ArticlePDF Available

Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming

June 2014
Nature 510(7504):254-8

DOI:10.1038/nature13327

Source
PubMed

Authors:

Wenju Cai

The Commonwealth Scientific and Industrial Research Organisation

Agus Santoso

UNSW Sydney

Guojian Wang

The Commonwealth Scientific and Industrial Research Organisation

Evan Weller

University of Auckland

Show all 8 authorsHide

The Indian Ocean dipole is a prominent mode of coupled ocean-atmosphere variability, affecting the lives of millions of people in Indian Ocean rim countries. In its positive phase, sea surface temperatures are lower than normal off the Sumatra-Java coast, but higher in the western tropical Indian Ocean. During the extreme positive-IOD (pIOD) events of 1961, 1994 and 1997, the eastern cooling strengthened and extended westward along the equatorial Indian Ocean through strong reversal of both the mean westerly winds and the associated eastward-flowing upper ocean currents. This created anomalously dry conditions from the eastern to the central Indian Ocean along the Equator and atmospheric convergence farther west, leading to catastrophic floods in eastern tropical African countries but devastating droughts in eastern Indian Ocean rim countries. Despite these serious consequences, the response of pIOD events to greenhouse warming is unknown. Here, using an ensemble of climate models forced by a scenario of high greenhouse gas emissions (Representative Concentration Pathway 8.5), we project that the frequency of extreme pIOD events will increase by almost a factor of three, from one event every 17.3 years over the twentieth century to one event every 6.3 years over the twenty-first century. We find that a mean state change--with weakening of both equatorial westerly winds and eastward oceanic currents in association with a faster warming in the western than the eastern equatorial Indian Ocean--facilitates more frequent occurrences of wind and oceanic current reversal. This leads to more frequent extreme pIOD events, suggesting an increasing frequency of extreme climate and weather events in regions affected by the pIOD.

Content uploaded by Guojian Wang

Content may be subject to copyright.

LETTER doi:10.1038/nature13327

Increased frequency of extreme Indian Ocean Dipole

events due to greenhouse warming

Wenju Cai

1,2

, Agus Santoso

, Guojian Wang

2,1

, Evan Weller

, Lixin Wu

, Karumuri Ashok

, Yukio Masumoto

5,6

& Toshio Yamagata

The Indian Ocean dipole is a prominent mode of coupled ocean–

atmosphere variability

1–4

, affecting the lives of millions of people in

IndianOceanrimcountries

5–15

.Initspositivephase,seasurface tem-

peratures are lower than normal off the Sumatra–Java coast, but higher

in the western tropical Indian Ocean. During the extreme positive-

IOD (pIOD) events of 1961, 1994 and 1997, the eastern cooling

strengthened and extended westward along the equatorial Indian

Ocean through strong reversal of both the mean westerly winds and

the associated eastward-flowing upper ocean currents

1,2

. This cre-

ated anomalously dry conditions from the eastern to the central Indian

Ocean along the Equator and atmospheric convergence farther west,

leading to catastrophic floods in eastern tropical African countries

13,14

but devastating droughts in eastern Indian Ocean rim countries

8–10,16,17

Despite these serious consequences, the response of pIOD events to

greenhouse warming is unknown. Here, using an ensemble of cli-

mate models forced by a scenario of high greenhouse gas emissions

(Representative Concentration Pathway 8.5), we project that the fre-

quency of extreme pIOD events will increase by almost a factor of

three, from one event every 17.3 years over the twentieth century to

one event every 6.3years over the twenty-first century. We find that

a mean state change—with weakening of both equatorial westerly winds

and eastward oceanic currents in association with a faster warming

in the western than the eastern equatorial Indian Ocean—facilitates

more frequent occurrences ofwind and oceanic current reversal. This

leads to more frequent extreme pIOD events, suggesting an increas-

ing frequency of extreme climate and weather events in regions affec-

ted by the pIOD.

In austral winter and spring, southeasterly trade winds that feed the

tropical convergence zone near the maritime continent are a feature of the

southern tropical Indian Ocean. During a pIOD event, an initial cooling off

Sumatra–Java, the eastern pole of theIndian Ocean dipole, suppresses local

convection, inducing easterly wind anomalies and a shallowing thermo-

cline.Thispromotes upwelling that in turn reinforces theinitial cooling

1,2,18

a process referred to as Bjerknes feedback. The growth of cool anomalies

causesanorthwestwardextensionofthesoutheasterlytradewinds

1,2,16

,with

anomalous easterlies along the equatorial Indian Ocean (Fig. 1a), where

weak westerlies normally prevail. Thechangeinwindpromotesconver-

gence, rainfall and warm anomalies in the equatorial western Indian Ocean.

The altered circulations induce droughts and bushfires in eastern Asia and

Australia

5–8

, floods in parts of the Indian subcontinent

and eastern

Africa

13,14

,coral reef death across western Sumatra

, and malaria outbreaks

in eastern Africa

. During extreme pIOD events, as occurred in 1961, 1994

and 1997, the anomalies, particularly the anomalous equatorial easterlies,

are far stronger (Fig. 1b), with commensurately greater impacts. During the

1997 event, devastating floods in Somalia, Ethiopia, Kenya, Sudan and

Uganda caused several thousand deaths and displaced hundreds of thou-

sands of people. In contrast, Indonesia suffered severe droughts and wild-

fires

2,16,17

made worse by the developing 1997 El Nin

˜o;theassociatedsmoke

and haze caused severe health problems to tens of millions of people in

Indonesia and surrounding countries

9,10

These dramatic impacts call for an urgent investigation into whether

extreme pIOD events will change in a warmer climate. Recent studies

have shown that greenhouse warming leads to a mean state change in the

equatorial Indian Ocean with an easterly wind trend and a faster warming

rate in the west than in the east, but referenced to the evolving mean state

there is no detectable change in either the overall frequency or amplitude

of pIOD events

19–21

. Here, using a suite of distinct process-based indica-

tors, we show that there is in fact a significant increase in the frequency of

the extreme pIOD events under greenhouse warming.

We characterize the observed extreme pIOD events in terms of their

contrast with moderate events, focusing on austral spring, the season in

which the IOD usually peaks. During extreme pIOD events, the cooling off

Sumatra is intensified by the large equatorial easterly anomalies through

generation of equatorial and coastally trapped upwelling Kelvin waves

22,23

enhanced evaporation

, and a weakening of the mean eastward oceanic

flows that transport heat eastward towards Sumatra

. The anomalous

convergence in the west, marked by increased rainfall and temperature, is

amplified through a series of processes: reduced wind speed and evapora-

tionassociatedwith the downstream extension of the southeasterly trades; a

deeper thermocline caused by the weaker eastward ocean heat transport

along the Equator

; and generation of equatorial downwelling Rossby

waves

3,4

. The warming in the west and cooling in theeast in turn strength-

ens the equatorial easterly anomalies, introducing a positive feedback

along the Equator that operates in addition to the Bjerknes feedback

centred off Sumatra–Java. The equatorial positive feedback, which is

far stronger during extreme pIOD events, leads to stronger equatorial

cooling (Extended Data Fig. 1a), and reversal of the equatorial winds and

ocean currents so that they flow towards the west (Extended Data Fig. 2f).

This creates a zone of atmospheric subsidence along the Equator char-

acterized by low rainfall and colder sea surface temperatures (SSTs) that

extend much farther to the west than during moderate pIOD events

(Fig. 1; Extended Data Fig. 1a).

Aheatbudgetanalysisfortheeastern-to-centralequatorialIndianOcean

during the IOD developing phase (July to October; Extended Data Figs 1

and 2 and Methods) clearly indicates the 1961, 1994 and 1997 events to be

the most extreme pIODs. The growth of equatorial SST anomalies during

these three events is dominated by nonlinear processes involving zonal

current anomalies. In particular, the nonlinear zonal advection, that is,

the product of the anomalous west-minus-east SST gradient with the

anomalous zonal currents (dark red bar, Extended Data Fig. 1c), sets these

three events apart from the rest. Essentially, the equatorial positive feedback

enhances anomalies of westward-flowing equatorial winds and currents,

allowing for an eventual reversal. This nonlinear process can be parame-

terized by the product of the equatorial easterly anomalies, which drive

thecurrent,andthedipolemodeindex(DMI)

, which measures the

west-minus-eastSST gradient (see Methods). Such nonlinearity also occurs

CSIRO Marine and Atmospheric Research, Aspendale, Victoria, 3195 Australia.

Physical Oceanography Laboratory, Qingdao Collaborative Innovation Center of Marine Science and Technology, Ocean

University of China, Qingdao, 266003 China.

Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, The University of New South Wales, Sydney, 2052 Australia.

Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pashan, Pune 411 008, India.

Department of Earth and Planetary Science, Graduate School of Science, The University of

Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

Climate Variation Predictability and Applicatability Research Program, Japan Agency for Marine-Earth Science and Technology (JAMSTEC),

3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan.

Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan.

254 | NATURE | VOL 510 | 12 JUNE 2014

in the eastern pole, rendering a negative skewness of SST, in that cool

anomalies off Sumatra grow to greater amplitude than warm anomalies

The strong nonlinearity along the Equator means that the representa-

tion of extreme pIOD impacts requires more than just the commonly

used DMI. This along-the-Equator nonlinearity can be represented by

two modes of empirical orthogonal function (EOF) of rainfall anomalies.

The pattern of the first EOF (EOF1, 43.4% of the total variance, Fig. 1c)

shows an east–west dipole of reduced convection, featuring anomalously

cold SSTs and a shallow thermocline in the east but anomalies of opposite

polarities in the west (Extended Data Fig. 3). This reflects characteristics

of pIOD events commonly depicted by the DMI. EOF2, which accounts

for 20.7% of the total variance, on the other hand, reflects pronounced

anomalous conditions during extreme pIOD events, as described above.

Both EOFs feature enhanced convection over the western tropical Indian

Ocean and equatorial Africa (Fig. 1d, Extended Data Fig. 3).

