383

This chapter should be cited as:

Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, E. Jansen, K. Lambeck,

J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. Ramesh, M. Rojas, X. Shao and A. Timmermann,

2013: Information from Paleoclimate Archives. 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:

Valérie Masson-Delmotte (France), Michael Schulz (Germany)

Lead Authors:

Ayako Abe-Ouchi (Japan), Jürg Beer (Switzerland), Andrey Ganopolski (Germany), Jesus Fidel

González Rouco (Spain), Eystein Jansen (Norway), Kurt Lambeck (Australia), Jürg Luterbacher

(Germany), Tim Naish (New Zealand), Timothy Osborn (UK), Bette Otto-Bliesner (USA), Terrence

Quinn (USA), Rengaswamy Ramesh (India), Maisa Rojas (Chile), XueMei Shao (China), Axel

Timmermann (USA)

Contributing Authors:

Kevin Anchukaitis (USA), Julie Arblaster (Australia), Patrick J. Bartlein (USA), Gerardo Benito

(Spain), Peter Clark (USA), Joseﬁno C. Comiso (USA), Thomas Crowley (UK), Patrick De Deckker

(Australia), Anne de Vernal (Canada), Barbara Delmonte (Italy), Pedro DiNezio (USA), Trond

Dokken (Norway), Harry J. Dowsett (USA), R. Lawrence Edwards (USA), Hubertus Fischer

(Switzerland), Dominik Fleitmann (UK), Gavin Foster (UK), Claus Fröhlich (Switzerland), Aline

Govin (Germany), Alex Hall (USA), Julia Hargreaves (Japan), Alan Haywood (UK), Chris Hollis

(New Zealand), Ben Horton (USA), Masa Kageyama (France), Reto Knutti (Switzerland), Robert

Kopp (USA), Gerhard Krinner (France), Amaelle Landais (France), Camille Li (Norway/Canada),

Dan Lunt (UK), Natalie Mahowald (USA), Shayne McGregor (Australia), Gerald Meehl (USA),

Jerry X. Mitrovica (USA/Canada), Anders Moberg (Sweden), Manfred Mudelsee (Germany),

Daniel R. Muhs (USA), Stefan Mulitza (Germany), Stefanie Müller (Germany), James Overland

(USA), Frédéric Parrenin (France), Paul Pearson (UK), Alan Robock (USA), Eelco Rohling

(Australia), Ulrich Salzmann (UK), Joel Savarino (France), Jan Sedláček (Switzerland), Jeremy

Shakun (USA), Drew Shindell (USA), Jason Smerdon (USA), Olga Solomina (Russian Federation),

Pavel Tarasov (Germany), Bo Vinther (Denmark), Claire Waelbroeck (France), Dieter Wolf-

Gladrow (Germany), Yusuke Yokoyama (Japan), Masakazu Yoshimori (Japan), James Zachos

(USA), Dan Zwartz (New Zealand)

Review Editors:

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(Switzerland)

Information from

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384

Table of Contents

Executive Summary ..................................................................... 385

5.1 Introduction ...................................................................... 388

5.2 Pre-Industrial Perspective on Radiative

Forcing Factors ................................................................. 388

5.2.1 External Forcings ....................................................... 388

5.2.2 Radiative Perturbations from Greenhouse Gases

and Dust ................................................................... 391

Box 5.1: Polar Ampliﬁcation ..................................................... 396

5.3 Earth System Responses and Feedbacks at

Global and Hemispheric Scales ................................... 398

5.3.1 High-Carbon Dioxide Worlds and Temperature.......... 398

5.3.2 Glacial–Interglacial Dynamics ................................... 399

Box 5.2: Climate-Ice Sheet Interactions ................................. 402

5.3.3 Last Glacial Maximum and Equilibrium

Climate Sensitivity .................................................... 403

5.3.4 Past Interglacials ....................................................... 407

5.3.5 Temperature Variations During the

Last 2000 Years ......................................................... 409

5.4 Modes of Climate Variability ....................................... 415

5.4.1 Tropical Modes .......................................................... 415

5.4.2 Extratropical Modes .................................................. 415

5.5 Regional Changes During the Holocene .................. 417

5.5.1 Temperature .............................................................. 417

5.5.2 Sea Ice ...................................................................... 420

5.5.3 Glaciers ..................................................................... 421

5.5.4 Monsoon Systems and Convergence Zones .............. 421

5.5.5 Megadroughts and Floods ........................................ 422

5.6 Past Changes in Sea Level ............................................ 425

5.6.1 Mid-Pliocene Warm Period ........................................ 425

5.6.2 The Last Interglacial .................................................. 425

5.6.3 Last Glacial Termination and Holocene ..................... 428

5.7 Evidence and Processes of Abrupt

Climate Change................................................................ 432

5.8 Paleoclimate Perspective on Irreversibility in

the Climate System ......................................................... 433

5.8.1 Ice Sheets .................................................................. 433

5.8.2 Ocean Circulation...................................................... 433

5.8.3 Next Glacial Inception ............................................... 435

5.9 Concluding Remarks ....................................................... 435

References .................................................................................. 436

Appendix 5.A: Additional Information on Paleoclimate

Archives and Models .................................................................. 456

Frequently Asked Questions

FAQ 5.1 Is the Sun a Major Driver of Recent Changes

in Climate? .............................................................. 392

FAQ 5.2 How Unusual is the Current Sea Level Rate

of Change? .............................................................. 430

385

Information from Paleoclimate Archives Chapter 5

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).

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).

Executive Summary

Greenhouse-Gas Variations and Past Climate Responses

It is a fact that present-day (2011) concentrations of the atmos-

pheric greenhouse gases (GHGs) carbon dioxide (CO

), methane

(CH

) and nitrous oxide (N

O) exceed the range of concentra-

tions recorded in ice cores during the past 800,000 years. Past

changes in atmospheric GHG concentrations can be determined with

very high conﬁdence

from polar ice cores. Since AR4 these records

have been extended from 650,000 years to 800,000 years ago. {5.2.2}

With very high conﬁdence, the current rates of CO

, CH

and N

rise in atmospheric concentrations and the associated radiative

forcing are unprecedented with respect to the highest resolu-

tion ice core records of the last 22,000 years. There is medium

conﬁdence that the rate of change of the observed GHG rise is also

unprecedented compared with the lower resolution records of the past

800,000 years. {5.2.2}

There is high conﬁdence that changes in atmospheric CO

con-

centration play an important role in glacial–interglacial cycles.

Although the primary driver of glacial–interglacial cycles lies in the

seasonal and latitudinal distribution of incoming solar energy driven by

changes in the geometry of the Earth’s orbit around the Sun (“orbital

forcing”), reconstructions and simulations together show that the full

magnitude of glacial–interglacial temperature and ice volume changes

cannot be explained without accounting for changes in atmospher-

ic CO

content and the associated climate feedbacks. During the last

deglaciation, it is very likely

that global mean temperature increased

by 3°C to 8°C. While the mean rate of global warming was very likely

0.3°C to 0.8°C per thousand years, two periods were marked by faster

warming rates, likely between 1°C and 1.5°C per thousand years,

although regionally and on shorter time scales higher rates may have

occurred. {5.3.2}

New estimates of the equilibrium climate sensitivity based on

reconstructions and simulations of the Last Glacial Maximum

(21,000 years to 19,000 years ago) show that values below 1°C

as well as above 6°C for a doubling of atmospheric CO

concen-

tration are very unlikely. In some models climate sensitivity differs

between warm and cold climates because of differences in the rep-

resentation of cloud feedbacks. {5.3.3}

With medium conﬁdence, global mean surface temperature

was signiﬁcantly above pre-industrial levels during several past

periods characterised by high atmospheric CO

concentrations.

During the mid-Pliocene (3.3 to 3.0 million years ago), atmospheric

concentrations between 350 ppm and 450 ppm (medium conﬁ-

dence) occurred when global mean surface temperatures were 1.9°C

to 3.6°C (medium conﬁdence) higher than for pre-industrial climate

{5.3.1}. During the Early Eocene (52 to 48 million years ago), atmos-

pheric CO

concentrations exceeded ~1000 ppm (medium conﬁdence)

when global mean surface temperatures were 9°C to 14°C (medium

conﬁdence) higher than for pre-industrial conditions. {5.3.1}

New temperature reconstructions and simulations of past

climates show with high conﬁdence polar ampliﬁcation in

response to changes in atmospheric CO

concentration. For high

climates such as the Early Eocene (52 to 48 million years ago) or

mid-Pliocene (3.3 to 3.0 million years ago), and low CO

climates such

as the Last Glacial Maximum (21,000 to 19,000 years ago), sea sur-

face and land surface air temperature reconstructions and simulations

show a stronger response to changes in atmospheric GHG concentra-

tions at high latitudes as compared to the global average. {Box 5.1,

5.3.1, 5.3.3}

Global Sea Level Changes During Past Warm Periods

The current rate of global mean sea level change, starting in the

late 19th-early 20th century, is, with medium conﬁdence, unu-

sually high in the context of centennial-scale variations of the

last two millennia. The magnitude of centennial-scale global mean

sea level variations did not exceed 25 cm over the past few millennia

(medium conﬁdence). {5.6.3}

There is very high conﬁdence that the maximum global mean

sea level during the last interglacial period (129,000 to 116,000

years ago) was, for several thousand years, at least 5 m higher

than present and high conﬁdence that it did not exceed 10 m

above present. The best estimate is 6 m higher than present. Based

on ice sheet model simulations consistent with elevation changes

derived from a new Greenland ice core, the Greenland ice sheet very

likely contributed between 1.4 and 4.3 m sea level equivalent, implying

with medium conﬁdence a contribution from the Antarctic ice sheet

tothe global mean sea level during the last interglacial period. {5.6.2}

There is high conﬁdence that global mean sea level was above

present during some warm intervals of the mid-Pliocene (3.3

to 3.0 million years ago), implying reduced volume of polar ice

sheets. The best estimates from various methods imply with high con-

ﬁdence that sea level has not exceeded +20 m during the warmest

periods of the Pliocene, due to deglaciation of the Greenland and West

Antarctic ice sheets and areas of the East Antarctic ice sheet. {5.6.1}

386

Chapter 5 Information from Paleoclimate Archives

Observed Recent Climate Change in the Context of

Interglacial Climate Variability

New temperature reconstructions and simulations of the warm-

est millennia of the last interglacial period (129,000 to 116,000

years ago) show with medium conﬁdence that global mean

annual surface temperatures were never more than 2°C higher

than pre-industrial. High latitude surface temperature, averaged over

several thousand years, was at least 2°C warmer than present (high

conﬁdence). Greater warming at high latitudes, seasonally and annu-

ally, conﬁrm the importance of cryosphere feedbacks to the seasonal

orbital forcing. During these periods, atmospheric GHG concentrations

were close to the pre-industrial level. {5.3.4, Box 5.1}

There is high conﬁdence that annual mean surface warming

since the 20th century has reversed long-term cooling trends

of the past 5000 years in mid-to-high latitudes of the Northern

Hemisphere (NH). New continental- and hemispheric-scale annual

surface temperature reconstructions reveal multi-millennial cooling

trends throughout the past 5000 years. The last mid-to-high latitude

cooling trend persisted until the 19th century, and can be attributed

with high conﬁdence to orbital forcing, according to climate model

simulations. {5.5.1}

There is medium conﬁdence from reconstructions that the cur-

rent (1980–2012) summer sea ice retreat was unprecedented

and sea surface temperatures in the Arctic were anomalously

high in the perspective of at least the last 1450 years. Lower than

late 20th century summer Arctic sea ice cover is reconstructed and sim-

ulated for the period between 8000 and 6500 years ago in response to

orbital forcing. {5.5.2}

There is high conﬁdence that minima in NH extratropical glacier

extent between 8000 and 6000 years ago were primarily due

to high summer insolation (orbital forcing). The current glacier

retreat occurs within a context of orbital forcing that would be favour-

able for NH glacier growth. If glaciers continue to reduce at current

rates, most extratropical NH glaciers will shrink to their minimum

extent, which existed between 8000 and 6000 years ago, within this

century (medium conﬁdence). {5.5.3}

For average annual NH temperatures, the period 1983–2012

was very likely the warmest 30-year period of the last 800 years

(high conﬁdence) and likely the warmest 30-year period of the

last 1400 years (medium conﬁdence). This is supported by com-

parison of instrumental temperatures with multiple reconstructions

from a variety of proxy data and statistical methods, and is consistent

with AR4. In response to solar, volcanic and anthropogenic radiative

changes, climate models simulate multi-decadal temperature changes

over the last 1200 years in the NH, that are generally consistent in

magnitude and timing with reconstructions, within their uncertainty

ranges. {5.3.5}

Continental-scale surface temperature reconstructions show,

with high conﬁdence, multi-decadal periods during the Medie-

val Climate Anomaly (950 to 1250) that were in some regions as

warm as in the mid-20th century and in others as warm as in the

late 20th century. With high conﬁdence, these regional warm peri-

ods were not as synchronous across regions as the warming since the

mid-20th century. Based on the comparison between reconstructions

and simulations, there is high conﬁdence that not only external orbit-

al, solar and volcanic forcing, but also internal variability, contributed

substantially to the spatial pattern and timing of surface temperature

changes between the Medieval Climate Anomaly and the Little Ice Age

(1450 to 1850). {5.3.5.3, 5.5.1}

There is high conﬁdence for droughts during the last millennium

of greater magnitude and longer duration than those observed

since the beginning of the 20th century in many regions. There

is medium conﬁdence that more megadroughts occurred in monsoon

Asia and wetter conditions prevailed in arid Central Asia and the South

American monsoon region during the Little Ice Age (1450 to 1850)

compared to the Medieval Climate Anomaly (950 to 1250). {5.5.4 and

5.5.5}

With high conﬁdence, ﬂoods larger than those recorded since

1900 occurred during the past ﬁve centuries in northern and

central Europe, western Mediterranean region and eastern Asia.

There is medium conﬁdence that modern large ﬂoods are comparable

to or surpass historical ﬂoods in magnitude and/or frequency in the

Near East, India and central North America. {5.5.5}

Past Changes in Climate Modes

New results from high-resolution coral records document with

high conﬁdence that the El Niño-Southern Oscillation (ENSO)

system has remained highly variable throughout the past 7000

years, showing no discernible evidence for an orbital modula-

tion of ENSO. This is consistent with the weak reduction in mid-Hol-

ocene ENSO amplitude of only 10% simulated by the majority of cli-

mate models, but contrasts with reconstructions reported in AR4 that

showed a reduction in ENSO variance during the ﬁrst half of the Hol-

ocene. {5.4.1}

With high conﬁdence, decadal and multi-decadal changes in the

winter North Atlantic Oscillation index (NAO) observed since

the 20th century are not unprecedented in the context of the

past 500 years. Periods of persistent negative or positive winter NAO

phases, similar to those observed in the 1960s and 1990 to 2000s,

respectively, are not unusual in the context of NAO reconstructions

during at least the past 500 years. {5.4.2}

The increase in the strength of the observed summer Southern

Annular Mode since 1950 has been anomalous, with medium

conﬁdence, in the context of the past 400 years. No similar spa-

tially coherent multi-decadal trend can be detected in tree-ring indices

from New Zealand, Tasmania and South America. {5.4.2}

Abrupt Climate Change and Irreversibility

With high conﬁdence, the interglacial mode of the Atlantic

Ocean meridional overturning circulation (AMOC) can recover

from a short-term freshwater input into the subpolar North

Atlantic. Approximately 8200 years ago, a sudden freshwater release

387

Information from Paleoclimate Archives Chapter 5

occurred during the ﬁnal stages of North America ice sheet melting.

Paleoclimate observations and model results indicate, with high con-

ﬁdence, a marked reduction in the strength of the AMOC followed by

a rapid recovery, within approximately 200 years after the perturba-

tion.{5.8.2}

Conﬁdence in the link between changes in North Atlantic climate

and low-latitude precipitation patterns has increased since AR4.

From new paleoclimate reconstructions and modelling studies, there is

very high conﬁdence that reduced AMOC and the associated surface

cooling in the North Atlantic region caused southward shifts of the

Atlantic Intertropical Convergence Zone, and also affected the Ameri-

can (North and South), African and Asian monsoon systems. {5.7}

It is virtually certain that orbital forcing will be unable to trig-

ger widespread glaciation during the next 1000 years. Paleo-

climate records indicate that, for orbital conﬁgurations close to the

present one, glacial inceptions only occurred for atmospheric CO

concentrations signiﬁcantly lower than pre-industrial levels. Climate

models simulate no glacial inception during the next 50,000 years if

concentrations remain above 300 ppm. {5.8.3, Box 6.2}

There is high conﬁdence that the volumes of the Greenland and

West Antarctic ice sheets were reduced during periods of the

past few million years that were globally warmer than pres-

ent. Ice sheet model simulations and geological data suggest that the

West Antarctic ice sheet is very sensitive to subsurface Southern Ocean

warming and imply with medium conﬁdence a West Antarctic ice sheet

retreat if atmospheric CO

concentration stays within or above the

range of 350 ppm to 450 ppm for several millennia. {5.3.1, 5.6.1, 5.8.1}

388

Chapter 5 Information from Paleoclimate Archives

5.1 Introduction

This chapter assesses the information on past climate obtained prior to

the instrumental period. The information is based on data from various

paleoclimatic archives and on modelling of past climate, and updates

Chapter 6 of AR4 of IPCC Working Group I (Jansen et al., 2007).

The Earth system has responded and will continue to respond to

various external forcings (solar, volcanic and orbital) and to changes

in atmospheric composition. Paleoclimate data and modelling pro-

vide quantitative information on the Earth system response to these

forcings. Paleoclimate information facilitates understanding of Earth

system feedbacks on time scales longer than a few centuries, which

cannot be evaluated from short instrumental records. Past climate

changes also document transitions between different climate states,

including abrupt events, which occurred on time scales of decades to

a few centuries. They inform about multi-centennial to millennial base-

line variability, against which the recent changes can be compared to

assess whether or not they are unusual.

Major progress since AR4 includes the acquisition of new and more

precise information from paleoclimate archives, the synthesis of

regional information, and Paleoclimate Modelling Intercomparison

Project Phase III (PMIP3) and Coupled Model Intercomparison Project

Phase 5 (CMIP5) simulations using the same models as for projections

(see Chapter 1). This chapter assesses the understanding of past cli-

mate variations, using paleoclimate reconstructions as well as climate

models of varying complexity, while the model evaluation based on

paleoclimate information is covered in Chapter 9. Additional paleo-

climate perspectives are included in Chapters 6, 10 and 13 (see Table

5.1).

The content of this chapter is largely restricted to topics for which sub-

stantial new information has emerged since AR4. Examples include

proxy-based estimates of the atmospheric carbon dioxide (CO

) con-

tent during the past ~65 million years (Section 5.2.2) and magnitude

of sea level variations during interglacial periods (Section 5.6.2). Infor-

mation from glacial climates has been included only if the underlying

processes are of direct relevance for an assessment of projected cli-

mate change. The impacts of past climate changes on biological sys-

tems and past civilizations are not covered, as these topics are beyond

the scope of Working Group I.

The chapter proceeds from evidence for pre-industrial changes in

atmospheric composition and external solar and volcanic forcings

(Section 5.2, FAQ 5.1), to global and hemispheric responses (Section

5.3). After evaluating the evidence for past changes in climate modes

of variability (Section 5.4), a speciﬁc focus is given to regional changes

in temperature, cryosphere and hydroclimate during the current inter-

glacial period (Section 5.5). Sections on sea level change (Section 5.6,

FAQ 5.2), abrupt climate changes (Section 5.7) and illustrations of irre-

versibility and recovery time scales (Section 5.8) conclude the chapter.

While polar ampliﬁcation of temperature changes is addressed in Box

5.1, the relationships between ice sheets, sea level, atmospheric CO

concentration and climate are addressed in several sections (Box 5.2,

Sections 5.3.1, 5.5, and 5.8.1).

Additional information to this chapter is available in the Appendix. Pro-

cessed data underlying the ﬁgures are stored in the PANGAEA data-

base (www.pangaea.de), while model output from PMIP3 is available

from pmip3.lsce.ipsl.fr. In all sections, information is structured by time,

going from past to present. Table 5.1 summarizes the past periods

assessed in the subsections.

5.2 Pre-Industrial Perspective on Radiative

Forcing Factors

5.2.1 External Forcings

5.2.1.1 Orbital Forcing

The term ‘orbital forcing’ is used to denote the incoming solar radiation

changes originating from variations in the Earth’s orbital parameters

as well as changes in its axial tilt. Orbital forcing is well known from

precise astronomical calculations for the past and future (Laskar et

al., 2004). Changes in eccentricity, longitude of perihelion (related to

precession) and axial tilt (obliquity) (Berger and Loutre, 1991) predom-

inantly affect the seasonal and latitudinal distribution and magnitude

of solar energy received at the top of the atmosphere (AR4, Box 6.1;

Jansen et al., 2007), and the durations and intensities of local seasons.

Obliquity also modulates the annual mean insolation at any given

latitude, with opposite effects at high and low latitudes. Orbital forc-

ing is considered the pacemaker of transitions between glacials and

interglacials (high conﬁdence), although there is still no consensus on

exactly how the different physical processes inﬂuenced by insolation

changes interact to inﬂuence ice sheet volume (Box 5.2; Section 5.3.2).

The different orbital conﬁgurations make each glacial and interglacial

period unique (Yin and Berger, 2010; Tzedakis et al., 2012a). Multi-mil-

lennial trends of temperature, Arctic sea ice and glaciers during the

current interglacial period, and speciﬁcally the last 2000 years, have

been related to orbital forcing (Section 5.5).

5.2.1.2 Solar Forcing

Solar irradiance models (e.g., Wenzler et al., 2005) have been improved

to explain better the instrumental measurements of total solar irradi-

ance (TSI) and spectral (wavelength dependent) solar irradiance (SSI).

Typical changes measured over an 11-year solar cycle are 0.1% for TSI

and up to several percent for the ultraviolet (UV) part of SSI (see Sec-

tion 8.4). Changes in TSI directly impact the Earth’s surface (see solar

Box 10.2), whereas changes in UV primarily affect the stratosphere, but

can inﬂuence the tropospheric circulation through dynamical coupling

(Haigh, 1996). Most models attribute all TSI and SSI changes exclusively

to magnetic phenomena at the solar surface (sunspots, faculae, mag-

netic network), neglecting any potential internal phenomena such as

changes in energy transport (see also Section 8.4). The basic concept in

solar models is to divide the solar surface into different magnetic fea-

tures each with a speciﬁc radiative ﬂux. The balance of contrasting dark

sunspots and bright faculae and magnetic network leads to a higher TSI

value during solar cycle maxima and at most wavelengths, but some

wavelengths may be out of phase with the solar cycle (Harder et al.,

2009; Cahalan et al., 2010; Haigh et al., 2010). TSI and SSI are calculated

by adding the radiative ﬂuxes of all features plus the contribution from

389

Information from Paleoclimate Archives Chapter 5

Notes:

Also known as Marine Isotopic Stage (MIS) 1 or current interglacial.

Also known as Medieval Climate Optimum or the Medieval Warm Period.

Also known as Termination I or the Last Deglaciation. Based on sea level, Last Glacial Termination occurred between ~19 and ~6 ka.

Also known as Greenland Stadial GS-1.

Also known as Greenland Interstadial GI-a-c-e.

Also known as MIS5e, which overlaps with the Eemian (Shackleton et al., 2003).

As estimated from the Greenland ice core GICC05 chronology (Rasmussen et al., 2006; Thomas et al., 2007).

In this chapter, when referring to comparison of radiative forcing or climate variables, pre-industrial refers to 1850 values in accordance with Taylor et al. (2012). Otherwise it refers to an extended

period of time before 1850 as stated in the text. Note that Chapter 7 uses 1750 as the reference pre-industrial period.

Different durations are reported in the literature. In Section 5.3.5, time intervals 950–1250 and 1450–1850 are used to calculate Northern Hemisphere temperature anomalies representative of

the MCA and LIA, respectively.

Note that CMIP5 “Last Millennium simulations” have been performed for the period 850–1850 (Taylor et al., 2012).

As dated on Tahiti corals (Deschamps et al., 2012).

The duration of Heinrich stadial 1 (e.g., Stanford et al., 2011) is longer than the associated Heinrich event, which is indicated by ice-rafted debris in deep sea sediment cores from the North

Atlantic Ocean (Hemming, 2004).

Period based on MARGO Project Members (2009). LGM simulations are performed for 21 ka. Note that maximum continental ice extent had already occurred at 26.5 ka (Clark et al., 2009).

Ages are maximum date for the onset and minimum age for the end from tectonically stable sites (cf. Section 5.6.2).

Dowsett et al. (2012).

Zachos et al. (2008).

Westerhold et al. (2007).

the magnetically inactive surface. These models can successfully repro-

duce the measured TSI changes between 1978 and 2003 (Balmaceda et

al., 2007; Crouch et al., 2008), but not necessarily the last minimum of

2008 (Krivova et al., 2011). This approach requires detailed information

of all the magnetic features and their temporal changes (Wenzler et al.,

2006; Krivova and Solanki, 2008) (see Section 8.4).

The extension of TSI and SSI into the pre-satellite period poses two

main challenges. First, the satellite period (since 1978) used to cali-

brate the solar irradiance models does not show any signiﬁcant long-

term trend. Second, information about the various magnetic features

at the solar surface decreases back in time and must be deduced from

proxies such as sunspot counts for the last 400 years and cosmogenic

Time Period Age

Chapter 5 Sections

Other

Chapters

5.2 5.3 5.4 5.5 5.6 5.7 5.8

Holocene

11.65 ka

to present

    

6, 9, 10

Pre-industrial period refers to times before 1850 or 1850 values

Little Ice Age (LIA) 1450–1850

    

Medieval Climate Anomaly (MCA)

950–1250

    

Last Millennium 1000–1999

    

9, 10

Mid-Holocene (MH) ~6 ka



9, 13

8.2-ka event ~8.2 ka



Last Glacial Termination

 

Younger Dryas

12.85–11.65 ka



Bølling-Allerød

14.64–12.85 ka



Meltwater Pulse 1A (MWP-1A) 14.65–14.31 ka



Heinrich stadial 1 (HS1) ~19–14.64 ka



Last Glacial Maximum (LGM) ~21–19 ka

  

6, 9

Last Interglacial (LIG)

~129–116 ka

   

Mid-Pliocene Warm Period (MPWP) ~3.3–3.0 Ma

   

Early Eocene Climatic Optimum (EECO) ~52–50 Ma

 

Paleocene-Eocene Thermal Maximum (PETM) ~55.5–55.3 Ma

  

Table 5.1 | Summary of past periods for which climate information is assessed in the various sections of this chapter and other chapters of AR5. Calendar ages are expressed

in Common Era (CE), geological ages are expressed in thousand years (ka) or million years (Ma) before present (BP), with present deﬁned as 1950. Radiocarbon-based ages are

quoted as the published calibrated ages.

radionuclides (

Be and

C) for the past millennium (Muscheler et al.,

2007; Delaygue and Bard, 2011) and the Holocene (Table 5.1) (Stein-

hilber et al., 2009; Vieira et al., 2011).

Be and

C records reﬂect not

only solar activity, but also the geomagnetic ﬁeld intensity and effects

of their respective geochemical cycles and transport pathways (Pedro

et al., 2011; Steinhilber et al., 2012). The corrections for these non-solar

components, which are difﬁcult to quantify, contribute to the overall

error of the reconstructions (grey band in Figure 5.1c).

TSI reconstructions are characterized by distinct grand solar minima

lasting 50 to 100 years (e.g., the Maunder Minimum, 1645–1715)

that are superimposed upon long-term changes. Spectral analysis of

TSI records reveals periodicities of 87, 104, 150, 208, 350, 510, ~980

390

Chapter 5 Information from Paleoclimate Archives

and ~2200 years (Figure 5.1d) (Stuiver and Braziunas, 1993), but with

time-varying amplitudes (Steinhilber et al., 2009; Vieira et al., 2011). All

reconstructions rely ultimately on the same data (sunspots and cosmo-

genic radionuclides), but differ in the details of the methodologies. As

a result the reconstructions agree rather well in their shape, but differ

in their amplitude (Figure 5.1b) (Wang et al., 2005; Krivova et al., 2011;

Lean et al., 2011; Schrijver et al., 2011) (see Section 8.4.1).

Since AR4, most recent reconstructions show a considerably smaller

difference (<0.1%) in TSI between the late 20th century and the Late

Maunder Minimum (1675–1715) when the sun was very quiet, com-

pared to the often used reconstruction of Lean et al. (1995b) (0.24%)

and Shapiro et al. (2011) (~0.4%). The Lean et al. (1995a) reconstruc-

tion has been used to scale solar forcing in simulations of the last

millennium prior to PMIP3/CMIP5 (Table 5.A.1). PMIP3/CMIP5 last

Figure 5.1 | (a) Two reconstructions of volcanic forcing for the past 1000 years derived from ice core sulphate and used for Paleoclimate Modelling Intercomparison Project

Phase III (PMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations (Schmidt et al., 2011). GRA: Gao et al. (2012); CEA: Crowley and Unterman (2013).

Volcanic sulphate peaks identiﬁed from their isotopic composition as originating from the stratosphere are indicated by squares (green: Greenland; brown: Antarctica) (Baroni et

al., 2008; Cole-Dai et al., 2009). (b) Reconstructed total solar irradiance (TSI) anomalies back to the year 1000. Proxies of solar activity (e.g., sunspots,

Be) are used to estimate

the parameters of the models or directly TSI. All records except LBB (Lean et al., 1995b) have been used for PMIP3/CMIP5 simulations (Schmidt et al., 2011). DB: Delaygue and

Bard (2011); MEA: Muscheler et al. (2007); SBF: Steinhilber et al. (2009); WLS: Wang et al. (2005); VSK: Vieira et al. (2011). For the years prior to 1600, the 11-year cycle has been

added artiﬁcially to the original data with an amplitude proportional to the mean level of TSI. (c) Reconstructed TSI anomalies (100-year low-pass ﬁltered; grey shading: 1 standard

deviation uncertainty range) for the past 9300 years (Steinhilber et al., 2009). The reconstruction is based on

Be and calibrated using the relationship between instrumental data

of the open magnetic ﬁeld, which modulates the production of

Be, and TSI for the past four solar minima. The yellow band indicates the past 1000 years shown in more details in

(a) and (b). Anomalies are relative to the 1976–2006 mean value (1366.14 W m

–2

) of Wang et al. (2005). (d) Wavelet analysis (Torrence and Compo, 1998) of TSI anomalies from

-1

-6000

-4000 -2000

2000

2048

1024

512

256

128

Period (yr)

1000 1100 1700 1800 1900 2000

-3

-2

-1

-15

-20

-5

-10

Volcanic Forcing

∆TSI (W m

-2

)

1200 1600

Solar Forcing

Time (Year BCE/CE)

CEA

GRA

1300 1400 1500

(a)

(b)

(c)

(d)

VSK

LBB

SBF

WLS

MEA

(W m

-2

)

1/64

1/32

1/16

1/8

1/4

1/2

∆TSI (W m

-2

)

Amplitude (dimensionless)

391

Information from Paleoclimate Archives Chapter 5

millennium simulations have used the weak solar forcing of recent

reconstructions of TSI (Schmidt et al., 2011, 2012b) calibrated (Mus-

cheler et al., 2007; Delaygue and Bard, 2011) or spliced (Steinhilber

et al., 2009; Vieira and Solanki, 2010) to Wang et al. (2005). The larger

range of past TSI variability in Shapiro et al. (2011) is not supported by

studies of magnetic ﬁeld indicators that suggest smaller changes over

the 19th and 20th centuries (Svalgaard and Cliver, 2010; Lockwood

and Owens, 2011).

Note that: (1) the recent new measurement of the absolute value of

TSI and TSI changes during the past decades are assessed in Section

8.4.1.1; (2) the current state of understanding the effects of galactic

cosmic rays on clouds is assessed in Sections 7.4.6 and 8.4.1.5 and

(3) the use of solar forcing in simulations of the last millennium is

discussed in Section 5.3.5.

5.2.1.3 Volcanic Forcing

Volcanic activity affects global climate through the radiative impacts

of atmospheric sulphate aerosols injected by volcanic eruptions (see

Sections 8.4.2 and 10.3.1). Quantifying volcanic forcing in the pre-sat-

ellite period is important for historical and last millennium climate

simulations, climate sensitivity estimates and detection and attribution

studies. Reconstructions of past volcanic forcing are based on sulphate

deposition from multiple ice cores from Greenland and Antarctica,

combined with atmospheric modelling of aerosol distribution and opti-

cal depth.

Since AR4, two new reconstructions of the spatial distribution of vol-

canic aerosol optical depth have been generated using polar ice cores,

spanning the last 1500 years (Gao et al., 2008, 2012) and 1200 years

(Crowley and Unterman, 2013) (Figure 5.1a). Although the relative size

of eruptions for the past 700 years is generally consistent among these

and earlier studies (Jansen et al., 2007), they differ in the absolute

amplitude of peaks. There are also differences in the reconstructions of

Icelandic eruptions, with an ongoing debate on the magnitude of strat-

ospheric inputs for the 1783 Laki eruption (Thordarson and Self, 2003;

Wei et al., 2008; Lanciki et al., 2012; Schmidt et al., 2012a). The recur-

rence time of past large volcanic aerosol injections (eruptions changing

the radiative forcing (RF) by more than 1 W m

–2

) varies from 3 to 121

years, with long-term mean value of 35 years (Gao et al., 2012) and 39

years (Crowley and Unterman, 2013), and only two or three periods of

100 years without such eruptions since 850.

Hegerl et al. (2006) estimated the uncertainty of the RF for a given

volcanic event to be approximately 50%. Differences between recon-

structions (Figure 5.1a) arise from different proxy data, identiﬁcation

of the type of injection, methodologies to estimate particle distribution

and optical depth (Kravitz and Robock, 2011), and parameterization of

scavenging for large events (Timmreck et al., 2009). Key limitations are

associated with ice core chronologies (Plummer et al., 2012; Sigl et al.,

2013), and deposition patterns (Moore et al., 2012).

A new independent methodology has recently been developed to

distinguish between tropospheric and stratospheric volcanic aerosol

deposits (Baroni et al., 2007). The stratospheric character of several

large eruptions has started to be assessed from Greenland and/or

Antarctic ice core sulphur isotope data (Baroni et al., 2008; Cole-Dai et

al., 2009; Schmidt et al., 2012b).

The use of different volcanic forcing reconstructions in pre-PMIP3/

CMIP5 (see AR4 Chapter 6) and PMIP3/CMIP5 last millennium simu-

lations (Schmidt et al., 2011) (Table 5.A.1), together with the methods

used to implement these volcanic indices with different representa-

tions of aerosols in climate models, is a source of uncertainty in model

intercomparisons. The impact of volcanic forcing on climate variations

of the last millennium climate is assessed in Sections 5.3.5, 5.4, 5.5.1

and 10.7.1.

5.2.2 Radiative Perturbations from Greenhouse

Gases and Dust

5.2.2.1 Atmospheric Concentrations of Carbon Dioxide,

Methane and Nitrous Oxide from Ice Cores

Complementing instrumental data, air enclosed in polar ice pro-

vides a direct record of past atmospheric well-mixed greenhouse gas

(WMGHG) concentrations albeit smoothed by ﬁrn diffusion (Joos and

Spahni, 2008; Köhler et al., 2011). Since AR4, the temporal resolution

of ice core records has been enhanced (MacFarling Meure et al., 2006;

Ahn and Brook, 2008; Loulergue et al., 2008; Lüthi et al., 2008; Mischler

et al., 2009; Schilt et al., 2010; Ahn et al., 2012; Bereiter et al., 2012).

During the pre-industrial part of the last 7000 years, millennial (20 ppm

, 125 ppb CH

) and centennial variations (up to 10 ppm CO

, 40 ppb

and 10 ppb N

O) are recorded (see Section 6.2.2 and Figure 6.6).

Signiﬁcant centennial variations in CH

during the last glacial occur in

phase with Northern Hemisphere (NH) rapid climate changes, while

millennial CO

changes coincide with their Southern Hemisphere (SH)

bipolar seesaw counterpart (Ahn and Brook, 2008; Loulergue et al.,

2008; Lüthi et al., 2008; Grachev et al., 2009; Capron et al., 2010b;

Schilt et al., 2010; Bereiter et al., 2012).

Long-term records have been extended from 650 ka in AR4 to 800 ka

(Figures 5.2 and 5.3) (Loulergue et al., 2008; Lüthi et al., 2008; Schilt et

al., 2010). During the last 800 ka, the pre-industrial ice core WMGHG

concentrations stay within well-deﬁned natural limits with maximum

interglacial concentrations of approximately 300 ppm, 800 ppb and

300 ppb for CO

, CH

and N

O, respectively, and minimum glacial

concentrations of approximately 180 ppm, 350 ppb, and 200 ppb. The

new data show lower than pre-industrial (280 ppm) CO

concentra-

tions during interglacial periods from 800 to 430 ka (MIS19 to MIS13)

(Figure 5.3). It is a fact that present-day (2011) concentrations of CO

(390.5 ppm), CH

(1803 ppb) and N

O (324 ppm) (Annex II) exceed the

range of concentrations recorded in the ice core records during the past

800 ka. With very high conﬁdence, the rate of change of the observed

anthropogenic WMGHG rise and its RF is unprecedented with respect

to the highest resolution ice core record back to 22 ka for CO

, CH

and

O, accounting for the smoothing due to ice core enclosure processes

(Joos and Spahni, 2008; Schilt et al., 2010). There is medium conﬁdence

that the rate of change of the observed anthropogenic WMGHG rise is

also unprecedented with respect to the lower resolution records of the

past 800 ka.

Progress in understanding the causes of past WMGHG variations is

reported in Section 6.2.

392

Chapter 5 Information from Paleoclimate Archives

Frequently Asked Questions

FAQ 5.1 | Is the Sun a Major Driver of Recent Changes in Climate?

Total solar irradiance (TSI, Chapter 8) is a measure of the total energy received from the sun at the top of the atmo-

sphere. It varies over a wide range of time scales, from billions of years to just a few days, though variations have

been relatively small over the past 140 years. Changes in solar irradiance are an important driver of climate vari-

ability (Chapter 1; Figure 1.1) along with volcanic emissions and anthropogenic factors. As such, they help explain

the observed change in global surface temperatures during the instrumental period (FAQ 5.1, Figure 1; Chapter 10)

and over the last millennium. While solar variability may have had a discernible contribution to changes in global

surface temperature in the early 20th century, it cannot explain the observed increase since TSI started to be mea-

sured directly by satellites in the late 1970s (Chapters 8, 10).

