

with changes in climate variables that are mediated by a change in

global mean surface temperature; they contribute to amplify or damp

global temperature changes via their impact on the radiative budget.

In this report, following an emerging consensus in the literature, the

traditional concept of radiative forcing (RF, deﬁned as the instanta-

neous radiative forcing with stratospheric adjustment only) is de-em-

phasized in favour of an absolute measure of the radiative effects of

all responses triggered by the forcing agent that are independent of

surface temperature change (see also Section 8.1). This new measure

of the forcing includes rapid adjustments and the net forcing with

these adjustments included is termed the effective radiative forcing

(ERF). The climate sensitivity to ERF will differ somewhat from tradi-

tional equilibrium climate sensitivity, as the latter include adjustment

effects. As shown in Figure 7.1, adjustments can occur through geo-

graphic temperature variations, lapse rate changes, cloud changes

577

Clouds and Aerosols Chapter 7

Aerosol–Radiation

Interactions (ari)

Aerosol–Cloud

Interactions (aci)

Effective Radiative

Forcing (ERF) and

Feedbacks

Aerosols

Clouds and

Precipitation

Radiation

Radiative Forcing

Anthropogenic

Sources

Global Surface

Temperature

Adjustments

Aerosol Feedbacks

Cloud Feedbacks

Other Feedbacks

Moisture and Winds

Temperature Profile

Regional Variability

Biosphere

Additional state

variables

Greenhouse

Gases

Figure 7.1 | Overview of forcing and feedback pathways involving greenhouse gases, aerosols and clouds. Forcing agents are in the green and dark blue boxes, with forcing

mechanisms indicated by the straight green and dark blue arrows. The forcing is modiﬁed by rapid adjustments whose pathways are independent of changes in the globally aver-

aged surface temperature and are denoted by brown dashed arrows. Feedback loops, which are ultimately rooted in changes ensuing from changes in the surface temperature,

are represented by curving arrows (blue denotes cloud feedbacks; green denotes aerosol feedbacks; and orange denotes other feedback loops such as those involving the lapse

rate, water vapour and surface albedo). The ﬁnal temperature response depends on the effective radiative forcing (ERF) that is felt by the system, that is, after accounting for rapid

adjustments, and the feedbacks.

and vegetation effects. Measures of ERF and rapid adjustments have

existed in the literature for more than a decade, with a number of

different terminologies and calculation methods adopted. These were

principally aimed to help quantify the effects of aerosols on clouds

(Rotstayn and Penner, 2001; Lohmann et al., 2010) and understand

different forcing agent responses (Hansen et al., 2005), but it is now

realized that there are rapid adjustments in response to the CO

forcing

itself (Section 7.2.5.6).

In principle rapid adjustments are independent of DT, while feedbacks

operate purely through DT. Thus, within this framework adjustments

are not another type of ‘feedback’ but rather a non-feedback phenom-

enon, required in the analysis by the fact that a single scalar DT cannot

fully characterize the system. This framework brings most efﬁcacies

close to unity although they are not necessarily exactly 1 (Hansen et al.,

2005; Bond et al., 2013). There is also no clean separation in time scale

between rapid adjustments and warming. Although the former occur

mostly within a few days of applying a forcing (Dong et al., 2009),

some adjustments such as those that occur within the stratosphere

and snowpack can take several months or longer. Meanwhile the land

surface warms quickly so that a small part of DT occurs within days to

weeks of an applied forcing. This makes the two phenomena difﬁcult to

isolate in model runs. Other drawbacks are that adjustments are difﬁ-

cult to observe, and typically more model-dependent than RF. However,

recent work is beginning to meet the challenges of quantifying the

adjustments, and has noted advantages of the new framework (e.g.,

Vial et al., 2013; Zelinka et al., 2013).

There is no perfect method to determine ERF. Two common meth-

ods are to regress the net energy imbalance onto DT in a transient

Figure 7.2 | Radiative forcing (RF) and effective radiative forcing (ERF) estimates

derived by two methods, for the example of 4 × CO

experiments in one climate model.

N is the net energy imbalance at the top of the atmosphere and DT the global mean

surface temperature change. The ﬁxed sea surface temperature ERF estimate is from an

atmosphere–land model averaged over 30 years. The regression estimate is from 150

years of a coupled model simulation after an instantaneous quadrupling of CO

, with

the N from individual years in this regression shown as black diamonds. The strato-

spherically adjusted RF is the tropopause energy imbalance from otherwise identical

radiation calculations at 1 × and 4 × CO

concentrations. (Figure follows Andrews et al.,

2012.) See also Figure 8.1.

ERF –ﬁxed sea surface temperature

ERF– regression

RF –stratospherically adjusted

N (W m

-2

)

ΔT (ºC)

0 123456

-2

578

Chapter 7 Clouds and Aerosols

warming simulation (Gregory et al., 2004; Figure 7.2), or to simulate

the climate response with sea surface temperatures (SSTs) held ﬁxed

(Hansen et al., 2005). The former can be complicated by natural var-

iability or time-varying feedbacks, while the non-zero DT from land

warming complicates the latter. Both methods are used in this chapter.

Figure 7.3 links the former terminology of aerosol direct, semi-direct

and indirect effects with the new terminology used in this chapter and

in Chapter 8. The RF from aerosol–radiation interactions (abbreviat-

ed RFari) encompasses radiative effects from anthropogenic aerosols

before any adjustment takes place and corresponds to what is usually

referred to as the aerosol direct effect. Rapid adjustments induced by

aerosol radiative effects on the surface energy budget, the atmospheric

proﬁle and cloudiness contribute to the ERF from aerosol–radiation

interactions (abbreviated ERFari). They include what has earlier been

referred to as the semi-direct effect. The RF from aerosol–cloud inter-

actions (abbreviated RFaci) refers to the instantaneous effect on cloud

albedo due to changing concentrations of cloud condensation and ice

nuclei, also known as the Twomey effect. All subsequent changes to

the cloud lifetime and thermodynamics are rapid adjustments, which

contribute to the ERF from aerosol–cloud interactions (abbreviated

ERFaci). RFaci is a theoretical construct that is not easy to separate

from other aerosol–cloud interactions and is therefore not quantiﬁed

in this chapter.

7.1.4 Chapter Roadmap

For the ﬁrst time in the IPCC WGI assessment reports, clouds and aer-

osols are discussed together in a single chapter. Doing so allows us

to assess, and place in context, recent developments in a large and

growing area of climate change research. In addition to assessing

cloud feedbacks and aerosol forcings, which were covered in previ-

ous assessment reports in a less uniﬁed manner, it becomes possible

to assess understanding of the multiple interactions among aerosols,

Direct Effect Semi-Direct Effects Lifetime (including glaciation

& thermodynamic) Effects

Cloud Albedo Effect

Radiative Forcing (RFari) Adjustments

Effective Radiative Forcing (ERFari)

AR4

AR5

Irradiance Changes from

Aerosol-Radiation Interactions (ari)

Adjustments

Effective Radiative Forcing (ERFaci)

Radiative Forcing (RFaci)

Irradiance Changes from

Aerosol-Cloud Interactions (aci)

Figure 7.3 | Schematic of the new terminology used in this Assessment Report (AR5) for aerosol–radiation and aerosol–cloud interactions and how they relate to the terminology

used in AR4. The blue arrows depict solar radiation, the grey arrows terrestrial radiation and the brown arrow symbolizes the importance of couplings between the surface and the

cloud layer for rapid adjustments. See text for further details.

clouds and precipitation and their relevance for climate and climate

change. This chapter assesses the climatic roles and feedbacks of water

vapour, lapse rate and clouds (Section 7.2), discusses aerosol–radiation

(Section 7.3) and aerosol–cloud (Section 7.4) interactions and quanti-

ﬁes the resulting aerosol RF on climate (Section 7.5). It also introduc-

es the physical basis for the precipitation responses to aerosols and

climate changes (Section 7.6) noted later in the Report, and assesses

geoengineering methods based on solar radiation management (Sec-

tion 7.7).

7.2 Clouds

This section summarizes our understanding of clouds in the current

climate from observations and process models; advances in the rep-

resentation of cloud processes in climate models since AR4; assessment

of cloud, water vapour and lapse rate feedbacks and adjustments; and

the RF due to clouds induced by moisture released by two anthropo-

genic processes (air trafﬁc and irrigation). Aerosol–cloud interactions

are assessed in Section 7.4. The ﬁdelity of climate model simulations of

clouds in the current climate is assessed in Chapter 9.

7.2.1 Clouds in the Present-Day Climate System

7.2.1.1 Cloud Formation, Cloud Types and Cloud Climatology

To form a cloud, air must cool or moisten until it is sufﬁciently super-

saturated to activate some of the available condensation or freezing

nuclei. Clouds may be composed of liquid water (possibly supercooled),

ice or both (mixed phase). The nucleated cloud particles are initially

very small, but grow by vapour deposition. Other microphysical mecha-

nisms dependent on the cloud phase (e.g., droplet collision and coales-

cence for liquid clouds, riming and Wegener–Bergeron–Findeisen pro-

cesses for mixed-phase clouds and crystal aggregation in ice clouds)

579

Clouds and Aerosols Chapter 7

can produce a broader spectrum of particle sizes and types; turbulent

mixing produces further variations in cloud properties on scales from

kilometres to less than a centimetre (Davis et al., 1999; Bodenschatz

et al., 2010). If and when some of the droplets or ice particles become

large enough, these will fall out of the cloud as precipitation.

Atmospheric ﬂows often organize convection and associated clouds

into coherent systems having scales from tens to thousands of kilo-

metres, such as cyclones or frontal systems. These represent a signiﬁ-

cant modelling and theoretical challenge, as they are usually too large

to represent within the limited domains of cloud-resolving models

(Section 7.2.2.1), but are also not well resolved nor parameterized by

most climate models; this gap, however, is beginning to close (Sec-

tion 7.2.2.2). Finally, clouds and cloud systems are organized by larg-

er-scale circulations into different regimes such as deep convection

near the equator, subtropical marine stratocumulus, or mid-latitude

storm tracks guided by the tropospheric westerly jets. Figure 7.4 shows

a selection of widely occurring cloud regimes schematically and as they

might appear in a typical geostationary satellite image.

New satellite sensors and new analysis of previous data sets have given

us a clearer picture of the Earth’s clouds since AR4. A notable example

Deep Tropics

Large-scale Subsidence

Trade Winds

Stratocumulus

Land/Sea Circulation

Shallow Cumulus

Convective Anvils

Thin Cirrus

(b)

(c)

Subtropics

OLD

Cirrus

Altostratus

Nimbostratus

ARM

Stratus

Polar (mixed phase) Stratus

High LatitudesMid-Latitudes

Melting Level

ARM

CEAN

COLD OCEAN

ARM

UBSIDING

EGIONS

(a)

17 km 10 km

(a)

(

)

(

)

Figure 7.4 | Diverse cloud regimes reﬂect diverse meteorology. (a) A visible-wavelength geostationary satellite image shows (from top to bottom) expanses and long arcs of cloud

associated with extratropical cyclones, subtropical coastal stratocumulus near Baja California breaking up into shallow cumulus clouds in the central Paciﬁc and mesoscale convec-

tive systems outlining the Paciﬁc Intertropical Convergence Zone (ITCZ). (b) A schematic section along the dashed line from the orange star to the orange circle in (a), through a

typical warm front of an extratropical cyclone.It shows (from right to left) multiple layers of upper-tropospheric ice (cirrus) and mid-tropospheric water (altostratus) cloud in the

upper-tropospheric outﬂow from the frontal zone, an extensive region of nimbostratus associated with frontal uplift and turbulence-driven boundary layer cloud in the warm sector.

(c) A schematic section along the dashed line from the red star to the red circle in (a),along the low-level trade wind ﬂow from a subtropical west coast of a continent to the ITCZ.It

shows (from right to left) typical low-latitude cloud mixtures, shallow stratocumulus trapped under a strong subsidence inversion above the cool waters of the oceanic upwelling

zone near the coast and shallow cumulus over warmer waters further offshore transitioning to precipitating cumulonimbus cloud systems with extensive cirrus anvils associated

with rising air motions in the ITCZ.

is the launch in 2006 of two coordinated, active sensors, the Cloud

Proﬁling Radar (CPR) on the CloudSat satellite (Stephens et al., 2002)

and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on

board the Cloud–Aerosol Lidar and Infrared Pathﬁnder Satellite Obser-

vations (CALIPSO) satellite (Winker et al., 2009). These sensors have

signiﬁcantly improved our ability to quantify vertical proﬁles of cloud

occurrence and water content (see Figures 7.5 and 7.6), and comple-

ment the detection capabilities of passive multispectral sensors (e.g.,

Stubenrauch et al., 2010; Chan and Comiso, 2011). Satellite cloud-ob-

serving capacities are reviewed by Stubenrauch et al. (2013).

Clouds cover roughly two thirds of the globe (Figure 7.5a, c), with a

more precise value depending on both the optical depth threshold

used to deﬁne cloud and the spatial scale of measurement (Wielicki

and Parker, 1992; Stubenrauch et al., 2013). The mid-latitude ocean-

ic storm tracks and tropical precipitation belts are particularly cloudy,

while continental desert regions and the central subtropical oceans are

relatively cloud-free. Clouds are composed of liquid at temperatures

above 0°C, ice below about –38°C (e.g., Koop et al., 2000), and either

or both phases at intermediate temperatures (Figure 7.5b). Throughout

most of the troposphere, temperatures at any given altitude are usually

warmer in the tropics, but clouds also extend higher there such that ice

580

Chapter 7 Clouds and Aerosols

cloud amounts are no less than those at high latitudes. At any given

time, most clouds are not precipitating (Figure 7.5d).

In this chapter cloud above the 440 hPa pressure level is considered

‘high’, that below the 680 hPa level ‘low’, and that in-between is con-

sidered ‘mid-level’. Most high cloud (mainly cirrus and deep cumulus

outﬂows) occurs near the equator and over tropical continents, but can

also be seen in the mid-latitude storm track regions and over mid-lati-

tude continents in summer (Figure 7.6a, e); it is produced by the storms

generating most of the global rainfall in regions where tropospheric air

motion is upward, such that dynamical, rainfall and high-cloud ﬁelds

closely resemble one another (Figure 7.6d, h). Mid-level cloud (Figure

7.6b, f), comprising a variety of types, is prominent in the storm tracks

and some occurs in the Intertropical Convergence Zone (ITCZ). Low

cloud (Figure 7.6c, g), including shallow cumulus and stratiform cloud,

occurs over essentially all oceans but is most prevalent over cooler

subtropical oceans and in polar regions. It is less common over land,

except at night and in winter.

Overlap between cloud layers has long been an issue both for sat-

ellite (or ground-based) detection and for calculating cloud radiative

effects. Active sensors show more clearly that low clouds are preva-

lent in nearly all types of convective systems, and are often under-

estimated by models (Chepfer et al., 2008; Naud et al., 2010; Haynes

et al., 2011). Cloud layers at different levels overlap less often than

typically assumed in General Circulation Models (GCMs), especially

Ice

(kg m

-2

)

Height (km)

0.2

0.1

Ice Water Path

Liquid Water Path

1 0 0.5 0.25 0.75

Fraction (or Occurrence Frequency)

Precipitation Occurrence (x2)

Cloud OccurrenceCloud Fraction

Condensate Path

Liquid

60ºS 60ºN 30º 30ºEq 60ºS 60ºN 30º 30ºEq

0ºC

-38ºC

over high-latitude continents and subtropical oceans (Naud et al.,

2008; Mace et al., 2009), and the common assumption that the radi-

ative effects of precipitating ice can be neglected is not necessarily

warranted (Waliser et al., 2011). New observations have led to revised

treatments of overlap in some models, which signiﬁcantly affects cloud

radiative effects (Pincus et al., 2006; Shonk et al., 2012). Active sensors

have also been useful in detecting low-lying Arctic clouds over sea ice

(Kay et al., 2008), improving our ability to test climate model simula-

tions of the interaction between sea ice loss and cloud cover (Kay et

al., 2011).

7.2.1.2 Effects of Clouds on the Earth’s Radiation Budget

The effect of clouds on the Earth’s present-day top of the atmosphere

(TOA) radiation budget, or cloud radiative effect (CRE), can be inferred

from satellite data by comparing upwelling radiation in cloudy and

non-cloudy conditions (Ramanathan et al., 1989). By enhancing the

planetary albedo, cloudy conditions exert a global and annual short-

wave cloud radiative effect (SWCRE) of approximately –50 W m

–2

and,

by contributing to the greenhouse effect, exert a mean longwave effect

(LWCRE) of approximately +30 W m

–2

, with a range of 10% or less

between published satellite estimates (Loeb et al., 2009). Some of the

apparent LWCRE comes from the enhanced water vapour coinciding

with the natural cloud ﬂuctuations used to measure the effect, so the

true cloud LWCRE is about 10% smaller (Sohn et al., 2010). The net

global mean CRE of approximately –20 W m

–2

implies a net cooling

Figure 7.5 | (a) Annual mean cloud fractional occurrence (CloudSat/CALIPSO 2B-GEOPROF-LIDAR data set for 2006–2011; Mace et al., 2009). (b) Annual zonal mean liquid water

path (blue shading, microwave radiometer data set for 1988–2005 from O’Dell et al. (2008)) and total water path (ice path shown with grey shading, from CloudSat 2C-ICE data

set for 2006–2011 from Deng et al. (2010) over oceans). The 90% uncertainty ranges, assessed to be approximately 60 to 140% of the mean for the liquid and total water paths,

are schematically indicated by the error bars. (c–d) latitude-height sections of annual zonal mean cloud (including precipitation falling from cloud) occurrence and precipitation

(attenuation-corrected radar reﬂectivity >0 dBZ) occurrence; the latter has been doubled to make use of a common colour scale (2B-GEOPROF-LIDAR data set). The dashed curves

show the annual mean 0°C and −38°C isotherms.

581

Clouds and Aerosols Chapter 7

Figure 7.6 | (a–d) December–January–February mean high, middle and low cloud cover from CloudSat/CALIPSO 2B-GEOPROF R04 and 2B-GEOPROF-LIDAR P1.R04 data sets

for 2006–2011 (Mace et al., 2009), 500 hPa vertical pressure velocity (colours, from ERA-Interim for 1979–2010; Dee et al., 2011), and Global Precipitation Climatology Project

(GPCP) version 2.2 precipitation rate (1981–2010, grey contours at 3 mm day

–1

in dash and 7 mm day

–1

in solid); (e–h) same as (a–d), except for June–July–August. For low clouds,

the GCM-Oriented CALIPSO Cloud Product (GOCCP) data set for 2007–2010 (Chepfer et al., 2010) is used at locations where it indicates a larger fractional cloud cover, because

the GEOPROF data set removes some clouds with tops at altitudes below 750 m. Low cloud amounts are probably underrepresented in regions of high cloud (Chepfer et al., 2008),

although not as severely as with earlier satellite instruments.

(a)

(b)

(c)

(e)

(f)

(g)

(d)

(h)

Fraction

Mid-troposphere Vertical Pressure Velocity (hPa day

-1

)

-50 50-50 0 25

0 0.80.2 0.4 0.6 1

High Cloud

Middle Cloud

Low Cloud

December–January–February June–July–August

582

Chapter 7 Clouds and Aerosols

effect of clouds on the current climate. Owing to the large magnitudes

of the SWCRE and LWCRE, clouds have the potential to cause signiﬁ-

cant climate feedback (Section 7.2.5). The sign of this feedback on cli-

mate change cannot be determined from the sign of CRE in the current

climate, but depends instead on how climate-sensitive the properties

are that govern the LWCRE and SWCRE.

The regional patterns of annual-mean TOA CRE (Figure 7.7a, b) reﬂect

those of the altitude-dependent cloud distributions. High clouds, which

are cold compared to the clear-sky radiating temperature, dominate

patterns of LWCRE, while the SWCRE is sensitive to optically thick

clouds at all altitudes. SWCRE also depends on the available sunlight,

so for example is sensitive to the diurnal and seasonal cycles of cloud-

iness. Regions of deep, thick cloud with large positive LWCRE and

large negative SWCRE tend to accompany precipitation (Figure 7.7d),

showing their intimate connection with the hydrological cycle. The net

CRE is negative over most of the globe and most negative in regions

of very extensive low-lying reﬂective stratus and stratocumulus cloud

such as the mid-latitude and eastern subtropical oceans, where SWCRE

is strong but LWCRE is weak (Figure 7.7c). In these regions, the spatial

distribution of net CRE on seasonal time scales correlates strongly with

measures of low-level stability or inversion strength (Klein and Hart-

mann, 1993; Williams et al., 2006; Wood and Bretherton, 2006; Zhang

et al., 2010).

Clouds also exert a CRE at the surface and within the troposphere, thus

affecting the hydrological cycle and circulation (Section 7.6), though

this aspect of CRE has received less attention. The net downward ﬂux

of radiation at the surface is sensitive to the vertical and horizontal

distribution of clouds. It has been estimated more accurately through

radiation budget measurements and cloud proﬁling (Kato et al., 2011).

Based on these observations, the global mean surface downward long-

wave ﬂux is about 10 W m

–2

larger than the average in climate models,

probably due to insufﬁcient model-simulated cloud cover or lower

tropospheric moisture (Stephens et al., 2012). This is consistent with a

global mean precipitation rate in the real world somewhat larger than

current observational estimates.

7.2.2 Cloud Process Modelling

Cloud formation processes span scales from the sub-micrometre scale

of CCN, to cloud-system scales of up to thousands of kilometres. This

range of scales is impossible to resolve with numerical simulations

on computers, and this is not expected to change in the foreseeable

future. Nonetheless progress has been made through a variety of mod-

elling strategies, which are outlined brieﬂy in this section, followed by

a discussion in Section 7.2.3 of developments in representing clouds

in global models. The implications of these discussions are synthesized

in Section 7.2.3.5.

7.2.2.1 Explicit Simulations in Small Domains

High-resolution models in small domains have been widely used to

simulate interactions of turbulence with various types of clouds. The

grid spacing is chosen to be small enough to resolve explicitly the dom-

inant turbulent eddies that drive cloud heterogeneity, with the effects

of smaller-scale phenomena parameterized. Such models can be run in

(a)

(b)

(c)

(d)

(mm day

-1

)

Cloud Radiative Effect (W m

-2

)

0 5 10

-100 0 100-50 50

Shortwave (global mean = –47.3 W m

-2

)

Longwave (global mean = 26.2 W m

-2

)

Net (global mean = –21.1 W m

-2

)

Precipitation (global mean = 2.7 mm day

-1

)

Figure 7.7 | Distribution of annual-mean top of the atmosphere (a) shortwave, (b)

longwave, (c) net cloud radiative effects averaged over the period 2001–2011 from

the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled

(EBAF) Ed2.6r data set (Loeb et al., 2009) and (d) precipitation rate (1981–2000 aver-

age from the GPCP version 2.2 data set; Adler et al., 2003).

583

Clouds and Aerosols Chapter 7

idealized settings, or with boundary conditions for speciﬁc observed

cases. This strategy is typically called large-eddy simulation (LES) when

boundary-layer eddies are resolved, and cloud-resolving model (CRM)

when only deep cumulus motions are well resolved. It is useful not

only in simulating cloud and precipitation characteristics, but also in

understanding how turbulent circulations within clouds transport and

process aerosols and chemical constituents. It can be applied to any

type of cloud system, on any part of the Earth. Direct numerical simula-

tion (DNS) can be used to study turbulence and cloud microphysics on

scales of a few metres or less (e.g., Andrejczuk et al., 2006) but cannot

span crucial meteorological scales and is not further considered here.

Cloud microphysics, precipitation and aerosol interactions are treated

with varying levels of sophistication, and remain a weak point in all

models regardless of resolution. For example, recent comparisons to

satellite data show that liquid water clouds in CRMs generally begin to

rain too early in the day (Suzuki et al., 2011). Especially for ice clouds,

and for interactions between aerosols and clouds, our understanding of

the basic micro-scale physics is not yet adequate, although it is improv-

ing. Moreover, microphysical effects are quite sensitive to co-variations

of velocity and composition down to very small scales. High-resolution

models, such as those used for LES, explicitly calculate most of these

variations, and so provide much more of the information needed for

microphysical calculations, whereas in a GCM they are not explicitly

available. For these reasons, low-resolution (e.g., climate) models will

have even more trouble representing local aerosol–cloud interactions

than will high-resolution models. Parameterizations are under develop-

ment that could account for the small-scale variations statistically (e.g.,

Larson and Golaz, 2005) but have not been used in the Coupled Model

Intercomparison Project Phase 5 (CMIP5) simulations.

High-resolution models have enhanced our understanding of cloud

processes in several ways. First, they can help interpret in situ and

high-resolution remote sensing observations (e.g., Stevens et al.,

2005b; Blossey et al., 2007; Fridlind et al., 2007). Second, they have

revealed important inﬂuences of small-scale interactions, turbulence,

entrainment and precipitation on cloud dynamics that must eventu-

ally be accounted for in parameterizations (e.g., Krueger et al., 1995;

Derbyshire et al., 2004; Kuang and Bretherton, 2006; Ackerman et al.,

2009). Third, they can be used to predict how cloud system properties

(such as cloud cover, depth, or radiative effect) may respond to cli-

mate changes (e.g., Tompkins and Craig, 1998; Bretherton et al., 2013).

Fourth, they have become an important tool in testing and improv-

ing parameterizations of cloud-controlling processes such as cumulus

convection, turbulent mixing, small-scale horizontal cloud variability

and aerosol–cloud interactions (Randall et al., 2003; Rio and Hourdin,

2008; Stevens and Seifert, 2008; Lock, 2009; Del Genio and Wu, 2010;

Fletcher and Bretherton, 2010), as well as the interplay between con-

vection and large-scale circulations (Kuang, 2008).

Different aspects of clouds, and cloud types, require different grid reso-

lutions. CRMs of deep convective cloud systems with horizontal resolu-

tions of 2 km or ﬁner (Bryan et al., 2003) can represent some statistical

properties of the cloud system, including fractional area coverage of

cloud (Xu et al., 2002), vertical thermodynamic structure (Blossey et

al., 2007), the distribution of updraughts and downdraughts (Khair-

outdinov et al., 2009) and organization into mesoscale convective

systems (Grabowski et al., 1998). Modern high-order turbulence clo-

sure schemes may allow some statistics of boundary-layer cloud distri-

butions, including cloud fractions and ﬂuxes of moisture and energy, to

be reasonably simulated even at horizontal resolution of 1 km or larger

(Cheng and Xu, 2006, 2008). Finer grids (down to hundreds of metres)

better resolve individual storm characteristics such as vertical velocity

or tracer transport. Some cloud ensemble properties remain sensitive

to CRM microphysical parameterization assumptions regardless of res-

olution, particularly the vertical distribution and optical depth of clouds

containing ice.

Because of these requirements, it is computationally demanding to run

a CRM in a domain large enough to capture convective organisation or

perform regional forecasts. Some studies have created smaller regions

of CRM-like resolution within realistically forced regional-scale models

(e.g., Zhu et al., 2010; Boutle and Abel, 2012; Zhu et al., 2012), a spe-

cial case of the common ‘nesting’ approach for regional downscaling

(see Section 9.6). One application has been to orographic precipitation,

associated both with extratropical cyclones (e.g., Garvert et al., 2005)

and with explicitly simulated cumulus convection (e.g., Hohenegger

et al., 2008); better resolution of the orography improves the simula-

tion of precipitation initiation and wind drift of falling rain and snow

between watersheds.

LES of shallow cumulus cloud ﬁelds with horizontal grid spacing of

about 100 m and vertical grid spacing of about 40 m produces vertical

proﬁles of cloud fraction, temperature, moisture and turbulent ﬂuxes

that agree well with available observations (Siebesma et al., 2003),

though the simulated precipitation efﬁciency still shows some sensi-

tivity to microphysical parameterizations (vanZanten et al., 2011). LES

of stratocumulus-topped boundary layers reproduces the turbulence

statistics and vertical thermodynamic structure well (e.g., Stevens et

al., 2005b; Ackerman et al., 2009), and has been used to study the

sensitivity of stratocumulus properties to aerosols (e.g., Savic-Jovcic

and Stevens, 2008; Xue et al., 2008) and meteorological conditions.

However, the simulated entrainment rate and cloud liquid water path

are sensitive to the underlying numerical algorithms, even with vertical

grid spacings as small as 5 m, due to poor resolution of the sharp cap-

ping inversion (Stevens et al., 2005a).

These grid requirements mean that low-cloud processes dominating

the known uncertainty in cloud feedback cannot be explicitly simulat-

ed except in very small domains. Thus, notwithstanding all of the above

beneﬁts of explicit cloud modeling, these models cannot on their own

quantify global cloud feedbacks or aerosol–cloud interactions deﬁni-

tively. They are important, however, in suggesting and testing feedback

and adjustment mechanisms (see Sections 7.2.5 and 7.4).

7.2.2.2 Global Models with Explicit Clouds

Since AR4, increasing computer power has led to three types of devel-

opments in global atmospheric models. First, models have been run

with resolution that is higher than in the past, but not sufﬁciently high

that cumulus clouds can be resolved explicitly. Second, models have

been run with resolution high enough to resolve (or ‘permit’) large

individual cumulus clouds over the entire globe. In a third approach,

the parameterizations of global models have been replaced by

584

Chapter 7 Clouds and Aerosols

embedded CRMs. The ﬁrst approach is assessed in Chapter 9. The other

two approaches are discussed below.

Global Cloud-Resolving Models (GCRMs) have been run with grid spac-

ings as small as 3.5 km (Tomita et al., 2005; Putman and Suarez, 2011).

At present GCRMs can be used only for relatively short simulations of a

few simulated months to a year or two on the fastest supercomputers,

but in the not-too distant future they may provide climate projections.

GCRMs provide a consistent way to couple convective circulations to

large-scale dynamics, but must still parameterize the effects of individ-

ual clouds, microphysics and boundary-layer circulations.

Because they avoid the use of uncertain cumulus parameterizations,

GCRMs better simulate many properties of convective circulations that

are very challenging for many current conventional GCMs, including

the diurnal cycles of precipitation (Sato et al., 2009) and the Asian

summer monsoon (Oouchi et al., 2009). Inoue et al. (2010) showed

that the cloudiness simulated by a GCRM is in good agreement with

observations from CloudSat and CALIPSO, but the results are sensitive

to the parameterizations of turbulence and cloud microphysics (Satoh

et al., 2010; Iga et al., 2011; Kodama et al., 2012).

Heterogeneous multiscale methods, in which CRMs are embedded in

each grid cell of a larger scale model (Grabowski and Smolarkiewicz,

1999), have also been further developed as a way to realize some of

the advantages of GCRMs but at less cost. This approach has come

to be known as super-parameterization, because the CRM effectively

replaces some of the existing GCM parameterizations (e.g., Khairoutdi-

nov and Randall, 2001; Tao et al., 2009). Super-parameterized models,

which are sometimes called multiscale modeling frameworks, occupy a

middle ground between high-resolution ‘process models’ and ‘climate

models’ (see Figure 7.8), in terms of both advantages and cost.

Like GCRMs, super-parameterized models give more realistic simula-

tions of the diurnal cycle of precipitation (Khairoutdinov et al., 2005;

Pritchard and Somerville, 2010) and the Madden-Julian Oscillation

(Benedict and Randall, 2009) than most conventional GCMs; they can

also improve aspects of the Asian monsoon and the El Niño–Southern

Oscillation (ENSO; Stan et al., 2010; DeMott et al., 2011). Moreover,

because they also begin to resolve cloud-scale circulations, both strat-

egies provide a framework for studying aerosol–cloud interactions

that conventional GCMs lack (Wang et al., 2011b). Thus both types of

global model provide important insights, but because neither of them

fully resolves cloud processes, especially for low clouds (see Section

7.2.2.1), their results must be treated with caution just as with con-

ventional GCMs.

7.2.3 Parameterization of Clouds in Climate Models

7.2.3.1 Challenges of Parameterization

The representation of cloud microphysical processes in climate models

is particularly challenging, in part because some of the fundamen-

tal details of these microphysical processes are poorly understood

(particularly for ice- and mixed-phase clouds), and because spatial

heterogeneity of key atmospheric properties occurs at scales signif-

icantly smaller than a GCM grid box. Such representation, however,

General Circulation Model (GCM)

Cloud-Resolving Model (CRM)

Large-Eddy Simulation (LES)

Climate System

Cloud Processes

Spatial scale (m)

Time scale (day)

Super Parameterization (MMF)

Global Cloud-Resolving Model (GCRM)

Figure 7.8 | Model and simulationstrategy for representing the climate system and

climate processes at different space and time scales. Also shown are the ranges of

space and time scales usually associated with cloud processes (orange, lower left) and

the climate system (blue, upper right). Classes of models are usually deﬁned based on

the range of spatialscales they represent, which in the ﬁgure is roughly spanned by the

text for each model class. Thetemporal scales simulated by a particular type of model

vary more widely. For instance, climate modelsare often run for afew time steps for

diagnostic studies, or can simulate millennia. Hence the ﬁgure indicatesthe typical time

scales for which a given model is used.Computational powerprevents one model from

covering all time and space scales. Since the AR4, thedevelopment of Global Cloud

Resolving Models (GCRMs), and hybrid approaches suchas General Circulation Models

(GCMs) using the ‘super-parameterization’ approach (sometimes called the Multiscale

Modelling Framework (MMF)), havehelped ﬁll the gap between climatesystem and

cloud process models.

affects many aspects of a model’s overall simulated climate including

the Hadley circulation, precipitation patterns, and tropical variability.

Therefore continuing weakness in these parameterizations affects not

only modeled climate sensitivity, but also the ﬁdelity with which these

other variables can be simulated or projected.

Most CMIP5 climate model simulations use horizontal resolutions of

100 to 200 km in the atmosphere, with vertical layers varying between

100 m near the surface to more than 1000 m aloft. Within regions

of this size in the real world, there is usually enormous small-scale

variability in cloud properties, associated with variability in humidity,

temperature and vertical motion (Figure 7.16). This variability must

be accounted for to accurately simulate cloud–radiation interaction,

condensation, evaporation and precipitation and other cloud processes

that crucially depend on how cloud condensate is distributed across

each grid box (Cahalan et al., 1994; Pincus and Klein, 2000; Larson et

al., 2001; Barker et al., 2003).

The simulation of clouds in modern climate models involves several

parameterizations that must work in unison. These include parame-

terization of turbulence, cumulus convection, microphysical processes,

radiative transfer and the resulting cloud amount (including the ver-

tical overlap between different grid levels), as well as sub-grid scale

transport of aerosol and chemical species. The system of parameter-

izations must balance simplicity, realism, computational stability and

efﬁciency. Many cloud processes are unrealistic in current GCMs, and

as such their cloud response to climate change remains uncertain.

Cloud processes and/or turbulence parameterization are important not

only for the GCMs used in climate projections but also for special-

ized chemistry–aerosol–climate models (see review by Zhang, 2008),

585

Clouds and Aerosols Chapter 7

for regional climate models, and indeed for the cloud process models

described in Section 7.2.2 which must still parameterize small-scale

and microphysical effects. The nature of the parameterization problem,

however, shifts as model scale decreases. Section 7.2.3.2 brieﬂy assess-

es recent developments relevant to GCMs.

7.2.3.2 Recent Advances in Representing Cloud

Microphysical Processes

7.2.3.2.1 Liquid clouds

Recent development efforts have been focused on the introduction of

more complex representations of microphysical processes, with the

dual goals of coupling them better to atmospheric aerosols and link-

ing them more consistently to the sub-grid variability assumed by the

model for other calculations. For example, most CMIP3 climate models

predicted the average cloud and rain water mass in each grid cell only

at a given time, diagnosing the droplet concentration using empiri-

cal relationships based on aerosol mass (e.g., Boucher and Lohmann,

1995; Menon et al., 2002), or altitude and proximity to land. Many

were forced to employ an arbitrary lower bound on droplet concentra-

tion to reduce the aerosol RF (Hoose et al., 2009). Such formulations

oversimplify microphysically mediated cloud variations.

By contrast, more models participating in CMIP5 predict both mass and

number mixing ratios for liquid stratiform cloud. Some determine rain

and snow number concentrations and mixing ratios (e.g., Morrison and

Gettelman, 2008; Salzmann et al., 2010), allowing treatment of aerosol

scavenging and the radiative effect of snow. Some models explicitly

treat sub-grid cloud water variability for calculating microphysical pro-

cess rates (e.g., Morrison and Gettelman, 2008). Cloud droplet activa-

tion schemes now account more realistically for particle composition,

mixing and size (Abdul-Razzak and Ghan, 2000; Ghan et al., 2011;

Liu et al., 2012). Despite such advances in internal consistency, a con-

tinuing weakness in GCMs (and to a much lesser extent GCRMs and

super-parameterized models) is their inability to fully represent turbu-

lent motions to which microphysical processes are highly sensitive.

7.2.3.2.2 Mixed-phase and ice clouds

Ice treatments are following a path similar to those for liquid water,

and face similar but greater challenges because of the greater com-

plexity of ice processes. Many CMIP3 models predicted the condensed

water amount in just two categories—cloud and precipitation—with

a temperature-dependent partitioning between liquid and ice within

either category. Although supersaturation with respect to ice is com-

monly observed at low temperatures, only one CMIP3 GCM (ECHAM)

allowed ice supersaturation (Lohmann and Kärcher, 2002).

Many climate models now include separate, physically based equations

for cloud liquid versus cloud ice, and for rain versus snow, allowing a

more realistic treatment of mixed-phase processes and ice supersatu-

ration (Liu et al., 2007; Tompkins et al., 2007; Gettelman et al., 2010;

Salzmann et al., 2010; see also Section 7.4.4). These new schemes are

tested in a single-column model against cases observed in ﬁeld cam-

paigns (e.g., Klein et al., 2009) or against satellite observations (e.g.,

Kay et al., 2012), and provide superior simulations of cloud structure

than typical CMIP3 parameterizations (Kay et al., 2012). However

new observations reveal complexities not correctly captured by even

relatively advanced schemes (Ma et al., 2012a). New representations

of the Wegener–Bergeron–Findeisen process in mixed-phase clouds

(Storelvmo et al., 2008b; Lohmann and Hoose, 2009) compare the rate

at which the pre-existing ice crystals deplete the water vapour with the

condensation rate for liquid water driven by vertical updraught speed

(Korolev, 2007); these are not yet included in CMIP5 models. Climate

models are increasingly representing detailed microphysics, including

mixed-phase processes, inside convective clouds (Fowler and Randall,

2002; Lohmann, 2008; Song and Zhang, 2011). Such processes can

inﬂuence storm characteristics like strength and electriﬁcation, and are

crucial for fully representing aerosol–cloud interactions, but are still

not included in most climate models; their representation is moreover

subject to all the caveats noted in Section 7.2.3.1.