EOF1 and EOF2 (or the DMI) display a nonlinear relationship (Fig. 1e,

f). During a moderateevent, the two EOFs are of opposite sign. Thus, the

associated rainfall anomalies tend to offset over the central Indian

Ocean. In contrast, during an extreme pIOD, both EOFs are positive,

rendering large negative rainfall anomalies that extend westward along

0º

30º N

30º S

0.05 N m–2

a 1982 rainfall and wind anomalies b 1997 rainfall and wind anomalies

–6 –4 –2 0 2 4 6

c Observed rst principal pattern d Observed second principal pattern

–3 –2 –1 0 1 2 3

–4 –2 0 2 4

–2

1982

1987

1994

1997

2002

2006

1961

First

rinci

al com

onent

Second principal component

e Observed relationship, 1979–2010

–1 0 1 2

–2

1982

1987

1994

1997

2002

2006

1961

ole mode index

(

ºC

)

Second principal component

f Observed relationship, 1979–2010

40º E 80º E 120º E

0.05 N m–2

40º E 80º E 120º E

0º

30º N

30º S

Rainfall anomaly (mm d–1)

0.01 N m–2 0.005 N m–2

0º

30º N

30º S

0º

30º N

30º S

40º E 80º E 120º E 40º E 80º E 120º E

Rainfall anomaly (mm d–1) (s.d.)–1

Figure 1

Comparison of moderateand extreme pIOD and identification of

extreme pIOD events. a,b, September–Novemberaverage rainfall (shading, in

units of mm day

) and wind stress (vector scale is shown in the top right

corner for each panel) anomalies associated with a moderate (1982) and

extreme (1997) pIOD. c,d, Principal variability patterns of rainfall obtained by

applying a statistical and signal processing method, EOF analysis, to a satellite-

era rainfall anomaly data from the Global Precipitation Climatology Project

version 2 (see Methods), in the equatorial region (10uS–10uN, 40uE–100uE).

The associated rainfall and wind stress vectors from reanalysis data (see

Methods) are presented as linear regression onto the EOF time series. The

colour scale indicates rainfall in mmday

per 1 s.d. change; blue or red

contours indicate increased or decreased rain. Note the different vector scales

in cand d. e, Relationship between the two principal component time series.

Values for 1961 are obtained by regressing the rainfall anomaly pattern from a

reanalysis onto the EOF1 and EOF2 pattern (see Methods). An extreme pIOD

event (red dots) is defined as when the first principal component is greater

than 1 s.d. and the second principal component is greater than 0.5s.d. A

moderate pIOD event (green dots) is determined from a detrended DMI

when its amplitude is greater than 0.75 s.d. other than the 1994 and 1997

events. Negative IOD and neutral years are indicated with blue dots.

f, Relationship between the second principal component time series and the

DMI.

LETTER RESEARCH

12 JUNE 2014 | VOL 510 | NATURE | 255

–0.06 –0.04 –0.02 0 0.02 0.04

a Extreme pIOD events

Zonal wind stress anomalies (N m–2)

Number of occurrences

1900–1999

2000–2099

–0.06 –0.04 –0.02 0 0.02 0.04

100

150

200

250

c All events other than extreme plOD events

Zonal wind stress anomalies (N m–2)

Number of occurrences

–0.2 –0.15 –0.1 –0.05 0

b Extreme pIOD events

Nonlinear zonal advection (N m–2 ºC)

Number of occurrences

–0.2 –0.15 –0.1 –0.05 0

100

200

300

400

500

600

700

800

d All events other than extreme plOD events

Nonlinear zonal advection (N m–2 ºC)

Number of occurrences

Figure 3

Multi-model statistics associated with

the increase in frequency of extreme pIOD

events. a, Multi-model ensemble histogram of zonal

wind stress t

anomalies in the equatorial Indian

Ocean (5uS–5uN, 60uE–100uE), referenced to the

‘control’ period. These are averaged over the July–

October months of Indian Ocean dipole development

phase. Values during extreme pIOD years in each

period are separated into 5 310

bins

centred at the tick point for the ‘control’ (blue) and

‘climate change’ (red) periods. The multi-model

median for the ‘control’ (dashed blue line) and the

‘climate change’ (dashed red line) periods are

indicated. b,Thesameasabut for the product of t

anomalies shown in amultiplied by the DMI

(separated into 0.01 Nm

uCbins),approximating

the nonlinear zonal advection (see Methods). c,d,The

same as aand bbut for all years excluding extreme

pIOD events. The histogram for extreme pIOD is

statistically different above the 95% confidence level

from that for non-extreme pIOD events, for both the

‘control’ and the ‘climate change’ periods. On average,

nonlinear advection is greater for extreme pIOD

events than for non-extreme pIOD events.

a Modelled rst principal pattern b Modelled second principal pattern

–3 –2 –1 0 1 2 3

–4 –2 0 2 4

–4

–2

First principal component

Second principal component

c ‘Control’ period, 1900–1999

Extreme = 133

Moderate = 392

–4 –2 0 2 4

–4

–2

First principal component

Second principal component

d ‘Climate change’ period, 2000–2099

Extreme = 367

Moderate = 293

0º

30º N

30º S

0.01 N m–2

0º

30º N

30º S

0.005 N m–2

40º E 80º E 120º E 40º E 80º E 120º E

Rainfall anomaly (mm d–1) (s.d.)–1

Figure 2

Multi-model ensemble average of the

principal variability patterns of austral spring

season rainfall and their nonlinear relationship.

a, b, First and second principal variability patterns

of rainfall anomalies referenced to the ‘control’

period (1900–1999), obtained by applying an EOF

analysis to rainfall anomalies in the equatorial region

(10uS–10uN, 40uE–100uE). Note the different

vector scales in aand b. The associated pattern and

wind stress vectors beyond the domain are obtained

by a linear regression onto the EOF time series. The

colour scale indicates rainfall in mm day

per 1.0 s.d.

change; blue or red contours indicate increased or

decreased rainfall. c,d, A nonlinear relationship

between the principal component time series for the

‘control’ (1900–1999) and ‘climate change’ (2000–

2099) periods. An extreme pIOD event (red dots) is

defined as when the first principal component is

greater than 1 s.d. and the second principal

component is greater than 0.5 s.d. A moderate pIOD

event (green dots) is determined from a detrended

DMI when its amplitude is greater than 0.75 s.d. but

is not an extreme pIOD event. Negative IOD and

neutral years are indicated with blue dots. The

number of extreme and moderate pIOD events is

indicated.

RESEARCH LETTER

256 | NATURE | VOL 510 | 12 JUNE 2014

the Equator. The pIOD events of 1961, 1994 and 1997 are determined

to be ‘extreme’ when EOF1 is greater than a 1-standard-deviation (s.d.)

value and EOF2 is greater than a positive 0.5-s.d. value. The characteristics

of the pIOD events are only fully captured by the superimposition of these

two EOFs (Extended Data Fig. 4). Without EOF2, the salient feature of the

westward extending equatorial anomalies that characterizes extreme pIOD

would be missed. A similar EOF analysis on verticalvelocity vat 500 mb, a

measure of convection, generates similar patterns (Extended Data Fig. 5).

To assess the influence of greenhouse warming, we use the Coupled

Model Intercomparison Project phase 5 (CMIP5

) multi-model database.

The coupled general circulation models (CGCMs) used in this study are

forced with historical anthropogenic andnatural forcings, and future green-

house-gas emission scenarios of Representative Concentration Pathway

(RCP) 8.5, covering the 1900–2099 period. Not all of the 31 CGCMs con-

sidered here are able to simulate the characteristics of observedpIOD events.

We focus on 23 CGCMs that simulate negative skewness of SST off Sumatra

as well as the nonlinear relationship of the tworainfall EOFs (Extended Data

Table 1, Fig. 2). An identical EOF analysis of vat 500 mb in 21 out of the 23

selected CGCMs, in which vis available, produces similar spatial patterns

and their nonlinear relationship (Extended Data Fig. 6). From these 23

CGCMs, we define extreme pIOD events in the same manner as for

the observed events, and compare their frequency in the first (1900–

1999) and second (2000–2099) hundred-year periods. These two adja-

cent periods within a transient scenario are referred to as the ‘control’

and ‘climate change’ periods, respectively.

In aggregation, the frequency of extreme pIOD events based on rainfall

EOFs increases by a factor of 2.7, from about one event every 17.3 years

(133 events in 2,300 years) in the ‘control’ period, to one every 6.3 years

(367 events in 2,300 years) in the ‘climate change’ period (Fig. 2c and d).

This is statistically significant according to a bootstrap test

,underscored

by a strong inter-model consensus, with 21 out of 23 models simulating an

increase (Extended Data Table 1). Sensitivity tests to varying definitions of

extreme pIOD further support the robustness of this result (Supplementary

Tables 1 and 2).

Development of pIOD events can interact with an El Nin

˜oevent

28–30

The 1997 extreme pIOD developed in conjunction with the strongest El

Nin

˜o of the twentieth century. The 1961 and 1994 extreme pIODs on the

other hand occurred without an El Nin

˜o, supporting the notion that the

generating mechanism behind an extreme pIOD event lies within the

Indian Ocean

.WefindnoevidencethattheincreaseinextremepIOD

events in the ‘climate change’ period is induced by a change in the fre-

quency of El Nin

˜oorElNin

˜o Modoki occurrences (see Methods and Ex-

tended Data Fig. 7).

Rather, the increase in extreme pIOD events appears to arise from mean

state changes within the Indian Ocean (Extended Data Fig. 8), consis-

tent with a weakening Walker circulation as projected under greenhouse

warming

19–21

. Relative to the ‘control’ period, the altered mean state is more

conducive to equatorial easterly winds, westward oceanic currents, an en-

hanced west-minus-east SST gradient, and the associated nonlinear zonal

advection. There is a strong link between climatologically stronger easterly

winds along the Equator and more occurrences of a given nonlinear advec-

tion (correlation coefficient r50.9, not shown). These changes lead to

increasing occurrences of extreme pIOD events, because a smaller per-

turbation is required in the ‘climate change’ period to generate the same

sizeofnonlinearzonaladvectionasseenduringextremepIOD events in the

‘control’ period (see Extended Data Fig. 9). Thus, there are increased occur-

rences of extreme pIOD events for a given size of the equatorial easterly

anomaly (Fig. 3a), or a given strength of nonlinear advection (Fig. 3b). On

the other hand, the changes associated with non-extreme pIOD events are

not as apparent (Fig. 3c, d).

