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14
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
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Review Editors:
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(New Zealand)
Climate Phenomena and
their Relevance for Future
Regional Climate Change
1218
14
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
14
1
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).
2
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
1
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
2
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}
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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}
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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}
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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
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)
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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).
AN
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
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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
s
–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
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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)
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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.
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
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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.
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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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,
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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).
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14
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
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Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change
14
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
0.5
1
1.5
2
Zonal
PAC
PAC
Zonal
Temperature ( C)
o
(%)
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
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Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14