1217

This chapter should be cited as:

Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. Dong, P. Goswami, A.

Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. Stephenson, S.-P. Xie and T. Zhou, 2013: Climate

Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Sci-

ence 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:

Jens Hesselbjerg Christensen (Denmark), Krishna Kumar Kanikicharla (India)

Lead Authors:

Edvin Aldrian (Indonesia), Soon-Il An (Republic of Korea), Iracema Fonseca Albuquerque

Cavalcanti (Brazil), Manuel de Castro (Spain), Wenjie Dong (China), Prashant Goswami (India),

Alex Hall (USA), Joseph Katongo Kanyanga (Zambia), Akio Kitoh (Japan), James Kossin (USA),

Ngar-Cheung Lau (USA), James Renwick (New Zealand), David B. Stephenson (UK), Shang-Ping

Xie (USA), Tianjun Zhou (China)

Contributing Authors:

Libu Abraham (Qatar), Tércio Ambrizzi (Brazil), Bruce Anderson (USA), Osamu Arakawa (Japan),

Raymond Arritt (USA), Mark Baldwin (UK), Mathew Barlow (USA), David Barriopedro (Spain),

Michela Biasutti (USA), Sébastien Biner (Canada), David Bromwich (USA), Josephine Brown

(Australia), Wenju Cai (Australia), Leila V. Carvalho (USA/Brazil), Ping Chang (USA), Xiaolong

Chen (China), Jung Choi (Republic of Korea), Ole Bøssing Christensen (Denmark), Clara Deser

(USA), Kerry Emanuel (USA), Hirokazu Endo (Japan), David B. Enﬁeld (USA), Amato Evan

(USA), Alessandra Giannini (USA), Nathan Gillett (Canada), Annamalai Hariharasubramanian

(USA), Ping Huang (China), Julie Jones (UK), Ashok Karumuri (India), Jack Katzfey (Australia),

Erik Kjellström (Sweden), Jeff Knight (UK), Thomas Knutson (USA), Ashwini Kulkarni (India),

Koteswara Rao Kundeti (India), William K. Lau (USA), Geert Lenderink (Netherlands), Chris

Lennard (South Africa), Lai-yung Ruby Leung (USA), Renping Lin (China), Teresa Losada (Spain),

Neil C. Mackellar (South Africa), Victor Magaña (Mexico), Gareth Marshall (UK), Linda Mearns

(USA), Gerald Meehl (USA), Claudio Menéndez (Argentina), Hiroyuki Murakami (USA/Japan),

Mary Jo Nath (USA), J. David Neelin (USA), Geert Jan van Oldenborgh (Netherlands), Martin

Olesen (Denmark), Jan Polcher (France), Yun Qian (USA), Suchanda Ray (India), Katharine

Davis Reich (USA), Belén Rodriguez de Fonseca (Spain), Paolo Ruti (Italy), James Screen (UK),

Jan Sedláček (Switzerland) Silvina Solman (Argentina), Martin Stendel (Denmark), Samantha

Stevenson (USA), Izuru Takayabu (Japan), John Turner (UK), Caroline Ummenhofer (USA), Kevin

Walsh (Australia), Bin Wang (USA), Chunzai Wang (USA), Ian Watterson (Australia), Matthew

Widlansky (USA), Andrew Wittenberg (USA), Tim Woollings (UK), Sang-Wook Yeh (Republic of

Korea), Chidong Zhang (USA), Lixia Zhang (China), Xiaotong Zheng (China), Liwei Zou (China)

Review Editors:

John Fyfe (Canada), Won-Tae Kwon (Republic of Korea), Kevin Trenberth (USA), David Wratt

(New Zealand)

Climate Phenomena and

their Relevance for Future

Regional Climate Change

1218

Table of Contents

Executive Summary ................................................................... 1219

14.1 Introduction .................................................................... 1222

14.1.1 Monsoons and Tropical Convergence Zones ........... 1222

14.1.2 Modes of Climate Variability ................................... 1222

14.1.3 Tropical and Extratropical Cyclones ......................... 1223

14.1.4 Summary of Climate Phenomena and their Impact

on Regional Climate ................................................ 1223

Box 14.1: Conceptual Deﬁnitions and Impacts of Modes

of Climate Variability .............................................................. 1223

14.2 Monsoon Systems ......................................................... 1225

14.2.1 Global Overview ..................................................... 1225

14.2.2 Asian-Australian Monsoon ..................................... 1227

14.2.3 American Monsoons ............................................... 1232

14.2.4 African Monsoon .................................................... 1234

14.2.5 Assessment Summary ............................................. 1234

14.3 Tropical Phenomena ..................................................... 1235

14.3.1 Convergence Zones ................................................. 1235

14.3.2 Madden–Julian Oscillation ...................................... 1237

14.3.3 Indian Ocean Modes ............................................... 1237

14.3.4 Atlantic Ocean Modes ............................................. 1239

14.3.5 Assessment Summary ............................................. 1240

14.4 El Niño-Southern Oscillation ..................................... 1240

14.4.1 Tropical Paciﬁc Mean State ..................................... 1240

14.4.2 El Niño Changes over Recent Decades and

in the Future ........................................................... 1240

14.4.3 Teleconnections....................................................... 1243

14.4.4 Assessment Summary ............................................. 1243

14.5 Annular and Dipolar Modes ....................................... 1243

14.5.1 Northern Modes ...................................................... 1244

14.5.2 Southern Annular Mode .......................................... 1245

14.5.3 Assessment Summary ............................................. 1246

Box 14.2: Blocking ................................................................... 1246

14.6 Large-scale Storm Systems ........................................ 1248

14.6.1 Tropical Cyclones .................................................... 1248

14.6.2 Extratropical Cyclones ............................................ 1251

14.6.3 Assessment Summary ............................................. 1252

14.7 Additional Phenomena of Relevance ...................... 1253

14.7.1 Paciﬁc–South American Pattern .............................. 1253

14.7.2 Paciﬁc–North American Pattern .............................. 1253

14.7.3 Paciﬁc Decadal Oscillation/Inter-decadal

Paciﬁc Oscillation .................................................... 1253

14.7.4 Tropospheric Biennial Oscillation ............................ 1253

14.7.5 Quasi-Biennial Oscillation ....................................... 1254

14.7.6 Atlantic Multi-decadal Oscillation ........................... 1254

14.7.7 Assessment Summary ............................................. 1255

14.8 Future Regional Climate Change .............................. 1255

14.8.1 Overview ................................................................. 1255

14.8.2 Arctic....................................................................... 1257

14.8.3 North America ......................................................... 1258

14.8.4 Central America and Caribbean .............................. 1260

14.8.5 South America ......................................................... 1261

14.8.6 Europe and Mediterranean ..................................... 1264

14.8.7 Africa ...................................................................... 1266

14.8.8 Central and North Asia ............................................ 1268

14.8.9 East Asia ................................................................. 1269

14.8.10 West Asia ................................................................ 1271

14.8.11 South Asia ............................................................... 1272

14.8.12 Southeast Asia ........................................................ 1273

14.8.13 Australia and New Zealand ..................................... 1273

14.8.14 Paciﬁc Islands Region .............................................. 1275

14.8.15 Antarctica ............................................................... 1276

References ................................................................................ 1290

Frequently Asked Questions

FAQ 14.1 How Is Climate Change

Affecting Monsoons? ........................................... 1228

FAQ 14.2 How Are Future Projections in Regional

Climate Related to Projections of Global

Means? .................................................................. 1256

Supplementary Material

Supplementary Material is available in online versions of the report.

1219

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

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).

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

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

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

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

Executive Summary

This chapter assesses the scientiﬁc literature on projected changes in

major climate phenomena and more speciﬁcally their relevance for

future change in regional climates, contingent on global mean temper-

atures continue to rise.

Regional climates are the complex result of processes that vary strong-

ly with location and so respond differently to changes in global-scale

inﬂuences. The following large-scale climate phenomena are increas-

ingly well simulated by climate models and so provide a scientiﬁc

basis for understanding and developing credibility in future regional

climate change. A phenomenon is considered relevant to regional cli-

mate change if there is conﬁdence

that it has inﬂuence on the regional

climate and there is conﬁdence that the phenomenon will change, par-

ticularly under the Representative Concentration Pathway 4.5 (RCP4.5)

or higher end scenarios. {Table 14.3}

Monsoon Systems

There is growing evidence of improved skill of climate models

in reproducing climatological features of the global mon-

soon. Taken together with identiﬁed model agreement on

future changes, the global monsoon, aggregated over all mon-

soon systems, is likely

to strengthen in the 21st century with

increases in its area and intensity, while the monsoon circula-

tion weakens. Monsoon onset dates are likely to become earlier

or not to change much and monsoon retreat dates are likely to

delay, resulting in lengthening of the monsoon season in many

regions. {14.2.1}

Future increase in precipitation extremes related to the monsoon is

very likely in South America, Africa, East Asia, South Asia, Southeast

Asia and Australia. Lesser model agreement results in medium conﬁ-

dence

that monsoon-related interannual precipitation variability will

increase in the future. {14.2.1, 14.8.5, 14.8.7, 14.8.9, 14.8.11, 14.8.12,

14.8.13}

Model skill in representing regional monsoons is lower compared to

the global monsoon and varies across different monsoon systems.

There is medium conﬁdence that overall precipitation associated with

the Asian-Australian monsoon will increase but with a north–south

asymmetry: Indian and East Asian monsoon precipitation is projected

to increase, while projected changes in Australian summer monsoon

precipitation are small. There is medium conﬁdence that the Indian

summer monsoon circulation will weaken, but this is compensated by

increased atmospheric moisture content, leading to more precipitation.

For the East Asian summer monsoon, both monsoon circulation and

precipitation are projected to increase. There is medium conﬁdence that

the increase of the Indian summer monsoon rainfall and its extremes

throughout the 21st century will be the largest among all monsoons.

{14.2.2, 14.8.9, 14.8.11, 14.8.13}

There is low conﬁdence in projections of changes in precipita-

tionamounts for the North American and South American monsoons,

but medium conﬁdence that the North American monsoon will arrive

and persist later in the annual cycle, and high conﬁdence in expansion

of the South American monsoon area. {14.2.3, 14.8.3, 14.8.4, 14.8.5}

There is low conﬁdence in projections of a small delay in the devel-

opment of the West African rainy season and an intensiﬁcation of

late-season rains. Model limitations in representing central features

of the West African monsoon result in low conﬁdence in future projec-

tions. {14.2.4, 14.8.7}

Tropical Phenomena

Based on models’ ability to reproduce general features of the

Indian Ocean Dipole and agreement on future projections, the

tropical Indian Ocean is likely to feature a zonal (east–west)

pattern of change in the future with reduced warming and

decreased precipitation in the east, and increased warming and

increased precipitation in the west, directly inﬂuencing East

Africa and Southeast Asia precipitation. {14.3, 14.8.7, 14.8.12}

A newly identiﬁed robust feature in model simulations of trop-

ical precipitation over oceans gives medium conﬁdence that

annual precipitation change follows a ‘warmer-get-wetter’

pattern, increasing where warming of sea surface temperature

exceeds the tropical mean and vice versa. There is medium con-

ﬁdence in projections showing an increase in seasonal mean precipi-

tation on the equatorial ﬂank of the Inter-Tropical Convergence Zone

(ITCZ) affecting parts of Central America, the Caribbean, South Ameri-

ca, Africa and West Asia despite shortcomings in many models in simu-

lating the ITCZ. There is medium conﬁdence that the frequency of zon-

ally oriented South Paciﬁc Convergence Zone events will increase, with

the South Paciﬁc Convergence Zone (SPCZ) lying well to the northeast

of its average position, a feature commonly reproduced in models that

simulate the SPCZ realistically, resulting in reduced precipitation over

many South Paciﬁc island nations. Similarly there is medium conﬁ-

dence that the South Atlantic Convergence Zone will shift southwards,

leading to an increase in precipitation over southeastern South Amer-

ica and a reduction immediately north thereof. {14.3, 14.8.4, 14.8.5,

14.8.7, 14.8.11, 14.8.14}

1220

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

There is low conﬁdence in projections of future changes in the

Madden–Julian Oscillation owing to poor ability of the models to

simulate it and its sensitivity to ocean warming patterns. The implica-

tions for future projections of regional climate extremes in West Asia,

South Asia, Southeast Asia and Australia are therefore highly uncertain

when associated with the Madden–Julian Oscillation. {14.3, 14.8.10,

14.8.11, 14.8.12, 14.8.13}

There is low conﬁdence in the projections of future changes for the

tropical Atlantic, both for the mean and interannual modes, because

of systematic errors in model simulations of current climate. The impli-

cations for future changes in Atlantic hurricanes and tropical South

American and West African precipitation are therefore uncertain. {14.3,

14.6.1, 14.8.5, 14.8.7 }

The realism of the representation of El Niño-Southern Oscilla-

tion (ENSO) in climate models is increasing and models simulate

ongoing ENSO variability in the future. Therefore there is high

conﬁdence that ENSO very likely remains as the dominant mode

of interannual variability in the future and due to increased

moisture availability, the associated precipitation variability on

regional scales likely intensiﬁes. An eastward shift in the patterns

of temperature and precipitation variations in the North Paciﬁc and

North America related to El Niño and La Niña (teleconnections), a fea-

ture consistently simulated by models, is projected for the future, but

with medium conﬁdence, while other regional implications including

those in Central and South America, the Caribbean, Africa, most of

Asia, Australia and most Paciﬁc Islands are more uncertain. However,

natural modulations of the variance and spatial pattern of ENSO are

so large in models that conﬁdence in any speciﬁc projected change in

its variability in the 21st century remains low. {14.4, 14.8.3, 14.8.4,

14.8.5, 14.8.7, 14.8.9, 14.8.11, 14.8.12, 14.8.13, 14.8.14}

Cyclones

Based on process understanding and agreement in 21st century

projections, it is likely that the global frequency of occurrence

of tropical cyclones will either decrease or remain essentially

unchanged, concurrent with a likely increase in both global

mean tropical cyclone maximum wind speed and precipitation

rates. The future inﬂuence of climate change on tropical cyclones

is likely to vary by region, but the speciﬁc characteristics of the

changes are not yet well quantiﬁed and there is low conﬁdence

in region-speciﬁc projections of frequency and intensity. How-

ever, better process understanding and model agreement in speciﬁc

regions provide medium conﬁdence that precipitation will be more

extreme near the centres of tropical cyclones making landfall in North

and Central America; East Africa; West, East, South and Southeast

Asia as well as in Australia and many Paciﬁc islands. Improvements in

model resolution and downscaling techniques increase conﬁdence in

projections of intense storms, and the frequency of the most intense

storms will more likely than not increase substantially in some basins.

{14.6, 14.8.3, 14.8.4, 14.8.7, 14.8.9, 14.8.10, 14.8.11, 14.8.12, 14.8.13,

14.8.14}

Despite systematic biases in simulating storm tracks, most

models and studies are in agreement on the future changes in

the number of extratropical cyclones (ETCs). The global number

of ETCs is unlikely to decrease by more than a few percent. A

small poleward shift is likely in the Southern Hemisphere (SH)

storm track. It is more likely than not, based on projections with

medium conﬁdence, that the North Paciﬁc storm track will shift pole-

ward. However, it is unlikely that the response of the North Atlantic

storm track is a simple poleward shift. There is low conﬁdence in the

magnitude of regional storm track changes, and the impact of such

changes on regional surface climate. It is very likely that increases in

Arctic, Northern European, North American and SH winter precipitation

by the end of the 21st century (2081–2100) will result from more pre-

cipitation in ETCs associated with enhanced extremes of storm-related

precipitation. {14.6, 14.8.2, 14.8.3, 14.8.5, 14.8.6, 14.8.13, 14.8.15}

Blocking

Increased ability in simulating blocking in models and higher

agreement on projections indicate that there is medium conﬁ-

dence that the frequency of Northern and Southern Hemisphere

blocking will not increase, while trends in blocking intensity and

persistence remain uncertain. The implications for blocking-related

regional changes in North America, Europe and Mediterranean and

Central and North Asia are therefore also uncertain. {14.8.3, 14.8.6,

14.8.8, Box 14.2}

Annular and Dipolar Modes of Variability

Models are generally able to simulate gross features of annular

and dipolar modes. Model agreement in projections indicates

that future boreal wintertime North Atlantic Oscillation is very

likely to exhibit large natural variations and trend of similar

magnitude to that observed in the past and is likely to become

slightly more positive on average, with some, but not well doc-

umented, implications for winter conditions in the Arctic, North

America and Eurasia. The austral summer/autumn positive trend

in Southern Annular Mode is likely to weaken considerably as

stratospheric ozone recovers through the mid-21st century with

some, but not well documented, implications for South Ameri-

ca, Africa, Australia, New Zealand and Antarctica. {14.5.1, 14.5.2,

14.8.2, 14.8.3, 14.8.5, 14.8.6, 14.8.7, 14.8.8, 14.8.13, 14.8.15}

Atlantic Multi-decadal Oscillation

Multiple lines of evidence from paleo reconstructions and model

simulations indicate that the Atlantic Multi-decadal Oscillation

(AMO) is unlikely to change its behaviour in the future as the

mean climate changes. However, natural ﬂuctuations in the AMO

over the coming few decades are likely to inﬂuence regional climates

at least as strongly as will human-induced changes, with implications

for Atlantic major hurricane frequency, the West African wet season,

North American and European summer conditions. {14.7.6, 14.2.4,

14.6.1, 14.8.3, 14.8.6}

1221

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Paciﬁc South American Pattern

Understanding of underlying physical mechanisms and the pro-

jected sea surface temperatures in the equatorial Indo-Paciﬁc

regions gives medium conﬁdence that future changes in the

mean atmospheric circulation for austral summer will project

on this pattern, thereby inﬂuencing the South American Conver-

gence Zone and precipitation over southeastern South America.

{14.7.2, 14.8.5}

1222

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

14.1 Introduction

Regional climates are the complex outcome of local physical processes

and the non-local responses to large-scale phenomena such as the El

Niño-Southern Oscillation (ENSO) and other dominant modes of cli-

mate variability. The dynamics of regional climates are determined by

local weather systems that control the net transport of heat, moisture

and momentum into a region. Regional climate is interpreted in the

widest sense to mean the whole joint probability distribution of cli-

mate variables for a region including the time mean state, the variance

and co-variance and the extremes.

This chapter assesses the physical basis of future regional climate

change in the context of changes in the following types of phenom-

ena: monsoons and tropical convergence zones, large-scale modes of

climate variability and tropical and extratropical cyclones. Assessment

of future changes in these phenomena is based on climate model

projections (e.g., the Coupled Model Intercomparison Project Phase

3 (CMIP3) and CMIP5 multi-model ensembles described in Chapter

12) and an understanding of how well such models represent the key

processes in these phenomena. More generic processes relevant to

regional climate change, such as thermodynamic processes and land–

atmosphere feedback processes, are assessed in Chapter 12. Local pro-

cesses such as snow–albedo feedback, moisture feedbacks due to local

vegetation, effects of steep complex terrain etc. can be important for

changes but are in general beyond the scope of this chapter. The main

focus here is on large-scale atmospheric phenomena rather than more

local feedback processes or impacts such as ﬂoods and droughts.

Sections 14.1.1 to 14.1.3 introduce the three main classes of phenom-

ena addressed in this Assessment and then Section 14.1.4 summarizes

their main impacts on precipitation and surface temperature. Specif-

ic climate phenomena are then addressed in Sections 14.2 to 14.7,

which build on key ﬁndings from the Fourth Assessment Report, AR4

(IPCC, 2007a), and provide an assessment of process understanding

and how well models simulate the phenomenon and an assessment of

future projections for the phenomena. In Section 14.8, future regional

climate changes are assessed, and where possible, interpreted in terms

of future changes in phenomena. In particular, the relevance of the var-

ious phenomena addressed in this chapter for future climate change in

the regions covered in Annex I are emphasized. The regions are those

deﬁned in previous regional climate change assessments (IPCC, 2007a,

2007b, 2012). Regional Climate Models (RCMs) and other downscaling

tools required for local impact assessments are assessed in Section 9.6

and results from these studies are used where such supporting infor-

mation adds additional relevant details to the assessment.

14.1.1 Monsoons and Tropical Convergence Zones

The major monsoon systems are associated with the seasonal move-

ment of convergence zones over land, leading to profound season-

al changes in local hydrological cycles. Section 14.2 assesses current

understanding of monsoonal behaviour in the present and future cli-

mate, how monsoon characteristics are inﬂuenced by the large-scale

tropical modes of variability and their potential changes and how the

monsoons in turn affect regional extremes. Convergence zones over

the tropical oceans not only play a fundamental role in determining

regional climates but also inﬂuence the global atmospheric circula-

tion. Section 14.3 presents an assessment of these and other important

tropical phenomena.

14.1.2 Modes of Climate Variability

Regional climates are strongly inﬂuenced by modes of climate variabil-

ity (see Box 14.1 for deﬁnitions of mode, regime and teleconnection).

This chapter assesses major modes such as El Niño-Southern Oscil-

lation (ENSO, Section 14.4), the North Atlantic Oscillation/Northern

Annular Mode (NAO/NAM) and Southern Annular Mode (SAM) in the

extratropics (Section 14.5) and various other well-known modes such

as the Paciﬁc North American (PNA) pattern, Paciﬁc Decadal Oscillation

(PDO), Atlantic Multi-decadal Oscillation (AMO), etc. (Section 14.7).

Many of these modes are described in previous IPCC reports (e.g., Sec-

tion 3.6 of AR4 WG1). Chapter 2 gives operational deﬁnitions of mode

indices (Box 2.5, Table 1) and an assessment of observed historical

behaviour (Section 2.7.8). Climate models are generally able to sim-

ulate the gross features of many of the modes of variability (Section

9.5), and so provide useful tools for understanding how modes might

change in the future (Müller and Roeckner, 2008; Handorf and Dethloff,

2009).

Modes and regimes provide a simpliﬁed description of variations in

the climate system. In the simplest paradigm, variations in climate var-

iables are described by linear projection onto a set of mode indices

(Baldwin et al., 2009; Baldwin and Thompson, 2009; Hurrell and Deser,

2009). For example, a large fraction of interannual variance in Northern

Hemisphere (NH) sea level pressure is accounted for by linear combi-

nations of the NAM and the PNA modes (Quadrelli and Wallace, 2004).

Alternatively, the nonlinear regime paradigm considers the probability

distribution of local climate variables to be a multi-modal mixture of

distributions related to a discrete set of regimes/types (Palmer, 1999;

Cassou and Terray, 2001; Monahan et al., 2001).

There is ongoing debate on the relevance of the different paradigms

(Stephenson et al., 2004; Christiansen, 2005; Ambaum, 2008; Fereday

et al., 2008), and care is required when interpreting these constructs

(Monahan et al., 2009; Takahashi et al., 2011).

Modes of climate variability may respond to climate change in one or

more of the following ways:

• No change—the modes will continue to behave as they have done

in the recent past.

• Index changes—the probability distributions of the mode indices

may change (e.g., shifts in the mean and/or variance, or more com-

plex changes in shape such as changes in local probability density,

e.g., frequency of regimes).

• Spatial changes—the climate patterns associated with the modes

may change spatially (e.g., new ﬂavours of ENSO; see Section

14.4 and Supplementary Material) or the local amplitudes of the

climate patterns may change (e.g., enhanced precipitation for a

given change in index (Bulic and Kucharski, 2012)).

1223

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

• Structural changes—the types and number of modes and their

mutual dependencies may change; completely new modes could

in principle emerge.

An assessment of changes in modes of variability can be problematic

for several reasons. First, interpretation depends on how one separates

modes of variability from forced changes in the time mean or variations

in the annual cycle (Pezzulli et al., 2005; Compo and Sardeshmukh,

2010). Modes of variability are generally deﬁned using indices based

on either detrended anomalies (Deser et al., 2010b) or anomalies

obtained by removing the time mean over a historical reference period

(see Box 2.5). The mode index in the latter approach will include

changes in the mean, whereas by deﬁnition there is no trend in a mode

index when it is based on detrended anomalies. Second, it can be difﬁ-

cult to separate natural variations from forced responses, for example,

warming trends in the N. Atlantic during the 20th century that may be

due to trends in aerosol and other forcings rather than natural internal

variability (see Sections 14.6.2 and 14.7.1). Finally, modes of climate

variability are nonlinearly related to one another (Hsieh et al., 2006)

and this relationship can change in time (e.g., trends in correlation

between ENSO and NAO indices).

Even when the change in a mode of variability index does not con-

tribute greatly to mean regional climate change, a climate mode may

still play an important role in regional climate variability and extremes.

Natural variations, such as those due to modes of variability, are a

major source of uncertainty in future projections of mean regional

climate (Deser et al., 2012). Furthermore, changes in the extremes of

regional climate are likely to be sensitive to small changes in variance

or shape of the distribution of the mode indices or the mode spatial

patterns (Coppola et al., 2005; Scaife et al., 2008).

14.1.3 Tropical and Extratropical Cyclones

Tropical and extratropical cyclones (TCs and ETCs) are important

weather phenomena intimately linked to regional climate phenomena

and modes of climate variability. Both types of cyclone can produce

extreme wind speeds and precipitation (see Section 3.4, IPCC Spe-

cial Report on Managing the Risks of Extreme Events and Disasters

to Advance Climate Change Adaptation (SREX; IPCC, 2012)). Sections

14.6.1 and 14.6.2 assess the recent progress in scientiﬁc understand-

ing of how these important weather systems are likely to change in

the future.

14.1.4 Summary of Climate Phenomena and their Impact

on Regional Climate

Box 14.1, Figure 1 illustrates the large-scale climate phenomena

assessed in this chapter. Many of the climate phenomena are evident

in the map of annual mean rainfall (central panel). The most abundant

annual rainfall occurs in the tropical convergence zones: Inter-Tropical

Convergence Zone (ITCZ) over the Paciﬁc, Atlantic and African equato-

rial belt (see Section 14.3.1.1), South Paciﬁc Convergence Zone (SPCZ)

over central South Paciﬁc (see Section 14.3.1.2) and South Atlantic

Convergence Zone (SACZ) over Southern South America and Southern

Atlantic (see Section 14.3.1.3). In the global monsoon domain (white

contours on the map), large amounts of precipitation occur but only

in certain seasons (see Section 14.2.1). Local maxima in precipitation

are also apparent over the major storm track regions in mid-latitudes

(see Section 14.7.2). Box 14.1 Figure 1 also illustrates surface air tem-

perature (left panels) and precipitation (right panels) teleconnection

patterns for ENSO (in December to February and June to August; see

Section 14.4), NAO (in December to February; see Section 14.5.1) and

SAM (in September to November; see Section 14.5.2). The telecon-

nection patterns were obtained by taking the correlation between

monthly gridded temperature and precipitation anomalies and indi-

ces for the modes (see Box 14.1 deﬁnitions). It can be seen that all

three modes have far-reaching effects on temperature and precipita-

tion in many parts of the world. Box 14.1, Table 1 brieﬂy summarizes

the main regional impacts of different well-known modes of climate

variability.

Box 14.1 | Conceptual Deﬁnitions and Impacts of Modes of Climate Variability

This box brieﬂy deﬁnes key concepts used to interpret modes of variability (below) and summarizes regional impacts associated with

well-known modes (Box 14.1, Table 1 and Box 14.1, Figure 1). The terms below are used to describe variations in time series variables

reported at a set of geographically ﬁxed spatial locations, for example, a set of observing stations or model grid points (based on the

more complete statistical and dynamical interpretation in Stephenson et al. (2004)).

Climate indices

Time series constructed from climate variables that provides an aggregate summary of the state of the climate system. For example,

the difference between sea level pressure in Iceland and the Azores provides a simple yet useful historical NAO index (see Section 14.5

and Box 2.5 for deﬁnitions of this and other well-known observational indices). Because of their maximum variance properties, climate

indices are often deﬁned using principal components.

Principal component

A linear combination of a set of time series variables that has maximum variance subject to certain normalization constraints. Principal

components are widely used to deﬁne optimal climate indices from gridded datasets (e.g., the Arctic Oscillation (AO) index, deﬁned as

the leading principal component of NH sea level pressure; Section 14.5). (continued on next page)

1224

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Box 14.1 (continued)

Climate pattern

A set of coefﬁcients obtained by ‘projection’ (regression) of climate variables at different spatial locations onto a climate index time series.

Empirical Orthogonal Function

The climate pattern obtained if the climate index is a principal component. It is an eigenvector of the covariance matrix of gridded

climate data.

Teleconnection

A statistical association between climate variables at widely separated, geographically ﬁxed spatial locations. Teleconnections are

caused by large spatial structures such as basin-wide coupled modes of ocean–atmosphere variability, Rossby wave-trains, mid-latitude

jets and storm-tracks, etc.

Teleconnection pattern

A correlation map obtained by calculating the correlation between variables at different spatial locations and a climate index. It is the

special case of a climate pattern obtained for standardized variables and a standardized climate index, that is, the variables and index

are each centred and scaled to have zero mean and unit variance. One-point teleconnection maps are made by choosing a variable at

one of the locations to be the climate index. (continued on next page)

Mode Regional Climate Impacts

ENSO

Global impact on interannual variability in global mean temperature. Inﬂuences severe weather and tropical cyclone activity worldwide. The diverse El Niño

ﬂavours present different teleconnection patterns that induce large impacts in numerous regions from polar to tropical latitudes (Section 14.4).

PDO

Inﬂuences surface air temperature and precipitation over the entire North American continent and extratropical North Paciﬁc. Modulates ENSO rainfall

teleconnections, e.g., Australian climate (Section 14.7.3).

IPO

Modulates decadal variability in Australian rainfall, and ENSO teleconnections to rainfall, surface temperature, river ﬂow and ﬂood risk over Australia,

New Zealand and the SPCZ (Section 14.7.3).

NAO

Inﬂuences the N. Atlantic jet stream, storm tracks and blocking and thereby affects winter climate in over the N. Atlantic and surrounding landmasses.

The summer NAO (SNAO) inﬂuences Western Europe and Mediterranean basin climates in the season (Section 14.5.1).

NAM

Modulates the intensity of mid-latitude storms throughout the Northern Hemisphere and thereby inﬂuences North America and Eurasia climates as well as

sea ice distribution across the Arctic sea (Section 14.5.1).

NPO

Inﬂuences winter air temperature and precipitation over much of western North America as well as Arctic sea ice in the Paciﬁc sector (Section 14.5.1).

SAM

Inﬂuences temperature over Antarctica, Australia, Argentina, Tasmania and the south of New Zealand and precipitation over southern South America,

New Zealand, Tasmania, Australia and South Africa (Section 14.5.2).

PNA

Inﬂuences the jet stream and storm tracks over the Paciﬁc and North American sectors, exerting notable inﬂuences on the temperature and precipitation in

these regions on intraseasonal and interannual time scales (Section 14.7.2).

PSA

Inﬂuences atmospheric circulation over South America and thereby has impacts on precipitation over the continent (Section 14.7.1).

AMO

Inﬂuences air temperatures and rainfall over much of the Northern Hemisphere, in particular, North America and Europe. It is associated with multidecadal

variations in Indian, East Asian and West African monsoons, the North African Sahel and northeast Brazil rainfall, the frequency of North American droughts

and Atlantic hurricanes (Section 14.7.6).

AMM

Inﬂuences seasonal hurricane activity in the tropical Atlantic on both decadal and interannual time scales. Its variability is inﬂuenced by other modes,

particularly ENSO and NAO (Section 14.3.4).

Affects the West African Monsoon, the oceanic forcing of Sahel rainfall on both decadal and interannual time-scales and the spatial extension of drought

in South Africa (Section 14.3.4).

IOB

Associated with the intensity of Northwest Paciﬁc monsoon, the tropical cyclone activity over the Northwest Paciﬁc and anomalous rainfall over East Asia

(Section 14.3.3).

IOD

Associated with droughts in Indonesia, reduced rainfall over Australia, intensiﬁed Indian summer monsoon, ﬂoods in East Africa, hot summers over Japan, and

anomalous climate in the extratropical Southern Hemisphere (Section 14.3.3).

TBO

Modulates the strength of the Indian and West Paciﬁc monsoons. Affects droughts and ﬂoods over large areas of south Asia and Australia (Section 14.7.4).

MJO

Modulates the intensity of monsoon systems around the globe and tropical cyclone activity in the Indian, Paciﬁc and Atlantic Oceans. Associated with enhanced

rainfall in Western North America, northeast Brazil, Southeast Africa and Indonesia during boreal winter and Central America/Mexico and Southeast Asia

during boreal summer (Section 14.3.2).

QBO

Strongly affects the strength of the northern stratospheric polar vortex as well as the extratropical troposphere circulation, occurring preferentially

in boreal winter (Section 14.7.5).

BLC

Associated with cold air outbreaks, heat-waves, ﬂoods and droughts in middle and high latitudes of both hemispheres (Box 14.2).

Box 14.1, Table 1 | Regional climate impacts of fundamental modes of variability.

Notes:

AMM: Atlantic Meridional Mode

AMO: Atlantic Multi-decadal Oscillation

AN: Atlantic Niño pattern

BLC: Blocking events

ENSO: El Niño-Southern Oscillation

IOB: Indian Ocean Basin pattern

IOD: Indian Ocean Dipole pattern

IPO: Interdecadal Paciﬁc Oscillation

MJO: Madden-Julian Oscillation

NAM: Northern Annular Mode

NAO: North Atlantic Oscillation

NPO: North Paciﬁc Oscillation

PDO: Paciﬁc Decadal Oscillation

PNA: Paciﬁc North America pattern

PSA: Paciﬁc South America pattern

QBO: Quasi-Biennial Oscillation

SAM: Southern Annular Mode

TBO: Tropospheric Biennial Oscillation

1225

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Box 14.1 (continued)

Mode of climate variability

Underlying space–time structure with preferred spatial pattern and temporal variation that helps account for the gross features in vari-

ance and for teleconnections. A mode of variability is often considered to be the product of a spatial climate pattern and an associated

climate index time series.

Climate regime

A set of similar states of the climate system that occur more frequently than nearby states due to either more persistence or more

often recurrence. In other words, a cluster in climate state space associated with a local maximum in the probability density function.

Annual precipitation

Winter storm-tracks Monsoon precipitation domains

Temperature

-0.8 -0.6 -0.4 -0.2 0.20.4 0.60.8

Precipitation

NAO

DJF

SOI

DJF

SOI

JJA

SON

SAM

NAO

DJF

SOI

DJF

SOI

JJA

SON

SAM

-0.8 -0.6 -0.4 -0.2 0.20.4 0.60.8

01030507090 120 150 200 250 300 400

Box 14.1, Figure 1 | Global distribution of average annual rainfall (in cm/year) from 1979–2010 Global Precipitation Climatology Project (GPCP) database, monsoon

precipitation domain (white contours) as deﬁned in Section 14.2.1, and winter storm-tracks in both hemispheres (black arrows). In left (right) column seasonal cor-

relation maps of North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI, the atmospheric component of El Niño-Southern Oscillation (ENSO)) and Southern

Annular Mode (SAM) mode indexes vs. monthly temperature (precipitation) anomalies in boreal winter (December, January and February (DJF)), austral winter (June, July

and August (JJA)) and austral spring (September, October and November (SON)). Black contours indicate a 99% signiﬁcance level. The mode indices were taken from

National Oceanic and Atmospheric Administration (NOAA, http://www.esrl.noaa.gov/psd/data/climateindices/list/), global temperatures from NASA Goddard Institute

of Space Studies Surface Temperature Analysis (GISTEMP, http://data.giss.nasa.gov/gistemp/) and global precipitations from GPCP (http://www.esrl.noaa.gov/psd/data/

gridded/data.gpcp.html).

14.2 Monsoon Systems

Monsoons are a seasonal phenomenon responsible for producing the

majority of wet season rainfall within the tropics. The precipitation

characteristics over the Asian-Australian, American and African mon-

soons can be viewed as an integrated global monsoon system, asso-

ciated with a global-scale atmospheric overturning circulation (Tren-

berth et al., 2000). In Section 14.2.1, changes in precipitation of the

global monsoon system are assessed. Changes in regional monsoons

are assessed in Sections 14.2.2 to 14.2.4.

14.2.1 Global Overview

The global land monsoon precipitation displays a decreasing trend

over the last half-century, with primary contributions from the weak-

ened summer monsoon systems in the NH (Wang and Ding, 2006).

The combined global ocean–land monsoon precipitation has inten-

siﬁed during 1979–2008, mainly due to an upward trend in the NH

summer oceanic monsoon precipitation (Zhou et al., 2008b; Hsu et al.,

2011; Wang et al., 2012b). Because the fractional increase in monsoon

area is greater than that in total precipitation, the ratio of the latter

to the former (a measure of the global monsoon intensity) exhibits a

1226

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

decreasing trend (Hsu et al., 2011). CMIP5 models generally reproduce

the observed global monsoon domain, but the disparity between the

best and poorest models is large (Section 9.5.2.4).

In the CMIP5 models the global monsoon area (GMA), the global

monsoon total precipitation (GMP) and the global monsoon precipi-

tation intensity (GMI) are projected to increase by the end of the 21st

century (2081–2100, Hsu et al., 2013; Kitoh et al., 2013; Figure 14.1).

See Supplementary Material Section 14.SM.1.2 for the deﬁnitions of

GMA, GMP and GMI. The CMIP5 model projections show an expan-

sion of GMA mainly over the central to eastern tropical Paciﬁc, the

southern Indian Ocean and eastern Asia. In all RCP scenarios, GMA is

very likely to increase, and GMI is likely to increase, resulting in a very

likely increase in GMP, by the end of the 21st century (2081–2100). The

100-year median changes in GMP are +5%, +8%, +10%, and +16%

in RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. Indices

of precipitation extremes such as simple daily precipitation intensity

index (SDII), deﬁned as the total precipitation divided by the number

of days with precipitation greater than or equal to 1 mm, annual max-

Figure 14.1 | (Upper) Observed (thick contour) and simulated (shading) global monsoon domain, based on the deﬁnition of Wang et al. (2011). The observations are based

on GPCP v2.2 data (Huffman et al., 2009), and the simulations are based on 26 CMIP5 multi-model mean precipitation with a common 2.5 by 2.5 degree grid in the present

day (1986–2005) and the future (2080–2099; RCP8.5 scenario). Orange (dark blue) shading shows monsoon domain only in the present day (future). Light blue shading shows

monsoon domain in both periods. (Lower) Projected changes for the future (2080–2099) relative to the present day (1986-2005) in the global monsoon area (GMA) and global

monsoon intensity (GMI), global monsoon total precipitation (GMP), standard deviation of interannual variability in seasonal average precipitation (Psd), simple daily precipitation

intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD) and monsoon season duration (DUR), under the RCP2.6

(dark blue; 18 models), RCP4.5 (light blue; 24 models), RCP6.0 (orange; 14 models) and RCP8.5 scenarios (red; 26 models). Units are % except for DUR (days). Box-and-whisker

plots show the 10th, 25th, 50th, 75th and 90th percentiles. All of the indices are calculated for the summer season (May to September in the Northern Hemisphere; November to

March in the Southern Hemisphere). The indices of Psd, SDII, R5d and CDD calculated for each model’s original grid, and then averaged over the monsoon domains determined by

each model at the present-day. The indices of DUR are calculated for seven regional monsoon domains based on the criteria proposed by Wang and LinHo (2002) using regionally

averaged climatological cycles of precipitation, and then their changes are averaged with weighting based on their area at the present day.

imum 5-day precipitation total (R5d) and consecutive dry days (CDD)

all indicate that intense precipitation will increase at larger rates than

those of mean precipitation (Figure 14.1). The standard deviation of

interannual variability in seasonal average precipitation (Psd) is pro-

jected to increase by many models but some models show a decrease

in Psd. This is related to uncertainties in projections of future chang-

es in tropical sea surface temperature (SST). Regarding seasonality,

CMIP5 models project that monsoon onset dates will come earlier or

not change much while monsoon retreat dates will delay, resulting in a

lengthening of the monsoon season in many regions.