The increased frequency in extreme pIODs does not translate to greater

intensityofrainfallanomalies over all regions affected by the pIOD (Fig. 4a–

c). Over northeastern equatorial Africa, the extreme pIOD-induced wet

events do become more intense in the ‘climate change’ period than in the

‘control’ period (Fig. 4d; the means are statistically different above the 95%

confidence level). In contrast, there is no statistically significant difference

betweenthetwoperiodsintheintensityofdryepisodesoverJava(Fig.4f).In

addition,thedifferenceinrainfallintensityoftheextremeeventsisgenerally

a ‘Control’ period b ‘Climate change’ period c ‘Climate change’ minus ‘Control’

–3 –2 –1 0 1 2 3

–3 –2 –1 0 1 2 3 4 5 6

Eastern Africa, extreme pIOD events

Number of

occurrences

1900−1999

2000−2099

–3 –2 –1 0 1 2 3 4 5 6

200

400

600

Eastern Africa, all other events

–8 –6 –4 –2 0 2 4 6 8 10

120

Java, extreme pIOD events

Rainfall anomalies (mm d–1)

–8 –6 –4 –2 0 2 4 6 8 10

200

400

600

Java, all other events

0º

30º N

30º S

40º E 80º E 120º E

0º

30º N

30º S

0º

30º N

30º S

40º E 80º E 120º E 40º E 80º E 120º E

Rainfall anomaly (mm d–1) (s.d.)–1

Figure 4

Multi-model ensemble average of rainfall anomalies (referenced

to the ‘control’ period)during extreme pIOD events and associatedstatistics

in affected regions. a,band c, Ensemble average rainfall anomalies in the

‘control’ and ‘climate change’ periods, and their difference (‘climate change’

minus ‘control’). Stippling in cindicates regions where the differences are

statistically significant at the 95% level as determined by a two-sided Student’s t-

test. d,e, Multi-model ensemble histogram of the rainfall anomalies over northern

equatorial East Africa (0u–5uN, 37.5uE–47uE) for extreme pIOD events and

all events other than extreme pIOD events, respectively. All extreme pIOD events

in each period are separated into 0.5 mm day

bins centred at the tick point

for the ‘control’ (blue) and ‘climate change’ (red) periods. The multi-model mean

for the ‘control’ (blue dashed line) and the ‘climate change’ (red dashed line)

periods are indicated. In each period the histograms for extreme pIOD (for

example, red bars in Fig. 4d) and non-extreme pIOD (for example, red bars in

Fig. 4e) are statistically different above the 95% confidence level. f,g,Thesameas

dand e, but for the Java region (8uS–6uS, 105.5uE–108.5uE), separated into

1mmday

bins. The two histograms in dand eare statistically different above

the 95% confidence level, but this is not the case for the two histograms in fand g.

LETTER RESEARCH

12 JUNE 2014 | VOL 510 | NATURE | 257

smaller than the difference in the mean rainfall(comparing Fig. 4a and

Extended Data Fig. 8a), despite the far greateranomalies during extreme

pIOD events. This illustrates that in general the impacts of extreme

pIOD events experienced in the ‘control’ period will repeat more fre-

quently in the ‘climate change’ period.

In summary, our finding of a greenhouse-induced increased frequency

of extreme pIOD events is in stark contrast with previous results of no

change in pIOD frequency about the evolving background state. By iden-

tifying nonlinear processes that give rise to extreme pIOD events, we show

that under greenhouse warming, the evolving equatorial Indian Ocean

towards climatologically stronger west-minus-east temperature gradients

and easterly winds is more susceptible to producing more frequent

extremepIODevents.Withtheprojectedlargeincreaseinextreme

pIOD events, we should expect more frequent occurrences of devastating

weather events in affected regions.

METHODS SUMMARY

The extreme pIOD events were diagnosed using a suite of distinct process-based

indicators—such as anomalous equatorial easterlies, low rainfall and atmospheric

subsidence—asinduced by a downstreamextension of the southeasterly trades. For

observations, we focus on historical events in the satellite era (1979–present) monthly

precipitationanalysis, SSTs and othercirculation fields from a globalreanalysis (see

Methods). We focus on austral spring, September–November, in whicha pIOD typ-

ically peaks. A heat budget analysis for the eastern-to-central equatorial Indian Ocean

using the European Centre for Medium-Range Weather Forecasts - Ocean Reanalysis

System 3 revealsthat the strong nonlinear zonal advection of heat sets the observed

1994 and 1997 extremepIOD events apart from other events.The nonlinearity sug-

gests that the traditional DMI, defined as the difference in SST anomalies between

the western (50uE–70uEand10uS–10uN) and eastern (90uE–110uE and 10uS–

0uS) parts of the Indian Ocean

is not sufficient to differentiate an extreme pIOD

event. Thus, we propose an identification method for extreme pIOD, in which we

apply EOF analysis to rainfall anomalies and vertical velocity vat 500 mb in the

equatorial Indian Ocean (40uE–100uE, 10uS–10uN). This produces two principal

variability patterns, one depicting an east–westpattern and the other depicting dry

conditions along the central Indian Ocean extending from the east. An extreme

pIOD event is defined as when the first principal time series is greater than 1 s.d.,

and the second greater than 0.5 s.d. This definition exclusively captures the three

observed extreme pIOD events. To select CGCMs, the method is applied to 31

CMIP5 CGCMs, each covering 105 years of a pre-twenty-first-century climate

changesimulation usinghistorical anthropogenic andnatural forcings(1901–2005)

and a further 95 years (2006–2100) under the RCP8.5 forcing scenario

Online Content Any additional Methods, ExtendedData display items and Source

Data are available in the online version of the paper; references unique to these

sections appear only in the online paper.

Received 28 November 2013; accepted 8 April 2014.

1. Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole in the

tropical Indian Ocean. Nature 401, 360–363 (1999).

2. Webster, P. J., Moore, A. M., Loschnigg, J. P. & Leben, R. R. Coupled oceanic-

atmospheric dynamics in the Indian Ocean during 1997–98. Nature 401,

356–360 (1999).

3. Yu, L. & Rienecker, M. M. Mechanisms for the Indian Ocean warming during the

1997–98 El Nin

˜o. Geophys. Res. Lett. 26, 735–738 (1999).

4. Murtugudde, R., McCreary, J. P. & Busalacchi, A. J. Oceanic processes associated

with anomalous events in the Indian Ocean with relevance to 1997–1998.

J. Geophys. Res. 105, 3295–3306 (2000).

5. Meyers, G. A., McIntosh, P. C., Pigot, L. & Pook, M. J. The years of El Nin

˜o, La Nin

˜a,

and interactions with the tropical Indian Ocean. J. Clim. 20, 2872–2880 (2007).

6. Ummenhofer, C. C. et al. What causes southeast Australia’s worst droughts?

Geophys. Res. Lett. 36, L04706 (2009).

7. Ashok, K., Guan, Z. & Yamagata, T. Influence of the Indian Ocean Dipole on the

Australian winter rainfall. Geophys. Res. Lett. 30, 1821 (2003).

8. Cai, W., Cowan, T. & Raupach, M. Positive Indian OceanDipole events precondition

southeast Australia bushfires. Geophys. Res. Lett. 36, L19710 (2009).

9. Emmanuel, S. Impact to lung health of haze from forest fires: the Singapore

experience. Respirology 5, 175–182 (2000).

10. Frankenberg, E., McKee, D. & Thomas, D. Health consequences of forest fires in

Indonesia. Demography 42, 109–129 (2005).

11. Zubair, L., Rao, S. A. & Yamagata, T. Modulation of Sri Lankan Maha rainfall by the

Indian Ocean dipole. Geophys. Res. Lett. 30, 1063 (2003).

12. Abram, N. J., Gagan, M. K.,McCulloch, M. T., Chappell,J. & Hantoro, W. S. Coral reef

death duringthe 1997 Indian Ocean Dipole linked to Indonesian wildfires. Science

301, 952–955 (2003).

13. Behera, S. K. et al. Paramount impact of the Indian Ocean Dipole on the East

African short rains: a CGCM study. J. Clim. 18, 4514–4530 (2005).

14. Black, E., Slingo, J. & Sperber, K. R. An observational study of the relationship

between excessively strong short rains in coastal East Africa and Indian Ocean

SST. Mon. Weath. Rev. 131, 74–94 (2003).

15. Hashizume, M., Chaves, L. F. & Minakawa, N. Indian Ocean Dipole drives malaria

resurgence in East African highlands. Sci. Rep. 2, 269 (2012).

16. Schott, F. A., Xie, S.-P. & McCreary, J. P. Indian Ocean circulation and climate

variability. Rev. Geophys. 47, RG1002 (2009).

17. Page, S. E. et al. The amount of carbon released from peat and forest fires in

Indonesia in 1997. Nature 420, 61–65 (2002).

18. Udea, H. & Matsumoto, J. A. Possible triggering process of east–west asymmetric

anomaliesover the Indian Ocean in relationto 1997/1998 El Nin

˜o. J. Meteorol.Soc.

Jpn 78, 803–818 (2000).

19. Vecchi, G. A. & Soden, B. J. Global warming and the weakening of the tropical

circulation. J. Clim. 20, 4316–4340 (2007).

20. Zheng, X. T. et al. Indian Ocean Dipole response to global warming in the CMIP5

multimodel ensemble. J. Clim. 26, 6067–6080 (2013).

21. Cai, W. et al. Projected response of the Indian Ocean Dipole to greenhouse

warming. Nature Geosci. 6, 999–1007 (2013).