The Sun’s core is a massive nuclear fusion reactor that converts hydrogen into helium. This process produces energy

that radiates throughout the solar system as electromagnetic radiation. The amount of energy striking the top of

Earth’s atmosphere varies depending on the generation and emission of electromagnetic energy by the Sun and on

the Earth’s orbital path around the Sun.

Satellite-based instruments have directly measured TSI since 1978, and indicate that on average, ~1361 W m

–2

reach-

es the top of the Earth’s atmosphere. Parts of the Earth’s surface and air pollution and clouds in the atmosphere act

as a mirror and reﬂect about 30% of this power back into space. Higher levels of TSI are recorded when the Sun is

more active. Irradiance variations follow the roughly 11-year sunspot cycle: during the last cycles, TSI values ﬂuctu-

ated by an average of around 0.1%.

For pre-satellite times, TSI variations have to be estimated from sunspot numbers (back to 1610), or from radioiso-

topes that are formed in the atmosphere, and archived in polar ice and tree rings. Distinct 50- to 100-year periods

of very low solar activity—such as the Maunder Minimum between 1645 and 1715—are commonly referred to as

grand solar minima. Most estimates of TSI changes between the Maunder Minimum and the present day are in the

order of 0.1%, similar to the amplitude of the 11-year variability.

How can solar variability help explain the observed global surface temperature record back to 1870? To answer

this question, it is important to understand that other climate drivers are involved, each producing characteristic

patterns of regional climate responses. However, it is the combination of them all that causes the observed climate

change. Solar variability and volcanic eruptions are natural factors. Anthropogenic (human-produced) factors, on

the other hand, include changes in the concentrations of greenhouse gases, and emissions of visible air pollution

(aerosols) and other substances from human activities. ‘Internal variability’ refers to ﬂuctuations within the climate

system, for example, due to weather variability or phenomena like the El Niño-Southern Oscillation.

The relative contributions of these natural and anthropogenic factors change with time. FAQ 5.1, Figure 1 illustrates

those contributions based on a very simple calculation, in which the mean global surface temperature variation rep-

resents the sum of four components linearly related to solar, volcanic, and anthropogenic forcing, and to internal

variability. Global surface temperature has increased by approximately 0.8°C from 1870 to 2010 (FAQ 5.1, Figure

1a). However, this increase has not been uniform: at times, factors that cool the Earth’s surface—volcanic eruptions,

reduced solar activity, most anthropogenic aerosol emissions—have outweighed those factors that warm it, such

as greenhouse gases, and the variability generated within the climate system has caused further ﬂuctuations unre-

lated to external inﬂuences.

The solar contribution to the record of global surface temperature change is dominated by the 11-year solar cycle,

which can explain global temperature ﬂuctuations up to approximately 0.1°C between minima and maxima (FAQ

5.1, Figure 1b). A long-term increasing trend in solar activity in the early 20th century may have augmented the

warming recorded during this interval, together with internal variability, greenhouse gas increases and a hiatus

in volcanism. However, it cannot explain the observed increase since the late 1970s, and there was even a slight

decreasing trend of TSI from 1986 to 2008 (Chapters 8 and 10).

Volcanic eruptions contribute to global surface temperature change by episodically injecting aerosols into the

atmosphere, which cool the Earth’s surface (FAQ 5.1, Figure 1c). Large volcanic eruptions, such as the eruption of

Mt. Pinatubo in 1991, can cool the surface by around 0.1°C to 0.3°C for up to three years. (continued on next page)

393

Information from Paleoclimate Archives Chapter 5

FAQ 5.1 (continued)

The most important component of internal cli-

mate variability is the El Niño Southern Oscillation,

which has a major effect on year-to-year variations

of tropical and global mean temperature (FAQ 5.1,

Figure 1d). Relatively high annual temperatures

have been encountered during El Niño events, such

as in 1997–1998.

The variability of observed global surface tempera-

tures from 1870 to 2010 (Figure 1a) reﬂects the com-

bined inﬂuences of natural (solar, volcanic, internal;

FAQ 5.1, Figure 1b–d) factors, superimposed on the

multi-decadal warming trend from anthropogenic

factors (FAQ 5.1, Figure 1e).

Prior to 1870, when anthropogenic emissions

of greenhouse gases and aerosols were smaller,

changes in solar and volcanic activity and internal

variability played a more important role, although

the speciﬁc contributions of these individual fac-

tors to global surface temperatures are less certain.

Solar minima lasting several decades have often

been associated with cold conditions. However,

these periods are often also affected by volcanic

eruptions, making it difﬁcult to quantify the solar

contribution.

At the regional scale, changes in solar activity have

been related to changes in surface climate and

atmospheric circulation in the Indo-Paciﬁc, North-

ern Asia and North Atlantic areas. The mechanisms

that amplify the regional effects of the relatively

small ﬂuctuations of TSI in the roughly 11-year solar

cycle involve dynamical interactions between the

upper and the lower atmosphere, or between the

ocean sea surface temperature and atmosphere,

and have little effect on global mean temperatures

(see Box 10.2).

Finally, a decrease in solar activity during the past

solar minimum a few years ago (FAQ 5.1, Figure

1b) raises the question of its future inﬂuence on

climate. Despite uncertainties in future solar activ-

ity, there is high conﬁdence that the effects of solar

activity within the range of grand solar maxima and

minima will be much smaller than the changes due

to anthropogenic effects.

FAQ 5.1, Figure 1 | Global surface temperature anomalies from 1870 to 2010,

and the natural (solar, volcanic, and internal) and anthropogenic factors that

inﬂuence them. (a) Global surface temperature record (1870–2010) relative to

the average global surface temperature for 1961–1990 (black line). A model

of global surface temperature change (a: red line) produced using the sum of

the impacts on temperature of natural (b, c, d) and anthropogenic factors (e).

(b) Estimated temperature response to solar forcing. (c) Estimated temperature

response to volcanic eruptions. (d) Estimated temperature variability due to

internal variability, here related to the El Niño-Southern Oscillation. (e) Esti-

mated temperature response to anthropogenic forcing, consisting of a warm-

ing component from greenhouse gases, and a cooling component from most

aerosols.

Anomaly (°C)

-0.2

0.0

-0.1

(d) Internal Variability

Anomaly (°C)

0.0

0.2

-0.2

(a) Global Surface Temperature

Anomaly (°C)

0.8

0.4

0.0

-0.4

-0.8

(b) Solar Component

Anomaly (°C)

0.2

0.0

0.1

(e) Anthropogenic Component

Anomaly (°C)

1880

0.0

0.2

0.4

0.6

0.8

200019801960194019201900

Year

394

Chapter 5 Information from Paleoclimate Archives

5.2.2.2 Atmospheric Carbon Dioxide Concentrations from

Geological Proxy Data

Geological proxies provide indirect information on atmospheric CO

concentrations for time intervals older than those covered by ice

core records (see Section 5.2.2.1). Since AR4, the four primary proxy

methods have undergone further development (Table 5.A.2). A

reassessment of biological respiration and carbonate formation has

reduced CO

reconstructions based on fossil soils by approximately

50% (Breecker et al., 2010). Bayesian statistical techniques for calibrat-

ing leaf stomatal density reconstructions produce consistently higher

estimates than previously assessed (Beerling et al., 2009), result-

ing in more convergence between estimates from these two terrestrial

proxies. Recent CO

reconstructions using the boron isotope proxy pro-

vide an improved understanding of foraminifer species effects and evo-

lution of seawater alkalinity (Hönisch and Hemming, 2005) and sea-

water boron isotopic composition (Foster et al., 2012). Quantiﬁcation

of the phytoplankton cell-size effects on carbon isotope fractionation

has also improved the consistency of the alkenone method (Henderiks

and Pagani, 2007). These proxies have also been applied more widely

and at higher temporal resolution to a range of geological archives,

resulting in an increased number of atmospheric CO

estimates since

65 Ma (Beerling and Royer, 2011). Although there is improved consen-

sus between the proxy CO

estimates, especially the marine proxy esti-

mates, a signiﬁcant degree of variation among the different techniques

remains. All four techniques have been included in the assessment, as

there is insufﬁcient knowledge to discriminate between different proxy

estimates on the basis of conﬁdence (assessed in Table 5.A.2).

In the time interval between 65 and 23 Ma, all proxy estimates of CO

concentration span a range of 300 ppm to 1500 ppm (Figure 5.2). An

independent constraint on Early Eocene atmospheric CO

concentra-

tion

is provided by the occurrence of the sodium carbonate mineral

nahcolite, in about 50 Ma lake sediments, which precipitates in asso-

ciation with halite at the sediment–water interface only at CO

levels

>1125 ppm (Lowenstein and Demicco, 2006), and thus provides a

potential lower bound for atmospheric concentration (medium conﬁ-

dence) during the warmest period of the last 65 Ma, the Early Eocene

Climatic Optimum (EECO; 52 to 50 Ma; Table 5.1), which is inconsist-

ent with lower estimates from stomata and paleosoils. Although the

reconstructions indicate a general decrease in CO

concentrations

since about 50 Ma (Figure 5.2), the large scatter of proxy data pre-

cludes a robust assessment of the second-order variation around this

overall trend.

Since 23 Ma, CO

proxy estimates are at pre-industrial levels with

exception of the Middle Miocene climatic optimum (17 to 15 Ma)

and the Pliocene (5.3 to 2.6 Ma), which have higher concentrations.

Although new CO

reconstructions for the Pliocene based on marine

proxies have produced consistent estimates mostly in the range 350

ppm to 450 ppm (Pagani et al., 2010; Seki et al., 2010; Bartoli et al.,

2011), the uncertainties associated with these marine estimates remain

difﬁcult to quantify. Several boron-derived data sets agree within error

(±25 ppm) with the ice core records (Foster, 2008; Hönisch et al., 2009),

but alkenone data for the ice core period are outside the error limits

(Figure 5.2). We conclude that there is medium conﬁdence that CO

levels were above pre-industrial interglacial concentration (~280 ppm)

and did not exceed ~450 ppm during the Pliocene, with interglacial

values in the upper part of that range between 350 and 450 ppm.

5.2.2.3 Past Changes in Mineral Dust Aerosol Concentrations

Past changes in mineral dust aerosol (MDA) are important for estimates

of climate sensitivity (see Section 5.3.3) and for its supply of nutrients,

especially iron to the Southern Ocean (see Section 6.2). MDA concen-

tration is controlled by variations in dust sources, and by changes in

atmospheric circulation patterns acting on its transport and lifetime.

Since AR4, new records of past MDA ﬂux have been obtained from

deep-sea sediment and ice cores. A 4 million-year MDA-ﬂux recon-

struction from the Southern Ocean (Figure 5.2) implies reduced dust

generation and transport during the Pliocene compared to Holo-

cene levels, followed by a signiﬁcant rise around 2.7 Ma when NH

ice volume increased (Martinez-Garcia et al., 2011). Central Antarc-

tic ice core records show that local MDA deposition ﬂuxes are ~20

times higher during glacial compared to interglacial periods (Fischer

et al., 2007; Lambert et al., 2008; Petit and Delmonte, 2009). This is

due to enhanced dust production in southern South America and per-

haps Australia (Gaiero, 2007; De Deckker et al., 2010; Gabrielli et al.,

2010; Martinez-Garcia et al., 2011; Wegner et al., 2012). The impact of

changes in MDA lifetime (Petit and Delmonte, 2009) on dust ﬂuxes in

Antarctica remains uncertain (Fischer et al., 2007; Wolff et al., 2010).

Equatorial Paciﬁc glacial–interglacial MDA ﬂuxes co-vary with Antarc-

tic records, but with a glacial–interglacial ratio in the range of approx-

imately three to four (Winckler et al., 2008), attributed to enhanced

dust production from Asian and northern South American sources in

glacial times (Maher et al., 2010). The dominant dust source regions

(e.g., North Africa, Arabia and Central Asia) show complex patterns

of variability (Roberts et al., 2011). A glacial increase of MDA source

strength by a factor of 3 to 4 requires low vegetation cover, seasonal

aridity, and high wind speeds (Fischer et al., 2007; McGee et al., 2010).

In Greenland ice cores, MDA ice concentrations are higher by a factor

of 100 and deposition ﬂuxes by a factor 20 during glacial periods

(Ruth et al., 2007). This is due mainly to changes in the dust sources

for Greenland (Asian desert areas), increased gustiness (McGee et al.,

2010) and atmospheric lifetime and transport of MDA (Fischer et al.,

2007). A strong coherence is observed between dust in Greenland ice

cores and aeolian deposition in European loess formations (Antoine et

al., 2009).

Global data synthesis shows two to four times more dust deposition

at the Last Glacial Maximum (LGM; Table 5.1) than today (Derbyshire,

2003; Maher et al., 2010). Based on data–model comparisons, esti-

mates of global mean LGM dust RF vary from –3 W m

–2

to +0.1 W m

–2

due to uncertainties in radiative properties. The best estimate value

remains at –1 W m

–2

as in AR4 (Claquin et al., 2003; Mahowald et

al., 2006, 2011; Patadia et al., 2009; Takemura et al., 2009; Yue et al.,

2010). Models may underestimate the MDA RF at high latitudes (Lam-

bert et al., 2013).

395

Information from Paleoclimate Archives Chapter 5

0102030405060

Age (Ma)

100

200

500

1000

2000

Atmospheric CO

(ppm)

proxies

Phytoplankton Boron

Stomata Liverworts

Nahcolite Paleosols

0123

Age (Ma)

100

200

300

400

500

Atmospheric CO

(ppm)

Tropical sea−surface

temperature (°C)

−100

Global

sea level (m)

Southern Ocean

Dust accumulation

(g m

−2

−1

)

MPWP

Figure 5.2 | (Top) Orbital-scale Earth system responses to radiative forcings and perturbations from 3.5 Ma to present. Reconstructed dust mass accumulation rate is from the

Atlantic sector of the Southern Ocean (red) (Martinez-Garcia et al., 2011). Sea level curve (blue) is the stacked d

O proxy for ice volume and ocean temperature (Lisiecki and Raymo,

2005) calibrated to global average eustatic sea level (Naish and Wilson, 2009; Miller et al., 2012a). Also shown are global eustatic sea level reconstructions for the last 500 kyr

based on sea level calibration of the d

O curve using dated coral shorelines (green line; Waelbroeck et al., 2002) and the Red Sea isotopic reconstruction (red line; Rohling et al.,

2009). Weighted mean estimates (2 standard deviation uncertainty) for far-ﬁeld reconstructions of eustatic peaks are shown for mid-Pliocene interglacials (red dots; Miller et al.,

2012a). The dashed horizontal line represents present-day sea level. Tropical sea surface temperature (black line) based on a stack of four alkenone-based sea surface temperature

reconstructions (Herbert et al., 2010). Atmospheric carbon dioxide (CO

) measured from Antarctic ice cores (green line, Petit et al., 1999; Siegenthaler et al., 2005; Lüthi et al., 2008),

and estimates of CO

from boron isotopes (d

B) in foraminifera in marine sediments (blue triangles; Hönisch et al., 2009; Seki et al., 2010; Bartoli et al., 2011), and phytoplankton

alkenone-derived carbon isotope proxies (red diamonds; Pagani et al., 2010; Seki et al., 2010), plotted with 2 standard deviation uncertainty. Present (2012) and pre-industrial CO

concentrations are indicated with long-dashed and short-dashed grey lines, respectively. (Bottom) Concentration of atmospheric CO

for the last 65 Ma is reconstructed from marine

and terrestrial proxies (Cerling, 1992; Freeman and Hayes, 1992; Koch et al., 1992; Stott, 1992; van der Burgh et al., 1993; Sinha and Stott, 1994; Kürschner, 1996; McElwain, 1998;

Ekart et al., 1999; Pagani et al., 1999a, 1999b, 2005a, 2005b, 2010, 2011; Kürschner et al., 2001, 2008; Royer et al., 2001a, 2001b; Beerling et al., 2002, 2009; Beerling and Royer,

2002; Nordt et al., 2002; Greenwood et al., 2003; Royer, 2003; Lowenstein and Demicco, 2006; Fletcher et al., 2008; Pearson et al., 2009; Retallack, 2009b, 2009a; Tripati et al.,

2009;Seki et al., 2010; Smith et al., 2010; Bartoli et al., 2011; Doria et al., 2011; Foster et al., 2012). Individual proxy methods are colour-coded (see also Table A5.1). The light blue

shading is a 1-standard deviation uncertainty band constructed using block bootstrap resampling (Mudelsee et al., 2012) for a kernel regression through all the data points with a

bandwidth of 8 Myr prior to 30 Ma, and 1 Myr from 30 Ma to present. Most of the data points for CO

proxies are based on duplicate and multiple analyses. The red box labelled

MPWP represents the mid-Pliocene Warm Period (3.3 to 3.0 Ma; Table 5.1).

396

Chapter 5 Information from Paleoclimate Archives

Box 5.1 | Polar Ampliﬁcation

Polar ampliﬁcation occurs if the magnitude of zonally averaged surface temperature change at high latitudes exceeds the globally

averaged temperature change, in response to climate forcings and on time scales greater than the annual cycle. Polar ampliﬁcation is

of global concern due to the potential effects of future warming on ice sheet stability and, therefore, global sea level (see Sections 5.6.1,

5.8.1 and Chapter 13) and carbon cycle feedbacks such as those linked with permafrost melting (see Chapter 6).

Some external climate forcings have an enhanced radiative impact at high latitudes, such as orbital forcing (Section 5.2.1.1), or black

carbon (Section 8.3.4). Here, we focus on the latitudinal response of surface temperature to CO

perturbations. The magnitude of polar

ampliﬁcation depends on the relative strength and duration of different climate feedbacks, which determine the transient and equilib-

rium response to external forcings. This box ﬁrst describes the different feedbacks operating in both polar regions, and then contrasts

polar ampliﬁcation depicted for past high CO

and low CO

climates with projected temperature patterns for the RCP8.5 future green-

house gas (WMGHG) emission scenario.

In the Arctic, the sea ice/ocean surface albedo feedback plays an important role (Curry et al., 1995; Serreze and Barry, 2011). With

retreating sea ice, surface albedo decreases, air temperatures increase and the ocean can absorb more heat. The resulting ocean

warming contributes to further sea ice melting. The sea ice/ocean surface albedo feedback can exhibit threshold behaviour when

temperatures exceed the freezing point of sea ice. This may also translate into a strong seasonality of the response characteristics.

Other feedbacks, including water vapour and cloud feedbacks have been suggested as important ampliﬁers of Arctic climate change

(Vavrus, 2004; Abbot and Tziperman, 2008, 2009; Graversen and Wang, 2009; Lu and Cai, 2009; Screen and Simmonds, 2010; Bintanja

et al., 2011). In continental Arctic regions with seasonal snow cover, changes in radiative forcing (RF) can heavily inﬂuence snow cover

(Ghatak et al., 2010), and thus surface albedo. Other positive feedbacks operating on time scales of decades-to-centuries in continen-

tal high-latitude regions are associated with surface vegetation changes (Bhatt et al., 2010) and thawing permafrost (e.g., Walter et

al., 2006). On glacial-to-interglacial time scales, the very slow ice sheet–albedo response to external forcings (see Box 5.2) is a major

contributor to polar ampliﬁcation in the Northern Hemisphere.

An ampliﬁed response of Southern Ocean sea surface temperature (SST) to radiative perturbations also emerges from the sea ice–

albedo feedback. However, in contrast to the Arctic Ocean, which in parts is highly stratiﬁed, mixed-layer depths in the Southern Ocean

typically exceed several hundreds of meters, which allows the ocean to take up vast amounts of heat (Böning et al., 2008; Gille, 2008;

Sokolov and Rintoul, 2009) and damp the SST response to external forcing. This process, and the presence of the ozone hole over the

Antarctic ice sheet (Thompson and Solomon, 2002, 2009), can affect the transient response of surface warming of the Southern Ocean

and Antarctica, and lead to different patterns of future polar ampliﬁcation on multi-decadal to multi-centennial time scales. In response

to rapid atmospheric CO

changes, climate models indeed project an asymmetric warming between the Arctic and Southern Oceans,

with an earlier response in the Arctic and a delayed response in the Southern Ocean (Section 12.4.3). Above the Antarctic ice sheet,

however, surface air temperature can respond quickly to radiative perturbations owing to the limited role of latent heat ﬂux in the

surface energy budget of Antarctica.

These differences in transient and equilibrium responses of surface temperatures on Antarctica, the Southern Ocean and over conti-

nents and oceans in the Arctic domain can explain differences in the latitudinal temperature patterns depicted in Box 5.1, Figure 1 for

past periods (equilibrium response) and future projections (transient response).

Box 5.1, Figure 1 illustrates the polar ampliﬁcation phenomenon for three different periods of the Earth’s climate history using tem-

perature reconstructions from natural archives and climate model simulations for: (i) the Early Eocene Climatic Optimum (EECO, 54 to

48 Ma) characterised by CO

concentrations of 1000 to 2000 ppm (Section 5.2.2.2) and the absence of continental ice sheets; (ii) the

mid-Pliocene Warm Period (MPWP, 3.3 to 3.0 Ma), characterized by CO

concentrations in the range of 350 to 450 ppm (Section 5.2.2.2)

and reduced Greenland and Antarctic ice sheets compared to today (see Section 5.6.1), (iii) the Last Glacial Maximum (LGM, 21 to 19

ka), characterized by CO

concentrations around 200 ppm and large continental ice sheets covering northern Europe and North America.

Throughout all three time periods, reconstructions and simulations reveal Arctic and Antarctic surface air temperature ampliﬁcation of

up to two times the global mean (Box 5.1, Figure 1c, d), and this bipolar ampliﬁcation appears to be a robust feature of the equilibrium

Earth system response to changes of CO

concentration, irrespective of climate state. The absence (EECO), or expansion (LGM) of conti-

nental ice sheets has the potential to affect the zonally averaged surface temperatures due to the lapse-rate effect (see Box 5.2), hence

contributing to polar ampliﬁcation. However, polar ampliﬁcation is also suppressed in zonally averaged gradients of SST compared

with terrestrial surface air temperature (Box 5.1, Figure 1), owing to the presence of high-latitude sea ice in the pre-industrial control,

(continued on next page)

397

Information from Paleoclimate Archives Chapter 5

Box 5.1 (continued)

which places a lower limit on SST. Global mean temperature estimates for these three past climates also imply an Earth system climate

sensitivity to radiative perturbations up to two times higher than the equilibrium climate sensitivity (Lunt et al., 2010; Haywood et al.,

2013) (see Section 5.3.1 and Box 12.2).

Polar ampliﬁcation explains in part why Greenland Ice Sheet (GIS) and the West Antarctic Ice Sheet (WAIS) appear to be highly sensitive

to relatively small increases in CO

concentration and global mean temperature. For example, global sea level during MPWP may have

been up to +20m higher than present day when atmospheric CO

concentrations were ~350 to 450 ppm and global mean surface

temperature was 2°C to 3°C above pre-industrial levels (see Sections 5.6.1 and 5.8.1). (continued on next page)

Box 5.1, Figure 1 | Comparison of data and multi-model mean (MMM) simulations, for four periods of time, showing (a) sea surface temperature (SST) anomalies,

(b) zonally averaged SST anomalies, (c) zonally averaged global (green) and land (grey) surface air temperature (SAT) anomalies and (d) land SAT anomalies. The time

periods are 2081–2100 for the Representative Concentration Pathway (RCP) 8.5 (top row), Last Glacial Maximum (LGM, second row), mid-Pliocene Warm Period

(MPWP, third row) and Early Eocene Climatic Optimum (EECO, bottom row). Model temperature anomalies are calculated relative to the pre-industrial value of

each model in the ensemble prior to calculating the MMM anomaly (a, d; colour shading). Zonal MMM gradients (b, c) are plotted with a shaded band indicating 2

standard deviations. Site speciﬁc temperature anomalies estimated from proxy data are calculated relative to present site temperatures and are plotted (a, d) using the

same colour scale as the model data, and a circle-size scaled to estimates of conﬁdence. Proxy data compilations for the LGM are from Multiproxy Approach for the

Reconstruction of the Glacial Ocean surface (MARGO) Project Members (2009) and Bartlein et al. (2011), for the MPWP are from Dowsett et al. (2012), Salzmann et

al. (2008) and Haywood et al. (2013) and for the EECO are from Hollis et al. (2012) and Lunt et al. (2012). Model ensemble simulations for 2081–2100 are from the

CMIP5 ensemble using RCP 8.5, for the LGM are seven Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) and Coupled Model Intercomparison Project

Phase 5 (CMIP5) models, for the Pliocene are from Haywood et al., (2013), and for the EECO are after Lunt et al. (2012).

EECO

54−48 Ma

−12 −10 −8 −6 −4 −2 024681012

SST anomaly (°C)

Confidence

Low Medium High Very High Not assessed

010

90°N

0°

90°S

+9.6°C

02040

90°N

0°

90°S

land

+14.1°C

+12.7°C

global

−24−20 −16−12 −8 −4 04812162

SAT anomaly (°C)

MPWP

3.3−3 Ma

010

90°N

0°

90°S

+1.7°C

02040

90°N

0°

90°S

land

+3.7°C

+2.7°C

global

LGM

21 ka

−100

90°N

0°

90°S

−2.2°C

−40−200

90°N

0°

90°S

land

−7.2°C

−4.4°C

global

(a)

RCP 8.5

SST anomaly (°C)

010

SST anomaly (°C)

90°N

0°

90°S

+2.5°C

(b)

02040

SAT anomaly (°C)

90°N

0°

90°S

land

+4.9°C

+3.7°C

global

(c)

SAT anomaly (°C)

(d)

2081−2100

398

Chapter 5 Information from Paleoclimate Archives

Box 5.1 (continued)

Based on earlier climate data–model comparisons, it has been claimed (summarised in Huber and Caballero, 2011), that models under-

estimated the strength of polar ampliﬁcation for high CO

climates by 30 to 50%. While recent simulations of the EECO and the MPWP

exhibit a wide inter-model variability, there is generally good agreement between new simulations and data, particularly if seasonal

biases in some of the marine SST proxies from high-latitude sites are considered (Hollis et al., 2012; Lunt et al., 2012; Haywood et al.,

2013).

Transient polar ampliﬁcation as recorded in historical instrumental data and as projected by coupled climate models for the 21st

century involves a different balance of feedbacks than for the “equilibrium” past states featured in Box 5.1, Figure 1. Since 1875, the

Arctic north of 60°N latitude has warmed at a rate of 1.36°C per century, approximately twice as fast as the global average (Bekryaev

et al., 2010), and since 1979, Arctic land surface has warmed at an even higher rate of 0.5°C per decade (e.g., Climatic Research Unit

(CRU) Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land version 4 (CRUTEM4), Jones et al.,

2012; Hadley Centre/CRU gridded surface temperature data set version 4 (HadCRUT4), Morice et al., 2012) (see Section 2.4). This recent

warming appears unusual in the context of reconstructions spanning the past 2000 years (Section 5.5) and has been attributed primari-

ly to anthropogenic factors (Gillett et al., 2008) (see Section 10.3.1.1.4). The fact that the strongest warming occurs in autumn and early

winter (Chylek et al., 2009; Serreze et al., 2009; Polyakov et al., 2010; Screen and Simmonds, 2010; Semenov et al., 2010; Spielhagen

et al., 2011) strongly links Arctic ampliﬁcation to feedbacks associated with the seasonal reduction in sea ice extent and duration, as

well as the insulating effect of sea ice in winter (e.g., Soden et al., 2008; Serreze et al., 2009; Serreze and Barry, 2011). For future model

projections (Box 5.1, Figure 1), following the RCP8.5 scenarios, annual mean Arctic (68°N to 90°N) warming is expected to exceed the

global average by 2.2 to 2.4 times for the period 2081–2100 compared to 1986–2005 (see Section 12.4.3.1), which corresponds to the

higher end of polar ampliﬁcation implied by paleo-reconstructions.

The transient response of Antarctic and Southern Ocean temperatures to the anthropogenic perturbation appears more complex, than

for the Arctic region. Zonal mean Antarctic surface warming has been modest at 0.1°C per decade over the past 50 years (Steig et al.,

2009; O’Donnell et al., 2010). The Antarctic Peninsula is experiencing one of the strongest regional warming trends (0.5°C per decade

over the past 50 years), more than twice that of the global mean temperature. Central West Antarctica may have also experienced

a similar strong warming trend, as depicted by the only continuous meteorological station during the last 50 years (Bromwich et al.,

2013), and borehole measurements spanning the same period (Orsi et al., 2012). Ice core records show enhanced summer melting in

the Antarctic Peninsula since the 1950, which is unprecedented over the past 1000 years (Abram et al., 2013), and warming in West

Antarctica that cannot be distinguished from natural variability over the last 2000 years (Steig et al., 2013) (see also Section 10.3.1.1.4,

and Section 5.5). Polar ampliﬁcation in the Southern Ocean and Antarctica is virtually absent in the transient CMIP5 RCP4.5 future

simulations (2081–2100 versus 1986–2005) (see Section 12.4.3.1), although CMIP5 RCP8.5 exhibits an ampliﬁed warming in the

Southern Ocean (Box 5.1, Figure 1), much smaller in magnitude than the equilibrium response implied from paleo-reconstructions for

a high-CO

world.

In summary, high conﬁdence exists for polar ampliﬁcation in either one or both hemispheres, based on robust and consistent evidence

from temperature reconstructions of past climates, recent instrumental temperature records and climate model simulations of past,

present and future climate changes.

5.3 Earth System Responses and Feedbacks

at Global and Hemispheric Scales

This section updates the information available since AR4 on changes in

surface temperature on million-year to orbital time scales and for the

last 2000 years. New information on changes of the monsoon systems

on glacial–interglacial time scales is also assessed.

5.3.1 High-Carbon Dioxide Worlds and Temperature

Cenozoic (last 65 Ma) geological archives provide examples of natural

climate states globally warmer than the present, which are associated

with atmospheric CO

concentrations above pre-industrial levels. This

relationship between global warmth and high CO

is complicated by

factors such as tectonics and the evolution of biological systems, which

play an important role in the carbon cycle (e.g., Zachos et al., 2008).

Although new reconstructions of deep-ocean temperatures have been

compiled since AR4 (e.g., Cramer et al., 2011), low conﬁdence remains

in the precise relationship between CO

and deep-ocean temperature

(Beerling and Royer, 2011).

Since AR4 new proxy and model data have become available from three

Cenozoic warm periods to enable an assessment of forcing, feedbacks

and the surface temperature response (e.g., Dowsett et al., 2012; Lunt et

al., 2012; Haywood et al., 2013). These are the Paleocene–Eocene Ther-

mal Maximum (PETM; Table 5.1), the Early Eocene Climatic Optimum

(EECO; Table 5.1) and the mid-Pliocene Warm Period (MPWP; Table 5.1).

Reconstructions of surface temperatures based on proxy data remain

399

Information from Paleoclimate Archives Chapter 5

challenged by (i) the limited number and uneven geographical dis-

tribution of sites, (ii) seasonal biases and (iii) the validity of assump-

tions required by each proxy method (assessed in Table 5.A.3). There

is also a lack of consistency in the way uncertainties are reported for

proxy climate estimates. In most cases error bars represent the analyti-

cal and calibration error. In some compilations qualitative conﬁdence

assessments are reported to account for the quality of the age control,

number of samples, fossil preservation and abundance, performance of

the proxy method utilized and agreement of multiple proxy estimates

(e.g., Multiproxy Approach for the Reconstruction of the Glacial Ocean

surface (MARGO) Project Members, 2009; Dowsett et al., 2012).

The PETM was marked by a massive carbon release and corresponding

global ocean acidiﬁcation (Zachos et al., 2005; Ridgwell and Schmidt,

2010) and, with low conﬁdence, global warming of 4°C to 7°C rel-

ative to pre-PETM mean climate (Sluijs et al., 2007; McInerney and

Wing, 2011). The carbon release of 4500 to 6800 PgC over 5 to 20 kyr

translates into a rate of emissions of ~0.5 to 1.0 PgC yr

–1

(Panchuk et

al., 2008; Zeebe et al., 2009). GHG emissions from marine methane

hydrate and terrestrial permafrost may have acted as positive feed-

backs (DeConto et al., 2012).

The EECO represents the last time atmospheric CO

concentrations

may have reached a level of ~1000 ppm (Section 5.2.2.2). There were

no substantial polar ice sheets, and oceanic and continental conﬁgu-

rations, vegetation type and distribution were signiﬁcantly different

from today. Whereas simulated SAT are in reasonable agreement with

reconstructions (Huber and Caballero, 2011; Lunt et al., 2012) (Box 5.1,

Figure 1d), there are still signiﬁcant discrepancies between simulat-

ed and reconstructed mean annual SST, which are reduced if seasonal

biases in some of the marine proxies are considered for the high-lati-

tude sites (Hollis et al., 2012; Lunt et al., 2012). Medium conﬁdence is

placed on the reconstructed global mean surface temperature anomaly

estimate of 9°C to 14°C.

The Pliocene is characterized by a long-term increase in global ice

volume and decrease in temperature from ~3.3–2.6 Ma (Lisiecki and

Raymo, 2005; Mudelsee and Raymo, 2005; Fedorov et al., 2013), which

marks the onset of continental-scale glaciations in the NH. Superim-

posed on this trend, benthic d

O (Lisiecki and Raymo, 2005) and an

ice proximal geological archive (Lisiecki and Raymo, 2005; Naish et al.,

2009a) imply moderate ﬂuctuations in global ice volume paced by the

41 kyr obliquity cycle. This orbital variability is also evident in far-ﬁeld

sea level reconstructions (Miller et al., 2012a), tropical Paciﬁc SST (Her-

bert et al., 2010) and Southern Ocean MDA records (Martinez-Garcia et

al., 2011), and indicate a close coupling between temperature, atmos-

pheric circulation and ice volume/sea level (Figure 5.2). The MPWP and

the following 300 kyr represent the last time atmospheric CO

concen-

trations were in the range 350 to 450 ppm (Section 5.2.2.2, Figure 5.2).

Model–data comparisons (Box 5.1, Figure 1) provide high conﬁdence

that mean surface temperature was warmer than pre-industrial for the

average interglacial climate state during the MPWP (Dowsett et al.,

2012; Haywood et al., 2013). Global mean SST is estimated at +1.7°C

(without uncertainty) above the 1901–1920 mean based on large data

syntheses (Lunt et al., 2010; Dowsett et al., 2012). General circulation

model (GCM) results agree with this SST anomaly (to within ±0.5°C),

and produce a range of global mean SAT of +1.9°C and +3.6°C rela-

tive to the 1901–1920 mean (Haywood et al., 2013). Weakened merid-

ional temperature gradients are shown by all GCM simulations, and

have signiﬁcant implications for the stability of polar ice sheets and

sea level (see Box 5.1 and Section 5.6). SST gradients and the Paciﬁc

Ocean thermocline gradient along the equator were greatly reduced

compared to present (Fedorov et al., 2013) (Section 5.4). Vegetation

reconstructions (Salzmann et al., 2008) imply that the global extent of

arid deserts decreased and boreal forests replaced tundra, and GCMs

predict an enhanced hydrological cycle, but with large inter-model

spread (Haywood et al., 2013). The East Asian Summer Monsoon, as

well as other monsoon systems, may have been enhanced at this time

(e.g., Wan et al., 2010).

Climate reconstructions for the warm periods of the Cenozoic also pro-

vide an opportunity to assess Earth-system and equilibrium climate

sensitivities. Uncertainties on both global temperature and CO

recon-

structions preclude deriving robust quantitative estimates from the

available PETM data. The limited number of models for MPWP, which

take into account slow feedbacks such as ice sheets and the carbon

cycle, imply with medium conﬁdence that Earth-system sensitivity may

be up to two times the model equilibrium climate sensitivity (ECS)

(Lunt et al., 2010; Pagani et al., 2010; Haywood et al., 2013). However,

if the slow amplifying feedbacks associated with ice sheets and CO

are considered as forcings rather than feedbacks, climate records of

the past 65 Myr yield an estimate of 1.1°C to 7°C (95% conﬁdence

interval) for ECS (PALAEOSENS Project Members, 2012) (see also Sec-

tion 5.3.3.2).

5.3.2 Glacial–Interglacial Dynamics

5.3.2.1 Role of Carbon Dioxide in Glacial Cycles

Recent modelling work provides strong support for the important role

of variations in the Earth’s orbital parameters in generating long-term

climate variability. In particular, new simulations with GCMs (Carlson

et al., 2012; Herrington and Poulsen, 2012) support the fundamental

premise of the Milankovitch theory that a reduction in NH summer

insolation generates sufﬁcient cooling to initiate ice sheet growth. Cli-

mate–ice sheet models with varying degrees of complexity and forced

by variations in orbital parameters and reconstructed atmospheric CO

concentrations simulate ice volume variations and other climate char-

acteristics during the last and several previous glacial cycles consistent

with paleoclimate records (Abe-Ouchi et al., 2007; Bonelli et al., 2009;

Ganopolski et al., 2010) (see Figure 5.3).

There is high conﬁdence that orbital forcing is the primary external

driver of glacial cycles (Kawamura et al,. 2007; Cheng et al., 2009;

Lisiecki, 2010; Huybers, 2011). However, atmospheric CO

content

plays an important internal feedback role. Orbital-scale variability

in CO

concentrations over the last several hundred thousand years

covaries (Figure 5.3) with variability in proxy records including recon-

structions of global ice volume (Lisiecki and Raymo, 2005), climatic

conditions in central Asia (Prokopenko et al., 2006), tropical (Herbert

et al., 2010) and Southern Ocean SST (Pahnke et al., 2003; Lang and

Wolff, 2011), Antarctic temperature (Parrenin et al., 2013), deep-ocean

temperature (Elderﬁeld et al., 2010), biogeochemical conditions in the

North Paciﬁc (Jaccard et al., 2010) and deep-ocean ventilation (Lisiecki

400

Chapter 5 Information from Paleoclimate Archives

Figure 5.3 | Orbital parameters and proxy records over the past 800 kyr. (a) Eccentricity. (b) Obliquity. (c) Precessional parameter (Berger and Loutre, 1991). (d) Atmospheric

concentration of CO

from Antarctic ice cores (Petit et al., 1999; Siegenthaler et al., 2005; Ahn and Brook, 2008; Lüthi et al., 2008). (e) Tropical sea surface temperature stack

(Herbert et al., 2010). (f) Antarctic temperature stack based on up to seven different ice cores (Petit et al., 1999; Blunier and Brook, 2001; Watanabe et al., 2003; European Project

for Ice Coring in Antarctica (EPICA) Community Members, 2006; Jouzel et al., 2007; Stenni et al., 2011). (g) Stack of benthic d

O, a proxy for global ice volume and deep-ocean

temperature (Lisiecki and Raymo, 2005). (h) Reconstructed sea level (dashed line: Rohling et al., 2010; solid line: Elderﬁeld et al., 2012). Lines represent orbital forcing and proxy

records, shaded areas represent the range of simulations with climate models (Grid Enabled Integrated Earth System Model-1, GENIE-1, Holden et al., 2010a; Bern3D, Ritz et al.,

2011), climate–ice sheet models of intermediate complexity (CLIMate and BiosphERe model, CLIMBER-2, Ganopolski and Calov, 2011) and an ice sheet model (ICe sheet model for

Integrated Earth system studies, IcIES, Abe-Ouchi et al., 2007) forced by variations of the orbital parameters and the atmospheric concentrations of the major greenhouse gases. (i)

Rate of changes of global mean temperature during Termination I based on Shakun et al. (2012).

et al., 2008). Such close linkages between CO

concentration and cli-

mate variability are consistent with modelling results suggesting with

high conﬁdence that glacial–interglacial variations of CO

and other

GHGs explain a considerable fraction of glacial–interglacial climate

variability in regions not directly affected by the NH continental ice

sheets (Timmermann et al., 2009; Shakun et al., 2012).