CMIP5 models show a decreasing trend of lower-troposphere wind

convergence (dynamical factor) throughout the 20th and 21st centu-

ries (Figure 14.2d). With increased moisture (see also Section 12.4),

the moisture ﬂux convergence shows an increasing trend from 1980

through the 21st century (Figure 14.2c). Surface evaporation shows

a similar trend (Figure 14.2b) associated with warmer SSTs. There-

fore, the global monsoon precipitation increases (Figure 14.2a) due to

increases in moisture ﬂux convergence and surface evaporation despite

1227

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.2 | Time series of simulated anomalies, smoothed with a 20-year running mean over the global land monsoon domain for (a) precipitation (mm day

–1

), (b) evaporation

(mm day

–1

), (c) water vapour ﬂux convergence in the lower (below 500 hPa) troposphere (mm day

–1

), and (d) wind convergence in the lower troposphere (10

–3

kg m

–2

–1

), relative

to the present-day (1986–2005), based on CMIP5 multi-model monthly outputs. Historical (grey; 29 models), RCP2.6 (dark blue; 20 models), RCP4.5 (light blue; 24 models), RCP6.0

(orange; 16 models), and RCP8.5 (red; 24 models) simulations are shown in the 10th and 90th percentile (shading), and in all model averages (thick lines).

a weakened monsoon circulation. Besides greenhouse gases (GHGs),

monsoons are affected by changes in aerosol loadings (Ramanathan

et al., 2005). The aerosol direct forcing may heat the atmosphere but

cools the surface, altering atmospheric stability and inducing horizon-

tal pressure gradients that modulate the large-scale circulation and

hence monsoon rainfall (Lau et al., 2008). However, the representation

of aerosol forcing differs among models, and remains an important

source of uncertainty (Chapter 7 and Section 12.2.2), particularly in

some regional monsoon systems.

14.2.2 Asian-Australian Monsoon

The seasonal variation in the thermal contrast between the large Eur-

asian landmass and the Paciﬁc-Indian Oceans drives the powerful

Asian-Australian monsoon (AAM) system (Figure 14.3), which consists

of ﬁve major subsystems: Indian (also known as South Asian), East

Asian, Maritime Continent, Australian, and Western North Paciﬁc mon-

soons. More than 85% of CMIP5 models show an increase in mean

precipitation of the East Asian summer (EAS) monsoon, while more

than 95% of models project an increase in heavy precipitation events

(Figure 14.4). All models and all scenarios project an increase in both

the mean and extreme precipitation in the Indian summer monsoon

(referred to as SAS in Figures 14.3 and 14.4) . In these two regions,

the interannual standard deviation of seasonal mean precipitation

also increases. Over the Australian-Maritime Continent (AUSMC)

monsoon region, agreement among models is low. Figure 14.5 shows

the time-series of circulation indices representing EAS, Indian (IND),

Western North Paciﬁc (WNP) and Australian (AUS) summer monsoon

systems. The Indian monsoon circulation index is likely to decrease in

the 21st century, while a slight increase in the East Asian monsoon

circulation is projected. Scatter among models is large for the western

North Paciﬁc and Australian monsoon circulation change.

Factors that limit the conﬁdence in quantitative assessment of mon-

soon changes include sensitivity to model resolution (Cherchi and

Navarra, 2007; Klingaman et al., 2011), model biases (Levine and

1228

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Frequently Asked Questions

FAQ 14.1 | How is Climate Change Affecting Monsoons?

Monsoons are the most important mode of seasonal climate variation in the tropics, and are responsible for a large

fraction of the annual rainfall in many regions. Their strength and timing is related to atmospheric moisture con-

tent, land–sea temperature contrast, land cover and use, atmospheric aerosol loadings and other factors. Overall,

monsoonal rainfall is projected to become more intense in future, and to affect larger areas, because atmospheric

moisture content increases with temperature. However, the localized effects of climate change on regional mon-

soon strength and variability are complex and more uncertain.

Monsoon rains fall over all tropical continents: Asia, Australia, the Americas and Africa. The monsoon circulation is

driven by the difference in temperature between land and sea, which varies seasonally with the distribution of solar

heating. The duration and amount of rainfall depends on the moisture content of the air, and on the conﬁguration

and strength of the atmospheric circulation. The regional distribution of land and ocean also plays a role, as does

topography. For example, the Tibetan Plateau—through variations in its snow cover and surface heating—modu-

lates the strength of the complex Asian monsoon systems. Where moist on-shore winds rise over mountains, as they

do in southwest India, monsoon rainfall is intensiﬁed. On the lee side of such mountains, it lessens.

Since the late 1970s, the East Asian summer monsoon has been weakening and not extending as far north as it used

to in earlier times , as a result of changes in the atmospheric circulation. That in turn has led to increasing drought

in northern China, but ﬂoods in the Yangtze River Valley farther south. In contrast, the Indo-Australian and West-

ern Paciﬁc monsoon systems show no coherent trends since the mid-20th century, but are strongly modulated by

the El Niño-Southern Oscillation (ENSO). Similarly, changes observed in the South American monsoon system over

the last few decades are strongly related to ENSO variability. Evidence of trends in the North American monsoon

system is limited, but a tendency towards heavier rainfalls on the northern side of the main monsoon region has

been observed. No systematic long-term trends have been observed in the behaviour of the Indian or the African

monsoons.

The land surface warms more rapidly than the ocean surface, so that surface temperature contrast is increasing in

most regions. The tropical atmospheric overturning circulation, however, slows down on average as the climate

warms due to energy balance constraints in the tropical atmosphere. These changes in the atmospheric circulation

lead to regional changes in monsoon intensity, area and timing. There are a number of other effects as to how

FAQ 14.1, Figure 1 | Schematic diagram illustrating the main ways that human activity inﬂuences monsoon rainfall. As the climate warms, increasing water vapour

transport from the ocean into land increases because warmer air contains more water vapour. This also increases the potential for heavy rainfalls. Warming-related

changes in large-scale circulation inﬂuence the strength and extent of the overall monsoon circulation. Land use change and atmospheric aerosol loading can also affect

the amount of solar radiation that is absorbed in the atmosphere and land, potentially moderating the land–sea temperature difference.

(a) present

solar radiation solar radiation

aerosols

changes

in aerosols

more rain

enhanced moisture moisture

weaker

circulation

land use land use

warm cool

(b) future

warmer warm

(continued on next page)

1229

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

FAQ 14.1 (continued)

climate change can inﬂuence monsoons. Surface heating varies with the intensity of solar radiation absorption,

which is itself affected by any land use changes that alter the reﬂectivity (albedo) of the land surface. Also, chang-

ing atmospheric aerosol loadings, such as air pollution, affect how much solar radiation reaches the ground, which

can change the monsoon circulation by altering summer solar heating of the land surface. Absorption of solar

radiation by aerosols, on the other hand, warms the atmosphere, changing the atmospheric heating distribution.

The strongest effect of climate change on the monsoons is the increase in atmospheric moisture associated with

warming of the atmosphere, resulting in an increase in total monsoon rainfall even if the strength of the monsoon

circulation weakens or does not change.

Climate model projections through the 21st century show an increase in total monsoon rainfall, largely due to

increasing atmospheric moisture content. The total surface area affected by the monsoons is projected to increase,

along with the general poleward expansion of the tropical regions. Climate models project from 5% to an approxi-

mately 15% increase of global monsoon rainfall depending on scenarios. Though total tropical monsoon rainfall

increases, some areas will receive less monsoon rainfall, due to weakening tropical wind circulations. Monsoon

onset dates are likely to be early or not to change much and the monsoon retreat dates are likely to delay, resulting

in lengthening of the monsoon season.

Future regional trends in monsoon intensity and timing remain uncertain in many parts of the world. Year-to-year

variations in the monsoons in many tropical regions are affected by ENSO. How ENSO will change in future—and

how its effects on monsoon will change—also remain uncertain. However, the projected overall increase in mon-

soon rainfall indicates a corresponding increase in the risk of extreme rain events in most regions.

Turner, 2012; Bollasina and Ming, 2013), poor skill in simulating the

Madden–Julian Oscillation (MJO; Section 9.1.3.3) and uncertainties in

projected ENSO changes (Collins et al., 2010; Section 14.4) and in the

representation of aerosol effects (Section 9.4.6).

14.2.2.1 Indian Monsoon

The Indian summer monsoon is known to have undergone abrupt

shifts in the past millennium, giving rise to prolonged and intense

droughts (Meehl and Hu, 2006; Sinha et al., 2011; see also Chapter

2). The observed recent weakening tendency in seasonal rainfall and

the regional re-distribution has been partially attributed to factors

such as changes in black carbon and/or sulphate aerosols (Chung and

Ramanathan, 2006; Lau et al., 2008; Bollasina et al., 2011), land use

(Niyogi et al., 2010; see also Chapter 10) and SSTs (Annamalai et al.,

2013). An increase in extreme rainfall events occurred at the expense

of weaker rainfall events (Goswami et al., 2006) over the central Indian

region, and in many other areas (Krishnamurthy et al., 2009). With a

declining number of monsoon depressions (Krishnamurthy and Ajay-

amohan, 2010), the upward trend in extreme rainfall events may be

due to enhanced moisture content (Goswami et al., 2006) or warmer

SSTs in the tropical Indian Ocean (Rajeevan et al., 2008).

CMIP3 projections show suppressed rainfall over the equatorial Indian

Ocean (Cai et al., 2011e; Turner and Annamalai, 2012), and an increase

in seasonal mean rainfall over India (Ueda et al., 2006; Annamalai

Figure 14.3 | Regional land monsoon domain based on 26 CMIP5 multi-model mean precipitation with a common 2.5° × 2.5° grid in the present-day (1986–2005). For regional

divisions, the equator separates the northern monsoon domains (North America Monsoon System (NAMS), North Africa (NAF), Southern Asia (SAS) and East Asian summer (EAS))

from the southern monsoon domains (South America Monsoon System (SAMS), South Africa (SAF), and Australian-Maritime Continent (AUSMC)), 60°E separates NAF from SAS,

and 20°N and 100°E separates SAS from EAS. All the regional domains are within 40°S to 40°N.

1230

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Figure 14.4 | Changes in precipitation indices over the regional land monsoon domains of (upper) East Asian summer (EAS), (middle) Southern Asia (SAS), and (lower) Australian-

Maritime Continent (AUSMC) based on CMIP5 multi-models. (Left) Time series of observed and model-simulated summer precipitation anomalies (%) relative to the present-day

average. All the time series are smoothed with a 20-year running mean. For the time series of simulations, all model averages are shown by thick lines for the historical (grey; 40

models), RCP2.6 (dark blue; 24 models), RCP4.5 (light blue; 34 models), RCP6.0 (orange; 20 models), and RCP8.5 scenarios (red; 32 models). Their intervals between 10th and

90th percentiles are shown by shading for RCP2.6 and RCP8.5 scenarios. For the time series of observations, Climate Research Unit (CRU) TS3.2 (update from Mitchell and Jones,

2005; dark blue), Global Precipitation Climatology Centre (GPCC) v6 (Becker et al., 2013; deep green), GPCC Variability Analysis of Surface Climate Observations (VASClimO; Beck

et al., 2005; light green), Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) v1101 (Yatagai et al., 2012; only for EAS and

SAS regions; light blue), Global Precipitation Climatology Project (GPCP) v2.2 (updated from Huffman et al., 2009; black), and Climate Prediction Center (NOAA) Merged Analysis of

Precipitation (CMAP) v1201 (updated from Xie and Arkin, 1997; black with dots) are shown. GPCC v6 with dot line, GPCC VASClimO, GPCP v2.2 and CMAP v1201 are calculated

using all grids for the period of 1901–2010, 1951–2000, 1979–2010, 1979–2010, respectively. CRU TS3.2, GPCC v6 with solid line, and APHRODITE v1101, are calculated using

only grid boxes (2.5° in longitude/latitude) where at least one observation site exists for more than 80% of the period of 1921–2005, 1921–2005, and 1951–2005, respectively.

(Right) Projected changes for the future (2080-2099) relative to the present-day average in averaged precipitation (Pav), standard deviation of interannual variability in seasonal

average precipitation (Psd), simple precipitation daily intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD),

monsoon onset date (ONS), retreat date (RET), and duration (DUR), under the RCP2.6 (18 models), RCP4.5 (24 models), RCP6.0 (14 models) and RCP8.5 scenarios (26 models).

Units are % in Pav, Psd, SDII, R5d, and CDD; days in ONS, RET, and DUR. Box-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All of the indices are calculated

for the summer season (May to September in the Northern Hemisphere; November to March in the Southern Hemisphere). The indices of Pav, Psd, SDII, R5d and CDD are calculated

for each model’s original grid, and then averaged over the monsoon domains determined by each model at the present day. The indices of ONS, RET and DUR are calculated based

on the criteria proposed by Wang and LinHo (2002) using regionally averaged climatological cycles of precipitation.

1231

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.5 | Time series of summer monsoon indices (21-year running mean) relative to the base period average (1986–2005). Historical (gray), RCP4.5 (light blue) and RCP8.5

(red) simulations by 39 CMIP5 model ensembles are shown in 10th and 90th (shading), and 50th (thick line) percentiles. (a) East Asian summer monsoon (deﬁned as June, July and

August (JJA) sea level pressure difference between 160°E and 110°E from 10°N to 50°N), (b) Indian summer monsoon (deﬁned as meridional differences of the JJA 850 hPa zonal

winds averaged over 5°N to 15°N, 40°E to 80°E and 20°N to 30°N, 60°E to 90°E), (c) western North Paciﬁc summer monsoon (deﬁned as meridional differences of the JJA 850

hPa zonal winds averaged over 5°N to 15°N, 100°E to 130°E and 20°N to 30°N, 110°E to 140°E), (d) Australian summer monsoon (deﬁned as December, January and February

(DJF) 850 hPa zonal wind anomalies averaged over 10°S to 0°, 120°E to 150°E). (See Wang et al. (2004) and Zhou et al. (2009c) for indices deﬁnitions.)

et al., 2007; Turner et al., 2007a; Kumar et al., 2011b; Sabade et al.,

2011). These results are generally conﬁrmed by CMIP5 projections

(Chaturvedi et al., 2012). The projected changes in Indian monsoon

rainfall increase with the anthropogenic forcing among RCPs (May,

2011; see Figure 14.4; SAS).

In a suite of models that realistically simulate ENSO–monsoon rela-

tionships, normal monsoon years are likely to become less frequent

in the future, but there is no clear consensus about the occurrence of

extrememonsoon years (Turner and Annamalai, 2012). CMIP3 models

indicate ENSO–monsoon relationships to persist in the future (Kumar

et al., 2011b), but there is low conﬁdence in the projection of ENSO

variability (Section 14.4). Sub-seasonal scale monsoon variability is

linked to the MJO but again the conﬁdence in the future projection of

MJO remains low (Section 14.3.2).

CMIP5 models project an increase in mean precipitation as well as

its interannual variability and extremes (Figure 14.4; SAS). All models

project an increase in heavy precipitation events but disagree on CDD

changes. Regarding seasonality, model agreement is high on an earlier

onset and later retreat, and hence longer duration. The monsoon cir-

culation weakens in the future (Figure 14.5; IND) but the precipitation

increases. Like the global monsoon (Section 14.2.1), the precipitation

increase is largely due to the increased moisture ﬂux from ocean to

land.

14.2.2.2 East Asian Monsoon

The East Asian monsoon is characterized by a wet season and

southerly ﬂow in summer and by dry cold northerly ﬂow in winter.

The East Asian summer (EAS) monsoon circulation has experienced

an inter-decadal weakening from the 1960s to the 1980s (Hori et

al., 2007; Li et al., 2010a), associated with deﬁcient rainfall in North

China and excessive rainfall in central East China along 30°N (Hu,

1997; Wang, 2001; Gong and Ho, 2002; Yu et al., 2004). The summer

monsoon circulation has begun to recover in recent decades (Liu et

al., 2012a; Zhu et al., 2012). The summer rainfall amount over East

Asia shows no clear trend during the 20th century (Zhang and Zhou,

2011), although signiﬁcant trends may be found in local station

records (Wang et al., 2006). The winter monsoon circulation weakened

signiﬁcantly after the 1980s (Wang et al., 2009a; Wang and Chen,

2010). See Supplementary Material Sections 14.SM.1.3 to 14.SM.1.7

for additional discussions of natural variability.

CMIP3 models show reasonable skill in simulating large-scale circula-

tion of the EAS monsoon (Boo et al., 2011), but their performance is

poor in reproducing the monsoon rainband (Lin et al., 2008a; Li and

Zhou, 2011). Only a few CMIP3 models reproduce the Baiu rainband

(Ninomiya, 2012) and high-resolution models (Kitoh and Kusunoki,

2008) show better performance than low resolution CMIP3 type

models in simulating the monsoon rainband (Kitoh and Kusunoki,

1232

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

2008). CMIP3 models show large uncertainties in projections of mon-

soon precipitation and circulation (Ding et al., 2007; Kripalani et al.,

2007a) but the simulation of interannual variability of the EAS mon-

soon circulation has improved from CMIP3 to CMIP5 (Sperber et al.,

2012). Climate change may bring a change in the position of the mon-

soon rain band (Li et al., 2010a).

CMIP5 projections indicate a likely increase in both the circulation

(Figure 14.5) and rainfall of the EAS monsoon (Figure 14.4) throughout

the 21st century. This is different from other Asian-Australian mon-

soon subsystems, where the increase in precipitation (Figure 14.4)

is generally associated with weakening monsoon circulation (Figure

14.5). Interannual variability of seasonal mean rainfall is very likely to

increase except for RCP2.6 (Figure 14.4). Heavy precipitation events

(SDII and R5d) are also very likely to increase. CMIP5 models project

an earlier monsoon onset and longer duration but the spread among

models is large (Figure 14.4).

14.2.2.3 Maritime Continent Monsoon

Interaction between land and water characterizes the Maritime Con-

tinent region located between the Asian continent and Australia. It

provides a land bridge along which maximum convection marches

from the Asian summer monsoon regime (generally peaking in June,

July and August) to the Australian summer monsoon system (generally

peaking in December, January and February).

Phenomena such as the MJO (Tangang et al., 2008; Section 14.3.2;

Hidayat and Kizu, 2010; Salahuddin and Curtis, 2011), and ENSO (Aldri-

an and Djamil, 2008; Moron et al., 2010; Section 14.4) inﬂuence Mari-

time Continent Monsoon variability. Rainfall extremes in the Maritime

Continent are strongly inﬂuenced by diurnal rainfall variability (Qian,

2008; Qian et al., 2010a; Robertson et al., 2011; Ward et al., 2011) as

well as the MJO. There have been no obvious trends in extreme rainfall

indices in Indonesia, except evidence of a decrease in some areas in

annual rainfall and an increase in the ratio of the wet to dry season

rainfall (Aldrian and Djamil, 2008).

Modelling the Maritime Continent monsoon is a challenge because

of the coarse resolution of contemporary large-scale coupled climate

models (Aldrian and Djamil, 2008; Qian, 2008). Most CMIP3 models

tend to simulate increasing precipitation in the tropical central Paciﬁc

but declining trends over the Maritime Continent for June to August

(Ose and Arakawa, 2011), consistent with a decreasing zonal SST gra-

dient across the equatorial Paciﬁc and a weakening Walker Circulation

(Collins et al., 2010). Projections of CMIP5 models are consistent with

those of CMIP3 models, with decreasing precipitation during boreal

summer and increasing precipitation during boreal winter, but model

agreement is not high (Figures AI.66-67; Figure 12.22, but see also

Figure 14.27).

14.2.2.4 Australian Monsoon

Some indices of the Australian summer monsoon (Wang et al., 2004; Li

et al., 2012a) show a clear post-1980 reduction, but another index by

Kajikawa et al. (2010) does not fully exhibit this change. Over north-

west Australia, summer rainfall has increased by more than 50% (Rot-

stayn et al., 2007; Shi et al., 2008b; Smith et al., 2008), whereas over

northeast Australia, summer rainfall has decreased markedly since

around 1980 (Li et al., 2012a).

Models in general show skill in representing the gross spatial char-

acteristics of Australian monsoon summer precipitation (Moise et al.,

2005). Further, atmospheric General Circulation Models (GCMs) forced

by SST anomalies can skilfully reproduce monsoon-related zonal wind

variability over recent decades (Zhou et al., 2009a). Recent analysis of

the skill of a suite of CMIP3 models showed a good representation in

the ensemble mean, but a very large range of biases across individu-

al models (more than a factor of 6; Colman et al., 2011). Most CMIP

models have biases in monsoon seasonality, but CMIP5 models gener-

ally perform better than CMIP3 (Jourdain et al., 2013).

In climate change projections, overall changes in tropical Australian

rainfall are small, with substantial uncertainties (Figure 14.4; Moise et

al., 2012; see also Figure 14.27). Using a group of CMIP5 models that

exhibit a realistic present-day climatology, most projections using the

RCP8.5 scenario produced 5% to 20% more monsoon rainfall by the

late 21st century compared to the pre-industrial period (Jourdain et

al., 2013). Most CMIP3 model projections suggest delayed monsoon

onset and reduced monsoon duration over northern Australia. Weaker

model agreement is seen over the interior of the Australian continent,

where ensembles show an approximate 7-day delay of both the onset

and retreat with little change in duration (Zhang et al., 2013a). CMIP5

model agreement in changes of monsoon precipitation seasonality is

low (Figure 14.4).

14.2.2.5 Western North Paciﬁc Monsoon

The western North Paciﬁc summer monsoon (WNPSM) occupies a

broad oceanic region of the South China and Philippine Seas, featuring

a monsoon trough and a subtropical anticyclonic ridge to the north

(Zhang and Wang, 2008).

The western North Paciﬁc monsoon does not show any trend during

1950–1999. Since the late 1970s, the correlation has strengthened

between interannual variability in the western North Paciﬁc monsoon

and ENSO (Section 14.4), a change mediated by Indian Ocean SST

(Huang et al., 2010; Xie et al., 2010a). This occurred despite a weaken-

ing of the Indian monsoon–ENSO correlation in this period (Wang et

al., 2008a).

CMIP5 models project little change in western North Paciﬁc monsoon

circulation (Figure 14.5) but enhanced precipitation (Figures AI.66-67;

Figure 12.22; but see also Figure 14.24) due to increased moisture con-

vergence (Chapter 12).

14.2.3 American Monsoons

The American monsoons, the North America Monsoon System (NAMS)

and the SouthAmerica Monsoon System (SAMS), are associated with

large inter-seasonal differences in precipitation, humidity and atmos-

pheric circulation (Vera et al., 2006; Marengo et al., 2010a). NAMS

and SAMS indices areoften, though not always, deﬁned in terms of

precipitation characteristics (Wang and LinHo, 2002).

1233

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

14.2.3.1 North America Monsoon System

The warm season precipitation in northern Mexico and the southwest-

ern USA is strongly inﬂuenced by the NAMS. It has been difﬁcult to

simulate many important NAMS-related phenomenon in global cli-

mate models (Castro et al., 2007; Lin et al., 2008b; Cerezo-Mota et al.,

2011), though the models capture gross-scale features associated with

the NAMSseasonal cycle (Liang et al., 2008b; Gutzler, 2009). See Sup-

plementary Material Section 14.SM.1.8 for a more detailed discussion

of NAMS dynamics.

In the NAMS core region, no distinct precipitation trends have been

seen over the last half of the 20th century (Anderson et al., 2010;

Arriaga-Ramirez and Cavazos, 2010), due to countervailing trends in

increasing intensity and decreasing frequency of events, as well as

the decreasing length of the monsoon season itself (Englehart and

Douglas, 2006). However, monsoonal stream ﬂow in western Mexico

has been decreasing, possibly as a result of changing precipitation

characteristics or antecedent hydrological conditions rather than over-

all precipitation amounts (Gochis et al., 2007). There has also been

a systematic delay in monsoon onset, peak and termination (Grantz

et al., 2007) as well as an increase in extreme precipitation events

associated with land-falling hurricanes (Cavazos et al., 2008). Finally,

positive trends in NAMS precipitation amounts have been detected

in the northern fringes of the core area, that is, Arizona and western

NewMexico (Anderson et al., 2010), consistent with northward NAMS

expansion during relatively warm periods in the Holocene (Petersen,

Figure 14.6 | As in Figure 14.4, except for (upper) North America Monsoon System (NAMS) and (lower) South America Monsoon System (SAMS).

1994; Mock and Brunelle-Daines, 1999; Harrison et al., 2003; Poore et

al., 2005; Metcalfe et al., 2010).

Over the coming century, CMIP5 simulations generally project a precip-

itation reduction in the core zone of the monsoon (Figures AI.27 and

Figure 14.6), but this signal is not particularly consistent across models,

even under the RCP8.5 scenario (Cook and Seager, 2013). Thus con-

ﬁdence in projections of monsoon precipitation changes is currently

low. CMIP5 models have no consensus on future changes ofmonsoon

timing (Figure 14.6). Temperature increases are consistently project-

ed in all models (Annex I). This will likely increase the frequency of

extreme summer temperatures (Diffenbaugh and Ashfaq, 2010; Ander-

son, 2011; Duffy and Tebaldi, 2012), together with projected increase

in consecutive dry days (Figure 14.6).

14.2.3.2 South America Monsoon System

The SAMS mainly inﬂuences precipitation in the South American trop-

ics and subtropics (Figure 14.1). The main characteristics of SAMS

onset are increased humidity ﬂux from the Atlantic Ocean over north-

ern South America, an eastward shift of the subtropical high, strong

northwesterly moisture ﬂux east of the tropical Andes, and establish-

ment of the Bolivian High (Raia and Cavalcanti, 2008; Marengo et al.,

2010a; Silva and Kousky, 2012). Recent SAMS indices have been cal-

culated based on different variables, such as a large scale index (Silva

and Carvalho, 2007), moisture ﬂux (Raia and Cavalcanti, 2008), and

wind (Gan et al., 2006), in addition to precipitation (Nieto-Ferreira and

1234

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Rickenbach, 2010; Seth et al., 2010; Kitoh et al., 2013). As seen below,

conclusions regarding SAMS changes can depend on the index chosen.

SAMS duration and amplitude obtained from the observed large-scale

index have both increased in the last 32 years (Jones and Carvalho,

2013). Increase of extreme precipitation and consecutive dry days have

been observed in the SAMS region from 1969 to 2009 (Skansi et al.,

2013). The overall annual cycle of precipitation in the SAMS region,

including SAMS onset and demise, is generally well represented by

models (Bombardi and Carvalho, 2009; Seth et al., 2010; Kitoh et al.,

2013). Extreme precipitation indices in SAMS region are also well sim-

ulated by CMIP5 models (Kitoh et al., 2013). CMIP5 models subjected

to historical forcing show increases in SAMS amplitude, earlier onset

and later demise during the 1951–2005 period (Jones and Carvalho,

2013). Using a precipitation based index, precipitation increases in

austral summer but decreases in austral spring, indicating delayed

SAMS onset in the CMIP3 projections (Seth et al., 2011). CMIP5 projec-

tions based on the global precipitation index (Section 14.2.1) consist-

ently show small precipitation increases and little change in onset and

retreat (Kitoh et al., 2013; Figure 14.6). On the other hand, when using

a different index, earlier onsets and later demises and thus, longer

duration of the SAMS by the end of the 21st century (2081–2100) has

been found (Jones and Carvalho, 2013). Thus there is medium con-

ﬁdence that SAMS overall precipitation will remain unchanged. The

different estimates of changes in timing underscores potential uncer-

tainties related to SAMS timing due todifferences in SAMS indices. The

models do show signiﬁcant and robust increases in extreme precipi-

tation indices in the SAMS region, such as seasonal maximum 5-day

precipitation total and number of consecutive dry days (Figure 14.6),

leading to medium conﬁdence in projections of these characteristics.

14.2.4 African Monsoon

In Africa, monsoon circulation affects precipitation in West Africa

where notable upper air ﬂow reversals are observed. East and south

African precipitation is generally described by variations in the tropi-

cal convergence zone rather than as a monsoon feature. This section

covers the West African monsoon, and Section 14.8.7 also covers the

latter two regions.

The West African monsoon develops during northern spring and

summer, with a rapid northward jump of the rainfall belt from along

the Gulf of Guinea at 5°N in May to June to the Sahel at 10°N in July

to August. Factors inﬂuencing the West African monsoon include inter-

annual to decadal variations, land processes and the direct response to

radiative forcing. Cross-equatorial tropical Atlantic SST patterns inﬂu-

ence the monsoon ﬂow and moistening of the boundary layer, so that

a colder northern tropical Atlantic induces negative rainfall anomalies

(Biasutti et al., 2008; Giannini et al., 2008; Xue et al., 2010; Rowell,

2011).

In CMIP3 simulations, rainfall is projected to decrease in the early part

but increase towards the end of the rainy season, implying a small

delay in the monsoon season and an intensiﬁcation of late-season rains

(Biasutti and Sobel, 2009; Biasutti et al., 2009; Seth et al., 2010). CMIP5

models, on the other hand, simulate the variability of tropical Atlantic

SST patterns with little credibility (Section 9.4.2.5.2) and model resolu-

tion is known to limit the ability to capture the mesoscale ‘squall line’

systems that form a central element in the maintenance of the rainy

season (Ruti and Dell’Aquila, 2010; see also Section 14.8.7). Therefore,

projections of the West African monsoon rainfall appear to be uncer-

tain, reﬂected by considerable model deﬁciencies and spread in the

projections (Figure 14.7). Note that this ﬁgure is based on a somewhat

eastward extended area for the West African monsoon (NAF in Figure

14.3), seen as a component of the global monsoon system (Section

14.2.1). The limitations of model simulations in the region arising from

the lack of convective organization (Kohler et al., 2010) leading to the

underestimation of interannual variability (Scaife et al., 2009) imply

that conﬁdence in projections of the African monsoon is low.

The limited information that could be deduced from CMIP3 has not

improved much in CMIP5. Figure 14.7 largely conﬁrms the ﬁndings

based on CMIP3. The CMIP5 model ensemble projects a modest

change in the onset date (depending on the scenario) and a small

delay in the retreat date, leading to a small increase in the duration

of the rainy season. The delay in the monsoon retreat is larger in the

high-end emission scenarios. The interannual variance and the 5-day

rain intensity show a robust increase, while a small increase in dry day

periods is less signiﬁcant.

14.2.5 Assessment Summary

It is projected that global monsoon precipitation will likely strengthen

in the 21st century with increase in its area and intensity while the

monsoon circulation weakens. Precipitation extremes including pre-

cipitation intensity and consecutive dry days are likely to increase at

higher rates than those of mean precipitation. Overall, CMIP5 models

project that the monsoon onset will be earlier or not change much

and the monsoon retreat dates will delay, resulting in a lengthening

of the monsoon season. Such features are likely to occur in most of

Asian-Australian Monsoon regions.

There is medium conﬁdence that overall precipitation associated with

the Asian-Australian monsoon will increase but with a north-south

asymmetry: Indian and East Asian monsoon precipitation is projected

to increase, while projected changes in Australian summer monsoon

precipitation are small. There is medium conﬁdence that the Indian

summer monsoon circulation will weaken, but this is compensated by

increased atmospheric moisture content, leading to more precipita-

tion. For the East Asian summer monsoon, both monsoon circulation

and precipitation are projected to increase. There is low conﬁdence

that over the Maritime Continent boreal summer rainfall will decrease

and boreal winter rainfall will increase. There is low conﬁdence that

changes in the tropical Australian monsoon rainfall are small. There

is low conﬁdence that Western North Paciﬁc summer monsoon circu-

lation changes are small, but with increased rainfall due to enhanced

moisture. There is medium conﬁdence in an increase of Indian summer

monsoon rainfall and its extremes throughout the 21st century under

all RCP scenarios. Their percentage change ratios are the largest and

model agreement is highest among all monsoon regions.

There is low conﬁdence in projections of American monsoon precipi-

tationchanges but there is high conﬁdence in increases of precipita-

tion extremes, of wet days and consecutive dry days. There is medium

1235

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.7 | As in Figure 14.4, except for (upper) North Africa (NAF) and (lower) South Africa (SAF).

conﬁdence in precipitation associated with the NAMSwill arrive later

in the annual cycle, and persist longer. Projections of changes in the

timing and duration of the SAMS remain uncertain. There is high conﬁ-

dence in the expansion of SAMS, resulting from increased temperature

and humidity.

Based on how models represent known drivers of the West African

monsoon, there is low conﬁdence in projections of its future develop-

ment based on CMIP5. Conﬁdence is low in projections of a small delay

in the onset of the West African rainy season with an intensiﬁcation of

late-season rains.

14.3 Tropical Phenomena

14.3.1 Convergence Zones

Section 7.6 presents a radiative perspective of changes in convection

(including the differences between GHG and aerosol forcings), and Sec-

tion 12.4.5.2 discusses patterns of precipitation change on the global

scale. The emphasis here is on regional aspects of tropical changes.

Tropical convection over the oceans, averaged for a month or longer,

is organized into long and narrow convergence zones, often anchored

by SST structures. Latent heat release in convection drives atmospher-

ic circulation and affects global climate. In model experiments where

spatially uniform SST warming is imposed, precipitation increases in

these tropical convergence zones (Xie et al., 2010b), following the

‘wet-get-wetter’ paradigm (Held and Soden, 2006). On the ﬂanks of

a convergence zone, rainfall may decrease because of the increased

horizontal gradient in speciﬁc humidity and the resultant increase in

dry advection into the convergence zone (Neelin et al., 2003).

Although these arguments based on moist atmospheric dynamics call

for changes in tropical convection to be organized around the clima-

tological rain band, studies since AR4 show that such changes in a

warmer climate also depend on the spatial pattern of SST warming.

As a result of the SST pattern effect, rainfall change does not generally

project onto the climatological convergence zones, especially for the

annual mean. In CMIP3/5 model projections, annual rainfall change

over tropical oceans follows a ‘warmer-get-wetter’ pattern, increas-

ing where the SST warming exceeds the tropical mean and vice versa

(Figure 14.8, Xie et al., 2010b; Sobel and Camargo, 2011; Chadwick et

al., 2013). Differences among models in the SST warming pattern are

an important source of uncertainty in rainfall projections, accounting

for a third of inter-model variability in annual precipitation change in

the tropics (Ma and Xie, 2013).

Figure 14.8 presents selected indices for several robust patterns of SST

warming for RCP8.5. They include greater warming in the NH than in

the Southern Hemisphere (SH), a pattern favouring rainfall increase at

locations north of the equator and decreases to the south (Friedman et

al., 2013); enhanced equatorial warming (Liu et al., 2005) that anchors

a pronounced rainfall increase in the equatorial Paciﬁc; reduced warm-

ing in the subtropical Southeast Paciﬁc that weakens convection there;

decreased zonal SST gradient across the equatorial Paciﬁc (see Sec-

tion 14.4) and increased westward SST gradient across the equatorial

1236

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Indian Ocean (see Section 14.3.3) that together contribute to the

weakened Walker cells.

Changes in tropical convection affect the pattern of SST change

(Chou et al., 2005) and such atmospheric and oceanic perturbations

are inherently coupled. The SST pattern effect dominates the annual

rainfall change while the wet-get-wetter effect becomes important for

seasonal mean rainfall in the summer hemisphere (Huang et al., 2013).

This is equivalent to an increase in the annual range of precipitation

in a warmer climate (Chou et al., 2013). Given uncertainties in SST

warming pattern, the conﬁdence is generally higher for seasonal than

annual mean changes in tropical rainfall.

14.3.1.1 Inter-Tropical Convergence Zone

The Inter-Tropical Convergence Zone (ITCZ) is a zonal band of persis-

tent low-level convergence, atmospheric convection, and heavy rainfall.

Over the Atlantic and eastern half of the Paciﬁc, the ITCZ is displaced

north of the equator due to ocean–atmosphere interaction (Xie et al.,

NH−SH EQ SE PAC IO r(T,Precip)

−2

−1.5

−1

−0.5

0.5

1.5

Zonal

PAC

Zonal

Temperature ( C)

(%)

Figure 14.8 | (Upper panel) Annual mean precipitation percentage change (dP/P in green/gray shade and white contours at 20% intervals), and relative SST change (colour

contours at intervals of 0.2°C; negative dashed) to the tropical (20°S to 20°N) mean warming in RCP8.5 projections, shown as 23 CMIP5 model ensemble mean. (Lower panel) Sea

surface temperature (SST) warming pattern indices in the 23-model RCP8.5 ensemble, shown as the 2081–2100 minus 1986–2005 difference. From left: Northern (EQ to 60°N)

minus Southern (60°S to EQ) Hemisphere; equatorial (120°E to 60°W, 5°S to 5°N) and Southeast (130°W to 70°W, 30°S to 15°S) Paciﬁc relative to the tropical mean warming;

zonal SST gradient in the equatorial Paciﬁc (120°E to 180°E minus 150°W to 90°W, 5°S to 5°N) and Indian (50°E to 70°E, 10°S to 10°N minus 90°E to 110°S, 10°S to EQ) Oceans.

(Rightmost) Spatial correlation (r) between relative SST change and precipitation percentage change (dP/P) in the tropics (20°S to 20°N) in each model. (The spatial correlation for

the multi-model ensemble mean ﬁelds in the upper panel is 0.63). The circle and error bar indicate the ensemble mean and ±1 standard deviation, respectively. The upper panel is

a CMIP5 update of Ma and Xie (2013), and see text for indices in the lower panel.

2007) and extratropical inﬂuences (Kang et al., 2008; Fučkar et al.,

2013). Many models show an unrealistic double-ITCZ pattern over the

tropical Paciﬁc and Atlantic, with excessive rainfall south of the equa-

tor (Section 9.4.2.5.1). This bias needs to be kept in mind in assessing

ITCZ changes in model projections, especially for boreal spring when

the model biases are largest.

The global zonal mean ITCZ migrates back and forth across the equator

following the sun. In CMIP5, seasonal mean rainfall is projected to

increase on the equatorward ﬂank of the ITCZ (Figure 14.9). The co-mi-

gration of rainfall increase with the ITCZ is due to the wet-get-wetter

effect while the equatorward displacement is due to the SST pattern

effect (Huang et al., 2013).

14.3.1.2 South Paciﬁc Convergence Zone

The South Paciﬁc Convergence Zone (SPCZ, Widlansky et al., 2011)

extends southeastward from the tropical western Paciﬁc to French Pol-

ynesia and the SH mid-latitudes, contributing most of the yearly rainfall

1237

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

to the many South Paciﬁc island nations under its inﬂuence. The SPCZ

is most pronounced during austral summer (December, January and

February (DJF)).

Zonal and meridional SST gradients, trade wind strength, and sub-

sidence over the eastern Paciﬁc are important mechanisms for SPCZ

orientation and variability (Takahashi and Battisti, 2007; Lintner and

Neelin, 2008; Vincent et al., 2011; Widlansky et al., 2011). Many GCMs

simulate the SPCZ as lying east–west, giving a ‘double-ITCZ’ structure

and missing the southeastward orientation (Brown et al., 2012a).

