1. Recent Progresses in Climate Predictions of the Indian Ocean Sector
Swadhin Behera, Jing-Jia Luo, Yukio Masumoto
and Toshio Yamagata
Research Institute for Global Change/JAMSTEC, Yokohama, Japan
Application Laboratory, JAMSTEC, Yokohama, Japan
School of Science, The University of Tokyo, Tokyo, Japan
Dept. of Ocean Technology, Policy, and Environment, The University of Tokyo, Japan

2. First two EOF modes of surface temperature from CTRL Experiment and non-ENSO Experiment
Behera et al. 2005, J. Clim.

3. The Indian Ocean Pressure Oscillation Caused by IOD and its role in Southern Oscillation
Behera and Yamagata 2003
Izumo et al Nature Geo Science.

4. IOD Influence on South American Rainfall
Observed
SINTEX-F model
Teleconnection
Observed climatology
Model climatology
Nov. 2008: Santa Catarina suffered constant rainfalls for over two months (during late September, October, and November) and associated land slide and flooding affected 60 towns and over 1.5 million people
Chan et al. 2008, GRL

5. Historical records of SST Dipole Index and Rainfall Dipole Index
noENSO
Obs
CTRL
Behera et al. 2008

6. SINTEX-F1 Coupled GCM Prediction System
OCEAN: OPA8.2 ORCAR2 Grid 20X1.50 Eq-0.5 Level 31
ATMOSPHERE: ECHAM4 T106 L19
T106L19 5
Non-flux adjustment
Every 2 hrs
2.2
The Earth Simulator

7. JAMSTEC seasonal prediction system: semi-multi-model ensemble
9-member ensemble hindcast experiments
Three models with different coupling physics:
(Each model has realistic ENSO & IOD simulations, Luo et al. 2005a)
m1: Ocean surface is solid to atmosphere.
(|Ua| Ua for Tau & heat flux)
　m2: Ocean surface current momentum is passed to atmosphere.
(|Ua-Uo| (Ua-Uo) for Tau & heat flux)
　m3: Ocean surface is solid to atmosphere, but
(|Ua-Uo| (Ua-Uo) for Tau)
2. Three initial conditions for each model:
• Model spin-up ( )
• A simple coupled SST-nudging initialization scheme
• １day, 2 days, 3 days (weekly NCEP Reynolds data)
Based on this model, we have designed a kind of semi-multi-model ensemble prediction scheme. We perturb the model coupling physics in three different ways to measure the large uncertainty in wind stress estimation. And for each model we use three initial conditions generated by a simple coupled nudging scheme and assimilating SST information only. This scheme produces realistic and ocean-atmosphere well-balanced initial conditions, which appear to be very important to long-lead predictions.
It works well for ENSO prediction because ENSO is mainly determined by air-sea coupling. But for the Indian Ocean prediction, assimilating subsurface important can be very important because of strong stochastic and influential intraseasonal disturbances there.
Forecast: 12 months from 1st day of every month during

8. based on a semi-multimodel ensemble prediction system
9-member mean ( )
based on a semi-multimodel ensemble prediction system
>0.9
Luo et al., J. Climate, 2005b.

9. ENSO prediction skill of 10 coupled GCMs
Nino3.4 index
( )
Adapted from Jin et al. 2008, APCC CliPAS

10. Indian Ocean Dipole Predictability
Both winter and spring barrier exist
Difficulties
Large deficiencies of current coupled GCMs in simulating Indian Ocean climate
Sparse subsurface observations in the Indian Ocean
Chaotic and active ISOs (initial conditions & predictions)
Strong monsoon influence (seasonal & interannual)
ENSO influence (require correct ENSO onset prediction)
(90º-110ºE, 10ºS-0º)
0.5
9-member ensemble hindcasts ( )
Predictable up to ~2 seasons ahead.
Luo et al., J. Climate, 2007,

11. Decoupled Hindcast Experiments El Niño and IOD Interaction
dPO: No air-sea coupling in the tropical Pacific (observed SST climatology is specified).
dIO: No air-sea coupling in the tropical Indian Ocean (observed SST climatology is specified).
(9-member, 12-month lead, )

