Toward Seasonal Climate Forecasting and Climate Projections in Future Akio KITOH

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

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 JMA webpage from BAMS, 1999, 80, S1-48

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

4. Atmosphere-ocean coupled models are necessary for the seasonal prediction of ENSO and its influences Short-term Prediction Model Seasonal Prediction Model Atmosphere – Land Models Atmosphere - Land – Ocean Coupled Models 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 ECHAM Wang et al. (2005)

6. Next JMA Seasonal Prediction Systemdeveloped by JMA/MRI JMA/MRI Coupled Model Ocean Initials and Data 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 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 JMA/MRI (170-120W, 5S-5N) NEW Operational NEW Persistent 持続予報 Operational OLD 持続予報 Persistent NEW NEW 気候値予報

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

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

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

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

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

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

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

15. Forecast quality of DEMETER hindcasts Skill depends on regions, seasons and variablesSignificant skills for precipitation in DJF_Amazon and JJA_Southeast AsiaJJA & DJF_East Asia and JJA_Australia for temperature 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. DEMETER

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

17. ENSO can be predicted out to 1-year lead and even up to 2-years ahead in some cases by SINTEX-F JAMSTEC Nino3.4 SSTA prediction (120º-170ºW, 5ºS-5ºN) 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 Luo et al. (2008)

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

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

20. … as occurrence location is related with ENSO 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 Wand and Chan (2002) etc

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

22. Toward further improvement of seasonal prediction 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. NWP model Typhoon prediction model El Niño prediction model Seasonal prediction model Climate model Earth system model It is necessary to explore other predictability sources in the Earth system

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

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 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. • 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

26. IPCC AR4 CMIP3 models 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

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

29. Radial Profile Change around TC Precipitation Surface Wind Present Experiment Future Experiment Change rate Radial Distance in km from Storm Center ・Large changes occur near inner-coreregion, 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.

30. Cooperation activities of the MRI group(by Earth Simulator computed model outputsfor adaptation studies) Adaptation study in Coastal Zones of Caribbean countries: Barbados(one, 2005), Belize (one, 2005) Adaptation studies in Colombiancoastal 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 This collaboration started after COP10 (2004) Other collaborations with India, Korea, Thailand, USA, Switzerland, …

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.