22. Wijffels, S. & Meyers, G. An intersection of oceanic waveguides: variability in the

Indonesian throughflow region. J. Phys. Oceanogr. 34, 1232–1253 (2004).

23. Feng, M. & Meyers, G. Interannual variability in the tropical Indian Ocean: a two-

year time scale of Indian Ocean Dipole. Deep-Sea Res. 50, 2263–2284 (2003) .

24. Wyrtki, K. An equatorial jet in the Indian Ocean. Science 181, 262–264 (1973).

25. Hong, C. C., Li, T., Lin, H. & Kug,J. S. Asymmetry of the Indian Ocean Dipole. PartI:

Observational analysis. J. Clim. 21, 4834–4848 (2008).

26. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the

experimental design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

27. Austin, P. C. Bootstrap methods for developing predictive models. Am. Stat. 58,

131–137 (2004).

28. Fischer, A. et al. Two independent triggers for theIndian Ocean dipole/zonalmode

in a coupled GCM. J. Clim. 18, 3428–3449 (2005).

29. Luo, J. J. et al. Interaction betweenEl Nin

˜o and extreme IndianOcean Dipole. J. Clim.

23, 726–742 (2010).

30. Izumo, T. et al. Influence of the state of the Indian Ocean Dipole on the following

year’s El Nin

˜o. Nature Geosci. 3, 168–172 (2010).

Supplementary Information is available in the online version of the paper.

Acknowledgements W.C. and E.W. are supported by the Australian Climate Change

Science Program,and the Goyder Institute. W.C. is also supported by a CSIRO Office of

Chief Executive Science Leader award. A.S. is supported by the Australian Research

Council.

Author Contributions W.C. conceived the study and directed the analysis. G.W. and

E.W. performed the model output analysis. A.S. conducted the heat budget analysis.

W.C. wrote the initial draft of the paper. All authors contributed to interpreting results,

discussion of the associated dynamics and improvement of this paper.

Author Information Reprints and permissions information is available at

www.nature.com/reprints. The authors declare no competing financial interests.

Readers are welcome to comment on theonline version of the paper. Correspondence

and requests for materials should be addressed to W.C. (wenju.cai@csiro.au).

RESEARCH LETTER

258 | NATURE | VOL 510 | 12 JUNE 2014

METHODS

Data, reanalyses and EOF analysis. We used data in the satellite era (1979–present)

which include Global Precipitation Climatology Project monthly precipitation ana-

lysis

, global analyses of SSTs

, and circulation fields from the National Center for

Environmental Prediction and National Center for Atmospheric Research global rea-

nalysis

. Ocean column data of velocities and temperatures for heat budget analysis

arebasedontheEuropeanCentre for Medium-Range Weather Forecasts - Ocean

Reanalysis System3 (ECMWF ORA-S3)

. We use a multivariate signal processing

method referred to as EOF analysis

to anomalies of rainfalland vertical velocity v

at 500 mb (ref. 33). The EOF techniquedeconvolves the spatio-temporalvariability

into orthogonal modes, eachdescribed by a principal spatial pattern and an assoc-

iated principal component time series.

Heat budget analysis. We examine the surface heat balance of the tropical Indian

Ocean which can be expressed as:

LTa=Lt~{½(uaLTa=LxzuLTa=LxzuaLT=Lx)

z(vaLTa=LyzvLTa=LyzvaLT=Ly)

z(waLTa=LzzwLTa=LzzwaLT=Lz)zQzresidual

ð1Þ

The variables T,u,vand ware potential temperature, and the zonal, meridional and

vertical ocean current velocities, respectively, averaged over the top 50 m. Differential

operators, x,y,zandt, are along the zonal, meridional and vertical directions, and time,

respectively. All variables are derived from the ECMWF ORA-S3 observational data

assimilation system

at a horizontal resolution of 1ulatitude by 1ulongitude, increasing

to0.3uinlatitude towards the Equator.Therate of change of themixedlayer temperature

(dT/dt) is calculated using a centred-difference approximation. Superscript ‘a’ and

overbar denote anomalous and long-term averaged quantities, respectively. Equation

(1) states that the rate of change or tendency of the surface temperature is balanced by

zonal advection of heat by the zonal current (first bracketed terms on the right hand

side), meridional advection (second bracketed terms), vertical advection (third brack-

eted terms), the net surface air–sea heat flux (Q), and all other factors not explicitly

expressed (residual), such as mixing and diffusion.

We use the entire reanalysis period of the ORA-S3, which spans 1959–2006, to

examine processes during the 1961 event. All variables in equation (1) are linearly de-

trended and averaged over the eastern-to-central equatorial region between 5uS–5uN

and 60uE–100uE, over which the 1961, 1994 and 1997 extreme events emerge as the

only pIODs that exhibit large anomalous cooling (Extended Data Fig. 1a). We examine

the heat budget terms averaged over the developing period of IndianOceandipole

events (July–October; Extended Data Fig. 1b). It may be noted that the 1997 event

exhibits exceptionally strong and prolonged cooling compared to the 1961 and 1994

events, which see an earlier start of the cooling at the end of boreal spring.

The nonlinear vertical advective process (waLTa=Lz), that is, the process associated

with anomalous upwelling and anomalous vertical temperature gradients, contributes

substantiallyto the coolingofthe equatorial Pacificduringthese events, especially during

moderate pIOD events (ExtendedData Fig. 1c). During the 1961, 1994 and 1997 events,

however, the nonlinear zonal advection term (uaLTa=Lx) is exceptionally strong,

extending notably farther to the west from the eastern Indian Ocean, as compared to

the other events (Extended Data Fig. 2a–d). Although the nonlinear vertical advection is

more prominent during the 1961 and 1997 events (Extended Data Fig. 1c), which is in

part also driven by the equatorial easterly winds, it is the nonlinear zonal advection that

setsthe1961, 1994 and 1997 eventsapartfromthe rest of the pIODs. Thisstemsfrom the

exceptionally strong westward current and its associated easterly winds (Extended Data

Fig. 2f).

As shown in Extended DataF ig.2f, the significant correlation between the zonal wind

and current (r50.87) means that the nonlinear zonal advection term over the equat-

orial region can be well approximated as a product between the zonal wind (averaged

over 5uS–5uN, 60uE–100uE) and the DMI

{uaLTa=Lx<txDMIað2Þ

with a~b=L,wherebis the regression coefficient between the zonal wind and zonal

current,andLis the longitudinal width of theequatorialbox(Extended Data Fig. 2a). This

parameterization is used to represent the nonlinear zonal advection term in the CGCMs.

Strikingly similar to the nonlinear zonal advection, the proxy exclusively identifies the

three observed extreme events. It may be noted that the proxy is further from the actual

value for the 1961 and 1997 events than for the 1994 event. This is expected, owing to the

particularlystrongnonlinearverticaladvection of the1961and 1997 events. Using93-year

time series of nine CMIP5 models for which we had access to the required variables, the

robustness of the proxy is signified by the high positive correlation coefficient with the

nonlinear advection, ranging from 0.59 to 0.92, significant above the 99% confidence level.

Characterization of extreme pIOD events. The extreme pIOD events were

characterized using a suite of distinctive process-based indicators, such as anomalous

equatorial easterlies, low rainfall and atmospheric subsidence as induced by a down-

stream extension of the southeasterly trade winds. For observations, we focus on histor-

ical events in the satellite era (1979–present) using Global Precipitation Climatology

Project monthly precipitation analysis

from http://www.esrl.noaa.gov/psd/data/

gridded/data.gpcp.html and SSTs

and other circulation fields from a global rea-

nalysis

. We focus on austral spring, September–November, in which a pIOD

typically peaks, and apply EOF analysis

to rainfall anomalies and v(ref. 33) at

500 mb in the equatorial Indian Ocean (40uE–100uE, 10uS–10uN). This produces

two principal variability patterns. The first principal pattern reflects a strong rain-

fall reduction over the eastern pole accompanied by a moderately rainfall increase

over the western equatorialIndian Ocean, and the second principal patternis char-

acterized by a westward extensionof rainfall reduction from the east, accompanied

by a rainfall increase farther west near equatorial east Africa.Note that the wetting

and northwesterly winds off Sumatra in EOF2are oppositeto the drying and south-

easterly anomalies in EOF1. Along the Equator, dry anomalies north of Sumatra

embedded in EOF2 oppose weak wet anomalies in EOF1. The opposing polarity

highlights that during extreme pIOD events, the centre of cold and dry anomalies

is not concentrated in the Sumatra region but shifts northward for a westward

extension along the Equator.

Model selection. We utilize 31 CMIP5 CGCMs forced with historical anthropogenic

and natural forcings, and future greenhouse gas under emission scenario of

Representative Concentration Pathway (RCP) 8.5

,coveringa200-yearperiod.Two

features of the nonlinearity associated with extreme pIODs are used to select models.

These are the negative skewness of SST anomalies over the eastern pole, and the non-

linear positive feedback along the Equator involving the west-minus-east SST gradient,

wind and oceanic currents, and nonlinear zonal advection, as indicated by a nonlinear

relationship between the two EOFs. These two features are not mutually inclusive and

are both used in our study.

Although the majority of CGCMs generate variability like that of the Indian Ocean

dipole, only a subgroupof CGCMs simulate the observed nonlinearocean–atmosphere

coupling over the eastern Indian Ocean as depicted by the negative skewness of SST

anomalies over the eastern pole during the austral spring (September–November),

which is 20.85 in observations since 1979.The level of nonlinearity varies vastly among

CGCMs, and we consider negative skewness of any extent. Out of the 31 CGCMs, 23

satisfytheSST skewness criterion. TheselectedCGCMs yield a mean skewnessof20.84,

close to the observed (Extended Data Table 1).

