Rethinking Indian monsoon rainfall prediction in the context of the recent

global warming

Bin Wang, Baoqiang Xiang, Juan Li, Peter. J. Webster, M. Rajeevan, Jian Liu, and Kyung-Ja Ha

May 2015

Nature Communication

IMD Uses

All-India Rainfall Index (AIRI)to measure and predict ISMR

The AIRI is the total amount of summer (June-to-September, JJAS) rainfall averaged over the entire

Indian subcontinent.

The AIRI represents the leading pattern of the ISMR variation very well (Mishra et al. 2012)

The composite ISMR for (a) 12 flood years (AIRI is greater than 110%) and (b) 15 drought years (AIRI is below 90%). The 27 events are selected during 1871-2012.

When the AIRI deviates by 10%, like-signed rainfall

anomalies tend to occur uniformly across India except

northeast India (Webster and Hoyos 2010).

Only 10% of the AIRI Variations can affect Indian food production and security, and Gross Domestic Product

(Gadgil and Gadgil 2006).

Great efforts have been made to understand the basic physics of the monsoon with some success (Walker 1923; Walker and Bliss 1932; Charney and Shukla 1981; Shukla and Mooley 1987; Webster et al. 1998).

However, Prediction efforts Since 1886 have not been a similar or consistent success (Rajeevan 2001; Rajeevan et al. 2007; Gadgil and Srinivasan 2011; Kim et al. 2012; Rajeevan et al. 2012)

The time series of observed (black line) and predicted AIRI The

corresponding MSSE skills for operational, ENSEMBLE and CliPAS are,

respectively, -0.36 (1989-2012), -1.92(1989-2008), and -0.86 (1989-2005).

Correlation skill of the IMD official operational

forecasts by SM for 1989-2012 is -0.12

A few more remarks

(a) The current dynamical models capture similar sources of predictability: The CC between the two MMEs’ hindcasts for 1989-2005 is 0.87.

(b) It is imparative to make aforecast mean bias-correction and variance-inflation. Both MMEs show large systematic biases in the climatological seasonal mean and small year-to-year variance compared to observations.

(c) The recent low skill of the ISMR prediction reflects secular changes in the prediction skills. The ENSEMBLE MME hindcast skill is 0.63 for 1960-1988, and 0.46 for 1960-2005.

(d) A reliable validation of the dynamical models’ performance may need hindcast data longer than 40-50 years.

(e) A more reliable estimation of the monsoon rainfall predictability needs centennial hindcast data.

Questions

How come the forecasting skill of the monsoon is so poor in the recent 2-to-3 decades?

Does the Indian monsoon possess intrinsically limited predictability? or

Is it because of the methodologies used or deficiencies in the numerical models?

Motivation

While the dynamical models are steadily improving, there are needs to develop alternative methods to

(a) possibly attain higher skills, (b) estimate the potential limit of predictability, and (c) help understanding causes of the models’ deficiencies.

Datasets (1871-2012)

(1) AIRI for the period from 1871 to 2012 from IITM and IMD

(2) National Climatic Data Center’s ERSST, v3b :1871–2012 (Smith et al. 2008).

(3) The 20C reanalysis data for the 850 hPa wind, SLP and T2 m for1871–2010 (Compo et al. 2011).

(4) The 20C merged statistical analyses of historical monthly precipitation anomalies reconstructed data from 1900 to 2008 (Smith et al. 2010).

(5) ERA-interim data for SLP for period 2011-2012 (Dee et al. 2011).

(A) 1871-1890 (B)1891-1910 (C) 1911-1930

(D)1931-1950 (E) 1951-1970 (F)1971-1990 (G)

1991-2010. The data are interpolated into 5*5

degree grid box.

Monthly accumulated numbers of SST observations and

locations of the spring predictors for JJAS mean AIRI

FORECAST datasets

1. IMD’s observations and operational forecasthttp://www.imdpune.gov.in/research/ncc/longrange/Previouslongrange/pre1989-2012.html

2. ENSEMBLES hindcast 1960-2005 (Weisheimer et al. 2009). Euro-Mediterranean Center for Climate Change (CMCC-INGV) ECMWF, Météo France (MF), UK Met Office (UKMO),

Leibniz Institute of Marine Sciences at Kiel University (IFM-GEOMAR).

