1. Potential Predictability of Drought and Pluvial Conditions over the Central United States on Interannual to Decadal Time Scales Siegfried Schubert, Max Suarez, Philip Pegion, Randal Koster and Julio Bacmeister Global Modeling and Assimilation Office Earth Sciences Directorate 29th Annual Climate Diagnostics and Prediction Workshop Madison, Wisconsin 18-22 October 2004

2. Problem and Approach Does the predictability of Great Plains precipitation change on inter-annual and longer time scales? If so - why? Examine the spread of an ensemble of century-long simulations forced with observed SSTs

3. AGCM: NSIPP-1 (NASA S-I Prediction Project) Climatology and Skill (Bacmeister et al. 2000, Pegion et al. 2000, Schubert et al. 2002) Great Plains drought (Schubert et al. 2003; 2004) Global grid point dynamical core, 4rth Order (Suarez and Takacs 1995) Relaxed Arakawa-Schubert Convection (Moorthi and Suarez 1992) Shortwave/Longwave Radiation (Chou et al. 1994, 1999) Mosaic interactive land model (Koster and Suarez 1992, 1996) 1 st Order PBL Turbulence Closure (Louis et al. 1982) C20C AGCM runs with Specified SST HadISST and sea ice dataset (1902-1999) 22 ensemble members - same SST, different ICs (14 with fixed CO 2, 8 with time varying CO 2 ) Model resolution: 3 degree latitude by 3.75 degree longitude (34 levels) Idealized AGCM runs forced with composite SST patterns

4. Observations Model ensemble mean C20C runs

5. CO 2 runs in blue

6. Quantities  - ensemble mean  2 - intra-ensemble variance (  ) 2 - intra-ensemble coefficient of variation

7. Great Plains Precipitation (Normalized , Normalized  2 )

9. Great Plains Precipitation (Normalized , Normalized   )

11. JFM0.11-.330.02-.15.37 FMA0.03-.530.02-.35.71 MAM-.26-.67-.12-.41.75 AMJ-.55-.76-.23-.38.67 MJJ-.52-.73-.23-.33.53 JJA-.39-.73-.12-.26.45 JAS-.08-.71.04-.26.49 ASO0.33-.53.19-.29.59 SON0.54-.46.38-.30.70 OND0.56-.38.32-.30.70 NDJ0.41-.28.27-.13.61 DJF0.19-.23.00-.11.23 (  ,  ) ((  ,  ) (  ,nino3) ((  ,nino3) ( ,nino3) Summary of Correlations

12. Results show that periods of less rain have greater relative variability than periods of more rain –implies that droughts are less predictable than pluvial conditions How do the SST influence precipitation variability in the Great Plains? –atmospheric variability –land/atmosphere coupling

13. Correlation Between Ensemble Mean (  ) GP Precipitation and SST

14. Correlation between SST and GP Precipitation  

16. Composites based on Great Plains Precipitation  

17. 200mb Z Composites Based On Largest/Smallest Values of Coefficient of Variation of GP Precipitation LargestSmallest

18. Difference in Composites of   of 200mb Z Dimensionless

19. Difference in Composites of   of Evaporation

20. Model Runs with Idealized SST Focus on AMJ Force model with 2 composite SST patterns –Positive: GP precip   > +1 STD –Negative: GP precip   < +1 STD 100 ensemble members (March 1 - June30) for each composite Initial soil moisture conditions are from AMIP runs Repeat both sets of runs with fixed soil moisture (fixed beta)

21. SST Forcing Fields °C GP precip   > +1 STD GP precip   < +1 STD

22. Differences in Idealized Runs-Precipitation Fixed Beta Interactive soil

23. Differences in Idealized Runs-Evaporation Fixed Beta Interactive soil

24. Soil Moisture From C20C Runs

25. WW WW EE EE W (soil moisture) Idealized run -1std Idealized run +1std

26. Interactive soil Fixed Beta Idealized run +1std Idealized run -1std C20C runs

27. Conclusions and Implications In the Great Plains, simulated droughts are less predictable than pluvial conditions Differences in ensemble spread are associated with changes in the strength of the atmosphere/land coupling Should also be true in other “hot spots” Future work - seasonality, model dependence, other regions (e.g. SW US), SST uncertainty

28. JJA Land-Atmosphere Coupling Strength, Averaged Across AGCMs