The majority of CMIP models simulate increased austral summer mean

precipitation in the SPCZ, with decreased precipitation at the eastern

edge of the SPCZ (Brown et al., 2012a; Brown et al., 2012b). The posi-

tion of the SPCZ varies on interannual to decadal time scales, shifting

northeast in response to El Niño (Folland et al., 2002; Vincent et al.,

2011). Strong El Niño events induce a zonally oriented SPCZ locat-

ed well northeast of its average position, while more moderate ENSO

(Section 14.4) events are associated with movement of the SPCZ to the

northeast or southwest, without a change in its orientation.

Models from both CMIP3 and CMIP5 that simulate the SPCZ well show

a consistent tendency towards much more frequent zonally oriented

SPCZ events in future (Cai et al., 2012b). The mechanism appears to be

associated with a reduction in near-equatorial meridional SST gradient,

a robust feature of modelled SST response to anthropogenic forcing

(Widlansky et al., 2013). An increased frequency of zonally oriented

Figure 14.9 | Seasonal cycle of zonal mean tropical precipitation change (2081–2100

in RCP8.5 minus 1986–2005) in CMIP5 multi-model ensemble (MME) mean. Eighteen

CMIP5 models were used. Stippling indicates that more than 90% models agree on the

sign of MME change. The red curve represents the meridional maximum of the climato-

logical rainfall. (Adapted from Huang et al., 2013.)

( )

SPCZ events would have major implications for regional climate, possi-

bly leading to longer dry spells in the southwest Paciﬁc.

14.3.1.3 South Atlantic Convergence Zone

The South Atlantic Convergence Zone (SACZ) extends from the Amazon

region through southeastern Brazil towards the Atlantic Ocean during

austral summer (Cunningham and Cavalcanti, 2006; Carvalho et al.,

2011; de Oliveira Vieira et al., 2013). Floods or dry conditions in south-

eastern Brazil are often related to SACZ variability (Muza et al., 2009;

Lima et al., 2010; Vasconcellos and Cavalcanti, 2010). A subset of CMIP

models simulate the SACZ (Vera and Silvestri, 2009; Seth et al., 2010;

Yin et al., 2012) and its variability as a dipolar structure (Junquas et al.,

2012; Cavalcanti and Shimizu, 2012).

A southward displacement of SACZ and intensiﬁcation of the southern

centre of the precipitation dipole are suggested in projections of CMIP3

and CMIP5 models (Seth et al., 2010; Junquas et al., 2012; Cavalcanti

and Shimizu, 2012). This displacement is consistent with the increased

precipitation over southeastern South America, south of 25°S, project-

ed for the second half of the 21st century, in CMIP3, CMIP5 and region-

al models (Figure AI.34, Figure 14.21). It is also consistent with the

southward displacement of the Atlantic subtropical high (Seth et al.,

2010) related to the southward expansion of the Hadley Cell (Lu et al.,

2007). Paciﬁc SST warming and the strengthening of the Paciﬁc–South

American (PSA)-like wave train (Section 14.6.2) are potential mech-

anisms for changes in the dipolar pattern resulting in SACZ change

(Junquas et al., 2012). This change is also supported by the intensiﬁca-

tion and increased frequency of the low level jet over South America in

future projections (Soares and Marengo, 2009; Seth et al., 2010).

14.3.2 Madden–Julian Oscillation

The MJO is the dominant mode of tropical intraseasonal (20 to 100

days) variability (Zhang, 2005). The MJO modulates tropical cyclone

activity (Frank and Roundy, 2006), contributes to intraseasonal ﬂuctu-

ations of the monsoons (Maloney and Shaman, 2008), and excites tel-

econnection patterns outside the tropics (L’Heureux and Higgins, 2008;

Lin et al., 2009). Simulation of the MJO by GCMs remains challenging,

but with some improvements made in recent years (Section 9.5.2.3).

Possible changes in the MJO in a future warmer climate have just

begun to be explored with models that simulate the phenomenon. In

the Max Planck Institute Earth System Model, MJO variance increas-

es appreciably with increasing warming (Schubert et al., 2013). The

change in MJO variance is highly sensitive to the spatial pattern of SST

warming (Maloney and Xie, 2013). In light of the low skill in simulating

MJO, and its sensitive to SST warming pattern, which in itself is subject

to large uncertainties, it is currently not possible to assess how the

MJO will change in a warmer climate.

14.3.3 Indian Ocean Modes

The tropical Indian Ocean SST exhibits two modes of interannual vari-

ability (Schott et al., 2009; Deser et al., 2010b): the Indian Ocean Basin

(IOB) mode featuring a basin-wide structure of the same sign, and the

Indian Ocean Dipole (IOD) mode with largest amplitude in the eastern

1238

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Indian Ocean off Indonesia, and weaker anomalies of the opposite

polarity over the rest of the basin (Box 2.5). Both modes are statisti-

cally signiﬁcantly correlated with ENSO (Section 14.4). CMIP models

simulate both modes well (Section 9.5.3.4.2).

The formation of IOB is linked to ENSO via an atmospheric bridge and

surface heat ﬂux adjustment (Klein et al., 1999; Alexander et al., 2002).

Ocean–atmosphere interactions within the Indian Ocean are impor-

tant for the long persistence of this mode (Izumo et al., 2008; Wu et

al., 2008; Du et al., 2009). The basin mode affects the termination of

ENSO events (Kug and Kang, 2006), it induces coherent atmospheric

anomalies in the summer following El Niño (Xie et al., 2009), including

supressed convection (Wang et al., 2003) and reduced tropical cyclone

activity (Du et al., 2011) over the Northwest Paciﬁc and anomalous

rainfall over East Asia (Huang et al., 2004).

IOD develops in July to November and involves Bjerknes feedback

between zonal SST gradient, zonal wind and thermocline tilt along the

equator (Saji et al., 1999; Webster et al., 1999). A positive IOD event

(with negative SST anomalies off Sumatra) is associated with droughts

in Indonesia, reduced rainfall over Australia, intensiﬁed Indian summer

monsoon, increased precipitation in East Africa and anomalous con-

ditions in the extratropical SH (Yamagata et al., 2004). Most CMIP3

models are able to reproduce the general features of the IOD, including

its phase lock onto the July to November season, while detailed analy-

sis of CMIP5 simulations are not yet available (Section 9.5.3.4.2)

Basin-mean SST has risen steadily for much of the 20th century, a trend

captured by CMIP3 20th century simulations (Alory et al., 2007). The

SST increase over the North Indian Ocean since about 1930 is notice-

ably weaker than for the rest of the basin. This spatial pattern is sug-

gestive of the effects of reduced surface solar radiation due to Asian

brown clouds (Chung and Ramanathan, 2006) and it affects Arabian

Latitude

Longitude

(mm per month)

Sea cyclones (Evan et al., 2011b). In the equatorial Indian Ocean, coral

isotope records off Indonesia indicate a reduced SST warming and/or

increased salinity during the 20th century (Abram et al., 2008). From

ship-borne surface measurements, an easterly wind change especially

during July to October has been observed over the past six decades, a

result consistent with a reduction of marine cloudiness in the east and

a decreasing precipitation trend over the maritime continent (Tokinaga

et al., 2012). Atmospheric reanalysis products have difﬁculty represent-

ing these changes (Han et al., 2010).

The projected changes over the equatorial Indian Ocean include east-

erly wind anomalies, a shoaling thermocline (Vecchi and Soden, 2007a;

Du and Xie, 2008) and reduced SST warming in the east (Stowasser et

al., 2009), a result conﬁrmed by CMIP5 multi-model analysis (Zheng

et al., 2013; Figure 14.10). The change in zonal SST gradient, in turn,

reinforces the easterly wind change, indicative of a positive feedback

between them as envisioned by Bjerknes (1969). This coupled pattern

is most pronounced during July to November, and is broadly consistent

with the observed changes in the equatorial Indian Ocean.

In one CMIP3 model, the IOB mode and its capacitor effect persist

longer, through summer into early fall towards the end of the century

(2081–2100, Zheng et al., 2011). This increased persistence intensiﬁes

ENSO’s inﬂuence on the Northwest Paciﬁc summer monsoon. The con-

ﬁdence level of this relationship is low due to the lack of multi-model

studies.

The IOD variability in SST remains nearly unchanged in future projec-

tions of CMIP3 and CMIP5 (Ihara et al., 2009; Figure 14.11a) despite

the easterly wind change that lifts the thermocline (Figure 14.10b)

and intensiﬁes thermocline feedback on SST in the eastern equatorial

Indian Ocean. The global increase in atmospheric dry static stability

weakens the atmospheric response to zonal SST gradient changes,

Figure 14.10 | September to November changes in a 22-model CMIP5 ensemble (2081–2100 in RCP8.5 minus 1986–2005 in historical run). (a) Sea surface temperature (SST,

colour contours at 0.1°C intervals) relative to the tropical mean (20°S to 20°N), and precipitation (shading and white contours at 20 mm per month intervals). (b) Surface wind velocity

(m s

–1

), and sea surface height deviation from the global mean (contours, centimetres). Over the equatorial Indian Ocean, ocean–atmospheric changes form Bjerknes feedback, with

the reduced SST warming and suppressed convection in the east. (Updated with CMIP5 from Xie et al., 2010b.)

1239

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.11 | CMIP5 multi-model ensemble mean standard deviations of interannual variability for September to November in pre-industrial (PiControl; blue bars) and RCP8.5

(red) runs: (a) the Indian Ocean dipole index deﬁned as the western (50°E to 70°E, 10°S to 10°N) minus eastern (90°E to 110°E, 10°S to 0°) SST difference; (b) zonal wind in the

central equatorial Indian Ocean (70°E to 90°E, 5°S to 5°N); and (c) sea surface height in the eastern equatorial Indian Ocean (90°E to 110°E, 10°S to 0°). The standard deviation

is normalized by the pre-industrial (PiControl) value for each model before ensemble average. Blue box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles of

51-year windows for PiControl, representing natural variability. Red box-and-whisker plots represent inter-model variability for RCP8.5, based on the nearest rank. (Adapted from

Zheng et al., 2013.)

(a) IOD Variance (b) Zonal Wind Variance (c) SSH Variance

0.5

0.7

0.9

1.1

Standard deviation

1.3

1.51.5

90%

75%

25%

50%

10%

countering the enhanced thermocline feedback (Zheng et al., 2010).

The weakened atmospheric feedback is reﬂected in a decrease in IOD

variance in both zonal wind and the thermocline depth (Zheng et al.,

2013; Figure 14.11b, c ).

14.3.4 Atlantic Ocean Modes

The Atlantic features a northward-displaced ITCZ (Section 14.3.1.1),

and a cold tongue that develops in boreal summer. Climate models

generally fail to simulate these characteristics of tropical Atlantic cli-

mate (Section 9.5.3.3). The biases severely limit model skill in simu-

lating modes of Atlantic climate variability and in projecting future

climate change in the Atlantic sector. In-depth analysis of the CMIP5

projections of Atlantic Ocean Modes has not yet been fully explored,

but see Section 12.4.3.

The inter-hemispheric SST gradient displays pronounced interannual to

decadal variability (Box 2.5, Figure 2), referred to as the Atlantic merid-

ional mode (AMM; Servain et al., 1999; Chiang and Vimont, 2004; Xie

and Carton, 2004). A thermodynamic feedback between surface winds,

evaporation and SST (WES; Xie and Philander, 1994) is fundamental to

the AMM (Chang et al., 2006). This mode affects precipitation in north-

eastern Brazil by displacing the ITCZ (Servain et al., 1999; Chiang and

Vimont, 2004; Xie and Carton, 2004), and Atlantic hurricane activity

(Vimont and Kossin, 2007; Smirnov and Vimont, 2011).

The Atlantic Niño mode represents interannual variability in the equa-

torial cold tongue, akin to ENSO (Box 2.5, Figure 2). Bjerknes feedback

is considered important for energizing the mode (Zebiak, 1993; Carton

and Huang, 1994; Keenlyside and Latif, 2007). This mode affects the

West Africa Monsoon (Vizy and Cook, 2002; Giannini et al., 2003).

Over the past century, the Atlantic has experienced a pronounced

and persistent warming trend. The warming has brought detectable

changes in atmospheric circulation and rainfall patterns in the region.

In particular, the ITCZ has shifted southward and land precipitation

has increased over the equatorial Amazon, equatorial West Africa, and

along the Guinea coast, while it has decreased over the Sahel (Deser

et al., 2010a; Tokinaga and Xie, 2011; see also Sections 2.5 and 2.7 ).

Atlantic Niño variability has weakened by 40% in amplitude from 1960

to 1999, associated with a weakening of the equatorial cold tongue

(Tokinaga and Xie, 2011).

The CMIP3 20th century climate simulations generally capture the

warming trend of the basin-averaged SST over the tropical Atlantic. A

majority of the models also seem to capture the secular trend in the

tropical Atlantic SST inter-hemispheric gradient and, as a result, the

southward shift of the Atlantic ITCZ over the past century (Chang et

al., 2011).

Many CMIP3 model simulations with the A1B emission scenario show

only minor changes in the SST variance associated with the AMM.

However, the few models that give the best AMM simulation over the

20th century project a weakening in future AMM activity (Breugem et

al., 2006), possibly due to the northward shift of the ITCZ (Breugem

et al., 2007). At present, model projections of future change in AMM

activity is considered highly uncertain because of the poorly simulat-

ed Atlantic ITCZ. In fact, uncertainty in projected changes in Atlantic

meridional SST gradient limits the conﬁdence in regional climate pro-

jections surrounding the tropical Atlantic Ocean (Good et al., 2008).

A majority of CMIP3 models forced with the A1B emission scenario

project no major change in Atlantic Niño activity in the 21st century,

1240

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

while a few models project a sizable decrease in future activity (Breu-

gem et al., 2006).

CMIP5 projections show an accelerated SST warming over much of the

tropical Atlantic (Figure 12.11). RCP8.5 projections of the inter-hemi-

spheric SST gradient change within the basin, however, are not consist-

ent among CMIP5 models as future GHG increase dominates over the

anthropogenic aerosol effect.

14.3.5 Assessment Summary

There is medium conﬁdence that annual rainfall changes over tropi-

cal oceans follow a ‘warmer-get-wetter’ pattern, increasing where the

SST warming exceeds the tropical mean and vice versa. One third of

inter-model differences in precipitation projection are due to those in

SST pattern. The SST pattern effect on precipitation change is a new

ﬁnding since AR4.

The wet-get-wetter effect is more obvious in the seasonal than annual

rainfall change in the tropics. Conﬁdence is generally higher in sea-

sonal than in annual mean changes in tropical precipitation. There is

medium conﬁdence that seasonal rainfall will increase on the equator-

ward ﬂank of the current ITCZ; that the frequency of zonally oriented

SPCZ events will increase, with the SPCZ lying well to the northeast

of its average position during those events; and that the SACZ shifts

southwards, in conjunction with the southward displacement of the

South Atlantic subtropical high, leading to an increase in precipitation

over southeastern South America.

Owing to models’ ability to reproduce general features of IOD and

agreement on future projections, it is likely that the tropical Indian

Ocean will feature a zonal pattern with reduced (enhanced) warm-

ing and decreased (increased) rainfall in the east (west), a pattern

especially pronounced during August to November. The Indian Ocean

dipole mode will very likely remain active, with interannual variability

unchanged in SST but decreasing in thermocline depth. There is low

conﬁdence in changes in the summer persistence of the Indian Ocean

SST response to ENSO and in ENSO’s inﬂuence on summer climate over

the Northwest Paciﬁc and East Asia.

The observed SST warming in the tropical Atlantic represents a reduc-

tion in spatial variation in climatology: the warming is weaker north

than south of the equator; and the equatorial cold tongue weakens

both in the mean and interannual variability. There is low conﬁdence

in projected changes over the tropical Atlantic, both for the mean and

interannual modes, because of large errors in model simulations of

current climate.

There is low conﬁdence in how MJO will change in the future due to

the poor skill of models in simulating MJO and the sensitivity of its

change to SST warming patterns that are themselves subject to large

uncertainties in the projections.

14.4 El Niño-Southern Oscillation

The ENSO is a coupled ocean–atmosphere phenomenon naturally

occurring at the interannual time scale over the tropical Paciﬁc (see

Box 2.5, Supplementary Material Section 14.SM.2, and Figure 14.12).

14.4.1 Tropical Paciﬁc Mean State

SST in the western tropical Paciﬁc has increased by up to 1.5°C per cen-

tury, and the warm pool has expanded (Liu and Huang, 2000; Huang

and Liu, 2001; Cravatte et al., 2009). Studies disagree on how the east–

west SST gradient along the equator has changed, some showing a

strengthening (Cane et al., 1997; Hansen et al., 2006; Karnauskas et al.,

2009; An et al., 2011) and others showing a weakening (Deser et al.,

2010a; Tokinaga et al., 2012), because of observational uncertainties

associated with limited data sampling, changing measurement tech-

niques, and analysis procedures. Most CMIP3 and CMIP5 models also

disagree on the response of zonal SST gradient across the equatorial

Paciﬁc (Yeh et al., 2012).

The Paciﬁc Ocean warms more near the equator than in the subtropics

in CMIP3 and CMIP5 projections (Liu et al., 2005; Gastineau and Soden,

2009; Widlansky et al., 2013; Figure 14.12) because of the difference

in evaporative damping (Xie et al., 2010b). Other oceanic changes

include a basin-wide thermocline shoaling (Vecchi and Soden, 2007a;

DiNezio et al., 2009; Collins et al., 2010; Figure 14.12), a weakening of

surface currents, and a slight upward shift and strengthening of the

equatorial undercurrent (Luo and Rothstein, 2011; Sen Gupta et al.,

2012). A weakening of tropical atmosphere circulation during the 20th

century was documented in observations and reanalyses (Vecchi et al.,

2006; Zhang and Song, 2006; Vecchi and Soden, 2007a; Bunge and

Clarke, 2009; Karnauskas et al., 2009; Yu and Zwiers, 2010; Tokinaga

et al., 2012) and in CMIP models (Vecchi and Soden, 2007a; Gastineau

and Soden, 2009). The Paciﬁc Walker Circulation, however, intensiﬁed

during the most recent two decades (Mitas and Clement, 2005; Liu and

Curry, 2006; Mitas and Clement, 2006; Sohn and Park, 2010; Li and

Ren, 2012; Zahn and Allan, 2011; Zhang et al., 2011a), illustrating the

effects of natural variability.

14.4.2 El Niño Changes over Recent Decades and

in the Future

The amplitude modulation of ENSO at longer time scales has been

observed in reconstructed instrumental records (Gu and Philander,

1995; Wang, 1995; Mitchell and Wallace, 1996; Wang and Wang, 1996;

Power et al., 1999; An and Wang, 2000; Yeh and Kirtman, 2005; Power

and Smith, 2007; Section 5.4.1), in proxy records (Cobb et al., 2003;

Braganza et al., 2009; Li et al., 2011c; Yan et al., 2011), and is also

simulated by coupled GCMs (Lau et al., 2008; Wittenberg, 2009). Some

studies have suggested that the modulation was due to changes in

mean climate conditions in the tropical Paciﬁc (An and Wang, 2000;

Fedorov and Philander, 2000; Wang and An, 2001, 2002; Li et al.,

2011c), as observed since the 1980s (An and Jin, 2000; An and Wang,

2000; Fedorov and Philander, 2000; Kim and An, 2011). With three

events during 2000-2010, which meets intensity in Nino4 being larger

than in Nino3, two events during 1990-2000 and only two events are

found for 1950-1990 the maximum SST warming during El Niño now

1241

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.12 | Idealized schematic showing atmospheric and oceanic conditions of the tropical Paciﬁc region and their interactions during normal conditions, El Niño condi-

tions, and in a warmer world. (a) Mean climate conditions in the tropical Paciﬁc, indicating sea surface temperatures (SSTs), surface wind stress and associated Walker Circulation,

the mean position of convection and the mean upwelling and position of the thermocline. (b) Typical conditions during an El Niño event. SSTs are anomalously warm in the east;

convection moves into the central Paciﬁc; the trade winds weaken in the east and the Walker Circulation is disrupted; the thermocline ﬂattens and the upwelling is reduced. (c) The

likely mean conditions under climate change derived from observations, theory and coupled General Circulation Models (GCMs). The trade winds weaken; the thermocline ﬂattens

and shoals; the upwelling is reduced although the mean vertical temperature gradient is increased; and SSTs (shown as anomalies with respect to the mean tropical-wide warm-

ing) increase more on the equator than off. Diagrams with absolute SST ﬁelds are shown on the left, diagrams with SST anomalies are shown on the right. For the climate change

ﬁelds, anomalies are expressed with respect to the basin average temperature change so that blue colours indicate a warming smaller than the basin mean, not a cooling (Collins

et al., 2010).

Thermocline

Upwelling

Walker circulaon

Warm pool Cold tongue

Thermocline

Upwelling

Thermocline

Upwelling

Thermocline

The

rmocline

Upwelling

Thermocline

The

rmocline

Upwelling

Normal conditions

El Niño conditions

El Niño Conditions

(SST anomalies)

Climate change

(SST anomalies)

(a)

(b)

(c)

1242

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

appears to occur more often in the central Paciﬁc (Figure 14.13; Ashok

et al., 2007; Kao and Yu, 2009; Kug et al., 2009; Section 9.5.3.4.1 and

Supplementary Material Section 14.SM.2; Yeh et al., 2009), with global

impacts that are distinct from ‘standard’ El Niño events where the

maximum warming is over the eastern Paciﬁc (Kumar et al., 2006a;

Ashok et al., 2007; Kao and Yu, 2009; Hu et al., 2012b). During the past

century, an increasing trend in ENSO amplitude was also observed (Li

et al., 2011c; Vance et al., 2012), possibly caused by a warming climate

(Zhang et al., 2008; Kim and An, 2011) although other reconstructions

in this data-sparse region dispute this trend (Giese and Ray, 2011).

Long coupled GCM simulations show that decadal-to-centennial mod-

ulations of ENSO can be generated without any change in external

forcing (Wittenberg, 2009; Yeh et al., 2011), with multi-decadal epochs

of anomalous ENSO behaviour. The modulations result from nonlinear

processes in the tropical climate system (Timmermann et al., 2003), the

interaction with the mean climate state (Ye and Hsieh, 2008; Choi et al.,

2009, 2011, 2012), or from random changes in ENSO activity triggered

by chaotic atmospheric variability (Power and Colman, 2006; Power et

al., 2006). There is little consensus as to whether the decadal modula-

tions of ENSO properties (amplitude and spatial pattern) during recent

decades are due to anthropogenic effects or natural variability. Instru-

mental SST records are available back to the 1850s, but good observa-

tions of the coupled air–sea feedbacks that control ENSO behaviour—

including subsurface temperature and current ﬂuctuations, and air–sea

exchanges of heat, momentum and water—are available only after the

late 1970s, making observed historical variations in ENSO feedbacks

highly uncertain (Chen, 2003; Wittenberg, 2004).

CMIP5 models show some improvement compared to CMIP3, espe-

cially in ENSO amplitude (Section 9.5.3.4.1). Selected CMIP5 models

that simulate well strong El Niño events show a gradual increase of El

Niño intensity, especially over the central Paciﬁc (Kim and Yu, 2012).

CMIP3 models suggested a westward shift of SST variability in future

projections (Boer, 2009; Yeh et al., 2009). Generally, however, future

changes in El Niño intensity in CMIP5 models are model dependent

(Guilyardi et al., 2012; Kim and Yu, 2012; Stevenson et al., 2012), and

Figure 14.13 | Intensities of El Niño and La Niña events for the last 60 years in the eastern equatorial Paciﬁc (Niño3 region) and in the central equatorial Paciﬁc (Niño4 region),

and the estimated linear trends, obtained from Extended Reconstructed Sea Surface Temperature v3 (ERSSTv3).

Figure 14.14 | Standard deviation in CMIP5 multi-model ensembles of sea surface

temperature variability over the eastern equatorial Paciﬁc Ocean (Nino3 region: 5°S-

5°N, 150°W-90°W), a measure of El Nino amplitude, for the pre-industrial (PI) control

and 20th century (20C) simulations, and 21st century projections using RCP4.5 and

RCP8.5. Thirty-one models are used for the ensemble average. Open circles indicate

multi-model ensemble means, and the red cross symbol is the observed standard devia-

tion for January 1870 – December 2011 obtained from HadISSTv1. The linear trend and

climatological mean of seasonal cycle have been removed. Box-whisker plots show the

16th, 25th, 50th, 75th, and 84th percentiles.

not signiﬁcantly distinguished from natural modulations (Stevenson,

2012; Figure 14.14). Because the change in tropical mean conditions

(especially the zonal gradient) in a warming climate is model depend-

ent (Section 14.4.1), changes in ENSO intensity for the 21st century

(Solomon and Newman, 2011; Hu et al., 2012a) are uncertain (Figure

14.14). Future changes in ENSO depend on competing changes in cou-

pled ocean–atmospheric feedback (Philip and Van Oldenborgh, 2006;

Collins et al., 2010; Vecchi and Wittenberg, 2010), and on the dynami-

cal regime a given model is in. There is high conﬁdence, however, that

ENSO will remain the dominant mode of natural climate variability in

the 21st century (Collins et al., 2010; Guilyardi et al. 2012; Kim and Yu

2012; Stevenson 2012).

1243

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

14.4.3 Teleconnections

There is little improvement in the CMIP5 ensemble relative to CMIP3

in the amplitude and spatial correlation metrics of precipitation tele-

connections in response to ENSO, in particular within regions of strong

observed precipitation teleconnections (equatorial South America, the

western equatorial Paciﬁc and a southern section of North America;

Langenbrunner and Neelin, 2013). Scenario projections in CMIP3 and

CMIP5 showed a systematic eastward shift in both El Niño- and La

Niña-induced teleconnection patterns over the extratropical NH

(Meehl and Teng, 2007; Stevenson et al., 2012; Figure 14.15), which

might be due to the eastward migration of tropical convection centres

associated with the expansion of the warm pool in a warm climate

(Muller and Roeckner, 2006; Müller and Roeckner, 2008; Cravatte et

al., 2009; Kug et al., 2010), or changes in the mid-latitude mean cir-

culation (Meehl and Teng, 2007). Some models produced an intensi-

ﬁed ENSO teleconnection pattern over the North Atlantic region in a

warmer climate (Müller and Roeckner, 2008; Bulic et al., 2012) and a

weakened teleconnection pattern over the North Paciﬁc (Stevenson,

2012). It is unclear whether the eastward shift of tropical convection is

related to longitudinal shifts in El Niño maximum SST anomalies (see

Supplementary Material Section 14.SM.2) or to changes in the mean

state in the tropical Paciﬁc. Some coupled GCMs, which do not show

an increase in the central Paciﬁc warming during El Nino in response

to a warming climate, do not produce a substantial change in the lon-

gitudinal location of tropical convection (Müller and Roeckner, 2008;

Yeh et al., 2009).

In a warmer climate, the increase in atmospheric moisture intensiﬁes

temporal variability of precipitation even if atmospheric circulation

El Nino DJF

20th century (historical)

Lat

120

160

120

La Nina DJF

Lon

Lat

El Nino DJF

21st century (RCP 4.5)

SLP (hPa)

−4

−3

−2

−1

120

160

120

Lon

La Nina DJF

SLP (hPa)

−4

−3

−2

−1

−10 0 10

−4

−3

−2

−1

Δ Lon (°E)

Δ Lat (

El Nino Aleutian Low shift: RCP 4.5

Northeast Northwest

Southwest

Southeast

(c)

(e)

(a)

(b) (d)

Figure 14.15 | Changes to sea level pressure (SLP) teleconnections during December, January and February (DJF) in the CMIP5 models. (a) SLP anomalies for El Niño during the

20th century. (b) SLP anomalies for La Nina during the 20th century. (c) SLP anomalies for El Niño during RCP4.5. (d) SLP anomalies for La Niña during RCP4.5. Maps in (a)–(d) are

stippled where more than two thirds of models agree on the sign of the SLP anomaly ((a),( b): 18 models; (c),(d): 12 models), and hatched where differences between the RCP4.5

multi-model mean SLP anomaly exceed the 60th percentile (red-bordered regions) or are less than the 40th percentile (blue-bordered regions) of the distribution of 20th century

ensemble means. In all panels, El Niño (La Niña) periods are deﬁned as years having DJF Nino3.4 SST above (below) one standard deviation relative to the mean of the detrended

time series. For ensemble mean calculations, all SLP anomalies have been normalized to the standard deviation of the ensemblemember detrended Nino3.4 SST. (e) Change in the

‘centre of mass’ of the Aleutian Low SLP anomaly, RCP4.5–20th century. The Aleutian Low SLP centre of mass is a vector with two elements (lat, lon), and is deﬁned as the sum of

(lat, lon) weighted by the SLP anomaly, over all points in the region 180°E to 120°E, 40°N to 60°N having a negative SLP anomaly during El Niño.

variability remains the same (Trenberth 2011; Section 12.4.5). This

applies to ENSO-induced precipitation variability but the possibility of

changes in ENSO teleconnections complicates this general conclusion,

making it somewhat regional-dependent (Seager et al. 2012)

14.4.4 Assessment Summary

ENSO shows considerable inter-decadal modulations in amplitude

and spatial pattern within the instrumental record. Models without

changes in external forcing display similar modulations, and there is

little consensus on whether the observed changes in ENSO are due to

external forcing or natural variability (see also Section 10.3.3 for an

attribution discussion).

There is high conﬁdence that ENSO will remain the dominant mode of

interannual variability with global inﬂuences in the 21st century, and

due to changes in moisture availability ENSO-induced rainfall variabil-

ity on regional scales will intensify. There is medium conﬁdence that

ENSO-induced teleconnection patterns will shift eastward over the

North Paciﬁc and North America. There is low conﬁdence in changes in

the intensity and spatial pattern of El Niño in a warmer climate.

14.5 Annular and Dipolar Modes

The North Atlantic Oscillation (NAO), the North Paciﬁc Oscillation (NPO)

and the Northern and Southern Annular Modes (NAM and SAM) are

dominant modes of variability in the extratropics. These modes are the

focus of much research attention, especially in impact studies, where

they are often used as aggregate descriptors of past regional

climate

1244

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

trends and variations over many parts of the world. For example, since

IPCC (2007a) more than 2000 scientiﬁc articles have been published,

which include NAO, AO, or NAM in either the title or abstract. This

assessment focusses on recent research on these modes that is most

relevant for future regional climate change. Past behaviour of these

modes inferred from observations is assessed in Section 2.7.8.

14.5.1 Northern Modes

The NAO is a well-established dipolar mode of climate variability

having opposite variations in sea level pressure between the Atlan-

tic subtropical high and the Iceland/Arctic low (Wanner et al., 2001;

Hurrell et al., 2003; Budikova, 2009). It is strongly associated with the

tropospheric jet, storms (see Section 14.6.2), and blocking that deter-

mine the weather and climate over the North Atlantic and surround-

ing continents (Hurrell and Deser, 2009; Box 14.2). The NAO exists in

boreal summer as well as in boreal winter, albeit with different physical

characteristics (Sun et al., 2008; Folland et al., 2009).

Over the North Paciﬁc, there is a similar wintertime dipolar mode

known as the NPO associated with north–south displacements of the

Asian-Paciﬁc jet stream and the Paciﬁc storm track. The NPO inﬂuences

winter air temperature and precipitation over much of western North

America as well as sea ice over the Paciﬁc sector of the Arctic, more so

than either ENSO (Section 14.4) or the PNA (Linkin and Nigam, 2008).

These dipolar modes have been interpreted as the regional manifes-

tation of an annular mode in sea level pressure known as the Arctic

Oscillation (AO; Thompson and Wallace, 1998) or the Northern Annular

Mode (NAM; Thompson and Wallace, 2000). The AO (NAM at 1000 hPa)

index and the NAO index (see Box 2.5, Table 1) are strongly correlated

but the AO spatial pattern is more zonally symmetric and so differs

from the NAO over the N. Paciﬁc (Ambaum et al., 2001; Feldstein and

Franzke, 2006). Hereafter, the term NAO is used to denote NAO, AO and

NAM in boreal winter unless further distinction is required.

Climate models are generally able to simulate the gross features of NAO

and NPO (see Section 9.5.3.2). It has been argued that these modes

may be a preferred pattern of response to climate change (Gerber et

al., 2008). However, this is not supported by a detailed examination

of the vertical structure of the simulated global warming response

(Woollings, 2008). Hori et al. (2007) noted that NAO variability did not

change substantially in the Special Report on Emission Scenarios (SRE-

S)-A1B and 20th century scenarios and so concluded that the trend in

the NAO index (deﬁned relative to a historical mean state) is a result

of an anthropogenic trend in the basic mean state rather than due

to changes in NAO variability. However, other research indicates that

there is a coherent two-way interaction between the trend in the mean

state and the NAO-like modes of variability—the mode and/or regime

structure change due to changes in the mean state (Branstator and

Selten, 2009 ; Barnes and Polvani, 2013). Section 14.6.2 assesses the

jet and storm track changes associated with the projected responses.

Model simulations have underestimated the magnitude of the large

positive trend from 1960-2000 in winter NAO observations, which

now appears to be more likely due to natural variability rather than

anthropogenic inﬂuences (see Section 10.3.3.2). Some studies have

even considered NAO to be a source of natural variability that needs to

be removed before detection and attribution of anthropogenic chang-

es (Zhang et al., 2006). Detection of regional surface air temperature

response to anthropogenic forcing has been found to be robust to the

exclusion of model-simulated AO and PNA changes (Wu and Karoly,

2007). Model projections of wintertime European precipitation have

been shown to become more consistent with observed trends after

removal of trends due to NAO (Bhend and von Storch, 2008). Underes-

timation of trends in NAO can lead to biases in projections of regional

climate, for example, Arctic sea ice (Koldunov et al., 2010).

Underestimation of NAO long-term variability may be due to missing

or poorly represented processes in climate models. Recent observation-

al and modelling studies have helped to conﬁrm that the lower strat-

osphere plays an important role in explaining recent more negative

NAO winters and long-term trends in NAO (Scaife et al., 2005; Dong

et al., 2011; Ouzeau et al., 2011; Schimanke et al., 2011). This is sup-

ported by evidence that seasonal forecasts of NAO can be improved

by inclusion of the stratospheric Quasi-Biennial Oscillation (QBO; Boer

and Hamilton, 2008; Marshall and Scaife, 2010). Other studies have

found that observed changes in stratospheric water vapour changes

from 1965–1995 led to an impact on NAO simulated by a model, and

have suggested that changes in stratospheric water vapour may be

another possible pathway for communicating tropical forcing to the

extratropics (Joshi et al., 2006; Bell et al., 2009). There is growing evi-

dence that future NAO projections are sensitive to how climate models

resolve stratospheric processes and troposphere–stratosphere interac-

tions (Sigmond and Scinocca, 2010; Scaife et al., 2011a; Karpechko and

Manzini, 2012).

Several recent studies of historical data have found a positive associa-

tion between solar activity and NAO (Haigh and Roscoe, 2006; Kodera

et al., 2008; Lockwood et al., 2010), while other studies have found

little imprint of solar and volcanic forcing on NAO (Casty et al., 2007).

Positive associations between NAO and solar forcing have been repro-

duced in recent modelling studies (Lee et al., 2008; Ineson et al., 2011)

but no signiﬁcant changes were found in CMIP5 projections of NAO

due to changes in solar irradiance or aerosol forcing.

Observational studies have noted weakening of NAO during periods of

reduced Arctic sea ice (Strong et al., 2009; Wu and Zhang, 2010). Sev-

eral modelling studies have also shown a negative NAO response to

the partial removal of sea ice in the Arctic or high latitudes (Kvamsto et

al., 2004; Magnusdottir et al., 2004; Seierstad and Bader, 2009; Deser

et al., 2010c; Screen et al., 2012). However, the strength and timing of

the response to sea ice loss varies considerably between studies, and

can be hard to separate from common responses to warming of the

troposphere and from natural climate variability. The impact of sea ice

loss in individual years on NAO is small and hard to detect (Bluthgen

et al., 2012). Reviews of the emerging literature on this topic can be

found in Budikova (2009) and Bader et al. (2011).

The NPO contributes to the excitation of ENSO events via the ‘Seasonal

Footprinting Mechanism’ (SFM; Anderson, 2003; Vimont et al., 2009;

Alexander et al., 2010). Some studies indicate that warm events in the

central tropical Paciﬁc Ocean may in turn excite the NPO (Di Lorenzo

et al., 2009).

1245

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

(a) NAO (b) NAM (c) SAM

(e) (f)(d)

Recent multi-model studies of NAO (Hori et al., 2007; Karpechko, 2010;

Zhu and Wang, 2010; Gillett and Fyfe, 2013) reconﬁrm the small pos-

itive response of boreal winter NAO indices to GHG forcing noted in

earlier studies reported in AR4 (Kuzmina et al., 2005; Miller et al., 2006;

Stephenson et al., 2006). Projected trends in wintertime NAO indices are

generally found to have small amplitude compared to natural internal

variations (Deser et al., 2012). Furthermore, there is substantial vari-

ation in NAO projections from different climate models. For example,

one study found no signiﬁcant NAO trends in two simulations with the

ECHAM4/OPYC3 model (Fischer-Bruns et al., 2009), whereas another

study found a strong positive trend in NAO in the ECHAM5/MPI-OM

SRES A1B simulations (Müller and Roeckner, 2008). The model depend-

ence of the response is an important source of uncertainty in the region-

al climate change response (Karpechko, 2010). A multi-model study of

24 climate model projections suggests that there are no major changes

in the NPO due to greenhouse warming (Furtado et al., 2011).

Figures 14.16a, b summarize the wintertime NAO and NAM indices

simulated by models participating in the CMIP5 experiment (Gillett

and Fyfe, 2013). The multi-model mean of the NAO and NAM indices

are similar and exhibit small linear trends in agreement with those

shown for the NAM index in AR4 (AR4, Figure 10.17a). The multi-model

mean projected increase of around 1 to 2 hPa from 1850 to 2100 is

smaller than the spread of around 2 to 4 hPa between model simula-

tions (Figure 14.16).

Some differences in model projections can be accounted for by chang-

es in the NAO spatial pattern, for example, northeastward shifts in

NAO centres of action have been found to be important for estimating

the trend in the NAO index (Ulbrich and Christoph, 1999; Hu and Wu,

2004). Individual model simulations have shown the spatial extent of

NAO inﬂuence decreases with GHG forcing (Fischer-Bruns et al., 2009),

a positive feedback between jet and storm tracks that enhances a

poleward shift in the NAO pattern (Choi et al., 2010), and changes in

the NAO pattern but with no changes in the propagation conditions for

Rossby waves (Brandefelt, 2006). One modelling study found a trend

in the correlation between NAO and ENSO during the 21st century

(Muller and Roeckner, 2006). Such changes in the structure of NAO

and/or its interaction with other modes of variability would could lead

to important regional climate impacts.

14.5.2 Southern Annular Mode

The Southern Annular Mode (SAM, also known as Antarctic Oscillation,

AAO), is the leading mode of climate variability in the SH extratropics,

describing ﬂuctuations in the latitudinal position and strength of the

mid-latitude eddy-driven westerly jet (see Box 2.5; Section 9.5.3.2).

SAM variability has a major inﬂuence on the climate of Antarctica,

Australasia, southern South America and South Africa (Watterson,

2009; Thompson et al., 2011 and references therein).

