1. The CFMIP-GCSS SCM/LES Case Study: Overview and Preliminary Results Minghua Zhang (Stony Brook University) Julio Bacmeister, Sandrine Bony, Chris Bretherton, Florent Brient, Anning Cheng, Charmaine Franklin, Chris Golaz, In-Sik Kang, Martin Koehler Adrian Lock, Ulrike Lohman, Marat Khairoutdinov, Martin Koehler, Roel Neggers, Sing-Bin Park, Pier Siebesma, Colombe Siegenthaler-Le Drian, Kuan-man Xu, Mark Webb, Ming Zhao

2. The ISSUE

4. (Cess et al. 1990)

5. The Idea

6. (moist adiabat) T(z) RH Fixed Warm PoolCold Tongue T(z) (Zhang and Bretherton, 2008)

7. GPCI

8. The objectives: 1.To understand the models 2.To understand the climate feedbacks in the models. 3.To compare SCM with LES/CRM simulations

9. Models WITH Results Submitted CAM3.1 (CAM3) CAM3.5 (CAM4) CSIRO ECHAM1 ECHAM2 ECMWF GFDL GSFC KNMI LaRC/UCLA* LMD SAM* SNU UKMO* UKMO-L38 UKMO-L63 Yet to Submit CCC Meteo-France GISS UUtah* UW* UWiscousin * Denotes LES/CRM

10. Some Philosophical Questions

11. Question #1 Can the idealized setup represent the large- scale atmospheric conditions at the selected locations? Question #2 Can the conditions represent those in the GCMs?

12. Question #3 Will the variation of clouds in the SCM be the same as that in the GCM at the same location? Question #4 How representative are the cloud responses at the selected locations to the GCM cloud feedback?

13. CAM-SP DLWP DSWCF DCRF

14. CAM3.5 DLWP DSWCF DCRF

15. Question #5 How do we know the simulated cloud feedbacks are correct or wrong? Can LES be used to answer this)? Question #6 What do we learn from it?

16. Control GCM Output at S9  SST=2K GCM Output at S9 Control SCM Output at S9 Under idealized forcing  SST=2K SCM Output at S9 Under idealized forcing Cloud Amount from CAM3.5

17. Forcing Data

18. T(p) at two latitudes. Stars are from ECMWF analysis

19. (moist adiabat) T(z) RH Fixed Warm PoolCold Tongue T(z) T RhRh_ec

20. u v  (control)  (p2k)

21. GPCI S6 S11 s12

22. S6 Shallow Cu S11 Stratocum ulus S12 Stratus Latitude (Degrees North) 17 o N32 o N35 o N Longitude (Degrees) 149 o W129 o W125 o W SLP (mb)1014.11020.81018.6 SST ( o C)25.619.317.8 Tair_surface ( o C)24.117.816.3 U_surface (m/s)-7.4-1.82.1 V_surface (m/s)-2.7-6.5-8.0 RH_surface (m/s)80% Mean TOA insolation (w/m2) 448.1471.5473.1 Mean daytime solar zenith angle 51.052.052.7 Daytime fraction on July 15 0.5390.5800.590 Eccentricity on July 15 0.967 Surface Albedo0.07

23. S11 S6 http://atmgcm.msrc.sunysb.edu/cfmips

24. Preliminary Results

25. Sample of Simulated Cloud Amount from Control Case at s6 (top row), s11 (middle row), and s12 (bottom row) CAM3.5 (1 st column), GFDL (2 nd Column),UKMO L38 (3 rd Column) LaRC/UCLA LES (4 th Column) In all 2-D plots that follow, the ordinate is pressure, the abscissa is time in days

26. CAM3.5 – s6 CAM3.5 – s11 CAM3.5 – s12 LaRC/UCLA – s6 LaRC/UCLA– s11 LaRC/UCLA – s12 UKMOL38 – s6 UKMOL38 – s11 UKMOL38 – s12 GFDL – s6 GFDL – s11 GFDL– s12 Cloud Amount in Control Simulation

27. Sample of Simulated Cloud Liquid Water from Control Case at s6 (top row), s11 (middle row), and s12 (bottom row) CAM3.5 (1 st column), GFDL (2 nd Column),UKMO L38 (3 rd Column) LaRC/UCLA LES (4 th Column)

28. CAM3.5 – s6 CAM3.5 – s11 CAM3.5 – s12 LaRC/UCLA – s6 LaRC/UCLA– s11 LaRC/UCLA – s12 UKMOL38 – s6 UKMOL38 – s11 UKMOL38 – s12 GFDL – s6 GFDL – s11 GFDL– s12 Cloud Liquid Water in Control Simulation

29. Cloud Amount at S11 from the Control Simulation in Different Models Next: Only Results from S11 Are Presented

30. ECMWF SAM-LES UKMOL38 SNUKNMIGFDLGSFC ECHAMCSIROCAM3.5 UKMO-LESLaRC/UCLA-LES Cloud Amount in Control Simulation at s11

31. Vertical Profiles of Cloud Amount at S11 from Control Simulation in Different Models

33. Vertical Profiles of Cloud Liquid Water Content at S11 from Control Simulation in Different Models

36. Comparison of Vertical Profiles of Cloud Amount at S11 Between Control (ctl, solid) and Perturbed (p2k, dashed)

38. Comparison of Vertical Profiles of Cloud Liquid Water at S11 Between Control (ctl, solid) and Perturbed (p2k, dashed)

40. Change of Net Cloud Radiative Forcing (p2k minus ctl) at s11

41. Cloud Change for Some Selected Models with Large Negative and Positive Feedbacks

42. Negative Feedback in CAM4 ctl p2k

43. Negative Feedback in CSIRO ctl p2k

44. Positive Feedback in GFDL ctl p2k

45. Positive Feedback in UKMO L38 ctl p2k

46. Positive Feedback in UKMO L63 ctl p2k

47. Summary 1.All models simulated stratocumulus clouds in the idealized case at s11. (The models also simulated shallow cumulus at s6.) 2.Cloud amount, cloud height, and liquid water content differ greatly in the models. 3.Both positive and negative cloud feedbacks are obtained in the set of models. 4.In-depth analyses are needed to understand each model at process levels. These will follow.

48. The interactions among PBL mixing, convection (shallow and deep), and cloud scheme lead to the unique cloud behaviors in each model

49. 1000mb 900mb 950mb 800mb 1010mb

50. (Albrecht 1996)

51. What’s Next? New participations still welcomed Interpretation of the SCM results Sensitivity of the LES Further refinement of the setup Connection to GCM cloud feedbacks

52. http://atmgcm.msrc.sunysb.edu/cfmip

53. Liquid Water Path in Control Simulation at s11

54. Change of Liquid Water Path (p2k minus ctl) at s11

56. Cloud Liquid Water at S11 from the Control Simulation in Different Models

57. Cloud Liquid in Control Simulation at s11 ECMWF SAM-LES UKMOL38 SNUKNMIGFDLGSFC ECHAMCSIROCAM3.5 UKMO-LESLaRC/UCLA-LES