5.3.2.2 Last Glacial Termination

It is very likely that global mean surface temperature increased by 3°C

to 8°C over the last deglaciation (see Table 5.2), which gives a very

likely average rate of change of 0.3 to 0.8°C kyr

–1

. Deglacial global

warming occurred in two main steps from 17.5 to 14.5 ka and 13.0

to 10.0 ka that likely reached maximum rates of change between

1°C kyr

–1

and 1.5°C kyr

–1

at the millennial time scale (cf. Shakun et

al., 2012; Figure 5.3i), although regionally and on shorter time scales

higher rates may have occurred, in particular during a sequence of

abrupt climate change events (see Section 5.7).

For the last glacial termination, a large-scale temperature reconstruc-

tion (Shakun et al., 2012) documents that temperature change in the

SH lead NH temperature change. This lead can be explained by the

bipolar thermal seesaw concept (Stocker and Johnsen, 2003) (see also

Section 5.7) and the related changes in the inter-hemispheric ocean

heat transport, caused by weakening of the Atlantic Ocean meridional

overturning circulation (AMOC) during the last glacial termination

(Ganopolski and Roche, 2009). SH warming prior to NH warming can

also be explained by the fast sea ice response to changes in austral

spring insolation (Stott et al., 2007; Timmermann et al., 2009). Accord-

ing to these mechanisms, SH temperature lead over the NH is fully

consistent with the NH orbital forcing of deglacial ice volume chang-

es (high conﬁdence) and the importance of the climate–carbon cycle

feedbacks in glacial–interglacial transitions. The tight coupling is fur-

ther highlighted by the near-zero lag between the deglacial rise in CO

and averaged deglacial Antarctic temperature recently reported from

improved estimates of gas-ice age differences (Pedro et al., 2012; Par-

renin et al., 2013). Previous studies (Monnin et al., 2001; Table 5.A.4)

401

Information from Paleoclimate Archives Chapter 5

Figure 5.4 | Inter-hemispheric response of monsoon systems at orbital, millennial and centennial scales. (a) Boreal summer insolation changes at 20°N (red) (W m

–2

) and austral

summer insolation changes at 20°S (blue). (b) Temperature changes in Greenland (degrees Celsius) reconstructed from North Greenland Ice Core Project (NGRIP) ice core on SS09

time scale (Huber et al., 2006), location indicated by orange star in c. (c) Location of proxy records displayed in panels a, b, d–i in relation to the global monsoon regions (cyan shad-

ing) (Wang and Ding, 2008). (d) Reconstructed (red) standardized negative d

O anomaly in East Asian Summer Monsoon region derived from Hulu (Wang et al., 2001) and Sanbao

(Wang et al., 2008) cave speleothem records, China and simulated standardized multi-model average (black) of annual mean rainfall anomalies averaged over region 108°E to

123°E and 25°N to 40°N using the transient runs conducted with LOch–Vecode-Ecbilt-CLio-agIsm Model (LOVECLIM, Timm et al., 2008), FAst Met Ofﬁce/UK Universities Simulator

(FAMOUS, Smith and Gregory, 2012), and the Hadley Centre Coupled Model (HadCM3) snapshot simulations (Singarayer and Valdes, 2010). (e) d

O from Xiaobailong cave, China

(Cai et al., 2010). (f) Standardized negative d

O anomalies (red) in Huangye (Tan et al., 2011) and Wanxian (Zhang et al., 2008) caves, China and simulated standardized annual

mean and 30-year low-pass ﬁltered rainfall anomalies (black) in region 100°E to 110°E, 20°N to 35°N, ensemble averaged over externally forced Atmosphere-Ocean General

Circulation Model (AOGCM) experiments conducted with Community Climate System Model-4 (CCSM4), ECHAM4+HOPE-G (ECHO-G), Max Planck Institute Earth System Model

(MPI-ESM), Commonwealth Scientiﬁc and Industrial Research Organisation model (CSIRO-Mk3L-1-2), Model for Interdisciplinary Research on Climate (MIROC), HadCM3 (Table

5.A.1). (g) Standardized negative d

O anomaly (blue) from Botuvéra speleothem, Brazil (Cruz et al., 2005) and simulated standardized multi-model average (black) of annual mean

rainfall anomalies averaged over region 45°W to 60°W and 35°S to 15°S using same experiments as in panel d. (h) Standardized d

O anomaly (blue) from Pacupahuain cave, Peru

(Kanner et al., 2012). (i) Standardized negative d

O anomalies (blue) from Cascayunga Cave, Peru (Reuter et al., 2009) and Pumacocha Lake, Peru (Bird et al., 2011) and simulated

standardized annual mean and 30-year low-pass ﬁltered rainfall anomalies (black) in region 76°W to 70°W, 16°S to 8°S, ensemble averaged over the same model simulations as

in f. HS4/5 denote Heinrich stadials 4 and 5, and LIA denotes Little Ice Age (Table 5.1).

1000 1500 2000

−2

−4

−2

020406080

440

460

480

20406080

−2

404550

−13

−12

−11

−10

−9

−17

−16

−15

−14

404550

−50

−45

−40

−35

(a) Insolation at 20°N/20°S

Age (ka)Age (ka)

Age (ka)

Age (ka) Age (ka)

Age (ka)

Time (Year CE)

(b) Greenland: NGRIP

(d) China: Hulu (d1), Sanbao (d2) caves (e) China: XiaoBailong cave

(g) Brazil: Botuvéra cave (h) Peru: Pacupahuain cave (i) Peru: Cascayunga (i1) cave, Pumacocha (i2) lake

(f) China: Huangye (f1), Wanxian (f2) caves

Standardized hydrological change

Insolation (W m

-2

)

Temperature (°C)

LIA

HS5

HS4

HS5

HS4

HS5

O (‰)δ

O (‰)

1000 1500 200020406080 404550

data (Clemens et al., 2010) document that speleothem d

O variations

in some monsoon regions can be explained as a combination of chang-

es in local precipitation and large-scale moisture transport.

This subsection focuses on the response of monsoon systems to orbital

forcing on glacial–interglacial time scales. Proxy data including spe-

leothem d

O from southeastern China (Wang et al., 2008), northern

Borneo (Meckler et al., 2013), eastern Brazil (Cruz et al., 2005) and

the Arabian Peninsula (Bar-Matthews et al., 2003), along with marine-

based records off northwestern Africa (Weldeab et al., 2007a) and from

the Arabian Sea (Schulz et al., 1998) document hydrological chang-

es that are dominated by eccentricity-modulated precessional cycles.

Increasing boreal summer insolation can generate a strong inter-hemi-

spheric surface temperature gradient that leads to large-scale decreas-

es in precipitation in the SH summer monsoon systems and increased

hydrological cycle in the NH tropics (Figure 5.4a, d, g). Qualitatively

suggesting a temperature lead of 800 ± 600 years over the deglacial

rise probably overestimated gas-ice age differences.

5.3.2.3 Monsoon Systems

Since AR4, new high-resolution hydroclimate reconstructions using

speleothems (Sinha et al., 2007; Hu et al., 2008; Wang et al., 2008; Cruz

et al., 2009; Asmerom et al., 2010; Berkelhammer et al., 2010; Stríkis et

al., 2011; Kanner et al., 2012), lake sediments (Shanahan et al., 2009;

Stager et al., 2009; Wolff et al., 2011), marine sediments (Weldeab et

al., 2007b; Mulitza et al., 2008; Tjallingii et al., 2008; Ponton et al.,

2012) and tree-ring chronologies (Buckley et al., 2010; Cook et al.,

2010a) have provided a more comprehensive view on the dynamics

of monsoon systems on a variety of time scales. Water isotope-ena-

bled modelling experiments (LeGrande and Schmidt, 2009; Lewis et

al., 2010; Pausata et al., 2011) and evaluation of marine and terrestrial

402

Chapter 5 Information from Paleoclimate Archives

Box 5.2 | Climate-Ice Sheet Interactions

Ice sheets have played an essential role in the Earth’s climate history (see Sections 5.3, 5.6 and 5.7). They interact with the atmosphere,

the ocean–sea ice system, the lithosphere and the surrounding vegetation (see Box 5.2, Figure 1). They serve as nonlinear ﬁlters and

integrators of climate effects caused by orbital and GHG forcings (Ganopolski and Calov, 2011), while at the same time affecting the

global climate system on a variety of time scales (see Section 5.7).

Ice sheets form when annual snow accumulation exceeds melting. Growing ice sheets expand on previously vegetated areas, thus

leading to an increase of surface albedo, further cooling and an increase in net surface mass balance. As ice sheets grow in height

and area, surface temperatures drop further as a result of the lapse-rate effect, but also snow accumulation decreases because colder

air holds less moisture (inlay in Box 5.2, Figure 1). This so-called elevation-desert effect (Oerlemans, 1980) is an important negative

feedback for ice sheets which limits their growth. Higher elevation ice sheets can be associated with enhanced calving at their margins,

because the ice ﬂow will be accelerated directly by increased surface slopes and indirectly by lubrication at the base of the ice sheet.

Calving, grounding line processes, basal lubrication and other forms of thermo-mechanical coupling may have played important roles

in accelerating glacial terminations following phases of relatively slow ice sheet growth, hence contributing to the temporal saw-tooth

structure of the recent glacial–interglacial cycles (Figure 5.3).

Large glacial ice sheets also deﬂect the path of the extratropical NH westerly winds (Cook and Held, 1988), generating anticyclonic

circulation anomalies (Box 5.2, Figure 1), which tend to warm the western side of the ice sheet and cool the remainder (e.g., Roe and

Lindzen, 2001). Furthermore, the orographic effects of ice sheets lead to reorganizations of the global atmosphere circulation by chang-

ing the major stationary wave patterns (e.g., Abe-Ouchi et al., 2007; Yin et al., 2008) and trade wind systems (Timmermann et al., 2004).

This allows for a fast transmission of ice sheet signals to remote regions.

The enormous weight of ice sheets depresses the underlying bedrocks causing a drop in ice sheet height and a surface warming as

a result of the lapse-rate effect. The lithospheric adjustment has been shown to play an important role in modulating the ice sheet

response to orbital forcing (Birchﬁeld et al., 1981; van den Berg et al., 2008). The presence of terrestrial sedimentary materials (regolith)

on top of the unweathered bedrock affects the friction at the base of an ice sheet, and may further alter the response of continental ice

sheets to external forcings, with impacts on the dominant periodicities of glacial cycles (Clark and Pollard, 1998).

An area of very active research is the interaction between ice sheets, ice shelves and the ocean (see Sections 4.4, 13.4.3 and 13.4.4).

The mass balance of marine ice sheets is strongly determined by ocean temperatures (Joughin and Alley, 2011). Advection of warmer

waters below ice shelves can cause ice shelf instabilities, reduced buttressing, accelerated ice stream ﬂow (De Angelis and Skvarca,

2003) and grounding line retreat in regions with retrograde bedrock slopes (Schoof, 2012), such as West Antarctica. On orbital and

millennial time scales such processes may have played an essential role in driving ice volume changes of the West Antarctic ice sheet

(Pollard and DeConto, 2009) and the Laurentide ice sheet (Alvarez-Solas et al., 2010). Massive freshwater release from retreating ice

sheets, can feed back to the climate system by altering sea level, oceanic deep convection, ocean circulation, heat transport, sea ice and

the global atmospheric circulation (Sections 5.6.3 and 5.7).

Whereas the initial response of ice sheets to external forcings can be quite fast, involving for instance ice shelf processes and outlet

glaciers (10 to 10

years), their long-term adjustment can take much longer (10

to 10

years) (see Section 12.5.5.3). As a result, the

climate–cryosphere system is not even in full equilibrium with the orbital forcing. This also implies that future anthropogenic radiative

perturbations over the next century can determine the evolution of the Greenland (Charbit et al., 2008) and Antarctic ice sheets for

centuries and millennia to come with a potential commitment to signiﬁcant global sea level rise (Section 5.8). (continued on next page)

similar, out-of-phase inter-hemispheric responses to insolation forcing

have also been documented in coupled time-slice and transient GCM

simulations (Braconnot et al., 2008; Kutzbach et al., 2008) (see also

Figure 5.4d, g). The similarity in response in both proxy records and

models provide high conﬁdence that orbital forcing induces inter-hem-

ispheric rainfall variability. Across longitudes, the response of precipi-

tation may, however, be different for the same orbital forcing (Shin et

al., 2006; Marzin and Braconnot, 2009). For example, in the mid-Holo-

cene drier conditions occurred in central North America (Diffenbaugh

et al., 2006) and wetter conditions in northern Africa (Liu et al., 2007b;

Hély et al., 2009; Tierney et al., 2011). There is further evidence for

east–west shifts of precipitation in response to orbital forcing in South

America (Cruz et al., 2009).

403

Information from Paleoclimate Archives Chapter 5

Box 5.2, Figure 1 | Schematic illustration of multiple interactions between ice sheets, solid earth and the climate system which can drive internal variability and

affect the coupled ice sheet–climate response to external forcings on time scales of months to millions of years. The inlay ﬁgure represents a typical height proﬁle of

atmospheric temperature and moisture in the troposphere.

Moisture

Temperature

Height

subglacial lakes

geothermal

heatux

ice shelf

iceberg

icebergs

sea-ice

calving

atmosphere-ice sheet

interaction

ocean-atmosphere

interaction

ocean

ocean-ice sheet

interaction

stationary wave feedback

land-ice sheet

interaction

albedo

moulins

snow

accumulation

dust

ablation

zone

–10

years

1–10

years

re

soot

bedrock adjustment

Box 5.2 (continued)

5.3.3 Last Glacial Maximum and Equilibrium

Climate Sensitivity

The LGM is characterized by a large temperature response (Section

5.3.3.1) to relatively well-deﬁned radiative perturbations (Section

5.2), linked to atmospheric CO

concentration around 200 ppm (Sec-

tion 5.2.2) and large ice sheets covering northern Europe and North

America. This can be used to evaluate climate models (Braconnot et

al. (2012b); see Sections 9.7 and 10.8) and to estimate ECS from the

combined use of proxy information and simulations (Section 5.3.3.2).

5.3.3.1 Last Glacial Maximum Climate

Since AR4, synthesis of proxy LGM temperature estimates was com-

pleted for SST (MARGO Project Members, 2009), and for land SAT

(Bartlein et al., 2011) (Box 5.1, Figure 1). The Multiproxy Approach

for the Reconstruction of the Glacial Ocean Surface (MARGO) SST

synthesis expanded earlier work (CLIMAP Project Members, 1976,

1981; Sarnthein et al., 2003a; Sarnthein et al., 2003b) by using multiple

proxies (Table 5.2). The land SAT synthesis is based on pollen data,

following the Cooperative Holocene Mapping Project (COHMAP Mem-

bers, 1988).

Climate models and proxy data consistently show that mean annual

SST change (relative to pre-industrial) is largest in the mid-latitude

North Atlantic (up to –10ºC), and the Mediterranean (about –6ºC)

(MARGO Project Members, 2009, Box 5.1, Table 5.2). Warming and

seasonally ice-free conditions are reconstructed, however, in the north-

eastern North Atlantic, in the eastern Nordic Seas and north Paciﬁc,

albeit with large uncertainty because of the different interpretation of

proxy data (de Vernal et al., 2006). SAT reconstructions generally shows

year-round cooling, with regional exceptions such as Alaska (Bartlein

et al., 2011). Modelling studies show how atmospheric dynamics inﬂu-

enced by ice sheets affect regional temperature patterns in the North

404

Chapter 5 Information from Paleoclimate Archives

Region Cooling (°C) 90% C.I. Methods Reference and remarks

Sea Surface Temperature (SST)

Global 0.7–2.7 Multi-proxy MARGO Project Members (2009)

Mid-latitude North Atlantic up to 10 Multi-proxy MARGO Project Members (2009)

Southern Ocean 2–6 Multi-proxy MARGO Project Members (2009)

Low-latitude

(30°S to 30°N)

0.3–2.7 Multi-proxy

MARGO Project Members (2009)

1.7°C ± 1°C: 15°S to 15°N

2.9°C ± 1.3°C: Atlantic 15°S to 15°N

1.2°C ± 1.1°C : Paciﬁc 15°S to 15°N (1.2°C ±1°C based on microfossil

assemblages; 2.5°C ±1°C based on Mg/Ca ratios and alkenones)

Low-latitude

(30°S to 30°N)

2.2–3.2 Multi-proxy Ballantyne et al. (2005)

Low-latitude (western and

eastern tropical Paciﬁc)

2–3 Multi-proxy

Lea et al. (2000); de Garidel-Thoron et al. (2007); Leduc et al.

(2007); Pahnke et al. (2007); Stott et al. (2007); Koutavas and

Sachs (2008); Steinke et al. (2008); Linsley et al. (2010)

Surface Air Temperature (SAT)

Eastern Antarctica 7–10 Water stable isotopes from ice core Stenni et al. (2010); Uemura et al. (2012)

Central Greenland 21–25 Borehole paleothermometry Cuffey et al. (1995); Johnsen et al. (1995); Dahl-Jensen et al. (1998)

Global 4.4–7.2

Single-EMIC ensemble with microfossil-

assemblage derived tropical Atlantic SST

Schneider von Deimling et al. (2006)

Global 4.6–8.3

Single-EMIC ensemble with multi-

proxy derived tropical SST

Holden et al. (2010a)

Global 1.7–3.7

Single-EMIC ensemble with

global multi-proxy data

Schmittner et al. (2011)

Global 3.9–4.6 Multi-proxy Shakun et al. (2012); for the interval 17.5–9.5ka

Global 3.4–4.6

Multi-AOGCM ensemble with

global multi-proxy data

Annan and Hargreaves (2013)

Global 3.1–5.9 Multi-AOGCM ensemble PMIP2 and PMIP3/CMIP5

Table 5.2 | Summary of Last Glacial Maximum (LGM) sea surface temperature (SST) and surface air temperature (SAT) reconstructions (anomalies with respect to pre-industrial

climate) using proxy data and model ensemble constrained by proxy data. Cooling ranges indicate 90% conﬁdence intervals (C.I., where available).

Notes:

AOGCM = Atmosphere-Ocean General Circulation Model; CMIP5 = Coupled Model Intercomparison Project Phase 5; EMIC = Earth System Model of Intermediate Complexity; MARGO =

Multiproxy Approach for the Reconstruction of the Glacial Ocean surface; PMIP2 and PMIP3 = Paleoclimate Modelling Intercomparison Project Phase II and III, respectively.

Atlantic region (Lainé et al., 2009; Pausata et al., 2011; Unterman et

al., 2011; Hofer et al., 2013), and in the north Paciﬁc region (Yanase

and Abe-Ouchi, 2010). Larger cooling over land compared to ocean

is a robust feature of observations and multiple atmosphere–ocean

general circulation models (AOGCM) (Izumi et al., 2013). As in AR4,

central Greenland temperature change during the LGM is underesti-

mated by PMIP3/CMIP5 simulations, which show 2°C to 18°C cooling,

compared to 21°C to 25°C cooling reconstructed from ice core data

(Table 5.2). A mismatch between reconstructions and model results

may arise because of missing Earth system feedbacks (dust, vegeta-

tion) (see Section 5.2.2.3) or insufﬁcient integration time to reach an

equilibrium for LGM boundary conditions.

Uncertainties remain on the magnitude of tropical SST cooling during

the LGM. Previous estimates of tropical cooling (2.7°C ± 0.5°C, Bal-

lantyne et al., 2005) are greater than more recent estimates (1.5°C ±

1.2°C, MARGO Project Members, 2009). Such discrepancies may arise

from seasonal productivity (Leduc et al., 2010) and habitat depth (Tel-

ford et al., 2013) biases. In the western and eastern tropical Paciﬁc,

many proxy records show 2°C to 3°C cooling relative to the pre-in-

dustrial period (Lea et al., 2000, 2006; de Garidel-Thoron et al., 2007;

Leduc et al., 2007; Pahnke et al., 2007; Stott et al., 2007; Koutavas and

Sachs, 2008; Steinke et al., 2008; Linsley et al., 2010). AOGCMs tend to

underestimate longitudinal patterns of tropical SST (Otto-Bliesner et

al., 2009) and atmospheric circulation (DiNezio et al., 2011).

Larger sea ice seasonality is reconstructed for the LGM compared to

the pre-industrial period around Antarctica (Gersonde et al., 2005). Cli-

mate models underestimate this feature, as well as the magnitude of

Southern Ocean cooling (Roche et al., 2012) (see Box 5.1, Figure 1). In

Antarctica 7°C to 10°C cooling relative to the pre-industrial period is

reconstructed from ice cores (Stenni et al., 2010; Uemura et al., 2012)

and captured in most PMIP3/CMIP5 simulations (Figure 5.5d).

The combined use of proxy reconstructions, with incomplete spatial

coverage, and model simulations is used to estimate LGM global mean

temperature change (Table 5.2). One recent such study, combining

multi-proxy data with multiple AOGCMs, estimates LGM global cooling

at 4.0°C ± 0.8°C (95% conﬁdence interval) (Annan and Hargreaves,

2013). This result contrasts with the wider range of global cooling

405

Information from Paleoclimate Archives Chapter 5

(1.7°C to 8.3°C) obtained using Earth-system models of intermediate

complexity (EMIC) (Schneider von Deimling et al., 2006; Holden et al.,

2010b; Schmittner et al., 2011). The source of these differences in the

estimate of LGM cooling may be the result of (i) the proxy data used

to constrain the simulations, such as data sets associated with mild

cooling in the tropics and the North Atlantic; (ii) model resolution and

structure, which affects their ability to resolve the land–sea contrast

(Annan and Hargreaves, 2013) and polar ampliﬁcation (Fyke and Eby,

2012); (iii) the experimental design of the simulations, where the lack

of dust and vegetation feedbacks (Section 5.2.2.3) and insufﬁcient

integration time. Based on the results and the caveats in the studies

assessed here (Table 5.2), it is very likely that global mean surface tem-

perature during the LGM was cooler than pre-industrial by 3°C to 8°C.

Some recent AOGCM simulations produce a stronger AMOC under

LGM conditions, leading to mild cooling over the North Atlantic and

GIS (Otto-Bliesner et al., 2007; Weber et al., 2007). This ﬁnding con-

trasts with proxy-based information (Lynch-Stieglitz et al., 2007; Hesse

et al., 2011). Changes in deep-ocean temperature and salinity during

the LGM have been constrained by pore-water chemistry in deep-sea

sediments. For example, pore-water data indicate that deep water in

the Atlantic Ocean cooled by between –1.7°C ± 0.9°C and –4.5°C ±

0.2°C, and became saltier by up to 2.4 ± 0.2 psu in the South Atlan-

tic and at least 0.95 ± 0.07 psu in the North Atlantic (cf. Adkins et

al., 2002). The magnitude of deep-water cooling is supported by other

marine proxy data (e.g., Dwyer et al., 2000; Martin et al., 2002; Elder-

ﬁeld et al., 2010), while the increase in salinity is consistent with inde-

pendent estimates based on d

O (Duplessy et al., 2002; Waelbroeck

et al., 2002). Average salinity increased due to storage of freshwater

in ice sheets. The much larger than average salinity increase in deep

Southern Ocean compared to the North Atlantic is probably due to

increased salt rejection through sea ice freezing processes around Ant-

arctica (Miller et al., 2012b) (see also Section 9.4.2.3.2 ).

As a result of prevailing LGM modelling uncertainties (Chavaillaz et al.,

2013; Rojas, 2013) and ambiguities in proxy interpretations (Kohfeld et

al., 2013), it cannot be determined robustly whether LGM SH wester-

lies changed in amplitude and position relative to today.

5.3.3.2 Last Glacial Maximum Constraints on Equilibrium

Climate Sensitivity

Temperature change recorded in proxies results from various feed-

back processes, and external forcings vary before equilibrium of the

whole Earth system is reached. Nevertheless, the equilibrium climate

sensitivity (ECS) can be estimated from past temperatures by explic-

itly counting the slow components of the processes (e.g., ice sheets)

as forcings, rather than as feedbacks (PALAEOSENS Project Members,

2012). This is achieved in three fundamentally different ways (Edwards

et al., 2007); see also Sections 9.7.3.2 and 10.8.2.4.

In the ﬁrst approach, ECS is estimated by scaling the reconstructed

global mean temperature change in the past with the RF difference of

the past and 2 × CO

(Hansen et al., 2008; Köhler et al., 2010) (Table

5.3). The results are subject to uncertainties in the estimate of global

mean surface temperature based on proxy records of incomplete spa-

tial coverage (see Section 5.3.3.1). Additional uncertainty is introduced

when the sensitivity to LGM forcing is scaled to the sensitivity to 2 ×

forcing, as some but not all (Brady et al., 2013) models show that

these sensitivities differ due to the difference in cloud feedbacks (Cruci-

ﬁx, 2006; Hargreaves et al., 2007; Yoshimori et al., 2011) (Figure 5.5a, b).

In the second approach, an ensemble of LGM simulations is carried

out using a single climate model in which each ensemble member

differs in model parameters and the ensemble covers a range of ECS

(Annan et al., 2005; Schneider von Deimling et al., 2006; Holden et al.,

2010a; Schmittner et al., 2011) (Table 5.3). Model parameters are then

constrained by comparison with LGM temperature proxy information,

generating a probability distribution of ECS. Although EMICSs are often

used in order to attain sufﬁciently large ensemble size, the uncertainty

arising from asymmetric cloud feedbacks cannot be addressed because

they are parameterized in the EMICs.

In the third approach, multiple GCM simulations are compared to proxy

data, and performance of the models and indirectly their ECSs are

assessed (Otto-Bliesner et al., 2009; Braconnot et al., 2012b). Because

the cross-model correlation between simulated LGM global cooling

Method (number follows the

“approach” in the text)

ECS Estimate (°C) Reference and Model Name

1. Proxy data

1.0–3.6 MARGO Project Members (2009)

1.4–5.2 Köhler et al. (2010)

2. Single-model ensemble constrained by proxy data

<6 Annan et al. (2005), MIROC3.2 AOGCM

1.2–4.3 Schneider von Deimling et al. (2006), CLIMBER-2

2.0–5.0 Holden et al. (2010a), GENIE-1

1.4–2.8 Schmittner et al. (2011), UVic

~3.6 Fyke and Eby (2012), UVic

3. Multi-GCM ensemble constrained by proxy data

1.2–4.2 Hargreaves et al. (2012), updated with addition of PMIP3/CMIP5 AOGCMs

1.6–4.5 Hargreaves et al. (2012), updated with addition of PMIP3/CMIP5 AOGCMs

Table 5.3 | Summary of equilibrium climate sensitivity (ECS) estimates based on Last Glacial Maximum (LGM) climate. Uncertainty ranges are 5 to 95% conﬁdence intervals,

with the exception of Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Project Members (2009), where the published interval is reported here.

Notes:

Temperature constraints in the tropics were lowered by 0.4°C according to Annan and Hargreaves (2013).

AOGCM = Atmosphere-Ocean General Circulation Model; CMIP5 = Coupled Model Intercomparison Project Phase 5; CLIMBER-2 = CLIMate and BiosphERe model-2; GENIE-1 = Grid ENabled

Integrated Earth system model, version 1; MIROC3.2 = Model for Interdisciplinary Research on Climate 3.2; UVic = University of Victoria Earth system model; PMIP3 = Paleoclimate Modelling

Intercomparison Project Phase III.

406

Chapter 5 Information from Paleoclimate Archives

(a)

(b)

Equilibrium climate sensitivity (°C)

Figure 5.5 | (a) Relation between equilibrium climate sensitivity (ECS) estimated from Last Glacial Maximum (LGM) simulations and that estimated from equilibrium 2 × CO

or abrupt 4 × CO

experiments. All experiments are referenced to pre-industrial simulations. Flexible Global Ocean Atmosphere Land System model version 1 (FGOALS1), Institut

Pierre Simon Laplace version 4 (IPSL4), Max Planck Institute version 5 (MPI5), and ECBILT of Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) models stand

for FGOALS-1.0g, IPSL-CM4-V1-MR, European Centre Hamburg Model 5 – and Max Planck Institute Ocean Model – Lund-Potsdam-Jena Dynamic Global Model (LPJ), and

Coupled Atmosphere Ocean Model from de Bilt (ECBilt) with Coupled Large-scale Ice-Ocean model (CLIO), respectively. Flexible Global Ocean Atmosphere Land System model-2

(FGOALS2), Institut Pierre Simon Laplace 5 (IPSL5), Model for Interdisciplinary Research on Climate (MIROC3), Max Planck Institute für Meteorologie Earth system model (MPIE),

Meteorological Research Institute of Japan Meteorological Agency version 3 (MRI3) and Centre National de Recherches Météorologiques version 5 (CNRM5) of Paleoclimate

Modelling Intercomparison Project Phase III (PMIP3) models stand for FGOALS-g2, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P, MRI-CGCM3, and CNRM-CM5, respectively. ECS

based on LGM simulations (abscissa) was derived by multiplying the LGM global mean temperature anomaly (DT

LGM

) and the ratio of radiative forcing between 2 × CO

and LGM

(DF

ratio

). DF

ratio

for three PMIP2 models (Community Climate System Model-3 (CCSM3), Met Ofﬁce Hadley Centre climate prediction models-3 (HadCM3) and IPSL4) were taken from

Cruciﬁx (2006), and it was taken from Yoshimori et al. (2009) for MIROC3. Its range, –0.80 to –0.56, with a mean of –0.69 was used for other PMIP2 and all PMIP3 models. ECS

of PMIP2 models (ordinate) was taken from Hargreaves et al. (2012). ECS of PMIP3 models was taken from Andrews et al. (2012) and Brady et al. (2013), or computed using the

method of Andrews et al. (2012) for FGOALS2. Also plotted is a one-to-one line. (b) Strength of individual feedbacks for the PMIP3/CMIP5 abrupt 4 × CO

(131 to 150 years) and

LGM (stable states) experiments following the method in Yoshimori et al. (2011). WV+LR, A, C

and C

denote water vapour plus lapse rate, surface albedo, shortwave cloud, and

longwave cloud feedbacks, respectively. ‘All’ denotes the sum of all feedbacks except for the Planck response. Feedback parameter here is deﬁned as the change in net radiation at

the top of the atmosphere due to the change in individual ﬁelds, such as water vapour, with respect to the pre-industrial simulations. It was normalized by the global mean surface

air temperature change. Positive (negative) value indicates that the feedback ampliﬁes (damp) the initial temperature response. Only models with all necessary data available for the

analysis are displayed. (c) Relation between LGM tropical (20°S to 30°N) surface air temperature anomaly from pre-industrial simulations and ECS across models. A dark blue bar

represents a 90% conﬁdence interval for the estimate of reconstructed temperature anomaly of Annan and Hargreaves (2013). A light blue bar represents the same with additional

0.4°C anomaly increase according to the result of the sensitivity experiment conducted in Annan and Hargreaves (2013), in which additional 1°C lowering of tropical SST proxy data

was assumed. (d) Same as in (c) but for the average of East Antarctica ice core sites of Dome F, Vostok, European Project for Ice Coring in Antarctica Dome C and Droning Maud

Land ice cores. A dark blue bar represents a range of reconstructed temperature anomaly based on stable isotopes at these core sites (Stenni et al., 2010; Uemura et al., 2012). A

value below zero is not displayed (the result of CNRM5). Note that the Antarctic ice sheet used for PMIP3 simulations, based on several different methods (Tarasov and Peltier, 2007;

Argus and Peltier, 2010; Lambeck et al., 2010) differs in elevation from that used in PMIP2 (d).

407

Information from Paleoclimate Archives Chapter 5

and ECS is poor (Figure 5.5a), ECS cannot be constrained by the LGM

global mean cooling. Models that show weaker sensitivity to LGM forc-

ing than 4 × CO

forcing in Figure 5.5a tend to show weaker shortwave

cloud feedback and hence weaker total feedback under LGM forcing

than 4 × CO

forcing (Figure 5.5b), consistent with previous studies

(Cruciﬁx, 2006). The relation found between simulated LGM tropical

cooling and ECS across multi-GCM (Hargreaves et al., 2012; Figure

5.5c) has been used to constrain the ECS with the reconstructed LGM

tropical cooling (Table 5.3). Again, the main caveats of this approach

are the uncertainties in proxy reconstructions and missing dust and

vegetation effects which may lead to underestimated LGM cooling in

the simulations.

Based on these different approaches, estimates of ECS yield low prob-

ability for values outside the range 1°C to 5°C (Table 5.3). Even though

there is some uncertainty in these studies owing to problems in both

the paleoclimate data and paleoclimate modelling as discussed in this

section, it is very likely that ECS is greater than 1°C, and very unlikely

that ECS exceeds 6°C.

5.3.4 Past Interglacials

Past interglacials are characterized by different combinations of orbit-

al forcing (Section 5.2.1.1), atmospheric composition (Section 5.2.2.1)

and climate responses (Tzedakis et al., 2009; Lang and Wolff, 2011).

Documenting natural interglacial climate variability in the past pro-

vides a deeper understanding of the physical climate responses to

orbital forcing. This section reports on interglacials of the past 800 kyr,

with emphasis on the Last Interglacial (LIG, Table 5.1) which has more

data and modelling studies for assessing regional and global tempera-

ture changes than earlier interglacials. The LIG sea level responses are

assessed in Section 5.6.2. Section 5.5 is devoted to the current inter-

glacial, the Holocene.

The phasing and strengths of the precessional parameter and obliquity

varied over past interglacials (Figure 5.3b, c), inﬂuencing their timing,

duration, and intensity (Tzedakis et al., 2012b; Yin and Berger, 2012)

(Figure 5.3e, f, h). Since 800 ka, atmospheric CO

concentrations during

interglacials were systematically higher than during glacial periods.

Prior to ~430 ka, ice cores from Antarctica record lower interglacial

concentrations than for the subsequent interglacial periods (Sec-

tion 5.2.2.1; Figure 5.3d). While LIG WMGHG concentrations were

similar to the pre-industrial Holocene values, orbital conditions were

very different with larger latitudinal and seasonal insolation variations.

Large eccentricity and the phasing of precession and obliquity (Figure

5.3a–c) during the LIG resulted in July 65°N insolation peaking at ~126

ka and staying above the Holocene maximum values from ~129 to 123

ka. The high obliquity (Figure 5.3b) contributed to small, but positive

annual insolation anomalies at high latitudes in both hemispheres and

negative anomalies at low latitudes.

New data and syntheses from marine and terrestrial archives, with

updated age models, have provided an expanded view of tempera-

ture patterns during interglacials since 800 ka (Masson-Delmotte et al.,

2010a; Lang and Wolff, 2011; Rohling et al., 2012). There is currently no

consensus on whether interglacials changed intensity after ~430 ka.

EPICA Dome C Antarctic ice cores record warmer temperatures after

this transition (Jouzel et al., 2007) and marine records of deep-wa-

ter temperatures are characterized by generally higher values during

later interglacials than earlier interglacials (Lang and Wolff, 2011). In

contrast, similar interglacial magnitudes are observed across the ~430

ka boundary in some terrestrial archives from Eurasia (Prokopenko et

al., 2002; Tzedakis et al., 2006; Candy et al., 2010). Simulations with

an EMIC relate global and southern high latitude mean annual surface

temperature variations to changes in CO

variations, while orbital forc-

ing and associated feedbacks of vegetation and sea ice have a major

impact on the simulated northern high-latitude mean annual surface

temperature (Yin and Berger, 2012). The highest and lowest interglacial

temperatures occur in models when WMGHG concentrations and local

insolation reinforce each other (Yin and Berger, 2010, 2012; Herold et

al., 2012).

At the time of the AR4, a compilation of Arctic records and two AOGCM

simulations allowed an assessment of LIG summer temperature chang-

es. New quantitative data syntheses (Figure 5.6a) now allow estima-

tion of maximum annual surface temperatures around the globe for

the LIG (Turney and Jones, 2010; McKay et al., 2011). A caveat is that

these data syntheses assume that the warmest phases were globally

synchronous (see Figure 5.6 legend for details). However, there is high

conﬁdence that warming in the Southern Ocean (Cortese et al., 2007;

Schneider Mor et al., 2012) and over Antarctica (Masson-Delmotte et

al., 2010b) occurred prior to peak warmth in the North Atlantic, Nordic

Seas, and Greenland (Bauch et al., 2011; Govin et al., 2012; North

Greenland Eemian Ice Drilling (NEEM) community members, 2013).

Overall, higher annual temperatures than pre-industrial are recon-

structed for high latitudes of both hemispheres. At ~128 ka, East Ant-

arctic ice cores record early peak temperatures ~5°C above the present

(Jouzel et al., 2007; Sime et al., 2009; Stenni et al., 2010). Higher tem-

peratures are derived for northern Eurasia and Alaska, with sites near

the Arctic coast in Northeast Siberia indicating warming of more than

10°C as compared to late Holocene (Velichko et al., 2008). Greenland

warming of 8°C ± 4°C at 126 ka is estimated from the new Greenland

NEEM ice core, after accounting for ice sheet elevation changes (NEEM

community members, 2013). Seasonally open waters off northern

Greenland and in the central Arctic are recorded during the LIG (Nør-

gaard-Pedersen et al., 2007; Adler et al., 2009). Changes in Arctic sea

ice cover (Sime et al., 2013) may have affected the Greenland water

stable isotope – temperature relationship, adding some uncertainty to

LIG Greenland temperature reconstructions. Marine proxies from the

Atlantic indicate warmer than late Holocene year-round SSTs north of

30°N, whereas SST changes were more variable south of this latitude

(McKay et al., 2011).