Figure 14.16 | Summary of multi-model ensemble simulations of wintertime (December to February) mean North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and

Southern Annular Mode (SAM) sea level pressure (SLP) indices for historical and RCP4.5 scenarios produced by 39 climate models participating in CMIP5. Panels (a)–(c) show time

series of the ensemble mean (black line) and inter-quartile range (grey shading) of the mean index for each model. Panels (d)–(f) show scatter plots of individual model 2081–2100

time means versus 1986–2005 time means (black crosses) together with (–2,+2) standard error bars. The NAO index is deﬁned here as the difference of regional averages: (90°W

to 60°E, 20°N to 55°N) minus (90°W to 60°E, 55°N to 90°N) (see Stephenson et al., 2006). The NAM and SAM are deﬁned as zonal indices: NAM as the difference in zonal mean

SLP at 35°N and 65°N (Li and Wang, 2003) and SAM as the difference in zonal mean SLP at 40°S and 65°S (Gong and Wang, 1999). All indices have been centred to have zero

time mean from 1861–1900. Comparison of simulated and observed trends from 1961–2011 is shown in Figure 10.13.

1246

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

The physical mechanisms of the SAM are well understood, and the

SAM is well represented in climate models, although the detailed spa-

tial and temporal characteristics vary between models (Raphael and

Holland, 2006). In the past few decades the SAM index has exhibited

a positive trend in austral summer and autumn (Figure 14.16, Mar-

shall, 2007; Jones et al., 2009b), a change attributed to the effects of

ozone depletion and, to a lesser extent, the increase in GHGs (Thomp-

son et al., 2011, see also Section 10.3.3.3). It is likely that these two

factors will continue to be the principal drivers into the future, but

as the ozone hole recovers they will be competing to push the SAM

in opposite directions (Arblaster et al., 2011; Thompson et al., 2011;

Bracegirdle et al., 2013), at least during late austral spring and summer,

when ozone depletion has had its greatest impact on the SAM. The

SAM is also inﬂuenced by teleconnections to the tropics, primarily

associated with ENSO (Carvalho et al., 2005; L’Heureux and Thompson,

2006). Changes to the tropical circulation, and to such teleconnections,

as the climate warms could further affect SAM variability (Karpechko

et al., 2010). See Supplementary Material Section 14.SM.3.1 for further

details on the observed variability of SAM.

The CMIP3 models projected a continuing positive trend in the SAM in

both summer and winter (Miller et al., 2006). However, those models

generally had poor simulations of stratospheric ozone, and tended to

underestimate natural variability and to misrepresent observed trends

in the SAM, indicating that care should be taken in interpretation of

their future SAM projections (Fogt et al., 2009). Arblaster et al. (2011)

showed that there can be large differences in the sensitivity of these

models to CO

increases, which affects their projected trends in the

SAM.

Since the AR4 a number of chemistry–climate models (CCMs) have

been run that have fully interactive stratospheric chemistry, although

unlike coupled atmosphere ocean models they are usually not coupled

to the oceans (see also Sections 9.1.3.2.8 and 9.4.6.2). The majority

of CCMs and coupled models, which generally compare well to rea-

nalyses (Gerber et al., 2010) although many exhibit biases in their

placement of the SH eddy-driven jet (Wilcox et al., 2012; Bracegirdle

et al., 2013), indicate that through to at least the mid-21st century

the current observed SAM changes are neutralized or reversed during

austral summer (Perlwitz et al., 2008; Son et al., 2010; Polvani et al.,

2011; Bracegirdle et al., 2013). Figure 14.16 shows the projected

ensemble-mean future SAM index evolution during DJF from a suite of

CMIP5 models, suggesting that the recent positive trend will weaken

considerably as stratospheric ozone concentrations recover over south-

ern high latitudes.

Projected 21st century changes in the SAM, and the closely associated

SH eddy-driven jet position, vary by season (Gillett and Fyfe, 2013),

and are sensitive to the rate of ozone recovery (Son et al., 2010; Eyring

et al., 2013) and to GHG emissions scenario (Swart and Fyfe, 2012;

Eyring et al., 2013). In the RCP2.6 scenario, with small increases in

GHGs, ozone recovery may dominate in austral summer giving a small

projected equatorward jet shift (Eyring et al., 2013) with little change

in the annual mean jet position (Swart and Fyfe, 2012). In RCP8.5 large

GHG increases are expected to dominate, giving an ongoing poleward

shift of the SH jet in all seasons (Swart and Fyfe, 2012; Eyring et al.,

2013). In RCP4.5 the inﬂuences of ozone recovery and GHG increas-

es are expected to approximately balance in austral summer, with an

ongoing poleward jet shift projected in the other seasons (Swart and

Fyfe, 2012; Eyring et al., 2013; Gillett and Fyfe, 2013).

14.5.3 Assessment Summary

Future boreal wintertime NAO is very likely to exhibit large natural var-

iations and trend of similar magnitude to that observed in the past; is

very likely to be differ quantitatively from individual climate model pro-

jections; is likely to become slightly more positive (on average) due to

increases in GHGs. The austral summer/autumn positive trend in SAM

is likely to weaken considerably as ozone depletion recovers through to

the mid-21st century. There is medium conﬁdence from recent studies

that projected changes in NAO and SAM are sensitive to boundary

processes, which are not yet well represented in many climate models

currently used for projections, for example, stratosphere-troposphere

interaction, ozone chemistry, solar forcing and atmospheric response

to Arctic sea ice loss. There is low conﬁdence in projections of other

modes such as the NPO due to the small number of modelling studies.

Box 14.2 | Blocking

Atmospheric blocking is associated with persistent, slow-moving high-pressure systems that interrupt the prevailing westerly winds of

middle and high latitudes and the normal eastward progress of extratropical storm systems. Overall, blocking activity is more frequent

at the exit zones of the jet stream and shows appreciable seasonal variability in both hemispheres, reaching a maximum in winter–

spring and a minimum in summer–autumn (e.g., Wiedenmann et al., 2002). In the Northern Hemisphere (NH), the preferred locations

for winter blocking are the North Atlantic and North Paciﬁc, whereas continental blocks are relatively more frequent in summer (Tyrlis

and Hoskins, 2008; Barriopedro et al., 2010). Southern Hemisphere (SH) blocking is less frequent than in the NH, and it tends to be

concentrated over the Southeast Paciﬁc and the Indian Ocean (Berrisford et al., 2007).

Blocking is a complex phenomenon that involves large- and small-scale components of the atmospheric circulation, and their mutual

interactions. Although there is not a widely accepted blocking theory, transient eddy activity is considered to play an important role in

blocking occurrence and maintenance through feedbacks between the large-scale ﬂow and synoptic eddies (e.g., Yamazaki and Itoh,

2009). (continued on next page)

1247

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Box 14.2 (continued)

Blocking is an important component of intraseasonal variability in the extratropics and causes climate anomalies over large areas of

Europe (Trigo et al., 2004; Masato et al., 2012), North America (Carrera et al., 2004), East Asia (e.g., Wang et al., 2010; Cheung et al.,

2012), high-latitude regions of the SH (Mendes et al., 2008) and Antarctica (Massom et al., 2004; Scarchilli et al., 2011). Blocking can

also be responsible for extreme events (e.g., Buehler et al., 2011; Pfahl and Wernli, 2012), such as cold spells in winter (e.g., 2008 in

China, Zhou et al., 2009d; or 2010 in Europe, Cattiaux, 2010) and summer heat waves in the NH (e.g., 2010 in Russia, Matsueda, 2011;

Lupo et al., 2012) and in southern Australia (Pezza et al., 2008).

At interannual time scales, there are statistically signiﬁcant relationships between blocking activity and several dominant modes of

atmospheric variability, such as the NAO (Section 14.5.1) and wintertime blocking in the Euro-Atlantic sector (Croci-Maspoli et al.,

2007a; Luo et al., 2010), the winter PNA (Section 14.7.1) and blocking frequency in the North Paciﬁc (Croci-Maspoli et al., 2007a), or

the SAM (Section 14.5.2) and winter blocking activity near the New Zealand sector (Berrisford et al., 2007). Multi-decadal variability in

winter blocking over the North Atlantic and the North Paciﬁc seem to be related, respectively, with the Atlantic Meridional Overturning

Circulation (AMOC; Häkkinen et al., 2011; Section 14.7.6) and the Paciﬁc Decadal Oscillation (PDO; Chen and Yoon, 2002; Section

14.7.3), although this remains an open question.

Other important scientiﬁc issues related to the blocking phenomenon include the mechanisms of blocking onset and maintenance, two-

way interactions between blocking and stratospheric processes (e.g., Martius et al., 2009; Woollings et al., 2010), inﬂuence on blocking

of slowly varying components of the climate system (sea surface temperature (SST), sea ice, etc., Liu et al., 2012b), and external forcings.

The most consistent long-term observed trends in blocking for the second half of the 20th century are the reduced winter activity

over the North Atlantic (e.g., Croci-Maspoli et al., 2007b), which is consistent with the observed increasing North Atlantic Oscillation

(NAO) trend from the 1960s to the mid-1990s (Section 2.7.8), as well as an eastward shift of intense winter blocking over the Atlantic

and Paciﬁc Oceans (Davini et al., 2012). The apparent decreasing trend in SH blocking activity (e.g., Dong et al., 2008) seems to be in

agreement with the upward trend in the SAM.

The AR4 (Section 8.4.5) reported a tendency for General Circulation Models (GCMs) to underestimate NH blocking frequency and per-

sistence, although most models were able to capture the preferred locations for blocking occurrence and their seasonal distributions.

Several intercomparison studies based on a set of CMIP3 models (Scaife et al., 2010; Vial and Osborn, 2012) revealed some progress

in the simulation of NH blocking activity, mainly in the North Paciﬁc, but only modest improvements in the North Atlantic. In the SH,

blocking frequency and duration was also underestimated, particularly over the Australia–New Zealand sector (Matsueda et al., 2010).

CMIP5 models still show a general blocking frequency underestimation over the Euro-Atlantic sector, and some tendency to overesti-

mate North Paciﬁc blocking (Section 9.5.2.2), with considerable inter-model spread (Box 14.2, Figure 1).

Model biases in the mean ﬂow, rather than in variability, can explain a large part of the blocking underestimation and they are usually

evidenced as excessive zonality of the ﬂow or systematic shifts in the latitude of the jet stream (Matsueda et al., 2010; Scaife et al.,

2011b; Barnes and Hartmann, 2012; Vial and Osborn, 2012; Anstey et al., 2013; Dunn-Sigouin and Son, 2013). Increasing the horizontal

resolution in atmospheric GCMs with prescribed SSTs has been shown to signiﬁcantly reduce blocking biases, particularly in the Euro-At-

lantic sector and Australasian sectors (e.g., Matsueda et al., 2010; Jung et al., 2011; Dawson et al., 2012; Berckmans et al., 2013), while

North Paciﬁc blocking could be more sensitive to systematic errors in tropical SSTs (Hinton et al., 2009). Also blocking biases are smaller

in those CMIP5 models with higher horizontal and vertical resolution (Anstey et al., 2013). However, the improvement of blocking sim-

ulation with increasing horizontal resolution is less clear in coupled models than in atmospheric GCMs with prescribed SSTs, indicating

that both SSTs and the relative coarse resolution in OGCM (Scaife et al., 2011b) are important causes of blocking biases.

Most CMIP3 models projected signiﬁcant reductions in NH annual blocking frequency (Barnes et al., 2012), particularly during winter,

but CMIP5 models seem to indicate weaker decreases in the future (Dunn-Sigouin and Son, 2013) and a more complex response than

that reported for CMIP3 models, including possible regional increases of blocking frequency in summer (Cattiaux et al., 2013; Masato

et al., 2013). There is high agreement that winter blocking frequency over the North Atlantic and North Paciﬁc will not increase under

enhanced GHG concentrations (Barnes et al., 2012; Dunn-Sigouin and Son, 2013). Future strengthening of the zonal wind and merid-

ional jet displacements may partially account for some of the projected changes in blocking frequency over the ocean basins of both

hemispheres (Matsueda et al., 2010; Barnes and Hartmann, 2012; Dunn-Sigouin and Son, 2013). Future trends in blocking intensity and

persistence are even more uncertain, with no clear signs of signiﬁcant changes. How the location and frequency of blocking events will

evolve in future are both critically important for understanding regional climate change in particular with respect to extreme conditions

(e.g., Sillmann et al., 2011; de Vries et al., 2013). (continued on next page)

1248

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Box 14.2 (continued)

In summary, the increased ability in simulating blocking in some models indicate that there is medium conﬁdence that the frequency of

NH and SH blocking will not increase, while trends in blocking intensity and persistence remain uncertain. The implications for blocking

related regional changes in North America, Europe and Mediterranean and Central and North Asia are therefore also uncertain [Box

14.2 and 14.8.3, 14.8.6, 14.8.8]

Box 14.2, Figure 1 | Annual mean blocking frequency in the NH (expressed in % of time, that is, 1% means about 4 days per year) as simulated by a set of CMIP5

models (colour lines) for the 1961–1990 period of one run of the historical simulation. Grey shading shows the mean model result plus/minus one standard deviation.

Black thick line indicates the observed blocking frequency derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research

(NCEP/NCAR) reanalysis. Only CMIP5 models with available 500 hPa geopotential height daily data at http://pcmdi3.llnl.gov/esgcet/home.htm have been used. Block-

ing is deﬁned as in Barriopedro et al. (2006), which uses a modiﬁed version of the(Tibaldi and Molteni, 1990) index. Daily data was interpolated to a common regular

2.5° × 2.5° longitude–latitude grid before detecting blocking.

CMIP5 models 1961-1990

-90 -60 -30 0 30 60 90 120 150 180 210 240 270

Longitude

Blocking frequency (%)

BCC-CSM1-1 BCC-CSM1-1-M BNU-ESM

CanESM2 CCSM4 CMCC-CM CMCC-CESM

CMCC-CMS CNRM-CM5 EC-EARTH FGOALS-g2

FGOALS-s2 GFDL-CM3 GFDL-ESM2M HadGEM2-CC

IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC5

MIROC-ESM MIROC-ESM-CHEM MPI-ESM-MR MPI-ESM-LR

MPI-ESM-P MRI-CGCM3 NorESM1-M

REANALYSIS

14.6 Large-scale Storm Systems

14.6.1 Tropical Cyclones

The potential for regional changes in future tropical cyclone frequency,

track and intensity is of great interest, not just because of the associat-

ed negative effects, but also because tropical cyclones can play a major

role in maintaining regional water resources (Jiang and Zipser, 2010;

Lam et al., 2012; Prat and Nelson, 2012). Past and projected increases

in human exposure to tropical cyclones in many regions (Peduzzi et al.,

2012) heightens the interest further.

14.6.1.1 Understanding the Causes of Past and Projected

Regional Changes

Detection of past trends in measures of tropical cyclone activi-

ty is constrained by the quality of historical records and uncertain

quantiﬁcation of natural variability in these measures (Knutson et al.,

2010; Lee et al., 2012; Seneviratne et al., 2012). Observed regional cli-

mate variability generally represents a complex convolution of natural

and anthropogenic factors, and the response of tropical cyclones to

each factor is not yet well understood (see also Section10.6.1.5 and

Supplementary Material Section 14.SM.4.1.2). For example, the steady

long-term increase in tropical Atlantic SST due to increasing GHGs can

be dominated by shorter-term decadal variability forced by both exter-

nal and internal factors (Mann and Emanuel, 2006; Baines and Folland,

2007; Evan et al., 2009, 2011a; Ting et al., 2009; Zhang and Delworth,

2009; Chang et al., 2011; Solomon and Newman, 2011; Booth et al.,

2012; Camargo et al., 2012; Villarini and Vecchi, 2012). Similarly, tropi-

cal upper-tropospheric temperatures, which modulate tropical cyclone

potential intensity (Emanuel, 2010), can be forced by slowly evolving

changes in the stratospheric circulation of ozone (Brewer–Dobson

circulation) due to climate change with occasional large amplitude

and persistent changes forced by volcanic eruptions (Thompson and

1249

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Solomon, 2009; Evan, 2012). This convolution of anthropogenic and

natural factors, as represented in a climate model, has also been shown

to be useful in prediction of Atlantic tropical storm frequency out to a

few years (Smith et al., 2010).

In addition to greenhouse warming scenarios, tropical cyclones can

also respond to anthropogenic forcing via different and possibly unex-

pected pathways. For example, increasing anthropogenic emissions of

black carbon and other aerosols in South Asia has been linked to a

reduction of SST gradients in the Northern Indian Ocean (Chung and

Ramanathan, 2006; Meehl et al., 2008), which has in turn been linked

to a weakening of the vertical wind shear in the region. Evan et al.

(2011b) linked the reduced wind shear to the observed increase in the

number of very intense storms in the Arabian Sea, including ﬁve very

severe cyclones that have occurred since 1998, but the fundamental

cause of this proposed linkage is not yet certain (Evan et al., 2012;

Wang et al., 2012a). Furthermore, it is possible that a substantial part

of the multi-decadal variability of North Atlantic SST is radiatively

forced, via the cloud albedo effect, by what are essentially pollution

aerosols emitted from North America and Europe (Baines and Folland,

2007; Booth et al., 2012), although the relative contribution of this

forcing to the observed variability has been questioned (Zhang et al.,

2013b). Note that in the North Atlantic, the evidence suggests that the

reduction of pollution aerosols is linked to tropical SST increases, while

in the northern Indian Ocean, increases in aerosol pollution have been

linked to reduced vertical wind shear. Both of these effects (increasing

SST and reduced shear) have been observed to be related to increased

tropical cyclone activity.

Finally, in addition to interannual-to-multi-decadal forcing of tropi-

cal Atlantic SST via radiative dimming (Evan et al., 2009; Evan et al.,

2011a), dust aerosols have a large and more immediate in situ effect

on the regional thermodynamic and kinematic environment (Dunion

and Marron, 2008; Dunion, 2011), and Saharan dust storms—whose

frequency has been linked to atmospheric CO

concentration (Mahow-

ald, 2007)—have also been linked to reduced strengthening of tropical

cyclones (Dunion and Velden, 2004; Wu, 2007). Direct in situ relation-

ships have also been identiﬁed between aerosol pollution concentra-

tions and tropical cyclone structure and intensity (Khain et al., 2008,

2010; Rosenfeld et al., 2011). Thus, when assessing changes in tropical

cyclone activity, it is clear that detection and attribution aimed simply

at long-term linear trends forced by increasing well-mixed GHGs is not

adequate to provide a complete picture of the potential anthropogenic

contributions to the changes in tropical cyclone activity that have been

observed (Section 10.6).

14.6.1.2 Regional Numerical Projections

Similar to observational analyses, conﬁdence in numerical simulations

of tropical cyclone activity (Supplementary Material Tables 14.SM.1

to 14.SM.4) is reduced when model spatial domain is reduced from

global to region-speciﬁc (IPCC SREX Box 3.2; see also Section 9.5.4.3).

The assessment provided by Knutson et al. (2010) of projections based

on the SRES A1B scenario concluded that it is likely that the global

frequency of tropical cyclones will either decrease or remain essential-

ly unchanged while mean intensity (as measured by maximum wind

speed) increases by +2 to +11% and tropical cyclone rainfall rates

increase by about 20% within 100 km of the cyclone centre. However,

inter-model differences in regional projections lead to lower conﬁdence

in basin-speciﬁc projections, and conﬁdence is particularly low for pro-

jections of frequency within individual basins. For example, a recent

study by Ying et al. (2012) showed that numerical projections of 21st

century changes in tropical cyclone frequency in the western North

Paciﬁc range broadly from –70% to +60%, while there is better model

agreement in measures of mean intensity and precipitation, which are

projected to change in the region by –3% to +18% and +5% to +30%,

respectively. The available modelling studies that are capable of pro-

ducing very strong cyclones typically project substantial increases in

the frequency of the most intense cyclones and it is more likely than

not that this increase will be larger than 10% in some basins (Emanuel

et al., 2008; Bender et al., 2010; Knutson et al., 2010, 2013; Yamada

et al., 2010; Murakami et al., 2012). It should be emphasized that this

metric is generally more important to physical and societal impacts

than overall frequency or mean intensity.

As seen in Tables 14.SM.1 to 14.SM.4 of the Supplementary Material,

as well as the previous assessments noted above, model projections

often vary in the details of the models and the experiments performed,

and it is difﬁcult to objectively assess their combined results to form a

consensus, particularly by region. It is useful to do this after normaliz-

ing the model output using a combination of objective and subjective

expert judgements. The results of this are shown in Figure14.17, and

are based on a subjective normalization of the model output to four

common metrics under a common future scenario projected through

the 21st century. The global assessment is essentially the same as

Knutson et al. (2010) and the assessment of projections in the west-

ern North Paciﬁc is essentially unchanged from Ying et al. (2012). The

annual frequency of tropical cyclones is generally projected to decrease

or remain essentially unchanged in the next century in most regions

although as noted above, the conﬁdence in the projections is lower

in speciﬁed regions than global projections. The decrease in storm

frequency is apparently related to a projected decrease of upward

deep convective mass ﬂux and increase in the saturation deﬁcit of

the middle troposphere in the tropics associated with global warming

(Bengtsson et al., 2007; Emanuel et al., 2008, 2012; Zhao et al., 2009;

Held and Zhao, 2011; Murakami et al., 2012; Sugi et al., 2012; Sugi and

Yoshimura, 2012).

A number of experiments that are able to simulate intense tropical

cyclones project increases in the frequency of these storms in some

regions, although there are presently only limited studies to assess and

there is insufﬁcient data to draw from in most regions to make a con-

ﬁdent assessment (Figure 14.17). Conﬁdence is somewhat better in

the North Atlantic and western North Paciﬁc basins where an increase

in the frequency of the strongest storms is more likely than not. The

models generally project an increase in mean lifetime-maximum inten-

sity of simulated storms (Supplementary Material Table 14.SM.3),

which is consistent with a projected increase in the frequency in the

more intense storms (Elsner et al., 2008). The projected increase in

intensity concurrent with a projected decrease in frequency can be

argued to result from a difference in scaling between projected chang-

es in surface enthalpy ﬂuxes and the Clausius–Clapeyron relationship

associated with the moist static energy of the middle troposphere

(Emanuel et al., 2008). The increase in rainfall rates associated with

1250

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

tropical cyclones is a consistent feature of the numerical models under

greenhouse warming as atmospheric moisture content in the tropics

and tropical cyclone moisture convergence is projected to increase

(Trenberth et al., 2005, 2007a; Allan and Soden, 2008; Knutson et al.,

2010, 2013). Although no broad-scale, detectable long-term changes in

tropical cyclone rainfall rates have been reported, preliminary evidence

for a detectable anthropogenic increase in atmospheric moisture con-

tent over ocean regions has been reported by Santer et al. (2007).

A number of studies since the AR4 have attempted to project future

changes in tropical cyclone tracks and genesis at inter- or intra-ba-

sin scale (Leslie et al., 2007; Vecchi and Soden, 2007b; Emanuel et al.,

2008; Yokoi and Takayabu, 2009; Zhao et al., 2009; Li et al., 2010b;

Murakami and Wang, 2010; Lavender and Walsh, 2011; Murakami

et al., 2011a, 2013). These studies suggest that projected changes in

tropical cyclone activity are strongly correlated with projected changes

in the spatial pattern of tropical SST (Sugi et al., 2009; Chauvin and

Royer, 2010; Murakami et al., 2011b; Zhao and Held, 2012) and asso-

ciated weakening of the Paciﬁc Walker Circulation (Vecchi and Soden,

2007a), indicating that reliable projections of regional tropical cyclone

activity depend critically on the reliability of the projected pattern of

SST changes. However, assessing changes in regional tropical cyclone

frequency is still limited because conﬁdence in projections critically

depend on the performance of control simulations (Murakami and

Sugi, 2010), and current climate models still fail to simulate observed

temporal and spatial variations in tropical cyclone frequency (Walsh et

al., 2012). As noted above, tropical cyclone genesis and track variability

is modulated in most regions by known modes of atmosphere–ocean

variability. The details of the relationships vary by region (e.g., El Niño

events tend to cause track shifts in western North Paciﬁc typhoons and

tend to suppress Atlantic storm genesis and development). Similarly,

it has been demonstrated that accurate modelling of tropical cyclone

activity fundamentally depends on the model’s ability to reproduce

these modes of variability (e.g., Yokoi and Takayabu, 2009). Reliable

projections of future tropical cyclone activity, both global and region-

al, will then depend critically on reliable projections of the behaviour

of these modes of variability (e.g., ENSO) under global warming, as

well as an adequate understanding of their physical links with tropical

cyclones. At present there is still uncertainty in their projected behav-

iours (e.g., Section 14.4).

The reduction in signal-to-noise ratio that accompanies changing focus

from global to regional scales also lengthens the emergence time

scale (i.e., the time required for a trend signal to rise above the nat-

ural variability in a statistically signiﬁcant way). Based on changes in

tropical cyclone intensity predicted by idealized numerical simulations

with carbon dioxide (CO

)-induced tropical SST warming, Knutson and

Tuleya (2004) suggested that clearly detectable increases may not be

manifest for decades to come. The more recent high-resolution dynam-

ical downscaling study of Bender et al. (2010) supports this argument

and suggests that the predicted increases in the frequency of the

strongest Atlantic storms may not emerge as a statistically signiﬁcant

signal until the latter half of the 21st century under the SRES A1B emis-

sionscenario. However, regional forcing by agents other than GHGs,

Tropical Cyclone (TC) Metrics:

I All TC frequency

II Category 4-5 TC frequency

III Lifetime Maximum Intensity

IV Precipitation rate

insf. d.insf. d.

I II IIII IV

−50

% Change

North Indian

insf. d.

I II IIII IV

−50

% Change

Eastern North Pacific

insf. d.

I II IIII IV

−50

% Change

South Indian

insf. d.

I II IIII IV

−50

% Change

Western North Pacific

I II IIII IV

−50

% Change

South Pacific

insf. d.

I II IIII IV

−50

% Change

North Atlantic

200 %

-100 %

I II IIII IV

−50

% Change

GLOBAL

I II IIII IV

−50

% Change

SOUTHERN HEMISPHERE

insf. d.

I II IIII IV

−50

% Change

NORTHERN HEMISPHERE

insf. d.

Figure 14.17 | General consensus assessment of the numerical experiments described in Supplementary Material Tables 14.SM.1 to 14.SM.4. All values represent expected percent

change in the average over period 2081–2100 relative to 2000–2019, under an A1B-like scenario, based on expert judgement after subjective normalization of the model projections.

Four metrics were considered: the percent change in (I) the total annual frequency of tropical storms, (II) the annual frequency of Category 4 and 5 storms, (III) the mean Lifetime

Maximum Intensity (LMI; the maximum intensity achieved during a storm’s lifetime) and (IV) the precipitation rate within 200 km of storm centre at the time of LMI. For each metric

plotted, the solid blue line is the best guess of the expected percent change, and the coloured bar provides the 67% (likely) conﬁdence interval for this value (note that this interval

ranges across –100% to +200% for the annual frequency of Category 4 and 5 storms in the North Atlantic). Where a metric is not plotted, there are insufﬁcient data (denoted ‘insf.

d.’) available to complete an assessment. A randomly drawn (and coloured) selection of historical storm tracks are underlain to identify regions of tropical cyclone activity.

1251

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

such as anthropogenic aerosols, is known to affect the regional climat-

ic conditions differently (e.g., Zhang and Delworth, 2009), and there

is evidence that tropical cyclone activity may have changed in some

regions because of effects from anthropogenic aerosol pollution. The

ﬁdelity of the emergence time scales projected under A1B warming

thus depends on the ﬁdelity of A1B aerosol projections, which are

known to be uncertain (Forster et al., 2007; Haerter et al., 2009).

14.6.2 Extratropical Cyclones

Some agreement on the response of storm tracks to anthropogenic

forcing started to emerge in the climate model projections from CMIP3,

with many models projecting a poleward shift of the storm tracks (Yin,

2005) and an expansion of the tropics (Lu et al., 2007). As stated in AR4

(Meehl et al., 2007) this response is particularly clear in the SH, but less

clear in the NH. Although clearer in zonal mean responses (Yin, 2005),

regional responses at different longitudes differ widely from this in

many models (Ulbrich et al., 2008). There is a strong two-way coupling

between storm tracks and the large-scale circulation (Lorenz and Hart-

mann, 2003; Robinson, 2006; Gerber and Vallis, 2007), which results in

associated shifts in storm tracks and westerly jet streams (Raible, 2007;

Athanasiadis et al., 2010).

14.6.2.1 Process Understanding of Future Changes

Future storm track change is the result of several different competing

dynamical inﬂuences (Held, 1993; O’Gorman, 2010; Woollings, 2010).

It is becoming apparent that the relatively modest storm track response

in many models reﬂects the partial cancelling of opposing tendencies

(Son and Lee, 2005; Lim and Simmonds, 2009; Butler et al., 2010).

One of the most important factors is the change in the meridional

temperature gradient from which ETCs draw most of their energy.

This gradient is projected to increase in the upper troposphere due to

tropical ampliﬁcation and decrease in the lower troposphere due to

polar ampliﬁcation, and it is still unclear whether this will lead to an

overall increase or decrease in ETC activity. The projected response can

involve an increase in eddy activity at upper levels and a decrease at

lower levels (Hernandez-Deckers and von Storch, 2010), although in

other models changes in low level eddy activity are more in line with

the upper level wind changes (Mizuta et al., 2011; Wu et al., 2011;

Mizuta, 2012). The projected warming pattern also changes vertical

temperature gradients leading to increased stability at low latitudes

and decreased stability at higher latitudes, and there is some modelling

evidence that this may be a strong factor in the response (Lu et al.,

2008, 2010; Kodama and Iwasaki, 2009; Lim and Simmonds, 2009).

Increasing depth of the troposphere might also be important for future

changes (Lorenz and DeWeaver, 2007).

Uncertainties in the projections of large-scale warming contribute to

uncertainty in the storm track response (Rind, 2008). Several mecha-

nisms have been proposed to explain how the storm tracks respond to

the large-scale changes, including changes in eddy phase speed (Chen

et al., 2007, 2008; Lu et al., 2008), eddy source regions (Lu et al., 2010)

and eddy length scales (Kidston et al., 2011) with a subsequent effect

on wave-breaking characteristics (Riviere, 2011). Furthermore, changes

in the large-scale warming might also be partly due to changes in the

storm tracks due to the two-way coupling between storm tracks and

the large-scale ﬂow. However, there is evidence that the amplitude of

the tropical and polar warming are largely determined by atmospheric

poleward heat ﬂuxes set by local processes (Hwang and Frierson, 2010).

Local processes could prove important for the storm track response

in certain regions, for example, sea ice loss (Kvamsto et al., 2004; Sei-

erstad and Bader, 2009; Deser et al., 2010c; Bader et al., 2011) and

spatial changes in SSTs (Graff and LaCasce, 2012). Local land–sea con-

trast in warming also has a local inﬂuence on baroclinicity along the

eastern continental coastlines (Long et al., 2009; McDonald, 2011). It is

not clear how the storm track responds to multiple forcings with some

studies suggesting a linear response (Lim and Simmonds, 2009) while

others suggest more complex interaction (Butler et al., 2010).

The projected increase in moisture content in a warmer atmosphere is

also likely to have competing effects. Latent heating has been shown

to play a role in invigorating individual ETCs, especially in the down-

stream development over eastern ocean (Dacre and Gray, 2009; Fink

et al., 2009, 2012). However, there is evidence that the overall effect of

moistening is to weaken ETCs by improving the efﬁciency of poleward

heat transport and hence reducing the dry baroclinicity (Frierson et al.,

2007; O’Gorman and Schneider, 2008; Schneider et al., 2010; Lucarini

and Ragone, 2011). Consistent with this, studies have shown that pre-

cipitation is projected to increase in ETCs despite no increase in wind

speed intensity of ETCs (Bengtsson et al., 2009; Zappa et al., 2013b).

14.6.2.2 Regional Projections

Large-scale projections of ETCs are assessed in Section 12.4.4.3. This

section complements this by presenting a more detailed assessment of

regional changes.

Individual model projections of regional storm track changes are often

comparable with the magnitude of interannual natural variability and

so the changes are expected to be relevant for regional climate. How-

ever, the magnitude of the response is model dependent at any given

location, especially over land (Harvey et al., 2012). There is also disa-

greement between different cyclone/storm track identiﬁcation meth-

ods, even when applied to the same data (Raible et al., 2008; Ulbrich

et al., 2009), although in the response to anthropogenic forcing, these

differences appear mainly in the statistics of weak cyclones (Ulbrich et

al., 2013). Conversely, when the same method is applied to different

models the spread between the model responses is often larger than

the ensemble mean response, especially in the NH (Ulbrich et al., 2008;

Laine et al., 2009).

The poleward shift of the SH storm track remains one of the most

reproducible projections, yet even here there is considerable quan-

titative uncertainty. This is partly associated with the varied model

biases in jet latitude (Kidston and Gerber, 2010) although factors such

as the varied cloud response may play a role (Trenberth and Fasullo,

2010). Many models project a similar poleward shift in the North

Paciﬁc (Bengtsson et al., 2006; Ulbrich et al., 2008; Catto et al., 2011),

although this is often weaker compared to natural variability and often

varies considerably between ensemble members (Pinto et al., 2007;

McDonald, 2011). Poleward shifts are generally less clear at the surface

than in the upper troposphere (Yin, 2005; McDonald, 2011; Chang et

1252

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

al., 2012), which reduces their relevance for regional impacts. However,

a shift in extreme surface winds is still detectable in the zonal mean,

especially in the subtropics and the southern high latitudes (Gastineau

and Soden, 2009). A weakening of the Mediterranean storm track is a

particularly robust response (Pinto et al., 2007; Loeptien et al., 2008;

Ulbrich et al., 2009; Donat et al., 2011) for which increasing static

stability is important (Raible et al., 2010). In general, the storm track

response in summer is weaker than in winter with less consistency

between models (Lang and Waugh, 2011).

The response of the North Atlantic storm track is more complex than a

poleward shift in many models, with an increase in storm activity and a

downstream extension of the storm track into Europe (Bengtsson et al.,

2006; Pinto et al., 2007; Ulbrich et al., 2008; Catto et al., 2011; McDon-

ald, 2011). In some models this regional response is important (Ulbrich

et al., 2009), with storm activity over Western Europe increasing by

50% (McDonald, 2011) or by an amount comparable to the natural

variability (Pinto et al., 2007; Woollings et al., 2012). The return periods

of intense cyclones are shortened (Della-Marta and Pinto, 2009) with

impact on potential wind damage (Leckebusch et al., 2007; Donat et

al., 2011) and economic losses (Pinto et al., 2012). This response is

related to the local minimum in warming in the North Atlantic ocean,

which serves to increase the meridional temperature gradient on its

southern side (Laine et al., 2009; Catto et al., 2011). The minimum in

warming arises due to the weakening of northward ocean heat trans-

ports by the Atlantic Meridional Overturning Circulation (AMOC),

and the varying AMOC responses of the models can account for a

large fraction of the variance in the Atlantic storm track projections

(Woollings et al., 2012). CMIP5 models show a similar, albeit weaker

extension of the storm track towards Europe, ﬂanked by reductions in

cyclone activity on both the northern and southern sides (Harvey et al.,

2012; Zappa et al., 2013b). Despite large biases in the mean state, the

model responses were found to agree with one another within sam-

pling variation caused by natural variability (Sansom et al., 2013). Colle

et al. (2013) noted similar reductions but also found that the higher

resolution CMIP5 models gave more realistic ETC performance in the

historical period. The best 7 models were found to give projections of

increased 10 to 20% increase in cyclone track density over the eastern

USA, including 10 to 40% more intense (<980 hPa) cyclones.

There is general agreement that there will be a small global reduction

in ETC numbers (Ulbrich et al., 2009). In individual regions there can

be much larger changes which are comparable to natural variations,

but these changes are not reproduced by the majority of the models

(e.g., Donat et al., 2011). ETC intensities are particularly sensitive to the

method and quantity used to deﬁne them, so there is little consensus

on changes in intensity (Ulbrich et al., 2009). While there are indica-

tions that the absolute values of pressure minima deepen in future

scenario simulations (Lambert and Fyfe, 2006), this is often associated

with large-scale pressure changes rather than changes in the pres-

sure gradients or winds associated with ETCs (Bengtsson et al., 2009;

Ulbrich et al., 2009; McDonald, 2011). The CMIP5 model projections

show little evidence of change in the intensity of winds associated with

ETCs (Zappa et al., 2013b).

There are systematic storm track biases common to many models,

which might have some inﬂuence on the projected storm track

response to forcing (Chang et al., 2012). Some models with improved

representation of the stratosphere have shown a markedly different

circulation response in the NH, with consequences for Atlantic/Euro-

pean storm activity in particular (Scaife et al., 2011a). Concerns over

the skill of many models in representing both the stratosphere and the

ocean mean that conﬁdence in NH storm track projections remains

low. Higher horizontal resolution can improve ETC representation, yet

there are still relatively few high-resolution global models which have

been used for storm track projections (Geng and Sugi, 2003; Bengtsson

et al., 2009; Catto et al., 2011; Colle et al., 2013; Zappa et al., 2013a).

Several studies have used RCMs to simulate storms at high resolu-

tion in particular regions. In multi-model experiments over Europe, the

ETC response is more sensitive to the choice of driving GCM than the

choice of RCM (Leckebusch et al., 2006; Donat et al., 2011), highlight-

ing the importance of large-scale circulation uncertainties. There has

been little work on potential changes to mesoscale storm systems,

although it has been suggested that polar lows may reduce in frequen-

cy due to an increase in static stability (Zahn and von Storch, 2010).

Higher resolution runs of one climate model also suggest an increase

in intensity of autumn ETCs due to increased transitioning of Atlantic

hurricanes (Haarsma et al., 2013).

14.6.3 Assessment Summary

The inﬂuence of past and future climate change on tropical cyclones is

likely to vary by region, but the speciﬁc characteristics of the changes

are not yet well understood, and the substantial inﬂuence of ENSO

and other known climate modes on global and regional tropical

cyclone activity emphasizes the need for more reliable assessments of

future changes in the characteristics of these modes. Recent advanc-

es in understanding and phenomenological evidence for shorter-term

effects on tropical cyclones from aerosol forcing are providing increas-

ingly greater conﬁdence that anthropogenic forcing has had a measur-

able effect on tropical cyclone activity in certain regions. Shorter term

increases such as those observed in the Atlantic over the past 30 to 40

years appear to be robust and have been hypothesized to be related, in

part, to regional external forcing by GHGs and aerosols, but the more

steady century-scale trends that may be expected from CO

forcing

alone are much more difﬁcult to assess given the data uncertainty in

the available tropical cyclone records.

Although projections under 21st century greenhouse warming indicate

that it is likely that the global frequency of tropical cyclones will either

decrease or remain essentially unchanged, concurrent with a likely

increase in both global mean tropical cyclone maximum wind speed

and rainfall rates, there is low conﬁdence in region-speciﬁc projections

of frequency and intensity. Still, based on high-resolution modelling

studies, the frequency of the most intense storms, which are associ-

ated with particularly extensive physical effects, will more likely than

not increase substantially in some basins under projected 21st century

warming and there is medium conﬁdence that tropical cyclone rainfall

rates will increase in every affected region.

The global number of ETCs is unlikely to decrease by more than a few

percent due to anthropogenic change. A small poleward shift is likely

in the SH storm track, but the magnitude is model dependent. There

is only medium conﬁdence in projections of storm track shifts in the

1253

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Northern Hemisphere. Nevertheless, model results suggests that it is

more likely than not that the N. Paciﬁc storm track will shift poleward,

and that it is unlikely that the N. Atlantic storm track will respond with

a simple poleward shift. There is low conﬁdence in the magnitude

of regional storm track changes, and the impact of such changes on

regional surface climate.

