1. Toward Seasonal Climate Forecasting and Climate Projections in Future Akio KITOH Meteorological Research Institute, Tsukuba, Japan Tokyo Climate Conference, 6 July 2009, Tokyo

2. ENSO influences worldwide climate even out of the tropical Pacific on seasonal to inter-annual scales. Sea Surface Temperature anomaly in November 1997 Accumulated Precipitation Anomaly during Nov.1997-Apr.1998 from BAMS, 1999, 80, S1-48 from JMA webpage

3. ENSO is the most successfully predicted large-scale phenomenon on seasonal to inter-annual scales Observation Dec1997 - Feb1998 Prediction from 31 July 1997 by JMA/MRI model Precipitation Surface Air Temperature Sea Surface Temperature JMA/MRI 4-month lead

4. Atmosphere - Land – Ocean Coupled Models Atmosphere – Land Models Atmosphere – Land Models Atmosphere-ocean coupled models are necessary for the seasonal prediction of ENSO and its influences Short-term Prediction Model Seasonal Prediction Model Given Sea Surface Temperature Coupled Ocean

5. Local Relationship between Sea Surface Temperature (SST) and Rain Anomalies in Coupled models is more realistic than in Atmospheric models Rain -> SST 1month lead Rain = SST Rain <- SST 1month lag Wang et al. (2005) ECHAM

6. Next JMA Seasonal Prediction System developed by JMA/MRI JMA/MRI Coupled Model JMA/MRI Unified Atmospheric Model 180km Resolution (TL95L40) Ocean Model (MRI.COM) 1.0°by 0.3-1.0° 50-layer 1-hour Coupling Wind-stress, Heat-flux Adjustment Ocean Initials and Data MOVE/MRI.COM Usui et al. (2006) 3D-VAR (T,S) TAO/TRITON array Altimeter Data Argo Float

7. Improved NINO3.4 SST Prediction Skill 持続予報 NEW Operational 持続予報 気候値予報 NEW Operational (170-120W, 5S-5N) NEW OLD Persistent NEW Persistent JMA/MRI

8. Jan 31 => Jun-Aug (1984-2005) Precipitation Anomaly Prediction Skill ROC: Relative Operating Characteristic Atmospheric Model Coupled model shows better skill than Atmosphere-only model blue region : Upper tercile ROC skill is better than climatological one JMA/MRI Coupled Model

9. Precipitation Anomaly Prediction Skill ROC: Relative Operating Characteristic Jan 31 => Jun-Aug Jul 31 => Dec-Feb Skill for boreal winter is higher than that for boreal summer blue region : Upper tercile ROC skill is better than climatological one JMA/MRI

10. Surface Air Temperature Anomaly Prediction Skill Jan 31 => Jun-Aug Jul 31 => Dec-Feb ROC: Relative Operating Characteristic blue region : Upper tercile ROC skill is better than climatological one JMA/MRI Prediction skill of temperature is higher than that of precipitation

11. South Asia Summer Monsoon Index (WYI) (4-month lead: JJA from JAN) AGCMCGCM WYI Definition: U850– U200 [0-20N,40-110E] Blue: Forecast Red: Analysis ACC: 0.59 Blue: Forecast Red: Analysis ACC: 0.35 JMA/MRI

12. East Asia Summer Monsoon Index (DU2) (4-month lead: JJA from JAN) DU2 Definition ： U850[5-15N,90-130E] - U850[22.5-32.5N,110-140E] Blue: Forecast Red: Analysis ACC: 0.58 Blue: Forecast Red: Analysis ACC: -0.05 CGCMAGCM JMA/MRI

13. Multi-Model Ensemble WMO Lead Centre for LRF MME APEC Climate Center From APCC HomepageFrom LRF MME Homepage

14. European DEMETER Project From ECMWF Web PageRPSS: Rank Probability Skill Score (Wilks 1995) Multi-model ensemble skill out-performs single model ensemble with the same member size DEMETER

15. Forecast quality of DEMETER hindcasts WCRP Position Paper on Seasonal Prediction. Report from the First WCRP Seasonal Prediction Workshop (Barcelona, Spain, 4-7 June 2007). February 2008. WCRP Informal Report No.3/2008, ICPO Publication No.127. Skill depends on regions, seasons and variables Significant skills for precipitation in DJF_Amazon and JJA_Southeast Asia JJA & DJF_East Asia and JJA_Australia for temperature DEMETER

16. SINTEX-F showed the highest ENSO prediction skill among 10 coupled GCMs Nino3.4 index (1982-2001) Adapted from Jin et al. 2008, APCC CliPAS JAMSTEC

17. Nino3.4 SSTA prediction Luo et al. (2008) (120º-170ºW, 5ºS-5ºN) ENSO can be predicted out to 1-year lead and even up to 2-years ahead in some cases by SINTEX-F JAMSTEC SINTEX-F Coupled Model Components AGCM (MPI, Germany): ECHAM4 (T106L19) OGCM (LODYC, France): OPA8 (2  x 0.5  2 , L31) Coupler (CERFACS, France): OASIS2 *No flux correction, no sea ice model