Transient LIG simulations with EMICs and low-resolution AOGCMs dis-

play peak NH summer warmth between 128 ka and 125 ka in response

to orbital and WMGHG forcings. This warming is delayed when NH ice

sheets are allowed to evolve (Bakker et al., 2013). Time-slice climate

simulations run by 13 modelling groups with a hierarchy of climate

models forced with orbital and WMGHG changes for 128 to 125 ka

(Figure 5.6b) simulate the reconstructed pattern of NH annual warm-

ing (Figure 5.6a). Positive feedbacks with the cryosphere (sea ice and

snow cover) provide the memory that allows simulated NH high-lati-

tude warming, annually as well as seasonally, in response to the sea-

sonal orbital forcing (Schurgers et al., 2007; Yin and Berger, 2012). The

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Chapter 5 Information from Paleoclimate Archives

magnitude of observed NH annual warming though is only reached

in summer in the simulations (Lunt et al., 2013) (Figure 5.6c, d). The

reasons for this discrepancy are not yet fully determined. Error bars on

temperature reconstructions vary signiﬁcantly between methods and

regions, due to the effects of seasonality and resolution (Kienast et al.,

2011; McKay et al., 2011; Tarasov et al., 2011). Differences may also be

related to model representations of cloud and sea ice processes (Born

et al., 2010; Fischer and Jungclaus, 2010; Kim et al., 2010; Otto-Bliesner

et al., 2013), and that most LIG simulations set the vegetation and ice

sheets to their pre-industrial states (Schurgers et al., 2007; Holden et

al., 2010b; Bradley et al., 2013). Simulations accounting for the bipolar

seesaw response to persistent iceberg melting at high northern lati-

tudes (Govin et al., 2012) and disintegration of the WAIS (Overpeck et

al., 2006) (see Section 5.6.2.3) are better able to reproduce the early

LIG Antarctic warming (Holden et al., 2010b).

From data synthesis, the LIG global mean annual surface temperature

is estimated to be ~1°C to 2°C warmer than pre-industrial (medium

conﬁdence) (Turney and Jones, 2010; Otto-Bliesner et al., 2013),

albeit proxy reconstructions may overestimate the global temperature

change. High latitude surface temperature, averaged over several thou-

sand years, was at least 2°C warmer than present (high conﬁdence). In

(a) Data

(b) Models [16]

(c)

SST anom (°C)

(d) SAT anom (°C)

Latitude (°)Latitude (°)

( )

Figure 5.6 | Changes in surface temperature for the Last Interglacial (LIG) as reconstructed from data and simulated by an ensemble of climate model experiments in response to

orbital and well-mixed greenhouse gas (WMGHG) forcings. (a) Proxy data syntheses of annual surface temperature anomalies as published by Turney and Jones (2010) and McKay et

al. (2011). McKay et al., (2011) calculated an annual anomaly for each record as the average sea surface temperature (SST) of the 5-kyr period centred on the warmest temperature

between 135 ka and 118 ka and then subtracting the average SST of the late Holocene (last 5 kyr). Turney and Jones (2010) calculated the annual temperature anomalies relative

to 1961–1990 by averaging the LIG temperature estimates across the isotopic plateau in the marine and ice records and the period of maximum warmth in the terrestrial records

(assuming globally synchronous terrestrial warmth). (b) Multi-model average of annual surface air temperature anomalies simulated for the LIG computed with respect to pre-

industrial. The results for the LIG are obtained from 16 simulations for 128 to 125 ka conducted by 13 modelling groups (Lunt et al., 2013). (c) Seasonal SST anomalies. Multi-model

zonal averages are shown as solid line with shaded bands indicating 2 standard deviations. Plotted values are the respective seasonal multi-mean global average. Symbols are

individual proxy records of seasonal SST anomalies from McKay et al. (2011). (d) Seasonal terrestrial surface temperature anomalies (SAT). As in (c) but with symbols representing

terrestrial proxy records as compiled from published literature (Table 5.A.5). Observed seasonal terrestrial anomalies larger than 10°C or less than –6°C are not shown. In (c) and

(d) JJA denotes June – July – August and DJF December – January – February, respectively.

409

Information from Paleoclimate Archives Chapter 5

response to orbital forcing and WMGHG concentration changes, time

slice simulations for 128 to 125 ka exhibit global mean annual surface

temperature changes of 0.0°C ± 0.5°C as compared to pre-industrial.

Data and models suggest a land–ocean contrast in the responses to

the LIG forcing (Figure 5.6c, d). Peak global annual SST warming is

estimated from data to be 0.7°C ± 0.6°C (medium conﬁdence) (McKay

et al., 2011). Models give more conﬁdence to the lower bound. The

ensemble of climate model simulations gives a large range of global

annual land temperature change relative to pre-industrial, –0.4°C to

1.7°C, when sampled at the data locations and cooler than when

averaged for all model land areas, pointing to difﬁculties in estimating

global mean annual surface temperature with current spatial data cov-

erage (Otto-Bliesner et al., 2013).

5.3.5 Temperature Variations During the Last 2000 Years

The last two millennia allow comparison of instrumental records with

multi-decadal-to-centennial variability arising from external forcings

and internal climate variability. The assessment beneﬁts from high-res-

olution proxy records and reconstructions of natural and anthropogen-

ic forcings back to at least 850 (Section 5.2), used as boundary condi-

tions for transient GCM simulations. Since AR4, expanded proxy data

networks and better understanding of reconstruction methods have

supported new reconstructions of surface temperature changes during

the last 2000 years (Section 5.3.5.1) and their associated uncertain-

ties (Section 5.3.5.2), and supported more extensive comparisons with

GCM simulations (Section 5.3.5.3).

5.3.5.1 Recent Warming in the Context of New Reconstructions

New paleoclimate reconstruction efforts since AR4 (Figure 5.7; Table

5.4; Appendix 5.A.1) have provided further insights into the charac-

teristics of the Medieval Climate Anomaly (MCA; Table 5.1) and the

Little Ice Age (LIA; Table 5.1). The timing and spatial structure of the

MCA and LIA are complex (see Box 6.4 in AR4 and Diaz et al., 2011;

and Section 5.5), with different reconstructions exhibiting warm and

cold conditions at different times for different regions and seasons. The

median of the NH temperature reconstructions (Figure 5.7) indicates

mostly warm conditions from about 950 to about 1250 and colder con-

ditions from about 1450 to about 1850; these time intervals are chosen

here to represent the MCA and the LIA, respectively.

Based on multiple lines of evidence (using different statistical meth-

ods or different compilations of proxy records; see Appendix 5.A.1

for a description of reconstructions and selection criteria), published

reconstructions and their uncertainty estimates indicate, with high con-

ﬁdence, that the mean NH temperature of the last 30 or 50 years very

likely exceeded any previous 30- or 50-year mean during the past 800

1 400 800 1200 1600 2000

-1.0

-0.5

0.0

0.5

1.0

NH temperature anomaly (

C from 1881-1980)

(a) Northern Hemisphere

PS04bore Ma08cpsl Ma08eivl LO12glac Sh13pcar LM08ave

Fr07treecps He07tls Da06treecps Mo05wave Ju07cvm Ma09regm Ma08min7eivf

CL12loc Lj10cps HadCRUT4 NH CRUTEM4 NH CRUTEM4 30-90N

1 400 800 1200 1600 2000

Year

-1.0

-0.5

0.0

0.5

1.0

SH (

C from 1881-1980)

(b) Southern Hemisphere

PS04bore Ma08cpsl Ma08eivl LO12glac

Ma08eivf HadCRUT4 SH CRUTEM4 SH

1 400 800 1200 1600 2000

Year

-1.0

-0.5

0.0

0.5

1.0

Global (

C from 1881-1980)

PS04bore Ma08eivl LO12glac

Ma08eivf HadCRUT4 GL CRUTEM4 GL

Figure 5.7 | Reconstructed (a) Northern Hemisphere and (b) Southern Hemisphere, and (c) global annual temperatures during the last 2000 years. Individual reconstructions

(see Appendix 5.A.1 for further information about each one) are shown as indicated in the legends, grouped by colour according to their spatial representation (red: land-only all

latitudes; orange: land-only extratropical latitudes; light blue: land and sea extra-tropical latitudes; dark blue: land and sea all latitudes) and instrumental temperatures shown in

black (Hadley Centre/ Climatic Research Unit (CRU) gridded surface temperature-4 data set (HadCRUT4) land and sea, and CRU Gridded Dataset of Global Historical Near-Surface

Air TEMperature Anomalies Over Land version 4 (CRUTEM4) land-only; Morice et al., 2012). All series represent anomalies (°C) from the 1881–1980 mean (horizontal dashed line)

and have been smoothed with a ﬁlter that reduces variations on time scales less than about 50 years.

410

Chapter 5 Information from Paleoclimate Archives

Table 5.4 | Comparison of recent hemispheric and global temperature estimates with earlier reconstructed values, using published uncertainty ranges to assess likelihood of

unusual warmth. Each reconstructed N-year mean temperature within the indicated period is compared with both the warmest N-year mean reconstructed after 1900 and with the

most recent N-year mean instrumental temperature, for N = 30 and N = 50 years. Blue symbols indicate the periods and reconstructions where the reconstructed temperatures are

very likely cooler than the post-1900 reconstruction (¢), or otherwise very likely () or likely (T) cooler than the most recent instrumental temperatures; £ indicates that some

reconstructed temperatures were as likely warmer or colder than recent temperatures.

Notes:

Symbols indicate the likelihood (based on the published multi-decadal uncertainty ranges) that each N-year mean of the reconstructed temperature during the indicated period was colder than

the warmest N-year mean after 1900. A reconstructed mean temperature X is considered to be likely (very likely) colder than a modern temperature Y if X+aE < Y, where E is the reconstruction

standard error and a = 0.42 (1.29) corresponding to a 66% (90%) one-tailed conﬁdence interval assuming the reconstruction error is normally distributed. Symbols indicate that the reconstructed

temperatures were either:

T likely colder than the 1983–2012 or 1963–2012 mean instrumental temperature;

 very likely colder than the 1983–2012 or 1963–2012 mean instrumental temperature;

¢ very likely colder than the 1983–2012 or 1963–2012 mean instrumental temperature and additionally very likely colder than the warmest 30- or 50-year mean of the post-1900 reconstruction

(which is typically not as warm as the end of the instrumental record);

£ indicates that at least one N-year reconstructed mean is about as likely colder or warmer than the 1983–2012 or 1963–2012 mean instrumental temperature.

No symbol is given where the reconstruction does not fully cover the indicated period.

Identiﬁcation and further information for each study is given in Table 5.A.6 of Appendix 5.A.1:

1 = Mo05wave; 2 = Ma08eivf; 3 = Ma09regm; 4 = Ju07cvm; 5 = LM08ave; 6 = Ma08cpsl; 7 = Ma08eivl; 8 = Sh13pcar; 9 = LO12gla; 10 = Lj10cps; 11 = CL12loc; 12 = He07tls; 13 = Da06treecps;

14 = Fr07treecps; 15 = PS04bore.

Region NH SH Global

Domain Land & Sea Land Extratropics Land Land

Study 1 2 3 4 5 6 7 8 9 10 11 12 13 14 7 6 9 7 9 15

50-year means

1600–1899

¢   ¢  ¢  ¢ ¢ ¢ ¢ ¢ ¢ ¢  ¢ ¢  ¢ ¢

1400–1899

¢   ¢  ¢  ¢ ¢ ¢ ¢ ¢ ¢  ¢ 

1200–1899

¢   ¢  ¢   ¢ ¢  ¢ ¢   

1000–1899

    T ¢    ¢  ¢    

800–1899

 T T £ ¢   £  ¢ T  

600–1899

 T T £ ¢   £  T  

400–1899

 T £ £    £ £

200–1899

 £   £ £

1–1899

  £

30-year means

1600–1899

¢   ¢  ¢  ¢ ¢ ¢ ¢ ¢ ¢  ¢ ¢  ¢

1400–1899

¢   ¢  ¢  ¢ ¢ ¢ ¢ ¢  ¢ 

1200–1899

¢   ¢  ¢  ¢ ¢ ¢ ¢ ¢   

1000–1899

     ¢    ¢ ¢    

800–1899

   T ¢   ¢ ¢   

600–1899

   T ¢   ¢   

400–1899

  T    

200–1899

 T   

1–1899

 

years (Table 5.4). The timing of warm and cold periods is mostly consist-

ent across reconstructions (in some cases this is because they use simi-

lar proxy compilations) but the magnitude of the changes is clearly sen-

sitive to the statistical method and to the target domain (land or land

and sea; the full hemisphere or only the extra-tropics; Figure 5.7a). Even

accounting for these uncertainties, almost all reconstructions agree that

each 30-year (50-year) period from 1200 to 1899 was very likely colder

in the NH than the 1983–2012 (1963–2012) instrumental temperature.

NH reconstructions covering part or all of the ﬁrst millennium suggest

that some earlier 50-year periods might have been as warm as the

1963–2012 mean instrumental temperature, but the higher temper-

ature of the last 30 years appear to be at least likely the warmest

30-year period in all reconstructions (Table 5.4). However, the conﬁ-

dence in this ﬁnding is lower prior to 1200, because the evidence is

less reliable and there are fewer independent lines of evidence. There

are fewer proxy records, thus yielding less independence among the

411

Information from Paleoclimate Archives Chapter 5

reconstructions while making them more susceptible to errors in indi-

vidual proxy records. The published uncertainty ranges do not include

all sources of error (Section 5.3.5.2), and some proxy records and

uncertainty estimates do not fully represent variations on time scales

as short as the 30 years considered in Table 5.4. Considering these

caveats, there is medium conﬁdence that the last 30 years were likely

the warmest 30-year period of the last 1400 years.

Increasing numbers of proxy records and regional reconstructions are

being developed for the SH (see Section 5.5), but few reconstructions

of SH or global mean temperatures have been published (Figure 5.7b,

c). The SH and global reconstructions with published uncertainty esti-

mates indicate that each 30- or 50-year interval during the last four

centuries was very likely colder than the warmest 30- or 50-year inter-

val after 1900 (Table 5.4). However, there is only limited proxy evidence

and therefore low conﬁdence that the recent warming has exceeded

the range of reconstructed temperatures for the SH and global scales.

5.3.5.2 Reconstruction Methods, Limitations and Uncertainties

Reconstructing NH, SH or global-mean temperature variations over

the last 2000 years remains a challenge due to limitations of spatial

sampling, uncertainties in individual proxy records and challenges

associated with the statistical methods used to calibrate and integrate

multi-proxy information (Hughes and Ammann, 2009; Jones et al.,

2009; Frank et al., 2010a). Since AR4, new assessments of the statis-

tical methods used to reconstruct either global/hemispheric temper-

ature averages or spatial ﬁelds of past temperature anomalies have

been published. The former include approaches for simple compositing

and scaling of local or regional proxy records into global and hemi-

spheric averages using uniform or proxy-dependent weighting (Hegerl

et al., 2007; Juckes et al., 2007; Mann et al., 2008; Christiansen and

Ljungqvist, 2012). The latter correspond to improvements in climate

ﬁeld reconstruction methods (Mann et al., 2009; Smerdon et al., 2011)

that apply temporal and spatial relationships between instrumen-

tal and proxy records to the pre-instrumental period. New develop-

ments for both reconstruction approaches include implementations of

Bayesian inference (Li et al., 2010a; Tingley and Huybers, 2010, 2012;

McShane and Wyner, 2011; Werner et al., 2013). In particular, Bayesian

hierarchical models enable a more explicit representation of the under-

lying processes that relate proxy (and instrumental) records to climate,

allowing a more systematic treatment of the multiple uncertainties

that affect the climate reconstruction process. This is done by speci-

fying simple parametric forms for the proxy-temperature relationships

that are then used to estimate a probability distribution of the recon-

structed temperature evolution that is compatible with the available

data (Tingley et al., 2012).

An improved understanding of potential uncertainties and biases asso-

ciated with reconstruction methods has been achieved, particularly by

using millennial GCM simulations as a surrogate reality in which pseu-

do-proxy records are created and reconstruction methods are replicated

and tested (Smerdon, 2012). A key ﬁnding is that the methods used for

many published reconstructions can underestimate the amplitude of

the low-frequency variability (Lee et al., 2008; Christiansen et al., 2009;

Smerdon et al., 2010). The magnitude of this amplitude attenuation in

real-world reconstructions is uncertain, but for affected methods the

problem will be larger: (i) for cases with weaker correlation between

instrumental temperatures and proxies (Lee et al., 2008; Christiansen

et al., 2009; Smerdon et al., 2011); (ii) if errors in the proxy data are

not incorporated correctly (Hegerl et al., 2007; Ammann et al., 2010);

or (iii) if the data are detrended in the calibration phase (Lee et al.,

2008; Christiansen et al., 2009). The 20th-century trends in proxies may

contain relevant temperature information (Ammann and Wahl, 2007)

but calibration with detrended or undetrended data has been an issue

of debate (von Storch et al., 2006; Wahl et al., 2006; Mann et al., 2007)

because trends in proxy records can be induced by other (non-temper-

ature) climate and non-climatic inﬂuences (Jones et al., 2009; Gagen

et al., 2011). Recent developments mitigate the loss of low-frequen-

cy variance in global and hemispheric reconstructions by increasing

the correlation between proxies and temperature through temporal

smoothing (Lee et al., 2008) or by correctly attributing part or all of the

temperature-proxy differences to imperfect proxy data (Hegerl et al.,

2007; Juckes et al., 2007; Mann et al., 2008). Pseudoproxy experiments

have shown that the latter approach used with a site-by-site calibra-

tion (Christiansen, 2011; Christiansen and Ljungqvist, 2012) can also

avoid attenuation of low-frequency variability, though it is debated

whether it might instead inﬂate the variability and thus constitute an

upper bound for low-frequency variability (Moberg, 2013). Even those

ﬁeld reconstruction methods that do not attenuate the low-frequency

variability of global or hemispheric means may still suffer from attenu-

ation and other errors at regional scales (Smerdon et al., 2011; Annan

and Hargreaves, 2012; Smerdon, 2012; Werner et al., 2013).

The fundamental limitations for deriving past temperature variabili-

ty at global/hemispheric scales are the relatively short instrumental

period and the number, temporal and geographical distribution, reli-

ability and climate signal of proxy records (Jones et al., 2009). The

database of high-resolution proxies has been expanded since AR4

(Mann et al., 2008; Wahl et al., 2010; Neukom and Gergis, 2011; PAGES

2k Consortium, 2013), but data are still sparse in the tropics, SH and

over the oceans (see new developments in Section 5.5). Integration of

low-resolution records (e.g., marine or some lake sediment cores and

some speleothem records) with high-resolution tree-ring, ice core and

coral records in global/hemispheric reconstructions is still challenging.

Dating uncertainty, limited replication and the possibility of tempo-

ral lags in low-resolution records (Jones et al., 2009) make regres-

sion-based calibration particularly difﬁcult (Christiansen et al., 2009)

and can be potentially best addressed in the future with Bayesian hier-

archical models (Tingley et al., 2012). The short instrumental period

and the paucity of proxy data in speciﬁc regions may preclude obtain-

ing accurate estimates of the covariance of temperature and proxy

records (Juckes et al., 2007), impacting the selection and weighting

of proxy records in global/hemispheric reconstructions (Bürger, 2007;

Osborn and Briffa, 2007; Emile-Geay et al., 2013b) and resulting in

regional errors in climate ﬁeld reconstructions (Smerdon et al., 2011).

Two further sources of uncertainty have been only partially considered

in the published literature. First, some studies have used multiple statis-

tical models (Mann et al., 2008) or generated ensembles of reconstruc-

tions by sampling parameter space (Frank et al., 2010b), but this type of

structural and parameter uncertainty needs further examination (Chris-

tiansen et al., 2009; Smerdon et al., 2011). Second, proxy-temperature

relationships may change over time due to the effect of other climate

412

Chapter 5 Information from Paleoclimate Archives

and non-climate inﬂuences on a proxy, a prominent example being the

divergence between some tree-ring width and density chronologies

and instrumental temperature trends during the last decades of the

20th century (Briffa et al., 1998). In cases that do show divergence, a

number of factors may be responsible, such as direct temperature or

drought stress on trees, delayed snowmelt, changes in seasonality and

reductions in solar radiation (Lloyd and Bunn, 2007; D’Arrigo et al.,

2008; Porter and Pisaric, 2011). However, this phenomenon does not

affect all tree-ring records (Wilson et al., 2007; Esper and Frank, 2009)

and in some cases where divergence is apparent it may arise from the

use of inappropriate statistical standardization of the data (Melvin and

Briffa, 2008; Briffa and Melvin, 2011) and not from a genuine change

in the proxy–temperature relationship. For the European Alps and Sibe-

ria, Büntgen et al., (2008) and Esper et al. (2010) demonstrate that

divergence can be avoided by careful selection of sites and standardi-

zation methods together with large sample replication.

Limitations in proxy data and reconstruction methods suggest that

published uncertainties will underestimate the full range of uncertain-

ties of large-scale temperature reconstructions (see Section 5.3.5.1).

While this has fostered debate about the extent to which proxy-based

reconstructions provide useful climate information (e.g., McShane and

Wyner, 2011 and associated comments and rejoinder), it is well estab-

lished that temperature and external forcing signals are detectable in

proxy reconstructions (Sections 5.3.5.3 and 10.7.2). Recently, model

experiments assuming a nonlinear sensitivity of tree-rings to climate

(Mann et al., 2012) have been used to suggest that the tree-ring

response to volcanic cooling may be attenuated and lagged. Tree-ring

data and additional tree-growth model assessments (Anchukaitis et

al., 2012; Esper et al., 2013) have challenged this interpretation and

analyses of instrumental data suggest hemispheric temperature recon-

structions agree well with the degree of volcanic cooling during early

19th-century volcanic events (Brohan et al., 2012; see Section 5.3.5.3).

These lines of evidence leave the representation of volcanic events in

tree-ring records and associated hemispheric scale temperature recon-

structions as an emerging area of investigation.

5.3.5.3 Comparing Reconstructions and Simulations

The number of GCM simulations of the last millennium has increased

since AR4 (Fernández-Donado et al., 2013). The simulations have

used different estimates of natural and anthropogenic forcings (Table

5.A.1). In particular, the PMIP3/CMIP5 simulations are driven by small-

er long-term changes in TSI (Section 5.2.1; Figure 5.1b): TSI increases

by ≤0.10% from the Late Maunder Minimum (LMM; 1675–1715) to

the late 20th century (Schmidt et al., 2011), while most previous simu-

lations use increases between 0.23% and 0.29% (Fernández-Donado

et al., 2013). Simulated NH temperatures during the last millennium lie

mostly within the uncertainties of the available reconstructions (Figure

5.8a). This agreement between GCM simulations and reconstructions

provides neither strong constraints on forcings nor on model sensitiv-

ities because internal variability and uncertainties in the forcings and

reconstructions are considerable factors.

Data have also been assimilated into climate models (see Sections 5.5

and 10.7, Figure 10.19) by either nudging simulations to follow local

or regional proxy-based reconstructions (Widmann et al., 2010) or by

selecting simulations from decade-by-decade ensembles to obtain the

closest match to reconstructed climate patterns (Annan and Harg-

reaves, 2012; Goosse et al., 2012a). The resulting simulations provide

insight into the relative roles of internal variability and external forcing

(Goosse et al., 2012b), and processes that may account for the spatial

distribution of past climate anomalies (Crespin et al., 2009; Palastanga

et al., 2011).

Figure 5.8b–d provides additional tests of model-data agreement by

compositing the temperature response to a number of distinct forcing

events. The models simulate a signiﬁcant NH cooling in response to

volcanic events (Figure 5.8b; peaks between 0.1°C and 0.5°C depend-

ing on model) that lasts 3 to 5 years, overlapping with the signal

inferred from reconstructions with annual resolution (0.05°C to 0.3°C).

CMIP5 simulations tend to overestimate cooling following the major

1809 and 1815 eruptions relative to early instrumental data (Brohan

et al., 2012). Such differences could arise from uncertainties in volcan-

ic forcing (Section 5.2.1.3) and its implementation in climate models

(Joshi and Jones, 2009) or from errors in the reconstructions (Section

5.3.5.2). Since many reconstructions do not have annual resolution,

similar composites (Figure 5.8c) are formed to show the response to

changes in multi-decadal volcanic forcings (representing clusters of

eruptions). Both the simulated and reconstructed responses are sig-

niﬁcant and comparable in magnitude, although simulations show a

faster recovery (<5 years) than reconstructions. Solar forcing estimat-

ed over the last millennium shows weaker variations than volcanic

forcing (Figure 5.8d), even at multi-decadal time scales. Compositing

the response to multi-decadal ﬂuctuations in solar irradiance shows

cooling in simulations and reconstructions of NH temperature between

0.0°C and 0.15°C. In both cases, the cooling may be partly a response

to concurrent variations in volcanic forcing (green line in Figure 5.8d).

Temperature differences between the warmest and coolest centennial

or multi-centennial periods provide an additional comparison of the

amplitude of NH temperature variations in the reconstructions and

simulations: between 950 and 1250 (nominally the MCA) and 1450–

1850 (nominally the LIA; Figures 5.8e; 5.9a–c) and between the LIA

and the 20th century (Figures 5.8f; 5.9g–i). Despite similar multi-mod-

el and multi-reconstruction means for the warming from the LIA to

the present, the range of individual results is very wide (see Sections

9.5.3.1 and 10.7.1 for a comparison of reconstructed and simulated

variability across various frequency ranges) and there is no clear dif-

ference between runs with weaker or stronger solar forcing (Figure

5.8f; Section 10.7.2). The difference between the MCA and LIA temper-

atures, however, has a smaller range for the model simulations than

the reconstructions, and the simulations (especially those with weaker

solar forcing) lie within the lower half of the reconstructed range of

temperature changes (Figure 5.8e). Recent studies have assessed the

consistency of model simulations and temperature reconstructions at

the hemispheric scale. Hind and Moberg (2012) found closer data-mod-

el agreement for simulations with 0.1% TSI increase than 0.24% TSI

increase, but the result is sensitive to the reconstruction uncertainty

and the climate sensitivity of the model. Simulations with an EMIC

using a much stronger solar forcing (0.44% TSI increase from LMM

to present, Shapiro et al., 2011) appear to be incompatible with most

temperature reconstructions (Feulner, 2011).

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Information from Paleoclimate Archives Chapter 5

(a) reconstructed (grey) and simulated (red/blue) NH temperature

1000 1200 1400 1600 1800 2000

Time (Year CE)

-0.5

0.0

0.5

1.0

Temperature anomaly (°C)

MCA LIA 20C

Strong

solar

variability

simulations

Weak

solar

variability

simulations

-6

-4

-2

Volcanic forcing (W m

-2

)

Forcing

(b)

-5 0 5 10

Year from peak forcing

-0.6

-0.4

-0.2

-0.0

NH temperature anomaly (°C)

Temperature

Individual volcanic events

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

Volcanic forcing (W m

-2

)

Forcing

(c)

-40 -20 0 20 40

Year from peak forcing

-0.6

-0.4

-0.2

-0.0

NH temperature anomaly (°C)

Temperature

Multi-decadal volcanic activity

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

Solar forcing (W m

-2

)

Volcanic forcing

coincident with

solar variability

Forcing

(d)

-40 -20 0 20 4

Year from peak forcing

-0.6

-0.4

-0.2

-0.0

NH temperature anomaly (°C)

Temperature

Multi-decadal solar variability

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

MCA - LIA NH temp (°C)

Fr07treecps

Ma08cpsl

Da06treecps

He07tls

Sh13pcar

Ju07cvm

Ma09regm

Lj10cps

Ma08min7eivf

Ma08eivl

Mo05wave

LM08ave

CL12loc

BCCcsm1-1-P

ECHAM5-SW

IPSLCM5A-P

CSIRO-Mk3L

MPI-ESM-P

CSM1.4

MIROC-P

CCSM3

CSIRO-Mk3L-P

HadCM3-P

FGOALS-g1

GISS-E2R-P

CCSM4-P

CNRM-CM3.3

ECHAM5-SS

ECHO-G

(e) NH temp (950-1250) minus (1450-1850)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

20C - LIA NH temp (°C)

Fr07treecps

Da06treecps

PS04bore

CL12loc

LO12glac

Ma08eivl

Ma09regm

Ma08cpsl

Lj10cps

Sh13pcar

Ju07cvm

Ma08min7eivf

Mo05wave

ECHAM5-SW

ECHAM5-SS

MIROC-P

HadCM3-P

CNRM-CM3.3

CSIRO-Mk3L

FGOALS-g1

GISS-E2R-P

CSIRO-Mk3L-P

CSM1.4

BCCcsm1-1-P

ECHO-G

MPI-ESM-P

CCSM3

CCSM4-P

IPSLCM5A-P

(f) NH temp (1900-2000) minus (1450-1850)

Figure 5.8 | Comparisons of simulated and reconstructed NH temperature changes. (a) Changes over the last millennium (Medieval Climate Anomaly, MCA; Little Ice Age, LIA;

20th century, 20C) (b) Response to individual volcanic events. (c) Response to multi-decadal periods of volcanic activity. (d) Response to multi-decadal variations in solar activity.

(e) Mean change from the MCA to the LIA. (f) Mean change from 20th century to LIA. Note that some reconstructions represent a smaller spatial domain than the full Northern

Hemisphere (NH) or a speciﬁc season, while annual temperatures for the full NH mean are shown for the simulations. (a) Simulations shown by coloured lines (thick lines: multi-

model-mean; thin lines: multi-model 90% range; red/blue lines: models forced by stronger/weaker solar variability, though other forcings and model sensitivities also differ between

the red and blue groups); overlap of reconstructed temperatures shown by grey shading; all data are expressed as anomalies from their 1500–1850 mean and smoothed with a

30-year ﬁlter. Superposed composites (time segments from selected periods positioned so that the years with peak negative forcing are aligned) of the forcing and temperature

response to: (b) 12 of the strongest individual volcanic forcing events after 1400 (the data shown are not smoothed); (c) multi-decadal changes in volcanic activity; (d) multi-decadal

changes in solar irradiance. Upper panels show volcanic or solar forcing for the individual selected periods together with the composite mean (thick line); in (d), the composite mean

of volcanic forcing (green) during the solar composite is also shown. Lower panels show the NH temperature composite means and 90% range of spread between simulations (red

line, pink shading) or reconstructions (grey line and shading), with overlap indicated by darker shading. Mean NH temperature difference between (e) MCA (950–1250) and LIA

(1450–1850) and (f) 20th century (1900–2000) and LIA, from reconstructions (grey), multi-reconstruction mean and range (dark grey), multi-model mean and range and individual

simulations (red/blue for models forced by stronger/weaker solar variability). Where an ensemble of simulations is available from one model, the ensemble mean is shown in solid

and the individual ensemble members by open circles. Results are sorted into ascending order and labelled. Reconstructions, models and further details are given in Appendix 5.A.1

and Tables 5.A.1 and 5.A.6.

414

Chapter 5 Information from Paleoclimate Archives

The spatial distributions of simulated and reconstructed (Mann et al.,

2009; Ljungqvist et al., 2012) temperature changes between the MCA,

LIA and 20th century are shown in Figure 5.9. Simulated changes tend

to be larger, particularly with stronger TSI forcing, over the continents

and ice/snow-covered regions, showing polar ampliﬁcation (see Box

5.1). The largest simulated and reconstructed changes are between

the LIA and present, with reconstructions (Figure 5.9i) indicating wide-

spread warming except for the cooling south of Greenland. Models also

simulate overall warming between the MCA and present (Figure 5.9d,

e), whereas the reconstructions indicate signiﬁcant regional cooling (in

the North Atlantic, southeastern North America, and the mid-latitudes

of the Paciﬁc Ocean). This is not surprising because greater regional

variability is expected in the reconstructions compared with the mean

of multiple model simulations, though reconstructed changes for such

areas with few or no proxy data (Figure 5.9i) should also be interpreted

with caution (Smerdon et al., 2011). The reconstructed temperature dif-

ferences between MCA and LIA (Figure 5.9c) indicate higher medieval

temperatures over the NH continents in agreement with simulations

(Figure 5.9a, b). The reconstructed MCA warming is higher than in the

simulations, even for stronger TSI changes and individual simulations

(Fernández-Donado et al., 2013). Simulations with proxy assimilation

show that this pattern of change is compatible with a direct response

Stronger TSI change

(a)

(b)

(d)

(c)

(e)

(f)

(g) (h) (i)

Weaker TSI change

ReconstructionsSimulations

−1.0−0.8 −0.6 −0.4 −0.20.0 0.20.4 0.60.8 1.0

MCA-LIA

Present-MCA

Present-LIA

Shaded and contours: Temperature difference (°C)

Dots in (c) are dimensionless proxy anomalies / 2

0200 400600 800100012001400160018002000

Starting year

Coral

Speleothem

Sediment

Documentary

Ice core

MXD

TRW

TRW-RC

CFR

(j)

Figure 5.9 | Simulated and reconstructed temperature changes for key periods in the last millennium. Annual temperature differences for: (a) to (c) Medieval Climate Anomaly (MCA,

950–1250) minus Little Ice Age (LIA, 1450–1850); (d) to (f) present (1950–2000) minus MCA; (g) to (i) present minus LIA. Model temperature differences (left and middle columns)

are average temperature changes in the ensemble of available model simulations of the last millennium, grouped into those using stronger (total solar irradiance (TSI) change from

the Late Maunder Minimum (LMM) to present >0.23%; left column; SS in Table 5.A.1) or weaker solar forcing changes (TSI change from the LMM to present <0.1%; middle column;

SW in Table 5.A.1). Right column panels (c, f, i) show differences (shading) for the Mann et al. (2009) ﬁeld reconstruction. In (c), dots represent additionally proxy differences from

Ljungqvist et al. (2012), scaled by 0.5 for display purposes. The distribution, type and temporal span of the input data used in the ﬁeld reconstruction of Mann et al. (2009) are shown in

(j); proxy types are included in the legend, acronyms stand for: tree-ring maximum latewood density (MXD); tree-ring width (TRW); regional TRW composite (TRW-RC); and multi-proxy

climate ﬁeld reconstruction (CFR). Dotted grid-cells indicate non-signiﬁcant differences (<0.05 level) in reconstructed ﬁelds (right) or that <80% of the simulations showed signiﬁcant

changes of the same sign (left and middle). For simulations starting after 950, the period 1000–1250 was used to estimate MCA values. Grid cells outside the domain of the Mann et

al. (2009) reconstruction are shaded grey in the model panels to enable easier comparison, though contours (interval 0.2 K) illustrate model output over the complete global domain.

Only simulations spanning the whole millennium and including at least solar, volcanic and greenhouse gas forcing have been used (Table 5.A.1): BCC-csm1-1 (1), CCSM3 (1), CCSM4

(1), CNRM-CM3.3, CSIRO-mk3L-1-2 (4), CSM1.4 (1), ECHAM5-MPIOM (8), ECHO-G (1), MPI-ESM-P (1), FGOALS-gl (1), GISS-E2-R (3), HadCM3 (1), IPSL-CM5A-LR (1). Averages for

each model are calculated ﬁrst, to avoid models with multiple simulations having greater inﬂuence on the ensemble means shown here.

415

Information from Paleoclimate Archives Chapter 5

to a relatively weak solar forcing and internal variability patterns sim-

ilar to a positive Northern Annular Mode (NAM) phase and northward

shifts of the Kuroshio and Gulf Stream currents (Goosse et al., 2012b).

For the tropical regions, an enhanced zonal SST gradient produced by

either a warmer Indian Ocean (Graham et al., 2011) or a cooler eastern

Paciﬁc (La Niña-like state) (Seager et al., 2007; Mann et al., 2009) could

explain the reconstructed MCA patterns (Figure 5.9c). However, the

enhanced gradients are not reproduced by model simulations (Figure

5.9a, b) and are not robust when considering the reconstruction uncer-

tainties and the limited proxy records in these tropical ocean regions

(Emile-Geay et al., 2013b) (Sections 5.4.1 and 5.5.1). This precludes an

assessment of the role of external forcing and/or internal variability in

these reconstructed patterns.

5.4 Modes of Climate Variability

Since AR4, new proxy reconstructions and model simulations have

provided additional insights into the forced and unforced behaviour

of modes of climate variability. This section focuses only on the inter-

annual ENSO, the NAM and NAO, the Southern Annular Mode (SAM)

and longer term variability associated with the Atlantic Multidecadal

Oscillation (AMO) (see Glossary and Chapter 14 for deﬁnitions and

illustrations and Box 2.5). It is organized from low to high latitudes and

from interannual to decadal-scale modes of variability.

5.4.1 Tropical Modes

During the MPWP, climate conditions in the equatorial Paciﬁc were

characterized by weaker zonal (Wara et al., 2005) and cross-equatorial

(Steph et al., 2010) SST gradients, consistent with the absence of an

eastern equatorial cold tongue. This state still supported interannual

variability, according to proxy records (Scroxton et al., 2011; Watanabe

et al., 2011). These results together with recent GCM experiments

(Haywood et al., 2007) indicate (medium conﬁdence) that interannual

ENSO variability existed, at least sporadically, during the warm back-

ground state of the Pliocene (Section 5.3.1).

LGM GCM simulations display wide ranges in the behaviour of ENSO

and the eastern equatorial Paciﬁc annual cycle of SST with little con-

sistency (Liu et al., 2007a; Zheng et al., 2008) (Figure 5.10). Currently

ENSO variance reconstructions for the LGM are too uncertain to help

constrain the simulated responses of the annual cycle and ENSO to

LGM boundary conditions. GCMs show that a reduced AMOC very

likely induces intensiﬁcation of ENSO amplitude and for the majority

of climate models also a reduction of the amplitude of the SST annual

cycle in the eastern equatorial Paciﬁc (Timmermann et al., 2007; Merkel

et al., 2010; Braconnot et al., 2012a) (Figure 5.10). About 75% of the

PMIP2 and PMIP3/CMIP5 mid-Holocene simulations exhibit a weak-

ening of interannual SST amplitude in the eastern equatorial Paciﬁc

relative to pre-industrial conditions. More than 87% of these simu-

lations also show a concomitant substantial weakening in the ampli-

tude of the annual cycle of eastern equatorial Paciﬁc SST. Model results

are consistent with a reduction of total variance of d

O variations of

individual foraminifera in the eastern equatorial Paciﬁc, indicative of

an orbital effect on eastern equatorial Paciﬁc SST variance (Koutavas

and Joanides, 2012). In contrast to these ﬁndings, a recent proxy study

using sub-annually resolved d

O from central equatorial Paciﬁc coral

segments (Cobb et al., 2013) reveals no evidence for orbitally-induced

changes in interannual ENSO amplitude throughout the last 7 ka (high

conﬁdence), which is consistent with the weak reduction in mid-Hol-

ocene ENSO amplitude of only ~10% simulated by the majority of cli-

mate models (Fig. 5.10), but contrasts with reconstructions reported

in AR4 that showed a reduction in ENSO variance during the ﬁrst half

of the Holocene. The same study revealed an ENSO system that expe-

rienced very large internal variance changes on decadal and centen-

nial time scales. This latter ﬁnding is also conﬁrmed by the analysis

of about 2000 years of annually varved lake sediments (Wolff et al.,

2011) in the ENSO-teleconnected region of equatorial East Africa. Fur-

thermore, Cobb et al. (2013) identify the late 20th century as a period

of anomalously high, although not unprecedented, ENSO variability

relative to the average reconstructed variance over the last 7000 years.