14.7 Additional Phenomena of Relevance

14.7.1 Paciﬁc–South American Pattern

The PSA pattern is a teleconnection from the tropics to SH extratrop-

ics through Rossby wave trains (Box 2.5). This pattern induces atmos-

pheric circulation anomalies over South America, affecting extreme

precipitation (Drumond and Ambrizzi, 2005; Cunningham and Cav-

alcanti, 2006; Muza et al., 2009; Vasconcellos and Cavalcanti, 2010).

PSA trends for recent decades depend on the choice of indices and are

hence uncertain (Section 2.7.8). The PSA pattern is reproduced in many

model simulations (Solman and Le Treut, 2006; Vera and Silvestri, 2009;

Bates, 2010; Rodrigues et al., 2011; Cavalcanti and Shimizu, 2012).

The intensiﬁcation and westward displacement of the PSA wave pat-

tern in projections of CMIP3 may be related to the increase in frequen-

cy and intensity of positive SST anomalies in the tropical Paciﬁc by the

end of the 21st century (2081–2100, Junquas et al., 2012). These per-

turbations of the PSA characteristics are linked with changes in SACZ

dipole precipitation and affect South America precipitation (Section

14.3.1.3). The PSA pattern occurrence and implications for precipita-

tion increase in the southeastern South America have been associated

with the zonally asymmetric part of the global SST change in the equa-

torial Indian–western Paciﬁc Oceans (Junquas et al., 2013).

Process understanding of the formation of the PSA gives medium con-

ﬁdence that future changes in the mean atmospheric circulation for

austral summer will project on the PSA pattern thereby inﬂuencing the

SACZ and precipitation over southeastern South America. However, the

literature is not sufﬁcient to assess more general changes in PSA, and

conﬁdence is low in its future projections.

14.7.2 Paciﬁc–North American Pattern

The PNA pattern, as deﬁned in Box 2.5, Table 1 and portrayed in Box

2.5, Figure 2 is a prominent mode of atmospheric variability over the

North Paciﬁc and the North American land mass. This phenomenon

exerts notable inﬂuences on the temperature and precipitation varia-

bility in these regions (e.g., Nigam, 2003). The PNA pattern is related to

ENSO events in the tropical Paciﬁc (see Section 14.4), and also serves

as a bridge linking ENSO and NAO variability (see Li and Lau, 2012).

The data records indicate a signiﬁcant positive trend in the wintertime

PNA index over the past 60 years (see Table 2.14 and Box 2.5, Figure 1).

Stoner et al. (2009) have assessed the capability of 22 CMIP3 GCMs

in replicating the essential aspects of the observed PNA pattern. Their

results indicate that a majority of the models overestimate the fraction

of variance explained by the PNA pattern, and that the spatial charac-

teristics of PNA patterns simulated in 14 of the 22 models are in good

agreement with the observations. The model-projected future evolu-

tion of the PNA pattern has not yet been fully assessed and therefore

conﬁdence in its future development is low.

14.7.3 Paciﬁc Decadal Oscillation/Inter-decadal

Paciﬁc Oscillation

The Paciﬁc Decadal Oscillation (PDO, Box 2.5) refers to the leading

Empirical Orthogonal Function (EOF) of monthly SST anomalies over

the North Paciﬁc (north of 20°N) from which the globally averaged

SST anomalies have been subtracted (Mantua et al., 1997). It exhibits

anomalies of one sign along the west coast of North America and of

opposite sign in the western and central North Paciﬁc (see also Sec-

tion 9.5.3.6 and Chapter 11). The PDO is closely linked to ﬂuctuations

in the strength of the wintertime Aleutian Low Pressure System. The

time scale of the PDO is around 20 to 30 years, with changes of sign

between positive and negative polarities in the 1920s, the late 1940s,

the late 1970s and around 2000.

The extension of the PDO to the whole Paciﬁc basin is known as the

Inter-decadal Paciﬁc Oscillation (IPO, Power et al., 1999). The IPO is

nearly identical in form to the PDO in the NH but is deﬁned globally, as

a leading EOF (principal component) of 13-year lowpass-ﬁltered global

SST anomalies (Parker et al., 2007) and has substantial amplitude in

the tropical and southern Paciﬁc. The time series of the PDO and IPO

correlate highly on an annual basis. The PDO/IPO pattern is considered

to be the result of internal climate variability (Schneider and Cornuelle,

2005; Alexander, 2010) and has not been observed to exhibit a long-

term trend. The PDO/IPO is associated with ENSO modulations, with

more El Niño activity during the positive PDO/IPO and more La Niña

activity during the negative PDO/IPO.

At the time of the AR4, little had been published on modelling of the

PDO/IPO or of its evolution in future. In a recent study, Furtado et al.

(2011) found that the PDO/IPO did not exhibit major changes in spa-

tial or temporal characteristics under GHG warming in most of the 24

CMIP3 models used, although some models indicated a weak shift

toward more occurrences of the negative phase of the PDO/IPO by

the end of the 21st century (2081–2100, Lapp et al., 2012). However,

given that the models strongly underestimate the PDO/IPO connection

with tropical Indo-Paciﬁc SST variations (Furtado et al., 2011; Lienert et

al., 2011), the credibility of the projections remains uncertain. Further-

more, internal variability is so high that it is hard to detect any forced

changes in the Aleutian Low for the next half a century (Deser et al.,

2012; Oshima et al., 2012). Therefore conﬁdence is low in projections

of future changes in PDO/IPO.

14.7.4 Tropospheric Biennial Oscillation

The Tropospheric Biennial Oscillation (TBO; Meehl, 1997) is a proposed

mechanism for the biennial tendency in large-scale drought and ﬂoods

of south Asia and Australia. Multiple studies imply that TBO involves

the Asian-Australian monsoon, the IOD and ENSO (Sections 14.2.2,

14.3.3 and 14.4; see also Supplementary Material Section 14.SM.5.2).

The IPO (Section 14.7.3) affects the decade-to-decade strength of the

TBO. A major contributor to recent change in the TBO comes from

1254

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

increase of SST in the Indian Ocean that contributes to stronger trade

winds in the Paciﬁc, one of the processes previously identiﬁed with

strengthening the TBO (Meehl and Arblaster, 2012). Thus, prediction of

decadal variability assessed in Chapter 11 that can be associated, for

example, with the IPO (e.g., Meehl et al., 2010) can inﬂuence the accu-

racy of shorter-term predictions of the TBO across the entire Indo-Pacif-

ic region (Turner et al., 2011), but the relevance for longer time scales

is uncertain.

Since AR4, little work has been done to document the ability of climate

models to simulate the TBO. However, Li et al. (2012b) showed that

there is an overall improvement in the seasonality of monsoons rainfall

related to changes in the TBO from CMIP3 to CMIP5, with most CMIP5

models better simulating both the monsoon timing and the very low

rainfall rates outside of the monsoon season (see also Section 14.2.2).

In addition they concluded that the India-Australia link seems to be

robust in all models.

With regard to possible future behaviour of the TBO, no analysis

using multiple GCMs has been made since the AR4. In models that

more accurately simulate the TBO in the present-day climate, the TBO

strengthens in a future warmer climate (Nanjundiah et al., 2005). How-

ever, as with ENSO (Section 14.4) and IOD (Section 14.3.3), internally

generated decadal variability complicates the interpretation of such

future changes. Therefore, it remains unclear how future changes in the

TBO will emerge and how this may inﬂuence regional climate in the

future. Conﬁdence in the projected future changes in TBO remains low.

14.7.5 Quasi-Biennial Oscillation

The QBO is a near-periodic, large-amplitude, downward propagating

oscillation in zonal winds in the equatorial stratosphere (Baldwin et

al., 2001). It is driven by vertically propagating internal waves that

are generated in the tropical troposphere (Plumb, 1977). The QBO has

substantial effects on the global stratospheric circulation, in particular

the strength of the northern stratospheric polar vortex as well as the

extratropical troposphere (Boer, 2009; Marshall and Scaife, 2009; Gar-

ﬁnkel and Hartmann, 2011). These extratropical effects occur primarily

in winter when the stratosphere and troposphere are strongly coupled

(Anstey and Shepherd, 2008; Garﬁnkel and Hartmann, 2011).

It has been unclear how the QBO will respond to future climate change

related to GHG increase and recovery of stratospheric ozone. Climate

models assessed in the AR4 did not simulate the QBO as they lacked

the necessary vertical resolution (Kawatani et al., 2011). Recent model

studies without using gravity wave parameterization (Kawatani et al.,

2011; Kawatani et al., 2012) showed that the QBO period and ampli-

tude may become longer and weaker, and the downward penetration

into the lowermost stratosphere may be more curtailed in a warmer

climate. This ﬁnding is attributed to the effect of increased equatorial

upwelling (stronger Brewer–Dobson circulation; Butchart et al., 2006;

Garcia and Randel, 2008; McLandress and Shepherd, 2009; Okamoto

et al., 2011) dominating the effect of increased wave forcing (more

convective activity). Two studies with gravity wave parameterization,

however, gave conﬂicting results depending on the simulated changes

in the intensity of the Brewer–Dobson circulation (Watanabe and

Kawatani, 2012).

There are limited published results on the future behaviour of the QBO,

using CMIP5 models. On the basis of the recent literature, it is uncer-

tain how the period or amplitude of the QBO may change in future and

conﬁdence in the projections remains low.

14.7.6 Atlantic Multi-decadal Oscillation

The AMO (Box 2.5; see also Section 9.5.3.3.2) is a ﬂuctuation seen in

the instrumental SST record throughout the North Atlantic Ocean and

is related to variability in the thermohaline circulation (Knight et al.,

2005). Area-mean North Atlantic SST shows variations with a range of

about 0.4°C (see Box 2.5) and a warming of a similar magnitude since

1870. The AMO has a quasi-periodicity of about 70 years, although the

approximately 150-year instrumental record possesses only a few dis-

tinct phases—warm during approximately 1930–1965 and after 1995,

and cool between 1900–1930 and 1965–1995. The phenomenon has

also been referred to as ‘Atlantic Multidecadal Variability’ to avoid the

implication of temporal regularity. Along with secular trends and Pacif-

ic variability, the AMO is one of the principal features of multidecadal

variability in the instrumental climate record.

The AR4 highlighted a number of important links between the AMO

and regional climates. Subsequent research using observational and

paleoclimate records, and climate models, has conﬁrmed and expand-

ed upon these connections, such as West African monsoon and Sahel

rainfall (Mohino et al., 2011; Section 14.2.4), summer climate in North

America (Seager et al., 2008; Section 14.8.3; Feng et al., 2011) and

Europe (Folland et al., 2009; Ionita et al., 2012; Section 14.8.6), the

Arctic (Chylek et al., 2009; Mahajan et al., 2011), and Atlantic major

hurricane frequency (Chylek and Lesins, 2008; Zhang and Delworth,

2009; Section 14.6.1). Further, the list of AMO inﬂuences around the

globe has been extended to include decadal variations in many other

regions (e.g., Zhang and Delworth, 2006; Kucharski et al., 2009a,

2009b; Huss et al., 2010; Marullo et al., 2011; Wang et al., 2011).

Paleo reconstructions of Atlantic temperatures show AMO-like variabili-

ty well before the instrumental era, as noted in the AR4 (Chapter 6; see

also Section 5.4.2). Recent analyses conﬁrm this, and suggest potential

for intermittency in AMO variability (Saenger et al., 2009; Zanchettin et

al., 2010; Chylek et al., 2012). Control simulations of climate models run

for hundreds or thousands of years also show long-lived Atlantic mul-

ti-decadal variability (Menary et al., 2012). These lines of evidence sug-

gest that AMO variability will continue into the future. No fundamental

changes in the characteristics of North Atlantic multi-decadal variability

in the 21st century are seen in CMIP3 models (Ting et al., 2011).

Many studies have diagnosed a trend towards a warm North Atlantic

in recent decades additional to that implied by global climate forcings

(Knight, 2009; Polyakov et al., 2010). It is unclear exactly when the

current warm phase of the AMO will terminate, but may occur within

the next few decades, leading to a cooling inﬂuence in the North Atlan-

tic and offsetting some of the effects characterizing global warming

(Keenlyside et al., 2008; see also Section 11.3.3.3).

Some similarity in the shape of the instrumental time series of global

and NH mean surface temperatures and the AMO has long been noted.

By removing an estimate of the effect of interannual variability phe-

1255

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

nomena like ENSO (Section 14.4), AMO transitions have been shown to

have the potential to produce large and abrupt changes in hemispheric

temperatures (Thompson et al., 2010). Estimates of the AMO’s contri-

bution to recent climate change are uncertain, however, as attribution

of the observed AMO requires a model (physical or conceptual) whose

assumptions are nearly always difﬁcult to verify (Knight, 2009).

14.7.7 Assessment Summary

Literature is generally insufﬁcient to assess future changes in behav-

iour of the PNA, PSA, TBO, QBO and PDO/IPO. Conﬁdence in the pro-

jections of changes in these modes is therefore low. However, process

understanding of the formation of the PSA gives medium conﬁdence

that future changes in the mean atmospheric circulation for austral

summer will project on the PSA pattern thereby inﬂuencing the SACZ

and precipitation over Southeastern South America.

Paleoclimate reconstructions and long model simulations indicate that

the AMO is unlikely to change its behaviour in the future as the mean

climate changes. However, natural ﬂuctuations in the AMO over the

coming few decades are likely to inﬂuence regional climates at least as

strongly as will human-induced changes.

14.8 Future Regional Climate Change

14.8.1 Overview

The following sections assess future climate projections for several

regions, and relate them, where possible, to projected changes in the

major climate phenomena assessed in Sections 14.2 to 14.7. The region-

al climate change assessments are mainly of mean surface air temper-

ature and mean precipitation based primarily on multi-model ensemble

projections from general circulation models. Reference is made to the

appropriate projection maps from CMIP5 (Taylor et al., 2011c) present-

ed in Annex I: Atlas of Global and Regional Climate Projections. Annex

I uses smaller sub-regions similar to those introduced by SREX (Sen-

eviratne et al., 2012). Table 14.1 presents a quantitative summary of

the regional area averages over three projection periods (2016–2035,

2046–2065 and 2081–2100 with respect to the reference period 1986–

2005, representing near future, middle century and end of century) for

the RCP4.5 scenario. The 26 land regions assessed here are presented in

Seneviratne et al., 2012, page 12 and the coordinates can be found from

their online Appendix 3.A. Added to this are six additional regions con-

taining the two polar regions, the Caribbean, Indian Ocean and Paciﬁc

Island States (see Annex I for further details). Table 14.1 identiﬁes the

smaller sub-domains grouped within the somewhat large regions that

are discussed in Sections 14.8.2 to 14.8.15. Tables for RCP2.6, RCP6.0

and RCP8.5 scenarios are presented in Supplementary Material Tables

14.SM.1a to 14.SM.1c. For continental-scale regions, projected chang-

es in mean precipitation between (2081–2100) and (1986–2005) are

compared in two generations of models forced under two comparable

emission scenarios: RCP4.5 in CMIP5 versus A1B in CMIP3. In contrast

to the Annex, the seasons here are chosen differently for each region

so as to best capture the regional features such as monsoons. Down-

scaling issues are illustrated in panels showing results from an ensem-

ble of high-resolution time-slice experiments with the Meteorological

Research Institute (MRI) model (Endo et al., 2012; Mizuta et al., 2012).

To facilitate a direct comparison across the scenarios, the precipitation

changes are normalized by the global annual mean surface air temper-

ature changes in each scenario. Published results using other downscal-

ing methods are also assessed when found essential to illustrate issues

related to regional climate change.

Regional climate projections are generally more uncertain than pro-

jections of global mean temperature but the sources of uncertainty

are similar (see Chapters 8, 11, and 12) yet differ in relative impor-

tance. For example, natural variability (Deser et al., 2012), aerosol

forcing (Chapter 7) and land use/cover changes (DeFries et al., 2002;

Moss et al., 2010) all become more important sources of uncertain-

ty on a regional scale. Regional climate assessments incur additional

uncertainty due to the cascade of uncertainty through the hierarchy

of models needed to generate local information (cf. downscaling in

Section 9.6). Calibration (bias correction) of model output to match

local observations is an additional important source of uncertainty in

regional climate projections (e.g., Ho et al., 2012), which should be

considered when interpreting the regional projections. Therefore, the

model spread shown in Annex 1 should not be interpreted as the ﬁnal

uncertainty in the observable regional climate change response.

Table 14.2 summarizes the assessed conﬁdence in the ability of CMIP5

models to represent regional scale present-day climate (temperature

and precipitation, based on Chapter 9), the main controlling phenom-

ena for weather and climate in that region and the assessed result-

ing conﬁdence in the future projections. There is generally less conﬁ-

dence in projections of precipitation than of temperature. For example,

in Annex I, the temperature projections for 2081–2100 are almost

always above the model estimates of natural variability, whereas the

precipitation projections less frequently rise above natural variability.

Although some projections are robust for reasons that are well under-

stood (e.g., the projected increase in precipitation at high latitudes),

many other regions have precipitation projections that vary in sign and

magnitude across the models. These issues are further discussed in Sec-

tion 12.4.5.2. Details on how the conﬁdence table is constructed are

found in the Supplementary Material.

Credibility in regional climate change projections is increased if it

is possible to ﬁnd key drivers of the change that are known to be

well-simulated and well-projected by climate models. Table 14.3

summarizes the assessment of how major climate phenomena might

be relevant for future regional climate change. For each entry in the

table, the relevance is based on an assessment of conﬁdence in future

change in the phenomenon and the conﬁdence in how the phenome-

non inﬂuences regional climate. For example, NAO is assigned high rel-

evance (red) for the Arctic region because NAO is known to inﬂuence

the Arctic and there is high conﬁdence that the NAO index will increase

in response to anthropogenic forcing. If there is low conﬁdence in how

a phenomenon might change (e.g., ENSO) but high conﬁdence that

it has a strong regional impact, then the cell in the table is assigned

medium relevance (yellow). It can be seen from the table that there are

many cases where major phenomena are assessed to have high (red)

or medium (yellow) relevance for future regional climate change. See

Supplementary Material Section 14.SM.6.1 for more details on how

this relevance table was constructed.

1256

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Frequently Asked Questions

FAQ 14.2 | How Are Future Projections in Regional Climate Related to Projections of

Global Means?

The relationship between regional climate change and global mean change is complex. Regional climates vary

strongly with location and so respond differently to changes in global-scale inﬂuences. The global mean change is,

in effect, a convenient summary of many diverse regional climate responses.

Heat and moisture, and changes in them, are not evenly distributed across the globe for several reasons:

• External forcings vary spatially (e.g., solar radiation depends on latitude, aerosol emissions have local sources,

land use changes regionally, etc.).

• Surface conditions vary spatially, for example, land/sea contrast, topography, sea surface temperatures, soil mois-

ture content.

• Weather systems and ocean currents redistribute heat and moisture from one region to another.

Weather systems are associated with regionally important climate phenomena such as monsoons, tropical conver-

gence zones, storm tracks and important modes of climate variability (e.g., El Niño-Southern Oscillation (ENSO),

North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), etc.). In addition to modulating regional warm-

ing, some climate phenomena are also projected to change in the future, which can lead to further impacts on

regional climates (see Table 14.3).

Projections of change in surface temperature and precipitation show large regional variations (FAQ 14.2, Figure 1).

Enhanced surface warming is projected to occur over the high-latitude continental regions and the Arctic ocean,

FAQ 14.2, Figure 1 | Projected 21st century changes in annual mean and annual extremes (over land) of surface air temperature and precipitation: (a) mean surface

temperature per °C of global mean change, (b) 90th percentile of daily maximum temperature per °C of global average maximum temperature, (c) mean precipitation (in

% per °C of global mean temperature change), and (d) fraction of days with precipitation exceeding the 95th percentile. Sources: Panels (a) and (c) projected changes

in means between 1986–2005 and 2081–2100 from CMIP5 simulations under RCP4.5 scenario (see Chapter 12, Figure 12.41); Panels (b) and (d) projected changes

in extremes over land between 1980–1999 and 2081–2100 (adapted from Figures 7 and 12 of Orlowsky and Seneviratne, 2012).

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0.51 1.52

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(a)(b)

(°C per °C)

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0.500.25 0.75 11.25 1.51.752

-6-12-9-3036912

(continued on next page)

1257

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

14.8.2 Arctic

This section is concerned with temperature and precipitation dimen-

sions of Arctic climate change, and their links to climate phenomena.

The reader is referred elsewhere for information on sea ice loss (Sec-

tions 4.2.2, 5.5.2 and Chapter 10), and projections of sea ice change

(Sections 9.4.3, 9.8.3 and Chapters 11 and 12).

Arctic climate is affected by three modes of variability: NAO (Section

14.5.1), PDO (Section 14.7.3) and AMO (Section 14.7.6). The NAO

index correlates positively with temperatures in the northeastern Eur-

asian sector, and correlates negatively with temperatures in the Bafﬁn

Bay and Canadian Archipelago, but exhibits little relationship with

central Arctic temperatures (Polyakov et al., 2003). The PDO plays a

role in temperature variability of Alaska and the Yukon (Hartmann and

Wendler, 2005). The AMO is positively associated with SST throughout

the Arctic (Chylek et al., 2009; Levitus et al., 2009; Chylek et al., 2010)

(Mahajan et al., 2011). ETCs are also mainly responsible for winter

precipitation in the region (see Table 14.3).

The surface and lower troposphere in the Arctic and surrounding land

areas show regional warming over the past three decades of about 1°C

per decade—signiﬁcantly greater than the global mean trend (Figures

2.22 and 2.25). According to temperature reconstructions, this signal

is highly unusual: Temperatures averaged over the Arctic over the past

few decades are signiﬁcantly higher than any seen over the past 2000

years (Kaufman et al., 2009). Temperatures 11 ka were greater than the

20th century mean, but this is probably a strongly forced signal, since

summer solar radiation was 9% greater than present (Miller et al.,

FAQ 14.2 (continued)

while over other oceans and lower latitudes changes are closer to the global mean (FAQ 14.2, Figure 1a). For

example, warming near the Great Lakes area of North America is projected to be about 50% greater than that

of the global mean warming. Similar large regional variations are also seen in the projected changes of more

extreme temperatures (FAQ 14.2, Figure 1b). Projected changes in precipitation are even more regionally variable

than changes in temperature (FAQ 14.2, Figure 1c, d), caused by modulation from climate phenomena such as the

monsoons and tropical convergence zones. Near-equatorial latitudes are projected to have increased mean precipi-

tation, while regions on the poleward edges of the subtropics are projected to have reduced mean precipitation.

Higher latitude regions are projected to have increased mean precipitation and in particular more extreme precipi-

tation from extratropical cyclones.

Polar regions illustrate the complexity of processes involved in regional climate change. Arctic warming is projected

to increase more than the global mean, mostly because the melting of ice and snow produces a regional feedback

by allowing more heat from the Sun to be absorbed. This gives rise to further warming, which encourages more

melting of ice and snow. However, the projected warming over the Antarctic continent and surrounding oceans is

less marked in part due to a stronger positive trend in the Southern Annular Mode. Westerly winds over the mid-

latitude southern oceans have increased over recent decades, driven by the combined effect of loss of stratospheric

ozone over Antarctica, and changes in the atmosphere’s temperature structure related to increased greenhouse

gas concentrations. This change in the Southern Annular Mode is well captured by climate models and has the

effect of reducing atmospheric heat transport to the Antarctic continent. Nevertheless, the Antarctic Peninsula is

still warming rapidly, because it extends far enough northwards to be inﬂuenced by the warm air masses of the

westerly wind belt.

2010). Finally, warmer temperatures have been sustained in pan-Arctic

land areas where a declining NAO over the past decade ought to have

caused cooling (Semenov, 2007; Turner et al., 2007b). Since AR4, evi-

dence has also emerged that precipitation has trended upward in most

pan-Arctic land areas over the past few decades (e.g., Pavelsky and

Smith, 2006; Rawlins et al., 2010), though the evidence remains mixed

(e.g., Dai et al., 2009). Increasing ETC activity over the Canadian Arctic

has also been observed (Section 2.6.4).

Since AR4, there has been progress in adapting RCMs for polar appli-

cations (Wilson et al., 2012). These models have been evaluated with

regard to their ability to simulate Arctic clouds, surface heat ﬂuxes, and

boundary layer processes (Tjernstrom et al., 2004; Inoue et al., 2006;

Rinke et al., 2006). They have been used to improve simulations of

Arctic-speciﬁc climate processes, such as glacial mass balance (Zhang

et al., 2007). A few regional models have been used for Arctic climate

change projections (e.g., Zahn and von Storch, 2010; Koenigk et al.,

2011; Döscher and Koenigk, 2012). For information on GCM quality in

the Arctic, see Chapter 9 and the brief summary of assessed conﬁdence

in the CMIP5 models in Table 14.2.

The CMIP5 model simulations exhibit an ensemble-mean polar ampli-

ﬁed warming, especially in winter, similar to CMIP3 model simulations

(Bracegirdle and Stephenson, 2012; see also Box 5.1). For RCP4.5,

ensemble-mean winter warming rises to 5.0°C over pan-Arctic land

areas by the end of the 21st century (2081–2100), and about 7.0°C

over the Arctic Sea (Table14.1). Throughout the century, the warming

exceeds simulated estimates of internal variability (Figure AI.8). The

RCP4.5 ensemble-mean warming is more modest in JJA (Table 14.1),

1258

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

reaching about 2.2°C by century’s end over pan-Arctic land areas, and

1.5°C over the Arctic Sea. The summer warming exceeds variability

estimates by about mid-century (Figure AI.9). These simulated anthro-

pogenic seasonal warming patterns match qualitatively the observed

warming patterns over the past six decades (AMAP, 2011), and the

observed warming patterns are likely to be at least partly anthropo-

genic in origin (Section 10.3.1.1.4). Given the magnitude of future

projected changes relative to variability, and the presence of anthro-

pogenic signals already, it is likely future Arctic surface temperature

changes will continue to be strongly inﬂuenced by the anthropogenic

forcing over the coming decades.

The CMIP5 models robustly project precipitation increases in the

pan-Arctic (both land and sea) region over the 21st century, as did

their CMIP3 counterparts (Kattsov et al., 2007; Rawlins et al., 2010).

Under the RCP4.5 scenario, the cold season, ensemble mean precip-

itation increases about 25% by the century’s end (Table 14.1), due

to enhanced precipitation in ETCs (Table 14.3). However, this signal

does not rise consistently above the noise of simulated variability

until mid 21

century (Figure AI.10). During the warm season, precip-

itation increases are smaller, about 15% (Table 14.1), though these

signals also rise above variability by mid 21

century (Figure AI.11). The

inter-model spread in the precipitation increase is generally as large as

the ensemble mean signal itself (similar to CMIP3 model behaviour,

Holland and Webster, 2007), so the magnitude of the future increase

is uncertain. However, since nearly all models project a large precipi-

tation increase rising above the variability year-round, it is likely the

pan-Arctic region will experience a statistically signiﬁcant increase in

precipitation by mid-century (see also Table 14.2). The small projected

increase in the NAO is likely to affect Arctic precipitation (and tem-

perature) patterns in the coming century (Section 14.5.1; Table 14.3),

though the importance of these signals relative to anthropogenic sig-

nals described here is unclear.

In summary: It is likely Arctic surface temperature changes will be

strongly inﬂuenced by anthropogenic forcing over the coming decades

dominating natural variability such as induced by NAO. It is likely the

pan-Arctic region will experience a signiﬁcant increase in precipitation

by mid-century due mostly to enhanced precipitation in ETCs.

14.8.3 North America

The climate of North America (NA) is affected by the following phenom-

ena: NAO (Section 14.5.1), ENSO (Section 14.4), PNA (Section 14.7.2),

PDO (Section 14.7.3), NAMS (Section 14.2.3), TCs and ETCs (Section

14.6). The NAO affects temperature and precipitation over Eastern NA

during winter (Hurrell et al., 2003). Positive PNA brings warmer tem-

peratures to northern Western NA and Alaska in winter, cooler tem-

peratures to the southern part of Eastern NA, and dry conditions to

much of Eastern NA (Nigam, 2003). The PNA can also be excited by

ENSO-related SST anomalies (Horel and Wallace, 1981; Nigam, 2003).

The PDO is linked to decadal climate anomalies resembling those of

the PNA. The NAMS brings excess summer rainfall to Central America

and Mexico and the southern portion of Western NA (Gutzler, 2004).

TCs also impact the Gulf Coast and Eastern NA (see Section 14.6.1).

The AMM and AMO may affect their frequency and intensity (Landsea

et al., 1999; Goldenberg et al., 2001; Cassou et al., 2007; Emanuel,

2007; Vimont and Kossin, 2007; Smirnov and Vimont, 2011). ETCs are

also mainly responsible for winter precipitation, especially in the north-

ern half of NA. See Table 14.3 for a summary of this information.

A general surface warming over NA has been documented over the

last century (see Section 2.4). It is particularly large over Alaska and

northern Western NA during winter and spring and the northern part

of Eastern NA during summer (Zhang et al., 2011b). There is also a

cooling tendency over Central and Eastern NA (i.e., the ‘warming hole’

discussed in Section 2.4.1) during spring, though it is absent in lower

tropospheric temperature (cf. Figure 2.25). The warming has coincided

with a general decline in NA snow extent and depth (Brown and Mote,

2009; McCabe and Wolock, 2010; Kapnick and Hall, 2012). Consistent

with surface temperature trends, temperature extremes also exhibit

secular changes. Cold days and nights have decreased in the last half

century, while warm days and nights have increased (see Chapter 2).

These changes are especially apparent for nightly extremes (Vincent

et al., 2007). It is unclear whether there have been mean precipita-

tion trends over the last 50 years (Section 2.5.1; Zhang et al., 2011b).

However, precipitation extremes increased, especially over Central and

Eastern NA (see Section 2.6.2 and Seneviratne et al., 2012).

Table 14.2 provides an assessment of GCM quality for simulations

of temperature, precipitation, and main phenomena in NA’s regions.

Regarding regional modelling experiments since AR4, biases have

decreased somewhat as resolutions increase. The North American

Regional Climate Change Assessment Program created a simulation

suite for NA at 50-km resolution. When forced by reanalyses, this suite

generally reproduces climate variability within observational error

(Leung and Qian, 2009; Wang et al., 2009b; Gutowski, 2010; Mearns

et al., 2012).Other regional modelling experiments covering parts or

all of NA have shown improvements as resolution increases (Liang et

al., 2008a; Lim et al., 2011; Yeung et al., 2011), including for extremes

(Kawazoe and Gutowski, 2013). Bias reductions are large for snowpack

in topographically complex Western NA, as revealed by 2- to 20-km res-

olution regional simulations (Qian et al., 2010b; Salathe Jr. et al., 2010;

Pavelsky et al., 2011; Rasmussen et al., 2011). Thus there has been

substantial progress since AR4 in understanding the value of regional

modelling in simulating NA climate. The added value of using regional

models to simulate climate change is discussed in Section 9.6.6.

NA warming patterns in RCP4.5 CMIP5 projections are generally sim-

ilar to those of CMIP3 (Figures AI.4 and AI.5, Table 14.1). In winter,

warming is greatest in Alaska, Canada, and Greenland (Figures AI.12

and AI.16), while in summer, maximum warming shifts south, to West-

ern, Central, and Eastern NA. Examining near-term (2016–2035) CMIP5

projections of the less sensitive models (25th percentile, i.e., upper

left maps in Figures AI.12, AI.13, AI.16, AI.17, AI.20 AI.21, AI.24 and

AI.25), the warming generally exceeds natural variability estimates.

Exceptions are Alaska, parts of Western, Central, and Eastern NA, and

Canada and Greenland during winter, when natural variability linked

to wintertime storms is particularly large. By 2046–2065, warming in

all regions exceeds the natural variability estimate for all models. Thus

it is very likely the warming signal will be large compared to natural

variability in all NA regions throughout the year by mid-century. This

warming generally leads to a two- to four fold increase in simulated

heat wave frequency over the 21st century (e.g., Lau and Nath, 2012).

1259

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Anthropogenic climate change may also bring systematic cold-season

precipitation changes. As with previous models, CMIP5 projections

generally agree in projecting a winter precipitation increase over the

northern half of NA (Figure 14.18 and AI.19). This is associated with

increased atmospheric moisture, increased moisture convergence, and

a poleward shift in ETC activity (Section 14.6.2 and Table 14.3). The

change is consistent with CMIP3 model projections of positive NAO

trends (Table 14.3; Hori et al., 2007; Karpechko, 2010; Zhu and Wang,

2010). Winter precipitation increases extend southward into the USA

(northern portions of SREX regions 3 to 5; Neelin et al., 2013) but

with decreasing strength relative to natural variability. This behaviour

is qualitatively reproduced in higher resolution simulations (Figure

14.18).

Warm-season precipitation also exhibits signiﬁcant increases in

Alaska, northern Canada, and Eastern NA by century’s end (Figures

14.18, AI.19, AI.22). However, CMIP5 models disagree on the sign

of the precipitation change over the rest of NA (Figures AI.26 and

AI.27), consistent with CMIP3 results (Figure 14.18; Neelin et al., 2006;

Rauscher et al., 2008; Seth et al., 2010). One set of high resolution

simulatons (Endo et al., 2012) shows a tendency towards more precipi-

tation than either CMIP3 or CMIP5 models (Figure 14.18), suggesting

the simulated warm-season precipitation change in the region may be

resolution-dependent. Future precipitation changes associated with

the NAMS are likewise uncertain, though there is medium conﬁdence

the phenomenon will move to later in the annual cycle (Section 14.2.3,

Table 14.3). As there is medium conﬁdence tropical cyclones will be

associated with greater rainfall rates, the Gulf and East coasts of NA

may be impacted by greater precipitation when tropical cyclones occur

(Table 14.3).

CMIP3 models showed a 21st century precipitation decrease across

much of southwestern NA, accompanied by a robust evaporation

increase characteristic of mid-latitude continental warming (Seager

et al., 2007; Seager and Vecchi, 2010) and an increase in drought fre-

quency (Shefﬁeld and Wood, 2008; Gutzler and Robbins, 2011). When

downscaled, CMIP3 models showed less drying in the region (Gao

et al., 2012c) and an extreme precipitation increase, despite overall

drying (Dominguez et al., 2012). CMIP5 models do not consistently

show such a precipitation decrease in this region (Neelin et al., 2013).

This is one of the few emerging differences between the two ensem-

bles in climate projections over NA. However, the CMIP5 models

still show a strong decrease in soil moisture here (Dai, 2013), due to

increasing evaporation.

In summary, it is very likely that by mid-century the anthropogenic

warming signal will be large compared to natural variability such as

that stemming from the NAO, ENSO, PNA, PDO, and the NAMS in all

Figure 14.18 | Maps of precipitation changes for North America in 2080–2099 with respect to 1986–2005 in June, July and August (above) and December to February (below)

in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes in 2075–2099 with

respect to 1979–2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2)

multi-physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light

hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of

models (or members) have the same sign with the ensemble mean changes.

1260

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

NA regions throughout the year. It is likely that the northern half of NA

will experience an increase in precipitation over the 21st century, due

in large part to a precipitation increase within ETCs.

14.8.4 Central America and Caribbean

The Central America and the Caribbean (CAC) region is affected by

several phenomena, including the ITCZ (Section 14.3.1.1), NAMS (Sec-

tion 14.2.3.1), ENSO (Section 14.4) and TCs (Section 14.6.1; Table 14.3;

also Gamble and Curtis, 2008). The annual cycle results from air–sea

interactions over the Western Hemisphere warm pool in the tropical

eastern north Paciﬁc and the Intra Americas Seas (Amador et al., 2006;

Wang et al., 2007). The Caribbean Low Level Jet is a key element of the

region’s summer climate (Cook and Vizy, 2010) and is controlled by

the size and intensity of the Western Hemisphere warm pool (Wang et

al., 2008b). It is also modulated by SST gradients between the eastern

equatorial Paciﬁc and tropical Atlantic (Taylor et al., 2011d). ENSO is

the main driver of climate variability, with El Niño being associated

with dry conditions and La Niña with wet conditions (Karmalkar et

al., 2011). Other teleconnection patterns, such as the NAO (Section

14.5.1) and the strength of boreal winter convection over the Amazon,

inﬂuence trade winds over the Tropical North Atlantic and can combine

with ENSO to modulate the summer Western Hemisphere warm pool

(e.g., Enﬁeld et al., 2006). Table 14.3 summarizes the main phenomena

and their relevance to climate change over the CAC.

Because inter-decadal climate variations can be large in the CAC

region, precipitation trends must beinterpreted carefully. From 1950

to 2003, negative trends were seen in several data sets in the Carib-

bean region and parts of Central America (Neelin et al., 2006). How-

ever, regarding secular trends (1901–2005), this signal was identiﬁed

only in the Caribbean region (Trenberth et al., 2007b). Prolonged dry

or wet periods are related to decadal variability of the adjacent Paciﬁc

and Atlantic (Mendoza et al., 2007; Seager et al., 2009; Mendez and

Magaña, 2010), and the intensity of easterlies over the region. For

instance, increased easterly surface winds over Puerto Rico from 1950

to 2000 disrupted a pattern of inland moisture convergence, leading to

a dramatic precipitation decrease (Comarazamy and Gonzalez, 2011).

Table 14.2 provides an overall assessment of GCM quality for simula-

tions of temperature, precipitation and main phenomena in the CAC

sub-regions. Annual cycles of temperature and precipitation are well

Figure 14.19 | Maps of precipitation changes for Central America and Caribbean in 2080–2099 with respect to 1986–2005 in June to September (above) and December to

March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes in

2075–2099 with respect to 1979–2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model

3.2 (AGCM3.2) multi-physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each

scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more

than 90% of models (or members) have the same sign with the ensemble mean changes.

1261

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

simulated by CMIP5 models, though precipitation from June to Octo-

ber is underestimated (Figure 9.38). Regional models also simulate

temperature and precipitation climatologies, and the magnitude and

annual cycle of the Caribbean Low-Level Jet reasonably well (Campbell

et al., 2010; Taylor et al., 2013).

CMIP3 models generally projected a precipitation reduction over much

of the Caribbean region, consistent with the observed negative trend

since 1950 (Neelin et al., 2006; Rauscher et al., 2008). The subtropics

are generally expected to dry as global climate warms (Held and Soden,

2006), but in both CMIP3 and CMIP5 models the CAC region shows the

greatest drying. Future drying may also be related to strengthening

of the Caribbean Low-Level Jet (Taylor et al., 2013) and subsidence

over the Caribbean region associated with warmer SSTs in the tropical

Paciﬁc than Atlantic (Taylor et al., 2011d). A high-resolution regional

Ocean GCM using a CMIP3 ensemble for boundary conditions conﬁrms

that the Intra American Seas circulation weakens by similar rate as the

reduction in Atlantic Meridional Overturning (Liu et al., 2012c). This

weakening causes the Gulf of Mexico to warm less than other oceans.

Downscaling experiments for the region have shown a mid-21st centu-

ry warming between 2°C and 3°C (Vergara et al., 2007; Rauscher et al.,

2008; Karmalkar et al., 2011). Precipitation decreases over most of the

CAC region, similar to the signal in driving global models (Campbell et

al., 2010; Hall et al., 2012). However, only a few downscaling studies

took into account key elements of the region’s climate, such as east-

erly wave activity, TCs, or interannual variability mechanisms linked to

ENSO (Karmalkar et al., 2011).