7.2.3.3 Recent Advances in Parameterizing Moist Turbulence

and Convection

Both the mean state and variability in climate models are sensitive to

the parameterization of cumulus convection. Since AR4, the develop-

ment of convective parameterization has been driven largely by rapidly

growing use of process models, in particular LES and CRMs, to inform

parameterization development (e.g., Hourdin et al., 2013).

Accounting for greater or more state-dependent entrainment of air into

deep cumulus updraughts has improved simulations of the Madden–

Julian Oscillation, tropical convectively coupled waves and mean rain-

fall patterns in some models (Bechtold et al., 2008; Song and Zhang,

2009; Chikira and Sugiyama, 2010; Hohenegger and Bretherton, 2011;

Mapes and Neale, 2011; Del Genio et al., 2012; Kim et al., 2012) but

usually at the expense of a degraded simulation of the mean state. In

another model, revised criteria for convective initiation and parame-

terizations of cumulus momentum ﬂuxes improved ENSO and tropical

vertical temperature proﬁles (Neale et al., 2008; Richter and Rasch,

2008). Since AR4, more climate models have adopted cumulus param-

eterizations that diagnose the expected vertical velocity in cumulus

updraughts (e.g., Del Genio et al., 2007; Park and Bretherton, 2009;

Chikira and Sugiyama, 2010; Donner et al., 2011), in principle allowing

more complete representations of aerosol activation, cloud microphys-

ical evolution and gravity wave generation by the convection.

Several new parameterizations couple shallow cumulus convection

more closely to moist boundary layer turbulence (Siebesma et al.,

2007; Neggers, 2009; Neggers et al., 2009; Couvreux et al., 2010)

including cold pools generated by nearby deep convection (Grandpeix

and Lafore, 2010). Many of these efforts have led to more accurate

simulations of boundary-layer cloud radiative properties and vertical

structure (e.g., Park and Bretherton, 2009; Köhler et al., 2011), and

have ameliorated the common problem of premature deep convective

initiation over land in one CMIP5 GCM (Rio et al., 2009).

7.2.3.4 Recent Advances in Parameterizing

Cloud Radiative Effects

Some models have improved representation of sub-grid scale cloud

variability, which has important effects on grid-mean radiative ﬂuxes

586

Chapter 7 Clouds and Aerosols

and precipitation ﬂuxes, for example, based on the use of probability

density functions of thermodynamic variables (Sommeria and Dear-

dorff, 1977; Watanabe et al., 2009). Stochastic approaches for radi-

ative transfer can account for this variability in a computationally

efﬁcient way (Barker et al., 2008). New treatments of cloud overlap

have been motivated by new observations (Section 7.2.1.1). Despite

these advances, the CMIP5 models continue to exhibit the ‘too few, too

bright’ low-cloud problem (Nam et al., 2012), with a systematic over-

estimation of cloud optical depth and underestimation of cloud cover.

7.2.3.5 Cloud Modelling Synthesis

Global climate models used in CMIP5have improved their represen-

tation of cloud processes relative to CMIP3, butstill face challenges

and uncertainties,especially regarding details of small-scale variability

that arecrucial foraerosol–cloud interactions (see Section 7.4). Finer-

scaleLES and CRM models are much better able to represent this vari-

ability andarean important research tool, but still suffer from imper-

fect representations of aerosol and cloud microphysics and known

biases. Most CRM and LES studies do not span the largespace and

time scales needed to fully determine the interactions amongdiffer-

entcloud regimes and the resulting net planetary radiative effects.Thus

our assessments in this chapter do notregard anymodel type on its

own as deﬁnitive, but weigh the implications ofprocess model studies

in assessing the quantitative results ofthe global models.

7.2.4 Water Vapour and Lapse Rate Feedbacks

Climate feedbacks determine the sensitivity of global surface temper-

ature to external forcing agents. Water vapour, lapse rate and cloud

feedbacks each involve moist atmospheric processes closely linked to

clouds, and in combination, produce most of the simulated climate

feedback and most of its inter-model spread (Section 9.7). The radia-

tive feedback from a given constituent can be quantiﬁed as its impact

(other constituents remaining equal) on the TOA net downward radi-

ative ﬂux per degree of global surface (or near-surface) temperature

increase, and may be compared with the basic ‘black-body’ response

of −3.4 W m

−2

°C

−1

(Hansen et al., 1984). This deﬁnition assigns posi-

tive values to positive feedbacks, in keeping with the literature on this

topic but contradictory to the conventions sometimes adopted in other

climate research.

7.2.4.1 Water Vapour Response and Feedback

As pointed out in previous reports (Section 8.6.3.1 in Randall et al.,

2007), physical arguments and models of all types suggest global

water vapour amounts increase in a warmer climate, leading to a

positive feedback via its enhanced greenhouse effect. The saturated

water vapour mixing ratio (WVMR) increases nearly exponentially and

very rapidly with temperature, at 6 to 10% °C

–1

near the surface, and

even more steeply aloft (up to 17% °C

–1

) where air is colder. Mounting

evidence indicates that any changes in relative humidity in warmer

climates would have much less impact on speciﬁc humidity than the

above increases, at least in a global and statistical sense. Hence the

overall WVMR is expected to increase at a rate similar to the saturated

WVMR.

Because global temperatures have been rising, the above arguments

imply WVMR should be rising accordingly, and multiple observing sys-

tems indeed show this (Sections 2.5.4 and 2.5.5). A study challenging

the water vapour increase (Paltridge et al., 2009) used an old reanalysis

product, whose trends are contradicted by newer ones (Dessler and

Davis, 2010) and by actual observations (Chapter 2). The study also

reported decreasing relative humidity in data from Australian radio-

sondes, but more complete studies show Australia to be exceptional

in this respect (Dai et al., 2011). Thus data remain consistent with the

expected global feedback.

Some studies have proposed that the response of upper-level humid-

ity to natural ﬂuctuations in the global mean surface temperature is

informative about the feedback. However, small changes to the global

mean (primarily from ENSO) involve geographically heterogeneous

temperature change patterns, the responses to which may be a poor

analogue for global warming (Hurley and Galewsky, 2010a). Most

climate models reproduce these natural responses reasonably well

(Gettelman and Fu, 2008; Dessler and Wong, 2009), providing addi-

tional evidence that they at least represent the key processes.

The ‘last-saturation’ concept approximates the WVMR of air by its sat-

uration value when it was last in a cloud (see Sherwood et al., 2010a

for a review), which can be inferred from trajectory analysis. Studies

since the AR4 using a variety of models and observations (including

concentrations of water vapour isotopes) support this concept (Sher-

wood and Meyer, 2006; Galewsky and Hurley, 2010). The concept has

clariﬁed what determines relative humidity in the subtropical upper

troposphere and placed the water vapour feedback on ﬁrmer theo-

retical footing by directly linking actual and saturation WVMR values

(Hurley and Galewsky, 2010b). CRMs show that convection can adopt

varying degrees of self-aggregation (e.g., Muller and Held, 2012),

which could modify the water vapour or other feedbacks if this were

climate sensitive, although observations do not suggest aggregation

changes have a large net radiative effect (Tobin et al., 2012).

In a warmer climate, an upward shift of the tropopause and poleward

shift of the jets and associated climate zones are expected (Sections

2.7.4 and 2.7.5) and simulated by most GCMs (Section 10.3.3). These

changes account, at least qualitatively, for robust regional changes in

the relative humidity simulated in warmer climate by GCMs, includ-

ing decreases in the subtropical troposphere and tropical uppermost

troposphere, and increases near the extratropical tropopause and

high latitudes (Sherwood et al., 2010b). This pattern may be ampliﬁed,

however, by non-uniform atmospheric temperature or wind changes

(Hurley and Galewsky, 2010b). It is also the apparent cause of most

model-predicted changes in mid- and upper-level cloudiness patterns

(Wetherald and Manabe, 1980; Sherwood et al., 2010b; see also Sec-

tion 7.2.5.2). Idealized CRM simulations of warming climates also

show upward shifts of the humidity patterns with little change in the

mean (e.g., Kuang and Hartmann, 2007; Romps, 2011).

It remains unclear whether stratospheric water vapour contributes

signiﬁcantly to climate feedback. Observations have shown decadal

variations in stratospheric water vapour, which may have affected the

planetary radiation budget somewhat (Solomon et al., 2010) but are

not clearly linked to global temperature (Section 3.4.2.4 in Trenberth et

587

Clouds and Aerosols Chapter 7

al., 2007). A strong positive feedback from stratospheric water vapour

was reported in one GCM, but with parameter settings that produced

an unrealistic present climate (Joshi et al., 2010).

7.2.4.2 Relationship Between Water Vapour and Lapse

Rate Feedbacks

The lapse rate (decrease of temperature with altitude) should, in the

tropics, change roughly as predicted by a moist adiabat, due to the

strong restoring inﬂuence of convective heating. This restoring inﬂu-

ence has now been directly inferred from satellite data (Lebsock et

al., 2010), and the near-constancy of tropical atmospheric stability and

deep-convective thresholds over recent decades is also now observ-

able in SST and deep convective data (Johnson and Xie, 2010). The

stronger warming of the atmosphere relative to the surface produces a

negative feedback on global temperature because the warmed system

radiates more thermal emission to space for a given increase in surface

temperature than in the reference case where the lapse rate is ﬁxed.

This feedback varies somewhat among models because lapse rates in

middle and high latitudes, which decrease less than in the tropics, do

so differently among models (Dessler and Wong, 2009).

As shown by Cess (1975) and discussed in the AR4 (Randall et al.,

2007), models with a more negative lapse rate feedback tend to have

a more positive water vapour feedback. Cancellation between these

is close enough that their sum has a 90% range in CMIP3 models of

only +0.96 to +1.22 W m

−2

°C

−1

(based on a Gaussian ﬁt to the data of

Held and Shell (2012), see Figure 7.9) with essentially the same range

in CMIP5 (Section 9.7). The physical reason for this cancellation is that

as long as water vapour infrared absorption bands are nearly saturat-

ed, outgoing longwave radiation is determined by relative humidity

(Ingram, 2010) which exhibits little global systematic change in any

model (Section 7.2.4.1). In fact, Held and Shell (2012) and Ingram

(2013a) argue that it makes more sense physically to redeﬁne feed-

backs in a different analysis framework in which relative humidity,

-3

-2

-1

Total Planck Lapse WVMR Planck

Lapse

Standard Decomposition

Feedback (W m

-2

ºC

-1

)

RH-based Decomposition

Figure 7.9 | Feedback parameters associated with water vapour or the lapse rate

predicted by CMIP3 GCMs, with boxes showing interquartile range and whiskers show-

ing extreme values. At left is shown the total radiative response including the Planck

response. In the darker shaded region is shown the traditional breakdown of this into

a Planck response and individual feedbacks from water vapour (labelled ‘WVMR’) and

lapse rate (labelled ‘Lapse’). In the lighter-shaded region at right are the equivalent

three parameters calculated in an alternative, relative humidity-based framework. In

this framework all three components are both weaker and more consistent among the

models. (Data are from Held and Shell, 2012.)

rather than speciﬁc humidity, is the feedback variable. Analysed in that

framework the inherent stabilization by the Planck response is weaker,

but the water vapour and lapse rate feedbacks are also very small; thus

the traditional view of large and partially compensating feedbacks has,

arguably, arisen from arbitrary choices made when the analysis frame-

work was originally set out, rather than being an intrinsic feature of

climate or climate models.

There is some observational evidence (Section 2.4.4) suggesting trop-

ical lapse rates might have increased in recent decades in a way not

simulated by models (Section 9.4.1.4.2). Because the combined lapse

rate and water vapour feedback depends on relative humidity change,

however, the imputed lapse rate variations would have little inﬂuence

on the total feedback or climate sensitivity even if they were a real

warming response (Ingram, 2013b). In summary, there is increased

evidence for a strong, positive feedback (measured in the tradition-

al framework) from the combination of water vapour and lapse rate

changes since AR4, with no reliable contradictory evidence.

7.2.5 Cloud Feedbacks and Rapid Adjustments to

Carbon Dioxide

The dominant source of spread among GCM climate sensitivities in AR4

was due to diverging cloud feedbacks, particularly due to low clouds,

and this continues to be true (Section 9.7). All global models continue to

produce a near-zero to moderately strong positive net cloud feedback.

Progress has been made since the AR4 in understanding the reasons

for positive feedbacks in models and providing a stronger theoretical

and observational basis for some mechanisms contributing to them.

There has also been progress in quantifying feedbacks—including

separating the effects of different cloud types, using radiative-kernel

residual methods (Soden et al., 2008) and by computing cloud effects

directly (e.g., Zelinka et al., 2012a)—and in distinguishing between

feedback and adjustment responses (Section 7.2.5.6).

Until very recently cloud feedbacks have been diagnosed in models

by differencing cloud radiative effects in doubled CO

and control cli-

mates, normalized by the change in global mean surface temperature.

Different diagnosis methods do not always agree, and some simple

methods can make positive cloud feedbacks look negative by failing to

account for the nonlinear interaction between cloud and water vapour

(Soden and Held, 2006). Moreover, it is now recognized that some of

the cloud changes are induced directly by the atmospheric radiative

effects of CO

independently of surface warming, and are therefore

rapid adjustments rather than feedbacks (Section 7.2.5.6). Most of the

published studies available for this assessment did not separate these

effects, and only the total response is assessed here unless otherwise

noted. It appears that the adjustments are sufﬁciently small in most

models that general conclusions regarding feedbacks are not signiﬁ-

cantly affected.

Cloud changes cause both longwave (greenhouse warming) and short-

wave (reﬂective cooling) effects, which combine to give the overall

cloud feedback or forcing adjustment. Cloud feedback studies point

to ﬁve aspects of the cloud response to climate change which are

distinguished here: changes in high-level cloud altitude, effects of

hydrological cycle and storm track changes on cloud systems, changes

588

Chapter 7 Clouds and Aerosols

in low-level cloud amount, microphysically induced opacity (optical

depth) changes and changes in high-latitude clouds. Finally, recent

research on the rapid cloud adjustments to CO

is assessed. Feedbacks

involving aerosols (Section 7.3.5) are not considered here, and the

discussion focuses only on mechanisms affecting the TOA radiation

budget.

7.2.5.1 Feedback Mechanisms Involving the Altitude of

High-Level Cloud

A dominant contributor of positive cloud feedback in models is the

increase in the height of deep convective outﬂows tentatively attribut-

ed in AR4 to the so-called ‘ﬁxed anvil-temperature’ mechanism (Hart-

mann and Larson, 2002). According to this mechanism, the average

outﬂow level from tropical deep convective systems is determined

in steady state by the highest point at which water vapour cools the

atmosphere signiﬁcantly through infrared emission; this occurs at a

particular water vapour partial pressure, therefore at a similar temper-

ature (higher altitude) as climate warms. A positive feedback results

because, since the cloud top temperature does not keep pace with that

of the troposphere, its emission to space does not increase at the rate

expected for the no-feedback system. This occurs at all latitudes and

has long been noted in model simulations (Hansen et al., 1984; Cess

et al., 1990). This mechanism, with a small modiﬁcation to account for

lapse rate changes, predicts roughly +0.5 W m

–2

°C

–1

of positive long-

wave feedback in GCMs (Zelinka and Hartmann, 2010), compared to

an overall cloud-height feedback of +0.35 (+0.09 to +0.58) W m

–2

°C

–1

(Figure 7.10). Importantly, CRMs also reproduce this increase in cloud

height (Tompkins and Craig, 1998; Kuang and Hartmann, 2007; Romps,

2011; Harrop and Hartmann, 2012).

On average, natural ﬂuctuations in tropical high cloud amount exert

little net TOA radiative effect in the current climate due to near-

-1.0

-0.5

0.0

0.5

1.0

1.5

Total High Middl e Low Amount Height Opacity

CFMIPCMIP3CMIP5 NetLW SW

Feedback (W m

-2

ºC

-1

)

CFMIP Models

(by cloud property)

CFMIP & CMIP3 Models

(by cloud level)

Figure 7.10 | Cloud feedback parameters as predicted by GCMs for responses to CO

increase including rapid adjustments. Total feedback shown at left, with centre light-

shaded section showing components attributable to clouds in speciﬁc height ranges

(see Section 7.2.1.1), and right dark-shaded panel those attributable to speciﬁc cloud

property changes where available. The net feedback parameters are broken down in

their longwave (LW) and shortwave (SW) components. Type attribution reported for

CMIP3 does not conform exactly to the deﬁnition used in the Cloud Feedback Model

Intercomparison Project (CFMIP) but is shown for comparison, with their ‘mixed’ cat-

egory assigned to middle cloud. CFMIP data (original and CFMIP2) are from Zelinka

et al. (2012a, 2012b; 2013); CMIP3 from Soden and Vecchi (2011); and CMIP5 from

Tomassini et al. (2013).

compensation between their longwave and shortwave cloud radiative

effects (Harrison et al., 1990; Figure 7.7). Similar compensation can

be seen in the opposing variations of these two components of the

high-cloud feedback across GCMs (Figure 7.10). This might suggest

that the altitude feedback could be similarly compensated. However,

GCMs can reproduce the observed compensation in the present cli-

mate (Sherwood et al., 1994) without producing one under global

warming. In the above-noted cloud-resolving simulations, the entire

cloud ﬁeld (including the typical base) moved upward, in accord with

a general upward shift of tropospheric ﬁelds (Singh and O’Gorman,

2012) and with drying at levels near cloud base (Minschwaner et al.,

2006; Sherwood et al., 2010b). This supports the prediction of GCMs

that the altitude feedback is not compensated by an increase in high-

cloud thickness or albedo.

The observational record offers limited further support for the altitude

increase. The global tropopause is rising as expected (Section 2.7.4).

Observed cloud heights change roughly as predicted with regional,

seasonal and interannual changes in near-tropopause temperature

structure (Xu et al., 2007; Eitzen et al., 2009; Chae and Sherwood,

2010; Zelinka and Hartmann, 2011), although these tests may not be

good analogues for global warming. Davies and Molloy (2012) report

an apparent recent downward mean cloud height trend but this is

probably an artefact (Evan and Norris, 2012); observed cloud height

trends do not appear sufﬁciently reliable to test this cloud-height feed-

back mechanism (Section 2.5.6).

In summary, the consistency of GCM responses, basic understanding,

strong support from process models, and weak further support from

observations give us high conﬁdence in a positive feedback contribu-

tion from increases in high-cloud altitude.

7.2.5.2 Feedback Mechanisms Involving the Amount of

Middle and High Cloud

As noted in Section 7.2.5.1, models simulate a range of nearly compen-

sating differences in shortwave and longwave high-cloud feedbacks,

consistent with different changes in high-cloud amount, but also show

a net positive offset consistent with higher cloud altitude (Figure 7.10).

However, there is a tendency in most GCMs toward reduced middle

and high cloud amount in warmer climates in low- and mid-latitudes,

especially in the subtropics (Trenberth and Fasullo, 2009; Zelinka and

Hartmann, 2010). This loss of cloud amount adds a positive shortwave

and negative longwave feedback to the model average, which causes

the average net positive feedback to appear to come from the short-

wave part of the spectrum. The net effect of changes in amount of all

cloud types averaged over models is a positive feedback of about +0.2

W m

–2

°C

–1

, but this roughly matches the contribution from low clouds

(see the following section), implying a near-cancellation of longwave

and shortwave effects for the mid- and high-level amount changes.

Changes in predicted cloud cover geographically correlate with sim-

ulated subtropical drying (Meehl et al., 2007), suggesting that they

are partly tied to large-scale circulation changes including the pole-

ward shifts found in most models (Wetherald and Manabe, 1980; Sher-

wood et al., 2010b; Section 2.7). Bender et al. (2012) and Eastman

and Warren (2013) report poleward shifts in cloud since the 1970s

589

Clouds and Aerosols Chapter 7

consistent with those reported in other observables (Section 2.5.6) and

simulated by most GCMs, albeit with weaker amplitude (Yin, 2005).

This shift of clouds to latitudes of weaker sunlight decreases the plan-

etary albedo and would imply a strong positive feedback if it were due

to global warming (Bender et al., 2012), although it is probably partly

driven by other factors (Section 10.3). The true amount of positive feed-

back coming from poleward shifts therefore remains highly uncertain,

but is underestimated by GCMs if, as suggested by observational com-

parisons, the shifts are underestimated (Johanson and Fu, 2009; Allen

et al., 2012).

The upward mass ﬂux in deep clouds should decrease in a warmer

climate (Section 7.6.2), which might contribute to cloudiness decreases

in storm tracks or the ITCZ (Chou and Neelin, 2004; Held and Soden,

2006). Tselioudis and Rossow (2006) predict this within the storm

tracks based on observed present-day relationships with meteorologi-

cal variables combined with model-simulated changes to those driving

variables but do not infer a large feedback. Most CMIP3 GCMs produce

too little storm-track cloud cover in the southern hemisphere compared

to nearly overcast conditions in reality, but clouds are also too bright.

Arguments have been advanced that such biases could imply either

model overestimation or underestimation of feedbacks (Trenberth and

Fasullo, 2010; Brient and Bony, 2012).

The role of thin cirrus clouds for cloud feedback is not known and

remains a source of possible systematic bias. Unlike high-cloud sys-

tems overall, these particular clouds exert a clear net warming effect

(Jensen et al., 1994; Chen et al., 2000), making a signiﬁcant cloud-

cover feedback possible in principle (e.g., Rondanelli and Lindzen,

2010). While this does not seem to be important in recent GCMs

(Zelinka et al., 2012b), and no speciﬁc mechanism has been suggested,

the representation of cirrus in GCMs appears to be poor (Eliasson et

al., 2011) and such clouds are microphysically complex (Section 7.4.4).

This implies signiﬁcant feedback uncertainty in addition to that already

evident from model spread.

Model simulations, physical understanding and observations thus pro-

vide medium conﬁdence that poleward shifts of cloud distributions

will contribute to positive feedback, but by an uncertain amount. Feed-

backs from thin cirrus amount cannot be ruled out and are an impor-

tant source of uncertainty.

7.2.5.3 Feedback Mechanisms Involving Low Cloud

Differences in the response of low clouds to a warming are responsible

for most of the spread in model-based estimates of equilibrium climate

sensitivity (Randall et al., 2007). Since the AR4 this ﬁnding has with-

stood further scrutiny (e.g., Soden and Vecchi, 2011; Webb et al., 2013),

holds in CMIP5 models (Vial et al., 2013) and has been shown to apply

also to the transient climate response (e.g., Dufresne and Bony, 2008).

This discrepancy in responses occurs over most oceans and cannot

be clearly conﬁned to any single region (Trenberth and Fasullo, 2010;

Webb et al., 2013), but is usually associated with the representation

of shallow cumulus or stratocumulus clouds (Williams and Tselioudis,

2007; Williams and Webb, 2009; Xu et al., 2010). Because the spread

of responses emerges in a variety of idealized model formulations

(Medeiros et al., 2008; Zhang and Bretherton, 2008; Brient and Bony,

2013), or conditioned on a particular dynamical state (Bony et al.,

2004), and is similar in equilibrium or transient simulations (Yokohata

et al., 2008), it appears to be attributable to how cloud, convective and

boundary layer processes are parameterized in GCMs.

The modelled response of low clouds does not appear to be dominated

by a single feedback mechanism, but rather the net effect of sever-

al potentially competing mechanisms as elucidated in LES and GCM

sensitivity studies (e.g., Zhang and Bretherton, 2008; Blossey et al.,

2013; Bretherton et al., 2013). Starting with some proposed negative

feedback mechanisms, it has been argued that in a warmer climate,

low clouds will be: (1) horizontally more extensive, because changes

in the lapse rate of temperature also modify the lower-tropospheric

stability (Miller, 1997); (2) optically thicker, because adiabatic ascent

is accompanied by a larger condensation rate (Somerville and Remer,

1984); and (3) vertically more extensive, in response to a weakening

of the tropical overturning circulation (Caldwell and Bretherton, 2009).

While these mechanisms may play some role in subtropical low cloud

feedbacks, none of them appears dominant. Regarding (1), dry static

stability alone is a misleading predictor with respect to climate chang-

es, as models with comparably good simulations of the current region-

al distribution and/or relationship to stability of low cloud can produce

a broad range of cloud responses to climate perturbations (Wyant et

al., 2006). Mechanism (2), discussed brieﬂy in the next section, appears

to have a small effect. Mechanism (3) cannot yet be ruled out but does

not appear to be the dominant factor in determining subtropical cloud

changes in GCMs (Bony and Dufresne, 2005; Zhang and Bretherton,

2008).

Since the AR4, several new positive feedback mechanisms have been

proposed, most associated with the marine boundary layer clouds

thought to be at the core of the spread in responses. These include the

ideas that: warming-induced changes in the absolute humidity lapse

rate change the energetics of mixing in ways that demand a reduction

in cloud amount or thickness (Webb and Lock, 2013; Bretherton et al.,

2013; Brient and Bony, 2013); energetic constraints prevent the sur-

face evaporation from increasing with warming at a rate sufﬁcient to

balance expected changes in dry air entrainment, thereby reducing the

supply of moisture to form clouds (Rieck et al., 2012; Webb and Lock,

2013); and that increased concentrations of GHGs reduce the radia-

tive cooling that drives stratiform cloud layers and thereby the cloud

amount (Caldwell and Bretherton, 2009; Stevens and Brenguier, 2009;

Bretherton et al., 2013). These mechanisms, crudely operating through

parameterized representations of cloud processes, could explain why

climate models consistently produce positive low-cloud feedbacks.

Among CFMIP GCMs, the low-cloud feedback ranges from −0.09 to

+0.63 W m

–2

°C

–1

(Figure 7.10), and is largely associated with a reduc-

tion in low-cloud amount, albeit with considerable spatial variabili-

ty (e.g., Webb et al., 2013). One ‘super-parameterized’ GCM (Section

7.2.2.2) simulates a negative low-cloud feedback (Wyant et al., 2006,

2009), but that model’s representation of low clouds was worse than

some conventional GCMs.

The tendency of both GCMs and process models to produce these

positive feedback effects suggests that the feedback contribution

from changes in low clouds is positive. However, deﬁcient representa-

tion of low clouds in GCMs, diverse model results, a lack of reliable

590

Chapter 7 Clouds and Aerosols

observational constraints, and the tentative nature of the suggested

mechanisms leave us with low conﬁdence in the sign of the low-cloud

feedback contribution.

7.2.5.4 Feedbacks Involving Changes in Cloud Opacity

It has long been suggested that cloud water content could increase in

a warmer climate simply due to the availability of more vapour for con-

densation in a warmer atmosphere, yielding a negative feedback (Pal-

tridge, 1980; Somerville and Remer, 1984), but this argument ignores

the physics of crucial cloud-regulating processes such as precipitation

formation and turbulence. Observational evidence discounting a large

effect of this kind was reported in AR4 (Randall et al., 2007).

The global mean net feedback from cloud opacity changes in CFMIP

models (Figure 7.10) is approximately zero. Optical depths tend to

reduce slightly at low and middle latitudes, but increase poleward

of 50°, yielding a positive longwave feedback that roughly offsets

the negative shortwave feedback. These latitude-dependent opacity

changes may be attributed to phase changes at high latitudes and

greater poleward moisture transport (Vavrus et al., 2009), and possibly

to poleward shifts of the circulation.

Studies have reported warming-related changes in cloud opacity tied

to cloud phase (e.g., Senior and Mitchell, 1993; Tsushima et al., 2006).

This might be expected to cause negative feedback, because at mixed-

phase temperatures of –38 to 0°C, cloud ice particles have typical

diameters of 10 to 100 µm (e.g., Figure 8 in Donovan, 2003), sever-

al-fold larger than cloud water drops, so a given mass of cloud water

would have less surface area and reﬂect less sunlight in ice form than

in liquid form. As climate warms, a shift from ice to liquid at a given

location could increase cloud opacity. An offsetting factor that may

explain the absence of this in CFMIP, however, is that mixed-phase

clouds may form at higher altitudes, and similar local temperatures, in

warmer climates (Section 7.2.5.1). The key physics is in any case not

adequately represented in climate models. Thus this particular feed-

back mechanism is highly uncertain.

7.2.5.5 Feedback from Arctic Cloud Interactions with Sea Ice

Arctic clouds, despite their low altitude, have a net heating effect at

the surface in the present climate because their downward emission of

infrared radiation over the year outweighs their reﬂection of sunlight

during the short summer season. Their net effect on the atmosphere is

cooling, however, so their effect on the energy balance of the whole

system is ambiguous and depends on the details of the vertical cloud

distribution and the impact of cloud radiative interactions on ice cover

(Palm et al., 2010).

Low-cloud amount over the Arctic oceans varies inversely with sea ice

amount (open water producing more cloud) as now conﬁrmed since

AR4 by visual cloud reports (Eastman and Warren, 2010) and lidar and

radar observations (Kay and Gettelman, 2009; Palm et al., 2010). The

observed effect is weak in boreal summer, when the melting sea ice

is at a similar temperature to open water and stable boundary layers

with extensive low cloud are common over both surfaces, and strong-

est in boreal autumn when cold air ﬂowing over regions of open water

stimulates cloud formation by boundary-layer convection (Kay and

Gettelman, 2009; Vavrus et al., 2011). Kay et al. (2011) show that a

GCM can represent this seasonal sensitivity of low cloud to open water,

but doing so depends on the details of how boundary-layer clouds are

parameterized. Vavrus et al. (2009) show that in a global warming sce-

nario, GCMs simulate more Arctic low cloud in all seasons, but espe-

cially during autumn and the onset of winter when open water and

very thin sea ice increase considerably, increasing upward moisture

transport to the clouds.

A few studies in the literature suggest negative feedbacks from Arctic

clouds, based on spatial correlations of observed warming and cloud-

iness (Liu et al., 2008) or tree-ring proxies of cloud shortwave effects

over the last millenium (Gagen et al., 2011). However, the spatial cor-

relations are not reliable indicators of feedback (Section 7.2.5.7), and

the tree-ring evidence (assuming it is a good proxy) applies only to the

shortwave effect of summertime cloud cover. The GCM studies would

be consistent with warmer climates being cloudier, but have opposite

radiative effects and positive feedback during the rest of the year. Even

though a small positive feedback is suggested by models, there is over-

all little evidence for signiﬁcant feedbacks from Arctic cloud.

7.2.5.6 Rapid Adjustments of Clouds to a Carbon

Dioxide Change

It is possible to partition the response of TOA radiation in GCMs to

an instantaneous doubling of CO

into a ‘rapid adjustment’ in which

the land surface, atmospheric circulations and clouds respond to the

radiative effect of the CO

increase, and an ‘SST-mediated’ response

that develops more slowly as the oceans warm (see Section 7.1.3).

This distinction is important not only to help understand model pro-

cesses, but because the presence of rapid adjustments would cause

clouds to respond slightly differently to a transient climate change (in

which SST changes have not caught up to CO

changes) or to a climate

change caused by other forcings, than they would to the same warm-

ing at equilibrium driven by CO

. There is also a rapid adjustment of the

hydrological cycle and precipitation ﬁeld, discussed in Section 7.6.3.

Gregory and Webb (2008) reported that in some climate models, rapid

adjustment of clouds can have TOA radiative effects comparable to

those of the ensuing SST-mediated cloud changes, though Andrews

and Forster (2008) found a smaller effect. Subsequent studies using

more accurate kernel-based techniques ﬁnd a cloud adjustment

of roughly +0.4 to +0.5 W m

–2

per doubling of CO

, with standard

deviation of about 0.3 W m

–2

across models (Vial et al., 2013; Zelinka

et al., 2013). This would account for about 20% of the overall cloud

response in a model with average sensitivity; and because it is not

strongly correlated with model sensitivity, it contributes perhaps 20%

of the inter-model response spread (Andrews et al., 2012; Webb et al.,

2013), which therefore remains dominated by feedbacks. The response

occurs due to a general decrease in cloud cover caused by the slight

stratiﬁcation driven by CO

warming of the troposphere, which espe-

cially for middle and low clouds has a net warming effect (Colman and

McAvaney, 2011; Zelinka et al., 2013). As explained at the beginning

of Section 7.2.5, feedback numbers given in this report already account

for these rapid adjustments.

591

Clouds and Aerosols Chapter 7

7.2.5.7 Observational Constraints on Global Cloud Feedback

A number of studies since AR4 have attempted to constrain overall

cloud feedback (or climate sensitivity) from observations of natural cli-

mate variability; here we discuss those using modern cloud, radiation

or other measurements (see a complementary discussion in Section

12.5 based on past temperature data and forcing proxies).

One approach is to seek observable aspects of present-day cloud

behaviour that reveal cloud feedback or some component thereof.

Varying parameters in a GCM sometimes produces changes in cloud

feedback that correlate with the properties of cloud simulated for

the present day, but this depends on the GCM (Yokohata et al., 2010;

Gettelman et al., 2013), and the resulting relationships do not hold

across multiple models such as those from CMIP3 (Collins et al., 2011).

Among the AR4 models, net cloud feedback correlates strongly with

mid-latitude relative humidity (Volodin, 2008), with TOA radiation at

high southern latitudes (Trenberth and Fasullo, 2010), and with humid-

ity at certain latitudes during boreal summer (Fasullo and Trenberth,

2012); if valid each of these regression relations would imply a rela-

tively strong positive cloud feedback in reality, but no mechanism has

been proposed to explain or validate them and such apparent skill can

arise fortuitously (Klocke et al., 2011). Likewise, Clement et al. (2009)

found realistic decadal variations of cloud cover over the North Paciﬁc

in only one model (HadGEM1) and argued that the relatively strong

cloud feedback in this model should therefore be regarded as more

likely, but this ﬁnding lacks a mechanistic explanation and may depend

on how model output is used (Broccoli and Klein, 2010). Chang and

Coakley (2007) examined mid-latitude maritime clouds and found

cloud thinning with increasing temperature, consistent with a positive

feedback, whereas Gordon and Norris (2010) found the opposite result

following a methodology that tried to isolate thermal and advective

effects. In summary, there is no evidence of a robust link between any

of the noted observables and the global feedback, though some appar-

ent connections are tantalizing and are being studied further.

Several studies have attempted to derive global climate sensitivity

from interannual relationships between global mean observations of

TOA radiation and surface temperature (see also Section 10.8.2.2).

One problem with this is the different spatial character of interannu-

al and long-term warming; another is that the methodology can be

confounded by cloud variations not caused by those of surface tem-

perature (Spencer and Braswell, 2008). A range of climate sensitivities

has been inferred based on such analyses (Forster and Gregory, 2006;

Lindzen and Choi, 2011). Crucially, however, among different GCMs

there is no correlation between the interannual and long-term cloud–

temperature relationships (Dessler, 2010; Colman and Hanson, 2012),

contradicting the basic assumption of these methods. Many but not

all atmosphere–ocean GCMs predict relationships that are consistent

with observations (Dessler, 2010, 2013). More recently there is interest

in relating the time-lagged correlations of cloud and temperature to

feedback processes (Spencer and Braswell, 2010) but again these rela-

tionships appear to reveal only a model’s ability to simulate ENSO or

other modes of interannual variability properly, which are not obvious-

ly informative about the cloud feedback on long-term global warming

(Dessler, 2011).

For a putative observational constraint on climate sensitivity to be

accepted, it should have a sound physical basis and its assumptions

should be tested appropriately in climate models. No method yet pro-

posed meets both conditions. Moreover, cloud responses to warming

can be sensitive to relatively subtle details in the geographic warm-

ing pattern, such as the slight hemispheric asymmetry due to the lag

of southern ocean warming relative to northern latitudes (Senior and

Mitchell, 2000; Yokohata et al., 2008). Cloud responses to speciﬁed

uniform ocean warming without CO

increases are not the same as

those to CO

-induced

global warming simulated with more realis-

tic oceans (Ringer et al., 2006), partly because of rapid adjustments

(Section 7.2.5.6) and because low clouds also feed back tightly to the

underlying surface (Caldwell and Bretherton, 2009). Simulated cloud

feedbacks also differ signiﬁcantly between colder and warmer climates

in some models (Cruciﬁx, 2006; Yoshimori et al., 2009) and between

volcanic and other forcings (Yokohata et al., 2005). These sensitivities

highlight the challenges facing any attempt to infer long-term cloud

feedbacks from simple data analyses.

7.2.6 Feedback Synthesis

Together, the water vapour, lapse rate and cloud feedbacks are the prin-

cipal determinants of equilibrium climate sensitivity. The water vapour

and lapse rate feedbacks, as traditionally deﬁned, should be thought of

as a single phenomenon rather than in isolation (see Section 7.2.4.2).

To estimate a 90% probability range for that feedback, we double the

variance of GCM results about the mean to account for the possibility

of errors common to all models, to arrive at +1.1 (+0.9 to +1.3) W m

−2

°C

−1

. Values in this range are supported by a steadily growing body

of observational evidence, model tests, and physical reasoning. As a

corollary, the net feedback from water vapour and lapse rate changes

combined is extremely likely positive, allowing for the possibility of

deep uncertainties or a fat-tailed error distribution. Key aspects of the

responses of water vapour and clouds to climate warming now appear

to be constrained by large-scale dynamical mechanisms that are not

sensitive to poorly represented small-scale processes, and as such, are

more credible. This feedback is thus known to be positive with high

conﬁdence, and contributes only a small part of the spread in GCM

climate sensitivity (Section 9.7). An alternative framework has recently

been proposed in which these feedbacks, and stabilization via ther-

mal emission, are all signiﬁcantly smaller and more consistent among

models; thus the range given above may overstate the true uncertainty.

Several cloud feedback mechanisms now appear consistently in GCMs,

summarized in Figure 7.11, most supported by other lines of evidence.

Nearly all act in a positive direction. First, high clouds are expected

to rise in altitude and thereby exert a stronger greenhouse effect in

warmer climates. This altitude feedback mechanism is well understood,

has theoretical and observational support, occurs consistently in GCMs

and CRMs and explains about half of the mean positive cloud feedback

in GCMs. Second, middle and high-level cloud cover tends to decrease

in warmer climates even within the ITCZ, although the feedback effect

of this is ambiguous and it cannot yet be tested observationally. Third,

observations and most models suggest storm tracks shift poleward in

a warmer climate, drying the subtropics and moistening the high lat-

itudes, which causes further positive feedback via a net shift of cloud

cover to latitudes that receive less sunshine. Finally, most GCMs also

592

Chapter 7 Clouds and Aerosols

predict that low cloud amount decreases, especially in the subtropics,

another source of positive feedback though one that differs signiﬁ-

cantly among models and lacks a well-accepted theoretical basis. Over

middle and high latitudes, GCMs suggest warming-induced transitions

from ice to water clouds may cause clouds to become more opaque,

but this appears to have a small systematic net radiative effect in

models, possibly because it is offset by cloud altitude changes.