3. APCC/CliPAS hindcast 1980-2009 (Wang et al. 2009)

NCEP CFS version 2 (Saha et al. 2006),

ABOM POAMA version 2.4 (Hudson et al. 2011),

GFDL CM version 2.1 (Delworth et al. 2006), and

FRCGC SINTEX-F model (Luo et al. 2005).

Approach: Physical-Empirical modelEstablish the P-E model predictors primarily based on

Understanding of the physical processes linking predictors with the predictand.

Statistical tests used as an auxiliary tool to confirm their significance and ascertain mutual independence.

Searching predictors is confined to only SST and SLP fields, and only two types of precursors,

Persistence precursor in spring that often hint positive atmosphere-ocean-land interaction processes maintaining the anomalies and the

Tendency precursor across spring that indicate the direction of the anomaly development.

AIRI-predictor relationships in the observation for the period 1900-1988.

The EP-ENSO

develops through

Bjerknes feedback,

i.e., the equatorial

zonal wind-SST

gradient feedback

through changing

oceanic thermocline depth and upwelling

1. Eastern Pacific ENSO predictor: EPT

CP-ENSO has

conspicuous impact

on ISMR (Kumar et

al. 2006) but no

predictor has been

proposed. A CP-

ENSO develops

mainly through

zonal advection by

ocean currents (Kug et al. 2009). The box locations not only physically motivated but also maximize the correlation to the predictand.

2. Central Pacific ENSO predictor: CPT

3. Mega-ENSO predictor: PSH

Mega-ENSO has

been recently

identified as a

major driver for the

NHSM including the

ISMR (Wang et al.

2013).

The mega-ENSO

involves an off-

equatorial

atmosphere-ocean

thermal feedback

between the two

PSHs/trades and

basin-wide SST

anomalies (Clement

et al. 2011)

Mega-ENSO has a pattern similar to Interdecadal Pacific Oscillation (IPO)

but involves both interannual and multidecadal variation of the Pacific

basin-wide SST variability, It is a integrated index reflecting ENSO, PDO

and IPO.

Mega-ENSO defined by SST anomaly field

Climate anomalies associated with Sevre drought and flood years during 1900-1988. Shown are the correlation coefficients between severe drought and floodand the April-May mean SLP. The severe years are defined by the observeddeviations larger than 10% of the long-term average of 848mm.

PSH predictor also capture Extreme events

All three ENSO-

related spring

predictors are

well correlated

with JJAS

growing ENSO

and the mature

ENSO anomalies

Three ENSO predictors have distinctive periodicities: the EPT peaks on

quasi-biennial and quasi-quadrennial periodicities; the CPT on four- and

eight-year cycles; and the PSH with significant quasi-biennial and multi-

decadal (60 years) peaks

Spectrum of three ENSO predictors

A decreasing SLP

tendency from March

to May in the vicinity of

Siberian High center

(near Lake of Baikal)

(NAT) represents a

spring warming and

low SLP anomaly over

the entire Asian

continent. It

foreshadows the

establishment of an

anomalous Asian

continental low in the

following summer

4. Summer Asian Low anomaly predictor: NAT

Selected four predictors

Using observation for the period 1900-1988.

EPT

CPT

PSH

NAT

Table 1 The definitions of the AIRI predictors

Name Definition meaning

EPT May-minus-March East-West SST dipole: DSST (20° S-5° N, 150° E-170° E) minus DSST (10° S-10° N, 110° W-80° W).

EP-ENSO predictor

CPT May minus April SST North-South dipole: DSST(10° S-25° S, 170° E-160° W) minus DSST (5° N-20° N, 180° -150° W)

CP-ENSO predictor

PSH April-May mean SLP averaged over (40° S-10° S, 160° W-90° W) and (10° N-30° N, 180° -130° W)

Mega-ENSO predictor

NAT May minus March SLP averaged over (45° N-60° N, 95° E-125° E)

Asian continental anomalous

warming predictor

Table 2 Correlation coefficients table summarizing the statistical

significance and mutual independence of the 4 predictors during

1900-1988. The bold (Italic) numbers denote statistically

significant at 99% (95%) confidence level.