12. Prediction plumes (9-member mean): dIO (no Indian Ocean) 1994/95
Nino3.4 SSTA (190º-240ºE, 5ºS-5ºN)
dIO (no Indian Ocean)
1994/95
1997/98
2006/07
CTL
0.5

13. Prediction plumes: CTL dPO (no El Niño) 1994 1997 2006
EIO SSTA (90º-110ºE, 10ºS-0º)
CTL
dPO (no El Niño)
1994
1997
2006

14. ACC RMSE Extended ENSO prediction: Nino3.4 SSTA prediction
Ensemble mean
Persistence
ACC
RMSE
0.5
Each member
Nino3.4 SSTA prediction
(120º-170ºW, 5ºS-5ºN)
Luo et al., J. Climate, 2008,

15. Nino3.4 SSTA forecast up to 2-year ahead (9-member)
1 Sep. 2006
Ensemble mean
The La Nina condition would be long-lived according to the model forecast.
1 Jan. 2007
Presented at WCRP Workshop on Seasonal Prediction
Barcelona Spain June 2007
1 May 2007

16. terrestrial 2-m air temperature anomaly
Extended ENSO prediction:
SSTA &
terrestrial 2-m air temperature
anomaly
Contour interval is 0.3ºC
Consistent with classical ENSO theory
Luo et al., J. Climate, 2008.

17. Role of Warming Trend on SIP
Detrended global mean surface air temperature (5-month running mean)
(60S-75N)
Trend
( )
practical (potential) predictability of the non-detrended and detrended global mean T2m anomalies (lines with and without closed circle mark). Red (blue) short-dashed lines show the predictability if a perfect warming trend of the NCEP observations (model ensemble mean ICs) were predicted at all lead times. Black (gray) thin long-dashed lines with open circles are the persistence forecasts for the non-detrended observations (model ICs).
perfect warming trend
Skills for original, detrended, and “perfect trend” time series
potential
persistence
Lead time (months)
Luo et al. 2010

18. http://www. jamstec. go. jp/frcgc/research/d1/iod/index
Latest Predictions - SINTEX-F
SON Precipitation
DJF Precipitation

19. Refer to http://www. jamstec. go. jp/frcgc/research/d1/iod/index
Refer to for our experimental real time forecasts updated every month:

20. Real time forecasts (27-member)
　23 Apr 2008, The 　　
　Weekly Times
Forecasts from
1 April 2008
24 Oct 2007, The Weekly Times
Societal impacts
of IOD forecasts
Press Release October 16, 2006
「The World‘s First Successful Prediction of the Indian Ocean Dipole Mode (IOD) - Alleviate Social Loss Caused by Floods and Drought -」
10／17　読売新聞　 10／17　日本経済新聞　 10／17　日刊工業新聞 10／17　毎日新聞　 10／17　時事通信 10／17　朝日新聞　 10／18　日本海事新聞
10／30 シンガポール聯合早報
11／10 THE ASAHI SHIMBUN ….
Real time forecasts (27-member)
(90º-110ºE, 10ºS-0º)
(190º-240ºE, 5ºS-5ºN)
2005
Jul
Jan
Luo et al. GRL2008, highlighted

21. Summary
IOD has its own predictability, more or less independent of El Niño.
IOD can fully drive El Niño-like signal as in 1994.
Intrinsic interaction between El Niño and IOD exists through the atmospheric Walker circulation.
crucial to the development (in particular, onset) of both El Niño and IOD.
El Niño-IOD interaction provides a new hope for
enhanced prediction skill of the both.
IOD change needs to be considered for the future
projection of ENSO under global warming.

22. Outstanding issues:
IOD and ENSO triggering and terminations – biases in models
Role of subsurface ocean (particularly the IO) – data assimilations, initializations…
IOD and ENSO teleconnections in model predictions
Extended ENSO predictions, role of warming trend
Decadal predictions – initialization, processes…

23. The Evolution of 2006 IOD in SINTEX-F and POAMA Predictions
The dotted lines indicate predictions for June and January DMI starting from initial months. SINTEX-F consistently predicts the 2006 IOD from November 2005.
Observed DMI

24. The Evolution of 1994 IOD in SINTEX-F and POAMA Predictions