Research Activity in Japan on Seasonal Forecasts by T.Ose (MRI/JMA) for 12 th WGSIP at RSMAS CHFP with JMA/MRI-CGCM03 from Yasuda, T. at MRI ENSO and IOD Prediction with SINTEX-F CGCM from Luo J.-J. at Frontier/JAMSTEC Near-Future Prediction in KAKUSHIN project from Prof. Kimoto at CCSR/Tokyo Solar cycle effect on climate from Kuroda, Y. at MRI River discharge predictability from Nakaegawa, T. at MRI

Seasonal Prediction Experiment in the new JMA/MRI Coupled Model The new system for forecasting SST in the equatorial Pacific using a coupled atmosphere-ocean model has been developed at JMA/MRI. This system is being used for the new JMA operational system for ENSO forecast since spring We have conducted the retrospective seasonal prediction experiments using this system based on the CHSP strategy. Yasuda, T. (MRI), Y. Takaya (JMA), Y. Naruse (JMA) and T.Ose (MRI)

Seasonal Forecast System and Experiments CGCM (JMA/MRI-CGCM03) System Components AGCM: JMA atmospheric model TL95L40 OGCM: MRI Community Ocean Model (MRI.COM) 1.0x( )L50 Coupling time: 1 hour Flux adjustment: Momentum and heat fluxes adjustment Experiments 7-month 10-member ensemble prediction initiated at the end of January, April, July and October from 1979 to Initial Conditions Atmosphere: JRA-25 reanalysis Ocean: Ocean Data Assimilation System “Multivariate Ocean Variational Estimation System (MOVE-G/MRI.COM)”

Asian Monsoon Precipitation is much improved by CGCM. CGCM MSSS CGCM COR AGCM MSSS AGCM COR

Asian Summer Monsoon Index (WYI) (4-month lead: JJA from JAN) AGCMCGCM WYI Definition ： (0-20N,40-110E) Mean of U850–U200 Blue: Forecast Red: Analysis ACC: 0.59 Blue: Forecast Red: Analysis ACC: 0.35

Seasonal-to-interannual climate prediction using SINTEX-F CGCM – ENSO and IOD prediction– Jing-Jia Luo ( 羅 京佳, Climate Variations Research Program Frontier Research Center for Global Change JAMSTEC, Japan Collaborators: Sebastien Masson, Swadhin Behera, Yukio Masumoto, Hirofumi Sakuma, and Toshio Yamagata

1. 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 2. International collaborators: LODYC: OPA model group INGV (Italy): Antonio Navarra’s group MPI-Met: ECHAM model group CERFACE: OASIS coupler group PRISM project group The SINTEX-F Coupled GCM (Luo et al. GRL 2003, J. Clim. 2005a; Masson et al. GRL 2005) Running on the Earth Simulator

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

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

Rainfall Anomalies Sep-Nov 2006Corresponding SST Anomalies More than 1 million people in Kenya, Somalia and neighboring countries were affected by the flooding. Severe drought devastated farmers in eastern Australia, estimated loss of 8 billion AUD. IOD Impacts in 2006 boreal fall fires in Borneo and Sumatra

Both winter and spring barrier exist (90º-110ºE, 10ºS-0º) 0.5 Luo et al., J. Climate, 2007, Predictable up to ~2 seasons ahead. Indian Ocean Dipole 9-member ensemble hindcasts ( )

ENSO can be predicted out to 1-year lead and even up to 2-years ahead in some cases. ISOs may limit ENSO predictability in certain cases. The results suggest a potential predictability for decadal ENSO-like process. Summary: Real time forecasts at one month intervals: IOD can be basically predicted up to ~2 seasons ahead. Extreme IOD events (and their climate impacts) can be predicted up to 1-year lead.

Ensemble hindcast/forecast Assimilation/Initialization A near-term prediction up to 2030 with a high- resolution coupled AOGCM –60km Atmos + 20x30km Ocean –w/ updated cloud PDF scheme, PBL, etc –advanced aerosol/chemistry Estimate of uncertainty due to initial conditions –10(?)-member ensemble –For impact applications water risk assessment system impacts on marine ecosystems etc. Test run w/ 20km AOGCM (in 2011) 110km mesh model 60km mesh model 5-min topography Japanese CLIMATE 2030 Project From Prof.Kimoto (CCSR)

Motizuki et al. (2009) Decadal Predictability? Assimilation vs. Hindcasts w/ & w/o initialization SPAM : S ystem for P rediction and A ssimilation by M IROC Global SATPDO

Solar cycle effect on climate Yuhji Kuroda (Meteorological Research Institute, JAPAN) -Review and recent works related on the modulation of the Annular Mode-

~0.1% variation of solar irradiance is observed for the 11-year Solar Cycle (SC)

Observation (ERA40) Zonal wind Contour greater than 0.5 Shading greater than 0.4 Correlation with S-SAM (Nov) 0.6Correlation with surface 0.4 larger S-SAM

Experiment with varying UV Ultra Solar (US) High Solar (HS) Low Solar (LS) UV:strong UV:weak Stratospheric SAM (S-SAM): EOF1-Z30 in late winter (Dec) Compares correlation with S-SAM

Zonal wind Contour greater than 0.5 Shading greater than 0.44 (95%) Correlation with S-SAM (Dec) 0.8Correlation with surface Stratosphere-troposphere coupling tends to be stronger with increasing UV!! Chemistry-Climate Model larger

1.Solar irradiance change is too small to change climate energetically. 2.UV change is one promising process. 3.Ozone anomaly changes temperature in the lower stratosphere to upper troposphere in summer. 4.Such temperature anomaly creates anomalous zonal wind. 5.Anomalous zonal wind modifies wave propagation. Possible Physical mechanism of the solar-cycle modulation of the SAM

Bibliography 1, Solar-cycle modulation of winter-NAO Kodera, K., GRL 2002, doi: /2001GL Ogi et al., GRL 2003, doi: /2003GL Kuroda et al., JGR 2008, doi: /2007jd in press Kuroda, Y., J. Meteorol. Soc. Japan 2007,Vol 85, , Solar-cycle modulation of late-winter/spring SAM Kuroda and Kodera, GRL 2005, doi: /2005GL Kuroda et al., GRL 2007, doi: /2007GL , Simulation of solar-cycle modulation of AO or SAM by CCM Tourpali et al., GRL 2005, doi: /2005GL Kuroda and Shibata, GRL 2006, doi: /2005GL025095

Potential predictability of seasonal mean river discharge in dynamical ensemble prediction using MRI/JMA GCM Tosiyuki Nakaegawa MRI, Japan

Physical characteristics of river discharge River discharge is a collection of total runoffs in an upper river basin, which is similar to the area average process. The collection is likely to reduce the unpredictable variability and, as a result, to enhance the predictability. P-E: each gridRiver discharge: accumulation

C20C Experiment setup AGCM: MJ98 ， T42 with 30 vertical layers River Routing Model: GRiveT, 0.5 o river channel network of TRIP, velocity: 0.4m/s Member: 6 SST & Sea Ice : HadISST (Rayner et al. 2003) CO 2 : annualy varying Integration period: Analysis period ：

Potential Predictability Definition: The maximum value that an ensemble approach can reach, assuming that perfectly predicted SSTs are available and that the model perfectly reproduces atmospheric and hydrological processes. Variance ratio ： measure of PP based on the ANOVA (Rowell 1998).

Collection Effect How much influence does the collection effect over a river basin have on the potential predictability of river discharge? Variance Ratio: (Discharge)-(P-E) Improvement Basin areas >10 6 km 2 Does not work effectively Cause deterioration