Reconstructions of ENSO for the last millennium also document mul-

ti-decadal-to-centennial variations in the amplitude of reconstructed

interannual eastern equatorial Paciﬁc SST anomalies (McGregor et al.,

2010; Wilson et al., 2010; Li et al., 2011; Emile-Geay et al., 2013a). Sta-

tistical efforts to determine ENSO variance changes in different annu-

ally resolved ENSO proxies (D’Arrigo et al., 2005; Braganza et al., 2009;

McGregor et al., 2010; Fowler et al., 2012; Hereid et al., 2013) and from

documentary sources (Garcia-Herrera et al., 2008; Gergis and Fowler,

2009) reveal (medium conﬁdence) extended periods of low ENSO

activity during parts of the LIA compared to the 20th century. Direct TSI

effects on reconstructed multi-decadal ENSO variance changes cannot

be identiﬁed (McGregor et al., 2010). According to reconstructions of

volcanic events (Section 5.2.1.3) and some ENSO proxies, a slightly

increased probability exists (medium conﬁdence) for the occurrence

of El Niño events 1 to 2 years after major volcanic eruptions (Adams

et al., 2003; McGregor et al., 2010; Wilson et al., 2010). This response

is not captured robustly by GCMs (McGregor and Timmermann, 2010;

Ohba et al., 2013).

5.4.2 Extratropical Modes

Robust evidence from LGM simulations indicates a weakening of the

NAM variability, connected with stronger planetary wave activity (Lü

et al., 2010). A signiﬁcant but model-dependent distortion of the sim-

ulated LGM NAO pattern may result from the strong topographic ice

sheet forcing (Justino and Peltier, 2005; Handorf et al., 2009; Pausata et

al., 2009; Riviere et al., 2010). A multimodel analysis of NAO behaviour

in mid-Holocene GCM simulations (Gladstone et al., 2005) reveals an

NAO structure, similar to its pre-industrial state, but a tendency for

more positive NAO values during the early Holocene (Rimbu et al.,

2003), with no consistent change in its interannual variability. Robust

proxy evidence to test these model-based results has not yet been

established. A new 5200-year-long lake sediment record from south-

western Greenland (Olsen et al., 2012) suggests that around 4500

and 650 years ago variability associated with the NAO changed from

generally positive to variable, intermittently negative conditions. Since

AR4, a few cold-season NAO reconstructions for the last centuries have

been published. They are based on long instrumental pressure series

(Cornes et al., 2012), a combination of instrumental and ship log-book

data (Küttel et al., 2010) and two proxy records (Trouet et al., 2009).

Whereas these and earlier NAO reconstructions (Cook et al., 2002;

416

Chapter 5 Information from Paleoclimate Archives

Luterbacher et al., 2002; Timm et al., 2004; Pinto and Raible, 2012)

differ in several aspects, and taking into consideration associated

reconstruction uncertainties, they demonstrate with high conﬁdence

that the strong positive NAO phases of the early 20th century and the

mid-1990s are not unusual in the context of the past half millennium.

Trouet et al. (2009) presented a winter NAO reconstruction that yielded

a persistent positive phase during the MCA in contrast to higher fre-

quency variability during the LIA. This is not consistent with the strong

NAO imprint in Greenland ice core data (Vinther et al., 2010) and recent

results from transient model simulations that neither support such a

persistent positive NAO during the MCA, nor a strong NAO phase shift

during the LIA (Lehner et al., 2012; Yiou et al., 2012). A recent pseu-

do-proxy-based assessment of low-frequency NAO behaviour (Lehner

et al., 2012) infers weaknesses in the reconstruction method used by

Trouet et al. (2009). Last millennium GCM simulations reveal no signif-

icant response of the NAO to solar forcing (Yiou et al., 2012), except

for the GISS-ER coupled model which includes ozone photochemistry,

extends into the middle atmosphere and exhibits changes in NAO that

are weak during the MCA compared to the LIA (Mann et al., 2009).

Changes in the SAM modulate the strength and position of the mean

SH westerlies, and leave an important signature on SH present-day sur-

face climate (Gillett et al., 2006) past tree-ring growth (e.g., Urrutia et

al., 2011), wildﬁres (Holz and Veblen, 2011) as well as on LGM climate

(Justino and Peltier, 2008). A ﬁrst hemispheric-wide, tree-ring-based

reconstruction of the austral summer SAM (Villalba et al., 2012) indi-

cates that the late 20th century positive trend may have been anoma-

lous in the context of the last 600 years, thus supporting earlier South

American proxy evidence for the last 400 years (e.g., Lara et al., 2008)

and GCM (Wilmes et al., 2012). Hence, there is medium conﬁdence that

the positive trend in SAM since 1950 may be anomalous compared to

the last 400 years.

The AMO (Delworth and Mann, 2000; Knight et al., 2005) (see also

Sections 9.5.3.3.2 and 14.7.6) has been reconstructed using marine

(Black et al., 2007; Kilbourne et al., 2008; Sicre et al., 2008; Chiessi et

al., 2009; Saenger et al., 2009) and terrestrial proxy records (Gray et

al., 2004; Shanahan et al., 2009) from different locations. Correlations

among different AMO reconstructions decrease rapidly prior to 1900

(Winter et al., 2011). An 8000-year long AMO reconstruction (Knudsen

et al., 2011) shows no correlation with TSI changes, and is interpreted

as internally generated ocean-atmosphere variability. However, GCM

experiments (Waple et al., 2002; Ottera et al., 2010) using solar and/

or volcanic forcing reconstructions indicate that external forcings may

have played a role in driving or at least acting as pacemaker for AMO

variations.

Mid Holocene LGM Hosing

−80

−60

−40

−20

Annual cycle

Relative change in annual cycle amplitude (%)

Mid Holocene LGM Hosing

−40

−30

−20

−10

ENSO

Relative change in ENSO amplitude (%)

Figure 5.10 | Relative changes in amplitude of the annual cycle of sea surface temperature (SST) in Niño 3 region (average over 5°S to 5°N and 150°W to 90°W) (left) and in

amplitude of interannual SST anomalies in the Niño 3.4 region (average over 5°S to 5°N and 170°W to 120°W) (right) simulated by an ensemble of climate model experiments

in response to external forcing. Left: Multi-model average of relative changes (%) in amplitude of the mean seasonal cycle of Niño 3 SST for mid Holocene (MH) and Last Glacial

Maximum (LGM) time-slice experiments and for freshwater perturbation experiments (Hosing) that lead to a weakening of the Atlantic Ocean meridional overturning circulation

(AMOC) by more than 50%. Bars encompass the 25 and 75 percentiles, with the red horizontal lines indicating the median in the respective multi-model ensemble, red crosses are

values in the upper and lower quartile of the distribution; Right: same as left, but for the SST anomalies in the Niño 3.4 region, representing El Niño-Southern Oscillation (ENSO)

variability. The MH ensemble includes 4 experiments performed by models participating in Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) (FGOALS1.0g, IPSL-

CM4, MIROC3.2 medres, CCSM3.0) and 7 experiments (mid-Holocene) performed by models participating in PMIP3/CMIP5 (CCSM4.0, CSIRO-Mk3-6-0, HadGEM2-CC, HadGEM2-

ES, MIROC-ESM, MPI-ESM-P, MRI-CGCM3). The LGM ensemble includes 5 experiments performed by models participating in PMIP2 (FGOALS1.0g,IPSL-CM4, MIROC3.2 medres,

CCSM3.0, HadCM3) and 5 experiments (LGM) performed by models participating in PMIP3/CMIP5 (CCSM4, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, and MPI-ESM-P). The changes

in response to MH and LGM forcing are computed with respect to the pre-industrial control simulations coordinated by PMIP2 and PMIP3/CMIP5. The results for Hosing are obtained

from freshwater perturbation experiments conducted with CCSM2.0, CCSM3.0, HadCM3, ECHAM5-MPIOM, GFDL-CM2.1 (Timmermann et al., 2007), CSM1.4 (Bozbiyik et al.,

2011) for pre-industrial or present-day conditions and with CCSM3 for glacial conditions (Merkel et al., 2010). The changes in response to fresh water forcing are computed with

respect the portion of simulations when the AMOC is high.

417

Information from Paleoclimate Archives Chapter 5

5.5 Regional Changes During the Holocene

Reconstructions and simulations of regional changes that have

emerged since AR4 are assessed. Most emphasis is on the last 2000

years, which has the best data coverage.

5.5.1 Temperature

5.5.1.1 Northern Hemisphere Mid to High Latitudes

New studies conﬁrm the spatial patterns of SAT and SST distribution as

summarised in AR4 (Jansen et al., 2007). According to a recent compi-

lation of proxy data, the global mean annual temperatures around 8 to

6 ka were about 0.7°C higher, and extratropical NH temperatures were

about 1°C higher than for pre-industrial conditions (Marcott et al.,

2013). Spatial variability in the temperature anomalies and the timing

of the thermal maximum implicate atmospheric or oceanic dynamical

feedbacks including effects from remaining ice sheets (e.g., Wanner

et al., 2008; Leduc et al., 2010; Bartlein et al., 2011; Renssen et al.,

2012). The peak early-to-mid-Holocene North Atlantic and sub-Arctic

SST anomalies are reconstructed and simulated to primarily occur in

summer and in the stratiﬁed uppermost surface-ocean layer (Hald et

al., 2007; Andersson et al., 2010). Terrestrial MH (~6 ka, Table 5.1) sum-

mer-season temperatures were higher than modern in the mid-to-high

latitudes of the NH, consistent with minimum glacier extents (Section

5.5.3) and PMIP2 and PMIP3/CMIP5 simulated responses to orbital

forcing (Figure 5.11) (Braconnot et al., 2007; Bartlein et al., 2011; Izumi

et al., 2013). There is also robust evidence for warmer MH winters com-

pared to the late 20th century (e.g., Wanner et al., 2008; Sundqvist et

al., 2010; Bartlein et al., 2011) (Figure 5.11), but the simulated high

latitude winter warming is model dependent and is sensitive to ocean

and sea-ice changes (Otto et al., 2009; Zhang et al., 2010). Overall,

models underestimate the reduction in the latitudinal gradient of Euro-

pean winter temperatures during the MH (Brewer et al., 2007). There

is a general, gradual NH cooling after ~5 ka, linked to orbital forcing,

and increased amplitude of millennial-scale variability (Wanner et al.,

2008; Vinther et al., 2009; Kobashi et al., 2011; Marcott et al., 2013).

Since AR4, regional temperature reconstructions have been produced

for the last 2 kyr (Figure 5.12; PAGES 2k Consortium, 2013). A recent

multi-proxy 2000-year Arctic temperature reconstruction shows that

temperatures during the ﬁrst centuries were comparable or even

higher than during the 20th century (Hanhijärvi et al., 2013; PAGES

2k Consortium, 2013). During the MCA, portions of the Arctic and

sub-Arctic experienced periods warmer than any subsequent period,

except for the most recent 50 years (Figure 5.12) (Kaufman et al., 2009;

Kobashi et al., 2010, 2011; Vinther et al., 2010; Spielhagen et al., 2011).

Tingley and Huybers (2013) provided a statistical analysis of northern

high-latitude temperature reconstructions back to 1400 and found that

recent extreme hot summers are unprecedented over this time span.

Marine proxy records indicate anomalously high SSTs north of Iceland

and the Norwegian Sea from 900 to 1300, followed by a generally

colder period that ended in the early 20th century. Modern SSTs in

this region may still be lower than the warmest intervals of the 900–

1300 period (Cunningham et al., 2013). Further north, in Fram Strait,

modern SSTs from Atlantic Water appear warmer than those recon-

structed from foraminifera for any prior period of the last 2000 years

(Spielhagen et al., 2011). However, different results are obtained using

dinocysts from the same sediment core (Bonnet et al. (2010) showing a

cooling trend over the last 2000 years without a 20th century rise, and

warmest intervals centered at years 100 and 600.

Tree-ring data and lake sediment information from the North American

treeline (McKay et al., 2008; Bird et al., 2009; D’Arrigo et al., 2009;

Anchukaitis et al., 2012) suggest common variability between different

records during the last centuries. An annually resolved, tree-ring based

800-year temperature reconstruction over temperate North America

(PAGES 2k Consortium, 2013) and a 1500-year long pollen-based tem-

perature estimate (Viau et al., 2012) show cool periods 500–700 and

1200–1900 as well as a warm period between 750 and 1100. The gen-

erally colder conditions until 1900 are in broad agreement with other

pollen, tree-ring and lake-sediment evidence from northwest Canada,

the Canadian Rockies and Colorado (Luckman and Wilson, 2005; Salzer

and Kipfmueller, 2005; Loso, 2009; MacDonald et al., 2009; Thomas

and Briner, 2009). It is very likely that the most recent decades have

been, on average, the warmest across mid-latitude western and tem-

perate North America over at least 500 years (Wahl and Smerdon,

2012; PAGES 2k Consortium, 2013; Figure 5.12).

New warm-season temperature reconstructions (PAGES 2k Consor-

tium, 2013; Figure 5.12) covering the past 2 millennia show that warm

European summer conditions were prevalent during 1st century, fol-

lowed by cooler conditions from the 4th to the 7th century. Persistent

warm conditions also occurred during the 8th–11th centuries, peaking

throughout Europe during the 10th century. Prominent periods with

cold summers occurred in the mid-15th and early 19th centuries. There

is high conﬁdence that northern Fennoscandia from 900 to 1100 was

as warm as the mid-to-late 20th century (Helama et al., 2010; Linder-

holm et al., 2010; Büntgen et al., 2011a; Esper et al., 2012a; 2012b;

McCarroll et al., 2013; Melvin et al., 2013). The evidence also suggests

warm conditions during the 1st century, but comparison with recent

temperatures is restricted because long-term temperature trends from

tree-ring data are uncertain (Esper et al., 2012a). In the European Alps

region, tree-ring based summer temperature reconstructions (Büntgen

et al., 2005; Nicolussi et al., 2009; Corona et al., 2010, 2011; Büntgen et

al., 2011b) show higher temperatures in the last decades than during

any time in the MCA, while reconstructions based on lake sediments

(Larocque-Tobler et al., 2010; Trachsel et al., 2012) show as high, or

slightly higher temperatures during parts of the MCA compared to

most recent decades. The longest summer temperature reconstructions

from parts of the Alps show several intervals during Roman and earlier

times as warm (or warmer) than most of the 20th century (Büntgen et

al., 2011b; Stewart et al., 2011).

Since AR4, new temperature reconstructions have also been generat-

ed for Asia. A tree-ring based summer temperature reconstruction for

temperate East Asia back to 800 indicates warm conditions during the

period 850–1050, followed by cooler conditions during 1350–1880

and a subsequent 20th century warming (Cook et al., 2012; PAGES

2k Consortium, 2013; Figure 5.12). Tree-ring reconstructions from the

western Himalayas, Tibetan Plateau, Tianshan Mountains and western

High Asia depict warm conditions from the 10th to the 15th centuries,

lower temperature afterwards and a 20th century warming (Esper et

al., 2007a; Zhu et al., 2008; Zhang et al., 2009; Yadav et al., 2011).

418

Chapter 5 Information from Paleoclimate Archives

-6

-3

MH -PIcontrol Anomaly(

MTWA (NorthernHemisphereExtratropics, Land)

Simulations Data

p()

-6

-3

MH -PIcontrolAnomaly (

MHMTCOData

PMIP2 ensemble

CMIP5 ensemble

PMIP23ensemble

PMIP2ensembleMH-OA

PMIP2ensembleMH-OAV

CMIP5 CCSM4OA

CMIP5 CNRM-CM5 OA

CMIP5 CSIRO-Mk3L-1-2 OA

CMIP5 CSIRO-Mk3-6-0 OA

CMIP5 EC-EARTH-2-2OA

CMIP5FGOALS-s2OA

CMIP5 FGOALS-g2OA

CMIP5GISS-E2-R OA

CMIP5 MPI-ESM-P OA

CMIP5MRI-CGCM3 OA

CMIP5BCC-CSM1.1 OAC

CMIP5 HadGEM2-CC OAC

CMIP5 HadGEM2-ES OAC

CMIP5IPSL-CM5A-LR OAC

CMIP5MIROC-ESM OAC

MTCO (Tropics,Land)

-6

-4

-2

MH -PI control Anomaly (

MH MTWAData

PMIP2ensemble

CMIP5ensemble

PMIP23 ensemble

PMIP2 ensembleMH-OA

PMIP2 ensemble MH-OAV

CMIP5 CCSM4OA

CMIP5 CNRM-CM5 OA

CMIP5 CSIRO-Mk3L-1-2 OA

CMIP5 CSIRO-Mk3-6-0OA

CMIP5EC-EARTH-2-2 OA

CMIP5FGOALS-s2 OA

CMIP5FGOALS-g2OA

CMIP5 GISS-E2-R OA

CMIP5 MPI-ESM-P OA

CMIP5 MRI-CGCM3 OA

CMIP5 BCC-CSM1.1 OAC

CMIP5 HadGEM2-CC OAC

CMIP5 HadGEM2-ES OAC

CMIP5 IPSL-CM5A-LR OAC

CMIP5 MIROC-ESM OAC

MTWA (Tropics, Land)

MTCO MH Anomalies

MTWA MH Anomalies

-6

-3

MH -PIcontrol Anomaly(

MTCO (NorthernHemisphereExtratropics, Land)

-2 -1.5 -1 -0.5

0 0.5 1 1.5

3 4 5

9 11 (°C)

-2 -1.5 -1 -0.5

0 0.5 1 1.5

3 4 5

9 11 (°C)

PMIP2 & CMIP5/PMIP3 ensemble averages

CMIP5/PMIP3 OA coupled models

CMIP5/PMIP3 OAC coupled models

Figure 5.11 | Model-data comparison of surface temperature anomalies for the mid-Holocene (6 ka). MTCO is the mean temperature of the coldest month; MTWA is the mean

temperature of the warmest month. Top panels are pollen-based reconstructions of Bartlein et al., (2011) with anomalies deﬁned as compared to modern, which varies among the

records. The bulk of the records fall within the range of 5.5 to 6.5 ka, with only 3.5% falling outside this range. Middle panels are corresponding surface temperature anomalies

simulated by the Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) and Paleoclimate Modelling Intercomparison Project Phase III (PMIP3)/ Coupled Model Inter-

comparison Project Phase 5 (CMIP5) models for 6 ka as compared to pre-industrial. Bottom panels contain boxplots for reconstructions (grey), for model ensembles and for the

individual CMIP5 models interpolated to the locations of the reconstructions. Included are OA (ocean–atmosphere), OAV (ocean–atmosphere–vegetation), and OAC (ocean–atmo-

sphere–carbon cycle) models. The boxes are drawn using the 25th, 50th and 75th percentiles (bottom, middle and top of the box, respectively), and whiskers extend to the 5th

and 95th percentiles of data or model results within each area. The northern extratropics are deﬁned as 30°N to 90ºN and the tropics as 30ºS to 30ºN. For additional model–data

comparisons for the mid-Holocene, see Section 9.4.1.4 and Figures 9.11 and 9.12.

419

Information from Paleoclimate Archives Chapter 5

Figure 5.12 | Regional temperature reconstructions, comparison with model simulations over the past millennium (950–2010). Temperature anomalies (thick black line), and

uncertainty estimated provided by each individual reconstruction (grey envelope). Uncertainties: Arctic: 90% conﬁdence bands. Antarctica, Australasia, North American pollen and

South America: ±2 standard deviation. Asia: ±2 root mean square error. Europe: 95% conﬁdence bands. North American trees: upper/lower 5% bootstrap bounds. Simulations are

separated into 2 groups: High solar forcing (red thick line), and weak solar forcing (blue thick line). For each model sub-group, uncertainty is shown as 1.645 times sigma level (light

red and blue lines). For comparison with instrumental record, the Climatic Research Unit (CRU) Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over

Land version 4 (CRUTEM4) data set is shown (yellow line). These instrumental data are not necessarily those used in calibration of the reconstructions, and thus may show greater

or lesser correspondence with the reconstructions than the instrumental data actually used for calibration; cut-off timing may also lead to end effects for the smoothed data shown.

Cf. PAGES 2k Consortium (2013, SOM) in this regard for the North America reconstruction. Green bars in rectangles on top of each panel indicate the 30 warmest years in the

950–1250 period (left rectangle) and 1800–2010 period (right rectangle). All lines are smoothed by applying a 30 year moving average. Map at bottom right shows the individual

regions for each reconstruction, and in bars the Medieval Climate Anomaly (MCA, 950-1250) – Little Ice Age (LIA, 1450-1850) differences over those regions. Reconstructions: from

PAGES 2k Consortium (2013). Models used: simulations with strong solar forcing (mostly pre-Paleoclimate Modelling Intercomparison Project Phase III (pre-PMIP3) simulations):

CCSM3 (1), CNRM-CM3.3 (1), CSM1.4 (1), CSIRO-MK3L-1-2 (3), ECHAM5/MPIOM (3), ECHO-G (1) IPSLCM4 (1), FGOALS-gl (1). Simulations with weak solar forcing (mostly PMIP3/

CMIP5 simulations): BCC-csm1-1 (1), CCSM4 (1), CSIRO-MK3L-1-2 (1), GISS-E2-R (3, ensemble members 121, 124, 127), HadCM3 (1), MPI-ESM, ECHAM5/MPIOM (5), IPSL-CM5A-

LR (1). In parenthesis are the number of simulations used for each model. All simulations are treated individually, in the time series as well as in the MCA–LIA bars. More information

about forcings used in simulations and corresponding references are given in Table 5.A.1. Time periods for averaging are JJA for June – July – August, SONDJF for the months from

September to February, and DJF for December – January – February, respectively, while ANN denotes annual mean.

SSA

AUS

ARC

ANT

0.2

0.4

(°C)

ASIA

NAM

0.2

0.4

(°C)

0.2

0.4

(°C)

0.4

(°C)

EUR

0.2

0.4

(°C)

0.2

0.4

(°C)

0.2

0.4

(°C)

Reconstruction

Weak solar forcing

Strong solar forcing

CRUTEM4

MCA - LIA

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2.5

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980 (°C)

Time (Year CE)

Arctic ANN

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980

(°C)

Time (Year CE)

Europe JJA

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980

(°C)

Time (Year CE)

Australasia SONDJF

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980

(°C)

Time (Year CE)

South America DJF

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980 (°C)

Time (Year CE)

Antarctica ANN

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850

(°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980

(°C)

Time (Year CE)

Asia JJA

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1500−1850 (°C)

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

−2

−1.5

−1

−0.5

0.5

1.5

Temp anomaly wrt 1881−1980 (°C)

Time (Year CE)

North

America ANN

420

Chapter 5 Information from Paleoclimate Archives

Taking associated uncertainties into consideration, 20th century tem-

peratures in those regions were likely not higher than during the ﬁrst

part of the last millennium. In different regions of China, temperatures

appear higher during recent decades than during earlier centuries,

although with large uncertainties (e.g., Ge et al., 2006, 2010; Wang

et al., 2007; Holmes et al., 2009; Yang et al., 2009; Zhang et al., 2009;

Cook et al., 2012). In northeast China, an alkenone-based reconstruc-

tion indicates that the growing season temperature during the peri-

ods 480–860, 1260–1300, 1510–1570 and 1800–1900 was about

1°C lower compared with the 20th century (Chu et al., 2011). These

reconstructed NH regional temperature evolutions appear consistent

with last millennium GCM simulations using a range of solar forcing

estimates (Figure 5.12).

There is high conﬁdence that in the extratropical NH, both regionally

and on a hemispheric basis, the surface warming of the 20th century

reversed the long term cooling trend due to orbital forcing.

5.5.1.2 Tropics

Marcott et al. (2013) provide a compilation of tropical SST reconstruc-

tions, showing a gradual warming of about 0.5°C until 5 ka and little

change thereafter. Holocene tropical SST trends are regionally hetero-

geneous and variable in magnitude. Alkenone records from the eastern

tropical Paciﬁc, western tropical Atlantic, and the Indonesian archipel-

ago document a warming trend of ~0.5°C to 2°C from the early Hol-

ocene to present (Leduc et al., 2010), consistent with local insolation.

In contrast, regional trends of planktonic foraminiferal Mg/Ca records

are heterogeneous, and imply smaller magnitude SST changes (Leduc

et al., 2010; Schneider et al., 2010). Foraminiferal Mg/Ca records in the

Indo-Paciﬁc warm-pool region show cooling trends with varying mag-

nitudes (Stott et al., 2004; Linsley et al., 2010). When comparing SST

records from different paleoclimate proxies it is important to note that

they can have different, and also regionally varying, seasonal biases

(Schneider et al., 2010).

Terrestrial temperature reconstruction efforts have mostly focussed on

Africa and to some extent on southeast Asia (Figure 5.11), with a lack

of syntheses from South America and Australia (Bartlein et al., 2011).

The PMIP2 and PMIP3/CMIP5 MH simulations show summer cooling

compared to pre-industrial conditions and a shorter growing season

in the tropical monsoon regions of Africa and southeast Asia (Figure

5.11), attributed to increased cloudiness and local evaporation (Brac-

onnot et al., 2007). In contrast, MH simulations and reconstructions for

the entire tropics (30°S to 30°N) show generally higher mean tempera-

ture of the warmest month and lower mean temperature of the coldest

month than for the mid-20th century.

5.5.1.3 Southern Hemisphere Mid to High Latitudes

In the high latitude Southern Ocean, Holocene SST trends follow the

decrease in austral summer duration, with a cooling trend from the

early Holocene into the late Holocene (Kaiser et al., 2008; Shevenell

et al., 2011). Similar cooling trends are found in the Australian-New

Zealand region (Bostock et al., 2013). Increased amplitude of millenni-

al-to-centennial scale SST variability between 5 ka and 4 ka is record-

ed in several locations, possibly due to variations in the position and

strength of the westerlies (Moros et al., 2009; Euler and Ninnemann,

2010; Shevenell et al., 2011). The Holocene land-surface temperature

history in the SH is difﬁcult to assess. Individual reconstructions gener-

ally track the trends registered by Antarctic ice core records with peak

values at around 12 to 10 ka (Masson-Delmotte et al., 2011b; Marcott

et al., 2013; Mathiot et al., 2013). Pollen-based records indicate posi-

tive MH temperature anomalies in southern South Africa that are not

reproduced in the PMIP3/CMIP5 simulations (Figure 5.11).

Indices for the position of Southern Ocean fronts and the strength and

position of the westerlies diverge (Moros et al., 2009; e.g., Shevenell

et al., 2011). For the mid-to-late-Holocene, climate models of different

complexity consistently show a poleward shift and intensiﬁcation of

the SH westerlies in response to orbital forcing (Varma et al., 2012).

However, the magnitude, spatial pattern and seasonal response vary

signiﬁcantly among the models.

New high-resolution, climate reconstructions for the last millennium

are based on tree-ring records from the subtropical and central Andes,

northern and southern Patagonia, Tierra del Fuego, New Zealand and

Tasmania (Cook et al., 2006; Boninsegna et al., 2009; Villalba et al.,

2009), ice cores, lake and marine sediments and documentary evidence

from southern South America (Prieto and García Herrera, 2009; Vimeux

et al., 2009; von Gunten et al., 2009; Tierney et al., 2010; Neukom et al.,

2011), terrestrial and shallow marine geological records from eastern

Antarctica (Verleyen et al., 2011), ice cores from Antarctica (Goosse

et al., 2012c; Abram et al., 2013; Steig et al., 2013), boreholes from

western Antarctica (Orsi et al., 2012) and coral records from the Indian

and Paciﬁc Oceans (Linsley et al., 2008; Zinke et al., 2009; Lough, 2011;

DeLong et al., 2012). There is medium conﬁdence that southern South

America (Neukom et al., 2011) austral summer temperatures during

950–1350 were warmer than the 20th century. A 1000-year temper-

ature reconstruction for land and ocean representing Australasia indi-

cates a warm period during 1160–1370 though this reconstruction is

based on only three records before 1430 (PAGES 2k Consortium, 2013).

In Australasia, 1971–2000 temperatures were very likely higher than

any other 30-year period over the last 580 years (PAGES 2k Consor-

tium, 2013).

Antarctica was likely warmer than 1971–2000 during the late 17th

century, and during the period from approximately the mid-2nd centu-

ry to 1250 (PAGES 2k Consortium, 2013).

In conclusion, continental scale surface temperature reconstructions

from 950 to 1250 show multi-decadal periods that were in some

regions as warm as in the mid-20th century and in others as warm as

in the late 20th century (high conﬁdence). These regional warm periods

were not as synchronous across regions as the warming since the mid-

20th century (high conﬁdence).

5.5.2 Sea Ice

Since AR4 several new Holocene sea ice reconstructions for the Arctic

and sub-Arctic have been made available that resolve multi-decadal

to century-scale variability. Proxies of sea-ice extent have been fur-

ther developed from biomarkers in deep sea sediments (e.g., IP25,

Belt et al., 2007; Müller et al., 2011) and from sea-ice biota preserved

421

Information from Paleoclimate Archives Chapter 5

in sediments (e.g., Justwan and Koç, 2008). Indirect information on

sea-ice conditions based on drift wood and beach erosion has also

been compiled (Funder et al., 2011). In general, these sea-ice recon-

structions parallel regional SST, yet they display spatial heterogeneity,

and differences between the methods, making it difﬁcult to provide

quantitative estimates of past sea-ice extent. Summer sea-ice cover

was reduced compared to late 20th century levels both in the Arctic

Ocean and along East Greenland between 8 ka and 6.5 ka (e.g., Moros

et al., 2006; Polyak et al., 2010; Funder et al., 2011), a feature which is

captured by some MH simulations (Berger et al., 2013). The response

of this sea ice cover to summer insolation warming was shown to be

central for explaining the reconstructed warmer winter temperatures

over the adjacent land (Otto et al., 2009; Zhang et al., 2010). During the

last 6 kyr available records show a long-term trend of a more extensive

Arctic sea ice cover driven by the orbital forcing (e.g., Polyak et al.,

2010), but punctuated by strong century-to-millennial scale variability.

Consistent with Arctic temperature changes (see Section 5.5.1), sea

ice proxies indicate relatively reduced sea-ice cover from 800 to 1200

followed by a subsequent increase during the LIA (Polyak et al., 2010).

Proxy reconstructions document the 20th-century ice loss trend, which

is also observed in historical sea ice data sets with a decline since

the late 19th century (Divine and Dick, 2006). There is medium con-

ﬁdence that the current ice loss was unprecedented and that current

SSTs in the Arctic were anomalously high at least in the context of the

last 1450 years (England et al., 2008; Kinnard et al., 2008; Kaufman et

al., 2009; Macias Fauria et al., 2010; Polyakov et al., 2010; Kinnard et

al., 2011; Spielhagen et al., 2011). Fewer high-resolution records exist

from the Southern Ocean. Data from the Indian Ocean sector docu-

ment an increasing sea-ice trend during the Holocene, with a rather

abrupt increase between 5 ka and 4 ka, consistent with regional tem-

peratures (see Section 5.5.1.3) (Denis et al., 2010).

5.5.3 Glaciers

Due to the response time of glacier fronts, glacier length variations

resolve only decadal- to centennial-scale climate variability. Since AR4

new and improved chronologies of glacier size variations were pub-

lished (Anderson et al., 2008; Joerin et al., 2008; Yang et al., 2008;

Jomelli et al., 2009; Licciardi et al., 2009; Menounos et al., 2009;

Schaefer et al., 2009; Wiles et al., 2011; Hughes et al., 2012). Studies

of sediments from glacier-fed lakes and marine deposits have allowed

new continuous reconstructions of glacier ﬂuctuations (Matthews and

Dresser, 2008; Russell et al., 2009; Briner et al., 2010; Bowerman and

Clark, 2011; Larsen et al., 2011; Bertrand et al., 2012; Vasskog et al.,

2012). Reconstructions of the history of ice shelves and ice sheets/caps

have also emerged (Antoniades et al., 2011; Hodgson, 2011; Simms et

al., 2011; Smith et al., 2011; Kirshner et al., 2012). New data conﬁrm

a general increase of glacier extent in the NH and decrease in the SH

during the Holocene (Davis et al., 2009; Menounos et al., 2009), con-

sistent with the local trends in summer insolation and temperatures.

Some exceptions exist (e.g., in the eastern Himalayas), where glaciers

were most extensive in the early Holocene (Gayer et al., 2006; Seong

et al., 2009), potentially due to monsoon changes (Rupper et al., 2009).

Due to dating uncertainties, incompleteness and heterogeneity of most

existing glacial chronologies, it is difﬁcult to compare glacier variations

between regions at centennial and shorter time scales (Heyman et al.,

2011; Kirkbride and Winkler, 2012). There are no deﬁnitive conclusions

regarding potential inter-hemispheric synchronicity of sub-millennial

scale glacier ﬂuctuations (Wanner et al., 2008; Jomelli et al., 2009; Lic-

ciardi et al., 2009; Schaefer et al., 2009; Winkler and Matthews, 2010;

Wanner et al., 2011).

Glacial chronologies for the last 2 kyr are better constrained (Yang et

al., 2008; Clague et al., 2010; Wiles et al., 2011; Johnson and Smith,

2012). Multi-centennial glacier variability has been linked with var-

iations in solar activity (Holzhauser et al., 2005; Wiles et al., 2008),

volcanic forcing (Anderson et al., 2008) and changes in North Atlantic

circulation (Linderholm and Jansson, 2007; Nesje, 2009; Marzeion and

Nesje, 2012). Glacier response is more heterogeneous and complex

during the MCA than the uniform global glacier recession observed at

present (see Section 4.3). Glaciers were smaller during the MCA than

in the early 21st

Century in the western Antarctic Peninsula (Hall et al.,

2010) and Southern Greenland (Larsen et al., 2011). However, promi-

nent advances occurred within the MCA in the Alps (Holzhauser et al.,

2005), Patagonia (Luckman and Villalba, 2001), New Zealand (Schaefer

et al., 2009), East Greenland (Lowell et al., 2013) and SE Tibet (Yang et

al., 2008). Glaciers in northwestern North America were similar in size

during the MCA compared to the peak during the LIA, probably driven

by increased winter precipitation (Koch and Clague, 2011).

There is high conﬁdence that glaciers at times have been smaller than

at the end of the 20th century in the Alps (Joerin et al., 2008; Ivy-Ochs

et al., 2009; Goehring et al., 2011), Scandinavia (Nesje et al., 2011),

Altai in Central Asia (Agatova et al., 2012), Bafﬁn Island (Miller et al.,

2005), Greenland (Larsen et al., 2011; Young et al., 2011), Spitsbergen

(Humlum et al., 2005), but the precise glacier extent in the previous

warm periods of the Holocene is often difﬁcult to assess. While ear-

ly-to-mid-Holocene glacier minima can be attributed with high con-

ﬁdence to high summer insolation (see Section 5.5.1.1), the current

glacier retreat, however, occurs within a context of orbital forcing that

would be favourable for NH glacier growth. If retreats continue at cur-

rent rates, most extratropical NH glaciers will shrink to their minimum

extent, that existed between 8 ka and 6 ka (medium conﬁdence) (e.g.,

Anderson et al., 2008); and ice shelves on the Antarctic peninsula will

retreat to an extent unprecedented through Holocene (Hodgson, 2011;

Mulvaney et al., 2012).

5.5.4 Monsoon Systems and Convergence Zones

This subsection focuses on internally and externally driven variability of

monsoon systems during the last millennium. Abrupt monsoon chang-

es associated with Dansgaard–Oeschger and Heinrich events (Figure

5.4b, e, h) are further assessed in Section 5.7.1. Orbital-scale monsoon

(Figure 5.4a, d, g) changes are evaluated in Section 5.3.2.3.

Hydrological proxy data characterizing the intensity of the East Asian

monsoon (South American monsoon) show decreased (increased)

hydrological activity during the LIA as compared to the MCA (medium

conﬁdence) (Figure 5.4f, i) (Zhang et al., 2008; Bird et al., 2011; Vuille et

al., 2012). These shifts were accompanied by changes in the occurrence

of megadroughts (high conﬁdence) in parts of the Asian monsoon

region (Buckley et al., 2010; Cook et al., 2010a) (Figure 5.13). Lake sed-

iment data from coastal eastern Africa document dry conditions in the

late MCA, a wet LIA, and return toward dry conditions in the 18th or

422

Chapter 5 Information from Paleoclimate Archives

early 19th century (Verschuren et al., 2000; Stager et al., 2005; Versch-

uren et al., 2009; Tierney et al., 2011; Wolff et al., 2011), qualitatively

similar to the South American monsoon proxies in Figure 5.4i, whereas

some inland and southern African lakes suggest dry spells during the

LIA (Garcin et al., 2007; Anchukaitis and Tierney, 2013). Rainfall pat-

terns associated with the Paciﬁc ITCZ also shifted southward during

the MCA/LIA transition in the central equatorial Paciﬁc (Sachs et al.,

2009). Extended intervals of monsoon failures and dry spells have been

reconstructed for the last few millennia for west Africa (Shanahan et

al., 2009), east Africa (Wolff et al., 2011), northern Africa (Esper et al.,

2007b; Touchan et al., 2008; Touchan et al., 2011), India and southeast-

ern Asia (Zhang et al., 2008; Berkelhammer et al., 2010; Buckley et al.,

2010; Cook et al., 2010a) and Australia (Mohtadi et al., 2011).

