1. Warm Season Precipitation Predictions over North America with the Eta Regional Climate Model Model Sensitivity to Initial Land States and Choice of Domain Size The 30 th Climate Diagnostic Prediction Workshop (Oct.24 - 28, 2005) State College, PA Rongqian Yang and Kenneth Mitchell Environmental Modeling Center National Centers for Environmental Prediction This development is sponsored by the GAPP program of NOAA/OAR/OGP

2. Presentation Outline Brief Introduction of the Eta RCM Model Predictions using CFS Hcst data Initial starting time (10 members from CFS, Mid April to Early May out to 6 months) Land States from Reanalysis II and Regional Reanalysis Big vs Small Domain 2004 results (NAME period, Precip, 500h, T2m etc.) Preliminary Conclusions Future Work

3. The Eta Regional Climate Model Development at NCEP Developed: uses very recent version of Eta Model physics As implemented in NCEP Regional Reanalysis As implemented in operational NCEP Eta on 24 Jul 01 Virtually exact match to Eta model in Regional Reanalysis (R/R) R/R domain and grid (32-km, 45-levels, large R/R domain) R/R Eta model physics, e.g. Noah Land Model 2.3, with 4 soil layers

4. Daily updates of several surface boundary fields Daily CFS predicted SST (or observed 1-deg weekly Reynolds/Stokes SST ) Satellite NDVI-based 0.15-degree monthly greenness (NESDIS) Seasonal 1.0-deg snow-free albedo climatology (NASA) Initial land states of soil moisture and soil temperature Soil moist/temp from : Regional Reanslysis Global Reanalysis II Snow depth: USAF operational 47-km daily global snow depth The Eta Regional Climate Model Development at NCEP (Cont’d)

5. Tests of different initial land states (GR2 or R/R) Test with both big and small domain size Predicted Lateral Boundary Conditions (CFS Hindcast) Predicted SSTs (CFS Hindcast) 10 summer members (mid-April/early May through mid-November) Currently 2004 (summer of NAME field campaign) 2000, 2001, 2002, and 2003 under way (to achieve 5yr model climo) Ultimate goal is to execute 10-20 yrs Model Prediction (Experiments)

6. The Big Domain

7. The Small Domain

8. Focus Issues Model's sensitivity to initial land states Choice of domain size Full prediction mode (i.e. using CFS predicted LBC and SSTs) To see if the regional model shows any skill in seasonal prediction mode, if any, where (in ensemble sense)? Focusing on precipitation over the CONUS domain and whether the skills are better than CFS

9. 10 Member Mean June Precipitation RCM RCM/R2 CFS Hcst CPC Analysis RCM/RR All missed this heavy rain Just a Hint Big Domain

10. CFS Hcst CPC Analysis RCM/R2 RCM/RR 10 Member Mean June Precipitation All missed this heavy rain with small domain too Nothing Here Small Domain

11. NAM 10 Member Mean July Precipitation RCM/R2 RCM/RR CPC Analysis CFS Hcst NAM: North American Monsoon Big Domain

12. NAM is weaker compared to Big Domain CFS Hcst CPC Analysis RCM/R2 RCM/RR 10 Member Mean July Precipitation Small Domain

13. Sustained NAM 10 Member Mean August Precipitation RCM/R2 RCM/RR CFS Hcst CPC Analysis Big Domain

14. CFS Hcst CPC Analysis RCM/R2 RCM/RR 10 Member Mean August Precipitation Small Domain

15. Dry in Southern Great Plains RCM/RR RCM/R2 CFS Hcst CPC Analysis 10 Member Mean JJA Precipitation Big Domain

16. Less dry in Southern Great Plains than the results using big domain CFS Hcst CPC Analysis RCM/R2 RCM/RR 10 Member Mean JJA Precipitation Small Domain

17. Eta-RCM and CFS Temperature is somewhat warmer in central US than the RR verification RCM/R2 CFS Hcst RR RCM/RR 10 Member Mean June 2m Temperature Orography signatures are much better revealed in RCM than in Global CFS Big Domain

18. Eta-RCM temperature is somewhat warmer than RR verification CFS Hcst RR Analysis RCM/R2 RCM/RR 10 Member Mean June 2m Temperature Small Domain

19. 500mb GPH is low compared to RR, RCM/R2 Better RCM/R2CFS Hcst RR Analysis RCM/RR 10 Member Mean June 500mb GPH GPH: Geo-potential Height Big Domain

20. 500mb GPH is high compared to RR verification RCM/R2 CFS Hcst RR Analysis RCM/RR 10 Member Mean June 500mb GPH Small Domain

21. Diffs in Initial Land States from One Member (April, 23) Comparison of the two Initial Land States Soil T Temp Total Soil Moisture 0-100cm percentage of Soil Saturation 0-10 cm Soil Moisture GR2 cooler GR2 drier

22. (Difference Fields: EtaRCM with GR2 Land States minus EtaRCM with RR Land States) Comparison of May Predicted Fields Latent Heat 500mb GPH 200mb GPH Precip

23. Comparison of Area-avgd Precipitation Timeseries (over CONUS) Area Avgd Precip is low at the beginning of integration with big domain Big Domain Small Domain Green: GR2 land states Yellow: R/R land states

24. Other Comparisons between the two domains using GR2 and RR land states

25. Ensemble Mean Kinetic Energy Big with R2 LS CFS Hindcast Big with R/R LS Small with R2 LS Small with R/R LS Regional Reanalysis Model Losing Kinetic Energy in May/June ?

26. Ensemble Mean Ratio of ACPCP/APCP R/R Small with R/R LS Small with R2 LS Big with R/R LS CFS Hcst Big with R2 LS Small domain has the highest percentage of convective Precip

27. Preliminary Conclusions Domain size choice is crucial to model results. Diffs caused by different land landstates is secondary. Big Domain EtaRCM yields better results than other combinations in general. The EtaRCM shows skills in warm season precipitation predictions (Compared to Obs and CFS hindcast, especially with features associated with NAM system), still problems? LBC errors plus physics?

28. Future Work More tests on different years (under way, 2000- 2003). To drive the Eta-RCM, CFS hindcasts need to be re-run (saved sigma files). It takes a lot CPU time (so only 2004 finished so far). Establishing model climatology to evaluate a relative dry/wet year with respect to model climatology. Further testing Land states from Regional Reanalysis and its impact on warm season precipitation.

29. Thanks to Suranjana Saha Wanqiu Wang Cathy Thiaw Jun Wang Kingtse Mo