Tropical Biases in Next-Generation Climate Models

1. Tropical Biases in Next-Generation Climate Models Stefan Tulich1 and George Kiladis2 1CIRES, University of Colorado, Boulder CO, USA 2NOAA ESRL, Boulder CO, USA

2. Coupled climate models are known to suffer from biases Kim et al. (2008) “High” = 1-2 deg. horizontal grid spacing (5 models)

3. Coupled climate models are known to suffer from biases Kim et al. (2008) “High” = 1-2 deg. horizontal grid spacing (5 models)

4. Uncoupled models too 12 CMIP3 AGCMs run in AMIP mode

5. Uncoupled models too 12 AGCMs run in AMIP mode (CMIP3 Archive)

6. Tropical variability also poorly simulated Lin et al. (2004)

7. Focus of this talk Mean climate and variability of 3 “next-generation” climate models: WRF tropical channel model (36-km, 45S-45N, Kain-Fritsch, AMIP-type run: 2000-2005) GFDL AM2.1 (0.5-deg, global, relaxed AS, SST = climatological ann. cycle, courtesy of G. Lau) SP-CAM (2.5-deg, global, 2D CRM, AMIP-type run, courtesy of M. Khairoutdinov)

8. Annual Rainfall Bias SPCAM WRF AM2.1

9. Annual Rainfall Bias SPCAM WRF AM2.1

10. Annual Rainfall Bias SPCAM WRF AM2.1

11. Annual Rainfall Bias SPCAM WRF AM2.1

12. Annual Rainfall Bias SPCAM WRF AM2.1

13. Annual Rainfall Bias SPCAM WRF AM2.1

14. Annual Rainfall Bias SPCAM WRF AM2.1

15. Basic Question • How do these mean rainfall biases (e.g., too much rain off the equator/too little rain near the equator) relate to the space-time variability of rainfall?

16. Rainfall Spectra (Symmetric) TRMM SPCAM WRF AM2.1

17. Rainfall Spectra (Symmetric) Kelvin waves Too weak TRMM SPCAM Too weak WRF AM2.1

18. Rainfall Spectra (Symmetric) TRMM SPCAM MJO No peak but.. WRF AM2.1

19. Rainfall Spectra (Symmetric) Easterly waves Too weak TRMM SPCAM Too strong Too weak Too slow WRF AM2.1

20. Rainfall Spectra (Symmetric) TRMM SPCAM Eq. Rossby waves Too strong WRF AM2.1 Too strong Too weak

21. Rainfall Spectra (Antisym.) TRMM SPCAM WRF AM2.1

22. Rainfall Spectra (Antisym.) MRG waves TRMM SPCAM n = 0 EIG waves WRF AM2.1

23. Summary thus far: • Kelvin waves generally too weak or non-existent; n = 0 EIG wave non-existent • Westward-moving waves better represented, but generally too active, especially in a relative sense: Westward Power (PW) /Eastward Power (PE)

24. Ratio of westward- to eastward-moving variance (k =1-25) AM2.1 WRF SPCAM 10 5 3.3 2.5

25. Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day) TRMM SPCAM AM2.1 WRF

26. Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day) TRMM SPCAM AM2.1 WRF

27. Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day) TRMM SPCAM AM2.1 WRF

28. Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day) TRMM SPCAM AM2.1 WRF

29. Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day) TRMM SPCAM AM2.1 WRF

30. Summary • Results of this study indicate a linkage between biases in simulated time-mean rainfall and its variability: • Too much rain at off-eq. latitudes • Too strong coupling between convection and westward-propagating (rotational) wave disturbances

31. Open questions • Is the westward bias in convective wave activity a consequence or a cause of time-mean biases in rainfall? • What determines the degree of coupling between convection and rotational (Rossby) vs. divergent (Kelvin, n = EIG wave modes)?

32. Proposal • A test case for measuring the strength of coupling between convection and rotational vs. divergent circulation anomalies • Basic idea: perturb a large-domain model by imposing 3D rotational vs. divergent circulations anomalies (i.e., a step beyond SCM approaches) • Parameterized vs. explicit • Coarse vs. fine resolution