All selected 23 CGCMs reproduce the observed IOD pattern obtained by regressing

September–November SST anomalies onto the DMI, with a pattern correlation greater

than 0.75 (Supplementary Table 3). The same EOF analysis is carried out for each

individual model using rainfall anomalies referenced to the mean over the ‘control’

period. Prior to the analysis, data are interpolated into a common grid of1.5ulatitude by

1.5ulongitude. Our EOF outputs are scaled so that the EOF time series have a standard

deviation of one to facilitate an inter-model comparison and aggregation.Details of the

variance explained by EOF1 and EOF2 are listed in Supplementary Table 3. All 23

models produce the nonlinear relationship between the two leading rainfall EOFs,

indicating their abil ity to generate the nonlinear equatorial positive feedback associated

withtheextreme pIOD. Outputsofvat 500 mb areavailable in 21of the23 CGCMs, and

anonlinearrelationship between thetwoleading vertical velocity EOFsisgenerated in all

the 21 models.

We derive changes in the occurrence of extreme pIOD events by comparing the

frequency of the first 100 years (‘control’ period) to that ofthe second 100 years (‘climate

change’period).OftheeightCGCMswhicharenotabletosimulatethenegativeSST

skewness, only three CGCMs are not able to reproduce the nonlinear relationship

between the two rainfall EOFs, suggesting that the negative skewness of SST anomalies

in the eastern pole is not a prerequisite for the equatorial positive feedback associated

with extreme pIOD events. We also test the sensitivity of our results to varying defini-

tions (Supplementary Tables 1 and 2), including a case in which the criterion of the

negative SST skewness is excluded: that is, including all 31 CGCMs. In all cases, there is a

statistically significant increase (greater than a 130% increase) in the occurrences of

extreme pIOD events from the ‘control’ to the ‘climate change’ period.

Occurrences of extreme pIOD and the El Nin

˜o. The modelled increase in extreme

pIOD events is not induced by a change in the frequency in El Nin

˜o occurrences,

because there is no inter-model consensus between the two periods in the frequency

change of El Nin

˜o defined as when the quadratically detrended Nin

˜o3 (5uS–5uN,

150uW–90uW) SST is greater than 0.5 s.d. (Extended Data Fig. 7a), consistent with

previous studies

36,37

. Nor is there a statistically significa nt relationship between changes

in the number of extreme pIOD events and changes in the number of El Nin

˜oevents

(ExtendedDataFig.7a),extremeElNin

˜odefinedaswithNin

˜o3 rainfall greater than a

threshold value (Extended Data Fig. 7b)

,ordetrendedNin

˜o3 SST greater than a

threshold value (for example, 1.5 s.d) (Extended Data Fig. 7c). In addition, there is

no systematic change in the relationship between the Indian Ocean dipole and the El

Nin

˜o/Southern Oscillation (ENSO) (Extended Data Fig. 7d)

. Similarly, there is no

inter-model consensus on how Modoki El Nin

˜o, defined as occurring when the index

LETTER RESEARCH

is greater than a 0.5 s.d, will change. Nor is there a statistically significant relationship

between changes in the number ofe xtremepIOD events and changes in the number of

Modoki El Nin

˜o events (Extended Data Fig. 7e), and there is little change in the

relationship between the Indian Ocean dipole and the Modoki ENSO (Extended

Data Fig. 7f).

Statistical significance test. We use a bootstrap method

to examine whether the

change in frequency of the extreme pIOD events is statistically significant. The 2,300

samples from the 23 CMIP5 CGCMs in the ‘control’ period are re-sampled randomly

to construct another 10,000 realizations of 2,300-year records. In the random re-

samplingprocess,anyextreme pIOD event is allowed to be selected again. The standard

deviation oftheextremepIODfrequencyin the inter-realizationis11.2 events per 2,300

years,farsmallerthanthedifferenceof234eventsper2,300yearsbetweenthe‘control’

and the ‘climate change’ periods (Fig. 2c, d), indicatinga strong statistical significance.

The maximum frequency is 176, far smaller than that in the ‘climate change’ period of

367. Increasing the realizations to 20,000 or 30, 000 yields essentially an identical result.

To further confirm the statistical significance of our result with ample samples of

IOD behaviour across a longer time series without climate change forcing, we use a

Canadian model (CanESM2), in which a pre-industrial simulation of 996 years. We

examinetherarityofextremepIODeventrelativetothatinthe‘climatechange’period

with this same model. In the pre-industrial period the frequency is one per 13 years, bu t

in the ‘climate change’ period there is a 180% increase to one event per 5 years. In the

pre-industrial period, such an extreme pIOD event is far rarer.

Dividing the 996 years into 9 sets of 100 years and a set of 96 years, we find no

frequency in these sets is as high as that in the ‘climate change’ period. The lowest

frequency is one event per 25 years, and the highest frequency, one event per 7.7 years, is

50% lower than the frequency in the ‘climate change’ period. This highlights the

robustness of the greenhouse-warming-induced increase in the extreme pIOD fre-

quency, above that generated by natural variability, whichis represented by the spread

of inter-model differences.

31. Adler, R. F. et al. The version 2 Global Precipitation Climatology Project (GPCP)

monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167

(2003).

32. Rayner, N. A. et al. Global analyses of sea surfacetemperature, sea ice, and night

marine air temperature since the late nineteenth century. J. Geophys. Res. 108,

4407 (2003).

33. Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol.

Soc. 77, 437–471 (1996).

34. Balmaseda, M. A., Vidard, A. & Anderson, D. The ECMWF ocean analysis system:

ORA-S3. Mon. Weath. Rev. 136, 3018–3034 (2008).

35. Lorenz, E. N. Empirical Orthogonal Functions and Statistical Weather Prediction.

Statistical Forecast Project Report 1 (MIT Department of Meteorology, 1956).

36. Guilyardi, E. et al. Understanding El Nin

˜o in ocean–atmosphere general

circulation models: progress and challenges. Bull. Am. Meteorol. Soc. 90,

325–340 (2009).

37. Collins,M. et al. The impact of global warmingon the tropical PacificOcean and El

Nin

˜o. Nature Geosci. 3, 391–397 (2010).

38. Cai, W. et al. Increasing frequency of extreme El Nin

˜o events due to greenhouse

warming. Nature Clim. Change 4, 111–116 (2014).

39. Ashok, K., Behera,S. K., Rao, S. A., Weng, H. & Yamagata,T. El Nin

˜o Modoki and its

possible teleconnection. J. Geophys. Res. 112, C11007 (2007).

RESEARCH LETTER

Extended Data Figure 1

Heat budget analysis of the extreme pIOD events

based on an ocean reanalysis

.a, Temperature anomalies averaged over

5uS–5uN and 60uE–100uE, over the top 50 m, and over September–

November. The filled blue and red circles indicate negative and positive DMI,

with the size of the markers indicating the relative strength of the DMI. b,The

rate of change of the temperatureanomalies as a function of calendar month for

all positive DMI values, with that of 1961 shown in green, 1994 in light red and

1997 in dark red, and all others in grey. c, The heat budget components

averaged over July–Octoberof Indian Ocean dipole development phase, for the

1961, 1994 and 1997 extreme events, and a composite of moderate pIOD events

in the satellite era (1982, 1987, 2002 and 2006). The uncertainty bar on each

composite represents the range of values over the four moderate pIOD events.

The nonlinear zonal advection term (uaLTa=Lx) (dark red in c) is particularly

large during the 1961, 1994 and 1997 events (see Methods for more details).

LETTER RESEARCH

Extended Data Figure 2

Nonlinear zonal advection term over the growth

phase of pIOD events. The nonlinear zonal advection term (uaLTa=Lx)

averaged over July to October for: a, a composite of moderate pIOD events,

b, the 1961 pIOD event, c, the 1994 pIOD event and d, the 1997 pIOD event.

The moderate pIOD events taken for the composite in aare the those in the

satellite era: the 1982, 1987, 2002 and 2006 events. Stippled locations in

aindicate composite values that are significant above the 95% confidence level

(P-value ,0.05) according to a Student’s t-test. e, The approximation of the

nonlinear advection term, uaLTa=Lx, averaged over the equatorial boxed

region (5uS–5uN and 60uE–100uE; as shown in a) using the product between

the corresponding zonal wind stress and the DMI (see Methods). The DMI is a

measure of zonal gradient of temperature anomalies averaged over the western

and eastern boxed regions in a.f, The total zonal current versus totalzonal wind

stress averaged over the equatorial box region in a. A particularly strong zonal

current reversal is seen during the 1961, 1994 and 1997 pIOD events (large red

dots in f, see Extended Data Fig. 1a).

RESEARCH LETTER

Extended Data Figure 3

Circulation anomalies associated with the

principal variability patterns of austral spring (September–November)

rainfall. a–c, Vertical velocity vat 500 mb (Pa s

) from reanalysis data

(positive indicating descending motion) (a), SST (uC) (ref. 32) (b) and

thermocline depth (m) (ref. 34)(c) anomalies associated with the first principal

variability pattern (Fig.1c). The patterns are obtained through linear regression

of the corresponding variables onto the principal component time series of

EOF1. d–f, The same as for a–c, but for the second principal variability pattern

(Fig. 1d).

LETTER RESEARCH

Extended Data Figure 4

Reconstruction of an extreme pIOD and a

moderate pIOD event using the first two principal rainfall variability

patterns. a–d, Composite of anomalies associated with the 1994 and 1997

extreme pIOD events, showing the observed rainfall and wind stress

anomalies, and anomalies reconstructed from the first principal, the second

principal, and the first and second principal components combined, using

satellite-era rainfall anomaly data from the Global Precipitation

Climatology Project version 2 (ref. 31) and reanalysis wind stress

. Note

the different vector scales shown in the top right corner for each panel.

e–h, The same as a–d, but for composites of anomalies associated with the

1982, 1987, 2002 and 2006 moderate pIOD events. The exercise highlights

that the difference between a moderate and an extreme pIOD depends on

the role of the second principal component, and can only be realized with

the use of both of the two principal components.