AIRI EPT CPT PSH NAT

EPT 0.51 0.22 0.19 0.10

CPT 0.35 0.22 0.12 -0.04

PSH 0.39 0.19 0.12 -0.20

NAT -0.24 0.10 -0.04 -0.20

26

List of the IMD PredictorsDR. PAI PARAMETERS FOR 2007

EAST ASIA PR ANOMALY

WARM WATER

VOLUME

N. ATL SST ANOMALY

EQ. SE INDIAN OCEAN SST ANOMALY

NORTH WEST EUROPE TEMP

ANOMALY

NINO 3.4 SST ANOMALY

NATL PR ANOMALY

NCPAC U850 ANOMALY

Compared to previous statistical models, the superior prediction

skill of the present P-E model arises primarily from use of the

three new predictors, i.e., predictors signifying CP-ENSO,

Pacific Subtropical Highs and abnormal decay of Siberian High

across spring. Additionally, use of tendency predictors appears

to provide more skillful predictions than those obtained with monthly anomalies

Rajeevan et al. 2007

Validation of the prediction model

Independent Forecast: Training 1900-1988, independent forecast 1989-2012

92-year “independent” retrospective forecast: 1921-2012. (50-y training 10 y forecast) from 1871-2012.

The reforecast correlation skill is 0.61 and the independent forecast (1989-2012) is

0.51. The 31-y sliding correlation between the predicted and observed AIRI.

24-y independent forecast validation

Training period 1900-1988 Forecast

92- y retrospective hindcast

Reliable estimation of the practical predictability requires centennial retrospective forecasts, we developed a suite of progression (forward rolling) multi-regression prediction equations using the same four predictors (Table 1) and 142-y observations from 1871 to 2012.

At each progression step, the prediction equation is derived using only 50-y training data and the AIRI is predicted for the ensuing 10 years. These 10-y rolling predictions are “independent” in the sense that no “future” information is used beyond the training period. Starting from 1871, totally, 10 segments of 10-y predictions were made for the 92 years (1921-2012).

The 92-year retrospective forecast correlation skill reaches

0.64 (Fig. 3a), and the truly independent forecast skill for

1989-2012 is 0.51

92-year retrospective forecast

For the 92-year forecast, the P-E model generally predicts the severe

drought and flood years with correct sigsn but the amplitude is

underestimated .

Prediction of Extreme Events

Why does the Prediction in recent decades have low skills?

Time series of the 23-y sliding independent hindcast skill. Statistical significant

correlation coefficient at 95% confidence level is ± 0.413

Secular Changes in the predictors and hindcast skill

The recent 24 years are indeed a challenging period for ISMR prediction.

Drastic weakening of the AIRI-EPT predictor relationship; the land

warming predictor (NAT) is complementary to EPT; AIRI-CPT relationship

became significant in the recent three decades,

AIRI-predictor

relationships in the

multi-model ensemble

(MME) and observation

for the period 1960-2005.

MME captures the EPT-

AIRI relationship but

overestimate it

compared with the

observed weakening

relationship. The MME

also captures the AIRI-

PSH relationship

reasonably well, but

totally missed the CP-

ENSO and NAT predictors.

Why the MME hindcast skill sharply declined after 1980s

Conclusions1. Four physically consequential predictors for AIRI foreshadow EP-ENSO, Mega-ENSO, CP-ENSO and anomalous Asian Low. The latter three are new to IMD model. The last two may represent new predictability sources emerging during the recent global warming.

2. The P-E model with the four predictors can produce a 92-y retrospective independent forecast skill of 0.64 for 1921-2012 and an independent forecast skill of 0.51 for 1989-2012.

3. Recent ISMR variability has declined relationship with EP-ENSO, but increased relationship with CP-ENSO, and the anomalous spring SLP drop over the Siberian High. Dynamical models do not capture these changes.

4. The 92-y hindcast skill (0.64) may provide an estimate for the lower bound of ISMR predictabilit y. The results reveal a considerable gap between ISMR prediction skill and predictability.

Discussion

Our explanations of the physical meanings of the predictors should be viewed as hypotheses. Comprehensive numerical experiments with coupled climate model should be conducted to test these hypotheses.