18. Seasonal prediction for # tropical cyclones ECMWF Newsletter No. 112 – Summer 2007 ECMWF has already started and shows some skill …

19. Occurrence location of tropical cyclones are well predicted in the Northwest Pacific latitude longitude JMA/MRI

20. … as occurrence location is related with ENSO Wand and Chan (2002) etc more tropical storms form in the SE quadrant during the warm phase, and in the NW quadrant during the cold phase, thus ENSO prediction is the key

21. Toward further improvement of seasonal prediction NWP model Typhoon prediction model El Niño prediction model Seasonal prediction model Climate model Earth system model Climate model development (IPCC AR4) It is necessary to explore other predictability sources in the Earth system

22. Toward further improvement of seasonal prediction NWP model Typhoon prediction model El Niño prediction model Seasonal prediction model Climate model Earth system model Improving atmosphere-ocean coupled models will lead to constant improvement of seasonal predictions based on slow-coupled process like ENSO. On the other hand, high predictability from ENSO seems to be limited within relatively low- latitudes. Therefore, for more complete seasonal prediction, we need to explore other influential elements that show relatively long-range persistency or predictability in the Earth system that consists of upper and/or polar atmosphere, land, snow and ice, chemical processes besides the low-latitude troposphere. It is necessary to explore other predictability sources in the Earth system

23. Xie et al. (1999) JAPAN Winter Temperature is significantly correlated with Arctic Oscillation besides ENSO ・ Atlantic SST anomaly ・ Snow over Eurasia ・ Arctic Sea Ice Cover ・ Stratosphere, Ozone ・ Volcano Eruption ・ Global Warming AO ENSO Possible Causes

24. Stratospheric Harbingers of Anomalous Weather (Troposphere-Stratosphere Interaction) Baldwin and Dunkerton (2001)

25. AR4 to AR5: Need of climate change information for adaptation studies in near future fill a gap between seasonal-to- interannual prediction and climate change projections sufficiently high resolution projection is needed for resolve weather extremes changes in weather extremes will become significant much earlier than mean climate change Another emerging issue is a projection of future changes in weather extremes in order to contribute to decision-makings for the disaster prevention and other adaptation studies under the global warming environment.

26. Projected changes in extremes Intensity of precipitation events is projected to increase. Even in areas where mean precipitation decreases, precipitation intensity is projected to increase but there would be longer periods between rainfall events. “It rains less frequently, but when it does rain, there is more precipitation for a given event.” (Tebaldi et al. 2006) Extremes will have more impact than changes in mean climate IPCC AR4 CMIP3 models

28. Number of TC Generated in Each Latitude Present-day(25yr) Future(25yr) Observation Latitude TC freqency ２０％ decrease Annual global average Present =82 Future =66 (20% decrease) (Observation:84)

29. Radial Profile Change around TC ・ Large changes occur near inner-core region, 40-60% for precipitation and 15-20% for surface wind. ・ A surface wind speed increase of more than 4% can be seen up to 500 km from storm center. Surface Wind Radial Distance in km from Storm Center Precipitation Future Experiment Present Experiment Change rate

30. Cooperation activities of the MRI group Earth Simulator computed model outputs for adaptation studies Cooperation activities of the MRI group (by Earth Simulator computed model outputs for adaptation studies)  Adaptation study in Coastal Zones of Caribbean countries: Barbados(one, 2005), Belize (one, 2005)  Adaptation studies in Colombian coastal areas, high mountain ecosystems: Colombia (two, 2005; 2009)  Adaptation to Climate Impacts in the Coastal Wetlands of the Gulf of Mexico : Mexico (two, 2006)  Adaptation to Rapid Glacier Retreat in the Tropical Andes: Peru (one, 2006), Ecuador (one, 2006; 2009), Bolivia (one, 2006; 2009)  Amazon Dieback : Brazil (two, 2008) Cooperation under the JICA (Japan International Cooperation Agency) funds  Adaptation studies in agriculture in Argentina: Argentina (three, 2008)  Adaptation studies in monsoon Asia: Bangladesh, Indonesia, Philippines, Thailand, Vietnam (one each, 2008 & 2009)  Adaptation studies in the Yucatan: Mexico (two, 2009) Cooperation under the World Bank funds Other collaborations with India, Korea, Thailand, USA, Switzerland, … This collaboration started after COP10 (2004)

31. SUMMARY ENSO is the major source of the predictability on seasonal to inter-annual time-scales at the present. ENSO prediction was much improved for the past a few decades, and can be extended up to 1-year lead or longer. Probabilistic representation using initial ensembles is adapted for seasonal prediction of precipitation and surface air temperature because of small ratios of signal to noise. Multi- model ensemble technique contributes to improvement of seasonal prediction skills. Seasonal prediction skills are strongly dependent on regions, seasons and the elements to predict as well as ENSO situations. In addition to steady improvement of atmosphere-ocean coupled models, it is necessary to explore other predictability sources in the Earth system in future. High resolution model is now used to project future changes in weather extremes and tropical cyclones under the global warming environment. Such data is useful for various application studies, including adaptation to climate change.