RESEARCH LETTER

Extended Data Figure 5

Principal variabilitypatterns of vertical velocity at

500 mb (

), their nonlinear relationship, and the associated wind stress

vectors during austral spring (September–November), based on a

reanalysis

. A positive vertical velocity indicates descending, while a negative

vindicates ascending motion. a,b, Spatial patterns obtained by applying a

statistical and signalprocessing method—EOF analysis—to thevertical velocity

anomalies in the equatorial region (10uS–10uN, 40uE–100uE) for data since

1979. The associated pattern and wind stress vectors from reanalysis data are

obtained by linear regression onto the principal component time series of the

EOFs. The first and second principal spatial pattern accounts for 32.6% and

16.8% of the total variance. The colour scale indicates vertical velocityin Pa s

per 1 s.d. change; blue or red contours indicate increased or decreased

convection. Note the different vector scales shown in the top right corner in

aand b. c, A nonlinear relationship between the associated principal

component time series. An extreme pIOD event (red dots) is defined as when

the first principal component is greater than 1 s.d., and the second principal

component is greater than 0.5s.d. A moderate pIOD event (green dots) is

determined from a detrended DMI when its amplitude is greater than 0.75 s.d.,

except for the 1994 and 1997 extreme pIOD events. Negative IOD and neutral

years are indicated with blue dots.d, Relationship between the second principal

component time series and rainfall over the eastern equatorial Pacific (Nin

˜o3)

region (5uS–5uN, 150uW–90uW). While the 1997 extreme pIOD was

associated with a large rainfall in the Nin

˜o3 region, the 1961 and 1994 extreme

pIODs were not.

LETTER RESEARCH

Extended Data Figure 6

Multi-model ensemble average of the principal

variability patterns of vertical velocity at 500mb (

), their nonlinear

relationship, and the associated wind stress vectors during austral spring

(September–November). A positive vertical velocity indicates descending,

while a negative vindicatesascending motion. a,b, Spatial patterns obtainedby

applying a statistical and signal processing method—EOF analysis—to the

vertical velocityanomalies in the equatorial region (10uS–10uN,40uE–100uE).

The associated pattern and wind stressvectors are obtained by linear regression

onto the principal component time series. The colour scale below gives vertical

velocity in m s

per 1 s.d. change; blue or red contours indicate increased or

decreased convection. Note the different vector scales shown in the top right

corner in aand b.cand d, A nonlinear relationship between the two principal

component time series for the ‘control’ (1900–1999) and ‘climate change’

(2000–2099) periods. An extreme pIOD event (red dots) is defined as when the

first principal component is greater than 1 s.d. and the second principal

component is greater than 0.5s.d. A moderate pIOD event (green dots) is

determined from a detrended DMI when its amplitudeis greater than 0.75s.d.

but is not an extreme pIOD event. NegativeIOD and neutral years are indicated

with blue dots. Number of extreme pIOD and moderate pIOD events is

indicated in cand d.

RESEARCH LETTER

Extended Data Figure 7

Multi-model statistics between El Nin

˜o and pIOD

in selected CGCMs. a, Changes (‘climate change’ minus ‘control’ period) in

the number of occurrences of extreme pIOD events versus changes in the

number of El Nin

˜o events defined as when the amplitude of the detrended

Nin

˜o3 (5uS–5uN, 150uW–90uW) SST index is greater than 0.5 s.d. b, Changes

in the number of extreme pIOD events versus changesin the number of El Nin

˜o

events defined as when the Nin

˜o3 total rainfall is greater than 5 mm day

as in

ref. 38. c, The same as b, except an extreme El Nin

˜o is determined from a

detrended Nin

˜o3 (5uS–5uN, 150uW–90uW) SST index when its amplitude is

greater than 1.5 s.d. d, Correlation between a detrended Nin

˜o3 index and a

detrended DMI index

for the ‘climate change’ (yaxis) and the ‘control’ periods

(xaxis). e, Changes in the number of occurrences of extreme pIOD events

versus changes in the number of Modoki El Nin

˜o events defined as when the

amplitude of a detrended index

(see Methods) is greater than 0.5 s.d.

f, Correlation between a detrended El Nin

˜o index and a detrended DMI index

for the ‘climate change’ (yaxis) and the ‘control’ periods (xaxis). The inter-

model correlation and its statistical significance or otherwise are indicated in

the bottom right corner of each panel, with a P-value less than 0.05, indicating

significance above the 95% confidence level, a condition not met in a,b,cand

e. Models with a stronger relationship between ENSO and the Indian Ocean

dipole in the ‘control’ period tend to have a stronger such relationship in the

‘climate change’ period, and the tendency is statistically significant, although

the relationship weakens slightly in the ‘climate change’ period. The same is

true for the Modoki relationship between ENSO and the Indian Ocean dipole.

LETTER RESEARCH

Extended Data Figure 8

Multi-model ensemble average of mean state

changes for selected CGCMs. The changes (‘climate change’ minus ‘control’

period) of the ensemble average mean state for: a, rainfall (mm day

), b, SST

(uC), c, wind stress vectors (N m

) and d, thermocline depth (m). The result

shows that rainfalloff Sumatra is decreasing, the southern eastern IndianOcean

is warming at a slower rate than the west, there is a trend of easterlies over the

equatorial Indian Ocean, and the thermocline is shallowing in the eastern

equatorial Indian Ocean. Areas where changes are statistically significant at the

95% confidence level are indicated with stipples, in a, b, and d.Inc, vectors in

bold indicate statistical significance at the 95% confidence level.

RESEARCH LETTER

Extended Data Figure 9

Schematic of extreme pIOD in response to

greenhouse warming. a, pIOD events are characterized by westward-flowing

wind anomalies (blue arrow at the surface) and the associated westward-

flowing current anomalies (blue arrow at depth) acting against the prevailing

background eastward circulations (black arrows), in association with the

anomalous positive west-minus-east SST gradient. These result in generally

weaker-than-normal eastward atmosphere and ocean circulations (grey

arrows), with anomalously wet condition in the west and dry in the east.

b, During extreme pIOD events, these anomalies are amplified, with

occurrences of strong reversals of the mean eastward winds and currents (grey

arrows). As the mean Walker circulation and the associated eastward-flowing

ocean current weaken (red arrows), wind and current reversals (orange arrows)

can occur more easily in association with pIOD anomalies. Greenhouse

warming thus induces more frequent occurrences of extreme pIOD events.

LETTER RESEARCH

Extended Data Table 1

Performance of 23 selected CMIP5 CGCMs forced under climate change emission scenario RCP8.5

These CGCMs are selected in terms of SST skewness in the eastern pole of the Indian Ocean dipole (IODE) and each model’s ability to simulate the nonlinear relationship between rainfall EOF1 and EOF2 (Fig. 2).

The sensitivity of changes in extreme pIOD events from the ‘control’ period to the ‘climate change’ period to different definitions is tested. An extreme pIOD event is defined as when the first principal component

time series is greater than 1 s.d., or 1.5s.d., and the second principal component time series is greater than 0.5 s.d. Numbers in red type indicate a decrease from the ‘control’ period (1900–1999) to ‘climate

change’ period (2000–2099). Multi-model average SST skewness in the eastern pole of the Indian Ocean dipole is 20.84, compared with the observed value of 20.85. The subscripts SST, vand tindicate that the

data of SST, vertical velocity at 500 mb and surface wind stress are available, respectively.

RESEARCH LETTER

Tailored Forecasts Can Predict Extreme Climate Informing Proactive Interventions in East Africa

Article

Full-text available

Jul 2023

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.

Zonal Indian Ocean Variability Drives Millennial‐Scale Precipitation Changes in Northern Madagascar

Article

Full-text available

Nov 2023

The low latitude Indian Ocean is warming faster than other tropical basins, and its interannual climate variability is projected to become more extreme under future emissions scenarios with substantial impacts on developing Indian Ocean rim countries. Therefore, it has become increasingly important to understand the drivers of regional precipitation in a changing climate. Here we present a new speleothem record from Anjohibe, a cave in northwest (NW) Madagascar well situated to record past changes in the Intertropical Convergence Zone (ITCZ). U‐Th ages date speleothem growth from 27 to 14 ka. δ¹⁸O, δ¹³C, and trace metal proxies reconstruct drier conditions during Heinrich Stadials 1 and 2, and wetter conditions during the Last Glacial Maximum and Bølling–Allerød. This is surprising considering hypotheses arguing for southward (northward) ITCZ shifts during North Atlantic cooling (warming) events, which would be expected to result in wetter (drier) conditions at Anjohibe in the Southern Hemisphere tropics. The reconstructed Indian Ocean zonal (west‐east) sea surface temperature (SST) gradient is in close agreement with hydroclimate proxies in NW Madagascar, with periods of increased precipitation correlating with relatively warmer conditions in the western Indian Ocean and cooler conditions in the eastern Indian Ocean. Such gradients could drive long‐term shifts in the strength of the Walker circulation with widespread effects on hydroclimate across East Africa. These results suggest that during abrupt millennial‐scale climate changes, it is not meridional ITCZ shifts, but the tropical Indian Ocean SST gradient and Walker circulation driving East African hydroclimate variability.

Mesophotic coral bleaching associated with changes in thermocline depth

Article

Full-text available

Oct 2023

As global temperatures continue to rise, shallow coral reef bleaching has become more intense and widespread. Mesophotic coral ecosystems reside in deeper (30–150 m), cooler water and were thought to offer a refuge to shallow-water reefs. Studies now show that mesophotic coral ecosystems instead have limited connectivity with shallow corals but host diverse endemic communities. Given their extensive distribution and high biodiversity, understanding their susceptibility to warming oceans is imperative. In this multidisciplinary study of an atoll in the Chagos Archipelago in the central Indian Ocean, we show evidence of coral bleaching at 90 m, despite the absence of shallow-water bleaching. We also show that the bleaching was associated with sustained thermocline deepening driven by the Indian Ocean Dipole, which might be further enhanced by internal waves whose influence varied at a sub-atoll scale. Our results demonstrate the potential vulnerability of mesophotic coral ecosystems to thermal stress and highlight the need for oceanographic knowledge to predict bleaching susceptibility and heterogeneity.