The P-E model can be extended to a wide range of climate predictability and prediction problems. the proposed understanding of the physical basis for lead-lag relationships represents advancement in climate study and it is more valuable for invention of prediction tools.

The dynamical models have been significantly improving and provide an ultimate prediction tool. Thus, an updated assessment of the current dynamical models’ hindcast skill is needed.

DiscussionThe decrease in the recent AIRI prediction skill

concurs with the most prominent recent global warming. Since late 1970s the breakdown of the EPT-AIRI relationship is unprecedented over the last century; and both the CP-ENSO and the Asian continent warming predictors have increased their correlation with AIRI.

More frequent occurrence of CP-ENSO has been speculated for both anthropogenic origin (Yeh et al. 2012) and multi-decadal origin (Xiang et al. 2013).

The secular change of the predictor-predictand relationships seems to be affected by both global warming and a multi-decadal natural variation, but the precise mechanisms require further studies.

How predictable is the anomaly pattern of the Indian summer

rainfall?

Juan Li and Bin Wang(Work in progress)

Finding form dynamical model prediction:

Prediction of the spatial distribution of the ISMR (areaaveraged TCC=0.16) is much more difficult thanprediction of the AIRI (TCC=0.43) for five modelsMME’s prediction during 1960-2005.

Outstanding problems :

• To what extent the ISMR distribution is predictable?

• What is the effective way for forecasting the ISMRanomaly pattern?

Data India gridded rainfall data：

Monthly high resolution (1ox1o) gridded rainfall dataset (Rajeevan et al. 2006;

Rajeevan et al. 2008) from IMD are used. The summer monsoon rainfall from

June to September (JJAS) is calculated for the period of 1960-2012.

SST data :

National Climatic Data Center’s Extended Reconstructed Sea Surface

Temperatur (ERSST, v3b) at 2o spatial resolution for the period 1960–

2012(Smith et al. 2008)

SLP and 850 hPa wind data:The gridded 2.5ox2.5o global monthly sea level pressure (SLP) and 850hpa

wind field are derived from NCEP/NCAR reanalysis dataset (Kalnay et al. 1996)

for the period 1960–2012.

Global rainfall data:

The twentieth century (20C) merged statistical analyses of historical monthly

precipitation anomalies reconstructed data at 2.5o spatial resolution for the

period from 1900 to 2008(Smith et al. 2010)

Dynamical models

The hindcasts of five state-of-the-art coupled dynamical models are derived

from the ENSEMBLES project (Weisheimer et al. 2009), including models from

the Euro-Mediterranean Center for Climate Change (CMCC-INGV), European

Centre for Medium-Range Weather Forecasts (ECMWF), the Leibniz Institute of

Marine Sciences at Kiel University (IFM-GEOMAR), Météo France (MF), and UK

Met Office (UKMO).

To what extent the ISMR distribution is predictable? Predictable mode analysis approach (PMA)

The predictable modes are determined by the following criteria:

• Represents major patterns of climate variability in the predictand field;

• Ideally they are statistically separable from other higher modes;

• The dynamical origins of these modes can be reasonably well understood;

• The dynamic models or/and P-E models are capable of predicting these modes with significant skills.

Assuming that the predictable modes can be predicted perfectly, the potential predictability can then be estimated by the fractional variance accounted for by the predictable modes.

Major patterns of the interannual variability

Physical interpretation of the major patterns

Simultaneous (JJAS) correlation fields between the (a) precipitation (shading) and

850hPa wind PC1 and (b) SST (shading), SLP (contours). (c) and (d) are same as (a)

and (b), respectively, but for PC2. (e) and (f) are same as (a) and (b), respectively, but

for PC3.

Identification of predictable modes

The first three modes are source

of the prediction skill in

dynamical model MME

Potential predictability: 0.61

What is the effective way for forecasting the ISMR

anomaly pattern? Physics-based Empirical model

Physics-based Empirical model (Wang et al., 2014; Yim et al., 2014) is

based on understanding of the physical linkages between the predictors

and predictand.

