Toward Seasonal Climate Forecasting and Climate Projections in Future Akio KITOH Meteorological Research Institute, Tsukuba, Japan FOCRAII, 6-8 April 2010, Beijing

Contents Seasonal Prediction of Tropical Cyclone Occurrences in the JMA/MRI System New MRI/JMA High-Resolution AGCM Stratosphere Effect on Prediction of Arctic Oscillation

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

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

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 ° 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

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

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

Seasonal Prediction of Tropical Cyclone Occurrences in the JMA/MRI System What is predictable?

Simulated Climatological Distribution of Tropical Depressions OBS Simulation Nakaegawa 2010 Initial: end of April Member: 10

ECMWF Newsletter No. 112 – Summer 2007 PredictionofTropicalCycloneOccurrences Takaya et al., 2010, submitted

SE SW NE NW SW NE NW r=0.74 r=0.46 r=0.34 r=0.03 SE 120E 17N Takaya et al., 2010, submitted Dependence of Tropical Cyclone Occurrence Prediction on Regions Wang and Chan, 2002

MRI/JMA High-Resolution AGCM impact of horizontal resolution

Future change in NH blocking frequency (JJA) The higher horizontal resolution is required to accurately simulate Euro-Atlantic blocking. The Euro-Atlantic blocking frequency is predicted to show a significant decrease in the future. 20km 120km 180km 60km Matsueda and Palmer (in progress)

AR4 models wet dry AR4 models predict wet (dry) conditions over north (south) Europe. Robust signals from AR4 models. MRI AGCM Much weaker signals at high resolution Precipitation change over Europe (JJA) 180km (climate) 20km (NWP) similar robust less blocking Matsueda and Palmer (in progress)

AR4 models wet dry AR4 models predict wet (dry) conditions over north (south) Europe. Robust signals from AR4 models. MRI AGCM Much weaker signals at high resolution Precipitation change over Europe (JJA) 180km (climate) 20km (NWP) similar robust less blocking Matsueda and Palmer (in progress) Unreliable ?

New MRI/JMA High-Resolution AGCM A new cumulus scheme impact on inter-annual performance

Old version in 2007New version in 2010 Horizontal Res.TL319 (60km) Vertical Res.L60 (top 0.1hPa)L64 (top 0.01hPa) Time-Step15 minutes20 minutes Cumulus SchemePrognostic ASYoshimura Scheme Cloud SchemeSmith (1990) & Kawai (2004) Tiedtke (1993) RadiationShibata & Uchiyama (1992)JMA Scheme (2004_r1) Gravity wave dragIwasaki et al. (1989) Upper TreatmentNewtonian RelaxationRayleigh Friction Ocean SurfaceJMA-SchemeMRI-Scheme & Skin Ocean Land SurfaceSIB0109 PBLMellor-Yamada Level-2 Direct Aerosol Effect SulphateSulphate, Organic & Black Carbon, Salt, Mineral Indirect EffectNone Two versions of MRI/JMA AGCMs

Old 60km model New 60km model New-Old ＴＲＭＭ 3A25 GPCP & JRA25 Reanalysis CMAP & JRA25 Reanalysis Asian Monsoon Climatology （ＪＪＡ）

Seasonal Progress of Monsoon Precipitation Black + ： CMAP Black ○ ： GPCP Red ： New Model Blue ： Old Model South Asia (India) Western North Pacific East Asia (Japan)

Inter-annual Precipitation Variability ( ＪＪＡ ) Old 60km New 60km GPCP ＥＯＦ １ ＥＯＦ ２

Inter-annual Precipitation Variability (DJF) Old 60km New 60km GPCP ＥＯＦ １ ＥＯＦ ２

( day band-pass-filtered precipitation anomaly ) Nov.-Apr. GPCP 1DD Variance of Intra-Seasonal Precipitation Old 60km New 60km May-Oct.

Next JMA/MRI-Coupled GCM for Seasonal Forecast Operational CGCM (Feb 2010) Plan for Next CGCM AGCMTL95L40 Top at 0.4hPa Gaussian Grids TL159L60 Top at 0.1hPa Reduced Grids OGCM75S-75N, 0-360E 1.0°by °L50 Longitude-Latitude Grids Climatological Sea Ice 90S-90N, 0-360E 1.0°by °L53 Tri-polar Orthogonal Grids Sea Ice Model Ocean Assimilation MOVE/MRI.COM Usui et al. (2006) 3D-VAR(T,S) T, S on GTS, COBE-SST, SSH vertical EOF modes of T-S coupling MOVE/MRI.COM for Tri-polar Orthogonal Grids + 3D-VAR Sea Ice Assimilation + Coupled Ensemble Method (Breeding)

Stratosphere Effect on Prediction of Arctic Oscillation

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

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

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

2010/3/3 平成 21 年度異常気象分析検討会 Normalized Surface Air Temperature Anomaly Dec Feb Quick Report Jan Originally From JMA Homepage

Most Negative AO index in 2009/10 Winter DJF AO Variability （上： ERA40+JRA の Z500- EOF1) （下： JRA+JCDAS の SLP- EOF1 ） 09/10 62/63 68/6976/77 85/8600/01 09/10 = Z500 Originally From JMA Homepage

From Science (2001)

Role of Stratosphere on Predictability of 2003/04 Winter Numerical Experiment Design 1.Initials from JMA Objective Analysis 2.Initial Observed SST Anomaly is Fixed 3.Prognostic Land Surface 4.20-member ensemble using every 6-hour Initials (4 times a day) during five days Observational Data NCEP/NCAR reanalysis Climatology MRI/JMA AGCM (1) Standard Model : TL95L40, Model-Top 0.4 hPa (2) No Stratosphere Model : TL95L29, Model-Top 40 hPa From Kuroda (2008)

06Z27Dec Z1Jan Initials Composite 2-month! Anomalous zonal-mean zonal wind at 60N （ 2003/2004 Winter ） Shade : Student’s-t ２：９５％ -significance ４：９９．９％ - significance ６： very-high-significance NCEP Reanalysis Prediction Experiment Sudden Stratosphere Warming occurs During early January From Kuroda (2008)

AGCM With Stratosphere AGCM Without Stratosphere Role of Stratosphere in Prediction 2-month! From Kuroda (2008)

1.Fixed to the Initially Observed SST Anomaly 2.Prognostic Land Surface 1.Climatological SST 2.Climatological Land Surface Impact of SST and Land Surface State on Prediction Forecasted anomalous zonal wind 2-month! From Kuroda (2008)

SUMMARY High predictability of tropical cyclones’ occurrences is confirmed in the eastern regions of the northwestern Pacific. Horizontal resolution matters. The new cumulus scheme improves the inter- annual and intra-seasonal variability of the Asian Monsoon simulation with the AGCM. Possibility of two-month lead prediction for negative-phase Arctic Oscillations after Stratosphere Sudden Warming.