Tropical Volcanic Eruptions and Low Frequency Indo‐Pacific Variability Drive Extreme Indian Ocean Dipole Events

Article

Full-text available

Oct 2023
GEOPHYS RES LETT

Volcanic eruptions can have significant climate impacts and serve as useful natural experiments for better understanding the effects of abrupt, externally forced climate change. Here, we investigate the Indian Ocean Dipole's (IOD) response to the largest tropical volcanic eruptions of the last millennium. Post‐eruption composites show a strong negative IOD developing in the eruption year, and a positive IOD the following year. The IOD and El Niño‐Southern Oscillation (ENSO) show a long‐term damped oscillatory response that can take up to 8 years to return to pre‐eruptive baselines. Moreover, the Interdecadal Pacific Oscillation (IPO) phase at the time of eruption controls the IOD response to intense eruptions, with negative (positive) IPO phasing favoring more negative (positive) IOD values via modulation of the background state of the eastern Indian Ocean thermocline depth. These results have important implications for climate risk in low‐likelihood, high‐impact scenarios, particularly in vulnerable communities unprepared for IOD and ENSO extremes.

Holocene extreme hydroclimate events in the Asian monsoon region were more frequent during cooler intervals

Article

Full-text available

Sep 2023

The areas affected by the East and South Asian monsoons contain almost half the world’s population. Understanding natural variability in these monsoons under warmer climates is critical for managing future changes. Here we present a high-resolution record of Holocene drought events derived from lake level changes in the South Asian monsoon region. By combining the published storm events in the East Asian monsoon region, we demonstrate that extreme hydroclimate events frequently occurred within a ca. 500-year cycle during the cool early-late Holocene, exhibiting a fierce Asian monsoon. In contrast, there were fewer extreme hydroclimate events during the warm mid-Holocene period. We propose that tropical temperatures and air-sea interaction in the Indo-Pacific Oceans are responsible for the occurrences of centennial-scale extreme events. Our findings suggest that tropical influences can module climate responses in monsoon regions, and the Asian monsoon may be more peaceful than hitherto expected under future global warming scenarios.

Assessing Methane Emissions From Tropical Wetlands: Uncertainties From Natural Variability and Drivers at the Global Scale

Article

Full-text available

Sep 2023
GLOBAL BIOGEOCHEM CY

Methane (CH 4 ) emissions from tropical wetlands represent half of the global wetland emissions, but uncertainties remain concerning the extent of tropical methane sources. One limitation is to conceive tropical wetlands as a single ecosystem, especially in global land surface models. We estimate CH 4 emissions and assess their environmental and anthropogenic drivers. We created a dataset with 101 studies involving 328‐point measurements, classified the sites into four wetland types, and included relevant biological and environmental information. We estimate the global CH 4 emission rate from tropical wetlands as 35 (5‐160) mg CH 4 m ‐2 d ‐1 (median, first and third quartile) and an annual global rate of 94 (56, 158) Tg y ‐1 . Fluxes differed among the wetland types, but except for anthropogenic factors, significant environmental drivers at the global scale could not be quantitatively identified due to high flux variability, even within wetland types. Coastal wetlands generate median emissions of 12(5‐23) Tg y ‐1 . Inland deep‐water wetlands emit 53 (32‐114) Tg y ‐1 , with highly variable areal extent. Emissions from inland shallow‐water wetlands are 52(33‐78) Tg y ‐1 with variation due to seasonal changes in water table level. Human‐made wetlands emit 17 (10‐4) Tg y ‐1 . Pollution and N inputs from agriculture are significant anthropogenic drivers of emissions from tropical wetlands. Specific drivers of change need to be considered according to wetland type when estimating global emissions, as well as their specific vulnerability to global change. Additionally, these differences should be contemplated when implementing wetland management practices aimed at decreasing methane emissions. This article is protected by copyright. All rights reserved.

Scientific Evidence Supporting the Progression of Climate Change–Induced Drought through History

Chapter

Jul 2023

Climate has varied during all timescales of Earth's history. Over the millions of years, Earth has swung between very cold conditions (ice age) known as the glacial periods to very warm conditions, where the annual mean temperatures rises above 10°C in polar regions, known as the interglacial period. The climate fluctuations of Earth's history suggest that no year is the same as the previous one. Climate change is observed through different atmospheric indicators, including precipitation, evaporation, temperature, solar irradiance, albedo, and changes in the greenhouse gases, and their effect on different crops, human beings, and animal behavior were reported. Also oceanic indicators like ocean heat content, sea level rise, wind waves, ocean acidification, and frequency of tropical cyclones give much information about the ongoing climate changes.

Origins of Underestimated Indian Ocean Dipole Skewness in CMIP5/6 Models

Article

Nov 2023

The Indian Ocean Dipole (IOD) is the dominant mode of interannual variability in the tropical Indian Ocean (TIO), characterized by warming (cooling) in western TIO and cooling (warming) in eastern TIO during its positive (negative) phase. Observed IOD events exhibit distinct amplitude asymmetry in relation to negative nonlinear dynamic heating. Nearly all models in phase 5 of the Coupled Model Inter-comparison Project (CMIP) simulate a less-skewed IOD than observed, but 6 out of 20 CMIP6 models can reproduce realistic high skewness. Analysis of less-skewed models indicates that the positive IOD-like biases in the mean state, which can be traced back to their weaker simulation of the preceding Indian summer monsoon, reduce the convective response to positive sea surface temperature anomalies in the western TIO, then leading to a weaker zonal wind response and weaker nonlinear zonal advection during positive IOD events. Besides, ocean stratification in the eastern TIO influences the IOD skewness: stronger stratification leads to larger mixed-layer temperature response to thermocline changes, contributing to larger anomalous vertical temperature gradient, larger nonlinear vertical advection, and thus more positive IOD skewness. Our findings underscore the importance of reducing Indian summer monsoon biases and eastern TIO stratification biases, for properly representing the IOD in Earth System Models.

Risks of Deserts Locust and Its Mitigation

Chapter

Nov 2023

Tauseef Khan Babar

The ancient migratory pest “Desert Locust” is one of the natural disasters that the generation of twenty-first century is still grappling with. Locusts belong to the family Acrididae of grasshoppers, which includes most short-horned grasshoppers. Globally, more than 10,000 grasshopper species are dispersed throughout tropical, temperate grassland, and desert areas. Of these, locusts are a group of 18–21 species with the ability to swarm over long distances. Desert locust, Schistocerca gregaria (Forksal), is the most dominant species of these with about 10 subspecies distributed in Europe, Asia, Africa, and Australia. Taxonomically, the locust and grasshopper are indistinguishable. But primary dissimilarity is that whether grasshopper species under favorable environment conditions forms a swarm (gregarious) or not (solitary). Often considered to be the “most important and dangerous of all migratory pests” which is known to invade 60 countries of the world. Interestingly, the proportion of crops damaged by locusts around the world is less than 0.2% but due to its voracious feeding, rapid reproduction, and extensive flight range, it can deprive a farmer from his sustenance in a single morning. Therefore, due to theses inherent features, the locusts have left indelible imprints in thoughts, views, scientific literature, along with arts of several cultures. Desert locust has three developmental stages (eggs, hoppers, adults), and its life cycle usually takes 2–4 months depending on the weather and ecological conditions. Although, the desert locust has been found in the Horn of Africa, since biblical times yet its intense outbreak in the recent past (2019–2020) is being linked with anthropogenic climate change and the increased frequency of extreme weather events such as El Niño and La Niña. Other factors like poor governance and the political uncertainties of these countries also intensified the plague and placed particular pressure on the already devastated nations. Unfortunately, the last locust outbreak (2019–2020) was the largest seen in the past 25, 30, 50, and 70 years in Somalia and Ethiopia, Pakistan, Iran, and Kenya, respectively, which posed serious economic, social, and environmental challenges. Overall, it affected 20 million persons in six (Ethiopia, Kenya, Somalia, South Sudan, Uganda, Tanzania) of the eight East African countries and put them at risk of acute food insecurity. The World Bank estimated that East Africa and Yemen alone suffered damages totaling US$8.5 billion. All over the world, maximum control measures implemented over years depended upon the application of conventional insecticides which are generally neurotoxic for locust. However, on account of their hazardous effects, there is an urge to invest for alternate controlling techniques which are ecofriendly as well as sustainable, like microbial insecticides. Other environmentally safe tools include georeferencing, global positioning systems, insect growth regulators, botanicals, and semiochemical traps. However, most of these are at infancy stages or not readily available in market. Herein, this chapter a review about the historical aspects, biology, and physiology of desert locusts is being presented. Furthermore, this chapter also sheds light on how climate change played a role in the in the irruption of the locust crisis, repercussions of the locusts invasion, and the potential of novel tools like remote sensing and the fungal-based biopesticides in its management is discussed.