Searching for the predictors :

1. Principle : physical meaning

2. Two fields : SST/SLP

3. Two types of precursory:

Persistent signal:April-May mean

Tendency signal : Long term tendency (from Dec.-Jan. to Apr.-

May)&Short-term tendency (March/April to May )

Stepwise regression -> significance & independency

Physical meaningful predictors -- PC1

Predictors Implication

SST over ocean (shading with blue to red), precipitation over land (shading

with yellow to green), SLP (contours) ,850 hPa winds (vectors)

SST over ocean (shading with blue to red), precipitation over land (shading with

yellow to green); SLP (contours);850 hPa winds (vectors)

Physical meaningful predictors – PC2Predictors Implication

Physical meaningful predictors – PC3

PredictorsImplication

PC3a

PC3b

Predictor Definition

PC1a May minus April N-S SST dipole: SST (10° S-50° S, 175° E-150° W) minus SST (0° N-25° N, 180° -140° W)

PC1b April-May minus December-January SST averaged over the western Pacific K-shape area minus eastern Pacific triangle (40° S-40° N, 145° E-80° W)

PC1c May minus March SST averaged over (10° S-30° S, 60° E-125° E)

PC2a April-May mean SLP averaged over (40° S-15° N, 155° E-130° W)

PC2b April-May minus December-January SST averaged over (20° S-5° S, 100° E-160° E)

PC3a April-May mean SLP averaged over (10° N-40° N, 175° -140° W) and (30° S-55° S, 160° E-150° W)

PC3b April-May mean SLP averaged over (30° N-45° N, 25° W-10° E) minus (50° N-70° N, 10° W-20° E)

(We use the grids in the defined region which correlation

coefficients with corresponding PC are significant at 95%

confidence level).

Definitions of predictors.

PC1 PC1a PC1b PC1c

PC1a 0.59 0.27 -0.32

PC1b 0.54 0.27 -0.51

PC1c -0.58 -0.32 -0.51

PC2 PC2a PC2b

PC2a 0.49 0.34

PC2b 0.49 0.34

PC3 PC3a PC3b

PC3a 0.57 0.35

PC3b 0.57 0.35

The correlation coefficients between each PC and corresponding predictors and

among each other during the period of 1960-2005. The bold (Italic) numbers denote

statistically significant at 99% (95%) confidence level.

Summary Of Predictors

(a) Cross-validated reforecast. Leave-five-out cross validation is used to validate the

reforecast skill.

(b) Independent forecast. For independent forecast, the step-wise regression model is

built using the data from a training period, no future information beyond the training

period are utilized.

(c) Rolling-retrospective forecast. Progressional prediction models are built for rolling-

retrospective forecast (20yr-10yr).

Three validation methods

The prediction skills (correlation coefficients) for WCI and PI. The bold (Italic) numbers

denote statistically significant at 99% (95%) confidence level.

PC1 PC2 PC3

Cross validated Reforecast (1960-2005) 0.71 0.56 0.63

Independent Forecast (2006-2012) 0.80 0.82 0.47

Rolling-Retrospective Forecast (1980-2012) 0.66 0.59 0.53

The hindcast skills of the P-E prediction model

NFig 8. The

corresponding PC of

the first three EOF

modes (a–c) in

observation (OBS),

P-E model (PEM)

cross-validated

prediction and Multi-

Model Ensemble

(MME)dynamical

prediction from 1960

to 2005. The

numbers within the

parenthesis in the

figure legend indicate

the TCC between the

observed and

predicted PC.

The correlation skills of (a) cross-validated reforecast, (b) independent forecast and (c)

rolling-retrospective forecast for each grid in Indian obtained from physical empirical

model. The number in the right upper corners indicates averaged correlation skill over

India.

Predicted ISMR precipitation anomaly pattern

The pattern correlation coefficient skill for ISMR prediction as a function of

forecast year using 5-year out cross-validated physical empirical prediction

(PEM), prediction with the MME’s 3 modes (MME3M) and potential attainable

forecast skill using observed 3 modes (OBS3M). The numbers within the

parenthesis in the legend indicate the averaged PCC skill.