By century’s end, CMIP5 models project greatest warming in the CAC

region in JJA. Warming is projected to be larger over Central America

than the Caribbean in summer and winter (Figures AI.24, AI.2, Table

14.1). From October to March, ensemble mean projections indicate pre-

cipitation decrease in northern Central America, including Mexico. In the

Caribbean precipitation is projected to decrease in the south (consistent

with the observed trends) but to increase in the north (Figure AI.26).

From April to September, the projected zone of precipitation reduction

expands over the entire CAC region, and this signal is generally larger

than the models’ estimates of natural variability (Figure AI.27). Precipi-

tation changes projected by CMIP3, CMIP5 and a high-resolution model

show a similar reduction in parts of Mexico and the southern Caribbe-

an in DJFM, and in Central America and the Caribbean in JJAS (Figure

14.19). The CMIP5 ensemble shows greater agreement in the DJFM pre-

cipitation increase in the northern Caribbean sector than CMIP3. These

projected changes are also reﬂected in Table 14.1. Figures AI.26, AI.27

and Figure 14.19 suggest an intensiﬁcation and southward displace-

ment of the East Paciﬁc ITCZ, which can contribute to drying in southern

Central America (Karmalkar et al., 2011).

ENSO will continue to inﬂuence CAC climate, but changes in ENSO

frequency or intensity remain uncertain (Section 14.4). Projected drier

conditions may also be related to decreased frequency of TCs, though

the associated rainfall rate of these systems are higher in future pro-

jections (Section 14.6.1).

In summary, owing to model agreement on projections and the

degree of consistency with observed trends, it is likely warm-season

precipitation will decrease in the Caribbean region, over the coming

century. However, there is only medium conﬁdencethat CentralAmer-

icawill experience a decrease in precipitation.

14.8.5 South America

South America (SA) is affected by several climate phenomena. ENSO

(Section 14.4) and Atlantic Ocean modes (Section14.3.4) have a role

in interannual variability of many regions. The SAMS (Section 14.2.3.2)

is responsible for rainfall over large areas, while the SACZ (Section

14.3.1.3) and Atlantic ITCZ (Section 14.3.1.1) also affect precipitation.

Teleconnections such as the PSA (Section 14.7.1), the SAM (Section

14.5.2) with related ETCs (Section 14.6.2)and the IOD (Section 14.3.3)

also inﬂuence climate variability. Table 14.3 summarizes the main phe-

nomena and their assessed relevance to climate change over SA.

Positive minimum temperature trends have been observed in SA

(Alexander et al., 2006; Marengo and Camargo, 2008; Rusticucci and

Renom, 2008; Marengo et al., 2009; Seneviratne et al., 2012; Skansi et

al., 2013). Glacial retreat in the tropical Andes was observed in the last

three decades (Vuille et al., 2008; Rabatel et al., 2013). In contrast to

the warming over the continental interior, a prominent but localized

coastal cooling was detected during the past 30 to 50 years, extending

from central Peru (Gutiérrez et al., 2011) to northern (Schulz et al.,

2012) and central Chile (Falvey and Garreaud, 2009). Observed pre-

cipitation changes include a signiﬁcant increase in precipitation during

the 20th century over the southern sector of southeastern SA, a nega-

tive trend in SACZ continental area (Section 2.5.1; Barros et al., 2008),

a negative trend in mean precipitation and precipitation extremes in

central-southern Chile, and a positive trend in southern Chile (Haylock

et al., 2006; Quintana and Aceituno, 2012). Other detected changes

include positive extreme precipitation trends in southeastern SA, cen-

tral-northern Argentina and northwestern Peru and Ecuador (Section

2.6.2; Haylock et al., 2006; Dufek et al., 2008; Marengo et al., 2009; Re

and Barros, 2009; Skansi et al., 2013).

Table 14.2 provides an overall assessment of GCM quality for simula-

tions of temperature, precipitation and main phenomena in the sub-re-

gions of SA. In general, GCM results are consistent with observed tem-

perature tendencies (e.g., Haylock et al., 2006). Trends toward warmer

nights in CMIP3 models (Marengo et al., 2010b; Rusticucci et al., 2010)

are consistent with observed trends. CMIP3 models, however, do not

simulate the cooling ocean and warming land trends observed in the

last 30 years along subtropical western SA noted above. The number of

warm nights in SA is well represented in CMIP5 simulations (Sillmann

et al., 2013). CMIP5 models reproduce the annual cycle of precipitation

over SA, though the multi-model mean underestimates rainfall over

some areas (Figure 9.38). In tropical SA, rainy season precipitation is

better reproduced in the CMIP5 ensemble than CMIP3 (Figure 9.39).

CMIP3 models were able to simulate extreme precipitation indices

over SA (Rusticucci et al., 2010), but CMIP5 models improved them

globally (Sillmann et al., 2013). CMIP5 also improved simulations

of precipitation indices in the SAMS region (Kitoh et al., 2013; Sec-

tion 14.2.3.2). The main precipitation features are well represented

by regional models in several areas of SA (Solman et al., 2008; Alves

and Marengo, 2010; Chou et al., 2012; Solman et al., 2013). However,

regional models underestimate daily precipitation intensity in the La

1262

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Plata Basin and in eastern Northeastern SA in DJF and almost over the

whole continent in JJA (Carril et al., 2012).

Regarding future projections, CMIP5 models indicate higher temper-

atures over all of SA, with the largest changes in southeastern Ama-

zonia by century’s end. (Figures AI.28, AI.29, Table 14.1). Temperature

changes projected by RCMs forced by a suite of CMIP3 models agree

thatthe largest warming occurs over the southern Amazon during aus-

tral winter. Regional models project a greater frequency of warm nights

over SA, except in parts of Argentina, and a reduction of cold nights

over the whole continent (Marengo et al., 2009). CMIP5 projections

conﬁrm the results of CMIP3 in AR4 and SREX (see Section 12.4.9).

CMIP5 results conﬁrm precipitation changes projected by CMIP3

models in the majority of SA regions, with increased conﬁdence, as

more models agree in the changes (AI.30, AI.34, AI31, AI35). Inter-mod-

el spread in precipitation also decreased in some SA regions from

CMIP3 to CMIP5 (Blázquez and Nuñez, 2012). CMIP5 precipitation

Figure 14.20 | Maps of precipitation changes for South America in 2080–2099 with respect to 1986–2005 in June to September (above) and December to March (below) in the

SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes in 2075–2099 with respect

to 1979–2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)- Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-

physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching

denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or

members) have the same sign with the ensemble mean changes.

1263

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

projections for the end of the twenty-ﬁrst century (2081–2100) show a

precipitation increase from October to March over the southern part of

Southeast Brazil and the La Plata Basin, the extreme south of Chile, the

northwest coast of SA, and the Atlantic ITCZ, extending to a small area

of the northeastern Brazil coast (Figures AI.30 and AI.34). Reduced

October to March rainfall isprojected in the extreme northern region

of SA, eastern Brazil, and central Chile. In eastern Amazonia and north-

eastern Brazil, CMIP5 models show both drying and moistening. This

uncertainty can also be seen in Table 14.1.

Figure 14.20 conﬁrms that changes in northwestern, southwestern and

southeastern SA are consistent among CMIP3 and CMIP5 ensembles

and a high-resolution model ensemble, which gives more conﬁdence in

these results. However, in eastern Amazonia and northeast and eastern

Brazil, there is less agreement. Results from high-resolution or regional

models forced by CMIP3 models provides further indication the pro-

jected changes are robust. A high-resolution regional model ensem-

ble projects precipitation increases during austral summer over the La

Plata Basin region, northwestern SA and southernmost Chile, and a

decrease over northern SA, eastern Amazonia, eastern Brazil, central

Chile and the Altiplano (Figure 14.21). Other regional models also pro-

ject a precipitation increase over the Peruvian coast and Ecuador and

a reduction in the Amazon Basin (Marengo et al., 2010b; Marengo et

al., 2012).

From April to September, the CMIP5 ensemble projects precipitation

increases over the La Plata Basin and northwestern SA near the coast

(Figures AI31, AI35). In contrast, a reduction is projected for northeast

-40

-20

-80

-60

-40

-20

ETA HADCM3

LMDZ IPSL

REMO ECHAM5

PROMES HADCM3

REGCM3 ECHAM5

RECGM3 HADCM3

RCA ECHAM5 1

RCA ECHAM5 2

RCA ECHAM5 3

LMDZ ECHAM5

Precipitation change A1B JJA

Precipitation change A1B DJF

-50

-40

-30

-20

-10

-50

-40

-30

-20

-10

(a)

(b)

(c)

(d)

SESA SACZ S-Amazonia

Figure 14.21 | (a) December, January and February (DJF) and (b) June, July and August (JJA) relative precipitation change in 2071–2100 with respect to 1961–1990 in the A1B

scenario from an ensemble of 10 Regional Climate Models (RCMs) participating in the Europe–South America Network for Climate Change Assessment and Impact Studies-La

Plata Basin (CLARIS-LPB) Project. Hatching denotes areas where 8 out of 10 RCMs agree in the sign of the relative change. (c) DJF and (d) JJA dispersion among regional model

projections of precipitation changes averaged over land grid points in Southeastern South America (SESA, 35°S to 25°S, 60°W to 50°W), South Atlantic Convergence Zone (SACZ,

25°S to 15°S, 45°W to 40°W) and southern Amazonia (15°S to 10°S, 65°W to 55°W), indicated by the boxes in (a).

1264

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Brazil and eastern Amazonia. Precipitation is projected to decrease in

Central Chile, but to increase over extreme southern areas. In CMIP3

models, a precipitation reduction in the central Andes resulted from a

moisture transport decrease from the continental interior to the Alti-

plano(Minvielle and Garreaud, 2011). CMIP3 and CMIP5 models are

consistent in projecting drier conditions in eastern Amazonia during

the dry season and wetter conditions in western Amazonia (Malhi et

al., 2008; Cook et al., 2011). The Amazon forest’s future is discussed in

Section 12.5.5.6.1. Areas of maximum change in CMIP5 are consistent

with those of CMIP3 in JJA, agreeing also with a high-resolution model

ensemble (Figure 14.20). Increased precipitation in southeastern SA

is projected by a high-resolution model ensemble in all four seasons

(Blázquez et al., 2012). The austral winter precipitation increase over

the La Plata Basin and southern Chile, and the reduction in eastern

Amazonia and northeast Brazil, are also projected by RCMs (Figure

14.21) as in CMIP5 models. A relevant result from a RCM is the pre-

cipitation decrease over most of SA north of 20°S in austral spring,

suggesting a longer dry season (Sörensson et al., 2010; see also Section

14.2.3.2). Note that average CMIP5 spatial values in Table 14.1 are

consistent with changes seen in the maps, unless for the west coast of

SA, where there are spatial variations within the area and the values

do not reﬂect the changes.

Regional model projections and a high-resolution model ensemble

indicate an increase in the number of consecutive dry days in north-

eastern SA (Marengo et al., 2009; Kitoh et al., 2011). An increase in

heavy precipitation events over almost the entire continent, especially

Amazonia, southern Brazil and northern Argentina, is projected by a

high-resolution model ensemble (Kitoh et al., 2011) and in subtropical

areas of South America by regional models (Marengo et al., 2009).

Seneviratne et al. (2012) indicated low to medium conﬁdence in CMIP3

SA precipitation trends. However, the increased ability of CMIP5 models

to represent extremes (Kitoh et al., 2013) provides higher conﬁdence in

the signals discussed above (Section 14.2.3.2), consistent with global

changes in land areas (Section 12.4.5).

Precipitation changes projected over SA are consistent with El Niño

inﬂuences,for example, rainfall increase over southeastern and north-

western SA and decrease over eastern Amazonia. However, CMIP3

models could not represent certain features of ENSO well (Roxy et al.,

2013) and there is no consensus about future ENSO behaviour (Coelho

and Goddard, 2009; Collins et al., 2010) even with CMIP5 results (Sec-

tion 14.4). As the various types of ENSO produce different impacts on

SA (Ashok et al., 2007; Hill et al., 2011; Tedeschi et al., 2013), future

ENSO effects remain uncertain. It is very likely that ENSO remains the

dominant mode of interannual variability in the future (Section 14.4.2).

Therefore, regions in SA currently inﬂuenced by Paciﬁc SST will contin-

ue to experience ENSO effects on precipitation and temperature.

Projected precipitation increases in the southern sector of south-

eastern SA are consistent with changes in the SACZ dipole (Section

14.3.1.3) and PSA (Section 14.7.1). Increased precipitation in this

region may also have a contribution from a more frequent and intense

Low Level Jet(Nuñez et al., 2009; Soares and Marengo, 2009). CMIP3

model analyses show little impact on extreme precipitation from SAM

changes toward century’s end, except in Patagonia (Menendez and

Carril, 2010). However, the southward shift of stormtracks associated

with the SAM’s projected positive trend (Reboita et al., 2009; Sec-

tion 14.5.2) impacts zones of cyclogenesis off the southeast SA coast

(Kruger et al., 2011; Section 12.4.4 ).

In summary, it is very likely temperatures will increase over the whole

continent, with greatest warming projected in southern Amazonia. It is

likely there will be an increase (reduction) in frequency of warm (cold)

nights in most regions. It is very likely precipitation willincrease in the

southern sector of southeastern and northwestern SA, and decrease

in Central Chile and extreme north of the continent. It is very likely

that less rainfall will occur in eastern Amazonia, northeast and east-

ern Brazil during the dry season. However, in the rainy season there

is medium conﬁdence in the precipitation changes over these regions.

There is high conﬁdence in an increase of precipitation extremes.

14.8.6 Europe and Mediterranean

This section assesses regional climate change in Europe and the North

African and West Asian rims of the Mediterranean basin. Area-average

summaries are presented for the three sub-regions of Northern Europe

(NEU), Central Europe (CEU) and Mediterranean (MED) (cf. Tables 14.1

to 14.2).

The most relevant climate phenomena for this region are NAO (Section

14.5.1), ETCs (Section 14.6.2) and blocking (Box 14.2, Folland et al.,

2009; Feliks et al., 2010; Dole et al., 2011; Mariotti and Dell’Aquila,

2012). These phenomena also interact with longer time-scale North

Atlantic ocean-atmosphere phenomena such as the AMO (Section

14.7.6, Mariotti and Dell’Aquila, 2012; Sutton and Dong, 2012). Other

phenomena have minor inﬂuence in limited sectors of the region (see

Supplementary Material Section 14.SM.6.3).

Recent 1981-2012 trends in annual mean temperature in each sub-

region exceed the global mean land trend as can be inferred from

Figure 2.22. Consistent with previous AR4 conclusions (Section 11.3),

recent studies of extreme events (Section 2.6.1) point to a very likely

increase of the number of warm days and nights, and decrease of the

number of cold days and nights, since 1950 in Europe. Heat waves

can be ampliﬁed by drier soil conditions resulting from warming (Vau-

tard et al., 2007; Seneviratne et al., 2010; Hirschi et al., 2011). Several

studies (Section 2.6.2.1) also indicate general increases in the intensity

and frequency of extreme precipitation especially in winter during the

last four decades however there are inconsistencies between studies,

regions and seasons.

The ability of climate models to simulate the climate in this region

has improved in many important aspects since AR4 (see Figure 9.38).

Particularly relevant for this region are increased model resolution and

a better representation of the land surface processes in many of the

models that participated in the recent CMIP5 experiment. Table 14.2

provides an assessment of the CMIP5 quality for simulations of tem-

perature, precipitation, and main phenomena in the region. The CMIP5

projections reveal warming in all seasons for the three sub-regions,

while precipitation projections are more variable across sub-regions

and seasons. In the winter half year (October to March), NEU and CEU

are projected to have increased mean precipitation associated with

increased atmospheric moisture, increased moisture convergence and

1265

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

intensiﬁcation in ETC activity (Section 14.6.2 and Table 14.3) and no

change or a moderate reduction in the MED. In the summer half year

(April to September) , NEU and CEU mean precipitation are projected

to have only small changes whereas there is a notable reduction in

MED (see Table 14.1, Figures AI.36 to AI.37 and AI.42-AI.43). Figure

14.22 illustrates that the precipitation changes are broadly consistent

with the ﬁndings CMIP3.

High-resolution projections from the Japanese high-resolution model

ensemble also agree with these ﬁndings and are consistent with

downscaling results from coordinated multi-model GCM/RCM exper-

iments (e.g., ENSEMBLES, Déqué et al., 2012). In general, regional cli-

mate change amplitudes for temperature and precipitation follow the

global warming amplitude although modulated both by changes in the

large-scale circulation and by regional feedback processes (Kjellstrom

et al., 2011), which conﬁrms assessments in AR4 (Christensen et al.,

2007).

Some new investigations have focussed on the uncertainties associat-

ed with model projections. A large ensemble of RCM-GCM shows that

the temperature response is robust in spite of a considerable uncer-

tainty related to choice of model combination (GCM/RCM) and sam-

pling (natural variability), even for the 2021–2050 time frame (Déqué

et al., 2012). Other studies based on CMIP3 projections suggest that

GHG-forced changes in the MED are likely to become distinguisha-

ble from the ‘noise’ created by internal decadal variations in decades

beyond 2020–2030 (Giorgi and Bi, 2009). It has been also shown using

an ensemble of RCM simulations that the removal of NAO-related var-

iability leads to an earlier emergence of change in seasonal mean tem-

peratures for some regions in Europe (Kjellström et al., 2013). Hence, in

the near term, decadal predictability is likely to be critically dependent

on the regional impacts of modes of variability ‘internal’ to the climate

system (Section 11.3). However, it has been shown that NAO trends do

not account for a large fraction of the long-term future change in mean

temperature or precipitation (Stephenson et al., 2006) and that large-

scale atmospheric circulation changes in CMIP5 models are not the

main driver of the warming projected in Europe by the end of the cen-

tury (2081–2100; Cattiaux et al., 2013). Therefore, changes in climate

phenomena contribute to the uncertainty in the near-term projections

rather than long-term changes in this region (Table 14.3), further sup-

porting the credibility in model projection (Table 14.2).

Recent studies have clearly identiﬁed a possible ampliﬁcation of tem-

perature extremes by changes in soil moisture (Jaeger and Seneviratne,

Figure 14.22 | Maps of precipitation changes for Europe and Mediterranean in 2080–2099 with respect to 1986–2005 in June to August (above) and December to Febru-

ary (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes in

2075–2099 with respect to 1979–2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation

Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air

temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense

hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes.

1266

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

2010; Hirschi et al., 2011), acting as a mechanism that further mag-

niﬁes the intensity and frequency of heat waves given the projected

enhance of summer drying conditions. This is in line with the assessed

results presented in SREX (Seneviratne et al., 2012). At the other end

of the spectrum, studies indicate that European winter variability may

be related to sea ice reductions in the Barents-Kara Sea (Petoukhov

and Semenov, 2010) and CMIP5 models in projections for the future in

general exhibit a similar relation until the summer sea ice has almost

disappeared (Yang and Christensen, 2012). Although the mechanism

behind this relation remains unclear this suggests that cold winters

in Europe will continue to occur in coming decades, despite an overall

warming.

Although climate models have improved ﬁdelity in simulating aspects

of regional climates over Europe and the Mediterranean, the spread in

projections is still substantial, partly due to large amounts of natural

variability in this region (particularly NAO and AMO), besides the inher-

ent model deﬁciencies .

In summary, there is high conﬁdence in model projections of mean

temperature in this region. It is very likely that temperatures will con-

tinue to increase throughout the 21st century over all of Europe and

the Mediterranean region. It is likely that winter mean temperature

will rise more in NEU than in CEU or MED, whereas summer warming

will likely be more intense in MED and CEU than in NEU. The length,

frequency, and/or intensity of warm spells or heat waves are assessed

to be very likely to increase throughout the whole region. There is

medium conﬁdence in an annual mean precipitation increase in NEU

and CEU, while a decrease is likely in MED summer mean precipitation.

14.8.7 Africa

The African continent encompasses a variety of climatic zones. Here

the continent is divided into four major sub-regions: Sahara (SAH),

Western Africa (WAF), Eastern Africa (EAF) and Southern Africa (SAF).

A ﬁfth Mediterranean region to the north of Sahara is discussed in

Section 14.8.6. In tropical latitudes, rainfall follows insolation (this sim-

pliﬁed picture is modiﬁed by the presence of orography, especially in

the Great Horn of Africa, the geography of the coastline, and by the

oceans). The most relevant phenomena affecting climate variability are

the monsoons (Section 14.2.4), ENSO (Section 14.4), Indian and Atlan-

tic Ocean SSTs (IOD, Section 14.3.3; AMM Section 14.3.4; AMO Section

14.7.6) and the atmospheric Walker Circulation (Section 2.7.5). Tropical

cyclones impact East African and Madagascan coastal regions (Section

14.6.1) and ETCs clearly impact southern Africa (Section 14.6.2).

Sub-Saharan Sahelian climate is dominated by the monsoonal system

that brings rainfall to the region during only one season (Polcher et al.,

2011). Most of the rain between May/June and September comes from

mesoscale ‘squall line’ systems that travel short distances in their life-

time (~1000 to 2000 km), and whose distribution is somewhat modi-

ﬁed by the synoptic scale African Easterly Wave (Ruti and Dell’Aquila,

2010). The onset of the rainy season in West Africa is a key parame-

ter triggering changes in the vegetation and surface properties, that

implies feedbacks to the local atmospheric heat and moisture cycle.

The length and frequency of dry spells as well as the length or cumu-

lated rainfall of the season also affect this. All are affected by a large

interannual variability (Janicot et al., 2011). When evaluating models

their ability to reproduce such characteristics of the African monsoon

is essential. A large effect of natural multi-decadal SST and warming of

the oceans on Sahel rainfall is very likely (Hoerling et al., 2006; Ting et

al., 2009, 2011; Mohino et al., 2011; Rodriguez-Fonseca et al., 2011).

East Africa experiences a semi-annual rainfall cycle, driven by the ITCZ

movement across the equator. Direct links between the region’s rainfall

and ENSO have been demonstrated (Giannini et al., 2008) and refer-

ences therein), but variations in Indian Ocean SST (phases of the IOD)

are recognized as the dominant driver of east African rainfall variability

(Marchant et al., 2007). This feature acts to enhance rainfall through

either anomalous low-level easterly ﬂow of moist air into the continent

(Shongwe et al., 2011), or a weakening of the low-level westerly ﬂow

over the northern Indian Ocean that transports moisture away from

the continent (Black et al., 2003). Although the effect of the IOD is

evident in the short rainy season, Shongwe et al. (2011) do not ﬁnd a

similar relationship for the long rains. Williams and Funk (2011), how-

ever, argue for a reduction in the long rains over Kenya and Ethiopia in

response to warmer Indian Ocean SSTs.

Variability in southern Africa’s climate is strongly inﬂuenced by its adja-

cent oceans (Rouault et al., 2003; Hansingo and Reason, 2008, 2009;

Hermes and Reason, 2009) as well as by ENSO (Vigaud et al., 2009;

Pohl et al., 2010). Although it is generally observed that El Niño events

correspond to conditions of below-average rainfall over much of south-

ern Africa (Mason, 2001; Giannini et al., 2008; Manatsa et al., 2008) the

ENSO teleconnection is not linear, but rather has complex inﬂuence in

which a number of regimes of local rainfall response can be identiﬁed

(Fauchereau et al., 2009). The extreme southwestern parts of southern

Africa receive rainfall in austral winter brought by mid-latitude frontal

systems mostly associated with passing ETCs, but the majority of the

region experiences a single summer rainfall season occurring between

November and April. A semi-permanent zone of sub-tropical conver-

gence is a major contributor to summer rainfall in sub-tropical southern

Africa (Fauchereau et al., 2009; Vigaud et al., 2012).

Because of its exceptional magnitude and its clear link to global SST,

20th century decadal rainfall variability in the Sahel is a test of GCMs

ability to produce realistic long-term changes in tropical precipitation.

Despite biases in the region (Cook and Vizy, 2006) the CMIP3 coupled

models overall can capture the observed correlation between Sahel

rainfall and basin-wide area averaged SST variability (Biasutti et al.,

2008) even though individual models may fail, especially at interan-

nual time scales (Lau et al., 2006; Joly et al., 2007). Recently, Ackerley

et al. (2011) used a perturbed physics ensemble and reached a similar

result for the role of atmospheric sulphate, conﬁrming previous results

(Rotstayn and Lohmann, 2002; Held et al., 2005). Since AR4, only lim-

ited information about improved performance has been documented

and only in WAF have major efforts been focussing on relating model

behaviour with ability to simulate local climate processes in such

details. However, in a comparative study of the ability of CMIP3 and

CMIP5 to simulate multiple SST–Africa teleconnections, Rowell (2013)

found varying degrees of success in simulating these. In particular, no

clear indication of an improvement in the CMIP5 models vs. the CMI3

models was identiﬁed.

1267

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

In projections of the 21st century, the CMIP3 models produced both

signiﬁcant drying and signiﬁcant moistening (Held et al., 2005; Biasutti

and Giannini, 2006; Cook and Vizy, 2006; Lau et al., 2006), and the

mechanisms by which a model dries or wets the Sahel are not fully

understood (Cook, 2008). At least qualitatively, the CMIP3 ensem-

ble simulates a more robust response during the pre-onset and the

demise portion of the rainy season (Biasutti and Sobel, 2009; Seth et

al., 2011). Rainfall is projected to decrease in the early phase of the

seasons—implying a small delay in the main rainy season, but is pro-

jected to increase at the end of the season—implying an intensiﬁca-

tion of late-season rains (d’Orgeval et al., 2006), although this appears

to be less robust in the CMIP5 models (Section 14.2.4). Projections of a

change in the timing of the rains is common to other monsoon regions

(Li et al., 2006; Biasutti and Sobel, 2009; Seth et al., 2011), including

southern Africa.

The relevance of a local effect is supported by several lines of evidence.

There is observational evidence that local soil moisture gradients can

trigger convective systems and that these surface contrasts are as

important as topography for generating these systems, which bring

most of the rain to the region (Taylor et al., 2011a, 2011b). Additional

evidence comes from simulations of future rainfall changes in West

Africa by RCMs subject to coupled model-derived boundary conditions

(Patricola and Cook, 2010), documenting a wetting response of the

Sahel to increased GHG in the absence of other forcings. But the rela-

tive importance of this effect versus the response to SST trends is not

well quantiﬁed, mostly due to the limitation of using a single RCM.

An evaluation of six GCMs over East Africa by Conway et al. (2007)

reveals no clear multi-model trend in mean annual rainfall by the

2080s, but some indications of increased SON and decreased March,

Figure 14.23 | Maps of precipitation changes for Africa in 2080–2099 with respect to 1986–2005 in June to September (above) and December to March (below) in the SRES

A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes in 2075–2099 with respect

to 1979–2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric Generl Circulation Model 3.2 (AGCM3.2) multi-

physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in

each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where

more than 90% of models (or members) have the same sign with the ensemble mean changes.

1268

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

April and May (MAM) rainfall are noted. They found inconsisten-

cy in how the models represent changes in the IOB and consequent

changes in rainfall over East Africa. Shongwe et al. (2011) analysed an

ensemble of 12 CMIP3 GCMs (forced with A1B emissions). They found

widespread increases in short season (OND) rainfall including extreme

precipitation across the region, with statistically signiﬁcant ensemble

mean increases. For the long rains (MAM), similar changes in the sign

and magnitude of mean and extreme seasonal rainfall were seen, but

model skill in simulating the MAM season is relatively poor. The chang-

es shown for the short rains are consistent with a differential warming

of 21st century Indian Ocean SSTs, which leads to a positive IOD-like

state (see Section 14.3.3). The atmospheric consequence of this is a

weakening of the descending branch of the East African Walker Cell

and enhancement of low-level moisture convergence over east Africa

(Vecchi and Soden, 2007a; Shongwe et al., 2011).

In an assessment of 19 CMIP3 models run with the A1B emissions

forcing, Giannini et al. (2008) note a tendency toward a persistent El

Niño-like pattern (see Section 14.4) in the equatorial Paciﬁc along with

a decrease in rainfall over southern Africa. Dynamical downscaling

of a single GCM by Engelbrecht et al. (2011) shows—for the austral

winter—an intensiﬁcation of the southern edge of the subtropical high

pressure belt resulting in southward displacement of the mid-latitude

systems that bring frontal rain to the south western parts of the con-

tinent, thus resulting in decrease in rainfall. The decrease in summer

rainfall is consistent with high-resolution (18 km) RCM simulations

done by Haensler et al. (2011) which indicate widespread reductions in

rainfall over southern Africa under the A1B scenario.

Shongwe et al. (2009) identiﬁed reduction in spring (SON) rainfall

throughout the eastern parts of southern Africa. There is good consen-

sus amongst the models used, with the spring anomalies indicating a

trend toward later onset of the summer rainy season. Autumn (MAM)

reductions are shown for most of southern Africa while eastern South

Africa experiences no change and eastern parts of southern Africa

show a small increase.

Table 14.2 provides an overall assessment of CMIP5 quality for simula-

tions of temperature, precipitation, and main phenomena in the differ-

ent sub-regions of Africa. Overall, conﬁdence in the projected precipi-

tation changes is at best medium. This is owing to the overall modest

ability of models to capture the most important phenomena having a

strong control on African climates (Table 14.3).

The ability of climate models to simulate historical climate, its change,

and its variability, has improved in many aspects since the AR4 (see

Section 9.6.1). But for Africa there is no clear evidence that the modest

increase in resolution and a better representation of the land surface

processes in many CMIP5 models have resulted in marked improve-

ments (e.g., Figure 9.39). The CMIP5 models projection for this century

is further warming in all seasons in the considered four sub-regions,

while precipitation show some distinct sub-regional and seasonally

dependent changes. In the October to March half year all four regions

are projected to receive practically unaltered precipitation amounts by

2081–2100, although somewhat elevated in RCP8.5. In the April to

September half year SAH, WAF and EAF will experience little change

but a quite notable reduction in SAF is projected (see Table 14.1,

Figures AI.40 to AI.51). This is consistent with the results from CMIP3

as depicted in Figure 14.23 in the West African monsoon wet season

and austral summer. High resolution information provided by the Japa-

nese high-resolution model ensemble also matches this ﬁnding.

In summary, given models’ ability to capture local processes, large

scale climate evolution and their linkages, it is very likely that all of

Africa will continue to warm during the 21st century. The overall qual-

ity of the CMIP5 models imply, that SAH already very dry is very likely

to remain very dry. But there is low conﬁdence in projection statements

about drying or wetting of WAF. Owing to models’ ability to capture

the overall monsoonal behaviour, there is medium conﬁdence in pro-

jections of a small delay in the rainy season with an increase at the

end of the season. There is medium conﬁdence in projections showing

little change in mean precipitation in EAF and reduced precipitation in

the Austral winter in SAF, as models tend to represent Indian Ocean

SST developments with credibility. Likewise, increasing rainfall in EAF

is likely for the short rainy season, but low conﬁdence exists in projec-

tions regarding drying or wetting in the long rainy season.

14.8.8 Central and North Asia

This area mostly covering the interior of a large continent extending

from the Tibetan plateau to the Arctic is mainly inﬂuenced by weath-

er systems coming from the west or south, giving some dependency

on the AAM (Section 14.2.2) on the one hand and NAO/NAM (Sec-

tion 14.5.1) on the other, with associated atmospheric blocking as an

additional phenomenon of inﬂuence related to the latter (Box 14.2). In

particular, the variability and long-term change of the climate system

in central Asia and northern Asia are closely related to variations of

the NAO and NAM (Takaya and Nakamura, 2005; Knutson et al., 2006;

Popova and Shmakin, 2010; Sung et al., 2010; Table 14.3).

As a part of the polar ampliﬁcation, large warming trends in recent

decades are observed in the northern Asian sector (e.g., Figure 2.22).

The warming trend was particularly strong in the cold season (Novem-

ber to March), with an increase of 2.4°C per 50 years in the mid-lat-

itude semi-arid area of Asia, where the annual rainfall is within the

range of 200 to 600 mm over the period of 1901–2009 (Huang et

al., 2012). The observations indicate some increasing trends of heavy

precipitation events in northern Asia, but no spatially coherent trends

in central Asia (Seneviratne et al., 2012).

The CMIP5 models generally have difﬁculties in representing the mean

climate expressed as the climatological means of both temperature

and precipitation (Table 14.2) for the sub-regions represented in this

area, which is partly related to the poor resolution unable to resolve

the complex mountainous terrain dominating this region. But the

scarceness of observational data and issues related to how these best

can be compared with coarse resolution models adds to the uncertain-

ty regarding model quality.

The model projections presented in AR4 (Section 11.4) indicated strong

warming in northern Asia during winter and in central Asia during

summer. Precipitation was projected to increase throughout the year

in northern Asia with the largest fractional increase during winter. For

central Asia, a majority of the CMIP3 models projected decreasing

1269

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Figure 14.24 | Maps of precipitation changes for Central, North, East and South Asia in 2080–2099 with respect to 1986–2005 in June to September (above) and December

to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes

in 2075–2099 with respect to 1979–2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation

Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air

temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense

hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes.

precipitation during spring and summer. Seneviratne et al. (2012) indi-

cate increases in all precipitation extreme indices for northern Asia and

in the 20-year return value of annual maximum daily precipitation for

central Asia. These projections are supported by output from CMIP5

models subject to various RCP scenarios (see Annex I). CMIP5 project-

ed temperature increase in Central Asia of comparable magnitude in

both JJA and in DJF. In North Asia, temperatures rise more in DJF than

in JJA, while less annual variation is found over Central Asia and the

Tibetan Plateau (Table 14.1, Figures AI.12 to AI.13, AI.52 to AI.55 and

AI.56 to AI.57).

With an RCM Sato et al. (2007) projected precipitation decreases over

northern Mongolia and increases over southern Mongolia in July. Soil

moisture over Mongolia decreases in July as a result of the combined

effect of decreased precipitation and increased potential evaporation

due to rising surface temperature. In North Asia, all CMIP5 models pro-

jects an increase in precipitation in the winter half year, and summer

half year precipitation is also projected to increase (Table 14.1; Figures

AI.14 to AI.15). In Central Asia and the Tibetan Plateau, model agree-

ment is lower on changes both for winter and summer precipitation

(Figure 14.24; Table 14.1; Figures AI.54 to AI.55 and AI.58 to AI.59). The

ability of these CMIP5 models to simulate precipitation over this region

varies (Table 14.3). The reasonable level of agreement in projections of

precipitation to be positive and signiﬁcantly above the 20-year natural

variability (Table 14.2), and therefore suggests that conﬁdence in the

sign of the projected change in future precipitation is medium.

In summary, all the areas are projected to warm, a stronger than global

mean warming trend is projected for northern Asia during winter.

For central Asia, warming magnitude is similar between winter and

summer. Precipitation in northern Asia will very likely increase, where-

as the precipitation over central Asia is likely to increase. Extreme pre-

cipitation events will likely increase in both regions.

14.8.9 East Asia

Summer is the rainy season for East Asia. The Meiyu-Changma-Baiu

rain band is the deﬁning feature of East Asian summer climate, extend-

ing from eastern China through central Japan (Ding and Chan, 2005;

Zhou et al., 2009b). The summer rain band is anchored by the sub-

tropical westerly jet (Sampe and Xie, 2010), and located on the north-

western ﬂank of the western North Paciﬁc subtropical high (Zhou and

Yu, 2005). The wintertime circulation is characterized by monsoonal

northerlies between the Siberian High and the Aleutian Low.

Both the East Asian summer and winter monsoon circulations have

experienced an inter-decadal scale weakening after the 1970s due to

natural variability of the coupled climate system, leading to enhanced

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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

mean and extreme precipitation along the Yangtze River Valley (30°N)

but deﬁcient mean precipitation in North China in summer (Figure

14.25), and a warmer climate in winter. The observed monsoon circu-

lation changes are partly reproduced by GCMs driven by PDO-related

SST patterns but the quality of precipitation simulation is poor (Zhou

et al., 2008a; Li et al., 2010a; Zhou and Zou, 2010).

In AR4, the regional warming is projected to be above the global

mean in East Asia (Christensen et al., 2007). “It is very likely that heat

waves/hot spells in summer will be of longer duration, more intense

and more frequent, but very cold days are very likely to decrease in

frequency. The precipitation is likely to increase in both boreal winter

and summer, while the frequency of intense precipitation events is very

likely to increase. Extreme rainfall and winds associated with tropical

cyclones are likely to increase”. CMIP5 results support many of these

assessments.

More recent analysis suggested that CMIP3 models projected

increased summer precipitation in amount and intensity over East

Asia (Figure 14.24 for SRES A1B scenario) due to enhanced mois-

ture convergence in a warmer climate (Ding et al., 2007; Sun and

Ding, 2010; Chen et al., 2011; Kusunoki and Arakawa, 2012), along

with an increase in interannual variability (Lu and Fu, 2010). CMIP5

projections for RCP4.5 support those from AR4 for summer (Figure

14.24), with 90% of the models projecting a precipitation increase

(% per 50 yr)

(a) PRCTOC

30 N

30 S

30 N

30 S

60 E

- 80 -48 -16 16 48 80

90 E 180 E 150 E 60 E 90 E 180 E 150 E

Figure 14.25 | Linear trend for local summer (a) total precipitation and (b) R95 (summer total precipitation when PR >95th percentile) during 1961–2006. The unit is % per

50-years. The trends statistically signiﬁcant at the 5% level are dotted. The daily precipitation data over Australia and China are produced by the Australian Water Availability Project

(AWAP, Jones et al., 2009a) and National Climate Centre China of China Meteorological Administration (Wu and Gao, 2013), respectively, while that over the other area is compiled

by the Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) project (Yatagai et al., 2012). The resolution of precipitation data

set is 0.5° × 0.5°. Local summer is deﬁned as June, July and August in the Northern Hemisphere, and December, January and February in the Southern Hemisphere.

in the winter half year (see Table 14.1 and Figures AI.56 to AI.59).

CMIP3 models projections indicated a decrease of winter precipita-

tion extending northeastward from South China Sea to south of Japan

under SRES A1B scenario, changes seen in CMIP5 projections but with

smaller spatial coverage (Figure 14.24).

An increase of extreme precipitation is projected over East Asia in a

warmer climate (Jiang et al., 2011; Lee et al., 2011; Li et al., 2011a,

2011b). A high-resolution model projects an increase of Meiyu pre-

cipitation in May through July, Changma precipitation over Korean

peninsula in May, and Baiu precipitation over Japan in July (Kusunoki

and Mizuta, 2008), and an increase of heavy precipitation over East

Asia under SRES A1B scenario (Kusunoki and Mizuta, 2008; Endo,

2012). CMIP3 models project a late withdrawal of Baiu (Kitoh and

Uchiyama, 2006), as has been observed in eastern and western Japan

(Endo, 2010). There is a signiﬁcant increase in mean, daily maximum

and minimum temperatures in southeastern China, associated with a

decrease in the number of frost days and an increase in the heat wave

duration under SRES A2 scenario (Chen et al., 2011). The CMIP5 model

projections also indicate an increase of temperature in both boreal

winter and summer over East Asia for RCP4.5 (Table 14.1). A decrease

of the annual and seasonal maximum wind speeds is found under SRES

A2 scenario due to both the reduced intensity of cold waves and the

reduced intensity of the winter monsoons (Jiang and Zhao, 2013).

1271

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

The future warming patterns simulated by RCMs essentially follow

those of the driving GCMs (e.g., Dairaku et al., 2008). For summer pre-

cipitation, however, RCM downscaling usually shows different regional

details due to more realistic topographic forcing than in GCMs (Gao et

al., 2008, 2012a). The uncertainty of precipitation projection in eastern

China is larger than that in western China (Gao et al., 2012b). RCM

downscaling indicates that both the seasonal mean summer rain-

fall and extreme precipitation around Japan Islands are projected to

increase (Im et al., 2008; Iizumi et al., 2012).