Currently, neither cloud process models (CRMs and LES) nor observa-

tions provide clear evidence to contradict or conﬁrm feedback mecha-

nisms involving low clouds. In some cases these models show stronger

low-cloud feedbacks than GCMs, but each model type has limitations,

and some studies suggest stronger positive feedbacks are more realistic

(Section 7.2.5.7). Cloud process models suggest a variety of potentially

opposing response mechanisms that may account for the current spread

of GCM feedbacks. In summary we ﬁnd no evidence to contradict either

the cloud or water vapour–lapse rate feedback ranges shown by current

GCMs, although the many uncertainties mean that true feedback could

still lie outside these ranges. In particular, microphysical mechanisms

affecting cloud opacity or cirrus amount may well be missing from

GCMs. Missing feedbacks, if any, could act in either direction.

Based on the preceding synthesis of cloud behaviour, the net radia-

tive feedback due to all cloud types is judged likely to be positive. This

is reasoned probabilistically as follows. First, because evidence from

observations and process models is mixed as to whether GCM cloud

feedback is too strong or too weak overall, and because the positive

feedback found in GCMs comes mostly from mechanisms now sup-

ported by other lines of evidence, the central (most likely) estimate of

the total cloud feedback is taken as the mean from GCMs (+0.6 W m

–2

°C

–1

). Second, because there is no accepted basis to discredit individual

GCMs a priori, the probability distribution of the true feedback cannot

be any narrower than the distribution of GCM results. Third, since feed-

back mechanisms are probably missing from GCMs and some CRMs

suggest feedbacks outside the range in GCMs, the probable range of

Equator 30º Pole60º

Broadening of the Hadley Cell

Rising of the Melting Level

More Polar Clouds

Poleward Shift of Storms

Less Low Clouds

Narrowing of Tropical Ocean Rainfall Zones

Rising High Clouds

Figure 7.11 | Robust cloud responses to greenhouse warming (those simulated by most models and possessing some kind of independent support or understanding). The tro-

popause and melting level are shown by the thick solid and thin grey dashed lines, respectively. Changes anticipated in a warmer climate are shown by arrows, with red colour

indicating those making a robust positive feedback contribution and grey indicating those where the feedback contribution is small and/or highly uncertain. No robust mechanisms

contribute negative feedback. Changes include rising high cloud tops and melting level, and increased polar cloud cover and/or optical thickness (high conﬁdence); broadening of

the Hadley Cell and/or poleward migration of storm tracks, and narrowing of rainfall zones such as the Intertropical Convergence Zone (medium conﬁdence); and reduced low-cloud

amount and/or optical thickness (low conﬁdence). Conﬁdence assessments are based on degree of GCM consensus, strength of independent lines of evidence from observations or

process models and degree of basic understanding.

the feedback must be broader than its spread in GCMs. We estimate

a probability distribution for this feedback by doubling the spread

about the mean of all model values in Figure 7.10 (in effect assuming

an additional uncertainty about 1.7 times as large asthat encapsu-

lated in the GCM range, added to it in quadrature). This yields a 90%

(very likely) range of −0.2 to +2.0 W m

–2

°C

–1

, with a 17% probability

of a negative feedback.

Note that the assessment of feedbacks in this chapter is independent

of constraints on climate sensitivity from observed trends or palaeocli-

mate information discussed in Box 12.2.

7.2.7 Anthropogenic Sources of Moisture and Cloudiness

Human activity can be a source of additional cloudiness through spe-

ciﬁc processes involving a source of water vapour in the atmosphere.

We discuss here the impact of aviation and irrigation on water vapour

and cloudiness. The impact of water vapour sources from combustion

at the Earth’s surface is thought to be negligible. Changes to the hydro-

logical cycle because of land use change are brieﬂy discussed in Sec-

tion 12.4.8.

7.2.7.1 Contrails and Contrail-Induced Cirrus

Aviation jet engines emit hot moist air, which can form line shaped

persistent condensation trails (contrails) in environments that are

supersaturated with respect to ice and colder than about –40°C. The

contrails are composed of ice crystals that are typically smaller than

those of background cirrus (Heymsﬁeld et al., 2010; Frömming et al.,

2011). Their effect on longwave radiation dominates over their short-

wave effect (Stuber and Forster, 2007; Rap et al., 2010b; Burkhardt and

Kärcher, 2011) but models disagree on the relative importance of the

two effects. Contrails have been observed to spread into large cirrus

sheets that may persist for several hours, and observational studies

conﬁrm their overall positive net RF impact (Haywood et al., 2009).

593

Clouds and Aerosols Chapter 7

Frequently Asked Questions

FAQ 7.1 | How Do Clouds Affect Climate and Climate Change?

Clouds strongly affect the current climate, but observations alone cannot yet tell us how they will affect a future,

warmer climate. Comprehensive prediction of changes in cloudiness requires a global climate model. Such models

simulate cloud ﬁelds that roughly resemble those observed, but important errors and uncertainties remain. Dif-

ferent climate models produce different projections of how clouds will change in a warmer climate. Based on all

available evidence, it seems likely that the net cloud–climate feedback ampliﬁes global warming. If so, the strength

of this ampliﬁcation remains uncertain.

Since the 1970s, scientists have recognized the critical importance of clouds for the climate system, and for climate

change. Clouds affect the climate system in a variety of ways. They produce precipitation (rain and snow) that is

necessary for most life on land. They warm the atmosphere as water vapour condenses. Although some of the con-

densed water re-evaporates, the precipitation that reaches the surface represents a net warming of the air. Clouds

strongly affect the ﬂows of both sunlight (warming the planet) and infrared light (cooling the planet as it is radi-

ated to space) through the atmosphere. Finally, clouds contain powerful updraughts that can rapidly carry air from

near the surface to great heights. The updraughts carry energy, moisture, momentum, trace gases, and aerosol

particles. For decades, climate scientists have been using both observations and models to study how clouds change

with the daily weather, with the seasonal cycle, and with year-to-year changes such as those associated with El Niño.

All cloud processes have the potential to change as the climate state changes. Cloud feedbacks are of intense inter-

est in the context of climate change. Any change in a cloud process that is caused by climate change—and in turn

inﬂuences climate—represents a cloud–climate feedback. Because clouds interact so strongly with both sunlight

and infrared light, small changes in cloudiness can have a potent effect on the climate system.

Many possible types of cloud–climate feedbacks have been suggested, involving changes in cloud amount, cloud-

top height and/or cloud reﬂectivity (see FAQ7.1, Figure 1). The literature shows consistently that high clouds amplify

global warming as they interact with infrared light emitted by the atmosphere and surface. There is more uncer-

tainty, however, about the feedbacks associated with low-altitude clouds, and about cloud feedbacks associated

with amount and reﬂectivity in general.

Thick high clouds efﬁciently reﬂect sunlight, and both thick and thin high clouds strongly reduce the amount of

infrared light that the atmosphere and surface emit to space. The compensation between these two effects makes

Greenhouse

Warming

Cloud

Response

Tropics

High clouds rise as troposphere

deepens, increasing difference

between cloud top and surface

temperature.

Feedback

Mechanism

Mid-latitudes

High clouds more effectively trap

infrared radiation, increasing

surface warming.

Reduction in mid- and low-level cloudiness (left).

Shift of cloudy storm tracks poleward into

regions with less sunlight (right).

Less sunlight reflected by clouds back to space,

increasing surface warming.

FAQ 7.1, Figure 1 | Schematic of important cloud feedback mechanisms.

(continued on next page)

594

Chapter 7 Clouds and Aerosols

FAQ 7.1 (continued)

the surface temperature somewhat less sensitive to changes in high cloud amount than to changes in low cloud

amount. This compensation could be disturbed if there were a systematic shift from thick high cloud to thin cirrus

cloud or vice versa; while this possibility cannot be ruled out, it is not currently supported by any evidence. On

the other hand, changes in the altitude of high clouds (for a given high-cloud amount) can strongly affect surface

temperature. An upward shift in high clouds reduces the infrared light that the surface and atmosphere emit to

space, but has little effect on the reﬂected sunlight. There is strong evidence of such a shift in a warmer climate.

This ampliﬁes global warming by preventing some of the additional infrared light emitted by the atmosphere and

surface from leaving the climate system.

Low clouds reﬂect a lot of sunlight back to space but, for a given state of the atmosphere and surface, they have

only a weak effect on the infrared light that is emitted to space by the Earth. As a result, they have a net cooling

effect on the present climate; to a lesser extent, the same holds for mid-level clouds. In a future climate warmed

by increasing greenhouse gases, most IPCC-assessed climate models simulate a decrease in low and mid-level cloud

amount, which would increase the absorption of sunlight and so tend to increase the warming. The extent of this

decrease is quite model-dependent, however.

There are also other ways that clouds may change in a warmer climate. Changes in wind patterns and storm tracks

could affect the regional and seasonal patterns of cloudiness and precipitation. Some studies suggest that the signal

of one such trend seen in climate models—a poleward migration of the clouds associated with mid-latitude storm

tracks—is already detectable in the observational record. By shifting clouds into regions receiving less sunlight, this

could also amplify global warming. More clouds may be made of liquid drops, which are small but numerous and

reﬂect more sunlight back to space than a cloud composed of the same mass of larger ice crystals. Thin cirrus cloud,

which exerts a net warming effect and is very hard for climate models to simulate, could change in ways not simu-

lated by models although there is no evidence for this. Other processes may be regionally important, for example,

interactions between clouds and the surface can change over the ocean where sea ice melts, and over land where

plant transpiration is reduced.

There is as yet no broadly accepted way to infer global cloud feedbacks from observations of long-term cloud trends

or shorter-time scale variability. Nevertheless, all the models used for the current assessment (and the preceding

two IPCC assessments) produce net cloud feedbacks that either enhance anthropogenic greenhouse warming or

have little overall effect. Feedbacks are not ‘put into’ the models, but emerge from the functioning of the clouds in

the simulated atmosphere and their effects on the ﬂows and transformations of energy in the climate system. The

differences in the strengths of the cloud feedbacks produced by the various models largely account for the different

sensitivities of the models to changes in greenhouse gas concentrations.

Aerosol emitted within the aircraft exhaust may also affect high-level

cloudiness. This last effect is classiﬁed as an aerosol–cloud interaction

and is deemed too uncertain to be further assessed here (see also Sec-

tion 7.4.4). Climate model experiments (Rap et al., 2010a) conﬁrm ear-

lier results (Kalkstein and Balling Jr, 2004; Ponater et al., 2005) that avi-

ation contrails do not have, at current levels of coverage, an observable

effect on the mean or diurnal range of surface temperature (medium

conﬁdence).

Estimates of the RF from persistent (linear) contrails often correspond

to different years and need to be corrected for the continuous increase

in air trafﬁc. More recent estimates tend to indicate somewhat smaller

RF than assessed in the AR4 (see Table 7.SM.1 and text in Supplemen-

tary Material). We adopt an RF estimate of +0.01 (+0.005 to +0.03) W

–2

for persistent (linear) contrails for 2011, with a medium conﬁdence

attached to this estimate. An additional RF of +0.003 W m

–2

is due to

emissions of water vapour in the stratosphere by aviation as estimated

by Lee et al. (2009).

Forster et al. (2007) quoted Sausen et al. (2005) to update the 2000

forcing for aviation-induced cirrus (including linear contrails) to +0.03

(+0.01 to +0.08) W m

–2

but did not consider this to be a best esti-

mate because of large uncertainties. Schumann and Graf (2013) con-

strained their model with observations of the diurnal cycle of contrails

and cirrus in a region with high air trafﬁc relative to a region with

little air trafﬁc, and estimated a RF of +0.05 (+0.04 to +0.08) W m

–2

for contrails and contrail-induced cirrus in 2006, but their model has

a large shortwave contribution, and larger estimates are possible. An

alternative approach was taken by Burkhardt and Kärcher (2011), who

estimated a global RF for 2002 of +0.03 W m

–2

from contrails and

contrail cirrus within a climate model (Burkhardt and Kärcher, 2009),

after compensating for reduced background cirrus cloudiness in the

main trafﬁc areas. Based on these two studies we assess the combined

contrail and contrail-induced cirrus ERF for the year 2011 to be +0.05

(+0.02 to +0.15) W m

–2

to take into uncertainties on spreading rate,

optical depth, ice particle shape and radiative transfer and the ongoing

increase in air trafﬁc (see also Supplementary Material). A low conﬁ-

dence is attached to this estimate.

595

Clouds and Aerosols Chapter 7

7.2.7.2 Irrigation-Induced Cloudiness

Boucher et al. (2004) estimated a global ERF due to water vapour from

irrigation in the range of +0.03 to +0.10 W m

–2

but the net climate

effect was dominated by the evaporative cooling at the surface and by

atmospheric thermal responses to low-level humidiﬁcation. Regional

surface cooling was conﬁrmed by a number of more recent region-

al and global studies (Kueppers et al., 2007; Lobell et al., 2009). The

resulting increase in water vapour may induce a small enhancement in

precipitation downwind of the major irrigation areas (Puma and Cook,

2010), as well as some regional circulation patterns (Kueppers et al.,

2007). Sacks et al. (2009) reported a 0.001 increase in cloud fraction

over land (0.002 over irrigated land). This suggests an ERF no more

negative than –0.1 W m

–2

with very low conﬁdence.

7.3 Aerosols

The section assesses the role of aerosols in the climate system, focus-

ing on aerosol processes and properties, as well as other factors, that

inﬂuence aerosol–radiation and aerosol–cloud interactions. Processes

directly relevant to aerosol–cloud interactions are discussed in Section

7.4, and estimates of aerosol RFs and ERFs are assessed in Section

7.5. The time evolution of aerosols and their forcings are discussed

in Chapters 2 and 8, with Chapter 8 also covering changes in natural

volcanic aerosols.

7.3.1 Aerosols in the Present-Day Climate System

7.3.1.1 Aerosol Formation and Aerosol Types

Atmospheric aerosols, whether natural or anthropogenic, originate

from two different pathways: emissions of primary particulate matter

Low volatility gases

(sulphuric & nitric acid,

ammonia, organics)

High volatility gases

(SO

, NO

, VOCs)

Secondary Particles

(inorganics, SOA)

Aged Aerosols

Primary Particles

(POA, BC, sea-salt, dust)

Atmospheric state,

Cloud distribution,

Surface properties,

Sun-earth geometry

ERFaci

Deposition Deposition

EmissionsEmissions

Chemical

Reactions

Coagulation

Gas Phase

Condensed Phase

Reactions &

Nucleation

Condensation & Cloud Processing

ERFari

Optical Properties

(optical depth, single scattering

albedo, asymmetry factor)

Cloud Activity

(cloud condensation nuclei,

ice nuclei)

Surface

and formation of secondary particulate matter from gaseous precur-

sors (Figure 7.12). The main constituents of the atmospheric aerosol

are inorganic species (such as sulphate, nitrate, ammonium, sea salt),

organic species (also termed organic aerosol or OA), black carbon (BC,

a distinct type of carbonaceous material formed from the incomplete

combustion of fossil and biomass based fuels under certain condi-

tions), mineral species (mostly desert dust) and primary biological

aerosol particles (PBAPs). Mineral dust, sea salt, BC and PBAPs are

introduced into the atmosphere as primary particles, whereas non-sea-

salt sulphate, nitrate and ammonium are predominantly from second-

ary aerosol formation processes. The OA has both signiﬁcant primary

and secondary sources. In the present-day atmosphere, the majority

of BC, sulphate, nitrate and ammonium come from anthropogenic

sources, whereas sea salt, most mineral dust and PBAPs are predomi-

nantly of natural origin. Primary and secondary organic aerosols (POA

and SOA) are inﬂuenced by both natural and anthropogenic sources.

Emission rates of aerosols and aerosol precursors are summarized in

Table 7.1. The characteristics and role of the main aerosol species are

listed in Table 7.2.

7.3.1.2 Aerosol Observations and Climatology

New and improved observational aerosol data sets have emerged since

AR4. A number of ﬁeld experiments have taken place such as the Inter-

continental Chemical Transport Experiment (INTEX, Bergstrom et al.,

2010; Logan et al., 2010), African Monsoon Multidisciplinary Analysis

(AMMA; Jeong et al., 2008; Hansell et al., 2010), Integrated Campaign

for Aerosols, gases and Radiation Budget (ICARB; Moorthy et al., 2008

and references therein), Megacity Impact on Regional and Global Envi-

ronments ﬁeld experiment (MILAGRO; Paredes-Miranda et al., 2009),

Geostationary Earth Radiation Budget Inter-comparisons of Longwave

and Shortwave (GERBILS, Christopher et al., 2009), Arctic Research

of the Composition of the Troposphere from Aircraft and Satellites

Figure 7.12 | Overview of atmospheric aerosol and environmental variables and processes inﬂuencing aerosol–radiation and aerosol–cloud interactions. Gas-phase variables and

processes are highlighted in red while particulate-phase variables and processes appear in green. Although this ﬁgure shows a linear chain of processes from aerosols to forcings

(ERFari and ERFaci), it is increasingly recognized that aerosols and clouds form a coupled system with two-way interactions (see Figure 7.16).

596

Chapter 7 Clouds and Aerosols

Year 2000

Emissions

Tg yr

–1

or TgS yr

–1

Anthropogenic

NMVOCs

Anthropogenic

Black Carbon

Anthropogenic

POA

Anthropogenic

Biomass Burning

Aerosols

Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max

Total 126.9 98.2 157.9 4.8 3.6 6.0 10.5 6.3 15.3 55.2 43.3 77.9 41.6 34.5 49.6 49.1 29.0 85.3

Western Europe 11.0 9.2 14.3 0.4 0.3 0.4 0.4 0.3 0.4 4.0 3.0 7.0 4.2 3.4 4.5 0.4 0.1 0.8

Central Europe 2.9 2.3 3.5 0.1 0.1 0.2 0.3 0.2 0.4 3.0 2.3 5.0 1.2 1.1 1.2 0.3 0.1 0.4

Former Soviet Union 9.8 6.5 15.2 0.3 0.2 0.4 0.7 0.5 0.9 5.2 3.0 7.0 1.7 1.5 2.0 5.4 3.0 7.9

Middle East 13.0 9.9 15.0 0.1 0.1 0.2 0.2 0.2 0.3 3.6 3.2 4.1 1.4 1.4 1.4 0.3 0.0 1.3

North America 17.8 14.5 20.9 0.4 0.3 0.4 0.5 0.4 0.6 8.7 7.8 10.4 4.6 3.8 5.5 2.0 0.8 4.4

Central America 3.8 2.9 4.4 0.1 0.1 0.1 0.3 0.2 0.3 2.1 1.9 2.8 1.1 1.1 1.2 1.44 0.3 2.7

South America 8.6 8.2 9.2 0.3 0.2 0.3 0.6 0.3 0.8 2.5 1.9 3.6 3.4 3.4 3.5 5.9 2.6 10.9

Africa 13.2 9.9 15.0 0.5 0.4 0.6 1.4 1.0 1.9 3.1 2.6 4.4 2.4 2.3 2.4 23.9 18.5 35.3

China 16.4 11.5 24.5 1.2 0.7 1.5 2.4 1.1 3.1 11.7 9.6 17.0 10.9 8.9 12.7 1.1 0.3 2.3

India 8.9 7.3 10.8 0.7 0.5 1.0 1.9 1.0 3.3 2.9 2.6 3.9 5.8 3.7 8.5 0.5 0.1 0.9

Rest of Asia 18.1 14.1 23.9 0.6 0.5 0.7 1.7 0.8 3.0 3.9 2.2 5.7 4.1 3.2 5.9 2.0 0.4 3.4

Oceania 1.2 1.0 1.5 0.03 0.03 0.04 0.05 0.04 0.08 1.2 0.9 1.4 0.7 0.7 0.7 5.8 2.7 16.8

International Shipping 2.1 1.3 3.0 0.1 0.1 0.1 0.1 0.1 0.1 3.3 2.1 5.5

Table 7.1 | (a) Global and regional anthropogenic emissions of aerosols and aerosol precursors. The average, minimum and maximum values are from a range of available inven-

tories (Cao et al., 2006; European Commission et al., 2009; Soﬁev et al., 2009; Lu et al., 2010, 2011; Granier et al., 2011 and references therein; Knorr et al., 2012). It should be

noted that the minimum to maximum range is not a measure of uncertainties which are often difﬁcult to quantify. Units are Tg yr

–1

and TgS yr

–1

for sulphur dioxide (SO

). NMVOCs

stand for non-methane volatile organic compounds. (b) Global natural emissions of aerosols and aerosol precursors. Dust and sea-spray estimates span the range in the historical

CMIP5 simulations. The ranges for monoterpenes and isoprene are from Arneth et al. (2008). There are other biogenic volatile organic compounds (BVOCs) such as sesquiterpenes,

alcohols and aldehydes which are not listed here. Marine primary organic aerosol (POA) and terrestrial primary biological aerosol particle (PBAP) emission ranges are from Gantt et

al. (2011) and Burrows et al. (2009), respectively. Note that emission ﬂuxes from mineral dust, sea spray and terrestrial PBAPs are highly sensitive to the cut-off radius. The conver-

sion rate of BVOCs to secondary organic aerosol (SOA) is also indicated using the range from Spracklen et al. (2011) and a lower bound from Kanakidou et al. (2005). Units are Tg

–1

except for BVOCs (monoterpenes and isoprene), in TgC yr

–1

, and dimethysulphide (DMS), in TgS yr

–1

Source

Natural Global

Min Max

Sea spray 1400 6800

including marine POA 2 20

Mineral dust 1000 4000

Terrestrial PBAPs 50 1000

including spores 28

Dimethylsulphide (DMS) 10 40

Monoterpenes 30 120

Isoprene 410 600

SOA production from all BVOCs 20 380

(ARCTAS, Lyapustin et al., 2010), the Amazonian Aerosol Character-

ization Experiment 2008 (AMAZE-08, Martin et al., 2010b), the Inte-

grated project on Aerosol Cloud Climate and Air Quality interactions

(EUCAARI, Kulmala et al., 2011) and Atmospheric Brown Clouds (ABC,

Nakajima et al., 2007), which have improved our understanding of

regional aerosol properties.

Long-term aerosol mass concentrations are also measured more sys-

tematically at the surface by global and regional networks (see Section

2.2.3), and there are institutional efforts to improve the coordination

and quality assurance of the measurements (e.g., GAW, 2011). A survey

(a)

(b)

of the main aerosol types can be constructed from such measurements

(e.g., Jimenez et al., 2009; Zhang et al., 2012b; Figure 7.13). Such analy-

ses show a wide spatial variability in aerosol mass concentration, dom-

inant aerosol type, and aerosol composition. Mineral dust dominates

the aerosol mass over some continental regions with relatively higher

concentrations especially in urban South Asia and China, accounting

for about 35% of the total aerosol mass with diameter smaller than

10 mm. In the urban North America and South America, organic carbon

(OC) contributes the largest mass fraction to the atmospheric aerosol

(i.e., 20% or more), while in other areas of the world the OC fraction

ranks second or third with a mean of about 16%. Sulphate normally

accounts for about 10 to 30% by mass, except for the areas in rural

Africa, urban Oceania and South America, where it is less than about

10%. The mass fractions of nitrate and ammonium are only around 6%

and 4% on average, respectively. In most areas, elemental carbon (EC,

which refers to a particular way of measuring BC) represents less than

5% of the aerosol mass, although this percentage may be larger (about

12%) in South America, urban Africa, urban Europe, South, Southeast

and East Asia and urban Oceania due to the larger impact of combus-

tion sources. Sea salt can be dominant at oceanic remote sites with 50

to 70% of aerosol mass.

Aerosol optical depth (AOD), which is related to the column-inte-

grated aerosol amount, is measured by the Aerosol Robotic Network

(AERONET, Holben et al., 1998), other ground-based networks (e.g.,

597

Clouds and Aerosols Chapter 7

Table 7.2 | Key aerosol properties of the main aerosol species in the troposphere. Terrestrial primary biological aerosol particles (PBAPs), brown carbon and marine primary

organic aerosols (POA) are particular types of organic aerosols (OA) but are treated here as separate components because of their speciﬁc properties. The estimated lifetimes in

the troposphere are based on the AeroCom models, except for terrestrial PBAPs which are treated by analogy to other coarse mode aerosol types.

Aerosol Species Size Distribution Main Sources Main Sinks

Tropospheric

Lifetime

Key Climate

Relevant Properties

Sulphate Primary: Aitken, accumulation

and coarse modes

Secondary: Nucleation, Aitken,

and accumulation modes

Primary: marine and volcanic emissions.

Secondary: oxidation of SO

and other S gases

from natural and anthropogenic sources

Wet deposition

Dry deposition

~ 1 week Light scattering. Very

hygroscopic. Enhances

absorption when deposited

as a coating on black

carbon. Cloud condensation

nuclei (CCN) active.

Nitrate Accumulation and coarse modes Oxidation of NO

Wet deposition

Dry deposition

~ 1 week Light scattering.

Hygroscopic. CCN active.

Black carbon Freshly emitted: <100 nm

Aged: accumulation mode

Combustion of fossil fuels, biofuels and biomass Wet deposition

Dry deposition

1 week to 10 days Large mass absorption

efﬁciency in the shortwave.

CCN active when coated.

May be ice nuclei (IN) active.

Organic aerosol POA: Aitken and accumulation

modes. SOA: nucleation, Aitken

and mostly accumulation modes.

Aged OA: accumulation mode

Combustion of fossil fuel, biofuel and biomass.

Continental and marine ecosystems.

Some anthropogenic and biogenic

non-combustion sources

Wet deposition

Dry deposition

~ 1 week Light scattering. Enhances

absorption when deposited

as a coating on black

carbon. CCN active

(depending on aging

time and size).

... of which

brown carbon

Freshly emitted: 100–400 nm

Aged: accumulation mode

Combustion of biofuels and biomass. Natural

humic-like substances from the biosphere

Wet deposition

Dry deposition

~ 1 week Medium mass absorption

efﬁciency in the UV and

visible. Light scattering.

... of which

terrestrial PBAP

Mostly coarse mode Terrestrial ecosystems Sedimentation

Wet deposition

Dry deposition

1 day to 1 week

depending on size

May be IN active. May

form giant CCN

Mineral dust Coarse and super-coarse modes,

with a small accumulation mode

Wind erosion, soil resuspension.

Some agricultural practices and

industrial activities (cement)

Sedimentation

Dry deposition

Wet deposition

1 day to 1 week

depending on size

IN active. Light scattering

and absorption.

Greenhouse effect.

Sea spray Coarse and accumulation modes Breaking of air bubbles induced e.g., by

wave breaking. Wind erosion.

Sedimentation

Wet deposition

Dry deposition

1 day to 1 week

depending on size

Light scattering. Very

hygroscopic. CCN active.

Can include primary

organic compounds in

smaller size range

... of which

marine POA

Preferentially Aitken and

accumulation modes

Emitted with sea spray in biologically

active oceanic regions

Sedimentation

Wet deposition

Dry deposition

~ 1 week CCN active.

Bokoye et al., 2001; Che et al., 2009) and a number of satellite-based

sensors. Instruments designed for aerosol retrievals such as Moderate

Resolution Imaging Spectrometer (MODIS; Remer et al., 2005; Levy et

al., 2010; Kleidman et al., 2012), Multi-angle Imaging Spectro-Radi-

ometer (MISR; Kahn et al., 2005; Kahn et al., 2007) and Polarization

and Directionality of the Earth’s Reﬂectances (POLDER)/Polarization

and Anisotropy of Reﬂectances for Atmospheric Sciences Coupled with

Observations from Lidar (PARASOL) (Tanré et al., 2011) are used pref-

erentially to less specialized instruments such as Advanced Very High

Resolution Radiometer (AVHRR; e.g., Zhao et al., 2008a; Mishchenko

et al., 2012), Total Ozone Mapping Spectrometer (TOMS; Torres et al.,

2002) and Along Track Scanning Radiometer (ATSR)/Advanced Along

Track Scanning Radiometer (AATSR) (Thomas et al., 2010) although

the latter are useful for building aerosol climatologies because of their

long measurement records (see Section 2.2.3). Although each AOD

retrieval by satellite sensors shows some skill against more accurate

sunphotometer measurements such as those of AERONET, there are

still large differences among satellite products in regional and seasonal

patterns because of differences and uncertainties in calibration, sam-

pling, cloud screening, treatment of the surface reﬂectivity and aero-

sol microphysical properties (e.g., Li et al., 2009; Kokhanovsky et al.,

2010). The global but incomplete sampling of satellite measurements

can be combined with information from global aerosol models through

data assimilation techniques (e.g., Benedetti et al., 2009; Figure 7.14a).

Owing to the heterogeneity in their sources, their short lifetime and the

dependence of sinks on the meteorology, aerosol distributions show

large variations on daily, seasonal and interannual scales.

The CALIPSO spaceborne lidar (Winker et al., 2009) complements

existing ground-based lidars. It now provides a climatology of the

aerosol extinction coefﬁcient (Figure 7.14b–e), highlighting that over

most regions the majority of the optically active aerosol resides in the

lowest 1 to 2 km. Yu et al. (2010) and Kofﬁ et al. (2012) found that

global aerosol models tend to have a positive bias in the aerosol extin-

ction scale height in some (but not all) regions, due to an overesti-

mate of aerosol concentrations above 6 km. There is less information

available on the vertical proﬁle of aerosol number and mass concen-

trations, although a number of ﬁeld experiments involving research

and commercial aircraft have measured aerosol concentrations (e.g.,

Heintzenberg et al., 2011). In particular vertical proﬁles of BC mixing

ratios have been measured during the Aerosol Radiative Forcing over

India (ARFI) aircraft/high altitude balloon campaigns (Satheesh et al.,

2008), Arctic Research of the Composition of the Troposphere from Air-

craft and Satellites (ARCTAS; Jacob et al., 2010), Aerosol, Radiation,

598

Chapter 7 Clouds and Aerosols

1: SO

2: OC

3: NO

5: EC

6: Mineral

or Sea Salt

4: NH

(3) Europe (4) Oceania

(2) N. America

urban rural urban rural urban

(1) S. America

concentration (μg m

-3

)

100

0.01

(5) Marine

100

0.01

N. Atlantic Ocean

(Mace Head)

100

0.01

Indian Ocean

(Amsterdam Is.

)

(6) Marine

High Asia

urban

S.E.-E. Asia

urban

S. Asia

urban

China

rural

concentration (μg m

-3

)

100

0.01

concentration (μg m

-3

)

100

0.01

urban

(7) Africa (8) Asia

concentration (μg m

-3

)

concentration (μg m

-3

)

concentration (μg m

-3

)

100

0.01

concentration (μg m

-3

)

100

0.01

concentration (μg m

-3

)

100

0.01

rural

China

123456 123456 123456 123456 1 23456 123456

1234561 23456

1 23456 123456 1 23456 123456 123456123456123456

Figure 7.13 | Bar chart plots summarizing the mass concentration (μg m

–3

) of seven major aerosol components for particles with diameter smaller than 10 μm, from various rural

and urban sites (dots on the central world map) in six continental areas of the world with at least an entire year of data and two marine sites. The density of the sites is a qualitative

measure of the spatial representativeness of the values for each area. The North Atlantic and Indian Oceans panels correspond to measurements from single sites (Mace Head and

Amsterdam Island, respectively) that are not necessarily representative. The relative abundances of different aerosol compounds are considered to reﬂect the relative importance

of emissions of these compounds or their precursors, either anthropogenic or natural, in the different areas. For consistency the mass of organic aerosol (OA) has been converted

to that of organic carbon (OC), according to a conversion factor (typically 1.4 to 1.6), as provided in each study. For each area, the panels represent the median, the 25th to 75th

percentiles (box), and the 10th to 90th percentiles (whiskers) for each aerosol component. These include: (1) South America (Artaxo et al., 1998; Morales et al., 1998; Artaxo et

al., 2002; Celis et al., 2004; Bourotte et al., 2007; Fuzzi et al., 2007; Mariani and Mello, 2007; de Souza et al., 2010; Martin et al., 2010a; Gioda et al., 2011); (2) North America

with urban United States (Chow et al., 1993; Kim et al., 2000; Ito et al., 2004; Malm and Schichtel, 2004; Sawant et al., 2004; Liu et al., 2005); and rural United States (Chow

et al., 1993; Malm et al., 1994; Malm and Schichtel, 2004; Liu et al., 2005); (3) Europe with urban Europe (Lenschow et al., 2001; Querol et al., 2001, 2004, 2006, 2008; Roosli

et al., 2001; Rodriguez et al., 2002, 2004; Putaud et al., 2004; Hueglin et al., 2005; Lonati et al., 2005; Viana et al., 2006, 2007; Perez et al., 2008; Yin and Harrison, 2008; Lodhi et

al., 2009); and rural Europe (Gullu et al., 2000; Querol et al., 2001, 2004, 2009; Rodriguez et al., 2002; Putaud et al., 2004; Puxbaum et al., 2004;Rodrıguez et al., 2004; Hueglin

et al., 2005; Kocak et al., 2007; Salvador et al., 2007; Yttri, 2007; Viana et al., 2008; Yin and Harrison, 2008;Theodosi et al., 2010); (4) urban Oceania (Chan et al., 1997; Maenhaut

et al., 2000; Wang and Shooter, 2001; Wang et al., 2005a; Radhi et al., 2010); (5) marine northern Atlantic Ocean (Rinaldi et al., 2009; Ovadnevaite et al., 2011); (6) marine

Indian Ocean (Sciare et al., 2009; Rinaldi et al., 2011); (7) Africa with urban Africa (Favez et al., 2008; Mkoma, 2008; Mkoma et al., 2009a); and rural Africa (Maenhaut et al.,

1996; Nyanganyura et al., 2007; Mkoma, 2008, 2009a, 2009b; Weinstein et al., 2010); (8) Asia with high Asia, with altitude larger than 1680 m (Shresth et al., 2000; Zhang et

al., 2001, 2008, 2012b; Carrico et al., 2003; Rastogi and Sarin, 2005; Ming et al., 2007a; Rengarajan et al., 2007; Qu et al., 2008; Decesari et al., 2010; Ram et al., 2010); urban

Southeast and East Asia (Lee and Kang, 2001; Oanh et al., 2006; Kim et al., 2007; Han et al., 2008; Khan et al., 2010); urban South Asia (Rastogi and Sarin, 2005; Kumar et

al., 2007; Lodhi et al., 2009; Chakraborty and Gupta, 2010; Khare and Baruah, 2010; Raman et al., 2010; Safai et al., 2010; Stone et al., 2010; Sahu et al., 2011); urban China

(Cheng et al., 2000; Yao et al., 2002; Zhang et al., 2002; Wang et al., 2003, 2005b, 2006; Ye et al., 2003; Xiao and Liu, 2004; Hagler et al., 2006; Oanh et al., 2006; Zhang et al.,

2011, 2012b); and rural China (Hu et al., 2002; Zhang et al., 2002; Hagler et al., 2006; Zhang et al., 2012b).

599

Clouds and Aerosols Chapter 7

-5

-4

-3

-2

-1

Extinction coefficient (km

-1

)

82ºS 60ºS 30ºS 30ºN 60ºN 82ºNEq 82ºS 60ºS 30ºS 30ºN 60ºN 82ºNEq

(d) 20ºW - 40ºE (e) 60ºE - 120ºE

(km)

(b) 180ºW - 120ºW (c) 120ºW - 60ºW

(km)

0.8

0.0

0.4

0.2

0.6

Optical Depth

(550 nm)

(a)

d e

b c

and Cloud Processes affecting Arctic Climate (ARCPAC; Warneke et

al., 2010), Aerosol Radiative Forcing in East Asia (A-FORCE; Oshima

et al., 2012) and HIAPER Pole-to-Pole Observations (HIPPO1; Schwarz

et al., 2010) campaigns. Comparison between models and observa-

tions have shown that aerosol models tend to underestimate BC mass

concentrations in some outﬂow regions, especially in Asia, but overes-

timate concentrations in remote regions, especially at altitudes (Koch

et al., 2009b; Figure 7.15), which make estimates of their RFari uncer-

tain (see Section 7.5.2) given the large dependence of RFari on the

vertical distribution of BC (Ban-Weiss et al., 2012). Absorption AOD

can be retrieved from sun photometer measurements (Dubovik et al.,

2002) or a combination of ground-based transmittance and satellite

reﬂectance measurements (Lee et al., 2007) in situations where AOD

Figure 7.14 | (a) Spatial distribution of the 550 nm aerosol optical depth (AOD, unitless) from the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated

Forecast System model with assimilation of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (Benedetti et al., 2009; Morcrette et al., 2009) averaged

over the period 2003–2010; (b–e) latitudinal vertical cross sections of the 532 nm aerosol extinction coefﬁcient (km

–1

) for four longitudinal bands (180°W to 120°W, 120°W to

60°W, 20°W to 40°E, and 60°E to 120°E, respectively) from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument for the year 2010 (nighttime all-sky data,

version 3; Winker et al., 2013).

is larger than about 0.4. Koch et al. (2009b) and Bond et al. (2013)

used AERONET-based retrievals of absorption AOD to show that most

AeroCom models underestimate absorption in many regions, but there

remain representativeness issues when comparing point observations

to a model climatology.

7.3.2 Aerosol Sources and Processes

7.3.2.1 Aerosol Sources

Sea spray is produced at the sea surface by bubble bursting induced

mostly, but not exclusively, by breaking waves. The effective emission

ﬂux of sea spray particles to the atmosphere depends on the surface

600

Chapter 7 Clouds and Aerosols

wind speed, sea state and atmospheric stability, and to a lesser extent

on the temperature and composition of the sea water. Our understand-

ing of sea spray emissions has increased substantially since AR4; how-

ever, process-based estimates of the total mass and size distribution

of emitted sea spray particles continue to have large uncertainties (de

Leeuw et al., 2011; Table 7.1). Sea spray particles are composed of sea

salt and marine primary organic matter, the latter being found prefer-

entially in particles smaller than 200 nm in diameter (Leck and Bigg,

2008; Russell et al., 2010). The emission rate of marine POA depends

on biological activity in ocean waters (Facchini et al., 2008) and its

global emission rate has been estimated to be in the range 2 to 20

Tg yr

–1

(Gantt et al., 2011). Uncertainty in the source and composi-

tion of sea spray translates into a significant uncertainty in the aerosol

number concentration in the marine atmosphere that, unlike aerosol

optical depth and mass concentrations, can only be constrained by in

situ observations (Heintzenberg et al., 2000; Jaeglé et al., 2011).