The year-to-year variation of the prediction skills of rainfall

pattern

Conclusion

• Major modes of ISMR (The first EOF mode represents a nearly uniform pattern acrossIndia. Second EOF mode is a north-south dipole rainfall pattern. Third EOF mode mainlystands for rainfall anomaly over north of Gangetic Plain and northeastern India. The firstthree EOF modes account for 51.2% of total interannual variance, or together they canexplain over half of Indian summer rainfall variability. )

• Dynamical origins of the first three modes (The SST anomalies associated withEOF1, EOF2 and EOF3 are characterized by an EP-type La Nina, a CP-type La Nina, and acooling center near dateline, respectively.)

• The first three modes can be regarded as potentially predictable modes.

• A set of P-E models is established to predict the principal component ofeach predictable mode based on the understanding of monsoon dynamicsand the lead-lag relationship between the predictors and predictand .

• The validated TCC skills of the P-E model here are more than doubled thatof dynamical models’ MME hindcast.

P-E model1 P-E model2 MME

1960-2005 0.67(cross-validatedreforecast)

0.72 (cross-validatedreforecast)

0.43 (all modes) 0.41 (3 modes)

1980-2005 0.44(rolling-retrospective forecast)

0.65 (rolling-retrospective forecast)

0.18 (all modes) 0.14 (3 modes)

Prediction skills of all Indian rainfall index (AIRI) made by P-E model (1 denotes

building for direct prediction of AIRI (Wang et al., 2014a); 2 denotes building from

predictable modes) and MME during the period of 1960-2005 and 1980-2005. The

bold (Italic) numbers denote statistically significant at 99% (95%) confidence level.

To what extent the P-E model can predict the All Indian

Rainfall Index (AIRI) compared with the dynamical models’

MME prediction

Discussion

• The methodology proposed here can be applied to a wide range of climateprediction and predictability studies. (Another independent forecast tool forAIRI.)

• Causative processes linking the predictors and the major modes of the ISMRhave been speculated, thus, further well-designed numerical experiments areneeded to validate or refuse the articulations proposed in the present study.

• 46-year retrospective cross-validated forecast correlation skills are likely inflatedbecause all the data (model development and validation) are used to select thepredictors

• The predictors derived from the current 46 years of data for ISMR patterns mayvary with time or experience secular changes.

• We anticipate that the established prediction equations will be useful for nextfew years, but continuous detection of secular changes and modifications of thepredictors/prediction equations are imperative.

Thank you for your comments

END

The ISMR variations are primarily driven by eastern Pacific (EP) ElNino-Southern Oscillation (ENSO) through tropical teleconnection

Climate anomalies associated with a positive AIRI.

Correlation map between AIRIand the snow cover during the winter and spring.

Spring reduced snow in Ural Mountain and northern Asia favors Siberian favors warming up of Asian land.

Sensitivity of location

AIRI EPT CPT PSH NAT

CPT_5-4 0.35 0.22 0.12 -0.04

CPT_paral 0.30 0.21 0.04 -0.05

PSH_45 0.39 0.19 0.12 -0.20

PSH_L 0.35 0.23 0.21 -0.11

Original CPT_paral PSH_L

Independent（1989-2012）

0.51 0.43 0.50

RetrospectiveIndependent(1921-2012）

0.63 0.60 0.63

Change location of CPT to parallel, the 92-y independent hindcast reduces from 0.63 to 0.60 and 24-y independent hindcast reduces from 0.51 to 0.43.

Juan: the 24-y hindcast is obtained by rolling forecast, right?

Fig. S3(new) The composite JJAS SLP differential anomalies between the extremely wet and extremely dry years during 1900-1988. The extremely wet (dry) years are defined by their deviation from mean are greater (smaller) than one standard deviation. The black lines outline the region for the predictor PSH (Table 1).

Fig. S The composite winter-spring mean SLP differential anomalies between the extremely wet and extremely dry years during 1900-1988. The extremely wet (dry) years are defined by their deviation from mean are greater (smaller) than one standard deviation. The black lines outline the region for the predictor PSH (Table 1).