Karakteristik Curah Hujan Monsunal Pada Periode ENSO dan IOD (Studi Kasus: Lampung, Surabaya dan Jayapura)

Article

Jul 2023

Variabilitas curah hujan di wilayah Indonesia sangat dipengaruhi oleh fenomena ENSO dan IOD. Penelitian ini bertujuan untuk mendiskripsikan karakteristik fenomena ENSO dan IOD selama periode 31 tahun, membandingkan karakteristik curah hujan dalam kondisi Normal dan karakteristik akibat fenomena ENSO dan IOD, serta menganalisis wilayah curah hujan monsunal (Lampung, Surabaya, Jayapura) yang dipengaruhi fenomena ENSO dan IOD. Hasil penelitian menunjukkan bahwa dalam dasawarsa pertama menuju dasawarsa kedua mengalami penurunan kejadian La Niña sebesar 3,4% kemudian meningkat dalam dasawarsa terakhir sebesar 4,3%, sebagaimana dominan kejadian La Niña dalam intensitas lemah yang mengalami peningkatan di setiap dasawarsanya. Pada saat fenomena IOD dalam dasawarsa pertama menuju dasawarsa kedua terjadi peningkatan IOD Positif sebesar 15,2% kemudian mengalami peningkatan yang cenderung drastis pada dasawarsa terakhir sebesar 27,2%. Pada wilayah Lampung dan Surabaya saat terjadi El Niño dan IOD Positif awal musim hujan cenderung datang lebih lambat, yang berarti musim kemarau lebih panjang terjadi pada Mei hingga November. Pada saat terjadi La Niña dan IOD Negatif awal musim hujan datang lebih cepat pada Juni hingga November dan mengalami musim hujan sepanjang tahun. Pada wilayah Jayapura curah hujan cenderung terjadi sepanjang tahun pada saat fenomena ENSO maupun IOD. Pengaruh yang ditimbulkan oleh fenomena ENSO yang terjadi di Samudra Pasifik dan IOD di Samudra Hindia lebih berpengaruh di Lampung dan Surabaya sedangkan Jayapura relatif tidak dipengaruhi oleh fenomena tersebut. Hal ini dimungkinkan oleh adanya bentangan elevasional Pegunungan Cyclops disebelah Barat Jayapura.

The ECMWF ocean analysis system ORA-S3

Article

Full-text available

Aug 2008

Influence of the state of the Indian Ocean Dipole on the following year's El Nino

Article

Full-text available

Jan 2010

El Niño-Southern Oscillation (ENSO) consists of irregular episodes of warm El Niño and cold La Niña conditions in the tropical Pacific Ocean1, with significant global socio-economic and environmental impacts1. Nevertheless, forecasting ENSO at lead times longer than a few months remains a challenge2, 3. Like the Pacific Ocean, the Indian Ocean also shows interannual climate fluctuations, which are known as the Indian Ocean Dipole4, 5. Positive phases of the Indian Ocean Dipole tend to co-occur with El Niño, and negative phases with La Niña6, 7, 8, 9. Here we show using a simple forecast model that in addition to this link, a negative phase of the Indian Ocean Dipole anomaly is an efficient predictor of El Niño 14 months before its peak, and similarly, a positive phase in the Indian Ocean Dipole often precedes La Niña. Observations and model analyses suggest that the Indian Ocean Dipole modulates the strength of the Walker circulation in autumn. The quick demise of the Indian Ocean Dipole anomaly in November–December then induces a sudden collapse of anomalous zonal winds over the Pacific Ocean, which leads to the development of El Niño/La Niña. Our study suggests that improvements in the observing system in the Indian Ocean region and better simulations of its interannual climate variability will benefit ENSO forecasts.

A Possible Triggering Process of East-West Asymmetric Anomalies over the Indian Ocean in Relation to 1997/98 El Niño

Article

Full-text available

Dec 2000

Anomalous east-west asymmetric anomalies were seen in sea surface temperature (SST), and convective activity over the equatorial Indian Ocean during October-December 1997. Using NCEP/NCAR reanalysis and sea surface height data obtained from TOPEX/POSEIDON satellite altimeter, its triggering process and strengthening mechanism are identified. The climatological wind over the equatorial Indian Ocean exhibits different seasonal cycle between its western and eastern region. During the summer monsoon season, westerly wind prevails over the western Indian Ocean, on the other hand, easterly wind is dominant over the eastern Indian Ocean at almost the same time. During the 1997 summer, divergent easterly wind anomalies were obvious over the equatorial Indian Ocean due to a warm episode of the El Niño, which weakened (accelerated) the climatological westerly (easterly) wind over the western (eastern) Indian Ocean. As a result, the east-west SST contrast was produced in the succeeding autumn through changing evaporative cooling and upwelling. Corresponding to these SST changes, the convective activities were enhanced (suppressed) over the western (eastern) Indian Ocean and actual wind became easterly in place of climatological westerly wind during October-December 1997. The above easterly anomalies induced westward-moving downwelling Rossby waves, and led to the maximum SST in January 1998 in the western Indian Ocean. On the other hand, eastward-moving downwelling Kelvin waves were generated after the termination of easterly wind anomalies, which were consistent with the SST warming in the eastern Indian Ocean for the period February-June 1998. In this manner, a coupling process between the modulated Walker Circulation associated with the El Niño event and the monsoon circulation from summer to autumn is a crucial factor for inducing the above asymmetric anomalies. Moreover, the oceanic waves are found to be closely related with enhancement of these asymmetric structures.

Indian Ocean Dipole Response to Global Warming in the CMIP5 Multimodel Ensemble*

Article

Full-text available

Aug 2013

The response of the Indian Ocean dipole (IOD) mode to global warming is investigated based on simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In response to increased greenhouse gases, an IOD-like warming pattern appears in the equatorial Indian Ocean, with reduced (enhanced) warming in the east (west), an easterly wind trend, and thermocline shoaling in the east. Despite a shoaling thermocline and strengthened thermocline feedback in the eastern equatorial Indian Ocean, the interannual variance of the IOD mode remains largely unchanged in sea surface temperature (SST) as atmospheric feedback and zonal wind variance weaken under global warming. The negative skewness in eastern Indian Ocean SST is reduced as a result of the shoaling thermocline. The change in interannual IOD variance exhibits some variability among models, and this intermodel variability is correlated with the change in thermocline feedback. The results herein illustrate that mean state changes modulate interannual modes, and suggest that recent changes in the IOD mode are likely due to natural variations.

Projected response of the Indian Ocean Dipole to greenhouse warming

Article

Full-text available

Dec 2013
NAT GEOSCI

Natural modes of variability centred in the tropics, such as the El Niño/Southern Oscillation and the Indian Ocean Dipole, are a significant source of interannual climate variability across the globe. Future climate warming could alter these modes of variability. For example, with the warming projected for the end of the twenty-first century, the mean climate of the tropical Indian Ocean is expected to change considerably. These changes have the potential to affect the Indian Ocean Dipole, currently characterized by an alternation of anomalous cooling in the eastern tropical Indian Ocean and warming in the west in a positive dipole event, and the reverse pattern for negative events. The amplitude of positive events is generally greater than that of negative events. Mean climate warming in austral spring is expected to lead to stronger easterly winds just south of the Equator, faster warming of sea surface temperatures in the western Indian Ocean compared with the eastern basin, and a shoaling equatorial thermocline. The mean climate conditions that result from these changes more closely resemble a positive dipole state. However, defined relative to the mean state at any given time, the overall frequency of events is not projected to change — but we expect a reduction in the difference in amplitude between positive and negative dipole events.

Global Analysis of Sea Surface Temperature, Sea Ice, and Night Marine Air Temperature since the Late Nineteenth Century

Article

Jan 2003

N.A. Rayner

A dipole mode in the tropical Indian Ocean

Article

Sep 1999

N. H. Saji, B. N. Goswami, P. N. Vinayachandran & T. Yamagata

For the tropical Pacific and Atlantic oceans, internal modes of variability that lead to climatic oscillations have been recognized1, ², but in the Indian Ocean region a similar ocean–atmosphere interaction causing interannual climate variability has not yet been found³. Here we report an analysis of observational data over the past 40 years, showing a dipole mode in the Indian Ocean: a pattern of internal variability with anomalously low sea surface temperatures off Sumatra and high sea surface temperatures in the western Indian Ocean, with accompanying wind and precipitation anomalies. The spatio-temporal links between sea surface temperatures and winds reveal a strong coupling through the precipitation field and ocean dynamics. This air–sea interaction process is unique and inherent in the Indian Ocean, and is shown to be independent of the El Niño/Southern Oscillation. The discovery of this dipole mode that accounts for about 12% of the sea surface temperature variability in the Indian Ocean—and, in its active years, also causes severe rainfall in eastern Africa and droughts in Indonesia—brightens the prospects for a long-term forecast of rainfall anomalies in the affected countries.

The NCEP/NCAR 40-years reanalysis project

Article

Jan 1996

E. Kalnay

Bootstrap Methods for Developing Predictive Models

Article

Feb 2004
AM STAT

Researchers frequently use automated model selection methods such as backwards elimination to identify variables that are independent predictors of an outcome under consideration. We propose using bootstrap resampling in conjunction with automated variable selection methods to develop parsimonious prediction models. Using data on patients admitted to hospitals with a heart attack, we demonstrate that selecting those variables that were identified as independent predictors of mortality in at least 60% of the bootstrap samples resulted in a parsimonious model with excellent predictive ability.

Increasing Frequency of Extreme El Nino Events due to Greenhouse Warming

Article

Jan 2014

El Niño events are a prominent feature of climate variability with global climatic impacts. The 1997/98 episode, often referred to as `the climate event of the twentieth century', and the 1982/83 extreme El Niño, featured a pronounced eastward extension of the west Pacific warm pool and development of atmospheric convection, and hence a huge rainfall increase, in the usually cold and dry equatorial eastern Pacific. Such a massive reorganization of atmospheric convection, which we define as an extreme El Niño, severely disrupted global weather patterns, affecting ecosystems, agriculture, tropical cyclones, drought, bushfires, floods and other extreme weather events worldwide. Potential future changes in such extreme El Niño occurrences could have profound socio-economic consequences. Here we present climate modelling evidence for a doubling in the occurrences in the future in response to greenhouse warming. We estimate the change by aggregating results from climate models in the Coupled Model Intercomparison Project phases 3 (CMIP3; ref. ) and 5 (CMIP5; ref. ) multi-model databases, and a perturbed physics ensemble. The increased frequency arises from a projected surface warming over the eastern equatorial Pacific that occurs faster than in the surrounding ocean waters, facilitating more occurrences of atmospheric convection in the eastern equatorial region.

Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming

Abstract

Recommended publications

A global climate change risk assessment of droughts and floods

More-frequent extreme northward shifts of eastern Indian Ocean tropical convergence under greenhouse...

Nonlinear processes reinforce extreme Indian Ocean Dipole events

Opposite response of strong and moderate positive Indian Ocean Dipole to global warming

Opposite response of strong and moderate positive Indian Ocean Dipole to global warming