On multi-decadal-to-centennial time scales, inﬂuences of North Atlan-

tic SST variations have been demonstrated for the North and South

African monsoon, and the Indian and East Asian summer monsoons

(see Figure 5.4c for monsoon regions), both using proxy reconstruc-

tions (Feng and Hu, 2008; Shanahan et al., 2009) and GCM simula-

tions (Lu et al., 2006; Zhang and Delworth, 2006; Wang et al., 2009;

Luo et al., 2011). These simulations suggest that solar and volcanic

forcing (Fan et al., 2009; Liu et al., 2009a; Man et al., 2012) may exert

only weak regional inﬂuences on monsoon systems (Figure 5.4f, i). A

ﬁve-member multi-model ensemble mean of PMIP3/CMIP5 simula-

tions (Table 5.A.1) exhibits decreased standardized monsoon rainfall

accompanying periods of reduced solar forcing during the LIA in the

East Asian monsoon regions (Figure 5.4f). There is, however, a consid-

erable inter-model spread in the simulated annual mean precipitation

response to solar forcing with the multi-model mean, explaining on

average only ~25 ± 15% (1 standard deviation) of the variance of the

individual model simulations. An assessment of the pre-instrumental

response of monsoon systems to volcanic forcing using paleo-proxy

data has revealed wetter conditions over southeast Asia in the year of

a major volcanic eruption and drier conditions in central Asia (Anchu-

kaitis et al., 2010), in contrast to GCM simulations (Oman et al., 2005;

Brovkin et al., 2008; Fan et al., 2009; Schneider et al., 2009).

5.5.5 Megadroughts and Floods

Multiple lines of proxy evidence from tree rings, lake sediments, and

speleothems indicate with high conﬁdence that decadal or mul-

ti-decadal episodes of drought have been a prominent feature of

North American Holocene hydroclimate (e.g., Axelson et al., 2009; St.

George et al., 2009; Cook et al., 2010a, 2010b; Shuman et al., 2010;

Woodhouse et al., 2010; Newby et al., 2011; Oswald and Foster, 2011;

Routson et al., 2011; Stahle et al., 2011; Stambaugh et al., 2011; Laird

et al., 2012; Ault et al., 2013). During the last millennium, western

North America drought reconstructions based on tree ring informa-

tion (Figure 5.13) show longer and more severe droughts than today,

particularly during the MCA in the southwestern and central United

States (Meko et al., 2007; Cook et al., 2010b). The mid-14th

century

cooling coincides in southwestern North America with a shift towards

overall wetter conditions (Cook et al., 2010a). In the Paciﬁc Northwest,

contrasting results emerge from lake sediment records, indicating

wetter conditions during the MCA (Steinman et al., 2013), and tree-

ring data showing no substantial change (Zhang and Hebda, 2005;

Cook et al., 2010a). In Scandinavia, new tree-ring based reconstruc-

tions show a multi-centennial summer drought phase during Medieval

times (900–1350) (Helama et al., 2009), while lake sediment proxies

from the same region suggest wetter winters (Luoto et al., 2013).

New tree-ring reconstructions from the southern-central (Wilson et al.,

2013) and southeastern British Isles (Cooper et al., 2013) do not reveal

multi- centennial drought during medieval times, but rather alternating

multidecades of dry and wet periods. Wilson et al. (2013) reconstructed

drier conditions between ~1300 and the early 16th century. Büntgen

et al. (2011b) identiﬁed exceptionally dry conditions in central Europe

from 200 to 350 and between 400 and 600. Numerous tree-ring

records from the eastern Mediterranean testify to the regular occur-

rence of droughts in the past few millennia (e.g., Akkemik et al., 2008;

Nicault et al., 2008; Luterbacher et al., 2012). In northern Africa, Esper

et al. (2007b) and Touchan et al. (2008; 2011) show severe drought

events through the last millennium, particularly prior to 1300, in the

1400s, between 1700 and 1900, and in the most recent instrumental

data. Using multiple proxies from Chile, Boucher et al. (2011) inferred

wetter conditions during 1000–1250, followed by much drier period

until 1400 and wetter conditions similar to present afterwards, while

Ledru et al. (2013) reconstructed a dry MCA-LIA transition until 1550.

For the South American Altiplano Morales et al. (2012) found periods

of drier conditions in the 14th, 16th, and 18th centuries, as well as a

modern drying trend.

Reconstruction of past ﬂooding from sedimentary, botanical and his-

torical records (Brázdil et al., 2006; Baker, 2008; Brázdil et al., 2012)

provides a means to compare recent large, rare ﬂoods, and to ana-

lyse links between ﬂooding and climate variability. During the last few

millennia, ﬂood records reveal strong decadal to secular variability

and non-stationarity in ﬂood frequency and clustering of paleoﬂoods,

which varied among regions. In Europe, modern ﬂood magnitudes are

not unusual within the context of the last 1000 years (e.g., Brázdil et

al., 2012). In Central Europe, the Elbe and the Oder/Odra Rivers show

a decrease in the frequency of winter ﬂoods during the last 80 to 150

years compared to earlier centuries, while summer ﬂoods indicate no

signiﬁcant trend (Mudelsee et al., 2003) (Figure 5.14f–i). In the Alps,

paleoﬂood records derived from lake sediments have shown a higher

ﬂood frequency during cool and/or wet phases (Stewart et al., 2011;

Giguet-Covex et al., 2012; Wilhelm et al., 2012), a feature also found

in Central Europe (Starkel et al., 2006) and the British Isles (Macklin

et al., 2012). In the western Mediterranean, winter ﬂoods were more

frequent during relatively cool and wet climate conditions of the LIA

(Benito et al., 2003b; Piccarreta et al., 2011; Luterbacher et al., 2012;

Figure 5.14a), whereas autumn ﬂoods reﬂect multi-decadal variations

(Benito et al., 2010; Machado et al., 2011; Figure 5.14b, c). In China,

extraordinary paleoﬂood events in the Yellow, Weihe and Qishuihe

rivers, occurred synchronously with severe droughts and dust accu-

mulations coinciding with a monsoonal shift, the most severe ﬂoods

dated at 3.1 ka (Zha et al., 2009; Huang et al., 2012). In India, ﬂood

frequencies since 1950 are the largest for the last several hundred

years for eight rivers, interpreted as a strengthening of the monsoon

conditions after the LIA (Kale, 2008). In southwestern United States,

increased frequency of high-magnitude paleoﬂoods coincide with peri-

ods of cool, wet climate, whereas warm intervals including the MCA,

corresponded with signiﬁcant decreases in the number of large ﬂoods

(Ely et al., 1993). In the Great Plains of North America, the frequency

of large ﬂoods increased signiﬁcantly around 850 with magnitudes

423

Information from Paleoclimate Archives Chapter 5

800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900

−4

−2

PDSI

105

120

135

120

105

1350 1450 1550 1650 1750 1850 1950

−2

−1

Time (Year CE)

PDSI

(c)

(a)

(d)

(f)

40 20 0

Count

0 2 4 6 8 10

−10

−8

−6

−4

−2

Duration (yr)

Severity (Cumulative PDSI)

100

150

200

250

300

Interval (yr)

(b)

Severity

Interval

60 40 20 0

Count

0 5 10 15

−25

−20

−15

−10

−5

Duration (yr)

Severity (Cumulative PDSI)

100

200

300

400

500

600

Interval (yr)

(e)

Severity

Interval

Time (Year CE)

Figure 5.13 | Severity, duration, and frequency of droughts in the Monsoon Asia (Cook et al., 2010b) and North American (Cook et al., 2004) Drought Atlases. The box in (a)

and (d) indicates the region over which the tree-ring reconstructed Palmer Drought Severity Index (PDSI) values have been averaged to form the regional mean time series shown

in (c) and (f), respectively. Solid black lines in (c) and (f) are a 9-year Gaussian smooth on the annual data shown by the red and blue bars. The covariance of drought (PDSI <0)

duration and cumulative severity for each region is shown in panels (b) and (e) by the red circles (corresponding to the left y-axes), along with the respective marginal frequency

histograms for each quantity. Not shown in b) is an outlier with an apparent duration of 24 years, corresponding to the ‘Strange Parallels’ drought identiﬁed in Cook et al. (2010b).

Intervals between droughts of given durations are shown in the same panels and are estimated as the mean interval between their occurrence, with the minimum and maximum

reconstructed intervals indicated (corresponding to the right y-axes, shown as connected lines and their corresponding range). No error bars are present if there are fewer than three

observations of a drought of that duration. The period of analysis is restricted by the availability of tree-ring data to the period 1300–1989 for Monsoon Asia, following Cook et al.

(2010a), and from 800 to 1978 for southwestern North America, following Cook et al. (2004).

424

Chapter 5 Information from Paleoclimate Archives

Figure 5.14 | Flood frequency from paleoﬂoods, historical and instrumental records in selected European rivers. Depicted is the number of ﬂoods exceeding a particular discharge

threshold or ﬂood height over periods of 20 years (bidecadal). Flood categories include rare or catastrophic ﬂoods (CAT) associated with high ﬂood discharge or severe damages,

and extraordinary ﬂoods (EXT) causing inundation of the ﬂoodplain with moderate-to-minor damages. Legend at each panel indicates for each category the period of record in

years, number of ﬂoods (n) over the period, and the average occurrence interval (T in years). (a) Tagus River combined paleoﬂood, historical and instrumental ﬂood records from

Aranjuez and Toledo with thresholds of 100–400 m

–1

(EXT) and >400 m

–1

(CAT) (data from Benito et al., 2003a; 2003b). (b) Segura River Basin (SE Spain) documentary and

instrumental records at Murcia (Barriendos and Rodrigo, 2006; Machado et al., 2011). (c) Gardon River combined discharges from paleoﬂoods at La Baume (Sheffer et al., 2008),

documented ﬂoods (since the 15th century) and historical and daily water stage readings at Anduze (1741–2005; Neppel et al., 2010). Discharge thresholds referred to Anduze are

1000 to 3000 m

–1

(EXT), >3000 m

–1

(CAT). At least ﬁve ﬂoods larger than the 2002 ﬂood (the largest in the gauged record) occurred in the period 1400–1800 (Sheffer et al.,

2008). (d) Tiber River ﬂoods in Rome from observed historical stages (since 1100; Camuffo and Enzi, 1996; Calenda et al., 2005) and continuous stage readings (1870 to present)

at the Ripetta landing (Calenda et al., 2005). Discharge thresholds set at 2300 to 2900 m

–1

(EXT) and >2900 m

–1

(CAT; >17 m stage at Ripetta). Recent ﬂooding is difﬁcult

to evaluate in context due to river regulation structures. (e) River Ouse at York combined documentary and instrumental ﬂood record (Macdonald and Black, 2010). Discharge

thresholds for large ﬂoods were set at 500 m

–1

(CAT) and 350 to 500 m

–1

(EXT). (f) Vltava River combined documentary and instrumental ﬂood record at Prague (Brázdil et al.,

2005) discharge thresholds: CAT, ﬂood index 2 and 3 or discharge >2900 m

–1

; EXT ﬂood index 1 or discharge 2000 to 2900 m

–1

. (g) Elbe River combined documentary and

instrumental ﬂood record (Mudelsee et al., 2003). Classes refer to Mudelsee et al. (2003) strong (EXT) and exceptionally strong (CAT) ﬂooding. (h) Oder River combined documentary

and instrumental ﬂood record (Mudelsee et al., 2003). The map shows the location of rivers used in the ﬂood frequency plots. Note that ﬂood frequencies obtained from historical

sources may be down biased in the early part of the reported periods owing to document loss.

(a) Tagus River at Aranjuez, Central Spain

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Large - catastrophic floods (437

; n= 9; T=48 yr)

Extraordinary floods (437 yr; n=31; T=14 yr)

Flow

regulation

(b) Segura River, SE Spain

1100

Large - catastrophic floods (600

; n=13; T=46 yr)

Extraordinary floods (270 yr; n=20; T=13 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Large - catastrophic floods (710

; n=49; T=15 yr)

Extraordinary floods (625

; n=105; T=6 yr)

1200 1300 1400 1500 1600 1700 1800 1900 2000

(d) Tiber River, Rome, Italy

Large - catastrophic floods (819

; n=20; T=41 yr)

Extraordinary floods (517

; n=40; T=13 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900

Large - catastrophic floods (685

; n=21; T=33 yr)

Extraordinary floods (941 yr; n=91; T=10 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Large -castastrophic floods (410

; n=25; T=16 yr)

Extraordinary floods (410

; n=65; T=6 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Time (Year CE)

(f) Vltava River at Prague, Czech Republic

Bidecadal frequency

Large - catastrophic floods (505

; n=19; T=26 yr)

Extraordinary floods (505 yr; n=63; T=8 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Large - catastrophic floods (504

; n=26; T=19 yr)

Extraordinary floods (650 yr; n=67; T=10 yr)

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

(h) Oder River, Western Poland/Germany

(g) Elbe River, Germany

(e) Ouse River at York, UK

500

10 00 15 00

(km)

50°40°30°20°

10°

0°

-10°-

20°

60°

50°

40°

30°

20°

10°

0°

Gauge

record

Bidecadal frequency

Bidecadal frequencyBidecadal frequency

Bidecadal frequency

Flow

regulation

Gauge

record

Gauge

record

Gauge

record

Gauge

record

Gauge

record

Discontinuous

documentary archives

Time (Year CE)

425

Information from Paleoclimate Archives Chapter 5

roughly two to three times larger than those of the 1972 ﬂood (Harden

et al., 2011). South America large ﬂooding in the Atacama and Peruvi-

an desert streams originated in the highland Altiplano and were par-

ticularly intense during El Niño events (Magilligan et al., 2008). In the

winter rainfall zone of southern Africa, the frequency of large ﬂoods

decreased during warmer conditions (e.g., from 1425 to 1600 and

after 1925) and increased during wetter, colder conditions (Benito et

al., 2011).

In summary, there is high conﬁdence that past ﬂoods larger than

recorded since the 20th century have occurred during the past 500

years in northern and central Europe, western Mediterranean region,

and eastern Asia. There is, however, medium conﬁdence that in the

Near East, India, central North America, modern large ﬂoods are com-

parable to or surpass historical ﬂoods in magnitude and/or frequency.

5.6 Past Changes in Sea Level

This section discusses evidence for global mean sea level (GMSL)

change from key periods. The MPWP (Table 5.1) has been selected as a

period of higher than present sea level (Section 5.6.1), warmer temper-

ature (Section 5.3.1) and 350-450 ppm atmospheric CO

concentration

(Section 5.2.2.2). Of the recent interglacial periods with evidence for

higher than present sea level, the LIG has the best-preserved record

(Section 5.6.2). For testing glacio-isostatic adjustment (GIA) models, the

principal characteristics of Termination I, including Meltwater Pulse-1A

(Section 5.6.3) is assessed. For the Holocene, the emphasis is on the

last 6000 years when ice volumes stabilized near present-day values,

providing the baseline for discussion of anthropogenic contributions.

5.6.1 Mid-Pliocene Warm Period

Estimates of peak sea levels during the MPWP (Table 5.1, Section

5.3.1) based on a variety of geological records are consistent in sug-

gesting higher-than-present sea levels, but they range widely (10 to 30

m; Miller et al., 2012a), and are each subject to large uncertainties. For

example, coastal records (shorelines, continental margin sequences)

are inﬂuenced by GIA, with magnitudes of the order of 5 m to 30 m

for sites in the far and near ﬁelds of ice sheets, respectively (Raymo et

al., 2011), and global mantle dynamic processes (Moucha et al., 2008;

Müller et al., 2008) may contribute up to an additional ±10 m. Conse-

quently, both signals can be as large as the sea level estimate itself and

current estimates of their amplitudes are uncertain.

Benthic d

O records are better dated than many coastal records

and provide a continuous time series, but the d

O signal reﬂects ice

volume, temperature and regional hydrographic variability. During the

mid-Pliocene warm interval, the 0.1 to 0.25‰ anomalies recorded in

the LR04 benthic d

O stack (Lisiecki and Raymo, 2005) would trans-

late into ~12 to 31 m higher than present GMSL, if they reﬂected only

ice volume. Conversely, these anomalies could be explained entirely by

warmer deep-water temperatures (Dowsett et al., 2009). Attempts to

constrain the temperature component in benthic d

O records conclude

higher than present GMSL during the MPWP with large uncertainties

(±10 m) (Dwyer and Chandler, 2009; Naish and Wilson, 2009; Sosdian

and Rosenthal, 2009; Miller et al., 2012a).

The ﬁrst appearance of ice-rafted debris across the entire North Atlan-

tic indicates that continental-scale ice sheets in North America and

Eurasia did not develop until about 2.7 Ma (Kleiven et al., 2002). This

suggests that MPWP high sea levels were due to mass loss from the

GIS, the WAIS and possibly the East Antarctic Ice sheet (EAIS). Sed-

imentary record from the Ross Sea indicates that the WAIS and the

marine margin of EAIS retreated periodically during obliquity-paced

interglacial periods of MPWP (Naish et al., 2009a). Reconstructed SSTs

for the ice free seasons in the Ross Sea range from 2°C to 8°C (McKay

et al., 2012b), with mean values >5°C being, according to one ice sheet

model, above the stability threshold for ice shelves and marine portions

of the WAIS and EAIS (Pollard and DeConto, 2009; see also Section

5.8.1). A synthesis of the geological evidence from the coastal regions

of the Transantarctic Mountains (Barrett, 2013) and an iceberg-raft-

ed debris record offshore of Prydz Bay (Passchier, 2011) also supports

coastal thinning and retreat of the EAIS between about 5 to 2.7 Ma.

In response to Pliocene climate, ice sheet models consistently produce

near-complete deglaciation of GIS (+7 m) and WAIS (+4 m) and retreat

of the marine margins of EAIS (+3 m) (Lunt et al., 2008; Pollard and

DeConto, 2009; Hill et al., 2010), altogether corresponding to a GMSL

rise of up to 14 m.

In summary, there is high conﬁdence that GMSL was above present,

due to deglaciation of GIS, WAIS and areas of EAIS, and that sea level

was not higher than 20 m above present during the interglacials of

the MPWP.

5.6.2 The Last Interglacial

Proxy indicators of sea level, including emergent shoreline deposits

(Blanchon et al., 2009; Thompson et al., 2011; Dutton and Lambeck,

2012) and foraminiferal d

O records (Siddall et al., 2003; Rohling et al.,

2008a; Grant et al., 2012) are used to reconstruct LIG sea levels. Implic-

it in these reconstructions is that geophysical processes affecting the

elevation of the sea level indicators (uplift, subsidence, GIA) have been

properly modelled and/or that the sea level component of the stable

isotope signal has been properly isolated. Particularly important issues

with regard to the LIG are (1) the ongoing debate on its initiation and

duration (cf. 130 to 116 ka, Stirling et al., 1998; 124 to 119 ka, Thomp-

son and Goldstein, 2005), due to coral geochronology issues; (2) the

magnitude of its maximum rise; and (3) sea level variability within the

interval. Foraminiferal d

O records can constrain (2) and possibly (3).

Emergent shorelines on tectonically active coasts can constrain (1) and

(3) and possibly (2) if vertical tectonic rates are independently known.

Shorelines on tectonically stable coasts can constrain all three issues.

Here evidence from emergent shorelines that can be dated directly is

emphasized.

5.6.2.1 Magnitude of the Last Interglacial Sea Level Rise

AR4 assessed that global sea level was likely between 4 and 6 m higher

during the LIG than in the 20th century. Since AR4, two studies (Kopp

et al., 2009; Dutton and Lambeck, 2012) have addressed GIA effects

from observations of coastal sites.

Kopp et al. (2009) obtained a probabilistic estimate of GMSL based

on a large and geographically broadly distributed database of LIG sea

426

Chapter 5 Information from Paleoclimate Archives

level indicators (Figure 5.15a). Their analysis accounted for GIA effects

as well as uncertainties in geochronology, the interpretation of sea

level indicators, and regional tectonic uplift and subsidence. They con-

cluded that GMSL was very likely +6.6 m and likely +8.0 m relative

to present, and that it is unlikely to have exceeded +9.4 m, although

some of the most rapid and sustained rates of change occur in the

early period when GMSL was still below present (Figure 5.15a).

Dutton and Lambeck (2012) used data from two tectonically stable far-

ﬁeld areas (areas far from the former centres of glaciation), Australia

and the Seychelles islands. At these sites, in contrast to sites near the

former ice margins, the isostatic signals are less sensitive to the choice

of parameters deﬁning the Earth rheology and the glacial ice sheets

(Lambeck et al., 2012). On the west coast of Australia, the highest LIG

reef elevations are at +3.5 m and the inferred paleo-sea level, allowing

for possible reef erosion, is about +5.5 m relative to present. In the

Seychelles, LIG coral reefs occur from 0 m to 6 m, but also possibly as

high as ~9 m (Israelson and Wohlfarth, 1999, and references therein).

Ten of the eleven LIG coral samples from the Seychelles used in Dutton

and Lambeck (2012) have reef elevation estimates ranging from +2.1

to +4 m relative to present; whereas a single LIG coral sample has a

reef elevation estimate of +6 m. Additional results are needed to sup-

port an estimate of a maximum LIG sea level at the Seychelles of + 9

m relative to present.

In conclusion, there is very high conﬁdence that the maximum GMSL

during the LIG was, for several thousand years, at least 5 m higher

than present but that GMSL at this time did not exceed 10 m (high

conﬁdence). The best estimate from the two available studies is 6 m

higher than present.

5.6.2.2 Evidence for Last Interglacial Sea Level Variability

Since AR4, there is evidence for meter-scale variability in local LIG sea

level between 126 ka and 120 ka (Thompson and Goldstein, 2005;

Hearty et al., 2007; Rohling et al., 2008a; Kopp et al., 2009; Thompson

et al., 2011). However, there are considerable differences in the timing

and amplitude of the reported ﬂuctuations due to regional sea level

variability and uncertainties in sea level proxies and their ages.

Two episodes of reef building during the LIG have been reported on the

Yucatan coast (Blanchon et al., 2009) and in the Bahamas (Chen et al.,

1991; Thompson et al., 2011). Blanchon et al. (2009) provide evidence

of Yucatan reef growth early in the LIG at a relative sea level of +3 m,

followed by a later episode at +6 m. Thompson et al. (2011) inferred a

+4 m relative sea level at ~123 ka, followed by a fall to near present,

and ﬁnally a rise to +6 m at ~119 ka. This yields a rate of sea level

change in the Bahamas of ~2.6 m kyr

–1

, although the higher estimate

at the end of the interval may reﬂect GIA effects that result in a rise in

relative sea level at these locations (Dutton and Lambeck, 2012).

LIG sea level rise rates of between 1.1 and 2.6 m per century have

been estimated based on a foraminiferal d

O record from the Red Sea

(Rohling et al., 2008a). However, the original Red-Sea chronology was

based on a short LIG duration of 124 to 119 ka, after Thompson and

Goldstein (2005). The longer LIG duration of 130 to 116 ka indicated

by the coral data (Stirling et al., 1998) reduces these rates to 0.4 to

0.9 m per century, and a revised chronology of the Red Sea sea level

record adjusted to ages from Soreq Cave yields estimates of sea level

rise rates of up to 0.7 m per century when sea level was above present

level during the LIG (Grant et al., 2012).

In their probabilistic assessment of LIG sea level, Kopp et al. (2013)

concluded that it was extremely likely that there were at least two

peaks in sea level during the LIG. They further concluded that during

the interval following the initial peak at ~126 ka (Figure 5.15a) it is

likely that there was a period in which GMSL rose at an average rate

exceeding 3 m kyr

–1

, but unlikely that this rate exceeded 7 m kyr

–1

In summary, there is evidence for two intra-LIG sea level peaks (high

conﬁdence) during which sea level varied by up to 4 m (medium conﬁ-

dence). The millennial-scale rate of sea level rise during these periods

exceeded 2 m kyr

–1

(high conﬁdence).

5.6.2.3 Implications for Ice Sheet Loss During the

Last Interglacial

The principal sources for the additional LIG meltwater are the GIS,

WAIS and the low elevation, marine-based margins of the EAIS. An

upper limit for the contributions from mountain glaciers is ~0.42 ±

0.11 m if all present-day mountain glaciers melted (cf. Section 4.3).

The estimated LIG ocean thermal expansion contribution is 0.4 ± 0.3

m (McKay et al., 2011).

Sedimentological evidence indicates that southern Greenland was not

ice-free during the LIG (Colville et al., 2011). Since AR4, the evidence

for LIG ice layers in Greenland ice cores, which was ambiguous from

Dye 3 and unequivocal from Summit and NGRIP ice cores (summarised

in Masson-Delmotte et al., 2011a), has been strengthened. Data from

the new NEEM ice core (NEEM community members, 2013; see Figure

5.16 for locations) point to an unequivocal existence of ice throughout

the LIG with elevations differing a few hundred meter from present,

possibly decreasing in elevation by ~ 400 m ± 350 m between 128 and

122 ka BP. GIS simulations give an average contribution of ~2.3 m to

LIG GMSL (1.5 m, 1.9 m, 1.4 m and 4.3 m respectively for four models

illustrated in Figure 5.16). Each model result has been selected from

a series of runs within a range of parameter uncertainties that yield

predictions consistent with the occurrence of ice at NEEM and the ele-

vation of that ice reconstructed from the ice core record. In summary,

the GIS simulations that are consistent with elevation changes from

the ice core analysis show limited ice retreat during this period such

that this ice sheet very likely contributed between 1.4 and 4.3 m sea

level equivalent, implying with medium conﬁdence a contribution from

the Antarctic ice sheet.

One model of WAIS glacial–interglacial variability shows very little

difference in ice volumes between the LIG and present (Pollard and

DeConto, 2009) (Figure 5.15g), when the surface climate and ocean

melt term were parameterised using the global benthic d

O record for

the last 5 Ma. Direct geological evidence of ﬂuctuations in the extent

of WAIS margin during the LIG is equivocal due to inadequate age

control on two sediment cores which imply that open-water conditions

existed in the southeastern sector of the Ross Ice Shelf at some time in

the last 1 Ma (Scherer et al., 1998; McKay et al., 2011; Vaughan et al.,

427

Information from Paleoclimate Archives Chapter 5

Figure 5.15 | Sea level during the Last Interglacial (LIG) period. (a) Proxy-derived estimate of global mean sea level (GMSL) anomaly from Kopp et al. (2013). Mean GMSL (red

line), 67% conﬁdence limits (blue dashed lines) and 95% conﬁdence limits (green dashed lines). The chronology is based on open-system U/Th dates. (b) Local LIG relative sea level

reconstructions from Western Australia based on in situ coral elevations (red) that pass diagenetic screening (Dutton and Lambeck, 2012). Age error bars correspond to 2 standard

deviation uncertainties. All elevations have been normalised to the upper growth limit of corals corresponding to mean low water spring or mean low sea level. The blue line indicates

the simplest interpretation of local sea level consistent with reef stratigraphy and should be considered as lower limits by an amount indicated by the blue upper limit error bars.

The chronology is based on closed system U/Th dates. (c) Predicted sea levels for selected sites in the Caribbean and North Atlantic in the absence of tectonics with the assump-

tion that ice volumes during the interval from 129 to 116 ka are equal to those of today (Lambeck et al., 2012) illustrating the spatial variability expected across the region due

to glacio-isostatic effects of primarily the MIS-6 and MIS-2 deglaciations (see also Raymo and Mitrovica, 2012). The reference ice-volume model for the LIG interval (blue shaded),

earth rheology and ice sheet parameters are based on rebound analyses from different regions spanning the interval from Marine Isotope Stage 6 to the present (c.f. Lambeck et al.,

2006). LIG sea level observations from these sites contain information on these ice histories and on GMSL. (d) Same as (c) but for different sites along the Western Australia coast

contributing to the data set in (b). The dependence on details of the ice sheet and on Earth-model parameters is less important at these sites than for those in (c). Thus data from

these locations, assuming tectonic stability, is more appropriate for estimating GMSL. (e) The Western Australian reconstructed evidence (blue) from (b), compared with the model-

predicted result (red) for a reference site midway between the northern and southern most localities. The difference between the reconstructed and predicted functions provides

an estimate of GMSL (green). Uncertainties in this estimate (67% conﬁdence limits) include the observational uncertainties from (b) and model uncertainties (see e.g., Lambeck et

al., 2004a, for a treatment of model errors). (f) Simulated contribution of GIS to GMSL change (black, Q, Quiquet et al. (2013); red, R, Robinson et al. (2011); blue, S, Stone et al.

(2013), The Q, R, S correspond to the labels in Figure 5.16). (g) Simulated total GMSL contribution from the GIS (Q, R, S as in panel (f)) and the Antarctic ice sheet contribution (PD)

according to Pollard and DeConto (2009). (h) Central Greenland surface-air temperature anomalies for summer (June–August, JJA) used for ice sheet simulations displayed in panel

(f) and in Figure 5.16. Anomalies in all panels are calculated relative to present.

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Chapter 5 Information from Paleoclimate Archives

2011) and in the vicinity of the northwestern Ross Ice Shelf within the

last 250,000 years during MIS 7 or LIG (McKay et al., 2012a). Ackert et

al. (2011) dated glacial erratics and moraines across the Ohio Moun-

tain Range of the Transantarctic Mountains and concluded that the

ice elevations were similar during the LIG and today, but such results

cannot be extrapolated beyond this region. East Antarctic ice core LIG

data may reﬂect the impact of a reduced WAIS due to climatic effects

(Holden et al., 2010b) but not through isostatic effects (Bradley et al.,

2012). Modelling and ice core data suggest EAIS may have retreated in

the Wilkes Basin (Bradley et al., 2012).

In summary, no reliable quantiﬁcation of the contribution of the Ant-

arctic ice volume to LIG sea level is currently possible. The only availa-

ble transient ice sheet model simulation (Pollard and DeConto, 2009)

does not have realistic boundary conditions, not enough is known

about the subsurface temperatures and there are few direct observa-

tional constraints on the location of ice margins during this period. If

the above inference of the contribution to GMSL from the GIS (5.6.2.1)

is correct, the full GMSL change (section 5.6.2.2) implies signiﬁcantly

less LIG ice in Antarctica than today, but as yet this cannot be support-

ed by the observational and model evidence.

5.6.3 Last Glacial Termination and Holocene

The onset of melting of the LGM ice sheets occurred at approximately

20 ka and was followed by a GMSL rise of ~130 m in ~13 kyr (Lambeck

et al., 2002b). Coeval with the onset of the Bølling warming in the NH,

a particularly rapid rise of ~ 20 m occurred within ~ 340 years (Melt-

water Pulse 1A, MWP-1A), as most recently documented by Deschamps

et al. (2012) from a new Tahiti coral record. At this location sea level

rose between 14 and 18 m at a rate approaching 5 m per century. The

source of MWP-1A continues to be widely debated with most attention

being on scenarios in which the Antarctic ice sheet contributed either

signiﬁcantly (Clark et al., 2002, 2009; Bassett et al., 2005) or very little

(Bentley et al., 2010; Mackintosh et al., 2011). Evidence of rapid WAIS

retreat at around the time of MWP-1A is also indicated by analysis of

marine sediment cores (e.g., Kilfeather et al., 2011; Smith et al., 2011).

If the Antarctic ice sheet was the major contributor to MWP-1A then

it must have contained at least 7·10

more ice than at present

(equivalent to ~17 m GMSL), which is about twice the difference in

Antarctic ice volume between the LGM and present found by White-

house et al. (2012). Because of the Earth-ocean (including gravita-

tional, deformational and rotational) response to rapid changes in ice

volume, the amplitude of the associated sea level change is spatially

variable (Clark et al., 2002) and can provide insight into the source

region. Based on the comparison of the new Tahiti record with records

from Barbados (Fairbanks, 1989) and the Sunda Shelf (Hanebuth et al.,

2011), Deschamps et al. (2012) conclude that a signiﬁcant meltwater

contribution to GMSL, of at least 7 m, originated from Antarctica. From

ice sheet modelling, Gregoire et al. (2012) argued that the separation

of the North American Laurentide and Cordilleran ice sheets may in

part be the cause of MWP-1A, contributing ~9 m in 500 years. Another

ice sheet modelling study Carlson et al. (2012) suggests a contribution

of 6 to 8 m in 500 years from the Laurentide at the onset of the Bølling

warming over North America. These studies indicate that there are no

glaciological impediments to a major North American contribution to

MWP-1A. In contrast, there are as yet no modelling results that show a

rapid retreat or partial collapse of the Antarctic ice sheet at that time.

Figure 5.16 | Simulated GIS elevation at the Last Interglacial (LIG) in transient (Q, R, S) and constant-forcing experiments (B). (Q) GRISLI ice sheet model with transient climate forc-

ing derived from IPSL simulations and paleoclimate reconstructions (Quiquet et al., 2013). (R) Simulation, most consistent with independent evidence from ice cores, from ensemble

runs SICOPOLIS ice sheet model driven by transient LIG climate simulations downscaled from CLIMBER2 with the regional model REMBO (Robinson et al., 2011). (S) As R but from

ensemble simulations with the Glimmer ice sheet model forced with transient climate forcing from 135 to 120 ka with HadCM3 (Stone et al., 2013). (B) SICOPOLIS ice model forced

with a constant Eemian climate simulation of IPSL (at 126 ka), running for 6000 years starting from fully glaciated present-day GIS (Born and Nisancioglu, 2012). White squares in

each panel show the locations of ice core sites: Greenland Ice Core Project/Greenland Ice Sheet Project (GRIP/GISP) from the summit (G), North Greenland Ice Core Project (NGRIP)

(NG), North Greenland Eemian Ice Drilling (NEEM) (NE), Camp Century (C), and Dye3 (D). For ice sheet simulations using transient climate forcing, the minimum in ice volume is

illustrated. All panels use original model resolution and grids. Below each panel, maximum contribution to global mean sea level rise and time of minimum ice volume are denoted

together with information on experimental design (either “transient” run of the Interglacial period starting from the former glacial or “equilibrium” run of a time slice at the peak

interglacial). The differences in model outputs regarding timing and ice elevations result from different methodologies (e.g., transient climate change or equilibrium climate, with the

latter assumption leading to the highest estimate of the four models), melt schemes (van de Berg et al., 2011), and the reference climate input (Quiquet et al., 2013).

429

Information from Paleoclimate Archives Chapter 5

-2

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(Giens, Port Gros, La Ciotat)

Laborel et al., 1994

relative sea level (m)

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North Queensland

Chapell et al., 1983

Lambeck, 2002

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Woodroffe et al., 2012

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Jevrejeva et al., 2008

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corrected for isostatic and tectonic contributions

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Age (ka BP)

(f)

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Lambeck et al., 2010

Age (year BP)

Relative sea level (m)

Age (ka BP)

Figure 5.17 | Observational evidence for sea level change in the recent and late Holocene. Left panels (a–d): High-resolution, relative sea level results from salt-marsh data at

representative sites, without corrections for glacial isostatic movement of land and sea surfaces. Locations are given on the map. The North Carolina (a) result is based on two nearby

locations, Tump Point (dark blue) and Sand Point (light blue). They are representative of other North American Atlantic coast locations (e.g., b; Kemp et al., 2011). The rate of change

occurring late in the 19th century are seen in all high resolution salt-marsh records—e.g., in New Zealand (c) (Gehrels et al., 2008; Gehrels and Woodworth, 2013) and in Spain

(d) (Leorri et al., 2008; García-Artola et al., 2009)—that extend into modern time and is consistent with Roman archaeological evidence (Lambeck et al., 2004b). The oscillation in

sea level seen in the North Carolina record at about 1000 years ago occurs in some (González and Törnqvist, 2009) but not all records (cf. Gehrels et al., 2011; Kemp et al., 2011).

Right hand side panels (g–j): Observational evidence for sea level change from lower resolution but longer period records. All records are uncorrected for isostatic effects resulting

in spatially variable near-linear trend in sea level over the 7000-year period. The Kiritimati record (Christmas Island) (g) consists of coral microatoll elevations whose fossil elevations

are with respect to the growth position of living microatolls (Woodroffe et al., 2012). The North Queensland record (h) is also based on microatoll evidence from several sites on

Orpheus Island (Chappell, 1983; Lambeck et al., 2002a). The data from Mediterranean France (i) is based on biological indicators (Laborel et al., 1994) restricted to three nearby

locations between which differential isostatic effects are less than the observational errors (Lambeck and Bard, 2000). The Spanish record (j) from estuarine sedimentary deposits is

for two nearby localities; Bilboa (dark blue) and Urdaibai (light blue) (Leorri et al., 2012). The two global records (central panels) are estimates of change in global mean sea level

from (i) the instrumental record (Jevrejeva et al., 2008) that overlaps the salt-marsh records, and (j) from a range of geological and archaeological indicators from different localities

around the world (Lambeck et al., 2010), with the contributing records corrected individually for the isostatic effects at each location.

Since AR4, high-resolution sea level records from different localities

suggest further periods of rapid ice-mass loss. For example, records

from Singapore indicate a rise of ~14 m from ~9.5 to 8.0 ka followed

by a short interval of a smaller rise centred on about 7.2 ka (Bird et

al., 2010, for Singapore) and records from the US Atlantic (Cronin et

al., 2007) and North Sea coasts (Hijma and Cohen, 2010) suggest a

rise at around ~9.0–7.5 ka that is possibly punctuated by one or two

short intervals of higher rates. These and similar rapid events have to

be interpreted against a background of rapid rise that is spatially vari-

able because of the residual isostatic response to the last deglaciation

(Milne and Mitrovica, 2008). Different explanations of these short-du-

ration events have been proposed: a multi-stage draining of glacial

Lake Agassiz (Hijma and Cohen, 2010), although estimates of the

amount of water stored in this lake are less than the required amount;

a rapid melting of the Labrador and Bafﬁn ice domes (Carlson et al.,

2007; Gregoire et al., 2012); or to Antarctic ice sheet decay (Bird et al.,

2007; Cronin et al., 2007).

Ocean volume between about 7 ka and 3 ka is likely to have increased

by an equivalent sea level rise of 2 to 3 m (Lambeck et al., 2004b, 2010)

(Figure 5.17). About 10% of this increase can be attributed to a mid-

to-late-Holocene ice reduction over Marie Byrd Land, West Antarctica

430

Chapter 5 Information from Paleoclimate Archives

Frequently Asked Questions

FAQ 5.2 | How Unusual is the Current Sea Level Rate of Change?

The rate of mean global sea level change—averaging 1.7 ± 0.2 mm yr

–1

for the entire 20th century and between

2.8 and 3.6 mm yr

–1

since 1993 (Chapter 13)—is unusual in the context of centennial-scale variations of the last two

millennia. However, much more rapid rates of sea level change occurred during past periods of rapid ice sheet dis-

integration, such as transitions between glacial and interglacial periods. Exceptional tectonic effects can also drive

very rapid local sea level changes, with local rates exceeding the current global rates of change.

‘Sea level’ is commonly thought of as the point where the ocean meets the land. Earth scientists deﬁne sea level as a

measure of the position of the sea surface relative to the land, both of which may be moving relative to the center

of the Earth. A measure of sea level therefore reﬂects a combination of geophysical and climate factors. Geophysi-

cal factors affecting sea level include land subsidence or uplift and glacial isostatic adjustments—the earth–ocean

system’s response to changes in mass distribution on the Earth, speciﬁcally ocean water and land ice.