Pressure (hPa)

1000

Pressure (hPa)

0.01 0.1 1 10 100 1000

BC MMR (ng kg

-1

)

0.01 0.1 1 10 100 1000

BC MMR (ng kg

-1

)

GMI

GOCART

IMPACT

INCA

ECHAM5-HAM

SPRINTARS

TM5

OsloCTM2

BCC

CAM4-Oslo

CAM5.1

HadGEM2

GISS-MATRIX

GISS-modelE

600

200

1000

600

200

HIPPO-1 (Jan 2009)

20ºS - 20ºN

HIPPO-1 (Jan 2009)

60ºS - 20ºS

A-FORCE (Mar-Apr. 2009)

26ºN - 38ºN

HIPPO-1 (Jan 2009)

20ºN - 60ºN

Figure 7.15 | Comparison of vertical proﬁles of black carbon (BC) mass mixing ratios (MMR, in ng kg

–1

) as measured by airborne single particle soot photometer (SP2) instruments

during the HIAPER Pole-to-Pole Observations (HIPPO1; Schwarz et al., 2010) and Aerosol Radiative Forcing in East Asia (A-FORCE; Oshima et al., 2012) aircraft campaigns and

simulated by a range of AeroCom II models (Schulz et al., 2009). The black solid lines are averages of a number of vertical proﬁles in each latitude zone with the horizontal lines

representing the standard deviation of the measurements at particular height ranges. Each HIPPO1 proﬁle is the average of about 20 vertical proﬁles over the mid-Paciﬁc in a two-

week period in January 2009. The A-FORCE proﬁle is the average of 120 vertical proﬁles measured over the East China Sea and Yellow Sea downstream of the Asian continent in

March to April 2009. The model values (colour lines) are monthly averages corresponding to the measurement location and month, using meteorology and emissions corresponding

to the year 2006.

Mineral dust particles are produced mainly by disintegration of aggre-

gates following creeping and saltation of larger soil particles over

desert and other arid surfaces (e.g., Zhao et al., 2006; Kok, 2011).

The magnitude of dust emissions to the atmosphere depends on the

surface wind speed and many soil-related factors such as its texture,

moisture and vegetation cover. The range of estimates for the global

dust emissions spans a factor of about ﬁve (Huneeus et al., 2011; Table

7.1). Anthropogenic sources, including road dust and mineral dust due

to human land use change, remain ill quantiﬁed although some recent

satellite observations suggest the fraction of mineral dust due to the

latter source could be 20 to 25% of the total (Ginoux et al., 2012a,

2012b).

The sources of biomass burning aerosols at the global scale are usually

inferred from satellite retrieval of burned areas and/or active ﬁres, but

inventories continue to suffer from the lack of sensitivity of satellite

601

Clouds and Aerosols Chapter 7

data to small ﬁres (Randerson et al., 2012) and uncertainties in emis-

sion factors. Terrestrial sources of PBAPs include bacteria, pollen, fungal

spores, lichen, viruses and fragments of animals and plants (Després

et al., 2012). Most of these particles are emitted in the coarse mode

(Pöschl et al., 2010) and the contribution to the accumulation mode is

thought to be small. There are only a few estimates of the global ﬂux

of PBAPs and these are poorly constrained (Burrows et al., 2009; Heald

and Spracklen, 2009; see Table 7.1).

The main natural aerosol precursors are dimethylsulphide (DMS) emit-

ted by the oceans and biogenic volatile organic compounds (BVOC)

emitted mainly by the terrestrial biosphere. BVOC emissions depend on

the amount and type of vegetation, temperature, radiation, the ambi-

ent CO

concentration and soil humidity (Grote and Niinemets, 2008;

Paciﬁco et al., 2009; Peñuelas and Staudt, 2010). While speciated BVOC

emission inventories have been derived for some continental regions,

global emission inventories or schemes are available only for isoprene,

monoterpenes and a few other compounds (Müller et al., 2008; Guen-

ther et al., 2012). The total global BVOC emissions have large uncer-

tainties, despite the apparent convergence in different model-based

estimates (Arneth et al., 2008).

The ratio of secondary to primary organic aerosol is larger than previ-

ously thought, but has remained somewhat ambiguous due to atmo-

spheric transformation processes affecting both these components

(Robinson et al., 2007; Jimenez et al., 2009; Pye and Seinfeld, 2010).

Globally, most of the atmospheric SOA is expected to originate from

biogenic sources, even though anthropogenic sources could be equally

important at northern mid-latitudes (de Gouw and Jimenez, 2009;

Lin et al., 2012). Recent studies suggest that the SOA formation from

BVOCs may be enhanced substantially by anthropogenic pollution due

to (1) high concentrations of nitrogen oxides (NO

) enhancing BVOC

oxidation, and (2) high anthropogenic POA concentrations that facili-

tate transformation of oxidized volatile organic compounds (VOCs) to

the particle phase (Carlton et al., 2010; Heald et al., 2011; Hoyle et

al., 2011). The uncertainty range of atmospheric SOA formation is still

large and estimated to be approximately 20 to 380 Tg yr

–1

(Hallquist et

al., 2009; Farina et al., 2010; Heald et al., 2010; Spracklen et al., 2011;

Table 7.1).

Anthropogenic sources of aerosol particles (BC, POA) and aerosol

precursors (sulphur dioxide, ammonia, NO

and NMVOCs, hereafter

also referred to as VOCs for simplicity) can be inferred from a priori

emission inventories (Table 7.1). They are generally better constrained

than natural sources, exceptions being anthropogenic sources of BC,

which could be underestimated (Bond et al., 2013), and anthropogenic

emissions of some VOCs, ﬂy-ash and dust which are still poorly known.

Since AR4, remote sensing by satellites has been increasingly used to

constrain natural and anthropogenic aerosol and aerosol precursor

emissions (e.g., Dubovik et al., 2008; Jaeglé et al., 2011; Huneeus et

al., 2012).

7.3.2.2 Aerosol Processes

New particle formation is the process by which low-volatility vapours

nucleate into stable molecular clusters, which under certain condens-

able vapour regimes can grow rapidly to produce nanometre-sized

aerosol particles. Since AR4, substantial progress in our understanding

of atmospheric nucleation and new particle formation has been made

(e.g., Zhang et al., 2012a). Multiple lines of evidence indicate that

while sulphuric acid is the main driver of nucleation (Kerminen et al.,

2010; Sipilä et al., 2010), the nucleation rate is affected by ammonia

and amines (Kurten et al., 2008; Smith et al., 2010; Kirkby et al., 2011;

Yu et al., 2012) as well as low-volatility organic vapours (Metzger et al.,

2010; Paasonen et al., 2010; Wang et al., 2010a). Nucleation pathways

involving only uncharged molecules are expected to dominate over

nucleation induced by ionization of atmospheric molecules in conti-

nental boundary layers, but the situation might be different in the free

atmosphere (Kazil et al., 2010; Hirsikko et al., 2011).

Condensation is the main process transferring low-volatility vapours to

aerosol particles, and also usually the dominant process for growth to

larger sizes. The growth of the smallest particles depends crucially on

the condensation of organic vapours (Donahue et al., 2011b; Riipinen et

al., 2011; Yu, 2011) and is therefore tied strongly with atmospheric SOA

formation discussed in Section 7.3.3.1. The treatment of condensation

of semi-volatile compounds, such as ammonia, nitric acid and most

organic vapours, remains a challenge in climate modeling. In addition,

small aerosol particles collide with one another and stick (coagulate),

one of the processes contributing to aerosol internal mixing. Coagula-

tion is an important sink for sub-micrometre size particles, typically

under high concentrations near sources and at lower concentrations in

locations where the aerosol lifetime is long and amount of condens-

able vapours is low. It is the main sink for the smallest aerosol particles

(Pierce and Adams, 2007).

Since AR4, observations of atmospheric nucleation and subsequent

growth of nucleated particles to larger sizes have been increasingly

reported in different atmospheric environments (Kulmala and Ker-

minen, 2008; Manninen et al., 2010; O’Dowd et al., 2010). Nucleation

and growth enhance atmospheric CCN concentrations (Spracklen et

al., 2008; Merikanto et al., 2009; Pierce and Adams, 2009a; Yu and

Luo, 2009) and potentially affect aerosol–cloud interactions (Wang

and Penner, 2009; Kazil et al., 2010; Makkonen et al., 2012a). However,

CCN concentrations may be fairly insensitive to changes in nucleation

rate because the growth of nucleated particles to larger sizes is limited

by coagulation (see Sections 7.3.3.3 and 7.4.6.2).

Aerosols also evolve due to cloud processing, followed by the aerosol

release upon evaporation of cloud particles, affecting the number con-

centration, composition, size and mixing state of atmospheric aerosol

particles. This occurs via aqueous-phase chemistry taking place inside

clouds, via altering aerosol precursor chemistry around and below

clouds, and via different aerosol–hydrometeor interactions. These pro-

cesses are discussed further in Section 7.4.1.2.

The understanding and modelling of aerosol sinks has seen less prog-

ress since AR4 in comparison to other aerosol processes. Improved dry

deposition models, which depend on the particle size as well as the

characteristics of the Earth’s surface, have been developed and are

increasingly being used in global aerosol models (Kerkweg et al., 2006;

Feng, 2008; Petroff and Zhang, 2010). Sedimentation throughout the

atmosphere and its role in dry deposition at the surface are impor-

tant for the largest particles in the coarse mode. The uncertainty in

602

Chapter 7 Clouds and Aerosols

the estimate of wet deposition by nucleation and impaction scaveng-

ing is controlled by the uncertainties in the prediction of the amount,

frequency and areal extent of precipitation, as well as the size and

chemical composition of aerosol particles. For insoluble primary parti-

cles like BC and dust, nucleation scavenging also depends strongly on

their degree of mixing with soluble compounds. Parameterization of

aerosol wet deposition remains a key source of uncertainty in aerosol

models, which affects the vertical and horizontal distributions of aer-

osols (Prospero et al., 2010; Vignati et al., 2010; Lee et al., 2011), with

further impact on model estimates of aerosol forcings.

7.3.3 Progress and Gaps in Understanding Climate

Relevant Aerosol Properties

The climate effects of atmospheric aerosol particles depend on their

atmospheric distribution, along with their hygroscopicity, optical prop-

erties and ability to act as CCN and IN. Key quantities for aerosol optical

and cloud forming properties are the particle number size distribution,

chemical composition, mixing state and morphology. These properties

are determined by a complex interplay between their sources, atmo-

spheric transformation processes and their removal from the atmo-

sphere (Section 7.3.2, Figures 7.12 and 7.16). Since AR4, measurement

of some of the key aerosol properties has been greatly improved in lab-

oratory and ﬁeld experiments using advanced instrumentation, which

allows for instance the analysis of individual particles. These experi-

mental studies have in turn stimulated improvement in the model rep-

resentations of the aerosol physical, chemical and optical properties

(Ghan and Schwartz, 2007). We focus our assessment on some of the

key issues where there has been progress since AR4.

7.3.3.1 Chemical Composition and Mixing State

Research on the climate impacts of aerosols has moved beyond the

simple cases of externally mixed sulphate, BC emitted from fossil fuel

combustion and biomass burning aerosols. Although the role of inor-

ganic aerosols as an important anthropogenic contributor to aerosol–

radiation and aerosol–cloud interactions has not been questioned, BC

has received increasing attention because of its high absorption as has

SOA because of its ubiquitous nature and ability to mix with other

aerosol types.

The physical properties of BC (strongly light-absorbing, refractory with

a vaporization temperature near 4000 K, aggregate in morphology,

insoluble in most organic solvents) allow a strict deﬁnition in principle

(Bond et al., 2013). Direct measurement of individual BC-containing

particles is possible with laser-induced incandescence (single particle

soot photometer, also called SP2; Gao et al., 2007; Schwarz et al., 2008a;

Moteki and Kondo, 2010), which has enabled accurate measurements

of the size of BC cores, as well as total BC mass concentrations. Con-

densation of gas-phase compounds on BC and coagulation with other

particles alter the mixing state of BC (e.g., Li et al., 2003; Pósfai et al.,

2003; Adachi et al., 2010), which can produce internally mixed BC in

polluted urban air on a time scale of about 12 hours (Moteki et al.,

2007; McMeeking et al., 2010). The resulting BC-containing particles

can become hygroscopic, which can lead to reduced lifetime and atmo-

spheric loading (Stier et al., 2006).

Formation processes of OA still remain highly uncertain, which is a

major weakness in the present understanding and model representa-

tion of atmospheric aerosols (Kanakidou et al., 2005; Hallquist et al.,

2009; Ziemann and Atkinson, 2012). Measurements by aerosol mass

spectrometers have provided some insights into sources and atmo-

spheric processing of OA (Zhang et al., 2005b; Lanz et al., 2007; Ulbrich

et al., 2009). Observations at continental mid-latitudes including urban

and rural/remote air suggest that the majority of SOA is probably

oxygenated OA (Zhang et al., 2005a, 2007a). Experiments within and

downstream of urban air indicate that under most circumstances SOA

substantially contributes to the total OA mass (de Gouw et al., 2005;

Volkamer et al., 2006; Zhang et al., 2007a).

There is a large range in the complexity with which OA is represent-

ed in global aerosol models. Some complex, yet still parameterized,

chemical schemes have been developed recently that account for mul-

tigenerational oxidation (Robinson et al., 2007; Jimenez et al., 2009;

Donahue et al., 2011a). Since AR4, some regional and global model

have used a new scheme based on lumping VOCs into volatility bins

(Robinson et al., 2007), which is an improved representation of the

two-product absorptive partitioning scheme (Kroll and Seinfeld, 2008)

for the formation and aging of SOA. This new framework includes

organic compounds of different volatility, produced from parent VOCs

by multi-generation oxidation processes and partitioned between the

aerosol and gas phases (Farina et al., 2010; Tsimpidi et al., 2010; Yu,

2011), which improves the agreement between observed and modeled

SOA in urban areas (Hodzic et al., 2010; Shrivastava et al., 2011). Field

observations and laboratory studies suggest that OA is also formed

efﬁciently in aerosol and cloud and liquid water contributing a sub-

stantial fraction of the organic aerosol mass (Sorooshian et al., 2007;

Miyazaki et al., 2009; Lim et al., 2010; Ervens et al., 2011a). Chemical

reactions in the aerosol phase (e.g., oligomerization) also make OA

less volatile and more hygroscopic, inﬂuencing aerosol–radiation and

aerosol–cloud interactions (Jimenez et al., 2009). As a consequence,

OA concentrations are probably underestimated in many global aero-

sol models that do not include these chemical processes (Hallquist et

al., 2009).

Some of the OA is light absorbing and can be referred to as brown

carbon (BrC; Kirchstetter et al., 2004; Andreae and Gelencser, 2006).

A fraction of the SOA formed in cloud and aerosol water is light-

absorbing in the visible (e.g., Shapiro et al., 2009), while SOA produced

from gas-phase oxidation of VOCs absorbs ultraviolet radiation (e.g.,

Nakayama et al., 2010).

Multiple observations show co-existence of external and internal mix-

tures relatively soon after emission (e.g., Hara et al., 2003; Schwarz

et al., 2008b; Twohy and Anderson, 2008). In biomass burning aero-

sol, organic compounds and BC are frequently internally mixed with

ammonium, nitrate, and sulphate (Deboudt et al., 2010; Pratt and

Prather, 2010). Over urban locations, as much as 90% of the particles

are internally mixed with secondary inorganic species (Bi et al., 2011).

Likewise mineral dust and biomass burning aerosols can become inter-

nally mixed when these aerosol types age together (Hand et al., 2010).

The aerosol mixing state can alter particle size distribution and hygro-

scopicity and hence the aerosol optical properties and ability to act as

CCN (Wex et al., 2010). Global aerosol models can now approximate

603

Clouds and Aerosols Chapter 7

the aerosol mixing state using size-resolving bin or modal schemes

(e.g., Stier et al., 2005; Kim et al., 2008; Mann et al., 2010).

7.3.3.2 Size Distribution and Optical Properties

Aerosol size distribution is a key parameter determining both the aero-

sol optical and CCN properties. Since the AR4, much effort has been

put into measuring and simulating the aerosol number rather than

volume size distribution. For instance, number size distributions in the

submicron range (30 to 500 nm) were measured at 24 sites in Europe

for two years (Asmi et al., 2011), although systematic measurements

are still limited in other regions. Although validation studies show

agreement between column-averaged volume size distribution from

sunphotometer measurements and direct in situ (surface as well as

aircraft-based) measurements at some locations (Gerasopoulos et al.,

2007; Radhi et al., 2010; Haywood et al., 2011), these inversion prod-

ucts have not been systematically validated. Satellite measurements

produce valuable but more limited information on aerosol size.

The aerosol scattering, absorption and extinction coefﬁcients depend

on the aerosol size distribution, aerosol refractive index and mixing

state. The humidiﬁcation of internally mixed aerosols further inﬂuenc-

es their light scattering and absorption properties, through changes

in particle shape, size and refractive index (Freney et al., 2010). Aer-

osol absorption is a key climate-relevant aerosol property and earlier

in situ methods to measure it suffered from signiﬁcant uncertainties

(Moosmüller et al., 2009), partly due to the lack of proper reference

material for instrument calibration and development (Baumgardner et

al., 2012). Recent measurements using photo-acoustic methods and

laser-induced incandescence methods are more accurate but remain

sparse. The mass absorption cross sections for freshly generated BC

were measured to be 7.5 ± 1.2 m

–1

at 550 nm (Bond et al., 2013).

Laboratory measurements conducted under well controlled conditions

show that thick coating of soluble material over BC cores enhance the

mass absorption cross section by a factor of 1.8 to 2 (Cross et al., 2010;

Shiraiwa et al., 2010). It is more difﬁcult to measure the enhance-

ment factor in mass absorption cross sections for ambient BC, partly

owing to the necessity of removing coatings of BC. Knox et al. (2009)

observed enhancement by a factor 1.2 to 1.6 near source regions. A

much lower enhancement factor was observed by Cappa et al. (2012)

by a new measurement technique, in contradiction to the laboratory

experiments and theoretical calculations. These results may not be rep-

resentative and would require conﬁrmation by independent measure-

ment methods.

As discussed in Section 7.3.1.2, the global mean AOD is not well con-

strained from satellite-based measurements and remains a signiﬁcant

source of uncertainty when estimating aerosol–radiation interactions

(Su et al., 2013). This is also true of the anthropogenic fraction of AOD

which is more difﬁcult to constrain from observations. AeroCom phase

II models simulate an anthropogenic AOD at 550 nm of 0.03 ± 0.01

(with the range corresponding to one standard deviation) relative

to 1850, which represents 24 ± 6% of the total AOD (Myhre et al.,

2013). This is less than suggested by some satellite-based studies, i.e.,

0.03 over the ocean only in Kaufman et al. (2005), and about 0.06

as a global average in Loeb and Su (2010) and Bellouin et al. (2013),

but more than in the CMIP5 models (see Figure 9.29). Overall there

is medium conﬁdence that between 20 and 40% of the global mean

AOD is of anthropogenic origin. There is agreement that the anthropo-

genic aerosol is smaller in size and more absorbing than the natural

aerosol (Myhre, 2009; Loeb and Su, 2010), but there is disagreement

on the anthropogenic absorption AOD and its contribution to the total

absorption AOD, that is, 0.0015 ± 0.0007 (one standard deviation) rel-

ative to 1850 in Myhre et al. (2013) but about 0.004 and half of the

total absorption AOD in Bellouin et al. (2013).

7.3.3.3 Cloud Condensation Nuclei

A subset of aerosol particles acts as CCN (see Table 7.2). The ability of

an aerosol particle to take up water and subsequently activate, thereby

acting as a CCN at a given supersaturation, is determined by its size

and composition. Common CCN in the atmosphere are composed of

sea salt, sulphates and sulphuric acid, nitrate and nitric acid and some

organics. The uptake of water vapour by hygroscopic aerosols strongly

affects their RFari.

CCN activity of inorganic aerosols is relatively well understood, and

lately most attention has been paid to the CCN activity of mixed

organic/inorganic aerosols (e.g., King et al., 2010; Prisle et al., 2010).

Uncertainties in our current understanding of CCN properties are

due primarily to SOA (Good et al., 2010), mainly because OA is still

poorly characterized (Jimenez et al., 2009). The important effect of the

formation of SOA is that internally mixed SOA contributes to the mass

of aerosol particles, and therefore to their sizes. The size of the CCN

has been found to be more important than their chemical composi-

tion at two continental locations as larger particles are more readily

activated than smaller particles because they require a lower critical

supersaturation (Dusek et al., 2006; Ervens et al., 2007). However, the

chemical composition may be important in other locations such as

the marine environment, where primary organic particles (hydrogels)

have been shown to be exceptionally good CCN (Orellana et al., 2011;

Ovadnevaite et al., 2011). For SOA it is not clear how important surface

tension effects and bulk-to-surface partitioning of surfactants are, and

if the water activity coefﬁcient changes signiﬁcantly as a function of

the solute concentration (Prisle et al., 2008; Good et al., 2010).

The bulk hygroscopicity parameter k has been introduced as a concise

measure of how effectively an aerosol particle acts as a CCN (Rissler

et al., 2004, 2010; Petters and Kreidenweis, 2007). It can be measured

experimentally and is increasingly being used as a way to character-

ize aerosol properties. Pringle et al. (2010) used surface and aircraft

measurements to evaluate the k distributions simulated by a global

aerosol model, and found generally good agreement. When the aerosol

is dominated by organics, discrepancies between values of k obtained

directly from both CCN activity measurements and sub-saturated par-

ticle water uptake measurements have been observed in some instanc-

es (e.g., Prenni et al., 2007; Irwin et al., 2010; Roberts et al., 2010),

whereas in other studies closure has been obtained (e.g., Duplissy et

al., 2008; Kammermann et al., 2010; Rose et al., 2011). Adsorption

theory (Kumar et al., 2011) replaces k-theory for CCN activation for

insoluble particles (e.g., mineral dust) while alternative theories are

still required for explanation of marine POA that seem to have peculiar

gel-like properties (Ovadnevaite et al., 2011).

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Chapter 7 Clouds and Aerosols

Available modelling studies (Pierce and Adams, 2009a; Wang and

Penner, 2009; Schmidt et al., 2012a) disagree on the anthropogen-

ic fraction of CCN (taken here at 0.2% supersaturation). Based on

these studies we assess this fraction to be between one fourth and

two thirds in the global mean with low conﬁdence, and highlight

large interhemispheric and regional variations. Models that neglect

or underestimate volcanic and natural organic aerosols would over-

estimate this fraction.

7.3.3.4 Ice Nuclei

Aerosols that act as IN are solid substances at atmospheric temper-

atures and supersaturations. Mineral dust, volcanic ash and primary

bioaerosols such as bacteria, fungal spores and pollen, are typically

known as good IN (Vali, 1985; Hoose and Möhler, 2012). Conﬂicting

evidence has been presented for the ability of BC, organic, organic

semi-solid/glassy organic and biomass burning particles to act as IN

(Hoose and Möhler, 2012; Murray et al., 2012). The importance of bio-

logical particles acting as IN is unclear. A new study ﬁnds evidence of

a large fraction of submicron particles in the middle-to-upper tropo-

sphere to be composed of biological particles (DeLeon-Rodriguez et

al., 2013); however global modelling studies suggest that their concen-

trations are not sufﬁcient to play an important role for ice formation

(Hoose et al., 2010a; Sesartic et al., 2012). Because BC has anthropo-

genic sources, its increase since pre-industrial times may have caused

changes to the lifetime of mixed-phase clouds (Section 7.4.4) and thus

to RF (Lohmann, 2002b; Section 7.5).

Four heterogeneous ice-nucleation modes are distinguished in the

literature: immersion freezing (initiated from within a cloud droplet),

condensation freezing (freezing during droplet formation), contact

freezing (due to collision with an IN) and deposition nucleation (that

refers to the direct deposition of vapour onto IN). Lidar observations

reveal that liquid cloud droplets are present before ice crystals form via

heterogeneous freezing mechanisms (Ansmann et al., 2008; de Boer et

al., 2011), indicating that deposition nucleation does not seem to be

important for mixed-phase clouds. IN can either be bare or mixed with

other substances. As bare particles age in the atmosphere, they acquire

liquid surface coatings by condensing soluble species and water

vapour or by coagulating with soluble particles, which may transform

IN from deposition or contact nuclei into possible immersion nuclei. A

change from contact to immersion freezing implies activation at colder

temperatures, with consequences for the lifetime and radiative effect

of mixed-phase clouds (Sections 7.4.4 and 7.5.3).

The atmospheric concentrations of IN are very uncertain because of

the aforementioned uncertainties in freezing mechanisms and the dif-

ﬁculty of measuring IN in the upper troposphere. The anthropogenic

fraction cannot be estimated at this point because of a lack of knowl-

edge about the anthropogenic fractions of BC and mineral dust acting

as IN and the contributions of PBAPs, other organic aerosols and other

aerosols acting as IN.

7.3.4 Aerosol–Radiation Interactions

7.3.4.1 Radiative Effects due to Aerosol–Radiation Interactions

The radiative effect due to aerosol–radiation interactions (REari),

formerly known as direct radiative effect, is the change in radiative

ﬂux caused by the combined scattering and absorption of radiation

by anthropogenic and natural aerosols. The REari results from well-

understood physics and is close to being an observable quantity, yet

our knowledge of aerosol and environmental characteristics needed

to quantify the REari at the global scale remains incomplete (Ander-

son et al., 2005; Satheesh and Moorthy, 2005; Jaeglé et al., 2011). The

REari requires knowledge of the spectrally varying aerosol extinction

coefﬁcient, single scattering albedo, and phase function, which can in

principle be estimated from the aerosol size distribution, shape, chemi-

cal composition and mixing state (Figure 7.12). Radiative properties

of the surface, atmospheric trace gases and clouds also inﬂuence the

REari. In the solar spectrum under cloud-free conditions the REari is

typically negative at the TOA, but it weakens and can become posi-

tive with increasing aerosol absorption, decreasing upscatter fraction

or increasing albedo of the underlying surface. REari is weaker in

cloudy conditions, except when the cloud layer is thin or when absorb-

ing aerosols are located above or between clouds (e.g., Chand et al.,

2009). The REari at the surface is negative and can be much stronger

than the REari at the TOA over regions where aerosols are absorbing

(Li et al., 2010). In the longwave part of the spectrum, TOA REari is

generally positive and mainly exerted by coarse-mode aerosols, such

as sea spray and desert dust (Reddy et al., 2005), and by stratospheric

aerosols (McCormick et al., 1995).

There have been many measurement-based estimates of shortwave

REari (e.g., Yu et al., 2006; Bergamo et al., 2008; Di Biagio et al., 2010;

Bauer et al., 2011) although some studies involve some degree of mod-

elling. In contrast, estimates of longwave REari remain limited (e.g.,

Bharmal et al., 2009). Observed and calculated shortwave radiative

ﬂuxes agree within measurement uncertainty when aerosol properties

are known (e.g., Osborne et al., 2011). Global observational estimates

of the REari rely on satellite remote sensing of aerosol properties and/

or measurements of the Earth’s radiative budget (Chen et al., 2011;

Kahn, 2012). Estimates of shortwave TOA REari annually averaged over

cloud-free oceans range from –4 to –6 W m

–2

, mainly contributed by

sea spray (Bellouin et al., 2005; Loeb and Manalo-Smith, 2005; Yu et

al., 2006; Myhre et al., 2007). However, REari can reach tens of W m

–2

locally. Estimates over land are more difﬁcult as the surface is less well

characterized (Chen et al., 2009; Jethva et al., 2009) despite recent

progress in aerosol inversion algorithms (e.g., Dubovik et al., 2011).

Attempts to estimate the REari in cloudy sky remain elusive (e.g.,

Peters et al., 2011b), although passive and active remote sensing of

aerosols over clouds is now possible (Torres et al., 2007; Omar et al.,

2009; Waquet et al., 2009; de Graaf et al., 2012). Notable areas of posi-

tive TOA REari exerted by absorbing aerosols include the Arctic over ice

surfaces (Stone et al., 2008) and seasonally over southeastern Atlantic

stratocumulus clouds (Chand et al., 2009; de Graaf et al., 2012). While

AOD and aerosol size are relatively well constrained, uncertainties in

the aerosol single-scattering albedo (McComiskey et al., 2008; Loeb

and Su, 2010) and vertical proﬁle (e.g., Zarzycki and Bond, 2010) con-

tribute signiﬁcantly to the overall uncertainties in REari. Consequently,

605

Clouds and Aerosols Chapter 7

diversity in large-scale numerical model estimates of REari increases

with aerosol absorption and between cloud-free and cloudy conditions

(Stier et al., 2013).

7.3.4.2 Rapid Adjustments to Aerosol–Radiation Interactions

Aerosol–radiation interactions give rise to rapid adjustments (see

Section 7.1), which are particularly pronounced for absorbing aero-

sols such as BC. Associated cloud changes are often referred to as the

semi-direct aerosol effect (see Figure 7.3). The ERF from aerosol–radia-

tion interactions is quantiﬁed in Section 7.5.2; only the corresponding

processes governing rapid adjustments are discussed here. Impacts on

precipitation are discussed in Section 7.6.3.

Since AR4, additional observational studies have found correlations

between cloud cover and absorbing aerosols (e.g., Brioude et al., 2009;

Wilcox, 2010), and eddy-resolving, regional and global scale modelling

studies have helped conﬁrm a causal link. Relationships between cloud

and aerosol reveal a more complicated picture than initially anticipat-

ed (e.g., Hill and Dobbie, 2008; Koch and Del Genio, 2010; Zhuang et

al., 2010; Sakaeda et al., 2011; Ghan et al., 2012).

Absorbing aerosols modify atmospheric stability in the boundary layer

and free troposphere (e.g., Wendisch et al., 2008; Babu et al., 2011).

The effect of this on cloud cover depends on the height of the aerosol

relative to the cloud and the type of cloud (e.g., Yoshimori and Broccoli,

2008; Allen and Sherwood, 2010; Koch and Del Genio, 2010; Persad et

al., 2012). Aerosol also reduces the downwelling solar radiation at the

surface. Together the changes in atmospheric stability and reduction

in surface ﬂuxes provide a means for aerosols to signiﬁcantly modify

the fraction of surface-forced clouds (Feingold et al., 2005; Sakaeda et

al., 2011). These changes may also affect precipitation as discussed in

Section 7.6.3.

Cloud cover is expected to decrease if absorbing aerosol is embedded

in the cloud layer. This has been observed (Koren et al., 2004) and sim-

ulated (e.g., Feingold et al., 2005) for clouds over the Amazon forest in

the presence of smoke aerosols. In the stratocumulus regime, absorb-

ing aerosol above cloud top strengthens the temperature inversion,

reduces entrainment and tends to enhance cloudiness. Satellite obser-

vations (Wilcox, 2010) and modelling (Johnson et al., 2004) of marine

stratocumulus show a thickening of the cloud layer beneath layers of

absorbing smoke aerosol, which induces a local negative forcing. The

responses of other cloud types, such as those associated with deep

convection, are not well determined.

Absorbing aerosols embedded in cloud drops enhance their absorp-

tion, which can affect the dissipation of cloud. The contribution to RFari

is small (Stier et al., 2007; Ghan et al., 2012), and there is contradictory

evidence regarding the magnitude of the cloud dissipation effect inﬂu-

encing ERFari (Feingold et al., 2005; Ghan et al., 2012; Jacobson, 2012;

Bond et al., 2013). Global forcing estimates are necessarily based on

global models (see Section 7.5.2), although the accuracy of GCMs in

this regard is limited by their ability to represent low cloud processes

accurately. This is an area of concern as discussed in Section 7.2 and

limits conﬁdence in these estimates.

7.3.5 Aerosol Responses to Climate Change and

Feedback

The climate drivers of changes in aerosols can be split into physical

changes (temperature, humidity, precipitation, soil moisture, solar radi-

ation, wind speed, sea ice extent, etc.), chemical changes (availability

of oxidants) and biological changes (vegetation cover and properties,

plankton abundance and speciation, etc). The response of aerosols

to climate change may constitute a feedback loop whereby climate

processes amplify or dampen the initial perturbation (Carslaw et al.,

2010; Raes et al., 2010). We assess here the relevance and strength

of aerosol–climate feedbacks in the context of future climate change

scenarios.

7.3.5.1 Changes in Sea Spray and Mineral Dust

Concentrations of sea spray will respond to changes in surface wind

speed, atmospheric stability, precipitation and sea ice cover (Struthers

et al., 2011). Climate models disagree about the balance of effects,

with estimates ranging from an overall 19% reduction in global sea

salt burden from the present-day to year 2100 (Liao et al., 2006), to

little sensitivity (Mahowald et al., 2006a), to a sizeable increase (Jones

et al., 2007; Bellouin et al., 2011). In particular there is little under-

standing of how surface wind speed may change over the ocean in a

warmer climate, and observed recent changes (e.g., Young et al., 2011;

Section 2.7.2) may not be indicative of future changes. Given that sea

spray particles comprise a signiﬁcant fraction of CCN concentrations

over the oceans, such large changes will feed back on climate through

changes in cloud droplet number (Korhonen et al., 2010b).

Studies of the effects of climate change on dust loadings also give

a wide range of results. Woodward et al. (2005) found a tripling of

the dust burden in 2100 relative to present-day because of a large

increase in bare soil fraction. A few studies projected moderate (10

to 20%) increases, or decreases (e.g., Tegen et al., 2004; Jacobson and

Streets, 2009; Liao et al., 2009). Mahowald et al. (2006b) found a 60%

decrease under a doubled CO

concentration due to the effect of CO

fertilization on vegetation. The large range reﬂects different responses

of the atmosphere and vegetation cover to climate change forcings,

and results in low conﬁdence in these predictions.

7.3.5.2 Changes in Sulphate, Ammonium and Nitrate Aerosols

The DMS–sulphate–cloud–climate feedback loop could operate in

numerous ways through changes in temperature, absorbed solar radia-

tion, ocean mixed layer depth and nutrient recycling, sea ice extent,

wind speed, shift in marine ecosystems due to ocean acidiﬁcation

and climate change, and atmospheric processing of DMS into CCN.

Although no study has included all the relevant effects, two decades of

research have questioned the original formulation of the feedback loop

(Leck and Bigg, 2007) and have provided important insights into this

complex, coupled system (Ayers and Cainey, 2007; Kloster et al., 2007;

Carslaw et al., 2010). There is now medium conﬁdence for a weak feed-

back due to a weak sensitivity of the CCN population to changes in

DMS emissions, based on converging evidence from observations and

Earth System model simulations (Carslaw et al., 2010; Woodhouse et

al., 2010; Quinn and Bates, 2011). Parameterizations of oceanic DMS

606

Chapter 7 Clouds and Aerosols

production nevertheless lack robust mechanistic justiﬁcation (Halloran

et al., 2010) and as a result the sensitivity to ocean acidiﬁcation and

climate change remains uncertain (Bopp et al., 2004; Kim et al., 2010;

Cameron-Smith et al., 2011).

Chemical production of sulphate increases with atmospheric tempera-

ture (Aw and Kleeman, 2003; Dawson et al., 2007; Kleeman, 2008), but

future changes in sulphate are found to be more sensitive to simulated

future changes in precipitation removal. Under fixed anthropogenic

emissions, most studies to date predict a small (0 to 9%) reduction in

global sulphate burden mainly because of future increases in precipita-

tion (Liao et al., 2006; Racherla and Adams, 2006; Unger et al., 2006;

Pye et al., 2009). However, Rae et al. (2007) found a small increase

in global sulphate burden from 2000 to 2100 because the simulated

future precipitation was reduced in regions of high sulphate abun-

dance.

Changes in temperature have a large impact on nitrate aerosol forma-

tion through shifting gas–particle equilibria. There is some agreement

among global aerosol models that climate change alone will contrib-

ute to a decrease in the nitrate concentrations (Liao et al., 2006; Rach-

erla and Adams, 2006; Pye et al., 2009; Bellouin et al., 2011) with the

exception of Bauer et al. (2007) who found little change in nitrate for

year 2030. It should be noted however that these modeling studies

have reported that changes in precursor emissions are expected to

increase nitrate concentrations in the future (Section 11.3.5). Besides

the changes in meteorological parameters, climate change can also

influence ammonium formation by changing concentrations of sul-

phate and nitrate, but the effect of climate change alone was found to

be small (Pye et al., 2009).

7.3.5.3 Changes in Carbonaceous Aerosols

There is evidence that future climate change could lead to increases

in the occurrence of wildﬁres because of changes in fuel availabili-

ty, readiness of the fuel to burn and ignition sources (Mouillot et al.,

2006; Marlon et al., 2008; Spracklen et al., 2009; Kloster et al., 2010;

Pechony and Shindell, 2010). However, vegetation dynamics may also

play a role that is not well understood. Increased ﬁre occurrence would

increase aerosol emissions, but decrease BVOC emissions. This could

lead to a small positive or negative net radiative effect and feedback

(Carslaw et al., 2010).

A large fraction of SOA forms from the oxidation of isoprene, mono-

terpenes and sesquiterpenes from biogenic sources (Section 7.3.3.1).

Emissions from vegetation can increase in a warmer atmosphere,

everything else being constant (Guenther et al., 2006). Global aerosol

models simulate an increase in isoprene emissions of 22 to 55% by

2100 in response to temperature change (Sanderson et al., 2003; Liao

et al., 2006; Heald et al., 2008) and a change in global SOA burden

of −6% to +100% through climate-induced changes in aerosol pro-

cesses and removal rates (Liao et al., 2006; Tsigaridis and Kanakidou,

2007; Heald et al., 2008). An observationally based study suggest a

small global feedback parameter of −0.01 W m

–2

°C

–1

despite larger

regional effects (Paasonen et al., 2013). Increasing CO

concentrations,

drought and surface ozone also affect BVOC emissions (Arneth et al.,

2007; Peñuelas and Staudt, 2010), which adds signiﬁcant uncertainty

to future global emissions (Makkonen et al., 2012b). Future changes

in vegetation cover, whether they are natural or anthropogenic, also

introduce large uncertainty in emissions (Lathière et al., 2010; Wu et

al., 2012). There is little understanding on how the marine source of

organic aerosol may change with climate, notwithstanding the large

range of emission estimates for the present day (Carslaw et al., 2010).

7.3.5.4 Synthesis

The emissions, properties and concentrations of aerosols or aerosol pre-

cursors could respond signiﬁcantly to climate change, but there is little

consistency across studies in the magnitude or sign of this response.

The lack of consistency arises mostly from our limited understanding

of processes governing the source of natural aerosols and the complex

interplay of aerosols with the hydrological cycle. The feedback param-

eter as a result of the future changes in emissions of natural aerosols

is mostly bracketed within ±0.1 W m

–2

°C

–1

(Carslaw et al., 2010). With

respect to anthropogenic aerosols, Liao et al. (2009) showed a signiﬁ-

cant positive feedback (feedback parameter of +0.04 to +0.15 W m

–2

°C

–1

on a global mean basis) while Bellouin et al. (2011) simulated a

smaller negative feedback of −0.08 to −0.02 W m

–2

°C

–1

. Overall we

assess that models simulate relatively small feedback parameters (i.e.,

within ±0.2 W m

–2

°C

–1

) with low conﬁdence, however regional effects

on the aerosol may be important.

