1. Young-Kwon Lim Collaborators: Climate modeling group (Drs. L. Stefanova, D.W. Shin, S. Cocke, and T. E. LaRow) at FSU/COAPS, Dr. G. Baigorria (Univ. of Florida), Dr. K. H. Seo (Pusan Nat ’ l Univ., Korea), Dr. S. Schubert (NASA/GSFC), Dr. H. Juang (NOAA) Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA Development of Multi-model High Resolution Seasonal Forecasting System: An Application to SE US

2. Current high-resolution seasonal forecast in FSU/COAPS  One dynamical model (FSU/COAPS RSM) and one statistical downscaling model at 20km resolution (Cocke et al. 2007; Lim et al. 2007, 2009).  Real-time seasonal forecasts (up to 6 month ahead) are updated four times a year through CMO web (http://coaps.fsu.edu/cmo).

3. Realtime high-resolution forecast by FSU/COAPS (example: Nov. 2009, 20km resolution) T mean T ano. Rainfall mean Rainfall anomaly

4.  Corr. skill of the current FSU/COAPS downscaling system: 1) Winter rainfall: Corr. > 0.5 (FL,GA) (Cocke et al. 2007) 2) Crop growing seasons (spring and summer) : Sfc. air T. (Lim et al. 2007): Corr.=0.3~0.8, Rainfall: Improvement of correlation over the large scale CFS. Statistical significance problem (Lim et al. 2009).  Skill (correlation and categorical predictability) tends to be model dependent (e.g., summer rainfall: higher skill over inland by FSU model, while higher skill over Florida peninsula by NCEP model)  Question: Can we improve the skill over the entire SE US with statistical signifcance via MM downscaling system? Capability of the current FSU/COAPS downscaling system, and Motivation

5. Error variance and Seasonal Anomaly Correlation (current downscaling system) (Lim et al. 2009)  Downscaled seasonal forecast with an improvement of Corr.  Reduction in Relative error variance (REV) (≈ 2  0.6~1.4)  Downscaled seasonal forecast with an improvement of Corr.  Reduction in Relative error variance (REV) (≈ 2  0.6~1.4) REVCorr. Corr. (0.~0.2) Corr. (0.4~0.6) REV > 6.0 REV < 1 Down. from FSU model

6. Categorical predictability (HSS) for the frequency of rainfall extremes (Lim et al. 2009) Downscaling Difference (Down. - Rescaling) Rescaling (OA) from the CFS  Downscaling: Florida and S. Georgia : > 0.1, Alabama and C. Georgia : -0.1 ~ 0.2,  Rescaling: -0.2 ~ 0.2 1 std. + climatology 0.1~0.5 -0.2 ~ 0.1 ≥0.1

7.  Dynamical models 1. FSU/COAPS NRSM 2. RSM (NCEP, ECPC) 3. RegCM3 (ICTP)  Statistical models 1. CRT (CSEOF + Regression + Time series generation) 2. NLCCA (Neural network based CCA) (Hsieh et al. 2006) 3. Geo-spatial weather generator (Baigorria et al. 2007) Dynamical and statistical models involved in the multi-model downscaling study

8. Difference between FSU/COAPS downscaling works and other downscaling projects NARCAPPMREDCORDEXFSU/COAPS Aim Climate change projection Seasonal forecast (winter) Climate change projection All seasonal forecast, Climate change proj. Downscaling Models RegCM3, CRCM, RSM, HadRM3, WRF, MM5 MM5, WRF, RSM RCMs…… FSU,RegCM3, RSM, + 3 Statistical models Global Models HadCM3,CG CM3,CCSM, GFDL CFS, GEOS-5 CMIP5 models FSU, CCSM, GEOS-5,CFS Resolution50km32km50km20km DomainN. AmericaUSWorldSE US

9. Procedures  Downscaling large-scale reanalysis using dynamical models for model validation (bias, reliable distribution)  Downscaling large-scale retrospective forecasts  High-resolution seasonal forecasts on real-time basis  Probabilistic forecasts and application of the MME for the improved deterministic forecasts  Expansion to high-resolution climate change projection  Downscaling large-scale reanalysis using dynamical models for model validation (bias, reliable distribution)  Downscaling large-scale retrospective forecasts  High-resolution seasonal forecasts on real-time basis  Probabilistic forecasts and application of the MME for the improved deterministic forecasts  Expansion to high-resolution climate change projection

10. Preliminary result: RCM response to downscaling (2.5˚ → 20km) 2m T. (JJA/2004)2m T. (JJA/2005)

11. Preliminary result: RCM response to downscaling (2.5˚ → 20km) Prcp. (JJA/2004)Prcp. (JJA/2005)

12. Preliminary result: Statistical downscaling models 2m T. (JJA/2004)2m T. (JJA/2005)

13. Preliminary result: Statistical downscaling models Prcp. (JJA/2004)Prcp. (JJA/2005)

14. Summary  Multi-model high-resolution seasonal forecasting system study at FSU/COAPS was begun in September.  Three dynamical and three statistical models have been involved in this study.  We aim at the spatial resolution as fine as 20km for the southeastern US (FL, GA, AL, NC, and SC).  Improvement of the skill over our existing downscaling system (one dynamical and one statistical model) is expected.