Climate inﬂuences include variations in ocean temperatures, which cause sea water to expand or contract, changes

in the volume of glaciers and ice sheets, and shifts in ocean currents. Local and regional changes in these climate

and geophysical factors produce signiﬁcant deviations from the global estimate of the mean rate of sea level

change. For example, local sea level is falling at a rate approaching 10 mm yr

–1

along the northern Swedish coast

(Gulf of Bothnia), due to ongoing uplift caused by continental ice that melted after the last glacial period. In con-

trast, local sea level rose at a rate of ~20 mm yr

–1

from 1960 to 2005 south of Bangkok, mainly in response to subsid-

ence due to ground water extraction.

For the past ~150 years, sea level change has been recorded at tide gauge stations, and for the past ~20 years, with

satellite altimeters. Results of these two data sets are consistent for the overlapping period. The globally averaged

rate of sea level rise of ~1.7 ± 0.2 mm yr

–1

over the 20th century—and about twice that over the past two decades—

may seem small compared with observations of wave and tidal oscillations around the globe that can be orders of

magnitude larger. However, if these rates persist over long time intervals, the magnitude carries important con-

sequences for heavily populated, low-lying coastal regions, where even a small increase in sea level can inundate

large land areas.

Prior to the instrumental period, local rates of sea level change are estimated from indirect measures recorded

in sedimentary, fossil and archaeological archives. These proxy records are spatially limited and reﬂect both local

and global conditions. Reconstruction of a global signal is strengthened, though, when individual proxy records

from widely different environmental settings converge on a common signal. It is important to note that geologic

archives—particularly those before about 20,000 years ago—most commonly only capture millennial-scale changes

in sea level. Estimates of century-scale rates of sea level change are therefore based on millennial-scale information,

but it must be recognised that such data do not necessarily preclude more rapid rates of century-scale changes in

sea level.

Sea level reconstructions for the last two millennia offer an opportunity to use proxy records to overlap with, and

extend beyond, the instrumental period. A recent example comes from salt-marsh deposits on the Atlantic Coast

of the United States, combined with sea level reconstructions based on tide-gauge data and model predictions, to

document an average rate of sea level change since the late 19th century of 2.1 ± 0.2 mm yr

–1

. This century-long rise

exceeds any other century-scale change rate in the entire 2000-year record for this same section of coast.

On longer time scales, much larger rates and amplitudes of sea level changes have sometimes been encountered.

Glacial–interglacial climate cycles over the past 500,000 years resulted in global sea level changes of up to about 120

to 140 m. Much of this sea level change occurred in 10,000 to 15,000 years, during the transition from a full glacial

period to an interglacial period, at average rates of 10 to 15 mm yr

–1

. These high rates are only sustainable when

the Earth is emerging from periods of extreme glaciation, when large ice sheets contact the oceans. For example,

during the transition from the last glacial maximum (about 21,000 years ago) to the present interglacial (Holocene,

last 11,650 years), fossil coral reef deposits indicate that global sea level rose abruptly by 14 to 18 m in less than 500

years. This event is known as Meltwater Pulse 1A, in which the rate of sea level rise reached more than 40 mm yr

–1

These examples from longer time scales indicate rates of sea level change greater than observed today, but it should

be remembered that they all occurred in special circumstances: at times of transition from full glacial to intergla-

cial condition; at locations where the long-term after-effects of these transitions are still occurring; at

locations of

(continued on next page)

431

Information from Paleoclimate Archives Chapter 5

(Stone et al., 2003). Elevation histories derived from central Greenland

ice core data (Vinther et al., 2009; Lecavalier et al., 2013) have present-

ed evidence for thinning from 8 ka to 6 ka but no integrated observa-

tion-based estimate for the total ice sheet is available. Contributions

from mountain glaciers for this interval are unknown.

Resolving decimeter-scale sea level ﬂuctuations is critical for under-

standing the causes of sea level change during the last few millen-

nia. Three types of proxies have this capability: salt-marsh plants and

microfauna (foraminifera and diatoms) that form distinctive elevation

zones reﬂecting variations in tolerances to the frequency and duration

of tidal inundation (Donnelly et al., 2004; Horton and Edwards, 2006;

Gehrels et al., 2008; Kemp et al., 2009; Long et al., 2012); coral micro-

atolls found in intertidal environments close to lowest spring tides

(Woodroffe and McLean, 1990; Smithers and Woodroffe, 2001; Good-

win and Harvey, 2008); and coastal archaeological features construct-

ed with direct (e.g., ﬁsh ponds and certain harbour structures) or indi-

rect (e.g., changes in water-table level in ancient wells) relationships

to sea level (Lambeck et al., 2004b; Sivan et al., 2004; Auriemma and

Solinas, 2009; Anzidei et al., 2011). Of these, the salt-marsh records

are particularly important because they have been validated against

regional tide-gauge records and because they can provide near-contin-

uous records. The most robust signal captured in the salt-marsh proxy

sea level records from both the NH and SH is an increase in rate, late

in the 19th or in the early 20th century (Figure 5.17), that marks a

transition from relatively low rates of change during the late Holocene

(order tenths of mm yr

–1

) to modern rates (order mm yr

–1

) (see also FAQ

5.2). Variability in both the magnitude and the timing (1840–1920) of

FAQ 5.2 (continued)

major tectonic upheavals or in major deltas, where subsidence due to sediment compaction—sometimes ampliﬁed

by ground-ﬂuid extraction—dominates.

The instrumental and geologic record support the conclusion that the current rate of mean global sea level change

is unusual relative to that observed and/or estimated over the last two millennia. Higher rates have been observed

in the geological record, especially during times of transition between glacial and interglacial periods.

FAQ 5.2, Figure 1 | (a) Estimates of the average rate of global mean sea level change (in mm yr

–1

) for ﬁve selected time intervals: last glacial-to-interglacial transition;

Meltwater Pulse 1A; last 2 millennia; 20th century; satellite altimetry era (1993–2012). Blue columns denote time intervals of transition from a glacial to an interglacial

period, whereas orange columns denote the current interglacial period. Black bars indicate the range of likely values of the average rate of global mean sea level

change. Note the overall higher rates of global mean sea level change characteristic of times of transition between glacial and interglacial periods. (b) Expanded view

of the rate of global mean sea level change during three time intervals of the present interglacial.

(a)

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Last 2

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20th CenturySatellite

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2,000 years ago

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1900-1999 1993-2012

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Rate of sea-level change (mm yr

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Rate of sea-level change (mm yr

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432

Chapter 5 Information from Paleoclimate Archives

this acceleration has been reported (Gehrels et al., 2006, 2008, 2011;

Kemp et al., 2009, 2011), but Gehrels and Woodworth (2013) have

concluded that these mismatches can be reconciled within the obser-

vational uncertainties. Combined with the instrumental evidence (see

Section 3.7) and with inferences drawn from archaeological evidence

from 2000 years ago (Lambeck et al., 2004b), rates of sea level rise

exceeded the late Holocene background rate after about 1900 (high

conﬁdence) (Figure 5.17).

Regionally, as along the US Atlantic coast and Gulf of Mexico coast,

the salt-marsh records reveal some consistency in multi-decadal and

centennial time scales deviations from the linear trends expected from

the GIA signal (see e.g., panels (a) and (b) in Figure 5.17) (van de Plass-

che et al., 1998; González and Törnqvist, 2009; Kemp et al., 2011) but

they have not yet been identiﬁed as truly global phenomena. For the

past 5 millennia the most complete sea level record from a single loca-

tion consists of microatoll evidence from Kiritimati (Christmas Island;

Paciﬁc Ocean) (Woodroffe et al., 2012) that reveals with medium conﬁ-

dence that amplitudes of any ﬂuctuations in GMSL during this interval

did not exceed approximately ±25 cm on time scales of a few hundred

years. Proxy data from other localities with quasi-continuous records

for parts of this pre-industrial period, likewise, do not identify signiﬁ-

cant global oscillations on centennial time scales (Figure 5.17).

5.7 Evidence and Processes of Abrupt

Climate Change

Many paleoclimate archives document climate changes that happened

at rates considerably exceeding the average rate of change for longer-

term averaging periods prior and after this change (see Glossary for

other deﬁnition of Abrupt Climate Change). A variety of mechanisms

have been suggested to explain the emergence of such abrupt climate

changes (see Section 12.5.5). Most of them invoke the existence of

nonlinearities or, more speciﬁcally, thresholds in the underlying dynam-

ics of one or more Earth-system components. Both internal dynamics

and external forcings can generate abrupt changes in the climate state.

Documentation of abrupt climate changes in the past using multiple

sources of proxy evidence can provide important benchmarks to test

instability mechanisms in climate models. This assessment of abrupt

climate change on time scales of 10 to 100 years focuses on Dans-

gaard-Oeschger (DO) events and iceberg/meltwater discharges during

Heinrich events, especially the advances since AR4 in reconstructing

and understanding their global impacts and in extending the record of

millennial-scale variability to about 800 ka.

Twenty-ﬁve abrupt DO events (North Greenland Ice Core Project mem-

bers, 2004) and several centennial-scale events (Capron et al., 2010b)

occurred during the last glacial cycle (see Section 5.3.2). DO events

in Greenland were marked by an abrupt transition (within a few dec-

ades) from a cold phase, referred to as Greenland Stadial (GS) into

a warm phase, known as Greenland Interstadial (GI). Subsequently

but within a GI, a gradual cooling preceded a rapid jump to GS that

lasted for centuries up to millennia. Thermal gas-fractionation methods

(Landais et al., 2004; Huber et al., 2006) suggest that for certain DO

events Greenland temperatures increased by up to 16°C ± 2.5°C (1

standard deviation) within several decades. Such transitions were also

accompanied by abrupt shifts in dust and deuterium excess, indicative

of reorganizations in atmospheric circulation (Steffensen et al., 2008;

Thomas et al., 2009). Reconstructions from the subtropical Atlantic and

Mediterranean reveal concomitant SST changes attaining values up to

5°C (e.g., Martrat et al., 2004; Martrat et al., 2007).

In spite of the visible presence of DO events in many paleoclimate

records from both hemispheres, the underlying mechanisms still remain

unresolved and range from internally generated atmosphere–ocean–

ice sheet events (Timmermann et al., 2003; Ditlevsen and Ditlevsen,

2009), to solar-forced variability (Braun et al., 2008; Braun and Kurths,

2010). However, given the lack of observational evidence for a direct

linear modulation of solar irradiance on DO time scales, (Muscheler

and Beer, 2006), solar forcing is an improbable candidate to generate

DO events. There is robust evidence from multiple lines of paleoceano-

graphic information and modelling that DO variability is often associ-

ated with AMOC changes, as suggested by climate models of varying

complexity (Ganopolski and Rahmstorf, 2001; Arzel et al., 2009) and

marine proxy records (Piotrowski et al., 2005; Kissel et al., 2008; Barker

et al., 2010; Roberts et al., 2010); but also potential inﬂuences of sea-

ice cover (Li et al., 2010b), atmosphere circulation and ice sheet topog-

raphy (Wunsch, 2006) have been proposed.

The widespread presence of massive layers of ice-rafted detritus in

North Atlantic marine sediments provide robust evidence that some

DO GS, known as Heinrich stadials, were associated with iceberg dis-

charges originating from the Northern Hemispheric ice sheets. During

these periods global sea level rose by up to several tens of meters

(Chappell, 2002; Rohling et al., 2008b; Siddall et al., 2008; González

and Dupont, 2009; Yokoyama and Esat, 2011), with remaining uncer-

tainties in timing and amplitude of sea level rise, stadial cooling and

ocean circulation changes relative to the iceberg discharge (Hall et al.,

2006; Arz et al., 2007; Siddall et al., 2008; González and Dupont, 2009;

Sierro et al., 2009; Hodell et al., 2010). Internal instabilities of the Lau-

rentide ice sheet can cause massive calving and meltwater events sim-

ilar to those reconstructed from proxy records (Calov et al., 2002, 2010;

Marshall and Koutnik, 2006). Alternatively, an initial weakening of the

AMOC can lead to subsurface warming in parts of the North Atlantic

(Shaffer et al., 2004) and subsequent basal melting of the Labrador

ice shelves, and a resulting acceleration of ice streams and iceberg

discharge (Alvarez-Solas et al., 2010; Marcott et al., 2011). At present,

unresolved dynamics in ice sheet models and limited proxy information

do not allow us to distinguish the two mechanisms with conﬁdence.

Since AR4, climate model simulations (Liu et al., 2009b; Otto-Bliesner

and Brady, 2010; Menviel et al., 2011; Kageyama et al., 2013) have

further conﬁrmed the ﬁnding (high conﬁdence) that changes in AMOC

strength induce abrupt climate changes with magnitude and patterns

resembling reconstructed paleoclimate-proxy data of DO and Heinrich

events.

Recent studies have presented a better understanding of the global

imprints of DO events and Heinrich events, for various regions. Wide-

spread North Atlantic cooling and sea-ice anomalies during GS induced

atmospheric circulation changes (high conﬁdence) (Krebs and Tim-

mermann, 2007; Clement and Peterson, 2008; Kageyama et al., 2010;

Merkel et al., 2010; Otto-Bliesner and Brady, 2010; Timmermann et

433

Information from Paleoclimate Archives Chapter 5

al., 2010) which in turn affected inter-hemispheric tropical rainfall

patterns, leading to drying in Northern South America (Peterson and

Haug, 2006), the Mediterranean (Fletcher and Sánchez Goñi, 2008;

Fleitmann et al., 2009), equatorial western Africa and Arabia (Higgin-

son et al., 2004; Ivanochko et al., 2005; Weldeab et al., 2007a; Mulitza

et al., 2008; Tjallingii et al., 2008; Itambi et al., 2009; Weldeab, 2012),

wide parts of Asia (Wang et al., 2008; Cai et al., 2010) (see Figure

5.4e) as well as in the Australian-Indonesian monsoon region (Mohtadi

et al., 2011). Concomitant wetter conditions have been reconstructed

for southwestern North America (Asmerom et al., 2010; Wagner et al.,

2010) and southern South America (Kanner et al., 2012) (Figure 5.4h).

Moreover, atmospheric circulation changes have been invoked (Zhang

and Delworth, 2005; Xie et al., 2008; Okumura et al., 2009) to explain

temperature variations in the North Paciﬁc that varied in unison with

abrupt climate change in the North Atlantic region (Harada et al., 2008,

2012; Pak et al., 2012). Other factors that may have contributed to

North Paciﬁc climate anomalies include large-scale Paciﬁc Ocean cir-

culation changes (Saenko et al., 2004; Schmittner et al., 2007; Harada

et al., 2009; Okazaki et al., 2010) during phases of a weak AMOC.

Recent high-resolution ice core studies (EPICA Community Members,

2006; Capron et al., 2010a, 2010b, 2012; Stenni et al., 2011) show

that Antarctica warmed gradually for most GS, reaching maximum

values at the time of GS/GI transitions, which is in agreement with

the bipolar seesaw concept (Stocker and Johnsen, 2003; Stenni et al.,

2011). A recent global temperature compilation (Shakun et al., 2012),

Southern Ocean temperature records (Lamy et al., 2007; Barker et al.,

2009; De Deckker et al., 2012), evidence from SH terrestrial records

(Kaplan et al., 2010; Putnam et al., 2010) and transient climate model

experiments (Menviel et al., 2011) provide multiple lines of evidence

for the inter-hemispheric character of millennial-scale variability during

the last glacial termination and for DO events (high conﬁdence).

Newly available marine records (Martrat et al., 2007; Grützner and Hig-

gins, 2010; Margari et al., 2010; Kleiven et al., 2011), Antarctic WMGHG

records (Loulergue et al., 2008; Schilt et al., 2010) and statistical analy-

ses of Antarctic ice core data (Siddall et al., 2010; Lambert et al., 2012)

combined with bipolar seesaw modelling (Siddall et al., 2006; Barker et

al., 2011) document with high conﬁdence that abrupt climate change

events, similar to the DO events and Heinrich stadials of the last glacial

cycle, occurred during previous glacial periods extending back about

800 ka and, with medium conﬁdence, to 1100 ka.

5.8 Paleoclimate Perspective on

Irreversibility in the Climate System

For an introduction of the concept of irreversibility see Glossary.

5.8.1 Ice Sheets

Modelling studies suggest the existence of multiple equilibrium

states for ice sheets with respect to temperature, CO

concentration

and orbital forcing phase spaces (DeConto and Pollard, 2003; Calov

and

Ganopolski, 2005; Ridley et al., 2010). This implies a possibility of

irreversible changes in the climate-cryosphere system in the past and

future.

The existence of threshold behaviour in the EAIS is consistent with

an abrupt increase in Antarctic ice volume at the Eocene/Oligocene

boundary, 33 Ma, attributed to gradual atmospheric CO

concentra-

tion decline on geological time scale (Pagani et al., 2005b; Pearson

et al., 2009) (Figure 5.2, Section 5.2.2). Ice sheet models produce a

hysteresis behaviour of the EAIS with respect to CO

concentrations,

leading to EAIS glaciation when CO

concentration declined to 600–

900 ppm (DeConto and Pollard, 2003; Langebroek et al., 2009) and

deglaciation for CO

above 1200 ppm (Pollard and DeConto, 2009).

Proxy records suggest that the WAIS might have collapsed during last

interglacials (Naish et al., 2009b; Vaughan et al., 2011) and was absent

during warm periods of the Pliocene when CO

concentration was 350

to 450 ppm (see Section 5.2.2.2) and global sea level was higher than

present (see Section 5.6.1). These reconstructions and one ice sheet

model simulation (Pollard and DeConto, 2009) suggest that WAIS is

very sensitive to the subsurface ocean temperature. This implies, with

medium conﬁdence, thata large part of the WAIS will be eventually

lost if the atmospheric CO

concentration stays within, or above, the

range of 350 to 450 ppm for several millennia.

Observational evidence suggest that the GIS was also much smaller

than today during the MPWP (see Sections 5.6.1 and 5.2.2), consist-

ent with the results of simulations with ice sheet models (Dolan et

al., 2011; Koenig et al., 2011). Ice sheet model simulations and proxy

records show that the volume of the GIS was also reduced during the

past interglacial period (Section 5.6.2). This supports modelling results

that indicate temperature or CO

thresholds for melting and re-growth

of the GIS may lie in close proximity to the present and future levels

(Gregory and Huybrechts, 2006; Lunt et al., 2008) (Section 5.6.1) and

that the GIS may have multiple equilibrium states under present-day

climate state (Ridley et al., 2010).

Therefore, proxy records and results of model simulations indicate

with medium conﬁdence that the GIS and WAIS could be destabi-

lized by projected climate changes, although the time scales of the ice

sheets response to climate change are very long (several centuries to

millennia).

5.8.2 Ocean Circulation

Numerous modelling studies demonstrate that increased freshwater

ﬂux into the North Atlantic leads to weakening of the AMOC. Results

of EMICs (Rahmstorf et al., 2005) and coupled GCMs also suggest that

AMOC may have multiple equilibrium states under present or glacial

climate conditions (Hawkins et al., 2011; Hu et al., 2012). Experiments

with climate models provide evidence that the sensitivity of the AMOC

to freshwater perturbation is larger for glacial boundary conditions

than for interglacial conditions (Swingedouw et al., 2009) and that the

recovery time scale of the AMOC is longer for LGM conditions than for

the Holocene (Bitz et al., 2007).

The abrupt climate-change event at 8.2 ka permits the study of

the recovery time of the AMOC to freshwater perturbation under

near-modern boundary conditions (Rohling and Pälike, 2005). Since

AR4, new proxy records and simulations conﬁrm that the pattern of

surface-ocean and atmospheric climate anomalies is consistent with

a reduction in the strength of the AMOC (Figure 5.18a, b, d). Available

434

Chapter 5 Information from Paleoclimate Archives

proxy records from the North Atlantic support the hypothesis that

freshwater input into the North Atlantic reduced the amount of deep

and central water-mass formation, Nordic Seas overﬂows, intermediate

water temperatures and the ventilation state of North Atlantic Deep

Water (Figure 5.18c, d) (McManus et al., 2004; Ellison et al., 2006; Kleiv-

en et al., 2008; Bamberg et al., 2010). A concomitant decrease of SST

and atmospheric temperatures in the North Atlantic and in Greenland

has been observed (Figure 5.18a, b) with the climate anomaly asso-

ciated with the event lasting 100–160 years (Daley et al., 2011). The

additional freshwater that entered the North Atlantic during the 8.2 ka

event is estimated between 1.6·10

and 8·10

(von Grafenstein

et al., 1998; Barber et al., 1999; Clarke et al., 2004). The duration of the

Figure 5.18 | Compilation of selected paleoenvironmental and climate model data for the abrupt Holocene cold event at 8.2 ka, documenting temperature and ocean-circulation

changes around the event and the spatial extent of climate anomalies following the event. Published age constraints for the period of release of freshwater from glacier lakes

Agassiz and Ojibway are bracketed inside the vertical blue bar. Vertical grey bar denotes the time of the main cold event as found in Greenland ice core records (Thomas et al.,

2007). Thick lines in (a–d) denote 5-point running mean of underlying data in thin lines. (a) Black curve: North Greenland Ice Core Project (NGRIP) d

O (temperature proxy) from

Greenland Summit (North Greenland Ice Core Project members, 2004). Red curve: Simulated Greenland temperature in an 8.2 ka event simulation with the ECBilt-CLIO-VECODE

model (Wiersma et al., 2011). Blue curve: Simulated Greenland temperature in an 8.2 ka event simulation with the CCSM3 model (Morrill et al., 2011). (b) North Atlantic/Nordic

Seas sea surface temperature (SST) reconstructions, age models are aligned on the peak of the cold-event (less than 100-year adjustment). Blue curve: Nordic Seas (Risebrobakken

et al., 2011). Black curve: Gardar Drift south of Iceland (Ellison et al., 2006). (c) Deep- and intermediate-water records. Black curve: Sortable silt (SS) record (overﬂow strength proxy)

from Gardar Drift south of Iceland (Ellison et al., 2006), Atlantic intermediate water temperature reconstruction (Bamberg et al., 2010). (d) Black curve: d

C (deep water ventilation

proxy) at 3.4 km water depth south of Greenland (Kleiven et al., 2008). Age model is aligned on the minimum overﬂow strength in (c) (less than 100-year adjustment). Modelled

change in the strength of the Atlantic Ocean meridional overturning circulation (AMOC)—Green curve: an 8.2 ka event simulation with the GISS model (LeGrande et al., 2006). Red

curve: an 8.2 ka event simulation with the ECBilt-CLIO-VECODE (v. 3) model (Wiersma et al., 2011). Blue curve: an 8.2 ka event simulation with the CCSM3 model (Morrill et al.,

2011). (e) Spatial distribution of the 4-member ensemble mean annual mean surface temperature anomaly (°C) compared with the control experiment from model simulations of

the effects of a freshwater release at 8.2 ka (based on Morrill et al., 2013a). White dots indicate regions where less than 3 models agree on the sign of change. Coloured circles show

paleoclimate data from records resolving the 8.2 ka event: purple = cold anomaly, yellow = warm anomaly, grey = no signiﬁcant anomaly. Data source and signiﬁcance thresholds

are as summarized by Morrill et al. (2013b). (f) Same as (e) but for annual mean precipitation anomalies in %. Coloured circles show paleoclimate data from records resolving the

8.2 ka event: purple = dry anomaly, yellow = wet anomaly, grey = no signiﬁcant anomaly.

∆ prec (%)

∆ T2m (°C)

1.2

0.4

0.8

-0.4

C C.wuellerstorfi (‰)

Sortable silt (SS µm)

9 8.5 87.5 7

Age (ka)

Drainage of

Lake Agassiz

8.8

9.2

9.6

10.4

10.8

11.2

SST (°C) Foram transfer function

12.5

11.5

10.5

Mg/Ca - IMWT (°C)

34.5

35.5

O Greenland ice cores (‰)

9.0

8.5

8.0

AMOC strength (%)

100

(a)

(b)

(c)

(d)

-28

-29

-30

Temperature (

(f)

(e)

435

Information from Paleoclimate Archives Chapter 5

meltwater release may have been as short as 0.5 years (Clarke et al.,

2004), but new drainage estimates indicate an up to 200 year-duration

in two separate stages (Gregoire et al., 2012). A four-model ensem-

ble with a one-year freshwater perturbation of 2.5 Sv only gives tem-

perature anomalies half of what has been reconstructed and with a

shorter duration than observed, resulting from unresolved processes

in models, imprecise representation of the initial climate state or a too

short duration of the freshwater forcing (Morrill et al., 2013a). These

marine-based reconstructions consistently show that the recovery time

scale of the shallow and deep overturning circulation is on the order

of 200 years (Ellison et al., 2006; Bamberg et al., 2010) (Figure 5.18c,

d), with one record pointing to a partial recovery on a decadal time

scale (Kleiven et al., 2008). Both recovery time scale and sensitivity

of the AMOC to the freshwater perturbation are generally consistent

with model experiments for the 8.2 ka event using coarse-resolution

models, GCMs and eddy permitting models (LeGrande and Schmidt,

2008; Spence et al., 2008; Li et al., 2009). The recovery of temperatures

out of the cold anomaly appears overprinted with natural variability

in the proxy data, and is more gradual in data than in the AOGCM

experiments (Figure 5.18c, d). In summary, multiple lines of evidence

indicate, with high conﬁdence, that the interglacial mode of the AMOC

can recover from a short-term freshwater input into the subpolar North

Atlantic.

The characteristic teleconnection patterns associated with a colder

North Atlantic Ocean as described in Section 5.7 are evident for the

8.2 ka event in both models and proxy data (Figure 5.18e, f).

5.8.3 Next Glacial Inception

Since orbital forcing can be accurately calculated for the future (see

Section 5.2.1), efforts can be made to predict the onset of the next

glacial period. However, the glaciation threshold depends not only on

insolation but also on the atmospheric CO

concentration (Archer and

Ganopolski, 2005). Models of different complexity have been used to

investigate the response to orbital forcing in the future for a range of

atmospheric CO

levels. These results consistently show that a glacial

inception is not expected to happen within the next approximate 50

kyr if either atmospheric CO

concentration remains above 300 ppm

or cumulative carbon emissions exceed 1000 PgC (Loutre and Berger,

2000; Archer and Ganopolski, 2005; Cochelin et al., 2006). Only if

atmospheric CO

content was below the pre-industrial level would a

glaciation be possible under present orbital conﬁguration (Loutre and

Berger, 2000; Cochelin et al., 2006; Kutzbach et al., 2011; Vettoretti and

Peltier, 2011; Tzedakis et al., 2012a). Simulations with climate–carbon

cycle models show multi-millennial lifetime of the anthropogenic CO

in the atmosphere (see Box 6.1). Even for the lowest RCP 2.6 scenario,

atmospheric CO

concentrations will exceed 300 ppm until the year

3000. It is therefore virtually certain that orbital forcing will not trigger

a glacial inception before the end of the next millennium.

5.9 Concluding Remarks

The assessments in this chapter are based on a rapidly growing body of

new evidence from the peer-review literature. Since AR4, there exists a

wide range of new information on past changes in atmospheric com-

position, sea level, regional climates including droughts and ﬂoods,

as well as new results from internationally coordinated model exper-

iments on past climates (PMIP3/CMIP5). At the regional scale proxy-

based temperature estimates are still scarce for key regions such as

Africa, India and parts of the Americas. Syntheses of past precipitation

changes were too limited to support regional assessments.

Precise knowledge of past changes in atmospheric concentrations of

well-mixed GHGs prior to the period for which ice core records are

available remains a strong limitation on assessing longer-term climate

change. Key limitations to our knowledge of past climate continues

to be associated with uncertainties of the quantitative information

derived from climate proxies, in particular due to seasonality effects,

the lack of proxy records sensitive to winter temperature, or the precise

water depth at which ocean proxies signals form. Moreover, method-

ological uncertainties associated with regional, hemispheric or global

syntheses need to be further investigated and quantiﬁed.

Despite progress on developing proxy records of past changes in sea

ice it is not yet possible to provide quantitative and spatially coherent

assessments of past sea ice cover in both polar oceans.

While this assessment could build on improved reconstructions of

abrupt climate changes during glacial periods, key questions remain

open regarding the underlying cause of these changes. Large uncer-

tainties remain on the variations experienced by the West and East

Antarctic ice sheets over various time scales of the past. Regarding

past sea level change, major difﬁculties are associated with deconvolv-

ing changes in ocean geodynamic effects, as well as for inferring global

signals from regional reconstructions.

The PMIP3/CMIP5 model framework offers the opportunity to directly

incorporate information from paleoclimate data and simulations into

assessments of future projections. This is an emerging ﬁeld for which

only preliminary information was available for AR5.

Acknowledgements

The compilation of this chapter has beneﬁted greatly from the techni-

cal support by the chapter’s scientiﬁc assistants Vera Bender (Germa-

ny), Hiroshi Kawamura (Germany/Japan), and Anna Peregon (France/

Russian Federation). We are indebted to Hiroshi and Vera for compiling

the various drafts, managing the ever-growing reference list and their

skilful stylistic overhaul of ﬁgures. Anna is thanked for help with output

from PMIP3 simulations, for tracking acronyms, and for identifying

entries for the glossary.

436

Chapter 5 Information from Paleoclimate Archives

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456

Chapter 5 Information from Paleoclimate Archives

Appendix 5.A: Additional Information on

Paleoclimate Archives and Models

Model (No. runs) Period Forcings

Reference

Pre PMIP3/CMIP5 Experiments

CCSM3

(1×) 1000–2000

(4×) 1500–2000

·V

·G

30,31,35

Hofer et al. (2011)

CNRM-CM3.3 (1×) 1001–1999 SS

·V

·G

30,34,35

·A

·L

Swingedouw et al. (2011)

CSM1.4 (1×) 850–1999 SS

·V

·G

30,31,35

·A

Ammann et al. (2007)

CSIRO-MK3L-1-2

(3×) 1–2001

(3×) 501–2001

·G

·O

·V

·G

·O

Phipps et al. (2013)

ECHAM4/OPYC (1×) 1500–2000 SS

·V

21,26

·G

·A

·L

Stendel et al. (2006)

ECHAM5/MPIOM

(5×) 800–2005

(3×) 800–2005

·V

·G

34,39

·A

·L

·O

·V

·G

34,39

·A

·L

·O

Jungclaus et al. (2010)

ECHO-G

(1×) 1000–1990

(2×) –7000–1998

·V

·G

31,36,37

·V

·G

31,36,37

·G

·O

González-Rouco et al. (2003)

González-Rouco et al. (2006)

Wagner et al. (2007)

HadCM3 (1×) 1492–1999 SS

·V

·G

·A

·L

50,54,55

·O

Tett et al. (2007)

IPSLCM4 (1×) 1001–2000 SS

·G

30,34,35

·A

·O

Servonnat et al. (2010)

FGOALS-gl (1×) 1000–1999 SS

·V

·G

30,31,35

Zhou et al. (2011)

PMIP3/CMIP5 Experiments

BCC-csm1-1 (1×) 850–2005 SW

·V

·G

30,33,34

· A

·O

CCSM4 (1×) 850–2004 SW

·V

·G

30,33,34

· A

·L

·O

Landrum et al. (2013)

CSIRO-MK3L-1-2 (1×) 851–2000 SW

·V

·G

30,33,34

·O

GISS-E2-R (8×) 850–2004

·V

·G

30,33,34

·A

·L

·O

·V

·G

30,33,34

·A

·L

·O

·G

30,33,34

·A

·L

·O

·V

·G

30,33,34

·A

·L

·O

·V

·G

30,33,34

·A

·L

·O

·G

30,33,34

·A

·L

·O

·V

·G

30,33,34

·A

·L

·O

·V

·G

30,33,34

·A

·L

·O

HadCM3 (1×) 800–2000 SW

·V

·G

30,32,34

·A

·L

·O

Schurer et al. (2013)

IPSL-CM5A-LR (1×) 850–2005 SW

·V

·G

30,33,34

·O

MIROC-ESM (1×) 850–2005 SW

·V

·G

30,34,39

·O

60 e

MPI-ESM-P (1×) 850–2005 SW

·V

·G

30,33,34

·A

·L

·O

Table 5.A.1 | Summary of the Atmosphere-Ocean General Circulation Model (AOGCM) simulations available and assessed for Sections 5.3.5 and 5.5.1. Acronyms describing

forcings are: SS (solar forcing, stronger variability), SW (solar forcing, weaker variability), V (volcanic activity), G (greenhouse gases concentration), A (aerosols), L (land use–land

cover), and O (orbital). The table is divided into Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) and

non-PMIP3/CMIP5 experiments (Braconnot et al., 2012b; Taylor et al., 2012). Superscript indices in forcing acronyms identify the forcing reconstructions used and are listed in the

table footnotes. PMIP3 experiments follow forcing guidelines provided in Schmidt et al. (2011, 2012b). See Fernández-Donado et al. (2013) for more information on pre-PMIP3/

CMIP5 forcing conﬁgurations. See Chapter 8 and Table 9.1 for the forcing and model speciﬁcations of the CMIP5 historical runs. The simulations highlighted in red were excluded

from Figures 5.8, 5.9 and 5.12 because they did not include at least solar, volcanic and greenhouse gas forcings, they did not span the whole of the last millennium, or for a reason

given in the table notes.

Notes:

Key for superscript indices in forcing acronyms:

[1] Solar:

[10] Bard et al. (2000)

[11] Bard et al. (2000) spliced to Lean et al. (1995a)

[12] Solanki et al. (2004)

[13] Krivova et al. (2007)

[14] Steinhilber et al. (2009) spliced to Wang et al. (2005)

[15] Vieira and Solanki (2010) spliced to Wang et al. (2005)

[16] Delaygue and Bard (2011) spliced to Wang

et al. (2005)

[2] Volcanic:

[20] Crowley (2000)

[21] Ammann et al. (2003)

[22] Total solar irradiances from Crowley (2000) converted

to aerosol masses using Ammann et al. (2003)

regression coefﬁcients.

[23] Crowley et al. (2003)

[24] Gao et al. (2008). In the GISS-E2-R simulations this

forcing was implemented twice as large as in Gao

et al. (2008).

[25] Crowley and Unterman (2013)

[26] Robertson et al. (2001)

[27] Ammann et al. (2007)

[3] WMGHGs:

[30] Flückiger et al., (1999; 2002); Machida et al. (1995)

[31] Etheridge et al. (1996)

[32] Johns et al. (2003)

[33] Hansen and Sato (2004)

[34] MacFarling Meure et al. (2006)

[35] Blunier et al. (1995)

[36] Etheridge et al. (1998)

[37] Battle et al. (1996)

[38] Robertson et al. (2001)

[39] CO

diagnosed by the model.

[4] Aerosols:

[40] Lefohn et al. (1999)

[41] Joos et al. (2001)

[42] Roeckner et al. (1999)

[43] Johns et al. (2003)

[44] Boucher and Pham (2002)

[45] Lamarque et al. (2010). See Sections 8.2 and 8.3

[5] Land use, land cover:

[50] Wilson and Henderson-Sellers (1985)

[51] Pongratz et al. (2009) spliced to Hurtt et al. (2006)

[52] Kaplan et al. (2011)

[53] Pongratz et al. (2008)

(continued on next page)

457

Information from Paleoclimate Archives Chapter 5

Method

Scientiﬁc

Rationale

Estimated

Applicability

Limitations Main Assumptions (relative conﬁdence)

Alkenone

(phytoplankton

biomarker)

carbon isotopes

Measurements of

carbon isotope ratios

of marine sedi-

mentary alkenones

(or other organic

compounds) allows

determination of the

isotopic fraction-

ation factor during

carbon ﬁxation (ε

)

from which pCO

can be calculated.

100 to

~4000 ppm;

0 to 100 Ma

Alkenones are often rare in

oligotrophic areas and some-

times absent. Method relies on

empirical calibration and d

C is

sensitive to other environmental

factors, especially nutrient-relat-

ed variables. Method has been

used successfully to reconstruct

glacial–interglacial changes.

Measured alkenone carbon isotope ratio is accurate and precise (high).

Ambient aqueous partial pressure of carbon dioxide (pCO

) has a quantiﬁable relation-

ship with ε

that can be distinguished from the nutrient-related physiological factors

such as algal growth rate, cell size, cell geometry and light-limited growth (medium).

Aqueous pCO

is in equilibrium with atmospheric pCO

(medium).

Carbon isotope fractionation in modern alkenone-producing species is the same in

ancient species and constant through time (medium).

Levels of biological productivity (e.g., dissolved phosphate concentrations) can

be calculated (high).

Carbon isotope ratio of aqueous CO

in the mixed layer can be determined (medium).

Sea surface temperature can be determined (high).

Atmospheric partial pressure of oxygen (pO

) is known or assumed (medium).

Diagenetic effects are minimal, or can be quantiﬁed (medium).

Boron isotopes

in foraminifera

Boron isotope ratios

B) in foraminifera

(or other calcifying

organisms) give

paleo-pH from

which pCO

can

be calculated if a

value for a second

carbonate system

parameter (e.g., alka-

linity) is assumed.

100 to

~4000 ppm;

0 to 100 Ma

Calculated pCO

is very sensi-

tive to the boron isotope ratio

of seawater which is relatively

poorly known, especially for

the earlier Cenozoic. Effects

of foraminiferal preserva-

tion are not well understood.

Method has been used

successfully to reconstruct

glacial–interglacial changes.

Measured boron isotope ratio is accurate and precise (high).

The equilibrium constant for dissociation of boric acid and boron isotopic fractionation

between B(OH)

and B(OH)

–

are well known (high).

Boron incorporation into carbonate is predominantly from borate ion (high).

Boron isotope ratio of foraminifer calciﬁcation reﬂects ambient surface seawater

pH (high).

Aqueous pCO

is in equilibrium with atmospheric pCO

(medium).

Habitats of extinct species can be determined (high).

There is no vital effect fractionation in extinct species, or it can be determined (medium).

The boron isotope ratio of seawater (d

) can be determined (medium).

Ocean alkalinity or concentration of Total Dissolved Inorganic Carbon can be

determined (high).

Sea surface temperature (SST) and salinity (SSS) can be determined (high).

Diagenetic effects are minimal or can be quantiﬁed (high).

Carbon isotopes

in soil carbonate

and organic

matter

Atmospheric pCO

affects the relation-

ship between the

C of soil CO

and the d

C of soil

organic matter at

depth in certain soil

types, hence mea-

surement of these

parameters in paleo-

sols can be used to

calculate past pCO

1000 to

~4000 ppm;

0 to 400 Ma

Method works better for

some soil types than others.

loss is difﬁcult to

quantify and method and

effects of late diagenesis may

be difﬁcult to determine.