7.4 Aerosol–Cloud Interactions

7.4.1 Introduction and Overview of Progress Since AR4

This section assesses our understanding of aerosol–cloud interac-

tions, emphasizing the ways in which anthropogenic aerosol may be

affecting the distribution and radiative properties of non- and weakly

precipitating clouds. The idea that anthropogenic aerosol is changing

cloud properties, thus contributing a substantial forcing to the climate

system, has been addressed to varying degrees in all of the previous

IPCC assessment reports.

Since AR4, research has continued to articulate new pathways through

which the aerosol may affect the radiative properties of clouds, as well

as the intensity and spatial patterns of precipitation (e.g., Rosenfeld

et al., 2008). Progress can be identiﬁed on four fronts: (1) global-scale

modelling now represents a greater diversity of aerosol–cloud interac-

tions, and with greater internal consistency; (2) observational studies

continue to document strong local correlations between aerosol and

cloud properties or precipitation, but have become more quantitative

and are increasingly identifying and addressing the methodological

challenges associated with such correlations; (3) regional-scale mod-

elling is increasingly being used to assess regional inﬂuences of aer-

osol on cloud ﬁeld properties and precipitation; (4) ﬁne-scale process

models have begun to be used more widely, and among other things

have shown how turbulent mixing, cloud and meso-scale circulations

may buffer the effects of aerosol perturbations.

This section focuses on the microphysics of aerosol–cloud interactions

in liquid, mixed-phase and pure ice clouds. Their radiative implications

are quantiﬁed in Section 7.5. This section also includes a discussion of

607

Clouds and Aerosols Chapter 7

aerosol inﬂuences on light precipitation in shallow clouds but defers

discussion of aerosol effects on more substantial precipitation from

mixed-phase clouds to Section 7.6.4.

7.4.1.1 Classiﬁcation of Hypothesized Aerosol–Cloud

Interactions

Denman et al. (2007) catalogued several possible pathways via which

the aerosol might affect clouds. Given the number of possible aero-

sol–cloud interactions, and the difﬁculty of isolating them individually,

there is little value in attempting to assess each effect in isolation,

especially because modelling studies suggest that the effects may

interact and compensate (Stevens and Feingold, 2009; Morrison and

Grabowski, 2011). Instead, all radiative consequences of aerosol–

cloud interactions are grouped into an ‘effective radiative forcing due

to aerosol–cloud interactions’, or ERFaci (Figure 7.3). ERFaci accounts

for aerosol-related microphysical modiﬁcations to the cloud albedo

(Twomey, 1977), as well as any secondary effects that result from

clouds adjusting rapidly to changes in their environment (i.e., ‘lifetime

effects’; Albrecht, 1989; Liou and Ou, 1989; Pincus and Baker, 1994).

We do assess the physical underpinnings of the cloud albedo effect,

but in contrast to previous assessments, no longer distinguish the

resultant forcing. Note that ERFaci includes potential radiative adjust-

ments to the cloud system associated with aerosol–cloud interactions

but does not include adjustments originating from aerosol–radiation

interactions (ERFari). Possible contributions to ERFaci from warm

(liquid) clouds are discussed in Section 7.4.3, separately from those

associated with adjustments by cold (ice or mixed-phase) clouds (Sec-

tion 7.4.4). Figure 7.16 shows a schematic of many of the processes to

be discussed in Sections 7.4, 7.5 and 7.6.

15 km

50 – 100 km

Ice Nucleation

Scavenging

Mixing

Cold Pool

Precipitation

New Particle Production

Convective Initiation

Surface fluxes

Stratiform Convective Interactions

Clear-sky Reﬂectance

Shortwave Irradiance

Ice Formation &

Precipitation Initiation

Longwave Irradiance

Mesoscale Downdraughts

Scavenging by

Droplet Coalesence

Aerosol Activation

Figure 7.16 | Schematic depicting the myriad aerosol–cloud–precipitation related processes occurring within a typical GCM grid box. The schematic conveys the importance of

considering aerosol–cloud–precipitation processes as part of an interactive system encompassing a large range of spatiotemporal scales. Cloud types include low-level stratocumu-

lus and cumulus where research focuses on aerosol activation, mixing between cloudy and environmental air, droplet coalescence and scavenging which results in cloud processing

of aerosol particles, and new particle production near clouds; cirrus clouds where a key issue is ice nucleation through homogeneous and heterogeneous freezing; and deep convec-

tive clouds where some of the key questions relate to aerosol inﬂuences on liquid, ice, and liquid–ice pathways for precipitation formation, cold pool formation and scavenging.

These processes inﬂuence the shortwave and longwave cloud radiative effect and hence climate. Primary processes that affect aerosol–cloud interactions are labelled in blue while

secondary processes that result from and inﬂuence aerosol–cloud interactions are in grey.

7.4.1.2 Advances and Challenges in Observing Aerosol–Cloud

Interactions

Since AR4, numerous ﬁeld studies (e.g., Rauber et al., 2007; Wood et

al., 2011b; Vogelmann et al., 2012) and laboratory investigations (e.g.,

Stratmann et al., 2009) of aerosol–cloud interactions have highlighted

the numerous ways that the aerosol impacts cloud processes, and how

clouds in turn modify the aerosol. The latter occurs along a number of

pathways including aqueous chemistry, which adds aerosol mass to

droplets (e.g., Schwartz and Freiberg, 1981; Ervens et al., 2011a); coa-

lescence scavenging, whereby drop collision–coalescence diminishes

the droplet (and aerosol) number concentration (Hudson, 1993) and

changes the mixing state of the aerosol; new particle formation in the

vicinity of clouds (Clarke et al., 1999); and aerosol removal by precipi-

tation (see also Section 7.3.2.2).

Satellite-based remote sensing continues to be the primary source of

global data for aerosol–cloud interactions but concerns persist regard-

ing how measurement artefacts affect retrievals of both aerosol (Tanré

et al., 1996; Tanré et al., 1997; Kahn et al., 2005; Jeong and Li, 2010)

and cloud properties (Platnick et al., 2003; Yuekui and Di Girolamo,

2008) in broken cloud ﬁelds. Two key issues are that measurements

classiﬁed as ‘cloud-free’ may not be, and that aerosol measured in the

vicinity of clouds is signiﬁcantly different than it would be were the

cloud ﬁeld, and its proximate cause (high humidity), not present (e.g.,

Loeb and Schuster, 2008). The latter results from humidiﬁcation effects

on aerosol optical properties (Charlson et al., 2007; Su et al., 2008;

Tackett and Di Girolamo, 2009; Twohy et al., 2009; Chand et al., 2012),

contamination by undetectable cloud fragments (Koren et al., 2007)

and the remote effects of radiation scattered by cloud edges on aerosol

retrieval (Wen et al., 2007; Várnai and Marshak, 2009).

608

Chapter 7 Clouds and Aerosols

While most passive satellite retrievals are unable to distinguish aerosol

layers above or below clouds from those intermingling with the cloud

ﬁeld, active space-based remote sensing (L’Ecuyer and Jiang, 2010) has

begun to address this problem (Stephens et al., 2002; Anderson et al.,

2005; Huffman et al., 2007; Chand et al., 2008; Winker et al., 2010).

Spectral polarization and multi-angular measurements can discrimi-

nate between cloud droplets and aerosol particles and thus improve

estimates of aerosol loading and absorption (Deuzé et al., 2001; Chow-

dhary et al., 2005; Mishchenko et al., 2007; Hasekamp, 2010).

Use of active remote sensing, both from monitoring ground stations

(e.g., McComiskey et al., 2009; Li et al., 2011) and satellites (Costantino

and Bréon, 2010), as well as aerosol proxies not inﬂuenced by cloud

contamination of retrievals (Jiang et al., 2008; Berg et al., 2011) have

emerged as a particular effective way of identifying whether aerosol

and cloud perturbations are intermingled.

Because the aerosol is a strong function of air-mass history and origin,

and is strongly inﬂuenced by cloud and precipitation processes (Clarke

et al., 1999; Petters et al., 2006; Anderson et al., 2009), and both are

affected by meteorology (Engström and Ekman, 2010; Boucher and

Quaas, 2013), correlations between the aerosol and cloud, or precipi-

tation, should not be taken as generally indicating a cloud response to

the aerosol (e.g., Painemal and Zuidema, 2010). Furthermore, attempts

to control for other important factors (air-mass history or cloud dynam-

ical processes) are limited by a lack of understanding of large-scale

cloud controlling factors in the ﬁrst place (Anderson et al., 2009; Siebe-

sma et al., 2009; Stevens and Brenguier, 2009). These problems are

increasingly being considered in observationally based inferences of

aerosol effects on clouds and precipitation, but ascribing changes in

cloud properties to changes in the aerosol remains a fundamental

challenge.

7.4.1.3 Advances and Challenges in Modelling

Aerosol–Cloud Interactions

Modelling of aerosol–cloud interactions must contend with the fact

that the key physical processes are fundamentally occurring at the

ﬁne scale and cannot be represented adequately based on large-scale

ﬁelds. There exist two distinct challenges: fundamental understanding

of processes and their representation in large-scale models.

Fine-scale LES and CRM models (Section 7.2.2.1) have greatly advanced

as a tool for testing the physical mechanisms proposed to govern aer-

osol–cloud–precipitation interactions (e.g., Ackerman et al., 2009; van-

Zanten et al., 2011). Their main limitation is that they are idealized, for

example, they do not resolve synoptic scale circulations or allow for

representation of orography. A general ﬁnding from explicit numerical

simulations of warm (liquid) clouds is that various aerosol impact mech-

anisms tend to be mediated (and often buffered) by interactions across

scales not included in the idealized albedo and lifetime effects (Stevens

and Feingold, 2009). Speciﬁc examples involve the interplay between

the drop-size distribution and mixing processes that determine cloud

macrostructure (Stevens et al., 1998; Ackerman et al., 2004; Brether-

ton et al., 2007; Wood, 2007; Small et al., 2009), or the dependence of

precipitation development in stratiform clouds on details of the vertical

structure of the cloud (Wood, 2007). Thus warm clouds may typically

be less sensitive to aerosol perturbations in nature than in large-scale

models, which do not represent all of these compensating processes.

Hints of similar behaviour in mixed-phase (liquid and ice) stratus are

beginning to be documented (Section 7.4.4.3) but process-level under-

standing and representation in models are less advanced.

Regional models include realism in the form of non-idealized meteor-

ology, synoptic scale forcing, variability in land surface, and diurnal/

monthly cycles (e.g., Iguchi et al., 2008; Bangert et al., 2011; Seifert et

al., 2012; Tao et al., 2012), however, at the expense of resolving ﬁne-

scale cloud processes. Regional models have brought to light the pos-

sibility of aerosol spatial inhomogeneity causing changes in circulation

patterns via numerous mechanisms including changes in the radiative

properties of cloud anvils (van den Heever et al., 2011), changes in the

spatial distribution of precipitation (Lee, 2012; Section 7.6.2) or gra-

dients in heating rates associated with aerosol–radiation interactions

(Lau et al., 2006; Section 7.3.4.2).

GCMs, our primary tool for quantifying global mean forcings, now rep-

resent an increasing number of hypothesized aerosol–cloud interac-

tions, but at poor resolution. GCMs are being more closely scrutinized

through comparisons to observations and to other models (Quaas et

al., 2009; Penner et al., 2012). Historically, aerosol–cloud interactions

in GCMs have been based on simple constructs (e.g., Twomey, 1977;

Albrecht, 1989; Pincus and Baker, 1994). There has been signiﬁcant

progress on parameterizing aerosol activation (e.g., Ghan et al., 2011)

and ice nucleation (Liu and Penner, 2005; Barahona and Nenes, 2008;

DeMott et al., 2010; Hoose et al., 2010b); however, these still depend

heavily on unresolved quantities such as updraught velocity. Similarly,

parameterizations of aerosol inﬂuences on cloud usually do not account

for known non-monotonic responses of cloud amount and properties

to aerosols (Section 7.4.3.2). Global models are now beginning to rep-

resent aerosol effects in convective, ice and mixed-phase clouds (e.g.,

Lohmann, 2008; Song and Zhang, 2011; Section 7.6.4). Nevertheless,

for both liquid-only and mixed-phase clouds, these parameterizations

are severely limited by the need to parameterize cloud-scale motions

over a huge range of spatio-temporal scales (Section 7.2.3).

Although advances have been considerable, the challenges remain

daunting. The response of cloud systems to aerosol is nuanced (e.g.,

vanZanten et al., 2011) and the representation of both clouds and

aerosol–cloud interactions in large-scale models remains primitive

(Section 7.2.3). Thus it is not surprising that large-scale models exhibit

a range of manifestations of aerosol–cloud interactions, which limits

quantitative inference (Quaas et al., 2009). This highlights the need to

incorporate into GCMs the lessons learned from cloud-scale models, in

a physically-consistent way. New ‘super-parameterization’ and prob-

ability distribution function approaches (Golaz et al., 2002; Rio and

Hourdin, 2008; Section 7.2.2.2) hold promise, with recent results sup-

porting the notion that aerosol forcing is smaller than simulated by

standard climate models (Wang et al., 2011b; see Section 7.5.3).

7.4.1.4 Combined Modelling and Observational Approaches

Combined approaches, which attempt to maximize the respective

advantage of models and observations, are beginning to add to

understanding of aerosol–cloud interactions. These include inversions

609

Clouds and Aerosols Chapter 7

of the observed historical record using simpliﬁed climate models(e.g.,

Forest et al., 2006; Aldrin et al., 2012) but also the use of reanalysis

and chemical transport models to help interpret satellite records (Cha-

meides et al., 2002; Koren et al., 2010a; Mauger and Norris, 2010), ﬁeld

study data to help constrain ﬁne-scale modelling studies (e.g., Acker-

man et al., 2009; vanZanten et al., 2011), or satellite/surface-based

climatologies to constrain large-scale modelling (Wang et al., 2012).

7.4.2 Microphysical Underpinnings of Aerosol–Cloud

Interactions

7.4.2.1 The Physical Basis

The cloud albedo effect (Twomey, 1977) is the mechanism by which

an increase in aerosol number concentration leads to an increase in

the albedo of liquid clouds (reﬂectance of incoming solar radiation)

by increasing the cloud droplet number concentration, decreasing the

droplet size, and hence increasing total droplet surface area, with the

liquid water content and cloud geometrical thickness hypothetically

held ﬁxed. Although only the change in the droplet concentration is

considered in the original concept of the cloud albedo effect, a change

in the shape of the droplet size distribution that is directly induced

by the aerosols may also play a role (e.g., Feingold et al., 1997; Liu

and Daum, 2002). In the Arctic, anthropogenic aerosol may inﬂuence

the longwave emissivity of optically thin liquid clouds and generate

a positive forcing at the surface (Garrett and Zhao, 2006; Lubin and

Vogelmann, 2006; Mauritsen et al., 2011), but TOA forcing is thought

to be negligible.

7.4.2.2 Observational Evidence for Aerosol–Cloud Interactions

The physical basis of the albedo effect is fairly well understood, with

research since AR4 generally reinforcing earlier work. Detailed in situ

aircraft observations show that droplet concentrations observed just

above the cloud base generally agree with those predicted based on

the aerosol properties and updraught velocity observed below the

cloud (e.g., Fountoukis et al., 2007). Vertical proﬁles of cloud droplet

effective radius also agree with those predicted by models that take

into account the effect of entrainment (Lu et al., 2008), although uncer-

tainties still remain in estimating the shape of the droplet size distri-

bution (Brenguier et al., 2011), and the degree of entrainment mixing

within clouds.

At relatively low aerosol loading (AOD less than about 0.3) there is

ample observational evidence for increases in aerosol resulting in an

increase in droplet concentration and decrease in droplet size (for

constant liquid water) but uncertainties remain regarding the magni-

tude of this effect, and its sensitivity to spatial averaging. Based on

simple metrics, there is a large range of physically plausible responses,

with aircraft measurements (e.g., Twohy et al., 2005; Lu et al., 2007,

2008;; Hegg et al., 2012) tending to show stronger responses than sat-

ellite-derived responses (McComiskey and Feingold, 2008; Nakajima

and Schulz, 2009; Grandey and Stier, 2010). At high AOD and high

aerosol concentration, droplet concentration tends to saturate (e.g.,

Verheggen et al., 2007) and, if the aerosol is absorbing, there may be

reductions in droplet concentration and cloudiness (Koren et al., 2008).

This absorbing effect originates from aerosol–radiation interactions

and is therefore part of ERFari (Section 7.3.4.2). Negative correlation

between AOD and ice particle size has also been documented in deep

convective clouds (e.g., Sherwood, 2002; Jiang et al., 2008).

7.4.2.3 Advances in Process Level Understanding

At the heart of the albedo effect lie two fundamental issues. The ﬁrst is

aerosol activation and its sensitivity to aerosol and dynamical param-

eters. The primary controls on droplet concentration are the aerosol

number concentration (particularly at diameters greater than about 60

nm) and cooling rate (proportional to updraught velocity). Aerosol size

distribution can play an important role under high aerosol loadings,

whereas aerosol composition tends to be much less important, except

perhaps under very polluted conditions and low updraught veloci-

ties (e.g., Ervens et al., 2005; McFiggans et al., 2006). This is partially

because aging tends to make particles more hygroscopic regardless of

their initial composition, but also because more hygroscopic particles

lead to faster water vapour uptake, which then lowers supersaturation,

limiting the initial increase in activation.

The second issue is that the amount of energy reﬂected by a cloud

system is a strong function of the amount of condensate. Simple argu-

ments show that in a relative sense the amount of reﬂected energy is

approximately two-and-a-half times more sensitive to changes in the

liquid water path than to changes in droplet concentration (Boers and

Mitchell, 1994). Because both of these parameters experience similar

ranges of relative variability, the magnitude of aerosol–cloud related

forcing rests mostly on dynamical factors such as turbulent strength

and entrainment that control cloud condensate, and a few key aerosol

parameters such as aerosol number concentration and size distribu-

tion, and to a much lesser extent, composition.

7.4.3 Forcing Associated with Adjustments in

Liquid Clouds

7.4.3.1 The Physical Basis for Adjustments in Liquid Clouds

The adjustments giving rise to ERFaci are multi-faceted and are asso-

ciated with both albedo and so-called ‘lifetime’ effects (Figure 7.3).

However, this old nomenclature is misleading because it assumes a

relationship between cloud lifetime and cloud amount or water con-

tent. Moreover, the effect of the aerosol on cloud amount may have

nothing to do with cloud lifetime per se (e.g., Pincus and Baker, 1994).

The traditional view (Albrecht, 1989; Liou and Ou, 1989) has been

that adjustment effects associated with aerosol–cloud–precipitation

interactions will add to the initial albedo increase by increasing cloud

amount. The chain of reasoning involves three steps: that droplet con-

centrations depend on the number of available CCN; that precipita-

tion development is regulated by the droplet concentration; and that

the development of precipitation reduces cloud amount (Stevens and

Feingold, 2009). Of the three steps, the ﬁrst has ample support in both

observations and theory (Section 7.4.2.2). More problematic are the

last two links in the chain of reasoning. Although increased droplet

concentrations inhibit the initial development of precipitation (see

Section 7.4.3.2.1), it is not clear that such an effect is sustained in

an evolving cloud ﬁeld. In the trade-cumulus regime, some modelling

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Chapter 7 Clouds and Aerosols

studies suggest the opposite, with increased aerosol concentrations

actually promoting the development of deeper clouds and invigorat-

ing precipitation (Stevens and Seifert, 2008; see discussion of similar

responses in deep convective clouds in Section 7.6.4). Others have

shown alternating cycles of larger and smaller cloud water in both

aerosol-perturbed stratocumulus (Sandu et al., 2008) and trade cumu-

lus (Lee et al., 2012), pointing to the important role of environmental

adjustment. There exists limited unambiguous observational evidence

(exceptions to be given below) to support the original hypothesised

cloud-amount effects, which are often assumed to hold universally and

have dominated GCM parameterization of aerosol–cloud interactions.

GCMs lack the more nuanced responses suggested by recent work,

which inﬂuences their ERFaci estimates.

7.4.3.2 Observational Evidence of Adjustments in Liquid Clouds

Since observed effects generally include both the albedo effect and

the adjustments, with few if any means of observing only one or the

other in isolation, in this section we discuss and interpret observation-

al ﬁndings that reﬂect both effects. Stratocumulus and trade cumulus

regimes are discussed separately.

7.4.3.2.1 Stratocumulus

The cloud albedo effect is best manifested in so-called ship tracks,

which are bright lines of clouds behind ships. Many ship tracks are

characterized by an increase in the droplet concentration resulting

from the increase in aerosol number concentration and an absence of

drizzle size drops, which leads to a decrease in the droplet radius and

an increase in the cloud albedo (Durkee et al., 2000), all else equal.

However, liquid water changes are the primary determinant of albedo

changes (Section 7.4.2.3; Chen et al., 2012), therefore adjustments are

key to understanding radiative response. Coakley and Walsh (2002)

showed that cloud water responses can be either positive or nega-

tive. This is supported by more recent shiptrack analyses based on new

satellite sensors (Christensen and Stephens, 2011): aerosol intrusions

result in weak decreases in liquid water (−6%) in overcast clouds, but

signiﬁcant increases in liquid water (+39%) and increases in cloud frac-

tion in precipitating, broken stratocumulus clouds. The global ERFaci of

visible ship tracks has been estimated from satellite and found to be

insigniﬁcant at about –0.5 mW m

–2

(Schreier et al., 2007), although

this analysis may not have identiﬁed all shiptracks. Some observational

studies downwind of ship tracks have been unable todistinguish aero-

sol inﬂuences frommeteorological inﬂuences on cloudmicrophysical

or macrophysical properties (Peters et al., 2011a), although it is not

clear whether their methodology had sufﬁcient sensitivity to detect the

aerosol effects. Notwithstanding evidence of shiptracks locally increas-

ing the cloud fraction and albedo of broken cloud scenes quite sig-

niﬁcantly (e.g., Christensen and Stephens, 2011; Goren and Rosenfeld,

2012), their contribution to global ERFaci is thought to be small. These

ship track results are consistent with satellite studies of the inﬂuence

of long-term degassing of low-lying volcanic aerosol on stratocumulus,

which point to smaller droplet sizes but ambiguous changes in cloud

fraction and cloud water (Gasso, 2008). The lack of clear evidence for

a global increase in cloud albedo from shiptracks and volcanic plumes

should be borne in mind when considering geoengineering methods

that rely on cloud modiﬁcation (Section 7.7.2.2).

The development of precipitation in stratocumulus, whether due to

aerosol or meteorological inﬂuence can, in some instances, change

a highly reﬂective closed-cellular cloud ﬁeld to a weakly reﬂective

broken open-cellular ﬁeld (Comstock et al., 2005; Stevens et al., 2005a;

vanZanten et al., 2005; Sharon et al., 2006; Savic-Jovcic and Stevens,

2008; Wang and Feingold, 2009a). In some cases, compact regions

(pockets) of open-cellular convection become surrounded by regions

of closed-cellular convection. It is, however, noteworthy that observed

precipitation rates can be similar in both open and closed-cell envi-

ronments (Wood et al., 2011a). The lack of any apparent difference in

the large-scale environment of the open cells, versus the surrounding

closed cellular convection, suggests the potential for multiple equilib-

ria (Baker and Charlson, 1990; Feingold et al., 2010). Therefore in the

stratocumulus regime, the onset of precipitation due to a dearth of

aerosol may lead to a chain of events that leads to a large-scale reduc-

tion of cloudiness in agreement with Liou and Ou (1989) and Albrecht

(1989). The transition may be bidirectional: ship tracks passing through

open-cell regions also appear to revert the cloud ﬁeld to a closed-cell

regime inducing a potentially strong ERFaci locally (Christensen and

Stephens, 2011; Wang et al., 2011a; Goren and Rosenfeld, 2012).

7.4.3.2.2 Trade-cumulus

Precipitation from trade cumuli proves difﬁcult to observe, as the

clouds are small, and not easily observed by space-based remote sens-

ing techniques (Stephens et al., 2008). Satellite remote sensing of trade

cumuli inﬂuenced by aerosol associated with slow volcanic degas-

sing points to smaller droplet size, decreased precipitation efﬁciency,

increased cloud amount and higher cloud tops (Yuan et al., 2011).

Other studies show that in the trade cumulus regime cloud amount

tends to increase with precipitation amount: for example, processes

that favour precipitation development also favour cloud development

(Nuijens et al., 2009); precipitation-driven colliding outﬂows tend to

regenerate clouds; and trade cumuli that support precipitation reach

heights where wind shear increases cloud fraction (Zuidema et al.,

2012).

While observationally based study of the microphysical aspects of aer-

osol–cloud interactions has a long history, more recent assessment of

the ability of detailed models to reproduce the associated radiative

effect in cumulus cloud ﬁelds is beginning to provide the important link

between aerosol–cloud interactions and total RF (Schmidt et al., 2009).

7.4.3.3 Advances in Process Level Understanding

Central to ERFaci is the question of how susceptible is precipitation

to droplet concentration, and by inference, to the available aerosol.

Some studies point to the droplet effective radius as a threshold indi-

cator of the onset of drizzle (Rosenfeld and Gutman, 1994; Gerber,

1996; Rosenfeld et al., 2012). Others focus on the sensitivity of the

conversion of cloud water to rain water (i.e., autoconversion) to droplet

concentration, which is usually in the form of (droplet concentration)

to the power -a. Both approaches indicate that from the microphysical

standpoint, an increase in the aerosol suppresses rainfall. Models and

theory show a ranging from ½ (Kostinski, 2008; Seifert and Stevens,

2010) to 2 (Khairoutdinov and Kogan, 2000), while observational stud-

ies suggest a = 1 (approximately the inverse of drop concentration;

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Clouds and Aerosols Chapter 7

Pawlowska and Brenguier, 2003; Comstock et al., 2005; vanZanten et

al., 2005). Note that thicker liquid clouds amplify rain via accretion

of cloud droplets by raindrops, a process that is relatively insensitive

to droplet concentration, and therefore to aerosol perturbations (e.g.,

Khairoutdinov and Kogan, 2000). The balance of evidence suggests

that a = ½ is more likely and that liquid water path (or cloud depth)

has signiﬁcantly more leverage over precipitation than does droplet

concentration. Many GCMs assume a much stronger relationship

between precipitation and cloud droplet number concentration (i.e., a

= 2) (Quaas et al., 2009).

Small-scale studies (Ackerman et al., 2004; Xue et al., 2008; Small et

al., 2009) and satellite observations (Lebsock et al., 2008; Christensen

and Stephens, 2011) tend to conﬁrm two responses of the cloud liquid

water to increasing aerosol. Under clean conditions when clouds are

prone to precipitation, an increase in the aerosol tends to increase

cloud amount as a result of aerosol suppression of precipitation.

Under non-precipitating conditions, clouds tend to thin in response

to increasing aerosol through a combination of droplet sedimentation

(Bretherton et al., 2007) and evaporation–entrainment adjustments

(e.g., Hill et al., 2009). Treatment of the subtlety of these responses and

associated detail in small-scale cloud processes is not currently feasi-

ble in GCMs, although probability distribution function approaches are

promising (Guo et al., 2010).

Since AR4, cloud resolving model simulation has begun to stress the

importance of scale interactions when addressing aerosol–cloud inter-

actions. Model domains on the order of 100 km allow mesoscale circu-

lations to develop in response to changes in the aerosol. These dynam-

ical responses may have a signiﬁcant impact on cloud morphology and

RF. Examples include the signiﬁcant changes in cloud albedo asso-

ciated with transitions between closed and open cellular states dis-

cussed above, and the cloud-free, downdraught ‘shadows’ that appear

alongside ship tracks (Wang and Feingold, 2009b). Similar examples

of large-scale changes in circulation associated with aerosol and asso-

ciated inﬂuence on precipitation are discussed in Section 7.6.4. These

underscore the large gap between our process level understanding of

aerosol–cloud–precipitation interactions and the ability of GCMs to

represent them.

7.4.3.4 Advances in and Insights Gained from Large-Scale

Modelling Studies

Regional models are increasingly including representation of aerosol–

cloud interactions using sophisticated microphysical models (Bangert

et al., 2011; Yang et al., 2011; Seifert et al., 2012). Some of these

regional models are operational weather forecast models that under-

go routine evaluation. Yang et al. (2011) show improved simulations of

stratocumulus ﬁelds when aerosol–cloud interactions are introduced.

Regional models are increasingly being used to provide the meteoro-

logical context for satellite observations of aerosol–cloud interactions

(see Section 7.4.1.4), with some (e.g., Painemal and Zuidema, 2010)

suggesting that droplet concentration differences are driven primarily

by synoptic scale inﬂuences rather than aerosol.

GCM studies that have explored sensitivity to autoconversion param-

eterization (Golaz et al., 2011) show that ERFaci can vary by 1 W m

–2

depending on the parameterization. Elimination of the sensitivity of

rain formation to the autoconversion process has begun to be consid-

ered in GCMs (Posselt and Lohmann, 2009). Wang et al. (2012) have

used satellite observations to constrain autoconversion and ﬁnd a

reduction in ERFaci of about 33% relative to a standard GCM autocon-

version parameterization. It is worth reiterating that these uncertain-

ties are not necessarily associated with uncertainties in the physical

process itself, but more so by the ability of a GCM to resolve the pro-

cesses (see Section 7.4.1.3).

7.4.4 Adjustments in Cold Clouds

7.4.4.1 The Physical Basis for Adjustments in Cold Clouds

Mixed-phase clouds, containing both liquid water and ice particles,

exist at temperatures between 0°C and –38°C. At warmer tempera-

tures ice melts rapidly, whereas at colder temperatures liquid water

freezes homogeneously. The formation of ice in mixed-phase clouds

depends on heterogeneous freezing, initiated by IN (Section 7.3.3.4),

which are typically solid or crystalline aerosol particles. In spite of their

very low concentrations (on the order of 1 per litre), IN have an impor-

tant inﬂuence on mixed-phase clouds. Mineral dust particles have been

identiﬁed as good IN but far less is known about the IN ability of other

aerosol types, and their preferred modes of nucleation. For example,

the ice nucleating ability of BC particles remains controversial (Hoose

and Möhler, 2012). Soluble matter can hinder glaciation by depress-

ing the freezing temperature of supercooled drops to the point where

homogeneous freezing occurs (e.g., Girard et al., 2004; Baker and

Peter, 2008). Hence anthropogenic perturbations to the aerosol have

the potential to affect glaciation, water and ice optical properties, and

their radiative effect.

Because the equilibrium vapour pressure with respect to ice is lower

than that with respect to liquid, the initiation of ice in a supercooled

liquid cloud will cause vapour to diffuse rapidly toward ice particles

at the expense of the liquid water (Wegener–Bergeron–Findeisen pro-

cess; e.g., Schwarzenbock et al., 2001; Verheggen et al., 2007; Hudson

et al., 2010). This favours the depositional growth of ice crystals, the

largest of which may sediment away from the water-saturated region

of the atmosphere, inﬂuencing the subsequent evolution of the cloud.

Hence anthropogenic perturbations to the IN can inﬂuence the rate at

which ice forms, which in turn may regulate cloud amount (Lohmann,

2002b; Storelvmo et al., 2011; see also Section 7.2.3.2.2), cloud optical

properties and humidity near the tropopause.

Finally, formation of the ice phase releases latent heat to the environ-

ment (inﬂuencing cloud dynamics), and provides alternate, complex

pathways for precipitation to develop (e.g., Zubler et al., 2011, and

Section 7.6.4).

7.4.4.2 Observations of ERFaci in Deep Convective Clouds

As noted in Section 7.4.2.2, observations have demonstrated corre-

lations between aerosol loading and ice crystal size but inﬂuence on

cloud optical depth is unclear (e.g., Koren et al., 2005). Satellite remote

sensing suggests that aerosol-related invigoration of deep convec-

tive clouds may generate more extensive anvils that radiate at cooler

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Chapter 7 Clouds and Aerosols

temperatures, are optically thinner, and generate a positive contribu-

tion to ERFaci (Koren et al., 2010b). The global inﬂuence on ERFaci is

unclear.

7.4.4.3 Observations of Aerosol Effects on Arctic Ice and

Mixed-Phase Stratiform Clouds

Arctic mixed-phase clouds have received a great deal of attention

since AR4, with major ﬁeld programs conducted in 2004 (Verlinde et

al., 2007) and 2008 (Jacob et al., 2010; Brock et al., 2011; McFarqu-

har et al., 2011), in addition to long-term monitoring at high north-

ern latitude stations (e.g., Shupe et al., 2008) and analysis of earlier

ﬁeld experiments (Uttal et al., 2002). Mixed-phase Arctic clouds persist

for extended periods of time (days and even weeks; Zuidema et al.,

2005), in spite of the inherent instability of the ice–water mix (see

also Section 7.2.3.2.2). In spite of their low concentrations, IN have

an important inﬂuence on cloud persistence, with clouds tending to

glaciate and disappear rapidly when IN concentrations are relatively

high and/or updraught velocities too small to sustain a liquid water

layer (e.g., Ovchinnikov et al., 2011). The details of the heterogeneous

ice-nucleation mechanism remain controversial but there is increasing

evidence that ice forms in Arctic stratus via the liquid phase (immersion

freezing) so that the CCN population also plays an important role (de

Boer et al., 2011; Lance et al., 2011). If ice indeed forms via the liquid

phase this represents a self-regulating feedback that helps sustain the

mixed-phase clouds: as ice forms, liquid water (the source of the ice)

is depleted, which restricts further ice formation and competition for

water vapour via the Wegener–Bergeron–Findeisen process (Morrison

et al., 2012).

7.4.4.4 Advances in Process Level Understanding

Since AR4, research on ice-microphysical processes has been very

active as evidenced by the abovementioned ﬁeld experiments (Sec-

tion 7.4.4.3). The persistence of some mixed-phase stratiform clouds

has prompted efforts to explain this phenomenon in a theoretical

framework (Korolev and Field, 2008). Predicting cloud persistence may

require a high level of understanding of very detailed processes. For

example, ice particle growth by vapour diffusion depends strongly on

crystal shape (Harrington et al., 2009), the details of which may have

similar relative inﬂuence on glaciation times to the representation of

ice nucleation mechanism (Ervens et al., 2011b). A recent review (Mor-

rison et al., 2012) discusses the myriad processes that create a resilient

mixed-phase cloud system, invoking the ideas of ‘buffering’ seen in

liquid clouds (Stevens and Feingold, 2009). Importantly, the Wegener–

Bergeron–Findeisen process does not necessarily destabilize the cloud

system, unless sufﬁcient ice exists (Korolev, 2007). Bistability has also

been observed in the mixed-phase Arctic cloud system; the resilient

cloud state is sometimes interrupted by a cloud-free state (Stramler

et al., 2011), but there is much uncertainty regarding the meteorolog-

ical and microphysical conditions determining which of these states is

preferred.

Signiﬁcant effort has been expended on heterogeneous freezing param-

eterizations employed in cloud or larger-scale models. Some parame-

terizations are empirical (e.g., Lohmann and Diehl, 2006; Hoose et al.,

2008; Phillips et al., 2008; Storelvmo et al., 2008a; DeMott et al., 2010;

Gettelman et al., 2010; Salzmann et al., 2010), whereas others attempt

to represent the processes explicitly (Jacobson, 2003) or ground the

development of parameterizations in concepts derived from classical

nucleation theory (Chen et al., 2008; Hoose et al., 2010b). The details

of how these processes are treated have important implications for

tropical anvils (Ekman et al., 2007; Fan et al., 2010).

Homogeneous ice nucleation in cirrus clouds (at temperatures lower

than about –38°C) depends crucially on the cloud updraught velocity

and hence the supersaturation with respect to ice. The onset relative

humidities for nucleation have been parameterized using results of

parcel model simulations (e.g., Sassen and Dodd, 1988; Barahona and

Nenes, 2009), airborne measurements in cirrus or wave clouds (Heyms-

ﬁeld and Miloshevich, 1995; Heymsﬁeld et al., 1998), extensions of clas-

sical homogeneous ice nucleation theory (Khvorostyanov and Sassen,

1998; Khvorostyanov and Curry, 2009) and data from laboratory meas-

urements (e.g., Bertram et al., 2000; Koop et al., 2000; Mohler et al.,

2003; Magee et al., 2006; Friedman et al., 2011). There is new evidence

that although ice nucleation in cirrus has traditionally been regarded as

homogeneous, the preferred freezing pathway may be heterogeneous

because it occurs at lower onset relative humidities (or higher onset

temperatures) than homogeneous nucleation (Jensen et al., 2010). The

onset relative humidities (or temperatures) for heterogeneous nuclea-

tion depend on the type and size of the IN (Section 7.3.3.4).

Cloud resolving modeling of deep convective clouds points to the

potential for aerosol-related changes in cirrus anvils (e.g., Morrison

and Grabowski, 2011; van den Heever et al., 2011; Storer and van den

Heever, 2013), but the physical mechanisms involved and their inﬂuence

on ERFaci are poorly understood, and their global impact is unclear.

7.4.4.5 Advances in and Insights Gained from Large-Scale

Modelling Studies

Since the AR4, mixed-phase and ice clouds have received signiﬁcant

attention, with effort on representation of both heterogeneous (mixed-

phase clouds) and homogeneous (cirrus) freezing processes in GCMs

(e.g., Lohmann and Kärcher, 2002; Storelvmo et al., 2008a). In GCMs

the physics of cirrus clouds usually involves only ice-phase microphys-

ical processes and is somewhat simpler than that of mixed-phase

clouds. Nevertheless, representation of aerosol–cloud interactions in

mixed-phase and ice clouds is considerably less advanced than that

involving liquid-only clouds.

Our poor understanding of the climatology and lifecycle of aerosol par-

ticles that can serve as IN complicates attempts to assess what consti-

tutes an anthropogenic perturbation to the IN population, let alone the

effect of such a perturbation. BC can impact background (i.e., non con-

trail) cirrus by affecting ice nucleation properties but the effect remains

uncertain (Kärcher et al., 2007). The numerous GCM studies that have

evaluated ERFaci for ice clouds are summarised in Section 7.5.4.