AIRI EPT CPT PSH NAT

EPT_5-3 0.51 0.22 0.19 0.10

CPT_5-4 0.35 0.22 0.12 -0.04

PSH_45 0.39 0.19 0.12 -0.20

NAT_5-3 -0.24 0.10 -0.04 -0.20

CPT_5-3 0.28 0.24 -0.11 -0.03

PSH_5-3 0.15 -0.03 -0.11 -0.21

Sensitivity of tendency

Original CPT_5-3 PSH_5-3

Independent（1989-2012）

0.51 0.35 0.55

RetrospectiveIndependent(1921-2012）

0.64 0.56 0.63

1900-1988 COR

The correlation between JJAS AIRI and tendency of SST from March to May

AIRI EPT CPT PSH NAT

EPT_5-3 -0.41 -0.24 -0.20 -0.11

CPT_5-3 0.42 -0.24 0.34 -0.003

PSH_45 0.40 -0.20 0.34 -0.20

NAT_5-3 -0.24 -0.11 -0.003 -0.20

Original New

Independent （1989-2012） 0.51 0.32

Retrospective Independent(1921-2012）

0.64 0.52

The correlation between JJAS AIRI and tendency of SST from April to May

Original New

Independent （1989-2012） 0.51 0.45

Retrospective Independent(1921-2012）

0.64 0.59

AIRI EPT CPT PSH NAT

EPT_5-4 -0.42 -0.22 -0.21 -0.01

CPT_5-4 0.45 -0.22 0.24 -0.03

PSH_45 0.40 -0.21 0.24 -0.20

NAT_5-3 -0.24 -0.01 -0.03 -0.20

(a)March-April-May

mean T2m/SSTduring the period of

1900-1988. The red

box denotes the

location in which the

NAT index is defined.

(b)JJAS mean SLP(contour) , land

precipitation (green

to yellow shading)

and 850 hPa winds

during 1900-1988.

Anomalies associated with the Land predictor reversed NAT.

92-y independent hindcast and secular changes of the forecast skill and AIRI-

predictor relationships a, The observed (black) and the 92-y independent hindcast (red)

AIRI with the empirical prediction model using four predictors listed in Table 1. The

correlation and MSSE skill (see supplementary information) are 0.64 and 0.40, respectively,

for the period 1921-2012. The independent forecast skill for 1989-2012 is 0.51. b, Time

series of the 23-y sliding independent hindcast skill (thick red) and the 23-y sliding

correlation coefficients between the JJAS AIRI and each of the four predictors: EPT, CPT,

reversed NAT and PSH. The statistical significant correlation coefficient at 95% confidence

level is ± 0.413 for the sample size of 23 which is indicated by the dashed lines. The

symbols for the predictors are explained in Table 1.

50y-1y 50y-5y 50y-10y

0.64 0.63 0.64

40y-1y 40y-5y 40y-10y

0.62 0.61 0.61

30y-1y 30y-5y 30y-10y

0.60 0.60 0.59

Table S1 Sensitivity of the retrospective prediction skills to

the training period and forecast period. The forecast skill is measured by the correlation coefficient between observation and progression forecast during 1921-2012. Symbol “50y-1y”

means training period is 50 years and the forecast period is 1 year, and so on.

Time series of 21-y sliding correlation coefficients (A) between the JJAS AIRI and

JJAS EP-ENSO and CP-ENSO indices and (B) between the JJAS AIRI and each

of the four predictors: EPT, CPT, reversed NAT and PSH. The statistical

significant correlation coefficient at 95% confidence level is ± 0.413 for sample

size of 21 which is indicated by the dashed lines. The symbols for the predictors

Comment on Fig. 6b Juan: Have you ever try to consider the dipole SST tendency between PC2b and the equatorial western Indian ocean cooling tendency ( 5S-10N, 40-60E)? If you did not, Can you try? Somehow, I feel PC 2 may be better predicted.

Yes, I tried that predictor before. The performance of the dipole SST tendencyis not as good as PC2b, so I do not use it.

15-y running correlation coefficients

Why is the Indian Ocean dipole SLP not taken?

We selected this predictor before, but it dropt a lot in recent year, so I do

not take it.

Is there any predictor from SST tendency?

April-May mean

May minus April

May minus

March

April-May minus Dec.-Jan.

Correlations between PC3 and 2m temperature/SST for 1960-2005

We selected some predictors from SST,but they are not good.

If IMD precipitation data are used, the precipitation

anomaly show an EOF3 pattern.