Isotopic composition of soil CO

is reﬂected in soil carbonates below a depth

of 50 cm. (medium).

The concentration of respired CO

in the soil is known or assumed (medium).

Isotopic composition of atmospheric CO

is known or can be inferred (low).

Soil carbonates were precipitated in the vadose zone in exchange with atmospheric

(high).

The original depth proﬁle of a paleosoil can be determined (low).

Burial (late) diagenetic effects are minimal or can be quantiﬁed (high).

Stomata in

plant leaves

The relative frequen-

cy of stomata on

fossil leaves (Stoma-

tal Index; (Salisbury,

1928) can be used to

calculate past atmo-

spheric CO

levels.

100 to

~1000 ppm;

0 to 400 Ma

Closely related species have

very different responses to

pCO

. The assumption that

short-term response is the same

as the evolutionary response

is difﬁcult to test. This and

the shape of the calibra-

tion curves mean that much

greater certainty applies to low

pCO

and short time scales.

Measured stomatal index is accurate and precise (high).

Measured stomatal index is representative of the plant (high).

The target plants adjust their stomatal index of leaves to optimize CO

uptake (medium).

Atmospheric pCO

close to the plant is representative of the atmosphere as

a whole (medium).

The quantitative relationship between stomatal index and CO

observed on short time

scales (ecophenotypic or ‘plastic response’) applies over evolutionary time (low).

Environmental factors such as irradiance, atmospheric moisture, water availability,

temperature, and nutrient availability do not affect the relationship between stomatal

index and CO

(medium).

Stomatal index response to CO

of extinct species can be determined or assumed (low).

Taphonomic processes do not affect stomatal index counts (high).

Diagenetic processes do not affect stomatal index counts (high).

Table 5.A.2 | Summary of atmospheric carbon dioxide (CO

) proxy methods and conﬁdence assessment of their main assumptions.

[54] Ramankutty and Foley (1999)

[55] Goldewijk (2001)

[6] Orbital:

[60] Berger (1978)

[61] Bretagnon and Francou (1988)

[62] Berger and Loutre (1991)

[63] Laskar et al. (2004)

Table 5.A.1 Notes (continued)

This simulation was only used in Figure 5.8, using NH temperature adjusted by Osborn et al. (2006).

The FGOALS-gl experiment is available in the PMIP3 repository, but the forcing conﬁguration is different from

Schmidt et al (2011; 2012b) recommendations so it is included here within the pre-PMIP3 ensemble.

The GISS-E2-R experiments with Gao et al. (2008) volcanic forcing were not used in Figures 5.8, 5.9 or 5.12. See [24].

This simulation was only used in Figure 5.8, using drift-corrected NH temperature.

458

Chapter 5 Information from Paleoclimate Archives

Table 5.A.3 | Summary of sea surface temperature (SST) proxy methods and conﬁdence assessment of their main assumptions.

Method Scientiﬁc Rationale

Estimated

Applica-

bility

Limitations Main Assumptions (relative conﬁdence)

O of mixed-

layer planktonic

foraminifera

Partitioning of

O from

seawater into calcite shells of

all foraminifera is temperature

dependent. Veriﬁed by theoretical,

ﬁeld and laboratory studies. Utilizes

extant and extinct species that

resided in the photic zone.

0°C to 50°C;

0 to 150 Ma

The

O ratios of recrystallized

planktonic foraminifer shells in carbonate-

rich sediments are biased toward colder

seaﬂoor temperatures, and at most, can

only constrain the lower limit of SST. The

transition in preservation is progressive with

age. Well-preserved forams from clay-rich

sequences on continental margins are

preferred. Diagenetic calcite is detectable

by visual and microscopic techniques.

Analytical errors are negligible (high).

Sensitivity to T is high and similar to modern descendants (high).

Seawater d

O is known. The uncertainty varies with time

depending on presence of continental ice-sheets, though

error is negligible in the Pleistocene and during minimal ice

periods such as the Eocene (<±0.25°C). Error doubles during

periods of Oligocene and early Neogene glaciation because

of weak constraints on ice-volume (medium to high).

Species lives in the mixed-layer and thus records SST (high).

Local salinity/seawater d

O is known (low to medium).

Carbonate ion/pH is similar to modern (medium, high).

Foraminifera from clay-rich sequences are well preserved

and

O ratios unaffected by diagenesis (high).

Foraminifera from carbonate-rich pelagic sequences are well

preserved and ratios unaffected by diagenesis (medium

to low; decreasing conﬁdence with age).

Biased towards summer SST in polar oceans (medium).

Mg/Ca in mixed-

layer planktonic

foraminifera

Partitioning of Mg/Ca from seawater

into calcite shells is temperature

dependent. Calibration to T is based

on empirical ﬁeld and laboratory

culturing studies, as Mg concentra-

tions of inorganically precipitated

calcite are an order of magnitude

higher than in biogenic calcite.

There is no ice-volume inﬂuence

on seawater Mg/Ca, though

sensitivity does change with

seawater Mg concentration.

5°C to 35°C;

0 to 65 Ma

Diagenetic recrystallization of foram shells

can bias ratios, though the direction of

bias is unknown and comparisons with

other proxies suggest it is minor. The Mg/

Ca is also slightly sensitive to seawater

pH. Long-term changes in seawater Mg/

Ca, on the order of a 2–5%/10 Myr,

must be constrained via models.

Analytical errors are negligible (high).

Mg containing oxide and organic contaminants have been

removed by oxidative/reductive cleaning (high).

Sensitivity to T in extinct species is similar to modern

species (medium).

Species lives in the mixed-layer and thus records SST (high).

Seawater Mg/Ca is known (high to low: decreasing

conﬁdence with time).

Surface water carbonate ion/pH is similar to modern

(medium).

Foraminfera from clay-rich sequences are well preserved and

ratios unaffected by diagenesis (high).

Foraminifera from carbonate-rich pelagic sequences are well

preserved and ratios unaffected by diagenesis (high to low;

decreasing conﬁdence with age).

Biased towards summer SST in polar oceans (medium).

TEX

index

in Archea

The ratio of cyclopentane rings in

archaeal tetraether lipids (TEX), i.e.,

isoprenoid glycerol dibiphytanyl

glycerol tetraethers (GDGTs), is sen-

sitive to the temperature of growth

environment. The relationship and

calibration with temperature is

empirical (based on core tops), as

the underlying mechanism(s) for this

relationship has yet to be identiﬁed.

Veriﬁcation of ﬁeld calibrations

with laboratory cultures is still

in progress. The compounds are

extracted from bulk sediments.

1°C to 40°C;

0 to 150 Ma

The depth from which the bulk of sedi-

mentary GDGT’s are produced is assumed

to be the mixed-layer though this cannot

be veriﬁed, for the modern or past. At

least two species with differing ecologies

appear to be producing the tetraethers. The

GDGT signal is ultimately an integrated

community signal allowing the potential for

evolutionary changes to inﬂuence regional

signals over time. Tetraethers are found in

measurable abundances on continental

shelves and/or organic rich sediments.

Analytical errors are small (high).

Sensitivity to T similar to modern (medium).

Species that produced tropical sedimentary GDGT’s resided

mainly in the mixed-layer and thus records SST

(high to medium).

Species that produced the sedimentary GDGT’s in the sub-polar

to polar regions mainly resided in the mixed-layer and thus

records SST (low).

No alteration of GDGT ratios during degradation of compounds

(medium to low: decreasing conﬁdence with age).

No contamination by GDGT’s derived from terrestrial sources

(high to medium if BIT index <0.3).

Biased towards summer SST in polar oceans (medium).

Index

in Algae

Based on the relative concentra-

tion of C

methyl ketonesderived

from the cells of haptophyte

phytoplankton. Calibrations

are empirically derived through

ﬁeld and culture studies.

5°C to 28°C;

0 to 50 Ma

The distribution of haptophyte algae

ranges from sub-polar to tropical.

Analytical errors are negligible (high).

Sensitivity to SST similar to modern (high to medium;decreasing

conﬁdence with time).

Species that produced the sedimentary alkenones lived in the

mixed-layer and thus record SST (high).

No alteration of alkenone saturation index during degradation

of compounds (medium; decreasing conﬁdence with age).

Biased towards summer SST in polar oceans (medium).

Microfossil

census modern

analogue

techniques

Utilises a statistical correla-

tion between extant planktonic

microfossil assemblage data (most

commonly foraminifera, but also

diatoms and radiolarians) and

climate parameters. Most commonly

used statistical methods are modern

analogue technique (MAT) and

artiﬁcial neural network (ANN).

0°C to 40°C;

0 to 5 Ma

Dependent on quality, coverage, size

and representativeness of the core

top modern analogue data base.

Extant species reduce with increasing age.

This and paleogeographic and ocean

circulation differences with age-limit

applicability to less than 1 Ma.

The composition of modern assemblages can be correlated

to SST (high).

Sensitivity of paleo-assemblages to SST is similar to modern

(high, but decreases with increasing age).

Eurythermal assemblages responding to non-temperature (e.g.,

nutrient availability) inﬂuences can be identiﬁed (medium).

That the extant species used to reconstruct SST mainly reside

in the mixed layer (medium to high).

Depositional and post-depositional processes have not biased

the assemblage (medium to high).

459

Information from Paleoclimate Archives Chapter 5

Table 5.A.4 | Assessment of leads and lags between Antarctic, hemispheric temperatures and atmospheric CO

concentration during terminations. Chronological synthesis of publications, main ﬁndings, incorporation in IPCC assessments

and key uncertainties.

(continued on next page)

Reference

Investigated

Period

Source CO

Data

Source Tempera-

ture Data

Lag Quantiﬁcation Method

Lag Between Tempera-

ture and CO

(positive,

temperature lead)

Key Limitations

TAR: From a detailed study of the last three glacial terminations in the Vostok ice core, Fischer et al. (1999) conclude that CO

increases started 600 ± 400 years after the Antarctic warming. However, considering the large uncertainty in the ages of the CO

and ice (1000 years or more if we consider the ice accumulation rate uncertainty), Petit et al. (1999) felt it premature to ascertain the sign of the phase relationship between CO

and Antarctic temperature at the initiation of the terminations. In any event,

changes parallel Antarctic temperature changes during deglaciations (Sowers and Bender, 1995; Blunier et al., 1997; Petit et al., 1999).

Fischer et

al. (1999)

Termination I

Terminations

I, II, III

Taylor Dome, Byrd

(CH

synchonized age scales)

Vostok

(gas age scales based

on ﬁrn modelling)

Byrd d

O, Vostok dD

(CH

synchonized age)

Vostok

dD (GT4

ice age scale)

Maximum at onset of

interglacial periods

Antarctica:

600 ± 400 years

Ice core synchronization for TerminationI (~300 years).

Gas age-ice age difference simulated by ﬁrn models for interglacial

conditions could be overestimated by ~400 years.

Signal-to-noise ratio.

Resolution of CO

measurements and ﬁrniﬁcation smoothing

(~300 years).

Petit et al.

(1999)

Pépin et al.

(2001)

Terminations

I, II, III, IV

Vostok

(GT4 gas age scale based

on ﬁrn modelling)

Vostok

(GT4 gas age scale)

Vostok dD

(GT4 ice age scale)

Vostok dD

(GT4 ice age scale)

Onset of transitions

Antarctica:

in phase within uncertainties

Positive

Gas age-ice age difference simulated by ﬁrn models for glacial

conditions could be overestimated by up to 1500 years.

Resolution of CO

measurements and ﬁrniﬁcation smoothing

(~300 years).

Signal to noise ratio (1 ice core).

Mudelsee

(2001)

0–420 ka

Lagged generalised least square

regression with parametric bootstrap

resampling, entire record

Antarctica :

1300 ± 1000 years

AR4: High-resolution ice core records of temperature proxies and CO

during deglaciation indicates that Antarctic temperature starts to rise several hundred years before CO

(Monnin et al., 2001; Caillon et al., 2003). During the last deglaciation, and possibly

also the three previous ones, the onset of warming at both high southern and northern latitudes preceded by several thousand years the ﬁrst signals of signiﬁcant sea level increase resulting from the melting of the northern ice sheets linked with the

rapid warming at high northern latitudes (Petit et al., 1999; Shackleton, 2000; Pépin et al., 2001). Current data are not accurate enough to identify whether warming started earlier in the SH or NH, but a major deglacial feature is the difference between

North and South in terms of the magnitude and timing of strong reversals in the warming trend, which are not in phase between the hemispheres and are more pronounced in the NH (Blunier and Brook, 2001).

Monnin et

al. (2001)

Termination I High resolution data from

EDC on EDC1 gas age scale

(based on ﬁrn modelling)

EDC (EPICA (European

Project for Ice Coring in

Antarctica Dome C) on

EDC1 ice age scale

Crossing points of linear ﬁt Antarctica:

800 ± 600 years

Gas age–ice age difference (±1000 years).

Signal to noise ratio (1 ice core).

Caillon et

al. (2003)

Termination III Vostok on GT4 gas age scale Vostok d

Ar on GT4

gas age scale

Maximum lagged correlation Antarctica:

800 ± 200 years

Relationship between d

Ar and temperature assumed to be

instantaneous. The 800 years is a minimum CO

-temperature lag

which does not account for a possible delayed response of ﬁrn

gravitational fractionation to surface temperature change.

AR5: For the last glacial termination, a large-scale temperature reconstruction (Shakun et al., 2012) documents that temperature change in the SH lead NH temperature change. This lead can be explained by the bipolar thermal seesaw concept (Stocker

and Johnsen, 2003) (see also Section 5.7) and the related changes in the inter-hemispheric ocean heat transport, caused by weakening of the Atlantic Ocean meridional overturning circulation (AMOC) during the last glacial termination (Ganopolski

and Roche, 2009). SH warming prior to NH warming can also be explained by the fast sea ice response to changes in austral spring insolation (Stott et al., 2007; Timmermann et al., 2009). According to these mechanisms, SH temperature lead over

the NH is fully consistent with the NH orbital forcing of deglacial ice volume changes (high conﬁdence) and the importance of the climate–carbon cycle feedbacks in glacial–interglacial transitions. The tight coupling is further highlighted by

the near-zero lag between the deglacial rise in CO

and averaged deglacial Antarctic temperature recently reported from improved estimates of gas-ice age differences (Pedro et al., 2012; Parrenin et al., 2013). Previous studies (Monnin et al., 2001)

suggesting a temperature lead of 800 ± 600 years over the deglacial CO

rise probably overestimated gas-ice age differences.

Shakun et

al. (2012)

Termination I EDC age scale synchro-

nized to GICC05

(Lemieux-

Dudon et al., 2010)

NH: stack of 50 records

including 2 Greenland

ice cores

SH: stack of 30 records

incl. 4 ice cores (Vostok,

EDML, EDC, Dome F)

their original age scale

Lag correlation (20–10 kyr) using

Monte-Carlo statistics

SH:

620 ± 660 years

NH:

–720 ± 660 years

Global:

–460 ± 340 years

Uncertainties in the original age scales of each record: e.g., reservoir

ages of marine sediments, radiocarbon calibration (intCal04),

Antarctic gas /ice chronology.

Assumption that time scale errors (e.g., from reservoir ages or ice core

chronologies) are independent from each other. This could lead

to higher-than-reported lag estimation uncertainties.

Similar limitations as in earlier studies for Antarctic temperature

lead on CO

Non stability of the phase lags: global temperature leads CO

at the

onset of deglacial warming.

460

Chapter 5 Information from Paleoclimate Archives

Reference

Investigated

Period

Source CO

Data

Source Tempera-

ture Data

Lag Quantiﬁcation Method

Lag Between Tempera-

ture and CO

(positive,

temperature lead)

Key Limitations

Pedro et al.

(2012)

Siple Dome and Byrd, synchro-

nized to GICC05

age scale

O composite (Law

Dome, Siple Dome, Byrd,

EDML and TALDICE

ice

cores) synchronized to

GICC05

using ﬁrn model-

ling (Pedro et al., 2011)

Lag correlation (9–21 kyr) and

derivative lag correlation

Antarctica:

–60 to 380 years

Uncertainty on gas – ice age difference in high accumulation

sites (<300 years) and on synchronization methods to GICC05.

Data resolution (145 year for Byrd CO

, 266 year for Siple CO

The CO

data were resampled at 20 year resolution prior to the

lag analysis, which may lead to an underestimation of the

statistical error in the lag determination.

Temperature versus other (e.g., elevation, moisture origin)

signals in coastal ice core d

Correlation method sensitive to minima, maxima and inﬂexion points.

(Parrenin et

al., 2013)

EDC, new gas age scale produced

from the modiﬁed EDC3 ice

age scale using lock-in depth

derived from d

N of N

and

adjusted to be consistent with

GICC05

gas age scale. Processes

affecting the gas lock-in depth

such as impurities are implicitly

taken into account when using

N (no use of ﬁrn models).

Stack temperature proﬁle

derived from water isotopes

from EDC

, Vostok

, Dome

Fuji

, TALDICE

and EDML

synchronized to a modiﬁed

EDC3 ice age scale

Monte-Carlo algorithm at

linear break points

Antarctica:

Warming onset:

–10 ± 160 years

Bølling onset:

260 ± 130 years

Younger Dryas onset:

–60 ± 120 years

Holocene onset:

500 ± 90 years

Accuracy, resolution and interpolation of d

N of N

; assumption

of no ﬁrn convective zone at EDC under glacial conditions.

Data resolution and noise (e.g., precipitation intermittency

biases in stable isotope records).

Table 5.A.4 (continued)

Notes:

Names of different Antarctic ice cores (Byrd, Taylor Dome, Vostok, Siple Dome, Law Dome, TALDICE, Dome Fuji, EDML, EDC), with different locations, surface climate and ﬁrniﬁcation conditions. For the most inland sites (Vostok, EDC, Dome Fuji), at a given ice

core depth, gas ages are lower than ice ages by 1500 to 2000 years (interglacial conditions) and 5000–5500 years (glacial conditions) while this gas age–ice age difference is lower (400 to 800. years) for coastal, higher accumulation sites (Byrd, Law Dome, Siple

Dome).

GICC05: Greenland Ice Core Chronology 2005, based on annual layer counting in Greenland (NGRIP, GRIP and DYE3 ice cores) (Rasmussen et al., 2006), back to 60 ka (Svensson et al., 2008). The synchronism between rapid shifts in Greenland climate and in

atmospheric CH

variations allows to transfer GICC05 to Greenland and then to Antarctic CH

variations (Blunier et al., 2007).

Additional point: CO

-Antarctic temperature phase during AIM events.

Studies on CO

phasing relative to CH

during Dansgaard Oeschger (DO) event onsets (Ahn and Brook, 2008; Ahn et al., 2012; Bereiter et al., 2012) suggest a lag of maximum CO

concentration relative to the Antarctic Isotope Maxima (AIM) 19, 20, 21, 23 and 24

by 260 ± 220 years during MIS5 and 670 to 870 years ± 360 years relative to AIM 12, 14, 17 during MIS3 (Bereiter et al., 2012). Accordingly, the lag is dependent on the climate state. A lag is not discernible for shorter AIM. This study avoids the ice age–gas

age difference problem, but relies on the bipolar seesaw concept, i.e., it assumes that maximum Antarctic temperatures are coincident to the onset of DO events and the concurrent CH

increase.

461

Information from Paleoclimate Archives Chapter 5

Table 5.A.5 | Summary of seasonal estimates of terrestrial surface temperature anomalies (°C) for the Last Interglacial (LIG) plotted in Figure 5.6. pdf-method stands for probability-density function method. Dating methods: AMS=Accelerator

mass spectrometry; IRSL=Infrared stimulated luminescence; OSL=Optically stimulated luminescence; TL=thermoluminescence.

Site

Latitude

(°N)

Longitude

(°E)

Elevation

(m asl)

Dating Proxy

Temperature

Anomaly (°C)

References

July January

Netherlands, Amsterdam Terminal 52.38 4.91 1 Eemian, U/Th Pollen, diatoms, molluscs, foraminifera, dinoﬂagellates, ostracods,

heavy minerals, paleomagnetism, grain-size, trace elements

2 3 (Zagwijn, 1996; van Leeuwen

et al., 2000; Beets et al., 2006)

E Canada, Addington

Forks, Nova Scotia

45.65 –62.1 50 Uranium-series Pollen 4 (Dreimanis, 1992)

NW America, Humptulips 47.28 –123.55 100 interpolation with

C dates

(peat) of the same core

Pollen 1 (Heusser and Heusser, 1990)

NE Siberia, Lake El’gygytgyn 67.5 172 492 TL Pollen 6 14 (Lozhkin and Anderson, 2006)

NW Alaska, Squirrel Lake 67.1 –160.38 91 TL Pollen, plant macrofossils 1.5 –2 (Berger and Anderson, 2000)

SE Bafﬁn Island, Robinson Lake 63.38 –64.25 170 TL, IRSL Pollen, diatoms, macrofossils 5 (Miller et al., 1999;

Fréchette et al., 2006)

Sweden, Leveäniemi 67.63 21.02 380 125 ka Pollen, pdf method 2.1 6.6 (Kühl, 2003, and ref. therein)

Finland, Evijärvi 63.43 23.33 67 125 ka Pollen, pdf method 2.3 10.3 (Kühl, 2003, and ref. therein)

Finland, Norinkylä 62.58 22.02 110 125 ka Pollen, pdf method 1.3 7.7 (Kühl, 2003, and ref. therein)

Estland, Prangli 59.65 25.08 5 125 ka Pollen, pdf method 1.7 3.2 (Kühl, 2003, and ref. therein)

Estland, Waewa-Ringen 58.33 26.73 50 125 ka Pollen, pdf method 1.3 6.8 (Kühl, 2003, and ref. therein)

Norway, Fjøsanger 60.35 5.33 5 125 ka Pollen, pdf method 2.9 1.6 (Kühl, 2003, and ref. therein)

Denmark, Hollerup 56.7 9.83 40 125 ka Pollen, pdf method 1.1 3.7 (Kühl, 2003, and ref. therein)

Germany, Husum 54.52 9.17 2 125 ka Pollen, pdf method 2.3 –0.3 (Kühl, 2003, and ref. therein)

Germany, Rederstall 54.28 9.25 0 125 ka Pollen, pdf method 0.3 1 (Kühl, 2003, and ref. therein)

Germany, Odderade 54.23 9.28 7 125 ka Pollen, pdf method 1.8 1.4 (Kühl, 2003, and ref. therein)

Germany, Helgoland 53.95 8.85 –1 125 ka Pollen & macrofossils, pdf method 2 0.6 (Kühl, 2003, and ref. therein)

Germany, Oerel 53.48 9.07 12.5 125 ka Pollen, pdf method 1.1 0.6 (Kühl, 2003, and ref. therein)

Germany, Quakenbrück 52.4 7.57 26 125 ka Pollen, pdf method 1.4 0.3 (Kühl, 2003, and ref. therein)

Netherlands, Amersfoort 52.15 5.38 3 125 ka Pollen, pdf method –0.3 0.5 (Kühl, 2003, and ref. therein)

Germany, Wallensen 52 9.4 160 125 ka Pollen & macrofossils, pdf method 1.9 –0.7 (Kühl, 2003, and ref. therein)

Germany, Neumark-Nord 51.33 11.88 90 125 ka Pollen & macrofossils, pdf method 1.4 0.5 (Kühl, 2003, and ref. therein)

Germany, Grabschütz 51.48 12.28 100 125 ka Pollen & macrofossils, pdf method 1.3 –0.2 (Kühl, 2003, and ref. therein)

Germany, Schönfeld 51.8 13.89 65 125 ka Pollen, pdf method –0.5 2.6 (Kühl, 2003, and ref. therein)

Germany, Kittlitz 51.43 14.78 150 125 ka Pollen, pdf method 1.4 2.4 (Kühl, 2003, and ref. therein)

Poland, Imbramovice 50.88 16.57 175 125 ka Pollen & macrofossils, pdf method 2.5 3.4 (Kühl, 2003, and ref. therein)

Poland, Zgierz-Rudunki 51.87 19.42 200 125 ka Pollen & macrofossils, pdf method 0.2 2.6 (Kühl, 2003, and ref. therein)

Poland, Wladyslawow 52.13 18.47 100 125 ka Pollen & macrofossils, pdf method 2.4 –0.9 (Kühl, 2003, and ref. therein)

Poland, Glowczyn 52.48 20.21 124 125 ka Pollen, pdf method 1.4 3.6 (Kühl, 2003, and ref. therein)

Poland, Gora Kalwaria 51.98 21.18 100 125 ka Pollen & macrofossils, pdf method 0.3 1.9 (Kühl, 2003, and ref. therein)

Poland, Naklo 53.15 17.6 62 125 ka Pollen & macrofossils, pdf method 0.6 3 (Kühl, 2003, and ref. therein)

Poland, Grudziadz 53.48 18.75 10 125 ka Pollen, pdf method 0.5 1.6 (Kühl, 2003, and ref. therein)

England, Wing 52.62 –0.78 119 125 ka Pollen, pdf method 2.4 –0.5 (Kühl, 2003, and ref. therein)

(continued on next page)

462

Chapter 5 Information from Paleoclimate Archives

Site

Latitude

(°N)

Longitude

(°E)

Elevation

(m asl)

Dating Proxy

Temperature

Anomaly (°C)

References

July January

England, Bobbitshole 52.05 1.15 3 125 ka Pollen & macrofossils, pdf method 2.5 –2.3 (Kühl, 2003, and ref. therein)

England, Selsey 50.42 0.48 0 125 ka Pollen & macrofossils, pdf method 0.7 –2.2 (Kühl, 2003, and ref. therein)

England, Stone 50.42 –1.02 0 125 ka Pollen & macrofossils, pdf method 2.9 –2.3 (Kühl, 2003, and ref. therein)

France, La Grande Pile 47.73 6.5 330 125 ka Pollen, pdf method 0.5 –0.7 (Kühl, 2003, and ref. therein)

Germany, Krumbach 48.04 9.5 606 125 ka Pollen, pdf method 0.5 2.3 (Kühl, 2003, and ref. therein)

Germany, Jammertal 48.1 9.72 578 125 ka Pollen, pdf method 0 0.5 (Kühl, 2003, and ref. therein)

Germany, Samerberg 47.75 12.2 600 125 ka Pollen & macrofossils, pdf method 2.7 4.1 (Kühl, 2003, and ref. therein)

Germany, Zeifen 47.93 12.83 427 125 ka Pollen & macrofossils, pdf method 3.4 2.5 (Kühl, 2003, and ref. therein)

Austria, Mondsee 47.51 13.21 534 125 ka Pollen & macrofossils, vmethod 4.3 1.3 (Kühl, 2003, and ref. therein)

Germany, Eurach 47.29 11.13 610 125 ka Pollen, pdf method 6.4 4.7 (Kühl, 2003, and ref. therein)

Germany, Füramoos 47.91 9.95 662 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic

spheres (PCS)

–2.8 –1.2 (Müller, 2001)

Swiss, Gondiswil-Seilern 47.12 7.88 639 125 ka Pollen, pdf method 0.1 0.4 (Kühl, 2003, and ref. therein)

Swiss, Meikirch 47 7.37 620 125 ka Pollen, pdf method 0.3 –0.3 (Kühl, 2003, and ref. therein)

Swiss, Meikirch II 47.01 7.33 620 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic

spheres (PCS)

–1.2 –4.5 (Welten, 1988)

Swiss, Beerenmösli 47.06 7.51 649 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic

spheres (PCS)

–1.1 –5.5 (Wegmüller, 1992)

France, Lac Du Bouchet 44.55 3.47 1200 125 ka Pollen, pdf method 1.7 –0.2 (Kühl, 2003, and ref. therein)

Italia, Valle di Castiglione 41.85 12.73 110 125 ka Pollen, pdf method –3.4 –5.9 (Kühl, 2003, and ref. therein)

Romania, Turbuta 47.25 23.3 275 U/Th, 125 ka Pollen, pdf method –1.2 2.4 (Kühl, 2003, and ref. therein)

Greece, Tenaghi Phillipon 41.17 24.33 40 125 ka Pollen, pdf method 0.9 2 (Kühl, 2003, and ref. therein)

Greece, Ioannina 39.67 20.85 472 125 ka Pollen, pdf method –1.9 –1.9 (Kühl, 2003, and ref. therein)

Germany, Bispingen 53.08 9.98 100 TL,125ka Pollen, pdf method 1.2 0.9 (Kühl, 2003, and ref. therein)

Germany, Gröbern 52.02 12.08 95 TL, 125 ka Pollen & macrofossils, pdf method 0.4 1.8 (Kühl, 2003, and ref. therein)

Germany, Klinge 51.75 14.52 10 pollen correlation Pollen (Grichuk, 1985) 0 2 (Novenko et al., 2008)

Germany, Ober-Rheinebene

near Darmstadt

49.82 8.4 90 Eem Vegetation, mammals (von Königswald, 2007)

France, La Flachere 45.23 5.58 333 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic

spheres (PCS)

–0.9 –14.4 (Peschke et al., 2000)

France, Lathuile 45.75 6.14 452 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic

spheres (PCS)

–0.5 –2.2 (Klotz et al., 2003)

France, La Grande Pile 47.73 6.5 330 TL, 125 ka Pollen, carbon isotopes 10 –15 (Rousseau et al., 2007)

Japan, Lake Biwa 35.33 136.17 86 tephrochronological and mag-

netostratigraphic information

Pollen –3 –2.5 (Nakagawa et al., 2008)

Pollen –5.5 –1.5 (Tarasov et al., 2011)

Siberia, Lake Baikal, Conti-

nent Ridge CON01-603-2

53.95 108.9 –386 AMS, 125 ka Pollen 2 –1 (Tarasov et al., 2005)

Bol’shoi Lyakhovsky Island 73.33 141.5 40 MIS 5, ca. 130–110 ka (IRSL) Pollen, beetles, chironomids, rhizopods, palaeomagnetic, BMA 4.5 (Andreev et al., 2004)

Wairarapa Valley; New Zealand –41.37 175.07 10 OSL, MIS 5e Beetles 2.8 winter 2.1 summer (Marra, 2003)

Table 5.A.5 (continued)

463

Information from Paleoclimate Archives Chapter 5

5.A.1 Additional Information to Section 5.3.5

Section 5.3.5 assesses knowledge of changes in hemispheric and

global temperature over the last 2 ka from a range of studies, recon-

structions and simulations. Tables 5.A.1 and 5.A.6 provide further

information about the datasets used in Figures 5.7–5.9 and 5.12, and

the construction of Figure 5.8 is described in more detail. All recon-

structions assessed in, or published since, AR4 were considered, but

those that have been superseded by a related study using an expanded

proxy dataset and/or updated statistical methods were excluded.

Figure 5.8 compares simulated and reconstructed NH temperature

changes (see caption). Some reconstructions represent a smaller spa-

tial domain than the full NH or a speciﬁc season, while annual tem-

peratures for the full NH mean are shown for the simulations. Mul-

ti-model means and estimated 90% multi-model ranges are shown by

the thick and thin lines, respectively, for two groups of simulations

(Table 5.A.1): those forced by stronger (weaker) solar variability in red

(blue). Note that the strength of the solar variability is not the only

difference between these groups: the GCMs and the other forcings are

also different between the groups. In Figure 5.7, the reconstructions

are shown as deviations from their 1881–1980 means, which allows

them to be compared with the instrumental record. In Figure 5.8a, all

timeseries are expressed as anomalies from their 1500–1850 mean

(prior to smoothing with a 30-year Gaussian-weighted ﬁlter, truncated

7 years from the end of each series to reduce end-effects of the ﬁlter)

because the comparison of simulations and reconstructions is less sen-

sitive to errors in anthropogenic aerosol forcing applied to the models

when a pre-industrial reference period is used, and less sensitive to

different realisations of internal variability with a multi-century refer-

ence period. The grey shading represents a measure of the overlapping

reconstruction conﬁdence intervals, with scores of 1 and 2 assigned

to temperatures within ±1.645 standard deviation (90% conﬁdence

range) or ±1 standard deviation, respectively, then summed over all

reconstructions and scaled so that the maximum score is dark grey,

and minimum score is pale grey. This allows the multi-model ensem-

bles to be compared with the ensemble of reconstructed NH tempera-

tures, taking into account the published conﬁdence intervals.

The superposed composites (time segments from selected periods

positioned so that the years with peak negative forcing are aligned;

top panels of Figure 5.8b–d) compare the simulated and reconstructed

temperatures (bottom panels) associated with (b) individual volcanic

forcing events; (c) multi-decadal changes in volcanic activity; (d) mul-

ti-decadal changes in solar irradiance. Only reconstructions capable of

resolving (b) interannual or (c, d) interdecadal variations are used. The

thick green line in Figure 5.8d shows the composite mean of the vol-

canic forcing, also band-pass ﬁltered, but constructed using the solar

composite periods to demonstrate the changes in volcanic forcing that

are coincident with solar variability. The composite of individual vol-

canic events shown in (b) is formed by aligned time segments centred

on the 12 years (1442, 1456, 1600, 1641, 1674, 1696, 1816, 1835,

1884, 1903, 1983 and 1992) during 1400–1999 that the Crowley and

Unterman (2013) volcanic forcing history exceeds 1.0 W m

–2

below the

1500–1899 mean volcanic forcing, excluding events within 7 years

(before or after) of a stronger event. The composite of multi-decadal

changes in volcanic forcing shown in (c) is formed from 80-year periods

centred on the ﬁve years (1259, 1456, 1599, 1695 and 1814) during

850–1999 when the Crowley and Unterman (2013) volcanic forcing,

smoothed with a 40-year Gaussian-weighted ﬁlter, exceeds 0.2 W m

–2

below the 1500–1899 mean volcanic forcing, except that a year is not

selected if it is within 39 years of another year that has a larger nega-

tive 40-year smoothed volcanic forcing. The composite of the strong-

est multidecadal changes in the solar forcing shown in (d) is formed

from 80-year periods centred on the seven years (1044, 1177, 1451,

1539, 1673, 1801 and 1905) during 850–1999 when the Ammann et

al. (2007) solar forcing, band-pass ﬁltered to retain variations on time

scales between 20 and 160 years, is reduced by at least 0.1 W m

–2

over

a 40-year period. Reconstructed and simulated temperature timeseries

were smoothed with a 40-year Gaussian-weighted ﬁlter in (c) or 20-

to-160-year band-pass ﬁltered in (d), and each composite was shifted

to have zero mean during the (b) 5 or (c, d) 40 years preceding the peak

negative forcing.

464

Chapter 5 Information from Paleoclimate Archives

Table 5.A.6 | Hemispheric and global temperature reconstructions assessed in Table 5.4 and used in Figures 5.7 to 5.9.

Reference

[Identiﬁer]

Period (CE) Resolution Region

Proxy Coverage

Method & Data

H M L O

Briffa et al. (2001) [only

used in Figure 5.8b–d due

to divergence issue]

1402–1960 Annual (summer) L 20°N to 90°N

 T £ £

Principal component forward regression of regional

composite averages.

Tree-ring density network, age effect removed via

age-band decomposition.

Christiansen and Ljungqvist

(2012) [CL12loc]

1–1973 Annual L+S 30°N to 90°N

  £ £

Composite average of local records

calibrated by local inverse regression.

Multi-proxy network.

D’Arrigo et al. (2006)

[Da06treecps]

713–1995 Annual L 20°N to 90°N

 T £ £

Forward linear regression of composite average.

Network of long tree-ring width chronologies, age

effect removed by Regional Curve Standardisation.

Frank et al. (2007) [Fr07treecps] 831–1992 Annual L 20°N to 90°N

T T £ £

Variance matching of composite average, adjusted

for artiﬁcial changes in variance.

Network of long tree-ring width chronologies, age

effect removed by Regional Curve Standardisation.

Hegerl et al. (2007) [He07tls] 558–1960 Decadal L 30°N to 90°N

T T £ £

Total Least Squares regression.

Multi-proxy network.

Juckes et al. (2007) [Ju07cvm] 1000–1980 Annual L+S 0° to 90°N

T T £ £

Variance matching of composite average.

Multi-proxy network.

Leclercq and Oerlemans

(2012) [LO12glac]

1600–2000 Multidecadal

L 0° to 90°N

L 90S to 0°

L 90°S to 90°N

T  T £

Inversion of glacier length response model.

308 glacier records.

Ljungqvist (2010) [Lj10cps] 1–1999 Decadal L+S 30°N to 90°N

 T £ T

Variance matching of composite average.

Multi-proxy network.

Loehle and McCulloch

(2008) [LM08ave]

16–1935 Multidecadal L+S mostly 0° to 90°N

T T £ T

Average of calibrated local records.

Multi-proxy network (almost no tree-rings).

Mann et al. (2008) [Ma08cpsl]

[Ma08eivl]

[Ma08eivf]

[Ma08min7eivf]

200–1980 Decadal

L [cpsl/eivl] and L+S [eivf]

versions, 0° to 90°N, 0° to

90°S, and 90°S to 90°N

  T T

(i) Variance matching of composite average.

(ii) Total Least Squares regression.

Multi-proxy network.

Mann et al. (2009) [Ma09regm] 500–1849 Decadal L+S 0 to 90°N

  T T

Regularized Expectation Maximization with

Truncated Total Least Squares.

Multi-proxy network.

Moberg et al. (2005)

[Mo05wave]

1–1979 Annual L+S 0° to 90°N

T  T £

Variance matching of composites of wavelet

decomposed records.

Tree-ring width network for short time scales;

non-tree-ring network for long time scales.

Pollack and Smerdon

(2004) [PS04bore]

1500–2000 Centennial

L 0° to 90°N

L 0° to 90°S

L 90°S to 90°N

T  T £

Borehole temperature proﬁles inversion

Shi et al. (2013) [Sh13pcar] 1000–1998 Annual L 0 to 90°N

 T £ £

Principal component regression with autoregressive

timeseries model.

Multi-proxy network (tree-ring and non-tree-ring

versions).

Notes:

Region: L = land only, L+S = land and sea, latitude range indicated.

Proxy location and coverage: H = high latitude, M = mid latitude, L = low latitude, O = oceans, £ = none or very few, T = limited,  = moderate

These studies also present versions without tree-rings or without seven inhomogeneous proxies (including the Lake Korttajärvi sediment records; Tiljander et al., 2003). The latter version is used

in Figure 5.7a (Ma08min7eivf) in preference to the reconstruction from the full network. The impact of these seven proxies on the other NH reconstructions is negligible (MA08cpsl) or results in

a slightly warmer pre-900 reconstruction compared to the version without them (Ma09regm).