7.4.5 Synthesis on Aerosol–Cloud Interactions

Earlier assessments considered the radiative implications of aerosol–

cloud interactions as two complementary processes—albedo and life-

time effects—that together amplify forcing. Since then the complexity

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Clouds and Aerosols Chapter 7

of cloud system responses to aerosol perturbations has become more

fully appreciated. Recent work at the process scale has identiﬁed com-

pensating adjustments that make the system less susceptible to pertur-

bation than might have been expected based on the earlier albedo and

lifetime effects. Increases in the aerosol can therefore result in either

an increase or a decrease in aerosol–cloud related forcing depending

on the particular environmental conditions. Because many current

GCMs do not include the possibility of compensating effects that are

not mediated by the large-scale state, there are grounds for expecting

these models to overestimate the magnitude of ERFaci. Nevertheless it

is also possible that poorly understood and unrepresented interactions

could cause real ERFaci to differ in either direction from that predicted

by current models. Forcing estimates are discussed in Section 7.5.3.

7.4.6 Impact of Cosmic Rays on Aerosols and Clouds

Many studies have reported observations that link solar activity to par-

ticular aspects of the climate system (e.g., Bond et al., 2001; Dengel et

al., 2009; Eichler et al., 2009). Various mechanisms have been proposed

that could amplify relatively small variations in total solar irradiance,

such as changes in stratospheric and tropospheric circulation induced

by changes in the spectral solar irradiance or an effect of the ﬂux of

cosmic rays on clouds. We focus in this subsection on the latter hypoth-

esis while Box 10.2 discusses solar inﬂuences on the climate system

more generally.

Solar activity variations inﬂuence the strength and three-dimensional

structure of the heliosphere. High solar activity increases the deﬂec-

tion of low energy cosmic rays, which reduces the ﬂux of cosmic rays

impinging upon the Earth’s atmosphere. It has been suggested that the

ionization caused by cosmic rays in the troposphere has an impact on

aerosols and clouds (e.g., Dickinson, 1975; Kirkby, 2007). This subsec-

tion assesses studies that either seek to establish a causal relationship

between cosmic rays and aerosols or clouds by examining empirical

correlations, or test one of the physical mechanisms that have been put

forward to account for such a relationship.

7.4.6.1 Observed Correlations Between Cosmic Rays and

Properties of Aerosols and Clouds

Correlation between the cosmic ray ﬂux and cloud properties has been

examined for decadal variations induced by the 11-year solar cycle,

shorter variations associated with the quasi-periodic oscillation in solar

activity centred on 1.68 years, or sudden and large variations known as

Forbush decrease events. It should be noted that long-term variations

in cloud properties are difﬁcult to detect (Section 2.5.6) while short-

term variations may be difﬁcult to attribute to a particular cause. More-

over, the cosmic ray ﬂux co-varies with other solar parameters such as

solar and UV irradiance. This makes any attribution of cloud changes to

the cosmic ray ﬂux problematic (Laken et al., 2011).

Some studies have shown co-variation between the cosmic ray ﬂux

and low-level cloud cover using global satellite data over periods of

typically 5 to 10 years (Marsh and Svensmark, 2000; Pallé Bagó and

Butler, 2000). Such correlations have not proved to be robust when

extending the time period under consideration (Agee et al., 2012),

and restricting the analysis to particular cloud types (Kernthaler et al.,

1999) or locations (Udelhofen and Cess, 2001; Usoskin and Kovaltsov,

2008). The purported correlations have also been attributed to ENSO

variability (Farrar, 2000; Laken et al., 2012) and artefacts of the sat-

ellite data cannot be ruled out (Pallé, 2005). Statistically signiﬁcant

(but weak) correlations between the diffuse fraction of surface solar

radiation and the cosmic ray ﬂux have been found at some locations

in the UK over the 1951–2000 period (Harrison and Stephenson,

2006). Harrison (2008) also found a unique 1.68-year periodicity in

surface radiation for two different UK sites between 1978 and 1990,

potentially indicative of a cosmic ray effect of the same periodicity.

Svensmark et al. (2009) found large global reductions in the aerosol

Ångström exponent, liquid water path, and cloud cover after large

Forbush decreases, but these results were not corroborated by other

studies that found no statistically signiﬁcant links between the cosmic

ray ﬂux and clouds at the global scale (Čalogović et al., 2010; Laken

and Čalogović, 2011). Although some studies found statistically signif-

icant correlations between the cosmic ray ﬂux and cloudiness at the

regional scale (Laken et al., 2010; Rohs et al., 2010), these correlations

were generally weak, cloud changes were small, and the results were

sensitive to how the Forbush events were selected and composited

(Kristjánsson et al., 2008; Laken et al., 2009).

7.4.6.2 Physical Mechanisms Linking Cosmic Rays to Cloudiness

The most widely studied mechanism proposed to explain the possible

inﬂuence of the cosmic ray ﬂux on cloudiness is the ‘ion-aerosol clear

air’ mechanism, in which atmospheric ions produced by cosmic rays

facilitate aerosol nucleation and growth ultimately impacting CCN

concentrations and cloud properties (Carslaw et al., 2002; Usoskin and

Kovaltsov, 2008). The variability in atmospheric ionization rates due to

changes in cosmic ray ﬂux can be considered relatively well quanti-

ﬁed (Bazilevskaya et al., 2008), whereas resulting changes in aerosol

nucleation rates are very poorly known (Enghoff and Svensmark, 2008;

Kazil et al., 2008). Laboratory experiments indicate that ionization

induced by cosmic rays enhances nucleation rates under middle and

upper tropospheric conditions, but not necessarily so in the continental

boundary layer (Kirkby et al., 2011). Field measurements qualitative-

ly support this view but cannot provide any ﬁrm conclusion due to

the scarcity and other limitations of free-troposphere measurements

(Arnold, 2006; Mirme et al., 2010), and due to difﬁculties in separating

nucleation induced by cosmic rays from other nucleation pathways in

the continental boundary layer (Hirsikko et al., 2011). Based on surface

aerosol measurements at one site, Kulmala et al. (2010) found no con-

nection between the cosmic ray ﬂux and new particle formation or any

other aerosol property over a solar cycle (1996–2008), although parti-

cles nucleated in the free troposphere are known to contribute to par-

ticle number and CCN concentrations in the boundary layer (Merikanto

et al., 2009). Our understanding of the ‘ion-aerosol clear air’ mecha-

nism as a whole relies on a few model investigations that simulate

changes in cosmic ray ﬂux over a solar cycle (Pierce and Adams, 2009b;

Snow-Kropla et al., 2011; Kazil et al., 2012) or during strong Forbush

decreases (Bondo et al., 2010; Snow-Kropla et al., 2011; Dunne et al.,

2012). Changes in CCN concentrations due to variations in the cosmic

ray ﬂux appear too weak to cause a signiﬁcant radiative effect because

the aerosol system is insensitive to a small change in the nucleation

rate in the presence of pre-existing aerosols (see also Section 7.3.2.2).

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Chapter 7 Clouds and Aerosols

A second pathway linking the cosmic ray ﬂux to cloudiness has been

proposed through the global electric circuit. A small direct current is

able to ﬂow vertically between the ionosphere and the Earth’s surface

over fair-weather regions because of cosmic-ray-induced atmospher-

ic ionization. Charge can accumulate at the upper and lower cloud

boundaries as a result of the effective scavenging of ions by cloud

droplets (Tinsley, 2008). This creates conductivity gradients at the cloud

edges (Nicoll and Harrison, 2010), and may inﬂuence droplet–droplet

collisions (Khain et al., 2004), cloud droplet–particle collisions (Tinsley,

2008) and cloud droplet formation processes (Harrison and Ambaum,

2008). These microphysical effects may potentially inﬂuence cloud

properties both directly and indirectly. Although Harrison and Ambaum

(2010) observed a small reduction in downward longwave radiation

that they associated with variations in surface current density, sup-

porting observations are extremely limited. Our current understanding

of the relationship between cloud properties and the global electric

circuit remains very low, and there is no evidence yet that associated

cloud processes could be of climatic signiﬁcance.

7.4.6.3 Synthesis

Correlations between cosmic ray ﬂux and observed aerosol or cloud

properties are weak and local at best, and do not prove to be robust

on the regional or global scale. Although there is some evidence that

ionization from cosmic rays may enhance aerosol nucleation in the

free troposphere, there is medium evidence and high agreement that

the cosmic ray-ionization mechanism is too weak to inﬂuence global

concentrations of CCN or droplets or their change over the last century

or during a solar cycle in any climatically signiﬁcant way.

7.5 Radiative Forcing and Effective Radiative

Forcing by Anthropogenic Aerosols

7.5.1 Introduction and Summary of AR4

In this section, aerosol forcing estimates are synthesized and updated

from AR4. As depicted in Figure 7.3, RF refers to the radiative forcing

due to either aerosol–radiation interactions (ari), formerly known as

the direct aerosol forcing, or aerosol–cloud interactions (aci), formerly

known as the ﬁrst indirect aerosol forcing or cloud albedo effect in

AR4. ERF refers to the effective radiative forcing and is typically esti-

mated from experiments with ﬁxed SSTs (see Sections 7.1.3 and 8.1).

It includes rapid adjustments, such as changes to the cloud lifetime,

cloud altitude, changes in lapse rate due to absorbing aerosols and

aerosol microphysical effects on mixed-phase, ice and convective

clouds.

Chapter 2 of AR4 (Forster et al., 2007) assessed RFari to be –0.5 ±

0.4 W m

–2

and broke this down into components associated with sev-

eral species. Land albedo changes associated with BC on snow were

assessed to be +0.1 ± 0.1 W m

–2

. The RFaci was assessed to be –0.70 W

–2

with a –1.8 to –0.3 W m

–2

uncertainty range. These uncertainty esti-

mates were based on a combination of model results and observations

from remote sensing. The semi-direct effect and other aerosol indirect

effects were assessed in Chapter 7 of AR4 (Denman et al., 2007) to

contribute additional uncertainty. The combined total aerosol forcing

was given as two distinct ranges: –2.3 to –0.2 W m

–2

from models and

a –1.7 to –0.1 W m

–2

range from inverse estimates.

As discussed in Section 7.4, it is inherently difﬁcult to separate RFaci

from subsequent rapid cloud adjustments either in observations or

model calculations (e.g., George and Wood, 2010; Lohmann et al.,

2010; Mauger and Norris, 2010; Painemal and Zuidema, 2010). For this

reason estimates of RFaci are of limited interest and are not assessed in

this report. This chapter estimates RFari, ERFari, and ERFari+aci based

purely on a priori approaches, and calculates ERFaci as the residual

between ERFari+aci and ERFari assuming the two effects are additive.

Inverse studies that estimate ERFari+aci from the observed rate of

planetary energy uptake and estimates of climate feedbacks and other

RFs are discussed in Section 10.8.

For consistency with AR4 and Chapter 8 of this Report, all quoted

ranges represent a 5 to 95% uncertainty range unless otherwise stated,

and we evaluate the forcings between 1750 and approximately 2010.

The reference year of 1750 is chosen to represent pre-industrial times,

so changes since then broadly represent the anthropogenic effect on

climate, although for several aerosol species (such as biomass burning)

this does not quite equate to the anthropogenic effect as emissions

started to be inﬂuenced by humans before the Industrial Revolution.

Many studies estimate aerosol forcings between 1850 and the present

day and any conversion to a forcing between 1750 and the present day

increases the uncertainty (Bellouin et al., 2008). This section principally

discusses global forcing estimates and attributes them to aerosol spe-

cies. Chapter 8 discusses regional forcings and additionally attributes

aerosol forcing to emission sources.

7.5.2 Estimates of Radiative Forcing and Effective

Radiative Forcing from Aerosol–Radiation

Interactions

Building on our understanding of aerosol processes and their radiative

effects (Section 7.3), this subsection assesses RFari and ERFari, but also

the forcings from absorbing aerosol (BC and dust) on snow and ice.

7.5.2.1 Radiative Forcing and Effective Radiative Forcing

from All Aerosols

Observations can give useful constraints to aspects of the global RFari

but cannot measure it directly (Section 7.3.4; Anderson et al., 2005;

Kahn, 2012). Remote sensing observations, in situ measurements of

ﬁne-mode aerosol properties and a better knowledge of bulk aerosol

optical properties make the estimate of total RFari more robust than

the RF for individual species (see Forster et al., 2007). Estimates of

RFari are either taken from global aerosol models directly (Schulz et al.,

2009; Myhre et al., 2013) or based mostly on observations, but using

supplemental information from models (e.g., Myhre, 2009; Loeb and

Su, 2010; Su et al., 2013). A number of studies (Bellouin et al., 2008;

Zhao et al., 2008b, 2011; Myhre, 2009) have improved aspects of the

satellite-based RFari estimate over those quoted in AR4. Of these, only

Myhre (2009) make the necessary adjustments to the observations to

account for forcing in cloudy regions and pre-industrial concentrations

to estimate a RFari of –0.3 ± 0.2 W m

–2

615

Clouds and Aerosols Chapter 7

A second phase of AeroCom model results gives an RFari estimate of

–0.35 W m

–2

, with a model range of about –0.60 to –0.13 W m

–2

, after

their forcings for 1850–2000 have been scaled by emissions to repre-

sent 1750–2010 changes (Myhre et al., 2013). Figure 7.17 shows the

zonal mean total RFari for AeroCom phase II models for 1850–2000.

Robust features are the maximum negative RF around 10°N to 50°N,

at latitudes of highest aerosol concentrations, and a positive RF at

higher latitudes due to the higher surface albedo there.

For observationally based estimates, a variety of factors are important

in constraining the radiative effect of aerosols (McComiskey et al.,

2008; Loeb and Su, 2010; Kahn, 2012). Particularly important are the

single scattering albedo (especially over land or above clouds) and the

AOD (see Section 7.3.4.1). Errors in remotely sensed, retrieved AOD

can be 0.05 or larger over land (Remer et al., 2005; Kahn et al., 2010;

Levy et al., 2010; Kahn, 2012). Loeb and Su (2010) found that the total

uncertainty in forcing was dominated by the uncertainty in single

scattering albedo, using single scattering albedo errors of ± 0.06 over

ocean and ± 0.03 over land from Dubovik et al. (2000), and assum-

ing errors can be added in quadrature. These retrieval uncertainties

could lead to a 0.5 to 1.0 W m

–2

uncertainty in RFari (Loeb and Su,

2010). However, model sensitivity studies and reanalyses can provide

additional constraints leading to a reduced error estimate. Ma et al.

(2012b) performed a sensitivity study in one model, ﬁnding a best

estimate of RFari of –0.41 W m

–2

with an asymmetrical uncertainty

range of –0.61 to –0.08 W m

–2

, with BC particle size and mixing state

having the largest effect of the parameters investigated. In models,

assumptions about surface albedo, background cloud distribution

and radiative transfer contribute a relative standard deviation of 39%

(Stier et al., 2013). Bellouin et al. (2013) quantiﬁed uncertainties in

RFari using reanalysis data that combined MODIS satellite data over

oceans with the global coverage of their model. This approach broke

down the uncertainty in aerosol properties into a local and a regional

error to ﬁnd a RFari standard deviation of 0.3 W m

–2

, not accounting

for uncertainty in the pre-industrial reference. When cloudy-sky and

pre-industrial corrections were applied an RFari best estimate of –0.4

W m

–2

was suggested.

The overall forcing uncertainty in RFari consists of the uncertainty in

the distribution of aerosol amount, composition and radiative prop-

erties (Loeb and Su, 2010; Myhre et al., 2013), the uncertainty in radi-

ative transfer (Randles et al., 2013) and the uncertainty owing to the

dependence of the forcing calculation on other uncertain parameters,

such as clouds or surface albedos (Stier et al., 2013). To derive a best

estimate and range for RFari we combine modelling and observation-

ally based studies. The best estimate is taken as –0.35 W m

–2

. This is

the same as the AeroCom II model estimate, and also the average of

the Myhre (2009) observationally based estimate (–0.3 W m

–2

) and

the Bellouin et al. (2013) reanalysis estimate (–0.4 W m

–2

). Models

probably underestimate the positive RFari from BC and the negative

forcing from OA aerosol (see Section 7.5.2.2), and currently there is

no evidence that one of these opposing biases dominates over the

other. The 5 to 95% range of RFari adopted in this assessment employs

the Bellouin et al. (2013) uncertainty to account for retrieval error in

observational quantities when constrained by global models, giving an

uncertainty estimate of ±0.49 W m

–2

. This is at the low end of the

uncertainty analysis of Loeb and Su (2010). However, our uncertainty

is partly based on models, and to account for this aspect, it is com-

bined in quadrature with a ±0.1 W m

–2

uncertainty from non-aerosol

related parameters following Stier et al. (2013). This gives an assessed

RFari of –0.35 ± 0.5 W m

–2

. This is a larger range than that exhibited

by the AeroCom II models. It is also a smaller magnitude but slightly

larger range than in AR4, with a more positive upper bound. This more

positive upper bound can be justiﬁed by the sensitivity to BC aerosol

(Ma et al., 2012b and Section 7.5.2.3). Despite the larger range, there

is increased conﬁdence in this assessment due to dedicated modelling

sensitivity studies, more robust observationally based estimates and

their better agreement with models.

ERFari adds the radiative effects from rapid adjustments onto RFari.

Studies have evaluated the rapid adjustments separately as a semi-di-

rect effect (see Section 7.3.4.2) and/or the ERFari has been directly

evaluated. Rapid adjustments are caused principally by cloud changes.

There is high conﬁdence that the local heating caused by absorbing

aerosols can alter clouds. However, there is low conﬁdence in deter-

mining the sign and magnitude of the rapid adjustments at the global

scale as current models differ in their responses and are known to

inadequately represent some of the important relevant cloud processes

(see Section 7.3.4). Existing estimates of ERFari nevertheless rely on

such global models. Five GCMs were analysed for RFari and ERFari in

Lohmann et al. (2010). Their rapid adjustments ranged from –0.3 to

+0.1 W m

–2

. In a further study, Takemura and Uchida (2011) found

a rapid adjustment of +0.06 W m

–2

. The sensitivity analysis of Ghan

et al. (2012) found a –0.1 to +0.1 W m

–2

range over model variants,

where an improved aging of the mixing state led to small negative

rapid adjustment of around –0.1 W m

–2

. Bond et al. (2013) assessed

scaled RF and efﬁcacy estimates from seven earlier studies focusing on

-2

-1

RFari (W m

-2

)

60ºS

Latitude

AeroCom mean

AeroCom 5%−95% range

Bellouin et al. (2013)

Su et al. (2013)

30ºS 30ºN 60ºNEq

Figure 7.17 | Annual zonal mean top of the atmosphere radiative forcing due to aero-

sol–radiation interactions (RFari, in W m

–2

) due to all anthropogenic aerosols from the

different AeroCom II models. No adjustment for missing species in certain models has

been applied. The multi-model mean and 5th to 95th percentile range from AeroCom

II models (Myhre et al., 2013) are shown with a black solid line and grey envelope. The

estimates from Bellouin et al. (2013) and Su et al. (2013) are shown with dotted and

dashed lines, respectively. The forcings are for the 1850 to 2000 period. See Supplemen-

tary Material for a ﬁgure with labelled individual AeroCom II model estimates.

616

Chapter 7 Clouds and Aerosols

BC and found a range of rapid adjustments between –0.2 and –0.01

W m

–2

. There is a potential additional rapid adjustment term from the

effect of cloud drop inclusions (see Section 7.3.4.2). Based on Ghan et

al. (2012) and Jacobson (2012), Bond et al. (2013) estimate an addi-

tional ERFari term of +0.2 W m

–2

, with an uncertainty range of –0.1 to

+0.9 W m

–2

; however there is very low conﬁdence in the sign or magni-

tude of this effect and we do not include it in our assessment. Overall a

best estimate for the rapid adjustment is taken to be –0.1 W m

–2

, with

a 5 to 95% uncertainty range of –0.3 to +0.1 W m

–2

. The best estimate

is based on Ghan et al. (2012) and the range on Lohmann et al. (2010).

The uncertainties are added in quadrature to the estimate of RFari and

rounded to give an overall assessment for ERFari of –0.45 ± 0.5 W m

–2

7.5.2.2 Radiative Forcing by Species

AeroCom II studies have calculated aerosol distributions using 1850

and 2000 simulations with the same meteorology to isolate RFari

for individual aerosol types (sulphate, BC fossil-fuel plus biofuel, OA

fossil-fuel and biofuel, biomass burning or BB, SOA, nitrate). Many of

these models account for internal mixing, so that partitioning RFari by

species is not straightforward, and different modelling groups adopt

different techniques (Myhre et al., 2013). Note also that due to internal

mixing of aerosol types the total RFari is not necessarily the sum of the

RFari from different types (Ocko et al., 2012). Unless otherwise noted

in the text below, the best estimate and 5 to 95% ranges for individual

types quoted in Figure 7.18 are solely based on the AeroCom II range

(Myhre et al., 2013) and the estimates have been scaled by emissions

to derive 1750–2010 RFari values. Note that although global numbers

are presented here, these RF estimates all exhibit large regional var-

iations, and individual aerosol species can contribute signiﬁcantly to

regional climate change despite rather small RF estimates (e.g., Wang

et al., 2010b).

For sulphate, AeroCom II models give a RF median and 5 to 95% uncer-

tainty range of –0.31 (–0.58 to –0.11) W m

–2

for the 1850–2000 period,

and –0.34 (–0.61 to –0.13) W m

–2

for the 1750–2010 period. This esti-

mate and uncertainty range are consistent with the AR4 estimate of

–0.4 ± 0.2 W m

–2

, which is retained as the best estimate for AR5.

RF from BC is evaluated in different ways in the literature. The BC RF

in this report is from fossil fuel and biofuel sources, while open burn-

ing sources are attributed separately to the biomass-burning aerosol,

which also includes other organic species (see Section 7.3.2). BC can

also affect clouds and surface albedo (see Sections 7.5.2.3 and Chapter

8). Here we only isolate the fossil fuel and biofuel RFari attributable

to BC over 1750–2010. Two comprehensive studies have quantiﬁed

the BC RFari and derive different central estimates and uncertainty

ranges. Myhre et al. (2013) quantify RF over 1850–2000 in the Aer-

oCom II generation of models and scale these up using emissions to

derive an RF estimate over 1750–2010 of +0.23 (+0.06 to +0.48) W

–2

for fossil fuel and biofuel emissions. Bond et al. (2013) employ an

observationally weighted scaling of an earlier generation of AeroCom

models, regionally scaling BC absorption to match absorption AOD as

retrieved at available AERONET sites. They derive a RF of +0.51 (+0.06

to +0.91) W m

–2

for fossil fuel and biofuel sources. There are known

biases in BC RF estimates from aerosol models. BC concentrations are

underestimated near source regions, especially in Asia, but overesti-

mated in remote regions and at altitude (Figure 7.15). Models also

probably underestimate the mass absorption cross-section probably

because enhanced absorption due to internal mixing is insufﬁciently

accounted for (see Section 7.3.3.2). Together these biases are expected

to cause the modelled BC RF to be underestimated. The Bond et al.

estimate accounted for these biases by scaling model results. However,

there are a number of methodological difﬁculties associated with the

absorption AOD retrieval from sunphotometer retrievals (see Section

7.3.3.2), the attribution of absorption AOD to BC, and the distribution

and representativeness of AERONET stations for constraining global

and relatively coarse-resolution models. Absorption by OA (see Section

7.3.3), which may amount to 20% of ﬁne-mode aerosol absorption

(Chung et al., 2012), is included into the BC RF estimate in Bond et

al. but is now treated separately in most AeroCom II models, some of

which have a global absorption AOD close to the Bond et al. estimate.

We use our expert judgement here to adopt a BC RF estimate that is

halfway between the two estimates and has a wider uncertainty range

from combining distributions. This gives a BC RF estimate from fossil

fuel and biofuel of +0.4 (+0.05 to +0.8) W m

–2

The AeroCom II estimate of the SOA RFari is –0.03 (–0.27 to –0.02) W

–2

and the primary OA from fossil fuel and biofuel estimate is –0.05

(–0.09 to –0.02) W m

–2

. An intercomparison of current chemistry–cli-

mate models found two models outside of this range for SOA RFari,

with one model exhibiting a signiﬁcant positive forcing from land

use and cover changes inﬂuencing biogenic emissions (Shindell et al.,

2013). We therefore adjust the upper end of the range to account for

this, giving an SOA RFari estimate of –0.03 (–0.27 to +0.20) W m

–2

. Our

assessment also scales the AeroComII estimate of the primary OA from

fossil fuel and biofuel by 1.74 to –0.09 (–0.16 to –0.03) W m

–2

to allow

for the underestimate of emissions identiﬁed in Bond et al. (2013). For

OA from natural burning, and for SOA, the natural radiative effects can

be an order of magnitude larger than the RF (see Sections 7.3.2 and

7.3.4, and O’Donnell et al., 2011) and they could thus contribute to

climate feedback (see Section 7.3.5).

The RFari from biomass burning includes both BC and OA species that

contribute RFari of opposite sign, giving a net RFari close to zero (Bond

et al., 2013; Myhre et al., 2013). The AeroCom II models give a 1750–

2010 RFari of 0.00 (–0.08 to +0.07) W m

–2

, and an estimate of +0.0

(–0.2 to +0.2) W m

–2

is adopted in this assessment, doubling the model

uncertainty range to account for a probable underestimate of their

emissions (Bond et al., 2013). Combining information in Samset et al.

(2013) and Myhre et al. (2013) would give a BC RFari contribution from

biomass burning of slightly less than +0.1 W m

–2

over 1850–2000 from

the models. However, this ignores a signiﬁcant contribution expected

before 1850 and the probable underestimate in emissions. Our assess-

ment therefore solely relies on Bond et al. (2013), giving an estimate

of +0.2 (+0.03 to +0.4) W m

–2

for the 1750–2010 BC contribution to

the biomass burning RFari. This is a 50% larger forcing than the earlier

generation of AeroCom models found (Schulz et al., 2006). Note that

we also expect an OA RFari of the same magnitude with opposite sign.

The AeroCom II RF estimate for nitrate aerosol gives an RFari of –0.11

(–0.17 to –0.03) W m

–2

, but comprises a relatively large 1850 to 1750

correction term. In these models ammonium aerosol is included within

the sulphate and nitrate estimates. An intercomparison of current

617

Clouds and Aerosols Chapter 7

chemistry–climate models found an RF range of –0.41 to –0.03 W m

–2

over 1850–2000. Some of the models with strong RF did not exhibit

obvious biases, whereas others did (Shindell et al., 2013). These sets of

estimates are in good agreement with earlier estimates (e.g., Adams et

al., 2001; Bauer et al., 2007; Myhre et al., 2009). Our assessment of the

RFari from nitrate aerosol is –0.11 (–0.3 to –0.03) W m

–2

. This is based

on AeroCom II with an increased lower bound.

Anthropogenic sources of mineral aerosols can result from changes in

land use and water use or climate change. Estimates of the RF from

anthropogenic mineral aerosols are highly uncertain, because natural

and anthropogenic sources of mineral aerosols are often located close

to each other (Mahowald et al., 2009; Ginoux et al., 2012b). Using

a compilation of observations of dust records over the 20th century

with model simulations, Mahowald et al. (2010) deduced a 1750–2000

change in mineral aerosol RFari including both natural and anthropo-

genic changes of –0.14 ± 0.11 W m

–2

. This is consistent within the AR4

estimate of –0.1 ± 0.2 W m

–2

(Forster et al., 2007) which is retained

here. Note that part of the dust RF could be due to feedback processes

(see Section 7.3.5).

Overall the species breakdown of RFari is less certain than the total

RFari. Fossil fuel and biofuel emissions contribute to RFari via sulphate

aerosol –0.4 (–0.6 to –0.2) W m

–2

; black carbon aerosol +0.4 (+0.05 to

+0.8) W m

–2

; and primary and secondary organic aerosol –0.12 (–0.4

to +0.1) W m

–2

(adding uncertainties in quadrature). Additional RFari

contributions are via biomass burning emissions, where black carbon

and organic aerosol changes offset each other to give an estimate of

+0.0 (–0.2 to +0.2) W m

–2

; nitrate aerosol –0.11 (–0.3 to –0.03) W

–2

; and a contribution from mineral dust of –0.1 (–0.3 to +0.1) W m

–2

that may not be entirely anthropogenic in origin. The sum of the RFari

−0.5

0.0

0.5

RFari (W m

-2

)

Sulphate

POA FF

SOA

Nitrate

Mineral

Total

BC FF

Figure 7.18 | Annual mean top of the atmosphere radiative forcing due to aerosol–

radiation interactions (RFari, in W m

–2

) due to different anthropogenic aerosol types,

for the 1750–2010 period. Hatched whisker boxes show median (line), 5th to 95th

percentile ranges (box) and min/max values (whiskers) from AeroCom II models (Myhre

et al., 2013) corrected for the 1750–2010 period. Solid coloured boxes show the AR5

best estimates and 90% uncertainty ranges. BC FF is for black carbon from fossil fuel

and biofuel, POA FF is for primary organic aerosol from fossil fuel and biofuel, BB is for

biomass burning aerosols and SOA is for secondary organic aerosols.

from these species agrees with, but is slightly weaker than, the best

estimate of the better-constrained total RFari.

7.5.2.3 Absorbing Aerosol on Snow and Sea Ice

Forster et al. (2007) estimated the RF for surface albedo changes from

BC deposited on snow to be +0.10 ± 0.10 W m

–2

, with a low level of

understanding, based largely on studies from Hansen and Nazarenko

(2004) and Jacobson (2004). Since AR4, observations of BC in snow

have been conducted using several different measurement techniques

(e.g., McConnell et al., 2007; Forsström et al., 2009; Ming et al., 2009;

Xu et al., 2009; Doherty et al., 2010; Huang et al., 2011; Kaspari et

al., 2011), providing data with which to constrain models. Laboratory

measurements have conﬁrmed the albedo reduction due to BC in snow

(Hadley and Kirchstetter, 2012). The albedo effects of non-BC constitu-

ents have also been investigated but not rigorously quantiﬁed. Remote

sensing can inform on snow impurity content in some highly polluted

regions. However, it cannot be used to infer global anthropogenic RF

because of numerous detection challenges (Warren, 2013).

Global modelling studies since AR4 have quantiﬁed present-day radi-

ative effects from BC on snow of +0.01 to +0.08 W m

–2

(Flanner et al.,

2007, 2009; Hansen et al., 2007; Koch et al., 2009a; Rypdal et al., 2009;

Skeie et al., 2011; Wang et al., 2011c; Lee et al., 2013). These studies

apply different BC emission inventories and atmospheric aerosol rep-

resentations, include forcing from different combinations of terrestrial

snow, sea ice, and snow on sea ice, and some include different rapid

adjustment effects such as snow grain size evolution and melt-induced

accumulation of impurities at the snow surface, observed on Tibetan

glaciers (Xu et al., 2012) and in Arctic snow (Doherty et al., 2013). The

forcing operates mostly on terrestrial snow and is largest during March

to May, when boreal snow and ice are exposed to strong insolation

(Flanner et al., 2007).

All climate modelling studies ﬁnd that the Arctic warms in response

to snow and sea ice forcing. In addition, estimates of the change in

global mean surface temperature per unit forcing are 1.7 to 4.5 times

greater for snow and sea ice forcing than for CO

forcing (Hansen and

Nazarenko, 2004; Hansen et al., 2005; Flanner et al., 2007; Flanner et

al., 2009; Bellouin and Boucher, 2010). The Koch et al. (2009a) estimate

is not included in this range owing to the lack of a clear signal in their

study. The greater response of global mean temperature occurs primar-

ily because all of the forcing energy is deposited directly into the cry-

osphere, whose evolution drives a positive albedo feedback on climate.

Key sources of forcing uncertainty include BC concentrations in snow

and ice, BC mixing state and optical properties, snow and ice coverage

and patchiness, co-presence of other light-absorbing particles in the

snow pack, snow effective grain size and its inﬂuence on albedo per-

turbation, the masking of snow surfaces by clouds and vegetation and

the accumulation of BC at the top of snowpack caused by melting and

sublimation. Bond et al. (2013) derive a 1750–2010 snow and sea ice

RF estimate of +0.046 (+0.015 to +0.094) W m

–2

for BC by (1) consid-

ering forcing ranges from all relevant global studies, (2) accounting for

biases caused by (a) modelled Arctic BC-in-snow concentrations using

measurements from Doherty et al. (2010), and (b) excluding mineral

dust, which reduces BC forcing by approximately 20%, (3) combining

in quadrature individual uncertainty terms from Flanner et al. (2007)

618

Chapter 7 Clouds and Aerosols

plus that originating from the co-presence of dust, and (4) scaling the

present-day radiative contributions from BB, biofuel and fossil fuel BC

emissions according to their 1750–2010 changes. Note that this RF

estimate allows for some rapid adjustments in the snowpack but is not

a full ERF as it does not account for adjustments in the atmosphere.

For this RF, we adopt an estimate of +0.04 (+0.02 to +0.09) W m

–2

and

note that the surface temperature change is roughly three (two to four)

times more responsive to this RF relative to CO

7.5.3 Estimate of Effective Radiative Forcing from

Combined Aerosol–Radiation and Aerosol–Cloud

Interactions

In addition to ERFari, there are changes due to aerosol–cloud interac-

tions (ERFaci). Because of nonlinearities in forcings and rapid adjust-

ments, the total effective forcing ERFari+aci does not necessarily equal

the sum of the ERFari and ERFaci calculated separately. Moreover a

strict separation is often difﬁcult in either state of the art models or

observations. Therefore we ﬁrst assess ERFari+aci and postpone our

assessment of ERFaci to Section 7.5.4. For similar reasons, we focus

primarily on ERF rather than RF.

ERFari+aci is deﬁned as the change in the net radiation at the TOA from

pre-industrial to present day. Climate model estimates of ERFari+aci in

the literature differ for a number of reasons. (1) The reference years

for pre-industrial and present-day conditions vary between estimates.

Studies can use 1750, 1850, or 1890 for pre-industrial; early estimates

of ERFari+aci used present-day emissions for 1985, whereas most

newer estimates use emissions for the year 2000. (2) The processes

they include also differ: aerosol–cloud interactions in large-scale liquid

stratiform clouds are typically included, but studies can also include

aerosol–cloud interactions for mixed-phase, convective clouds and/or

cirrus clouds. (3) The way in which ERFari+aci is calculated can also

differ between models, with some earlier studies only reporting the

change in shortwave radiation. Changes in longwave radiation arise

from rapid adjustments, or from aerosol–cloud interactions involving

mixed-phase or ice clouds (e.g., Storelvmo et al., 2008b, 2010; Ghan et

al., 2012), and tend to partially offset changes in shortwave radiation.

In the estimates discussed below and those shown in Figure 7.19, we

refer to estimates of the change in net (shortwave plus longwave) TOA

radiation whenever possible, but report changes in shortwave radia-

tion when changes in net radiation are not available. While this mostly

affects earlier studies, the subset of models that we concentrate on

all include both shortwave and longwave radiative effects. However,

for the sake of comparison, the satellite studies must be adjusted to

account for missing longwave contributions as explained below.

Early GCM estimates of ERFari+aci only included aerosol–cloud inter-

actions in liquid phase stratiform clouds; some of these were already

considered in AR4. Grouping these early estimates with similar (liquid

phase only) estimates from publications since the AR4 yields a median

value of ERFari+aci of –1.5 W m

–2

with a 5 to 95% range between –2.4

and –0.6 W m

–2

(Figure 7.19). In those studies that attempt a more

complete representation of aerosol–cloud interactions, by including

aerosol–cloud interactions in mixed-phase and/or convective cloud,

the magnitude of the ERF tends to be somewhat smaller (see Figure

7.19). The physical explanation for the mixed-phase reduction in the

magnitude of the ERF is that some aerosols also act as IN causing

supercooled clouds to glaciate and precipitate more readily. This reduc-

tion in cloud cover leads to less reﬂected shortwave radiation and a

less negative ERFari+aci. This effect can however be offset if the IN

become coated with soluble material, making them less effective at

nucleating ice, leading to less efﬁcient precipitation production and

more reﬂected shortwave radiation (Hoose et al., 2008; Storelvmo

et al., 2008a). Models that have begun to incorporate aerosol–cloud

interactions in convective clouds also have a tendency to reduce the

magnitude of the ERF, but this effect is less systematic (Jacobson, 2003;

Lohmann, 2008; Suzuki et al., 2008) and reasons for differences among

the models in this category are less well understood.

For our expert judgment of ERFari+aci a subset of GCM studies, which

strived for a more complete and consistent treatment of aerosol–cloud

interactions (by incorporating either convective or mixed-phase process-

es) was identiﬁed and scrutinized. The ERFari+aci derived from these

models is somewhat less negative than in the full suite of models, and

ranges from –1.68 and –0.81 W m

–2

with a median value of –1.38 W

–2

. Because in some cases a number of studies have been performed

with the same GCM, in what might be described as an evolving effort,

our assessment is further restricted to the best (usually most recent)

estimate by each modelling group (see black symbols in Figure 7.19 and

Table 7.4). This ensures that no single GCM is given a disproportionate

weight. Further, we consider only simulations not constrained by the

historical temperature rise, motivated by the desire to emphasize a pro-

cess-based estimate. Although it may be argued that greater uncertain-

ty is introduced by giving special weight to models that only incorporate

more comprehensive treatments of aerosol–cloud interactions, and for

processes that (as Section 7.4 emphasizes) are on the frontier of under-

standing, it should be remembered that aerosol–cloud interactions for

liquid-phase clouds remain very uncertain. Althoughtheunderstanding

and treatment of aerosol–cloud interactions inconvective ormixed-

phase clouds are also very uncertain, as discussed in Section 7.4.4,we

exercise our best judgment of their inﬂuence.