Projections with a 5-km RCM show that the heaviest hourly precipita-

tion is projected to increase even in the near future (2030s) when tem-

perature increase is modest (Kitoh et al., 2009). A southwest expan-

sion of the subtropical anticyclone over the northwestern Paciﬁc Ocean

associated with El Niño-like mean state changes in the Paciﬁc and a

dry air intrusion in the mid-troposphere from the Asian continent gives

a favourable condition for intense precipitation in the Baiu season in

Japan (Kanada et al., 2010). Increased water vapour supply from the

south of the Baiu front and an intensiﬁed frontal zone with intense

mean updrafts contribute to the increased occurrence of intense daily

precipitation during the late Baiu season (Kanada et al., 2012).

In summary, based on CMIP5 model projections, there is medium con-

ﬁdence that with an intensiﬁed East Asian summer monsoon,summer

precipitation over East Asia will increase (Table 14.3). Under RCP4.5

scenario, precipitation increase is likely over East Asia during the Mei-

yu-Changma-Baiu season in May to July, and precipitation extremes

are very likely to increase over the eastern Asian continent in all sea-

sons and over Japan in summer. However, there is only low conﬁdence

in more speciﬁc details of the projected changes due to the limited

skill of CMIP5 models in simulating monsoon features such as the East

Asian monsoon rainband (Table 14.2).

14.8.10 West Asia

This region extends from the Mediterranean to the western fringes of

South Asia, covering the Middle East and the Arabian Peninsula and

includes large areas of barren desert. The climate over this region

varies from arid to semi-arid and precipitation is primarily received in

the cold season.

The western part of the region is on the margin of Atlantic and Medi-

terranean inﬂuences, primarily the NAO (Section 14.5.1) during winter

months, and indirectly the monsoon heat low (Section 14.2.2.1) in the

summer months. Precipitation in this region comes largely from pass-

ing ETCs (Section 14.6.2). Land-falling TCs (Section 14.6.1) that occa-

sionally inﬂuence the eastern part of the Arabian Peninsula are notable

extreme events. Paciﬁc Ocean variability, associated with ENSO (Sec-

tion 14.2.4), and the ITCZ (Section 14.3.1) are also known to impact

weather and climate in different parts of West Asia.

In recent decades, there appears to be a weak but non-signiﬁcant

downward trend in mean precipitation (Zhang et al., 2005; Alpert et al.,

2008; AlSarmi and Washington, 2011; Tanarhte et al., 2012), although

intense weather events appear to be increasing (Alpert et al., 2002;

Yosef et al., 2009). In contrast, upward temperature trends are notable

and robust (Alpert et al., 2008; AlSarmi and Washington, 2011; Tanar-

hte et al., 2012).

The ability of climate models to simulate historical climate, its change

and its variability, has improved in many important aspects since the

AR4 (see Figure 9.39 in Chapter 9). CMIP5 models tend to be able to

reproduce the basic climate state of the region as well as the main

phenomena affecting it with some ﬁdelity (Table 14.2), but the region

is at the fringes of the inﬂuence of different drivers of European, Asian

and African climates and remains poorly analysed in the peer-reviewed

literature with respect to climate model performances.

The CMIP5 model projections for this century are for further warming

in all seasons, while precipitation shows some distinct sub-regional

and seasonally dependent changes, characterized by model scatter. In

both winter (October to March) and summer (April to September) pre-

cipitation in general is projected to decrease, (see Table 14.1, Figures

AI.52 to AI.55). However, the various interacting dynamical inﬂuenc-

es on precipitation of the region (that models have varying success

in capturing in the current climate) results in uncertainty in both the

patterns and magnitude of future precipitation change. Indeed, while

the overall pattern of change has remained the same between CMIP3

and CMIP5, the conﬁdence has decreased somewhat and the boundary

between the Mediterranean decreases and the general mid-latitude

increase to the north has shifted closer to the region (Figures 14.26 and

AI.54 to AI.55). So, although the Mediterranean side still appears likely

to become drier, the likely precipitation changes for the interior land

masses are less clear and the intensiﬁed and northward shifting ITCZ

may imply an increase in precipitation in the most southern part of the

Arabian Peninsula. Overall, the projections by the end of the century

(2081–2100) indicates little overall change, although with a tendency

for reduced precipitation, particular in the high end scenarios (Figures

AI.5 to AI.55). However, regardless of the sign of precipitation change

in the high mountain regions of the interior, the inﬂuence of warming

on the snow pack will very likely cause important changes in the timing

and amount of the spring melt (Diffenbaugh et al., 2013).

Recent downscaling results (Lionello et al., 2008; Evans, 2009; Jin et

al., 2010; Dai, 2011) suggest that the eastern Mediterranean will expe-

rience a decrease in precipitation during the rainy season due to a

northward displacement of the storm tracks (Section 14.6.2). A north-

ward shift in the ITCZ results in more precipitation in the southern part,

not previously being seriously affected by it. A moderate change in the

annual cycle of precipitation has also been simulated by some models.

Precipitation and temperature statistics in RCMs for an area consist-

ing of the western part of the Arab Peninsula was assessed by Black

(2009) and Onol and Semazzi (2009) conﬁrming GCM-based ﬁndings.

Increased drought duration has been projected (Kim and Byun, 2009).

Inland from the Mediterranean coastal areas, resolution of the terrain

becomes more important and, while downscaled results (Evans, 2008;

Marcella and Eltahir, 2011; Lelieveld et al., 2012) broadly agree with

GCM projections, higher resolution results in some differences associ-

ated with mountain barrier jets (Evans, 2008; see also Figure 14.26).

In summary, since AR4 climate models appear to have only modest-

ly improved ﬁdelity in simulating aspects of large-scale climate

1272

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

phenomena inﬂuencing regional climates over West Asia. Model

agreement, however, indicates that it is very likely that temperatures

will continue to increase. But at the same time, model agreement on

projected precipitation changes have reduced, resulting in medium

conﬁdence in projections showing an overall reduction in precipitation.

14.8.11 South Asia

From June through September, the Indian summer monsoon (Section

14.2.2.1) dominates South Asia, while the northeast winter monsoon

contributes substantially to annual rainfall over southeastern India and

Sri Lanka. The winter weather systems are also important in northern

parts of South Asia, that is, the western Himalayas.

Seasonal mean rainfall shows interdecadal variability, noticeably a

declining trend with more frequent deﬁcit monsoons (Kulkarni, 2012).

There are regional inhomogeneities: precipitation decreased over cen-

tral India along the monsoon trough (Figure 14.25) thought to be due

to a number of factors (Section 14.2.2) including black carbon, sul-

phate aerosols (Chung and Ramanathan, 2007; Bollasina et al., 2011),

land use changes (Niyogi et al., 2010) and SST rise over the Indo-Paciﬁc

Figure 14.26 | Maps of precipitation changes for West Asia in 2080–2099 with respect to 1986–2005 in June, July and August (above) and December, January and February

(below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). The ﬁgures on the right are the precipitation changes

in 2075–2099 with respect to 1979–2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation

Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air

temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense

hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes.

warm pool (Annamalai et al., 2013). The increase in the number of

monsoon break days over India (Dash et al., 2009), and the decline in

the number of monsoon depressions (Krishnamurthy and Ajayamohan,

2010), are consistent with the overall decrease in seasonal mean rain-

fall. The frequency of heavy precipitation events is increasing (Rajeevan

et al., 2008; Krishnamurthy et al., 2009; Sen Roy, 2009; Pattanaik and

Rajeevan, 2010), while light rain events are decreasing (Goswami et

al., 2006).

CMIP models reasonably simulate the annual cycle of precipitation and

temperature over South Asia (Table 14.2; Figure 9.38) but are limited

in simulating ﬁne structures of rainfall variability on sub-seasonal and

sub-regional scales (Turner and Annamalai, 2012). CMIP5 models show

improved skill in simulating monsoon variability compared to CMIP3

(Sperber et al., 2012; Section 14.2.2).

Summer precipitation changes in South Asia are consistent overall

between CMIP3 and CMIP5 (Figure 14.24), but model scatter is large

in winter precipitation change (Figures 14.24 and AI.62). Changes in

the summer monsoon dominate annual rainfall (see Section 14.2.2).

The CMIP3 multi-model ensemble shows an increase in summer

1273

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

precipitation (Kumar et al., 2011a; May, 2011; Sabade et al., 2011),

although there are wide variations among model projections (Annam-

alai et al., 2007; Kripalani et al., 2007b). Spatially, the rainfall increase

is stronger over northern parts of South Asia, Bangladesh and Sri

Lanka, with a weak decrease over Pakistan (Turner and Annamalai,

2012). In RCP6.0 and RCP8.5 scenarios, frequency of extreme precip-

itation days shows consistent increasing trends in 2060 and beyond

(Chaturvedi et al., 2012; Figure AI.63). In six CMIP3 models, precipita-

tion anomalies during Indian summer monsoon breaks strengthen in a

warmer climate, but changes in the timing and duration of active/break

spells are variable among models (Mandke et al., 2007). Note that the

active/break spells of the monsoon are related to the MJO (see Section

14.3.2), a phenomenon that models simulate poorly (Section 9.5.2.3;

Lin et al., 2008a; Sperber and Annamalai, 2008).

High-resolution RCM and GCM projections showed an overall increase

of precipitation over a large area of peninsular India (Rupa Kumar et

al., 2006; Stowasser et al., 2009; Kumar et al., 2011a), but a signiﬁcant

reduction in orographic rainfall in both seasonal mean and extreme

events on west coasts of India (Rajendran and Kitoh, 2008; Ashfaq et

al., 2009; Kumar et al., 2013). Such spatial variations in projected pre-

cipitation near orography are noticeable in Figure 14.24 on the back-

ground of the overall increase.

CMIP5 models project a clear increase in temperature over India

especially in winter (Figures AI.60 to AI.61), with enhanced warming

during night than day (Kumar et al., 2011a) and over northern India

(Kulkarni, 2012). In summer, extremely hot days and nights are project-

ed to increase. Table 14.1 summarizes the projected temperature and

precipitation changes for SAS in the RCP4.5 scenario based on CMIP5.

In summary, there is high conﬁdence in projected rise in temperature.

There is medium conﬁdence in summer monsoon precipitation increase

in the future over South Asia. Model projections diverge on smaller

regional scales.

14.8.12 Southeast Asia

Southeast Asia features a complex range of terrains and land–sea con-

trasts. Across the region, temperature has been increasing at a rate of

0.14°C to 0.20°C per decade since the 1960s (Tangang et al., 2007),

coupled with a rising number of hot days and warm nights, and a

decline in cooler weather (Manton et al., 2001; Caesar et al., 2011).

A positive trend in the occurrence of heavy (top 10% by rain amount)

and light (bottom 5%) rain events and a negative trend in moderate

(25 to 75%) rain events has been observed (Lau and Wu, 2007). Annual

total wet-day rainfall has increased by 22 mm per decade, while rain-

fall from extreme rain days has increased by 10 mm per decade (Alex-

ander et al., 2006; Caesar et al., 2011).

Several large-scale phenomena inﬂuence the climate of this region.

While ENSO (Section 14.4) inﬂuence is predominant in East Malay-

sia and areas east of it, Maritime continent monsoon (Section 14.2.3)

inﬂuences the climate in Peninsular Malaya. The impact of the IOD

(Section 14.3.3) is more prominent in eastern Indonesia. Thus climate

variability and trends differ vastly across the region and between

seasons. Between 1955 and 2005 the ratio of rainfall in the wet to

the dry seasons increased (Aldrian and Djamil, 2008). This appears to

be at least in part consistent with an upward trend of the IOD. While

an increasing frequency of extreme events has been reported in the

northern parts of South East Asia, decreasing trends in such events are

reported in Myanmar (Chang, 2011); see also Figure 14.25.

For a given region, strong seasonality in change is observed. In Penin-

sular Malaya during the southwest monsoon season, total rainfall and

the frequency of wet days decreased, but rainfall intensity increased in

much of the region (Deni et al., 2010). During the northeast monsoon,

total rainfall, the frequency of extreme rainfall events, and rainfall

intensity all increased over the peninsula (Suhaila et al., 2010).

High-resolution model simulations are necessary to resolve com-

plex terrain such as in Southeast Asia (Nguyen et al., 2012; Section

14.2.2.4). In a RCM downscaling simulation using the A1B emission

scenario (Chotamonsak et al., 2011), regional average rainfall was

projected to increase, consistent with a combination of the ‘warmer

getting wetter’ mechanism (Section 14.3.1), an increase in summer

monsoon, though there is a lack of consensus on future ENSO changes.

The spatial pattern of change is similar to that projected in the AR4

(Christensen et al., 2007, Section 11.4).

The median increase in temperature over land ranges from 0.8°C in

RCP2.6 to 3.2°C in RCP8.5 by the end of this century (2081–2100). A

moderate increase in precipitation is projected for the region: 1% in

RCP2.6 increasing to 8% in RCP8.5 by 2100 (Table 14.1, Supplemen-

tary Material Table 14.SM.1a to 14.SM.1c, Figures 14.27 and AI.64 to

AI.65). On islands neighbouring the southeast tropical Indian Ocean,

rainfall is projected to decrease during July to November (the IOD prev-

alent season), consistent with a slower oceanic warming in the east

than in the west tropical Indian Ocean, despite little change projected

in the IOD (Section 14.3.3).

In summary, warming is very likely to continue with substantial sub-re-

gional variations. There is medium conﬁdence in a moderate increase in

rainfall, except on Indonesian islands neighbouring the southeast Indian

Ocean. Strong regional variations are expected because of terrain.

14.8.13 Australia and New Zealand

The climate of Australia is a mix of tropical and extratropical inﬂuenc-

es. Northern Australia lies in the tropics and is strongly affected by

the Australian monsoon circulation (Section 14.2.2) and ENSO (Section

14.4). Southern Australia extends into the extratropical westerly circu-

lation and is also affected by the middle latitude storm track (Section

14.6.2), the SAM (Section 14.5.2), mid-latitude transient wave propa-

gation, and remotely by the IOD (Section 14.3.3) and ENSO.

Eastern–northeastern Australian rainfall is strongly inﬂuenced by the

ENSO cycle, with La Niña years typically associated with wet conditions

and more frequent and intense tropical cyclones in summer, and El

Niño years with drier than normal conditions, most notably in spring.

The SAM plays a signiﬁcant role in modulating southern Australian

rainfall, the positive SAM being associated with generally above-nor-

mal rainfall during summer (Hendon et al., 2007; Thompson et al.,

2011), but in winter with reduced rainfall, particularly in Southwest

1274

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Western Australia (Hendon et al., 2007; Meneghini et al., 2007; Pezza

et al., 2008; Risbey et al., 2009; Cai et al., 2011c). Rossby wavetrains

induced by tropical convective anomalies associated with the IOD (Cai

et al., 2009), and associated with ENSO through its coherence with

the IOD (Cai et al., 2011b) also have a strong impact, leading to lower

winter and spring rainfall particularly over Southeastern Australia

during positive IOD and El Niño events. Along the eastern seaboard,

ETCs (Section 14.6.2) exert a strong inﬂuence on the regional climate,

while ENSO and other teleconnections play a lesser role (Risbey et al.,

2009; Dowdy et al., 2012).

Signiﬁcant trends have been observed in Australian rainfall over recent

decades (Figure 14.25), varying vastly by region and season. Increasing

summer rainfall and decreasing temperature trends over northwest

Australia have raised the question of whether aerosols originating in

the NH play a role (Rotstayn et al., 2007; Shi et al., 2008b; Smith et al.,

2008; Rotstayn et al., 2009; Cai et al., 2011d), but there is no consensus

at present. By contrast, a prominent rainfall decline has been expe-

rienced in austral winter over southwest Western Australia (Cai and

Cowan, 2006; Bates et al., 2008) and in mid-to-late autumn over south-

Figure 14.27 | Maps of precipitation changes for Southeast Asia, Australia and New Zealand in 2080–2099 with respect to 1986–2005 in June to September (above) and Decem-

ber to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right ﬁgures are the precipitation changes

in 2075–2099 with respect to 1979–2003 in the SRES A1B scenario with the 12-member 60- km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation

Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air

temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense

hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes.

eastern Australia (Murphy and Timbal, 2008). Over southwest Western

Australia, the decrease in winter rainfall since the late 1960s of about

20% have led to an even bigger (~50%) drop in inﬂow into dams. The

rainfall decline has been linked to changes in large-scale mean sea

level pressure (Bates et al., 2008), shifts in synoptic systems (Hope et

al., 2006), changes in baroclinicity (Frederiksen and Frederiksen, 2007),

the SAM (Cai and Cowan, 2006; Meneghini et al., 2007), land cover

changes (Timbal and Arblaster, 2006), anthropogenic forcing (Timbal

et al., 2006), Indian Ocean warming (England et al., 2006) and tele-

connection to Antarctic precipitation (van Ommen and Morgan, 2010).

Over southeastern Australia, the decreasing rainfall trend is largest

in autumn with sustained declines during the drought of 1997–2009,

especially in May (Cai and Cowan, 2008; Murphy and Timbal, 2008; Cai

et al., 2012a). The exact causes remain contentious, and for the decrease

in May, may include ENSO variability and long-term Indian Ocean

warming (Cai and Cowan, 2008; Ummenhofer et al., 2009b), a weak-

ening of the subtropical storm track due to decreasing baroclinic insta-

bility of the subtropical jet (Frederiksen et al., 2010; Frederiksen et al.,

2011a, 2011b) and a poleward shift the ocean–atmosphere circulation

1275

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

(Smith and Timbal, 2012; Cai and Cowan, 2013). The well-documented

poleward expansion of the subtropical dry zone (Seidel et al., 2008;

Johanson and Fu, 2009; Lucas et al., 2012), particularly in April and

May, is shown to account for much of the April–May reduction (Cai

et al., 2012a). Rainfall trends over southeastern Australia in spring, far

weaker but with a signature in the subtropical ridge (Cai et al., 2011a;

Timbal and Drosdowsky, 2012), have been shown to be linked with

trends and variability in the IOD (Cai et al., 2009; Ummenhofer et al.,

2009b). Antarctic proxy data that capture both eastern Australian rain-

fall and ENSO variability (Vance et al., 2012) show a predominance of

El Niño/drier conditions in the 20th century than was the average over

the last millennium.

On seasonal to decadal time scales, New Zealand precipitation is

modulated by the SAM (Kidston et al., 2009; Thompson et al., 2011),

ENSO (Kidson and Renwick, 2002; Ummenhofer and England, 2007)

and the IPO (Grifﬁths, 2007). Increased westerly ﬂow across New Zea-

land, associated with negative SAM and with El Niño events, leads

to increased rainfall and generally lower than normal temperatures

in western regions. The positive SAM and La Niña conditions are gen-

erally associated with increased rainfall in the north and east of the

country, and warmer than normal conditions. On longer time scales,

a drying trend since 1979 across much of New Zealand during austral

summer is consistent with recent trends in the SAM and to a lesser

extent ENSO and the IPO (Grifﬁths, 2007; Ummenhofer et al., 2009a).

In western regions, however, the drying is accompanied by a trend

towards increased heavy rainfall (Grifﬁths, 2007). Temperatures over

New Zealand have risen by just under 1°C over the past century (Dean

and Stott, 2009). The upward trend has been modulated by an increase

in the frequency of cool southerly wind ﬂows over the country since the

1950s, without which the observed warming is consistent with large-

scale anthropogenic forcing (Dean and Stott, 2009).

A recent analysis (Irving et al., 2012; their Figure 9) shows that climate

projections over Australia using CMIP5 models, which generally sim-

ulate the climate of Australia well (Watterson et al., 2013), are highly

consistent with existing CMIP3-derived projections. The projected

changes include a further 1.0 to 5.0°C temperature rise by the year

2070 (relative to 1990); a long-term drying over southern areas during

winter, particularly in the southwest (Figure 14.27), that is consistent

with an upward trend of the SAM (Pitman and Perkins, 2008; Shi et al.,

2008a; Cai et al., 2011c); a long-term rainfall decline over southern and

eastern areas during spring, in part consistent with a upward trend of

the IOD index (Smith and Chandler, 2010; Zheng et al., 2010; Weller

and Cai, 2013; Zheng et al., 2013). Precipitation change in northeast

Australia remains uncertain (Moise et al., 2012), related to the lack of

consensus over how ENSO may change (Collins et al., 2010; Section

14.4). In terms of climate extremes, more frequent hot days and nights

and less frequent cold days and nights are projected (Alexander and

Arblaster, 2009). Changes in the intensity and frequency of extreme

rainfall events generally follow the mean rainfall change (Kharin et al.,

2007), although there is an increase in most regions in the intensity of

short duration extremes (e.g., Alexander and Arblaster, 2009).

For New Zealand, future climate projections suggest further increases

in the westerlies in winter and spring, though model biases in jet lat-

itude in the present climate reduce conﬁdence in the detail of future

projections (Barnes et al., 2010). The inﬂuence of poleward expansion

of the subtropical high-pressure belt is projected to lead to drier condi-

tions in parts of the country (Figure 14.27; Table 14.1), and a decrease

in westerly wind strength in northern regions. Such projections imply

increased seasonality of rainfall in many regions of New Zealand (Reis-

inger et al., 2010). Both ﬂood and drought occurrence is projected

to approximately double over New Zealand during the 21st century,

under the SRES A1B scenario. Temperatures are projected to rise at

about 70% of the global rate, because of the buffering effect of the

oceans around New Zealand. Temperature rises are projected to be

smallest in spring (SON) while the season of greatest warming varies

by region around the country. Continued decreases in frost frequen-

cy, and increases in the frequency of high-temperature extremes, are

expected, but have not been quantiﬁed (Reisinger et al., 2010).

In summary, based on understanding of recent trends and on CMIP5

results, it is likely that cool season precipitation will decrease over

southern Australia associated in part with trends in the SAM, the IOD

and a poleward shift and expansion of the subtropical dry zone. It is

very likely that Australia will continue to warm through the 21st cen-

tury, at a rate similar to the global land surface mean. The frequency

of very warm days is very likely to increase through this century, across

the whole country.

It is very likely that temperatures will continue to rise over New Zea-

land. Precipitation is likely to increase in western regions in winter and

spring, but the magnitude of change is likely to remain comparable to

that of natural climate variability through the rest of the century. In

summer and autumn, it is as likely as not that precipitation amounts

will change.

14.8.14 Paciﬁc Islands Region

The Paciﬁc Islands region includes the northwest tropical Paciﬁc, and

the tropical southwest Paciﬁc. North of the Equator, the wet season

occurs from May to November. In the south, the wet seasons occurs

from November to April.

The phenomena mainly responsible for climate variations in the Pacif-

ic Islands are ENSO (Section 14.4), the SPCZ (Section 14.3.1.2), the

ITCZ (Section 14.3.1.1) and the WNPSM (Section 14.2.2.5). During El

Niño events, the ITCZ and SPCZ move closer to the equator, rainfall

decreases in western regions and increases in the central Paciﬁc, and

tropical cyclone numbers tend to increase and to occur farther east

than normal (Diamond et al., 2012). During La Niña, the western trop-

ical Paciﬁc tends to experience above-average numbers of tropical

cyclones (Nicholls et al., 1998; Lavender and Walsh, 2011).

The seasonal evolution of the SPCZ has a strong inﬂuence on the

seasonality of the climate of the southern tropical Paciﬁc, particularly

during the wet season. The SPCZ moves northward during moderate

El Niño events and southward during La Niña events (Folland et al.,

2002; Vincent et al., 2011). During El Niño events, southwest Paciﬁc

Island nations experience an increased occurrence of forest ﬁres and

droughts (Salinger et al., 2001; Kumar et al., 2006b), and an increased

probability of tropical cyclone damage, as tropical cyclogenesis tends

to reside within 6° to 10° south of the SPCZ (Vincent et al., 2011).

1276

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Nauru experiences drought during La Niña as the SPCZ and ITCZ move

to the west (Brown et al., 2012c). During strong El Niño events (e.g.,

1982/1983, 1997/1998) the SPCZ undergoes an extreme swing of up

to 10 degrees towards the equator and collapses to a more zonally

oriented structure (Vincent et al., 2011; Section 14.3.2). The impacts

from these zonal SPCZ events are much more severe than those from

moderate El Niño events (Vincent et al., 2011; Cai et al., 2012b), and

can induce massive droughts and food shortages (Barnett, 2011).

Temperatures have increased at a rate between 0.1°C and 0.2°C per

decade throughout the Paciﬁc Islands during the 20th century (Folland

et al., 2003). Changes in temperature extremes have followed those of

mean temperatures (Manton et al., 2001; Grifﬁths et al., 2005). During

1961–2000, locations to the northeast of the SPCZ became wetter,

with the largest trends occurring in the eastern Paciﬁc Ocean (east of

160°W), while locations to the southwest of the SPCZ became drier

(Grifﬁths et al., 2003), indicative of a northeastward shift of the SPCZ.

Trends in the frequency of rain days were generally similar to those of

total annual rainfall (Manton et al., 2001; Grifﬁths et al., 2003). Since

1980, western Paciﬁc monsoon- and ITCZ-related rain during June to

August has decreased (Hennessy et al., 2011).

Future projections for tropical Paciﬁc Island nations are based on direct

outputs from a suite of CMIP3 models, updated using CMIP5 wherever

available (Brown et al., 2011; Hennessy et al., 2011; Irving et al., 2011;

Moise and Delage, 2011; Perkins, 2011; Perkins et al., 2012). These

projections carry a large uncertainty, even in the sign of change, as

discussed below and as evident in Table 14.1.

Annual average air and sea surface temperature are projected to con-

tinue to increase for all tropical Paciﬁc countries. By 2055, under the

high A2 emissions scenario, the increase is projected to be 1°C to 2°C.

A rise in the number of hot days and warm nights is also projected, and

a decline in cooler weather, as already observed (Manton et al., 2001).

For a low-emission scenario, the lower range decreases about 0.5ºC

while the upper range reduces by between 0.2°C and 0.5°C.

To a large extent, the response of the ITCZ, the SPCZ, and the WNPSM

to greenhouse warming will determine how rainfall patterns will

change in tropical Paciﬁc. In northwestern and near-equatorial regions,

rainfall during all seasons is projected to increase in the 21st century.

Wet season increases are consistent with the expected intensiﬁcation

of the WNPSM and the ITCZ (Smith et al., 2012a). For the southwest-

ern tropical Paciﬁc, the CMIP3 and CMIP5 ensemble mean change in

summer rainfall is far smaller than the inter-model range (Brown et

al., 2012b; Widlansky et al., 2013). There is a projected intensiﬁcation

in the western part of the SPCZ and near the equator with little mean

change in SPCZ position (Brown et al., 2012a; Brown et al., 2012b).

For the southern group of the Cook Islands, the Solomon Islands, and

Tuvalu, average rainfall during the wet season is projected to increase;

and for Vanuatu, Tonga, Samoa, Niue, Fiji, a decrease in dry season

rainfall is accompanied by an increase in the wet season, indicating an

intensiﬁed seasonal cycle.

Extreme rainfall days are likely to occur more often in all regions related

to an intensiﬁcation of the ITCZ and the SPCZ (Perkins, 2011). Although

the intensiﬁcation appears to be reproduced in CMIP5 models (Brown

et al., 2012a), it has recently been questioned (Widlansky et al., 2013;

see Section 14.3.1). There are two competing mechanisms, the ‘wet

regions getting wetter’ and the ‘warmest getting wetter, or coldest get-

ting drier’ paradigms. These two mechanisms compete within much of

the SPCZ region. Based on a multi-model ensemble of 55 greenhouse

warming experiments, in which model biases were corrected, tropical

SST changes between 2°C to 3°C resulted in a 5% decrease of austral

summer moisture convergence in the current SPCZ region (Widlansky et

al., 2013). This projects a diminished rainy season for most Southwest

Paciﬁc island nations. In Samoa and neighbouring islands, summer rain-

fall may decrease on average by 10 to 20% during the 21st century as

simulated by the hierarchy of bias-corrected atmospheric model experi-

ments. Less rainfall, combined with increasing surface temperatures and

enhanced potential evaporation, could increase the chance for longer-

term droughts in the region. Such projections are completely opposite

to those based on direct model outputs (Figure 14.27).

Recent downscaling experiments support the above conclusion regard-

ing the impact of biases on the SPCZ change, and suggest that the

projected intensiﬁcation of the ITCZ may have uncertainties of a simi-

lar nature (Chapter 7 of Hennessy et al., 2011). In these experiments a

bias correction is applied to average sea surface temperatures, and the

atmosphere is forced with the ‘correct’ climatological seasonal cycle

together with warming derived from large-scale model outputs. The

results show opposite changes in much of the SPCZ and some of the

ITCZ regions, resulting in much lower conﬁdence in rainfall projections.

Despite the uncertainty, there is general agreement in model projec-

tions regarding an increase in rainfall along the equator (Tables 14.1

and 14.2), and regarding a faster warming rate in the equatorial Paciﬁc

than the off-equatorial regions (Xie et al., 2010b). A potential conse-

quence is an increase in the frequency of the zonal SPCZ events (Cai

et al., 2012b).

In summary, based on CMIP3 and CMIP5 model projections and

recently observed trends, it is very likely that temperatures, including

the frequency and magnitude of extreme high temperatures, will con-

tinue to increase through the 21st century. In equatorial regions, the

consistency across model projections suggests that rainfall is likely to

increase. However, given new model results and physical insights since

the AR4, the rainfall outlook is uncertain in regions directly affected by

the SPCZ and western portion of the ITCZ.

14.8.15 Antarctica

Much of the climate variability of Antarctica is modulated by the South-

ern Annular Mode (SAM, Section 14.5.2), the high-latitude atmospher-

ic response to ENSO (Section 14.4) and interactions between the two

(Stammerjohn et al., 2008; Fogt et al., 2011; see also Sections 2.7

and 10.3.3). Signatures of the SAM and ENSO in Antarctic tempera-

ture, snow accumulation and sea ice have been documented by many

observational and modelling studies (Bromwich et al., 2004; Guo et

al., 2004; Kaspari et al., 2004; van den Broeke and van Lipzig, 2004;

Marshall, 2007).

The positive SAM is associated on average with warmer conditions over

the Peninsula and colder conditions over East Antarctica, with a mixed

1277

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

and generally non-signiﬁcant impact over West Antarctica (Kwok and

Comiso, 2002; Thompson and Solomon, 2002; van den Broeke and van

Lipzig, 2004). ENSO is associated with circulation anomalies over the

southeast Paciﬁc that primarily affect West Antarctica (Bromwich et al.,

2004; Guo et al., 2004; Turner, 2004). ENSO variability tends to produce

out-of-phase variations between the western and eastern sectors of

West Antarctica (Bromwich et al., 2004; Kaspari et al., 2004), in associ-

ation with the PSA pattern (Section 14.7.1).

The positive summer/autumn trend in the SAM index in recent dec-

ades (Section 14.5.2) has been related to the contrasting temperature

trend patterns observed in these two seasons, with warming in the

east and north of the Antarctic Peninsula and cooling (or no signiﬁcant

temperature change) over much of East Antarctica (Turner et al., 2005;

Thompson et al., 2011). The high polarity of the SAM is also consistent

with the signiﬁcant increase in snow accumulation observed in the

southern part of the Peninsula (Thomas et al., 2008).

Unlike the eastern Antarctic Peninsula, its western coast shows maxi-

mum warming in austral winter (when the SAM does not exhibit any

signiﬁcant trend), which has been attributed to reduced sea ice con-

centrations in the Bellingshausen Sea. Recent studies have emphasized

the role of tropical SST forcing not directly linked to ENSO to explain

the prominent spring- and wintertime atmospheric warming in West

Antarctica (Ding et al., 2011; Schneider et al., 2012). There is further

evidence of tropical SST inﬂuence on Antarctic temperatures and pre-

cipitation on decadal to inter-decadal time scales (Monaghan and

Bromwich, 2008; Okumura et al., 2012).

Modelling of Antarctic climate remains challenging, in part because of

the nature of the high-elevation ice sheet in the east Antarctic and its

effects on regional climate (Section 9.4.1.1). Moreover, modelling ice

properties themselves, for both land ice and sea ice, is an area that is

still developing despite improvements in recent years (Vancoppenolle

et al., 2009; Picard et al., 2012; Section 9.4.3). Modelling the role of the

stratosphere and of ozone recovery is critical for Antarctic climate, as

stratospheric change is intimately linked to trends in the SAM (Section

14.5.2).

The projected easing of the positive SAM trend in austral summer

(Section 14.5.2) may act to delay future loss of Antarctic sea ice (Bitz

and Polvani, 2012; Smith et al., 2012b). It is unclear what effect ENSO

will have on future Antarctic climate change as the ENSO response to

climate change remains uncertain (see 12.4.4.1 and 14.5.2 for more

information). Seasonally, changes in the strength of the circumpo-

lar westerlies are also expected during the 21st century as a result

of changes in the semi-annual oscillation caused by alterations in the

mid- to high-latitude temperature gradient in the SH. Bracegirdle et

al. (2008) considered modelled circulation changes over the Southern

Ocean and found a more pronounced strengthening of the autumn

peak of the semi-annual oscillation compared with the spring peak.

Future changes in surface temperature over Antarctica are likely to be

smaller than the global mean, and much smaller than those projected

for the Arctic, because of the buffering effect of the southern oceans,

and the thermal mass of the east Antarctic ice sheet (Section 12.4.6).

Warming is likely to bring increased precipitation on average across

Antarctica (Bracegirdle et al., 2008), but the spatial pattern of precipi-

tation change remains uncertain.

In summary, consistency across CMIP5 projections suggests it is very

likely that Antarctic temperatures will increase through the rest of

the century, but more slowly than the global mean rate of increase

(Table 14.1). SSTs of the oceans around Antarctica are likely to rise

more slowly than surface air temperature over the Antarctic land mass.

As temperatures rise, it is also likely that precipitation will increase

(Table 14.1), up to 20% or more over the East Antarctic. However, given

known difﬁculties associated with correctly modelling Antarctic cli-

mate, and uncertainties associated with future SAM and ENSO trends

and the extent of Antarctic sea ice, precipitation projections have only

medium conﬁdence.

1278

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.1 | Temperature and precipitation projections by the CMIP5 global models. The ﬁgures shown are averages over SREX regions (Seneviratne et al., 2012) of the projections

by a set of 42 global models for the RCP4.5 scenario. Added to the SREX regions are a six other regions including the two Polar Regions, the Caribbean, Indian Ocean and Paciﬁc

Island States (see Annex I for further details). The 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central

North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), Southeastern South America

(SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF),

Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeastern Asia (SEA), Northern Australia (NAS) and

Southern Australia/New Zealand (SAU). The area-mean temperature and precipitation responses are ﬁrst averaged for each model over the 1986–2005 period from the historical

simulations and the 2016–2035, 2046–2065 and 2081–2100 periods of the RCP4.5 experiments. Based on the difference between these two periods, the table shows the 25th,

50th and 75th percentiles, and the lowest and highest response among the 42 models, for temperature in degrees Celsius and precipitation as a percent change. Regions in which

the middle half (25 to 75%) of this distribution is all of the same sign in the precipitation response are coloured light brown for decreasing precipitation and light green for increas-

ing precipitation. Information is provided for land areas contained in the boxes unless otherwise indicated. The temperature responses are averaged over the boreal winter and

summer seasons; December, January and February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter;

October, November, December, January, February and March (ONDJFM) and summer; April, May, June, July, August and September (AMJJAS).