A less negative ERFari+aci (–0.93 to –0.45 W m

–2

with a median of

–0.85 W m

–2

, Figure 7.19 and Table 7.4) is found in studies that use vari-

ability in the present day satellite record to infer aerosol–cloud inter-

actions, or that constrain GCM parameterizations to optimize agree-

ment with satellite observations. Because some groups have published

multiple estimates as better information became available, only their

latest study was incorporated into this assessment. Moreover, if a study

did not report ERFari+aci but only evaluated changes in ERFaci, their

individual estimate was combined with the average ERFari of –0.45 W

–2

from Section 7.5.2. Likewise, those (all but one) studies that only

accounted for changes in shortwave radiation when computing ERFaci

were corrected by adding a constant factor of +0.2 W m

–2

, taken from

the lower range of the modeled longwave effects which varied from

+0.2 to +0.6 W m

–2

in the assessed models. These procedures result in

the ﬁnal estimates of ERFari+aci shown as black symbols in Figure 7.19

and in Table 7.4. This resulted in a median ERFari+aci of –0.85 W m

–2

for satellite-based ERFari+aci estimates. Results of pure satellite-based

studies are sensitive to the spatial scale of measurements (Grandey

and Stier, 2010; McComiskey and Feingold, 2012; Section 7.4.2.2), as

well as to how pre-industrial conditions and variations between pre-

industrial and present-day conditions are inferred from the observed

619

Clouds and Aerosols Chapter 7

Estimate Acronym References

Effective radiative forcing due to aerosol–cloud

interactions (ERFaci) published prior to and considered

in AR4

AR4 Lohmann and Feichter (1997); Rotstayn (1999); Lohmann et al., (2000); Ghan et al. (2001); Jones

et al. (2001); Rotstayn and Penner (2001); Williams et al. (2001); Kristjánsson (2002); Lohmann

(2002a); Menon et al. (2002); Peng and Lohmann (2003); Penner et al. (2003); Easter et al. (2004);

Kristjánsson et al. (2005); Ming et al. (2005); Rotstayn and Liu (2005); Takemura et al. (2005);

Johns et al. (2006); Penner et al. (2006); Quaas et al. (2006); Storelvmo et al. (2006)

ERFaci published since AR4 AR5 Menon and Del Genio (2007); Ming et al. (2007b); Kirkevåg et al. (2008); Seland et al. (2008); Storelvmo

et al. (2008a); Hoose et al. (2009); Quaas et al. (2009); Rotstayn and Liu (2009); Chen et al. (2010); Ghan

et al. (2011); Penner et al. (2011); Makkonen et al. (2012a); Takemura (2012); Kirkevåg et al. (2013)

Effective radiative forcing due to aerosol–radiation

and aerosol–cloud interactions (ERFari+aci) in liquid

phase stratiform clouds published prior to AR4

AR4 Lohmann and Feichter (2001); Quaas et al. (2004); Menon and Rotstayn (2006); Quaas et al. (2006)

ERFari+aci in liquid phase stratiform clouds

published since AR4

AR5 Lohmann et al. (2007); Rotstayn et al. (2007); Posselt and Lohmann (2008); Posselt and Lohmann

(2009); Quaas et al. (2009); Salzmann et al. (2010); Bauer and Menon (2012); Gettelman et al.

(2012); Ghan et al. (2012); Makkonen et al. (2012a); Takemura (2012); Kirkevåg et al. (2013)

ERFari+aci in liquid and mixed-phase stratiform clouds with mixed-

phase clouds

Lohmann (2004); Jacobson (2006); Lohmann and Diehl (2006); Hoose et al. (2008); Storelvmo et al. (2008a);

Lohmann and Hoose (2009); Hoose et al. (2010b); Lohmann and Ferrachat (2010);

Salzmann et al. (2010); Storelvmo et al. (2010)

ERFari+aci in stratiform and convective clouds with convective

clouds

Menon and Rotstayn (2006); Menon and Del Genio (2007); Lohmann (2008);

Koch et al. (2009a); Unger et al. (2009); Wang et al. (2011b)

ERFari+aci including satellite observations Satellites Lohmann and Lesins (2002); Sekiguchi et al. (2003); Quaas et al. (2006); Lebsock et al. (2008);

Quaas et al. (2008); Quaas et al. (2009); Bellouin et al. (2013)

Table 7.3 | List of references for each category of estimates displayed in Figure 7.19.

Figure 7.19 | (a) GCM studies and studies involving satellite estimates of RFari (red), ERFaci (green) and ERFari+aci (blue in grey-shaded box). Each symbol represents the best

estimate per model and paper (see Table 7.3 for references). The values for RFari are obtained from the CMIP5 models. ERFaci and ERFari+aci studies from GCMs on liquid phase

stratiform clouds are divided into those published prior to and included in AR4 (labelled AR4, triangles up), studies published after AR4 (labelled AR5, triangles down) and from the

CMIP5/ACCMIP models (ﬁlled circles). GCM estimates that include adjustments beyond aerosol–cloud interactions in liquid phase stratiform clouds are divided into those includ-

ing aerosol–cloud interactions in mixed-phase clouds (stars) and those including aerosol–cloud interactions in convective clouds (diamonds). Studies that take satellite data into

account are labelled as ‘satellites’. Studies highlighted in black are considered for our expert judgement of ERFari+aci. (b) Whisker boxes from GCM studies and studies involving

satellite data of RFari, ERFaci and ERFari+aci. They are grouped into RFari from CMIP5/ACCMIP GCMs (labelled CMIP5 in red), ERFaci from GCMs (labelled AR4, AR5 in green), all

estimates of ERFari+aci shown in the upper panel (labelled ‘All’ in blue), ERFari+aci from GCMs highlighted in the upper panel (labelled ‘Highlighted GCMs’ in blue), ERFari+aci

from satellites highlighted in the upper panel (labelled ‘Highlighted Satellites’ in blue), and our expert judgement based on estimates of ERFari+aci from these GCM and satellite

studies (labelled ‘Expert Judgement’ in blue). Displayed are the averages (cross sign), median values (middle line), 17th and 83th percentiles (likely range shown as box boundaries)

and 5th and 95th percentiles (whiskers). References for the individual estimates are provided in Table 7.3. Table 7.4 includes the values of the GCM and satellite studies considered

for the expert judgement of ERFari+aci that are highlighted in black.

CMIP5 AR4, AR5 All

Highlighted

GCMs

Highlighted

Satellites

Expert

Judgement

Aerosol Forcing (W m

-2

)Aerosol Forcing (W m

-2

)

-1

-2

-3

-1

-2

-3

(b)

RFari

CMIP5/ACCMIP

AR4 AR5

with mixed-phase ACI

with convective ACI

satellites

ERFaci ERFari+aci

(a)

620

Chapter 7 Clouds and Aerosols

Category Best Estimate Climate Model and/or Satellite Instrument Reference

with mixed-phase clouds –1.55 CAM Oslo Hoose et al. (2010b)

with mixed-phase clouds –1.02 ECHAM Lohmann and Ferrachat (2010)

with mixed-phase clouds –1.68 GFDL Salzmann et al. (2010)

with mixed-phase clouds –0.81 CAM Oslo Storelvmo et al. (2008b; 2010)

with convective clouds –1.50 ECHAM Lohmann (2008)

with convective clouds –1.38 GISS Koch et al. (2009a)

with convective clouds –1.05 PNNL-MMF Wang et al. (2011b)

Satellite-based –0.85 ECHAM + POLDER Lohmann and Lesins (2002)

Satellite-based –0.93 AVHRR Sekiguchi et al. (2003)

Satellite-based –0.67 CERES / MODIS Lebsock et al. (2008)

Satellite-based –0.45 CERES / MODIS Quaas et al. (2008)

Satellite-based –0.95 Model mean + MODIS Quaas et al. (2009)

Satellite-based –0.85 MACC + MODIS Bellouin et al. (2013)

Table 7.4 | List of ERFari+aci values (W m

–2

) considered for the expert judgement of ERFari+aci (black symbols in Figure 7.19). For the GCM studies only the best estimate per

modelling group is used. For satellite studies the estimates are corrected for the ERFari and for the longwave component of ERFari+aci when these are not included (see text).

AVHRR = Advanced Very High Resolution Radiometer.

CERES = Clouds and the Earth’s Radiant Energy System.

MACC = Monitoring Atmospheric Composition and Climate.

MODIS = Moderate Resolution Imaging Spectrometer.

variability in present-day aerosol and cloud properties (Quaas et al.,

2011; Penner et al., 2012). In addition, all (model- and satellite-based)

estimates of ERFari+aci are very sensitive to the assumed pre-industrial

or natural cloud droplet concentration (Hoose et al., 2009). The large

spatial scales of satellite measurements relative to in situ measure-

ments generally suggest smaller responses in cloud droplet number

increases for a given aerosol increase (Section 7.4.2.2). Satellite studies,

however, show a strong effect of aerosol on cloud amount, which could

be a methodological artefact as GCMs associate clouds with humidity

and aerosol swelling (Quaas et al., 2010). There are thus possible biases

in both directions, so the sign and magnitude of any net bias is not clear.

In large-scale models for which cloud-scale circulations are not explic-

itly represented, it is difﬁcult to capture all relevant cloud controlling

processes (Section 7.2.2). Because the response of clouds to aerosol

perturbations depends critically on the interplay of poorly understood

physical processes, global model-based estimates of aerosol–cloud

interactions remain uncertain (Section 7.4). Moreover, the connection

between the aerosol amount and cloud properties is too direct in the

large-scale modelling studies (as it relies heavily on the autoconversion

rate). Because of this, GCMs tend to overestimate the magnitude of the

aerosol effect on cloud properties (Section 7.4.5; see also discussion in

Section 7.5.4). This view has some support from studies that begin to

incorporate some cloud, or cloud-system scale responses to aerosol–

cloud interactions. For instance, in an attempt to circumvent some of

difﬁculties of parameterizing clouds, some groups (e.g., Wang et al.,

2011b) have begun developing modelling frameworks that can explic-

itly represent cloud-scale circulations, and hence the spatio-temporal

covariances of cloud-controlling processes. Another group (Khairout-

dinov and Yang, 2013) has used the same cloud-resolving model in a

radiative convective equilibrium approach, and compared the relative

contribution of aerosol–cloud interactions to warming from the dou-

bling of atmospheric CO

. In both studies a smaller (–1.1 and –0.8 W

–2

, respectively) ERFari+aci than for the average GCM was found.

Furthermore, the study best resolving the cloud-scale circulations

(Wang et al., 2011b) found little change in cloud amount in response to

changes in aerosol, consistent with other ﬁne-scale modelling studies

discussed in Section 7.4.

Based on the above considerations, we assess ERFari+aci using expert

judgement to be –0.9 W m

–2

with a 5 to 95% uncertainty range of

–1.9 to –0.1 W m

–2

(medium conﬁdence), and a likely range of –1.5 to

–0.4 W m

–2

. These ranges account for the GCM results by allowing for

an ERFari+aci somewhat stronger than what is estimated by the sat-

ellite studies with a longer tail in the direction of stronger effects, but

(for reasons given above) give less weight to the early GCM estimates

shown in Figure 7.19. The ERFari+aci can be much larger regionally

but the global value is consistent with several new lines of evidence

suggesting less negative estimates of aerosol–cloud interactionsthan

the corresponding estimate in Chapter 7 of AR4 of –1.2 W m

–2

. The

AR4 estimate was based mainly on GCM studies that did not take

secondary processes (such as aerosol effects on mixed-phase and/or

convective clouds) into account, did not beneﬁt as much from the use

of the recent satellite record, and did not account for the effect of

rapid adjustments on the longwave radiative budget. This uncertainty

range is slightly smaller than the –2.3 to –0.2 W m

–2

in AR4, with a

less negative upper bound due to the reasons outlined above. The best

estimate of ERFari+aci is not only consistent with the studies allowing

cloud-scale responses (Wang et al., 2011b; Khairoutdinov and Yang,

2013) but also is in line with the average ERFari+aci from the CMIP5/

ACCMIP models (about –1 W m

–2

, see Table 7.5), which as a whole

reproduce the observed warming of the 20th century (see Chapter 10).

Studies that infer ERFari+aci from the historical temperature rise are

discussed in Section 10.8.

7.5.4 Estimate of Effective Radiative Forcing from

Aerosol–Cloud Interactions Alone

ERFaci refers to changes in TOA radiation since pre-industrial times

due only to aerosol–cloud interactions, i.e., albedo effects augmented

by possible changes in cloud amount and lifetime. As stated in Sec-

tion 7.5.1, we do not discuss RFaci by itself because it is an academic

POLDER = Polarization and Directionality of the Earth’s Reﬂectances.

621

Clouds and Aerosols Chapter 7

construct. However, processes in GCMs that tend to affect RFaci such

as changes to the droplet size distribution breadth (e.g., Rotstayn

and Liu, 2005) will also affect ERFaci. Early studies evaluated just the

change in shortwave radiation or cloud radiative effect for ERFaci, but

lately the emphasis has changed to report changes in net TOA radia-

tion for ERFaci. As discussed in Section 7.5.3, evaluating ERFaci from

changes in net TOA radiation is the only correct method, and therefore

this is used whenever possible also in this section. However some ear-

lier estimates of ERFaci only reported changes in cloud radiative effect,

which we show in Figure 7.19 as the last resort. However, estimates

of changes in cloud radiative effect can differ quite substantially from

those in net radiation if rapid adjustments to aerosol–cloud interac-

tions induce changes in clear-sky radiation.

Cloud amount and lifetime effects manifest themselves in GCMs via

their representation of autoconversion of cloud droplets to rain, a pro-

cess that is inversely dependent on droplet concentration. Thus, ERFaci

and ERFari+aci have been found to be very sensitive to the autoconver-

sion parameterization (Rotstayn, 2000; Golaz et al., 2011; Wang et al.,

2012). GCMs probably underestimate the extent to which precipitation

is formed via raindrop accretion of cloud droplets (Wood, 2005), a pro-

cess that is insensitive to aerosol and droplet concentration. Indeed,

models that remedy this imbalance in precipitation formation between

autoconversion and accretion (Posselt and Lohmann, 2009; Wang et

al., 2012) exhibit weaker ERFaci in agreement with small-scale stud-

ies that typically do not show a systematic increase in cloud lifetime

because of entrainment and because smaller droplets also evaporate

more readily (Jiang et al., 2006; Bretherton et al., 2007). Bottom-up

estimates of ERFaci are shown in Figure 7.19. Their median estimate of

–1.4 W m

–2

is more negative than our expert judgement of ERFari+aci

because of the limitations of these studies discussed above.

Modelling Group Model Name ERFari+aci from All Anthropogenic Aerosols ERFari+aci from Sulphate Aerosols Only

CCCma CanESM2 –0.87 –0.90

CSIRO-QCCCE CSIRO-Mk3-6-0

–1.41 –1.10

GFDL GFDL-AM3 –1.60 (–1.44

) –1.62

GISS GISS-E2-R

–1.10

–0.61

GISS GISS-E2-R-TOMAS

–0.76

IPSL IPSL-CM5A-LR –0.72 –0.71

LASG-IAP FGOALS-s2

–0.38 –0.34

MIROC MIROC-CHEM

–1.24

MIROC MIROC5 –1.28 –1.05

MOHC HadGEM2-A –1.22 –1.16

MRI MRI-CGM3 –1.10 –0.48

NCAR NCAR-CAM5.1

–1.44

NCC NorESM1-M –0.99

Ensemble mean –1.08

Standard deviation +0.32

Table 7.5 | Estimates of aerosol 1850–2000 effective radiative forcing (ERF, in W m

–2

) in some of the CMIP5 and ACCMIP models. The ERFs are estimated from ﬁxed-sea-surface

temperature (SST) experiments using atmosphere-only version of the models listed. Different models include different aerosol effects. The CMIP5 and ACCMIP protocols differ, hence

differences in forcing estimates for one model.

Notes:

From ACCMIP (Shindell et al., 2013).

These models include the black carbon on snow effect.

This model does not include the ERF from aerosol–cloud interactions.

There is conﬂicting evidence for the importance of ERFaci associated

with cirrus, ranging from a statistically signiﬁcant impact on cirrus cov-

erage (Hendricks et al., 2005) to a very small effect (Liu et al., 2009).

Penner et al. (2009) obtained a rather large negative RFaci of anthro-

pogenic ice-forming aerosol on upper tropospheric clouds of –0.67 to

–0.53 W m

–2

; however, they ignored potential compensating effects

on lower lying clouds. A new study based on two GCMs and different

ways to deduce ERFaci on cirrus clouds estimates ERFaci to be +0.27

± 0.1 W m

–2

(Gettelman et al., 2012), thus rendering aerosol effects on

cirrus clouds smaller than previously estimated and of opposite sign.

One reason for having switched to providing an expert judgment esti-

mate of ERFari+aci rather than of ERFaci is that the individual con-

tributions are very difﬁcult to disentangle. The individual components

can be isolated only if linearity of ERFari and ERFaci is assumed but

there is no a priori reason why the ERFs should be additive because

by deﬁnition they occur in a system that is constantly readjusting to

multiple nonlinear forcings. Nevertheless assuming additivity, ERFaci

could be obtained as the difference between ERFari+aci and ERFari.

This yields an ERFaci estimate of –0.45 W m

–2

, that is, much smaller

than the median ERFaci value of –1.4 W m

–2

(see above and Figure

7.19). This discrepancy arises because the GCM estimates of ERFaci do

not consider secondary processes and because these studies are not

necessarily conducted with the same GCMs that estimate ERFari+aci.

This difference could also be a measure of the non-linearity of the ERFs.

A 90% uncertainty range of –1.2 to 0 W m

–2

is adopted for ERFaci,

which accounts for the error covariance between ERFari and ERFaci

and the larger uncertainty on the lower bound. In summary, there is

much less conﬁdence associated with the estimate of ERFaci than with

the estimate of ERFari+aci.

ACCMIP = Atmospheric Chemistry and Climate Model Intercomparison Project.

CMIP5 = Coupled Model Intercomparison Project Phase 5.

622

Chapter 7 Clouds and Aerosols

Frequently Asked Questions

FAQ 7.2 | How Do Aerosols Affect Climate and Climate Change?

Atmospheric aerosols are composed of small liquid or solid particles suspended in the atmosphere, other than larger

cloud and precipitation particles. They come from natural and anthropogenic sources, and can affect the climate in

multiple and complex ways through their interactions with radiation and clouds. Overall, models and observations

indicate that anthropogenic aerosols have exerted a cooling inﬂuence on the Earth since pre-industrial times, which

has masked some of the global mean warming from greenhouse gases that would have occurred in their absence.

The projected decrease in emissions of anthropogenic aerosols in the future, in response to air quality policies,

would eventually unmask this warming.

Atmospheric aerosols have a typical lifetime of one day to two weeks in the troposphere, and about one year in the

stratosphere. They vary greatly in size, chemical composition and shape. Some aerosols, such as dust and sea spray,

are mostly or entirely of natural origin, while other aerosols, such as sulphates and smoke, come from both natural

and anthropogenic sources.

Aerosols affect climate in many ways. First, they scatter and absorb sunlight, which modiﬁes the Earth’s radiative

balance (see FAQ.7.2, Figure 1). Aerosol scattering generally makes the planet more reﬂective, and tends to cool

the climate, while aerosol absorption has the opposite effect, and tends to warm the climate system. The balance

between cooling and warming depends on aerosol properties and environmental conditions. Many observational

studies have quantiﬁed local radiative effects from anthropogenic and natural aerosols, but determining their

FAQ 7.2, Figure 1 | Overview of interactions between aerosols and solar radiation and their impact on climate. The left panels show the instantaneous radiative effects

of aerosols, while the right panels show their overall impact after the climate system has responded to their radiative effects.

(a)

(c)

(a) (b)

(d)

(c)

Scattering aerosols

Aerosol-radiation interactions

Absorbing aerosols

Cooling

The atmospheric circulation and mixing processes spread

the cooling regionally and in the vertical.

Aerosols scatter solar radiation. Less solar radiation

reaches the surface, which leads to a localised cooling.

At the larger scale there is a net warming of the surface and

atmosphere because the atmospheric circulation and

mixing processes redistribute the thermal energy.

Warming

Aerosols absorb solar radiation. This heats the aerosol

layer but the surface, which receives less solar radiation,

can cool locally.

(continued on next page)

623

Clouds and Aerosols Chapter 7

FAQ 7.2 (continued)

global impact requires satellite data and models.

One of the remaining uncertainties comes from

black carbon, an absorbing aerosol that not

only is more difﬁcult to measure than scattering

aerosols, but also induces a complicated cloud

response. Most studies agree, however, that

the overall radiative effect from anthropogenic

aerosols is to cool the planet.

Aerosols also serve as condensation and ice

nucleation sites, on which cloud droplets and ice

particles can form (see FAQ.7.2, Figure 2). When

inﬂuenced by more aerosol particles, clouds of

liquid water droplets tend to have more, but

smaller droplets, which causes these clouds to

reﬂect more solar radiation. There are however

many other pathways for aerosol–cloud inter-

actions, particularly in ice—or mixed liquid and

ice—clouds, where phase changes between

liquid and ice water are sensitive to aerosol con-

centrations and properties. The initial view that

an increase in aerosol concentration will also

increase the amount of low clouds has been

challenged because a number of counteracting

processes come into play. Quantifying the overall

impact of aerosols on cloud amounts and proper-

ties is understandably difﬁcult. Available studies,

based on climate models and satellite observa-

tions, generally indicate that the net effect of

anthropogenic aerosols on clouds is to cool the

climate system.

Because aerosols are distributed unevenly in the

atmosphere, they can heat and cool the climate

system in patterns that can drive changes in the

weather. These effects are complex, and hard to

simulate with current models, but several stud-

ies suggest signiﬁcant effects on precipitation in

certain regions.

Because of their short lifetime, the abundance of

aerosols—and their climate effects—have varied

over time, in rough concert with anthropogenic

emissions of aerosols and their precursors in the gas phase such as sulphur dioxide (SO

) and some volatile organic

compounds. Because anthropogenic aerosol emissions have increased substantially over the industrial period, this

has counteracted some of the warming that would otherwise have occurred from increased concentrations of well

mixed greenhouse gases. Aerosols from large volcanic eruptions that enter the stratosphere, such as those of El

Chichón and Pinatubo, have also caused cooling periods that typically last a year or two.

Over the last two decades, anthropogenic aerosol emissions have decreased in some developed countries, but

increased in many developing countries. The impact of aerosols on the global mean surface temperature over this

particular period is therefore thought to be small. It is projected, however, that emissions of anthropogenic aero-

sols will ultimately decrease in response to air quality policies, which would suppress their cooling inﬂuence on the

Earth’s surface, thus leading to increased warming.

Aerosols serve as cloud condensation nuclei upon which

liquid droplets can form.

Aerosol-cloud interactions

More aerosols result in a larger concentration of smaller

droplets, leading to a brighter cloud. However there are

many other possible aerosol–cloud–precipitation

processes which may amplify or dampen this effect.

(b)

(a)

FAQ 7.2, Figure 2 | Overview of aerosol–cloud interactions and their impact

on climate. Panels (a) and (b) represent a clean and a polluted low-level cloud,

respectively.

624

Chapter 7 Clouds and Aerosols

7.6 Processes Underlying Precipitation

Changes

7.6.1 Introduction

In this section we outline some of the main processes thought to con-

trol the climatological distribution of precipitation and precipitation

extremes. Emphasis is placed on large-scale constraints that relate

to processes, such as changes in the water vapour mixing ratio that

accompany warming, or changes in atmospheric heating rates that

accompany changing GHG and aerosol concentrations, which are dis-

cussed earlier in this chapter. The ﬁdelity with which large-scale models

represent different aspects of precipitation, ranging from the diurnal

cycle to extremes, is discussed in Section 9.4.1. Building on, and adding

to, concepts developed here, Section 11.3.2 presents near term pro-

jections of changes in regional precipitation features. Projections of

changes on longer time scales, again with more emphasis on regionally

speciﬁc features and the coupling to the land surface, are presented

in Section 12.4.5. The effect of processes discussed in this section on

speciﬁc precipitation systems, such as the monsoon, the intertropical

convergence zones, or tropical cyclones are presented in Chapter 14.

Precipitation is sustained by the availability of moisture and energy.

In a globally averaged sense the oceans provide an unlimited supply

of moisture, so that precipitation formation is energetically limited

(Mitchell et al., 1987). Locally precipitation can be greatly modiﬁed by

limitations in the availability of moisture (for instance over land) and

the effect of circulation systems, although these too are subject to local

energetic constraints (Neelin and Held, 1987; Raymond et al., 2009).

There are many ways to satisfy these constraints, and climate models

still exhibit substantial biases in their representation of the spatio-tem-

poral distribution of precipitation (Stephens et al., 2010; Liepert and

Previdi, 2012; Section 9.5). Nonetheless, through careful analysis, it is

possible to identify robust features in the simulated response of precip-

itation to changes in precipitation drivers. In almost every case these

can be related to well understood processes, as described below.

7.6.2 The Effects of Global Warming on Large-Scale

Precipitation Trends

The atmospheric water vapour mixing ratio is expected to increase with

temperature roughly following the saturation value (e.g., with increas-

es in surface values ranging from 6 to 10% °C

–1

and larger increases

aloft, see Section 7.2.4.1). Increases in global mean precipitation are,

however, constrained by changes in the net radiative cooling rate of

the troposphere. GCMs, whose detailed treatment of radiative trans-

fer provides a basis for calculating these energetic limitations, sug-

gest that for the CO

forcing, globally-averaged precipitation increases

with global mean surface temperature at about 1 to 3% °C

–1

(Mitch-

ell et al., 1987; Held and Soden, 2006; Richter and Xie, 2008). Pre-

cipitation changes evince considerable regional variability about the

globally averaged value; generally speaking precipitation is expected

to increase in the wettest latitudes, whereas dry latitudes may even

see a precipitation decrease (Mitchell et al., 1987; Allen and Ingram,

2002; Held and Soden, 2006). On smaller scales, or near precipitation

margins, the response is less clear due to model-speciﬁc, and less well

understood, regional circulation shifts (Neelin et al., 2006), but there

is some evidence that the sub-tropical dry zones are expanding (Sec-

tion 7.2.5.2 and Section 2.7.5), both as a result of the tropical conver-

gence zones narrowing (Neelin et al., 2006; Chou et al., 2009), and the

storm tracks moving poleward (Allen et al., 2012) and strengthening

(O’Gorman and Schneider, 2008).

The ‘wet-get-wetter’ and ‘dry-get-drier’ response that is evident at

large scales over oceans can be understood as a simple consequence

of a change in the water vapour content carried by circulations, which

otherwise are little changed (Mitchell et al., 1987; Held and Soden,

2006). Wet regions are wet because they import moisture from dry

regions, increasingly so with warmer temperatures. These ideas have

withstood additional analysis and scrutiny since AR4 (Chou et al.,

2009; Seager et al., 2010; Muller and O’Gorman, 2011), are evident

in 20th century precipitation trends (Allan and Soden, 2007; Zhang et

al., 2007b; see Section 2.5.1) and are assessed on different time scales

in Chapters 11 and 12. Because the wet-get-wetter argument implies

that precipitation changes associated with warming correlate with the

present-day pattern of precipitation, biases in the simulation of pres-

ent-day precipitation will lead to biases in the projections of future

precipitation change (Bony et al., 2013).

The wet-get-wetter and dry-get-drier response pattern is mitigated,

particularly in the dry regions, by the anticipated slowdown of the

atmospheric circulation (as also discussed in Section 7.2.5.3), as well

as by gains from local surface evaporation. The slowdown within the

descent regions can be partly understood as a consequence of the

change in the dry static stability of the atmosphere with warming. And

although this line of argument is most effective for explaining changes

over the ocean (Chou et al., 2009; Bony et al., 2013), it can also be

used to understand the GCM land responses to some extent (Muller

and O’Gorman, 2011).

The non-uniform nature of surface warming induces regional circula-

tion shifts that affect precipitation trends. In the tropics SSTs warm

more where winds are weak and thus are less effective in damping

surface temperature anomalies, and precipitation systematically shifts

to regions that warm more (Xie et al., 2010). The greater warming over

land, and its regional variations, also affect the regional distributions

of precipitation (Joshi et al., 2008). However, low understanding of soil

moisture–precipitation feedbacks complicates interpretations of local

responses to warming over land (Hohenegger et al., 2009), so that the

effect of warming on precipitation at the scale of individual catch-

ments is not well understood. Some broad-scale responses, particularly

over the ocean, are more robust and relatively well understood.

7.6.3 Radiative Forcing of the Hydrological Cycle

In the absence of a compensating temperature change, an increase in

well-mixed GHG concentrations tends to reduce the net radiative cool-

ing of the troposphere. This reduces the rainfall rate and the strength

of the overturning circulation (Andrews et al., 2009; Bony et al., 2013),

such that the increase in global mean precipitation would be 1.5 to

3.5% °C

–1

due to temperature alone but is reduced by about 0.5% °C

–1

due to the effect of CO

(Lambert and Webb, 2008). The dynamic effects

are similar to those that result from the effect of atmospheric warming

on the lapse rate, which also reduces the strength of the atmospheric

625

Clouds and Aerosols Chapter 7

overturning circulation (e.g., Section 7.6.2), and are robustly evident

over a wide range of models and model conﬁgurations (Bony et al.,

2013; see also Figure 7.20). These circulation changes inﬂuence the

regional response, and are more pronounced over the ocean, because

asymmetries in the land-sea response to changing concentrations of

GHGs (Joshi et al., 2008) amplify the maritime and dampen or even

reverse the terrestrial signal (Wyant et al., 2012; Bony et al., 2013).

The dependence of the intensity of the hydrological cycle on the tro-

pospheric cooling rate helps to explain why perturbations having

the same RF do not produce the same precipitation responses. Apart

from the relatively small increase in absorption by atmospheric water

vapour, increased solar forcing does not directly affect the net tropo-

spheric cooling rate. As a result the hydrological cycle mostly feels the

subsequent warming through its inﬂuence on the rate of tropospheric

cooling (Takahashi, 2009). This is why modeling studies suggest that

solar radiation management (geoengineering) methods that maintain

a constant surface temperature will lead to a reduction in globally

averaged precipitation as well as different regional distributions of

precipitation (Schmidt et al., 2012b; Section 7.7.3).

Changes in cloud radiative effects, and aerosol RF can also be effec-

tive in changing the net radiative heating rate within the troposphere

(Lambert and Webb, 2008; Pendergrass and Hartmann, 2012). Most

land

ocean

Tropical ΔT

(ºC)

Change in overturning strength (%)

-5

-10

global

024 6

Abrupt 4xCO

in CMIP5 Coupled Model

RCP 8.5 scenario at 4xCO

4xCO

in AGCMs (ﬁxed SST)

4xCO

in aqua-planet AGCMs (ﬁxed SST

)

Figure 7.20 | Illustration of the response of the large-scale atmospheric overturning

to warming (adapted from Bony et al., 2013). The overturning intensity is shown on the

y-axis and is measured by the difference between the mean motion in upward moving

air and the mean motion in downward moving air. The warming is shown on the x-axis

and is measured by the change in surface temperature averaged over the Tropics, ΔT

after an abrupt quadrupling of atmospheric CO

. The grey region delineates responses

for which ΔT

is zero by deﬁnition. Nearly one half of the ﬁnal reduction in the intensity

of the overturning is evident before any warming is felt, and can be associated with

a rapid adjustment of the hydrological cycle to changes in the atmospheric cooling

rate accompanying a change in CO

. With warming the circulation intensity is further

reduced. The rapid adjustment, as measured by the change in circulation intensity for

zero warming, is different over land and ocean. Over land the increase in CO

initially

causes an intensiﬁcation of the circulation. The result is robust in the sense that it is

apparent in all of the 15 CMIP5 models analysed, irrespective of the details of their

conﬁguration.

prominently, absorption of solar radiation by atmospheric aerosols

is understood to reduce the globally averaged precipitation. But this

effect may be offset by the tendency of absorbing aerosols to reduce

the planetary albedo, thereby raising surface temperature, leading to

more precipitation (Andrews et al., 2009). Heterogeneously distributed

precipitation drivers such as clouds, aerosols and tropospheric ozone

will also induce circulations that may amplify or dampen their local

impact on the hydrological cycle (Ming et al., 2010; Allen et al., 2012;

Shindell et al., 2012). Such regional effects are discussed further in

Chapter 14 for the case of aerosols.

7.6.4 Effects of Aerosol–Cloud Interactions on

Precipitation

Aerosol–cloud interactions directly inﬂuence the cloud microphysical

structure, and only indirectly (if at all) the net atmospheric heating

rate, and for this reason have mostly been explored in terms of their

effect on the character and spatio-temporal distribution of precipita-

tion, rather than on the globally-averaged amount of precipitation.

The sensitivity of simulated clouds to their microphysical development

(e.g., Fovell et al., 2009; Parodi and Emanuel, 2009) suggests that they

may be susceptible to the availability of CCN and IN. For instance, an

increase in CCN favours smaller cloud droplets, which delays the onset

of precipitation and the formation of ice particles in convective clouds

(Rosenfeld and Woodley, 2001; Khain et al., 2005). It has been hypoth-

esized that such changes may affect the vertical distribution and total

amount of latent heating in ways that would intensify or invigorate

convective storms, as measured by the strength and vertical extent

of the convective updraughts (Andreae et al., 2004; Rosenfeld et al.,

2008; Rosenfeld and Bell, 2011; Tao et al., 2012). Support for the idea

that the availability of CCN inﬂuences the vigour of convective systems

can be found in some modelling studies, but the strength, and even

sign, of such an effect has been shown to be contingent on a variety

of environmental factors (Seifert and Beheng, 2006; Fan et al., 2009;

Khain, 2009; Seifert et al., 2012) as well as on modelling assumptions

(Ekman et al., 2011).

Observational studies, based on large data sets that sample many

convective systems, report systematic correlations between aerosol

amount and cloud-top temperatures (Devasthale et al., 2005; Koren

et al., 2010a; Li et al., 2011). Weekly cycles in cloud properties and

precipitation, wherein convective intensity, cloud cover or precipita-

tion increases during that part of the week when aerosol concentra-

tions are largest, have also been reported (Bäumer and Vogel, 2007;

Bell et al., 2008; Rosenfeld and Bell, 2011). Both types of studies have

been interpreted in terms of an aerosol inﬂuence on convective cloud

systems. However, whether or not these ﬁndings demonstrate that a

greater availability of CCN systematically invigorates, or otherwise

affects, convection remains controversial. Many of the weekly cycle

studies are disputed on statistical or other methodological grounds

(Barmet et al., 2009; Stjern, 2011; Tuttle and Carbone, 2011; Sanchez-

Lorenzo et al., 2012; Yuter et al., 2013). Even in cases where relation-

ships between aerosol amount and some measure of convective inten-

sity appear to be unambiguous, the interpretation that this reﬂects an

aerosol effect on the convection is less clear, as both aerosol properties

and convection are strongly inﬂuenced by meteorological factors that

626

Chapter 7 Clouds and Aerosols

are not well controlled for (e.g., Boucher and Quaas, 2013). Studies

that have used CRMs to consider the net effect of aerosol–cloud inter-

actions integrated over many storms, or in more of a climate context

wherein convective heating must balance radiative cooling within the

atmosphere, also do not support a strong and systematic invigoration

effect resulting from very large (many fold) changes in the ambient

aerosol (Morrison and Grabowski, 2011; van den Heever et al., 2011;

Seifert et al., 2012; Khairoutdinov and Yang, 2013). Locally in space or

time, however, radiative processes are less constraining, leaving open

the possibility of stronger effects from localized or transient aerosol

perturbations.

Because precipitation development in clouds is a time-dependent

process, which proceeds at rates that are partly determined by the

cloud microphysical structure (Seifert and Zängl, 2010), aerosol–cloud

interactions may lead to shifts in topographic precipitation to the lee-

ward side of mountains when precipitation is suppressed, or to the

windward side in cases when it is more readily initiated. Orographic

clouds show a reduction in the annual precipitation over topographical

barriers downwind of major urban areas in some studies (Givati and

Rosenfeld, 2004; Jirak and Cotton, 2006) but not in others (Halfon et

al., 2009). Even in cases where effects are reported, the results have

proven sensitive to how the data are analysed (Alpert et al., 2008;

Levin and Cotton, 2009).

In summary, it is unclear whether changes in aerosol–cloud interac-

tions arising from changes in the availability of CCN or IN can affect,

and possibly intensify, the evolution of individual precipitating cloud

systems. Some observational and modelling studies suggest such an

effect, but are undermined by alternative interpretations of the obser-

vational evidence, and a lack of robustness in the modelling studies.

The evidence for systematic effects over larger areas and long time

periods is, if anything, more limited and ambiguous.

7.6.5 The Physical Basis for Changes in Precipitation

Extremes

The physical basis for aerosol microphysical effects on convective

intensity was discussed in the previous section. Here we brieﬂy dis-

cuss process understanding of the effect of warming on precipitation

extremes; observed trends supporting these conclusions are presented

in Section 2.6.2.

Precipitation within individual storms is expected to increase with the

available moisture content in the atmosphere or near the surface rather

than with the global precipitation (Allen and Ingram, 2002; Held and

Soden, 2006), which leads to a 6 to 10% °C

–1

increase, but with longer

intervals between storms (O’Gorman and Schneider, 2009). Because

GCMs are generally poor at simulating precipitation extremes (Ste-

phens et al., 2010) and predicted changes in a warmer climate vary

(Kharin et al., 2007; Sugiyama et al., 2010), they are not usually thought

of as a source of reliable information regarding extremes. Howev-

er, a recent study (O’Gorman, 2012) shows that GCM predictions of

extremes can be constrained by observable relationships in the pres-

ent day climate, and upon doing so become broadly consistent with

the idea that extreme precipitation increases by 6 to 10% per °C of

warming. Central estimates of sensitivity of extreme (99.9th percentile)

Change in precipitation (% ºC

-1

)

A. GCM

B. Observed Trends

C. GCM Constrained by Obs

D. CRM – RCE

E. LES – RCE

tropics

only

extratropics

only

Increasing CO

temperature

only

Time average 24 hr Extreme ≤ 1hr Extreme

daily rainfall to global temperature from this study were 10% °C

–1

the tropics (O’Gorman, 2012), compared to 5% °C

–1

predicted by the

models in the extratropics, where they may be more reliable (O’Gorman

and Schneider, 2009). How precipitation extremes depend on temper-

ature has also been explored using cloud resolving simulations (but

only for tropical conditions) which produce similar increases in extreme

instantaneous rain rate (Romps, 2011) and daily or hourly rain totals

(Muller et al., 2011) within storms (Figure 7.21). Because these latter

studies are conﬁned to small domains, they may exclude important

synoptic or larger-scale dynamical changes such as increases in ﬂow

convergence (Chou et al., 2009; Sugiyama et al., 2010).

By taking advantage of natural variability in the present day climate,

a number of studies have correlated observed rainfall extremes with

local temperature variations. In the extratropics, these studies docu-

ment sensitivities of extreme precipitation to temperature much higher

than those reported above (Lenderink and Van Meijgaard, 2008), but

sensitivities vary with temperature (Lenderink et al., 2011), are often

negative in the tropics (Hardwick Jones et al., 2010) and usually

strengthen at the shortest (e.g., hourly or less) time scales (e.g., Haerter

et al., 2010; Hardwick Jones et al., 2010). However, local temperature

changes may not be a good proxy for global warming because they

tend to co-vary with other meteorological factors (such as humidity,

atmospheric stability, or wind direction) in ways that are uncharac-

teristic of changes in the mean temperature (see Section 7.2.5.7),

and these other meteorological factors may dominate the observed

signal (e.g., Haerter and Berg, 2009). Thus, the idea that precipitation

extremes depend much more strongly on temperature than the 5 to

Figure 7.21 | Estimate (5 to 95% range) of the increase in precipitation amount per

degree Celsius of global mean surface temperature change. At left (blue) are climate

model predictions of changes in time-averaged global precipitation; at centre and right

(orange) are predictions or estimates of the typical or average increase in local 99.9th

percentile extremes, over 24 hours (centre) and over one hour or less (right). Data

are adapted from (A) GCM studies (Allen and Ingram, 2002; and Lambert and Webb,

2008, for time average; O’Gorman and Schneider, 2009 for extremes), (B) long-term

trends at many sites globally (Westra et al., 2013), (C) GCMsconstrained by present-

day observations of extremes (O’Gorman, 2012), (D, E) cloud-resolving model (CRM)

and large-eddy simulation (LES) studies of radiative convective equilibrium (Muller et

al., 2011; Romps, 2011).

627

Clouds and Aerosols Chapter 7

10% increase per degree Celsius attributable to water vapour changes,

remains controversial.

Following the AR4, studies have also continued to show that extremes

in precipitation are associated with the coincidence of particular

weather patterns (e.g., Lavers et al., 2011). We currently lack an ade-

quate understanding of what controls the return time and persistence

of such rare events.

From the aforementioned model and observational evidence, there is

high conﬁdence that the intensity of extreme precipitation events will

increase with warming, at a rate well exceeding that of the mean pre-

cipitation. There is medium conﬁdence that the increase is roughly 5 to

10% °C

–1

warming but may vary with time scale, location and season.

7.7 Solar Radiation Management and

Related Methods

7.7.1 Introduction

Geoengineering—also called climate engineering—is deﬁned as a

broad set of methods and technologies that aim to deliberately alter

the climate system in order to alleviate impacts of climate change

(Keith, 2000; Izrael et al., 2009; Royal Society, 2009; IPCC, 2011). Two

main classes of geoengineering are often considered. Solar Radiation

Management (SRM) proposes to counter the warming associated with

increasing GHG concentrations by reducing the amount of sunlight

absorbed at the surface. A related method seeks to alter high-altitude

cirrus clouds to reduce their greenhouse effect. Another class of geoen-

gineering called Carbon Dioxide Removal (CDR) is discussed in Section

6.5. This section assesses how the climate system might respond to

some proposed SRM methods and related methods thought to have

the potential to inﬂuence the global energy budget by at least a few

tenths of a W m

–2

but it does not assess technological or economi-

cal feasibility, or consider methods targeting speciﬁc climate impacts

(MacCracken, 2009). Geoengineering is quite a new ﬁeld of research,

and there are relatively few studies focussed on it. Assessment of SRM

is limited by (1) gaps in understanding of some important processes;

(2) a relative scarcity of studies; and (3) a scarcity of studies using

similar experimental design. This section discusses some aspects of

SRM potential to mitigate global warming, outlines robust conclusions

where they are apparent, and evaluates uncertainties and potential

side effects. Additional impacts of SRM are assessed in Section 19.5.4

of the WGII report, while some of the socio-economic issues are

assessed in Chapters 3, 6 and 13 of the WGIII report.

7.7.2 Assessment of Proposed Solar Radiation

Management Methods

A number of studies have suggested reducing the amount of sunlight

reaching the Earth by placing solid or refractive disks, or dust particles,

in outer space (Early, 1989; Mautner, 1991; Angel, 2006; Bewick et al.,

2012). Although we do not assess the feasibility of these methods, they

provide an easily described mechanism for reducing sunlight reach-

ing the planet, and motivate the idealized studies discussed in Section

7.7.3.

7.7.2.1 Stratospheric Aerosols

Some SRM methods propose increasing the amount of stratospher-

ic aerosol to produce a cooling effect like that observed after strong

explosive volcanic eruptions (Budyko, 1974; Crutzen, 2006). Recent

studies have used numerical simulations and/or natural analogues to

explore the possibility of forming sulphuric acid aerosols by injecting

sulphur-containing gases into the stratosphere (Rasch et al., 2008b).

Because aerosols eventually sediment out of the stratosphere (within

roughly a year or less), these methods require replenishment to main-

tain a given level of RF. Research has also begun to explore the efﬁcacy

of other types of aerosol particles (Crutzen, 2006; Keith, 2010; Ferraro

et al., 2011; Kravitz et al., 2012) but the literature is much more limited

and not assessed here.

The RF depends on the choice of chemical species (gaseous sulphur

dioxide (SO

), sulphuric acid (H

) or sprayed aerosols), location(s),

rate and frequency of injection. The injection strategy affects particle

size (Rasch et al., 2008a; Heckendorn et al., 2009; Pierce et al., 2010;

English et al., 2012), with larger particles producing less RF (per unit

mass) and more rapid sedimentation than smaller particles, affecting

the efﬁcacy of the method. The aerosol size distribution is controlled

by an evolving balance between new particle formation, condensation

of vapour on pre-existing particles, evaporation of particles, coagula-

tion and sedimentation. Models that more fully account for aerosol

processes (Heckendorn et al., 2009; Pierce et al., 2010; English et al.,

2012) found smaller aerosol burdens, larger particles and weaker RF

than earlier studies that prescribed the particle size over the particle

lifetime. Current modeling studies indicate that injection of sulphate

aerosol precursors of at least 10 Mt S (approximately the amount of

sulphur injected by the Mount Pinatubo eruption) would be needed

annually to maintain a RF of –4 W m

–2

, roughly equal but opposite to

that associated with a doubling of atmospheric CO

(Heckendorn et al.,

2009; Pierce et al., 2010; Niemeier et al., 2011). Stratospheric aerosols

may affect high clouds in the tropopause region, and one study (Kueb-

beler et al., 2012) suggests signiﬁcant negative forcing would result,

but this is uncertain given limited understanding of ice nucleation in

high clouds (Section 7.4.4.4).

Along with its potential to mitigate some aspects of global warming,

the potential side effects of SRM must also be considered. Tilmes et al.

(2008; 2009) estimated that stratospheric aerosols SRM might increase

chemical ozone loss at high latitudes and delay recovery of the Antarc-

tic ozone hole (expected at the end of this century) by 30 to 70 years,

with changes in column ozone of –3 to –10% in polar latitudes and

+3 to +5% in the tropics. A high latitude ozone loss is expected to

increase UV radiation reaching the surface there, although the effect

would be partially compensated by the increase in attenuation by the

aerosol itself (Vogelmann et al., 1992; Tilmes et al., 2012). A decrease

in direct radiation and increase in diffuse radiation reaching the Earth’s

surface would occur and would be expected to increase photosynthesis

in terrestrial ecosystems (Mercado et al., 2009; see Section 6.5.4) and

decrease the efﬁciency of some solar energy technologies (see WGII

AR5, Section 19.5.4). Models indicate that stratospheric aerosol SRM

would not pose a surface acidiﬁcation threat with maximum acid dep-

osition rates estimated to be at least 500 times less than the threshold

of concern for the most sensitive land ecosystems (Kuylenstierna et al.,

628

Chapter 7 Clouds and Aerosols

2001; Kravitz et al., 2009); contributions to ocean acidiﬁcation are also

estimated to represent a very small fraction of that induced by anthro-

pogenic CO

emissions (Kravitz et al., 2009). There are other known

side effects that remain unquantiﬁed, and limited understanding (and

limited study) make additional impacts difﬁcult to anticipate.

7.7.2.2 Cloud Brightening

Boundary layer clouds act to cool the planet, and relatively small

changes in albedo or areal extent of low cloud can have profound

effects on the Earth’s radiation budget (Section 7.2.1). Theoretical,

modelling and observational studies show that the albedo of these

types of cloud systems are susceptible to changes in their droplet con-

centrations, but the detection and quantiﬁcation of RF attributable to

such effects is difﬁcult to separate from meteorological variability (Sec-

tion 7.4.3.2). Nonetheless, by systematically introducing CCN into the

marine boundary layer, it should be possible to locally increase bound-

ary layer cloud albedo as discussed in Section 7.4.2. These ideas under-

pin the method of cloud brightening, for instance through the direct

injection (seeding) of sea-spray particles into cloud-forming air masses

(Latham, 1990). An indirect cloud brightening mechanism through

enhanced DMS production has also been proposed (Wingenter et al.,

2007) but the efﬁcacy of the DMS mechanism is disputed (Vogt et al.,

2008; Woodhouse et al., 2008).

The seeding of cloud layers with a propensity to precipitate may

change cloud structure (e.g., from open to closed cells) and/or increase

liquid water content (Section 7.4.3.2.1), in either case changing albedo

and producing strong negative forcing. A variety of methods have been

used to identify which cloud regions are most susceptible to an aerosol

change (Oreopoulos and Platnick, 2008; Salter et al., 2008; Alterskjær

et al., 2012). Marine stratocumulus clouds with relatively weak precipi-

tation are thought to be an optimal cloud type for brightening because

of their relatively low droplet concentrations, their expected increase in

cloud water in response to seeding (Section 7.4.3.2.1), and the longer

lifetime of sea salt particles in non- or weakly precipitating environ-

ments. Relatively strong local ERFaci (–30 to –100 W m

–2

) would be

required to produce a global forcing of –1 to –5 W m

–2

if only the more

susceptible clouds were seeded.

Simple modelling studies suggest that increasing droplet concentra-

tions in marine boundary layer clouds by a factor of ﬁve or so (to con-

centrations of 375 to 1000 cm

–3

) could produce an ERFaci of about –1

W m

–2

if 5% of the ocean surface area were seeded, and an ERFaci as

strong as –4 W m

–2

if that fraction were increased to 75% (Latham et

al., 2008; Jones et al., 2009; Rasch et al., 2009). Subsequent studies

with more complete treatments of aerosol–cloud interactions have pro-

duced both stronger (Alterskjær et al., 2012; Partanen et al., 2012) and

weaker (Korhonen et al., 2010a) changes. Because the initial response

to cloud seeding is local, high-resolution, limited-domain simulations

are especially useful to explore the efﬁcacy of seeding. One recent

study of this type (Wang et al., 2011a) found that cloud brightening

is sensitive to cloud dynamical adjustments that are difﬁcult to treat

in current GCMs (Sections 7.4.3 and 7.5.3) and concluded that the

seeding rates initially proposed for cloud seeding may be insufﬁcient

to produce the desired cloud brightening. Recent studies accounting

for clear-sky brightening from increased aerosol concentrations (i.e.,

ERFari) also found an increase in the amplitude of the ERF by 30 to

50% (Hill and Ming, 2012; Partanen et al., 2012), thereby making the

aerosol seeding more effective than previous estimates that neglected

that effect.

In summary, evidence that cloud brightening methods are effective

and feasible in changing cloud reﬂectivity is ambiguous and subject to

many of the uncertainties associated with aerosol–cloud interactions

more broadly. If cloud brightening were to produce large local chang-

es in ERF, those changes would affect the local energy budget, with

further impacts on larger-scale oceanic and atmospheric circulations.

Possible side effects accompanying such large and spatially heteroge-

neous changes in ERF have not been systematically studied.

7.7.2.3 Surface Albedo Changes

A few studies have explored how planetary albedo might be increased

by engineering local changes to the albedo of urban areas, croplands,

grasslands, deserts and the ocean surface. Effects from whitening of

urban areas have been estimated to yield a potential RF of –0.17 W m

–2

(Hamwey, 2007) although subsequent studies (Lenton and Vaughan,

2009; Oleson et al., 2010; Jacobson and Ten Hoeve, 2012) suggest that

this estimate may be at the upper end of what is achievable. Larger

effects might be achievable by replacing native grassland or cropland

with natural or bioengineered species with a larger albedo. A hypo-

thetical 25% increase in grassland albedo could yield a RF as large as

–0.5 W m

–2

(Lenton and Vaughan, 2009), with the maximum effect in

the mid-latitudes during summer (Ridgwell et al., 2009; Doughty et al.,

2011). The feasibility of increasing crop and grassland albedo remains

unknown, and there could be side effects on photosynthetic activity,

carbon uptake and biodiversity. The low albedo and large extent of

oceanic surfaces mean that only a small increase in albedo, for exam-

ple, by increasing the concentration of microbubbles in the surface

layer of the ocean (Evans et al., 2010; Seitz, 2011), could be sufﬁcient

to offset several W m

–2

of RF by GHGs. Neither the extent of micro-

bubble generation and persistence required for a signiﬁcant climate

impact, nor the potential side effects on the ocean circulation, air-sea

ﬂuxes and marine ecosystems have been assessed.

7.7.2.4 Cirrus Thinning

Although not strictly a form of SRM, proposals have been made to cool

the planet by reducing the coverage or longwave opacity of high thin

cirrus clouds, which act to warm the surface through their greenhouse

effect (see Section 7.2.1.2). A proposal for doing so involves adding

efﬁcient IN in regions prone to forming thin cirrus cloud (Mitchell and

Finnegan, 2009). To the extent such a proposal is feasible, one mod-

elling study suggests that an ERFaci of as strong as –2 W m

–2

could

be achieved (Storelvmo et al., 2013), with further negative forcing

caused by a reduction in humidity of the upper troposphere associat-

ed with the cloud changes. However, lack of understanding of cirrus

cloud formation processes, as well as ice microphysical processes (Sec-

tion 7.4.4), makes it difﬁcult to judge the feasibility of such a method,

particularly in light of the fact that increasing ice nucleation can also

increase cirrus opacity, under some circumstances producing an oppo-

site, positive forcing (Storelvmo et al., 2013). Side effects speciﬁc to the

‘cirrus thinning’ method have not been investigated.

629

Clouds and Aerosols Chapter 7

7.7.3 Climate Response to Solar Radiation

Management Methods

As discussed elsewhere in this and other chapters of this assessment,

signiﬁcant gaps remain in our understanding of the climate response

to forcing mechanisms. Geoengineering is also a relatively new ﬁeld of

research. The gaps in understanding, scarcity of studies and diversity

in the model experimental design make quantitative model evaluation

and intercomparison difﬁcult, hindering an assessment of the efﬁcacy

and side effects of SRM. This motivates dividing the discussion into two

sections, one that assesses idealized studies that focus on conceptual

issues and searches for robust responses to simple changes in the bal-

ance between solar irradiance and CO

forcing, and another discussing

studies that more closely emulate speciﬁc SRM methods.

7.7.3.1 Climate Response in Idealized Studies

Perhaps the simplest SRM experiment that can be performed in a

climate model consists of a speciﬁed reduction of the total solar

irradiance, which could approximate the radiative impact of space

reﬂectors. Reductions in solar irradiance in particular regions (over

land or ocean, or in polar or tropical regions) could also provide useful

information. Idealized simulations often focus on the effects of a com-

plete cancellation of the warming from GHGs, but the rate of warming

is occasionally explored by producing a negative RF that partially can-

cels the anthropogenic forcing (e.g., Eliseev et al., 2009). They can also

provide insight into the climate response to other SRM methods, and

can provide a simple baseline for examining other SRM techniques.

The most comprehensive and systematic evaluation of idealized SRM

to date is the Geoengineering Model Intercomparison Project (Kravitz

et al., 2011). Together with earlier model studies, this project found

robust surface temperature reductions when the total solar irradiance is

reduced: when this reduction compensates for CO

RF, residual effects

appear regionally, but they are much smaller than the warming due to

the CO

RF alone (Kravitz et al., 2011; Schmidt et al., 2012b; Figures

7.22a, b and 7.23a–d) The substantial warming from 4 × CO

at high

latitudes (4°C to 18°C) is reduced to a warming of 0°C to 3°C near

Change in temperature (°C)

(

)

Latitude

(d)

Change in precipitation (mm day

-1

)

Latitude

(c)

(b)

90°S 60°S 30°S Eq 30°N 60°N 90°N

−1

−0.5

0.5

1.5

90°S 60°S 30°S Eq 30°N 60°N 90°N

−1

− 0.5

0.5

1.5

Ensemble Mean

90°S 60°S 30°S Eq 30°N 60°N 90°N

BNU-ESM

CanESM2

CESM-CAM5.1-FV

EC-Earth

GISS-E2-R

CCSM4

HadCM3

HadGEM2-ES

MIROC-ESM

MPI-ESM-LR

NorESM1-M

IPSL-CM5A-LR

Quadrupling CO

and Reducing Solar Input

Figure 7.22 | Zonally and annually averaged change in surface air temperature (°C) for (a) an abrupt 4 × CO

experiment and (b) an experiment where the 4 × CO

forcing is

balanced by a reduction in the total solar irradiance to produce a global top of the atmosphere ﬂux imbalance of less than ±0.1 W m

–2

during the ﬁrst 10 years of the simulation

(Geoengineering Model Intercomparison Project (GeoMIP) G1 experiment; Kravitz et al., 2011). (c, d) Same as (a) and (b) but for the change in precipitation (mm day

–1

). The multi-

model ensemble mean is shown with a thick black solid line. All changes are relative to the pre-industrial control experiment and averaged over years 11 to 50. The ﬁgure extends

the results from Schmidt et al. (2012b) and shows the results from an ensemble of 12 coupled ocean–atmosphere general circulation models.

630

Chapter 7 Clouds and Aerosols

Figure 7.23 | Multi-model mean of the change in surface air temperature (°C) averaged over December, January and February (DJF) for (a) an abrupt 4 × CO

simulation and (b)

an experiment where the 4 × CO

forcing is balanced by a reduction in the total solar irradiance to produce a global top of the atmosphere ﬂux imbalance of less than ±0.1 W m

–2

during the ﬁrst 10 years of the simulation (Geoengineering Model Intercomparison Project (GeoMIP) G1 experiment; Kravitz et al., 2011). (c, d) Same as (a-b) but for June, July and

August (JJA). (e–h) same as (a–d) but for the change in precipitation (mm day

–1

). All changes are relative to the pre-industrial control experiment and averaged over years 11 to 50.

The ﬁgure extends the results from Schmidt et al. (2012b) and shows the results from an ensemble of 12 coupled ocean–atmosphere general circulation models. Stippling denotes

agreement on the sign of the anomaly in at least 9 out of the 12 models.

the winter pole. The residual surface temperature changes are gener-

ally positive at mid- and high-latitudes, especially over continents, and

generally negative in the tropics (Bala et al., 2008; Lunt et al., 2008;

Schmidt et al., 2012b). These anomalies can be understood in terms of

the difference between the more uniform longwave forcing associated

with changes in long-lived GHGs and the less uniform shortwave forc-

ing from SRM that has a stronger variation in latitude and season. The

compensation between SRM and CO

forcing is inexact in other ways.

For example, SRM will change heating rates only during daytime, but

increasing greenhouse effect changes temperatures during both day

and night, inﬂuencing the diurnal cycle of surface temperature even if

compensation for the diurnally averaged surface temperature is correct.

Although increasing CO

concentrations lead to a positive RF that

warms the entire troposphere, SRM produces a negative RF that tends

to cool the surface. The combination of RFs produces an increase in

631

Clouds and Aerosols Chapter 7

stability that leads to less global precipitation as seen in Figures 7.22c,

d and 7.23e–g (Bala et al., 2008; Andrews et al., 2010; Schmidt et al.,

2012b) and discussed in Section 7.6.3. The reduction in precipitation

shows similarities to the climate response induced by the Pinatubo

eruption (Trenberth and Dai, 2007). Although the impact of changes in

the total solar irradiance on global mean precipitation is well under-

stood and robust in models, there is less understanding and agree-

ment among models in the spatial pattern of the precipitation changes.

Modelling studies suggest that some residual patterns may be robust

(e.g., approximately 5% reduction in precipitation over Southeast Asia

and the Paciﬁc Warm Pool in June, July and August), but a physical

explanation for these changes is lacking. Some model results indicate

that an asymmetric hemispheric SRM forcing would induce changes in

some regional precipitation patterns (Haywood et al., 2013).

Figure 7.24 | Time series of globally averaged (a) surface temperature (°C) and (b)

precipitation (mm day

–1

) changes relative to each model’s 1 × CO

reference simula-

tion. Solid lines are for simulations using solar radiation management (SRM) through

an increasing reduction of the total solar irradiance to balance a 1% yr

–1

increase in

concentration until year 50, after which SRM is stopped for the next 20 years

(Geoengineering Model Intercomparison Project (GeoMIP) G2 experiment; Kravitz et al.,

2011). Dashed lines are for 1% CO

increase simulations with no SRM. The multi-model

ensemble mean is shown with thick black lines.

Change in temperature (°C)

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Change in precipitation (mm day

-1

)

-0.08

-0.06

-0.04

-0.02

0.02

0.04

0.06

0.08

0.10

Year

0102030405060

(a)

(b)

Ensemble Mean

BNU-ESM

CanESM2

CESM-CAM5.1-FV

EC-Earth

GISS-E2-R

CCSM4

HadCM3

HadGEM2-ES

MIROC-ESM

MPI-ESM-LR

NorESM1-M

IPSL-CM5A-LR

High CO

concentrations from anthropogenic emissions will persist

in the atmosphere for more than a thousand years in the absence of

active efforts to remove atmospheric CO

(see Chapter 6). If SRM were

used to counter positive forcing, it would be needed as long as the

concentrations remained high (Boucher et al., 2009). If GHG con-

centrations continued to increase, then the scale of SRM to offset the

resulting warming would need to increase proportionally, amplifying

residual effects from increasingly imperfect compensation. Figure 7.24

shows projections of the globally averaged surface temperature and

precipitation changes associated with a 1% yr

–1

increase, with and

without SRM. The scenario includes a hypothetical, abrupt termination

of SRM at year 50, which could happen due to any number of unfore-

seeable circumstances. After this event, all the simulations predict a

return to temperature levels consistent with the CO

forcing within one

to two decades (high conﬁdence), and with a large rate of temperature

change (see also Irvine et al., 2012). Precipitation, which drops by 1%

over the SRM period, rapidly returns to levels consistent with the CO

forcing upon SRM termination. The very rapid warming would prob-

ably affect ecosystem and human adaptation, and would also weaken

carbon sinks, accelerating atmospheric CO

accumulation and contrib-

uting to further warming (Matthews and Caldeira, 2007).

Research suggests that this ‘termination effect’ might be avoided if

SRM were used at a modest level and for a relatively short period of

time (less than a century) when combined with aggressive CO

removal

efforts to minimize the probability that the global mean temperature

might exceed some threshold (Matthews, 2010; Smith and Rasch,

2012).

7.7.3.2 Climate Response to Speciﬁc Solar Radiation

Management Methods

Several studies examined the model response to more realistic strat-

ospheric aerosol SRM (Rasch et al., 2008b; Robock et al., 2008; Jones

et al., 2010; Fyfe et al., 2013). These studies produced varying aerosol

burdens, and RF and model responses also varied more strongly than

in idealized experiments. Although these studies differ in details, their

climate responses were generally consistent with the idealized experi-

ments described in Section 7.7.3.1.

Studies treating the interaction between the carbon cycle, the hydro-

logic cycle, and SRM indicate that SRM could affect the temperature-

driven suppression of some carbon sinks, and that the increased sto-

matal resistance with increased CO

concentrations combined with

less warming, may further affect the hydrological cycle over land (Mat-

thews and Caldeira, 2007; Fyfe et al., 2013), with larger impacts on

precipitation for stratospheric aerosol SRM than for a uniform reduc-

tion in incoming sunlight.

Coupled ocean–atmosphere–sea ice models have also been used to

assess the climate impacts of cloud brightening due to droplet concen-

tration changes (Jones et al., 2009; Rasch et al., 2009; Baughman et al.,

2012; Hill and Ming, 2012). The patterns of temperature and precipita-

tion change differ substantially between models. These studies showed

larger residual temperature changes than the idealized SRM studies,

with more pronounced cooling over the regions of enhanced albedo.

The cooling over the seeded regions (the marine stratocumulus regions)

632

Chapter 7 Clouds and Aerosols

Frequently Asked Questions

FAQ 7.3 | Could Geoengineering Counteract Climate Change and What Side Effects

Might Occur?

Geoengineering—also called climate engineering—is deﬁned as a broad set of methods and technologies that aim

to deliberately alter the climate system in order to alleviate impacts of climate change. Two distinct categories of

geoengineering methods are usually considered: Solar Radiation Management (SRM, assessed in Section 7.7) aims

to offset the warming from anthropogenic greenhouse gases by making the planet more reﬂective while Carbon

Dioxide Removal (CDR, assessed in Section 6.5) aims at reducing the atmospheric CO

concentration. The two cat-

egories operate on different physical principles and on different time scales. Models suggest that if SRM methods

were realizable they would be effective in countering increasing temperatures, and would be less, but still, effective

in countering some other climate changes. SRM would not counter all effects of climate change, and all proposed

geoengineering methods also carry risks and side effects. Additional consequences cannot yet be anticipated as the

level of scientiﬁc understanding about both SRM and CDR is low. There are also many (political, ethical, and practi-

cal) issues involving geoengineering that are beyond the scope of this report.

Carbon Dioxide Removal Methods

CDR methods aim at removing CO

from the atmosphere by deliberately modifying carbon cycle processes, or by

industrial (e.g., chemical) approaches. The carbon withdrawn from the atmosphere would then be stored in land,

ocean or in geological reservoirs. Some CDR methods rely on biological processes, such as large-scale afforestation/

reforestation, carbon sequestration in soils through biochar, bioenergy with carbon capture and storage (BECCS)

and ocean fertilization. Others would rely on geological processes, such as accelerated weathering of silicate and

carbonate rocks—on land or in the ocean (see FAQ.7.3, Figure 1). The CO

removed from the atmosphere would

Marine Cloud

Brightening

Stratospheric

Aerosol

Injection

Deployment

Of Space

Mirrors

Ocean Brightening

With Microbubbles

Crop

Brightening

Whitening

Rooftops

Direct Air Capture

Biomass Energy

With Carbon

Capture

And Storage

Afforestation

Ocean

Fertilisation

Alkalinity

Addition

To The

Ocean

Accelerated

Weathering

CARBON DIOXIDE REMOVALSOLAR RADIATION MANAGEMENT

FAQ 7.3, Figure 1 | Overview of some proposed geoengineering methods as they have been suggested. Carbon Dioxide Removal methods (see Section 6.5 for

details): (A) nutrients are added to the ocean (ocean fertilization), which increases oceanic productivity in the surface ocean and transports a fraction of the resulting

biogenic carbon downward; (B) alkalinity from solid minerals is added to the ocean, which causes more atmospheric CO

to dissolve in the ocean; (C) the weathering

rate of silicate rocks is increased, and the dissolved carbonate minerals are transported to the ocean; (D) atmospheric CO

is captured chemically, and stored either

underground or in the ocean; (E) biomass is burned at an electric power plant with carbon capture, and the captured CO

is stored either underground or in the ocean;

and (F) CO

is captured through afforestation and reforestation to be stored in land ecosystems. Solar Radiation Management methods (see Section 7.7 for details): (G)

reﬂectors are placed in space to reﬂect solar radiation; (H) aerosols are injected in the stratosphere; (I) marine clouds are seeded in order to be made more reﬂective; (J)

microbubbles are produced at the ocean surface to make it more reﬂective; (K) more reﬂective crops are grown; and (L) roofs and other built structures are whitened.

(continued on next page)

633

Clouds and Aerosols Chapter 7

FAQ 7.3 (continued)

then be stored in organic form in land reservoirs, or in inorganic form in oceanic and geological reservoirs, where

it would have to be stored for at least hundreds of years for CDR to be effective.

CDR methods would reduce the radiative forcing of CO

inasmuch as they are effective at removing CO

from the

atmosphere and keeping the removed carbon away from the atmosphere. Some methods would also reduce ocean

acidiﬁcation (see FAQ 3.2), but other methods involving oceanic storage might instead increase ocean acidiﬁcation

if the carbon is sequestered as dissolved CO

. A major uncertainty related to the effectiveness of CDR methods

is the storage capacity and the permanence of stored carbon. Permanent carbon removal and storage by CDR

would decrease climate warming in the long term. However, non-permanent storage strategies would allow CO

return back to the atmosphere where it would once again contribute to warming. An intentional removal of CO

by CDR methods will be partially offset by the response of the oceanic and terrestrial carbon reservoirs if the CO

atmospheric concentration is reduced. This is because some oceanic and terrestrial carbon reservoirs will outgas to

the atmosphere the anthropogenic CO

that had previously been stored. To completely offset past anthropogenic

emissions, CDR techniques would therefore need to remove not just the CO

that has accumulated in the

atmosphere since pre-industrial times, but also the anthropogenic carbon previously taken up by the terrestrial

biosphere and the ocean.

Biological and most chemical weathering CDR methods cannot be scaled up indeﬁnitely and are necessarily limited

by various physical or environmental constraints such as competing demands for land. Assuming a maximum CDR

sequestration rate of 200 PgC per century from a combination of CDR methods, it would take about one and half

centuries to remove the CO

emitted in the last 50 years, making it difﬁcult—even for a suite of additive CDR meth-

ods—to mitigate climate change rapidly. Direct air capture methods could in principle operate much more rapidly,

but may be limited by large-scale implementation, including energy use and environmental constraints.

CDR could also have climatic and environmental side effects. For instance, enhanced vegetation productivity may

increase emissions of N

O, which is a more potent greenhouse gas than CO

. A large-scale increase in vegetation

coverage, for instance through afforestation or energy crops, could alter surface characteristics, such as surface

reﬂectivity and turbulent ﬂuxes. Some modelling studies have shown that afforestation in seasonally snow-covered

boreal regions could in fact accelerate global warming, whereas afforestation in the tropics may be more effective

at slowing global warming. Ocean-based CDR methods that rely on biological production (i.e., ocean fertilization)

would have numerous side effects on ocean ecosystems, ocean acidity and may produce emissions of non-CO

greenhouse gases.

Solar Radiation Management Methods

The globally averaged surface temperature of the planet is strongly inﬂuenced by the amount of sunlight absorbed

by the Earth’s atmosphere and surface, which warms the planet, and by the existence of the greenhouse effect,

the process by which greenhouse gases and clouds affect the way energy is eventually radiated back to space. An

increase in the greenhouse effect leads to a surface temperature rise until a new equilibrium is found.If less incom-

ing sunlight is absorbed because the planet has been made more reﬂective, or if energy can be emitted to space

more effectively because the greenhouse effect is reduced, the average global surface temperature will be reduced.

Suggested geoengineering methods that aim at managing the Earth’s incoming and outgoing energy ﬂows are

based on this fundamental physical principle. Most of these methods propose to either reduce sunlight reaching

the Earth or increase the reﬂectivity of the planet by making the atmosphere, clouds or the surface brighter (see

FAQ 7.3, Figure 1). Another technique proposes to suppress high-level clouds called cirrus, as these clouds have a

strong greenhouse effect. Basic physics tells us that if any of these methods change energy ﬂows as expected, then

the planet will cool. The picture is complicated, however, because of the many and complex physical processes

which govern the interactions between the ﬂow of energy, the atmospheric circulation, weather and the resulting

climate.

While the globally averaged surface temperature of the planet will respond to a change in the amount of sunlight

reaching the surface or a change in the greenhouse effect, the temperature at any given location and time is inﬂu-

enced by many other factors and the amount of cooling from SRM will not in general equal the amount of warm-

ing caused by greenhouse gases. For example, SRM will change heating rates only during daytime, but increasing

greenhouse gases can change temperatures during both day and night. This inexact compensation can inﬂuence

(continued on next page)

634

Chapter 7 Clouds and Aerosols

FAQ 7.3 (continued)

the diurnal cycle of surface temperature, even if the average surface temperature is unchanged. As another exam-

ple, model calculations suggest that a uniform decrease in sunlight reaching the surface might offset global mean

-induced warming, but some regions will cool less than others. Models suggest that if anthropogenic green-

house warming were completely compensated by stratospheric aerosols, then polar regions would be left with a

small residual warming, while tropical regions would become a little cooler than in pre-industrial times.

SRM could theoretically counteract anthropogenic climate change rapidly, cooling the Earth to pre-industrial levels

within one or two decades. This is known from climate models but also from the climate records of large volcanic

eruptions. The well-observed eruption of Mt Pinatubo in 1991 caused a temporary increase in stratospheric aerosols

and a rapid decrease in surface temperature of about 0.5°C.

Climate consists of many factors besides surface temperature. Consequences for other climate features, such as

rainfall, soil moisture, river ﬂow, snowpack and sea ice, and ecosystems may also be important. Both models and

theory show that compensating an increased greenhouse effect with SRM to stabilize surface temperature would

somewhat lower the globally averaged rainfall (see FAQ 7.3, Figure 2 for an idealized model result), and there

also could be regional changes. Such imprecise compensation in

regional and global climate patterns makes it improbable that SRM

will produce a future climate that is ‘just like’ the one we experi-

ence today, or have experienced in the past. However, available

climate models indicate that a geoengineered climate with SRM

and high atmospheric CO

levels would be generally closer to 20th

century climate than a future climate with elevated CO

concentra-

tions and no SRM.

SRM techniques would probably have other side effects. For exam-

ple, theory, observation and models suggest that stratospheric

sulphate aerosols from volcanic eruptions and natural emissions

deplete stratospheric ozone, especially while chlorine from chlo-

roﬂuorocarbon emissions resides in the atmosphere. Stratospheric

aerosols introduced for SRM are expected to have the same effect.

Ozone depletion would increase the amount of ultraviolet light

reaching the surface damaging terrestrial and marine ecosystems.

Stratospheric aerosols would also increase the ratio of direct to dif-

fuse sunlight reaching the surface, which generally increases plant

productivity. There has also been some concern that sulphate aero-

sol SRM would increase acid rain, but model studies suggest that

acid rain is probably not a major concern since the rate of acid rain

production from stratospheric aerosol SRM would be much smaller

than values currently produced by pollution sources. SRM will also

not address the ocean acidiﬁcation associated with increasing atmo-

spheric CO

concentrations and its impacts on marine ecosystems.

Without conventional mitigation efforts or potential CDR meth-

ods, high CO

concentrations from anthropogenic emissions will

persist in the atmosphere for as long as a thousand years, and SRM

would have to be maintained as long as CO

concentrations were

high. Stopping SRM while CO

concentrations are still high would

lead to a very rapid warming over one or two decades (see FAQ7.3,

Figure 2), severely stressing ecosystem and human adaptation.

If SRM were used to avoid some consequences of increasing CO

concentrations, the risks, side effects and short-

comings would clearly increase as the scale of SRM increase. Approaches have been proposed to use a time-limited

amount of SRM along with aggressive strategies for reducing CO

concentrations to help avoid transitions across

climate thresholds or tipping points that would be unavoidable otherwise; assessment of such approaches would

require a very careful risk beneﬁt analysis that goes much beyond this Report.

Change in temperature (°C)

010203040506

-0.5

0.5

1.5

2.5

Change in precipitation (%)

Year

010203040506

-2

-1

(a)

(b)

FAQ 7.3, Figure 2 | Change in globally averaged (a) sur-

face temperature (°C) and (b) precipitation (%) in two ideal-

ized experiments. Solid lines are for simulations using Solar

Radiation Management (SRM) to balance a 1% yr

–1

increase in

concentration until year 50, after which SRM is stopped.

Dashed lines are for simulations with a 1% yr

–1

increase in

concentration and no SRM. The yellow and grey envelopes

show the 25th to 75th percentiles from eight different models.

635

Clouds and Aerosols Chapter 7

and a warmer North Paciﬁc adjacent to a cooler northwestern Canada,

produced a SST response with a La Niña-like pattern. One study has

noted regional shifts in the potential hurricane intensity and hurricane

genesis potential index in the Atlantic Ocean and South China Sea in

response to cloud brightening (Baughman et al., 2012), due primarily

to decreases in vertical wind shear, but overall the investigation and

identiﬁcation of robust side effects has not been extensively explored.

Irvine et al. (2011) tested the impact of increasing desert albedo up

to 0.80 in a climate model. This cooled surface temperature by –1.1°C

(versus –0.22°C and –0.11°C for their largest crop and urban albedo

change) and produced very signiﬁcant changes in regional precipita-

tion patterns.

7.7.4 Synthesis on Solar Radiation Management Methods

Theory, model studies and observations suggest that some SRM meth-

ods may be able to counteract a portion of global warming effects (on

temperature, sea ice and precipitation) due to high concentrations of

anthropogenic GHGs (high conﬁdence). But the level of understanding

about SRM is low, and it is difﬁcult to assess feasibility and efﬁcacy

because of remaining uncertainties in important climate processes and

the interactions among those processes. Although SRM research is still

in its infancy, enough is known to identify some potential beneﬁts,

which must be weighed against known side effects (there could also

be side effects that have not yet been identiﬁed). All studies suggest

there would be a small but measurable decrease in global precipita-

tion from SRM. Other side effects are speciﬁc to speciﬁc methods, and

a number of research areas remain largely unexplored. There are also

features that develop as a consequence of the combination of high CO

and SRM (e.g., effects on evapotranspiration and precipitation). SRM

counters only some consequences of elevated CO

concentrations; it

does not in particular address ocean acidiﬁcation.

Many model studies indicate that stratospheric aerosol SRM could

counteract some changes resulting from GHG increases that produce

a RF as strong as 4 W m

–2

(medium conﬁdence), but they disagree on

details. Marine cloud brightening SRM has received less attention, and

there is no consensus on its efﬁcacy, in large part due to the high level

of uncertainty about cloud radiative responses to aerosol changes.

There have been fewer studies and much less attention focused on

all other SRM methods, and it is not currently possible to provide a

general assessment of their speciﬁc efﬁcacy, scalability, side effects

and risks.

There is robust agreement among models and high conﬁdence that

the compensation between GHG warming and SRM cooling is impre-

cise. SRM would not produce a future climate identical to the present

(or pre-industrial) climate. Nonetheless, although models disagree on

details, they consistently suggest that a climate with SRM and high

atmospheric CO

levels would be closer to that of the last century than

a world with elevated CO

concentrations and no SRM (Lunt et al., 2008;

Ricke et al., 2010; Moreno-Cruz et al., 2011), as long as the SRM could

be continuously sustained and calibrated to offset the forcing by GHGs.

Aerosol-based methods would, however, require a continuous program

of replenishment to achieve this. If CO

concentrations and SRM were

increased in concert, the risks and residual climate change produced

by the imprecise compensation between SRM and CO

forcing would

also increase. If SRM were terminated for any reason, a rapid increase

in surface temperatures (within a decade or two) to values consistent

with the high GHG forcing would result (high conﬁdence). This rate of

climate change would far exceed what would have occurred without

geoengineering, causing any impacts related to the rate of change to

be correspondingly greater than they would have been without geoen-

gineering. In contrast, SRM in concert with aggressive CO

mitigation

might conceivably help avoid transitions across climate thresholds or

tipping points that would be unavoidable otherwise.

Acknowledgements

Thanks go to Anne-Lise Barbanes (IPSL/CNRS, Paris), Bénédicte Fisset

(IPSL/CNRS, Paris) and Edwina Berry for their help in assembling the list

of references. Sylvaine Ferrachat (ETH Zürich) is acknowledged for her

contribution to drafting Figure 7.19.

636

Chapter 7 Clouds and Aerosols

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