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

(land) DJF 2035 0.6 1.5 1.7 2.2 4.2 3 7 9 11 19

 2065 0.4 3.0 3.4 4.5 8.0 5 14 17 21 37

 2100 –0.9 3.7 5.0 6.2 10.0 –2 18 24 30 50

 JJA 2035 0.3 0.8 1.0 1.2 3.0 –3 4 5 7 20

 2065 0.5 1.3 1.8 2.3 4.8 1 7 10 12 34

 2100 0.3 1.8 2.2 3.0 6.0 –2 10 13 17 39

 Annual 2035 0.4 1.3 1.5 1.7 3.8 1 5 6 8 20

  2065 0.3 2.4 2.8 3.5 6.4 3 11 13 15 35

  2100 –0.4 3.0 3.9 4.7 7.8 –2 14 17 21 43

(sea) DJF 2035 0.2 2.2 2.8 3.3 6.7 –1 7 9 15 25

 2065 –0.5 4.2 5.1 6.8 11.4 –2 14 18 25 39

 2100 –2.2 5.4 7.0 9.1 14.8 –10 23 26 37 48

 JJA 2035 0.1 0.5 0.6 0.7 1.9 –3 4 6 7 17

 2065 0.0 0.8 1.2 1.4 2.9 –2 9 11 14 23

 2100 –0.3 1.2 1.5 2.1 4.0 –3 12 16 18 29

 Annual 2035 0.2 1.5 2.0 2.3 4.7 0 6 8 9 21

  2065 –0.1 2.9 3.7 4.7 7.4 –1 11 13 20 28

  2100 –1.0 3.7 4.9 6.5 9.3 –7 16 21 26 37

High latitudes

Canada/ DJF 2035 –0.2 1.2 1.7 1.9 3.1 0 4 5 9 14

Greenland/ 2065 0.6 2.8 3.4 3.9 6.6 3 9 12 15 21

Iceland 2100 –0.5 3.2 4.6 5.6 8.1 –2 11 15 22 32

 JJA 2035 0.1 0.7 1.0 1.2 3.0 0 2 3 4 8

 2065 0.5 1.3 1.8 2.3 4.5 2 5 6 9 16

 2100 0.2 1.7 2.3 3.0 5.6 1 6 9 12 20

 Annual 2035 0.2 1.1 1.3 1.6 2.9 0 3 4 6 9

  2065 0.4 2.0 2.5 2.9 5.2 3 7 9 11 17

  2100 –0.2 2.6 3.2 4.0 6.4 0 10 11 15 22

North Asia DJF 2035 0.5 1.1 1.5 2.2 4.0 2 6 8 10 22

 2065 1.2 2.3 3.0 3.6 6.0 5 11 14 18 34

 2100 0.2 3.0 3.8 4.9 7.8 5 13 18 22 44

 JJA 2035 0.1 0.8 1.0 1.4 2.5 1 2 4 6 16

 2065 0.8 1.5 2.0 2.7 4.4 –1 5 8 10 21

 2100 0.8 1.9 2.4 3.5 5.1 –3 6 9 12 30

 Annual 2035 0.4 1.1 1.3 1.6 3.0 1 4 5 7 18

  2065 0.8 2.0 2.4 2.9 4.9 2 8 9 12 25

  2100 0.2 2.5 3.2 3.8 5.8 1 10 12 15 35

1279

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

North America

Alaska/ DJF 2035 0.0 1.1 1.7 2.4 3.4 –1 3 5 8 12

NW Canada 2065 1.2 2.8 3.6 4.8 7.4 3 9 11 17 29

 2100 2.3 3.5 4.8 5.9 9.7 7 11 17 21 42

 JJA 2035 0.3 0.7 1.0 1.4 2.8 –1 2 5 7 16

 2065 0.7 1.3 1.8 2.3 4.9 –2 6 10 12 29

 2100 0.9 1.8 2.2 3.1 5.2 –2 9 12 16 34

 Annual 2035 0.4 1.0 1.4 1.8 2.8 0 3 6 7 14

  2065 1.4 2.1 2.7 3.6 5.2 4 8 10 13 28

  2100 1.7 2.5 3.5 4.3 6.7 3 11 14 17 33

West North DJF 2035 –0.4 0.7 1.1 1.5 2.5 –2 0 3 4 8

America 2065 0.9 1.7 2.2 2.6 4.0 –3 3 4 6 11

 2100 1.3 2.2 2.6 3.4 5.2 –4 4 6 8 17

 JJA 2035 0.3 0.9 1.1 1.3 2.1 –6 –1 1 3 9

 2065 0.8 1.7 2.0 2.6 3.4 –7 –1 1 4 10

 2100 0.9 2.1 2.5 3.4 4.6 –8 –1 2 6 10

 Annual 2035 0.3 0.8 1.0 1.3 1.9 –4 –1 2 3 6

  2065 0.9 1.7 2.0 2.5 3.4 –3 1 3 5 11

  2100 1.1 2.0 2.6 3.4 4.3 –4 2 4 6 14

Central North DJF 2035 –0.1 0.7 1.1 1.6 2.9 –8 –1 1 5 11

America 2065 0.9 1.6 2.2 2.7 4.2 –7 1 4 7 17

 2100 1.2 2.0 2.7 3.6 4.9 –6 –1 4 9 18

 JJA 2035 0.3 0.8 1.1 1.4 2.3 –7 –2 0 3 9

 2065 0.9 1.7 2.1 2.5 3.5 –16 –1 2 5 12

 2100 1.0 2.1 2.5 3.1 4.6 –13 –1 2 5 13

 Annual 2035 0.4 0.9 1.1 1.3 2.0 –4 –1 1 3 7

  2065 1.0 1.7 2.0 2.4 3.4 –7 0 3 4 14

  2100 1.1 2.0 2.6 3.1 4.3 –4 0 3 6 10

Eastern North DJF 2035 0.0 0.8 1.1 1.7 2.2 –6 0 3 7 12

America 2065 0.9 1.7 2.4 2.8 4.1 –2 4 7 9 18

 2100 0.7 2.2 2.9 3.8 4.8 –4 6 9 12 20

 JJA 2035 0.1 0.8 1.0 1.2 1.9 –4 0 3 5 9

 2065 0.8 1.5 2.0 2.4 3.9 –6 2 4 6 14

 2100 1.0 2.0 2.5 3.1 4.8 –7 2 5 7 14

 Annual 2035 0.4 0.8 1.1 1.3 1.9 –4 1 3 5 9

  2065 1.0 1.7 2.1 2.4 3.5 –1 3 5 7 14

  2100 1.0 2.1 2.7 3.1 4.2 –2 4 7 9 14

1280

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

Central America

Central DJF 2035 0.3 0.6 0.8 0.9 1.3 –8 –3 –1 2 10

America 2065 0.7 1.2 1.5 1.7 2.1 –15 –4 –2 3 10

 2100 1.0 1.6 1.8 2.4 2.7 –22 –5 0 2 11

 JJA 2035 0.5 0.7 0.8 1.0 1.4 –8 –3 –1 2 7

 2065 1.1 1.3 1.6 1.9 2.5 –15 –6 –2 1 6

 2100 1.1 1.6 2.0 2.5 3.2 –17 –6 –2 1 12

 Annual 2035 0.4 0.7 0.9 0.9 1.3 –8 –3 –1 1 6

  2065 1.0 1.3 1.5 1.8 2.4 –14 –6 –2 1 6

  2100 1.2 1.6 1.9 2.5 3.0 –17 –5 –2 1 9

Caribbean DJF 2035 0.3 0.5 0.6 0.7 1.0 –13 –4 0 3 8

(land and sea) 2065 0.6 1.0 1.2 1.4 1.8 –14 –6 –1 3 16

 2100 0.7 1.2 1.4 1.9 2.4 –22 –6 0 5 15

 JJA 2035 0.3 0.5 0.6 0.7 1.1 –17 –9 –6 0 11

 2065 0.7 0.9 1.1 1.4 2.0 –25 –16 –11 –4 16

 2100 0.7 1.1 1.3 1.8 2.5 –36 –18 –10 –3 13

 Annual 2035 0.3 0.5 0.6 0.7 1.1 –12 –5 –3 1 8

  2065 0.6 0.9 1.1 1.4 1.9 –19 –11 –5 –2 17

  2100 0.7 1.2 1.4 1.9 2.4 –29 –10 –5 –1 14

South America

Amazon DJF 2035 0.4 0.7 0.8 0.9 1.6 –12 –2 0 2 4

 2065 0.8 1.3 1.6 1.9 3.0 –22 –3 –1 2 6

 2100 0.7 1.7 2.0 2.5 3.7 –22 –4 –1 1 8

 JJA 2035 0.5 0.8 1.0 1.1 1.8 –14 –3 0 2 5

 2065 1.0 1.5 1.8 2.1 3.3 –25 –4 –1 2 11

 2100 1.3 1.8 2.2 2.8 4.2 –31 –4 –1 1 9

 Annual 2035 0.4 0.8 0.9 1.0 1.8 –13 –2 0 1 4

  2065 0.9 1.4 1.7 2.1 3.3 –23 –3 –1 1 7

  2100 1.0 1.8 2.1 2.8 4.0 –25 –4 –1 1 7

Northeast DJF 2035 0.4 0.6 0.7 0.9 1.3 –10 –2 1 3 17

Brazil 2065 0.8 1.3 1.5 1.7 2.3 –15 –5 0 4 21

 2100 0.8 1.6 1.8 2.4 2.9 –17 –5 –1 5 25

 JJA 2035 0.3 0.7 0.8 1.0 1.7 –16 –6 –3 2 15

 2065 0.8 1.4 1.6 1.9 3.0 –29 –10 –5 1 18

 2100 1.1 1.7 1.9 2.5 3.3 –39 –14 –9 –4 27

 Annual 2035 0.4 0.7 0.8 0.9 1.4 –11 –3 0 3 13

  2065 0.8 1.4 1.6 1.8 2.6 –17 –6 –2 3 20

  2100 1.0 1.7 1.9 2.5 3.1 –19 –7 –3 3 26

West Coast DJF 2035 0.5 0.7 0.8 0.9 1.2 –4 –1 1 3 6

South America 2065 0.9 1.2 1.5 1.7 2.1 –7 –1 1 4 7

 2100 1.0 1.6 1.9 2.2 2.9 –8 0 2 5 9

 JJA 2035 0.5 0.7 0.9 0.9 1.3 –9 –1 0 2 7

 2065 1.1 1.3 1.5 1.8 2.5 –10 –2 –1 2 9

 2100 1.3 1.6 1.9 2.4 3.0 –11 –2 1 4 11

 Annual 2035 0.5 0.7 0.8 0.9 1.2 –4 0 1 2 5

  2065 1.0 1.2 1.5 1.7 2.3 –6 –1 1 2 5

  2100 1.1 1.5 1.8 2.3 2.8 –7 0 2 4 7

1281

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

Southeastern DJF 2035 0.2 0.6 0.7 0.9 1.4 –7 0 2 4 10

South America 2065 0.7 1.1 1.3 1.6 2.4 –6 0 3 6 15

 2100 0.6 1.3 1.7 2.2 3.0 –6 1 4 7 18

 JJA 2035 0.0 0.4 0.6 0.8 1.2 –12 –1 2 6 19

 2065 0.4 1.0 1.2 1.5 2.1 –13 1 5 7 17

 2100 0.9 1.3 1.5 1.9 2.7 –18 1 4 8 27

 Annual 2035 0.3 0.5 0.6 0.8 1.3 –6 0 1 4 12

  2065 0.6 1.0 1.3 1.6 2.3 –6 1 3 6 13

  2100 0.7 1.3 1.6 2.2 2.7 –8 1 4 7 17

Europe

Northern Europe DJF 2035 –0.3 0.6 1.3 2.3 3.0 –4 2 4 6 12

 2065 –0.5 1.8 2.7 3.5 5.7 –1 3 8 11 24

 2100 –3.2 2.6 3.4 4.4 6.0 2 7 11 14 25

 JJA 2035 0.2 0.6 0.9 1.3 2.6 –6 2 4 6 11

 2065 0.0 1.2 1.8 2.5 3.6 –10 2 3 8 18

 2100 –1.1 1.6 2.2 3.0 4.7 –4 2 5 8 23

 Annual 2035 0.1 0.8 1.1 1.6 2.7 –2 2 3 6 12

  2065 –0.5 1.6 2.0 2.8 3.8 –5 3 5 9 17

  2100 –2.3 2.1 2.7 3.5 4.5 1 5 8 10 24

Central Europe DJF 2035 –0.4 0.6 1.2 1.7 2.5 –4 0 3 5 11

 2065 0.3 1.4 2.1 2.7 3.6 –3 2 6 10 17

 2100 –0.8 2.0 2.6 3.4 5.1 –4 3 7 11 18

 JJA 2035 0.3 0.9 1.1 1.5 2.4 –8 –3 0 4 9

 2065 0.4 1.7 2.0 2.6 4.3 –13 –4 1 3 8

 2100 0.4 2.0 2.7 3.0 4.6 –16 –6 0 5 13

 Annual 2035 0.3 0.7 1.1 1.4 2.3 –3 –1 2 3 8

  2065 0.4 1.5 1.9 2.4 3.2 –6 0 3 5 9

  2100 –0.3 2.0 2.6 3.1 4.0 –5 0 4 6 14

Southern Europe/ DJF 2035 –0.1 0.6 0.8 1.0 1.5 –11 –4 –2 2 8

Mediterranean 2065 0.1 1.2 1.5 1.8 2.3 –15 –6 –3 0 7

 2100 –0.2 1.5 2.0 2.4 3.0 –19 –7 –4 –1 9

 JJA 2035 0.6 0.9 1.2 1.4 2.9 –16 –7 –4 –1 5

 2065 1.0 1.9 2.2 2.6 4.3 –24 –12 –9 –4 5

 2100 1.2 2.3 2.8 3.3 5.5 –28 –17 –11 –6 2

 Annual 2035 0.3 0.8 1.0 1.2 2.0 –12 –4 –2 0 3

  2065 0.7 1.5 1.7 2.1 3.1 –14 –8 –5 –2 3

  2100 0.6 2.0 2.3 2.7 4.0 –19 –10 –6 –3 4

Africa

Sahara DJF 2035 0.1 0.8 1.0 1.1 1.5 –43 –11 –2 6 33

 2065 0.6 1.5 1.7 2.0 2.5 –29 –15 –7 1 92

 2100 0.7 1.8 2.2 2.6 3.1 –42 –14 –7 4 98

 JJA 2035 0.4 0.9 1.1 1.2 2.0 –25 –5 3 8 45

 2065 0.9 1.7 2.0 2.4 3.5 –31 –11 1 14 70

 2100 1.1 2.2 2.4 3.2 4.5 –28 –15 –1 10 108

 Annual 2035 0.4 0.9 1.0 1.1 1.5 –25 –7 0 7 45

  2065 1.0 1.6 1.8 2.2 2.8 –31 –11 –3 8 57

 2100 1.0 2.0 2.2 2.9 3.8 –27 –14 –6 9 86

1282

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

West Africa DJF 2035 0.4 0.8 0.9 1.0 1.3 –5 –1 2 3 9

 2065 0.9 1.4 1.6 1.9 2.7 –10 1 4 5 7

 2100 1.3 1.7 2.0 2.5 3.6 –5 1 4 6 11

 JJA 2035 0.6 0.7 0.8 0.9 1.2 –4 0 1 2 6

 2065 1.0 1.3 1.5 1.9 2.6 –9 –1 2 3 6

 2100 0.9 1.6 1.8 2.6 3.3 –12 0 2 4 9

 Annual 2035 0.6 0.7 0.8 0.9 1.2 –4 –1 1 3 8

  2065 1.1 1.3 1.5 1.9 2.5 –10 0 2 4 6

  2100 1.0 1.6 1.9 2.6 3.2 –8 1 3 4 8

East Africa DJF 2035 0.4 0.7 0.8 1.0 1.2 –4 –1 1 5 10

 2065 0.8 1.3 1.5 1.8 2.5 –3 –1 3 7 19

 2100 1.0 1.6 1.9 2.4 3.2 –6 –1 5 10 25

 JJA 2035 0.5 0.7 0.9 1.0 1.2 –8 –3 0 2 12

 2065 0.8 1.4 1.6 1.9 2.4 –10 –4 1 3 18

 2100 0.7 1.7 2.0 2.5 3.1 –12 –4 0 5 19

 Annual 2035 0.5 0.7 0.8 0.9 1.2 –5 –2 1 3 10

  2065 1.0 1.3 1.6 1.9 2.4 –6 –2 1 6 17

  2100 1.0 1.6 2.0 2.5 3.1 –7 –2 2 8 21

Southern DJF 2035 0.6 0.7 0.9 1.1 1.3 –11 –4 –2 0 3

Africa 2065 1.0 1.4 1.7 2.0 2.6 –19 –5 –3 –1 4

 2100 1.1 1.8 2.1 2.7 3.3 –19 –7 –3 1 5

 JJA 2035 0.5 0.8 0.9 1.0 1.5 –18 –9 –4 –1 9

 2065 1.1 1.5 1.7 2.0 2.5 –29 –13 –8 –3 4

 2100 1.4 1.8 2.1 2.6 3.3 –29 –18 –9 –3 12

 Annual 2035 0.6 0.8 0.9 1.0 1.4 –13 –5 –2 0 4

  2065 1.1 1.5 1.7 2.1 2.6 –15 –7 –4 –1 4

  2100 1.4 1.8 2.1 2.7 3.3 –20 –7 –5 –1 5

West Indian DJF 2035 0.3 0.5 0.6 0.7 1.0 –10 0 2 3 10

Ocean 2065 0.6 1.0 1.1 1.3 1.8 –10 –1 2 5 13

 2100 0.8 1.2 1.4 1.8 2.3 –9 –1 2 6 22

 JJA 2035 0.4 0.5 0.6 0.7 1.0 –5 –1 2 5 12

 2065 0.6 0.9 1.1 1.3 1.8 –7 –1 1 5 12

 2100 0.7 1.2 1.4 1.8 2.3 –7 0 2 5 19

 Annual 2035 0.3 0.5 0.6 0.7 1.0 –5 1 2 3 7

  2065 0.6 1.0 1.1 1.3 1.8 –4 –1 2 4 11

  2100 0.8 1.2 1.4 1.8 2.2 –5 0 2 5 19

Asia

West Asia DJF 2035 0.0 0.8 1.1 1.4 1.8 –12 0 3 6 14

 2065 0.5 1.5 1.9 2.3 3.2 –10 –1 2 7 21

 2100 0.6 1.9 2.4 2.9 3.8 –11 –3 4 9 20

 JJA 2035 0.2 0.9 1.1 1.3 2.1 –10 –2 1 5 55

 2065 1.1 1.7 2.1 2.6 4.0 –20 –6 –3 2 51

 2100 1.2 2.0 2.7 3.4 4.7 –29 –6 –1 4 60

 Annual 2035 0.1 0.9 1.0 1.2 1.8 –9 –2 3 4 27

  2065 0.7 1.7 1.9 2.3 3.2 –12 –2 0 4 27

  2100 0.9 2.1 2.5 3.1 4.1 –19 –2 1 6 28

1283

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

Central Asia DJF 2035 –0.1 0.8 1.3 1.6 2.4 –6 0 4 8 19

 2065 0.6 1.7 2.4 2.9 4.0 –9 –2 4 10 17

 2100 1.0 2.3 2.7 3.3 5.4 –12 –1 5 12 25

 JJA 2035 0.3 0.9 1.1 1.4 2.1 –13 –3 2 6 17

 2065 1.1 1.7 2.1 2.6 4.3 –22 –5 1 6 16

 2100 0.9 2.1 2.7 3.4 5.0 –17 –3 1 5 18

 Annual 2035 0.2 0.8 1.1 1.3 2.0 –6 –1 2 6 13

  2065 0.7 1.7 2.2 2.5 3.6 –13 –2 2 6 16

  2100 0.8 2.2 2.6 3.2 4.8 –12 –4 4 8 18

Eastern Asia DJF 2035 0.3 0.8 1.0 1.3 2.3 –9 –1 1 3 7

 2065 0.8 1.6 2.0 2.5 3.4 –5 3 5 9 16

 2100 0.9 2.1 2.7 3.1 4.7 –9 5 9 15 30

 JJA 2035 0.4 0.7 0.9 1.1 1.6 –3 0 2 3 6

 2065 0.7 1.4 1.9 2.3 3.1 –2 3 6 8 18

 2100 0.7 1.8 2.2 2.8 3.9 1 4 7 11 24

 Annual 2035 0.3 0.9 0.9 1.1 1.7 –3 0 2 3 7

  2065 0.9 1.6 1.9 2.2 3.0 –1 4 6 8 18

  2100 0.7 1.9 2.4 3.0 3.9 –1 5 7 11 21

Tibetan DJF 2035 0.0 0.9 1.2 1.5 2.2 –3 2 4 8 15

Plateau 2065 0.9 1.9 2.3 2.9 3.9 –1 6 8 12 17

 2100 1.4 2.3 2.8 3.5 5.5 2 6 11 16 25

 JJA 2035 0.4 0.9 1.1 1.3 2.3 –5 1 3 5 12

 2065 1.0 1.7 2.1 2.5 4.4 –3 2 6 9 25

 2100 0.9 2.2 2.5 3.1 5.4 –4 5 9 13 37

 Annual 2035 0.3 0.9 1.2 1.4 2.0 –2 1 4 5 11

  2065 1.0 1.8 2.2 2.6 3.6 –1 4 7 9 22

  2100 0.9 2.2 2.6 3.3 4.9 –1 6 9 14 32

South Asia DJF 2035 0.1 0.7 1.0 1.1 1.4 –18 –6 –1 4 8

 2065 0.6 1.6 1.8 2.3 2.6 –17 –3 4 7 13

 2100 1.4 2.0 2.3 3.0 3.7 –14 0 8 14 28

 JJA 2035 0.3 0.6 0.7 0.9 1.3 –3 2 3 6 9

 2065 0.9 1.1 1.3 1.7 2.6 –3 5 7 11 33

 2100 0.7 1.4 1.7 2.2 3.3 –7 8 10 13 37

 Annual 2035 0.2 0.7 0.8 1.0 1.3 –2 1 3 4 7

  2065 0.8 1.4 1.6 1.9 2.5 –2 3 7 9 26

  2100 1.3 1.7 2.1 2.7 3.5 –3 6 10 12 27

North Indian DJF 2035 0.1 0.5 0.6 0.7 1.0 –16 –3 1 7 22

Ocean 2065 0.5 1.0 1.2 1.5 1.9 –7 1 5 15 33

 2100 0.8 1.3 1.5 2.0 2.5 –9 5 9 20 41

 JJA 2035 0.2 0.5 0.6 0.7 1.0 –8 –1 2 5 16

 2065 0.6 1.0 1.2 1.4 1.9 –7 2 6 9 23

 2100 0.8 1.3 1.4 1.9 2.5 –10 5 8 12 36

 Annual 2035 0.2 0.5 0.6 0.7 1.0 –5 0 1 4 12

  2065 0.5 1.0 1.1 1.4 1.9 –4 3 6 9 22

  2100 0.9 1.3 1.5 2.0 2.5 –5 5 9 13 38

1284

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.1 (continued)

(continued on next page)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

Southeast DJF 2035 0.3 0.5 0.7 0.8 1.1 –2 1 2 4 12

Asia (land) 2065 0.6 1.1 1.3 1.6 2.2 –1 1 3 8 13

 2100 0.8 1.4 1.6 2.2 3.0 –5 2 6 9 19

 JJA 2035 0.3 0.6 0.7 0.8 1.2 –3 0 1 3 7

 2065 0.7 1.1 1.2 1.5 2.2 –2 0 3 7 13

 2100 0.8 1.4 1.5 2.0 2.7 –3 2 4 9 19

 Annual 2035 0.3 0.6 0.7 0.8 1.2 –2 0 1 3 8

  2065 0.7 1.1 1.2 1.6 2.2 –1 1 3 7 13

  2100 0.8 1.4 1.6 2.1 2.7 –2 2 5 10 18

Southeast DJF 2035 0.3 0.5 0.6 0.7 1.1 –3 0 2 3 9

Asia (sea) 2065 0.6 0.9 1.1 1.3 1.9 –4 0 3 6 10

 2100 0.9 1.2 1.4 1.7 2.5 –5 1 3 6 11

 JJA 2035 0.3 0.5 0.6 0.6 1.0 –4 0 1 2 7

 2065 0.7 0.9 1.1 1.3 1.9 –2 2 3 5 9

 2100 0.9 1.2 1.4 1.7 2.5 –1 2 3 6 16

 Annual 2035 0.3 0.5 0.6 0.7 1.0 –4 0 2 3 8

  2065 0.6 1.0 1.1 1.3 1.9 –2 1 3 5 7

  2100 0.9 1.2 1.4 1.7 2.5 –3 2 4 6 9

Australia

North Australia DJF 2035 0.2 0.6 0.9 1.1 1.9 –20 –5 –2 3 8

 2065 0.6 1.2 1.5 2.1 3.4 –18 –6 0 3 12

 2100 1.1 1.6 2.0 2.6 4.0 –31 –8 –4 3 9

 JJA 2035 0.4 0.8 0.9 1.1 1.4 –48 –10 –4 1 15

 2065 0.9 1.4 1.6 1.9 2.3 –53 –15 –7 –1 17

 2100 0.9 1.7 2.0 2.5 2.9 –46 –19 –8 2 11

 Annual 2035 0.3 0.7 0.9 1.1 1.6 –24 –6 –3 1 7

  2065 0.7 1.3 1.6 1.9 2.6 –21 –7 –2 2 11

  2100 1.0 1.7 2.0 2.5 3.4 –33 –9 –4 1 8

South Australia/ DJF 2035 –0.1 0.6 0.8 1.0 1.2 –27 –5 –2 2 7

New Zealand 2065 0.4 1.2 1.5 1.7 2.2 –18 –4 0 2 11

 2100 0.7 1.5 1.8 2.3 3.0 –17 –6 –2 2 8

 JJA 2035 0.2 0.6 0.7 0.8 1.0 –22 –3 –1 1 4

 2065 0.6 1.1 1.2 1.4 1.6 –21 –6 –3 2 11

 2100 0.7 1.4 1.6 1.8 2.4 –20 –9 –3 2 7

 Annual 2035 0.1 0.6 0.7 0.8 1.0 –24 –3 –2 1 5

  2065 0.6 1.1 1.3 1.5 1.7 –18 –5 –1 1 10

  2100 0.9 1.5 1.8 2.0 2.4 –17 –9 –2 2 7

1285

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Table 14.1 (continued)

RCP4.5   Temperature (°C) Precipitation (%)

REGION MONTH

Year min 25% 50% 75% max min 25% 50% 75% max

The Paciﬁc

Northern DJF 2035 0.2 0.5 0.6 0.7 0.9 –7 –2 0 3 11

Tropical Paciﬁc 2065 0.7 1.0 1.1 1.4 1.9 –4 –2 1 6 12

 2100 0.9 1.2 1.4 1.7 2.4 –6 –1 1 5 20

 JJA 2035 0.3 0.5 0.6 0.7 1.0 –11 –2 1 3 8

 2065 0.6 0.9 1.0 1.3 2.0 –9 –2 2 5 9

 2100 0.8 1.1 1.4 1.8 2.6 –11 –1 2 4 16

 Annual 2035 0.3 0.5 0.6 0.7 1.0 –8 –2 1 3 7

  2065 0.6 1.0 1.1 1.3 1.9 –7 –1 1 4 9

  2100 0.9 1.2 1.4 1.7 2.4 –8 0 1 4 18

Equatorial Paciﬁc DJF 2035 0.1 0.5 0.6 0.7 1.2 –9 –1 7 11 44

 2065 0.5 1.0 1.2 1.4 2.5 –4 5 12 19 226

 2100 0.4 1.2 1.5 1.8 3.3 –27 7 16 29 309

 JJA 2035 0.1 0.5 0.6 0.7 1.1 –18 5 10 14 40

 2065 0.7 1.0 1.1 1.4 2.3 0 11 15 25 143

 2100 0.5 1.2 1.5 1.8 2.9 –19 13 23 33 125

 Annual 2035 0.1 0.5 0.7 0.7 1.1 –11 3 7 12 40

  2065 0.7 1.0 1.2 1.4 2.3 –1 7 12 24 194

  2100 0.5 1.2 1.4 1.8 2.9 –23 13 19 29 225

Southern Paciﬁc DJF 2035 0.3 0.4 0.5 0.6 0.9 –7 –1 1 2 6

 2065 0.6 0.8 1.0 1.2 1.5 –22 0 2 4 6

 2100 0.8 1.0 1.3 1.5 2.0 –24 –1 3 5 8

 JJA 2035 0.3 0.4 0.5 0.6 0.9 –10 0 1 3 8

 2065 0.6 0.8 1.0 1.1 1.6 –18 –1 1 4 7

 2100 0.8 1.0 1.2 1.5 2.1 –17 –2 2 4 10

 Annual 2035 0.3 0.4 0.5 0.6 0.9 –8 0 1 2 7

  2065 0.6 0.8 1.0 1.1 1.6 –21 0 2 3 5

  2100 0.8 1.1 1.2 1.5 2.0 –21 0 2 4 6

Antarctica

(land) DJF 2035 0.1 0.5 0.6 0.8 1.3 –3 1 3 4 8

 2065 0.1 1.0 1.3 1.6 2.3 –7 3 5 8 14

 2100 0.5 1.5 1.7 2.1 3.1 –5 4 8 10 17

 JJA 2035 –0.5 0.6 0.8 0.9 1.8 –3 2 5 6 13

 2065 –0.1 1.2 1.4 1.8 2.5 1 6 8 13 16

 2100 –0.3 1.5 1.9 2.4 3.8 –1 9 12 15 23

 Annual 2035 –0.1 0.5 0.7 0.9 1.3 –3 2 4 5 9

  2065 0.0 1.1 1.3 1.7 2.3 –3 4 7 10 14

  2100 0.1 1.5 1.8 2.3 3.2 –3 7 9 13 21

(sea) DJF 2035 –0.3 0.2 0.4 0.5 0.7 –1 1 3 3 5

 2065 –0.4 0.5 0.6 0.9 1.3 0 3 4 5 8

 2100 –0.3 0.6 0.9 1.2 1.8 0 4 5 7 11

 JJA 2035 –0.7 0.4 0.6 1.0 1.9 0 2 2 4 5

 2065 –0.6 0.7 1.1 1.6 3.3 2 4 5 7 10

 2100 –0.8 1.1 1.4 2.2 3.8 3 5 7 10 13

 Annual 2035 –0.4 0.3 0.5 0.7 1.3 0 2 2 4 5

  2065 –0.5 0.5 0.8 1.2 2.3 2 3 4 6 9

  2100 –0.5 0.8 1.2 1.7 2.6 1 4 6 9 12

Notes:

Precipitation changes cover 6 months; ONDJFM and AMJJAS for winter and summer (Northern Hemisphere).

1286

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.2 | Assessed conﬁdence (high, medium, low) in climate projections of regional temperature and precipitation change from the multi-model ensemble of CMIP5 models

for the RCP4.5 scenario. Column 1 refers to the SREX regions (cf. Seneviratne et al., 2012, page 12. The region’s coordinates can be found from their online Appendix 3.A) and six

additional regions including the two polar regions, the Caribbean, Indian Ocean and Paciﬁc Island States (see Annex I for further details). Columns 2 to 4 show conﬁdence in models’

ability to simulate present-day mean temperature and precipitation as well as the most important phenomena for that region based on Figures 9.39, 9.40, and 9.45. In column 4,

the individual phenomena are listed, with associated conﬁdence levels shown below, in the same order as the phenomena. Note that only phenomena assessed in Figure 9.45 are

listed. Column 5 is an interpretation of the relevance of the main climate phenomena for future regional climate change, based on Table 14.3. Note that the SREX regions are smaller

than the regions listed in Table 14.3. Columns 6 and 7 express conﬁdence in projected temperature and precipitation changes, based solely on model agreement for 2080–2099

vs. 1985–2005, as listed in Table 14.1 and in the maps shown in Annex I. The conﬁdence is assessed for two periods for temperature (DJF and JJA) and two-half year periods for

precipitation (October to March and April to September). When the projections are consistent with no signiﬁcant change, it is marked by an asterisk (*) and the assigned conﬁdence

is medium. Further details on how conﬁdence levels have been assigned are provided in the Supplementary Material (Section 14.SM.6.1).

Present Future

SREX Region Temperature Precipitation Main Phenomenon

Relevance of

Main Phenomena

Temperature Precipitation

1. ALA M L PNA/PDO

M/M

H H/H H/H

2. CGI

H M NAO

H H/H H/H

3. WNA

M L PNA/ENSO/PDO/Monsoon

M/M/M/M

M-H H/H M/M*

4. CNA

L H PNA/ENSO

M/M

M-H H/H M*/M*

5. ENA

H H PNA/ENSO/NAO/Monsoon

M/M/H/M

M-H H/H H/M

6. CAM

H M ENSO/TC

M/H

M-H H/H M*/M*

7. AMZ

H L ENSO

M H/H M*/M*

8. NEB

H M ENSO

M-H H/H M*/L

9. WSA

M L ENSO /SAM

M/M

M-H H/H L/M*

10. SSA

H L ENSO/SAM

M/M

M-H H/H L/L

11. NEU

M H NAO/blocking

H/L

H H/H H/L

12. CEU

H M NAO/blocking

H/L

H H/H M/M*

13. MED

H H NAO/blocking

H/L

H H/H L/M

14. SAH

M L NAO

H H/H M*/M*

15. WAF

M L Monsoon/AMO

M/M

M H/H L/M*

16. EAF

H L IOD

M H/H M*/M*

17. SAF

H L SAM/TC

M/H

H H/H M*/L

18. NAS

M L NAO/Blocking

H/L

M H/H H/H

19. WAS

H L NAO/IOD/TC

H/M/H

M-H H/H M*/M*

20. CAS

M L N/A N/A H/H M*/M*

21. TIB

M L Monsoon

M H/H H/H

22. EAS

M M ENSO/Monsoon/TC

M/M/H

M-H H/H M/H

23. SAS

M M Monsoon/IOD/ENSO/TC/MJO

M/M/M/H/L

L-H H/H M*/H

24. SEA

H M Monsoon/IOD/ENSO/TC/MJO

M/M/M/H/L

L-H H/H M/M

(continued on next page)

1287

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Present Future

SREX Region Temperature Precipitation Main Phenomenon

Relevance of

Main Phenomena

Temperature Precipitation

25. NAU

H M ENSO/Monsoon/TC/IOD/MJO

M/M/H/M/L

L-H H/H M*/M*

26. SAU

H L SAM

M-H H/H M*/M*

1. Arctic (land) H L NAO

H H/H H/H

2. Arctic (sea) H L NAO

H H/H H/H

3. Antarctic (land) M M SAM

L-H H/H H/H

4. Antarctic (sea) M M SAM

L-H H/H H/H

5. Caribbean H L TC/ENSO

H/M

M-H H/H M*/M

6. West Indian

Ocean

H M IOD

N/A H/H M*/M*

7. North Indian

Ocean

H M Monsoon/MJO

M/L

N/A H/H L/M

8. SE Asia (sea) H M Monsoon/IOD/ENSO/TC/MJO

M/M/M/H/L

L-H H/H L/M

9. Northern

Tropical Paciﬁc

H L ENSO/TC

M/H

M-H H/H M*/M*

10. Equatorial

Tropical Paciﬁc

H M ENSO/MJO

M/L

M-H H/H M/M

11. Southern

Tropical Paciﬁc

H H ENSO//MJO

M/L

M-H H/H M*/M*

Table 14.2 (continued)

1288

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

Table 14.3 | Summary of the relevance of projected changes in major phenomena for mean change in future regional climate. The relevance is classiﬁed into high (red), medium (yellow), low (cyan), and ‘no obvious relevance’ (grey), based

on conﬁdence that there will be a change in the phenomena (‘HP’ for high, ‘MP’ for medium, ‘LP’ for low), and conﬁdence in the impact of the phenomena on each region (‘HI’ for high, ‘MI’ for medium, ‘LI’ for low). More information on how

these assessments have been constructed is given in the Supplementary Material (Section 14.SM.6.1).

Phenomena

Regions

Section

Monsoon Systems

MP—see Section 14.2

Tropical Phenomena

HP/MP/LP/LP—See Section 14.3

ENSO

LP—See Section 14.4

Annular and Dipolar Modes

HP—See Section 14.5

Tropical Cyclones

MP—See Section 14.6.1

Extratropical Cyclones

MP/HP—See Section 14.6.2

Arctic 14.8.2 HP/HI

The small projected increase

in NAO is likely to contrib-

ute to wintertime changes in

temperature and precipitation.

MP/HI

Projected increase in precipitation

in extratropical cyclones is likely

to enhance mean precipitation.

North America 14.8.3 MP/HI

It is likely the number of

consecutive dry days will

increase, and overall water

availability will be reduced.

HP/LI

Projected ITCZ shifts unre-

lated to ENSO changes will impact

temperature and precipita-

tion, especially in winter.

LP/HI

Likely changes in N. American

precipitation if ENSO changes.

HP/MI

The small projected increase in the

NAO index is likely to contribute to

wintertime temperature and pre-

cipitation changes in NE America.

MP/HI

Projected increases in extreme

precipitation near the centres

of tropical cyclones making

landfall along the western coast

of the USA and Mexico, the

Gulf Mexico, and the eastern

coast of the USA and Canada.

MP/HI

Projected increases in precipita-

tion in extratropical cyclones will

lead to large increases in

wintertime precipitation over the

northern third of the continent.

Central America

and Caribbean

14.8.4 MP/HI

Projected reduction in

mean precipitation .

HP/HI

Reduced mean precipitation

in southern Central America if

there is a southward displace-

ment of the East Paciﬁc ITCZ.

LP/HI

Reduced mean precipitation if

El Niño events become more

frequent and/or intense.

MP/HI

More extreme precipitation near

the centres of tropical cyclones

making landfall along the

eastern and western coasts.

South America 14.8.5 MP/HI

Projected increase in extre-

me precipitation and in the

extension of monsoon area.

HP/HI

Projected increase in the mean

precipitation in the southeast

due to the projected southward

displacement of the SACZ.

LP/HI

Reduced mean precipita-

tion in eastern Amazonia

and increased precipitation

in the La Plata Basin.

HP/HI

Poleward shift of storm tracks

due to projected positive trend

in SAMS phase leads to less

precipitation in central Chile and

increased precipitation in the

southern tip of South America.

HP/HI

Southward displacement

of cyclogenesis activity

increases the precipitation

in the extreme south.

Europe and

Mediterranean

14.8.6 HP/HI

Projected increase in the NAO will

lead to enhanced winter warming

and precipitation over NW Europe.

MP/HI

Enhanced extremes of storm-

related precipitation and decreased

frequency of storm-related precipi-

tation over the E. Mediterranean.

Africa 14.8.7 MP/HI

Projected enhancement

of summer precipita-

tion in West Africa.

HP/LI

Enhanced precipitation in parts

of East Africa due to pro-

jected shifts in ITCZ. Modiﬁed

precipitation in West or East

Africa according to variations in

Atlantic or Indian Ocean SSTs.

LP/HI

Increased precipitation in

East Africa and decreased

precipitation and enhanced

warming in southern Africa if

El Niño events become more

frequent and/or intense.

HP/HI

Enhanced winter warming

over southern Africa due to

projected increase in SAM.

MP/HI

Projected increase in extreme

precipitation near the centres

of tropical cyclones making

landfall along the eastern coast

(including Madagascar).

HP/HI

Enhanced extremes of storm-

related precipitation and decreased

frequency of storm-related precipi-

tation over southwestern Africa.

Central and

North Asia

14.8.8 MP/MI

Projected enhancement

in summer mean

precipitation.

HP/LI

Projected enhancement in winter

warming over North Asia.

East Asia 14.8.9 MP/MI

Enhanced summer precipita-

tion due to intensiﬁcation

of East Asian summer

monsoon circulation.

LP/HI

Enhanced warming if El

Niño events become more

frequent and/or intense.

MP/HI

Projected increase in extreme

precipitation near the centres of

tropical cyclones making landfall

in Japan, along coasts of east

China Sea and Sea of Japan.

MP/MI

Projected reduction in

midwinter precipitation.

(continued on next page)

1289

Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14

Table 14.3 (continued)

Phenomena

Regions

Section

Monsoon Systems

MP—see Section 14.2

Tropical Phenomena

HP/MP/LP/LP—See Section 14.3

ENSO

LP—See Section 14.4

Annular and Dipolar Modes

HP—See Section 14.5

Tropical Cyclones

MP—See Section 14.6.1

Extratropical Cyclones

MP/HP—See Section 14.6.2

West Asia 14.8.10 HP/LI

Enhanced precipitation in southern

parts of West Asia due to projected

northward shift in ITCZ.

MP/HI

Projected increase in extreme

precipitation near the centres of

tropical cyclones making landfall

on the Arabian Peninsula.

MP/LI

Projected decrease in mean

precipitation due to north-

ward shift of storm tracks.

South Asia 14.8.11 MP/MI

Enhanced summer

precipitation associated

with Indian Monsoon.

LP/MI

Strengthened break mon-

soon precipitation anomalies

associated with MJO.

LP/HI

Enhanced warming and

increased summer season

rainfall variability due to ENSO.

MP/HI

Projected increase in extreme

precipitation near the centres

of tropical cyclones making

landfall along coasts of Bay

of Bengal and Arabian Sea.

Southeast Asia 14.8.12 LP/MI

Decrease in precipitation

over Maritime continent.

HP/MI

Projected changes in

IOD-like warming pattern will

reduce mean precipitation in

Indonesia during Jul-Oct.

LP/HI

Reduction in mean precipitation

and enhanced warming if El

Niño events become more

frequent and/or intense.

MP/HI

Projected increase in extreme

precipitation near the centres of

tropical cyclones making landfall

along coasts of South China Sea,

Gulf of Thailand, and Andaman Sea.

Australia and

New Zealand

14.8.13 MP/LI

Mean monsoon pre-

cipitation may increase

over northern Australia.

HP/LI

More frequent zonal SPCZ

episodes may reduce pre-

cipitation in NE Australia.

LP/HI

Reduced precipitation in North

and East Australia and NZ if

El Niño events become more

frequent and/or intense.

HP/MI

Increased warming and

reduced precipitation in NZ and

South Aust. due to projected

positive trend in SAM.

MP/HI

More extreme precipitation

near the centres of tropical

cyclones making landfall along

the eastern, western, and

northern coasts of Australia.

HP/HI

Projected increase in extremes

of storm-related precipitation.

Paciﬁc Islands

Region

14.8.14 HP/LI

Increased mean precipitation along

equator with ITCZ intensiﬁcation.

More frequent zonal SPCZ episodes

leading to reduced precipitation in

southwest and increases in east.

HP/LI

Increased mean precipita-

tion in central/east Paciﬁc if

El Niño events become more

frequent and/or intense.

HP/HI

More extreme precipitation near

the centres of tropical cyclones

passing over or near Paciﬁc islands.

Antarctica 14.8.15 LP/MI

Increased warming over

Antarctic Peninsula and

reduced across central Paciﬁc

if El Niño events become more

frequent and/or intense.

HP/HI

Increased warming over

Antarctic Peninsula and west

Antarctic related to positive

trend projected in SAM.

HP/MI

Increased precipitation in

coastal areas due to projected

poleward shift of storm track.

1290

Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change

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