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

The ISSUE

(Cess et al. 1990)

The Idea

(moist adiabat) T(z) Warm Pool T(z) RH Fixed Cold Tongue

(Zhang and Bretherton, 2008)

GPCI

The objectives: 1. To understand the models 2. To understand the climate feedbacks in the models.

3. To compare SCM with LES/CRM simulations

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

Some Philosophical Questions

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?

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?

CAM-SP DLWP DSWCF DCRF

CAM3.5

DLWP DSWCF DCRF

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?

Cloud Amount from CAM3.5

Control GCM Output at S9 D SST=2K GCM Output at S9 Control SCM Output at S9 Under idealized forcing D SST=2K SCM Output at S9 Under idealized forcing

Forcing Data

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

T Rh (moist adiabat) T(z) Warm Pool RH Fixed T(z) Cold Tongue Rh_ec

u v

w

(control)

w

(p2k)

GPCI S6 S11

s12

Latitude (Degrees North) Longitude (Degrees) SLP (mb) SST ( o C) Tair_surface ( o C) U_surface (m/s) V_surface (m/s) RH_surface (m/s) Mean TOA insolation (w/m2) Mean daytime solar zenith angle Daytime fraction on July 15 Eccentricity on July 15 Surface Albedo S6 Shallow Cu 17 o N S11 Stratocum ulus 32 o N 149 o W 1014.1

25.6

24.1

-7.4

-2.7

80% 448.1

129 o W 1020.8

19.3

17.8

-1.8

-6.5

80% 471.5

51.0

0.539

52.0

0.580

0.967

0.07

0.967

0.07

S12 Stratus 35 o N 125 o W 1018.6

17.8

16.3

2.1

-8.0

80% 473.1

52.7

0.590

0.967

0.07

S11 S6

http://atmgcm.msrc.sunysb.edu/cfmips

Preliminary Results

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 LaRC/UCLA LES (4 th Column) Column) In all 2-D plots that follow, the ordinate is pressure, the abscissa is time in days

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

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 LaRC/UCLA LES (4 th Column) Column)

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

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

ECMWF Cloud Amount in Control Simulation at s11 CAM3.5

CSIRO ECHAM GFDL GSFC KNMI SNU UKMOL38 LaRC/UCLA-LES SAM-LES UKMO-LES

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

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

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

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

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

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

ctl Negative Feedback in CAM4 p2k

ctl Negative Feedback in CSIRO p2k

ctl Positive Feedback in GFDL p2k

ctl Positive Feedback in UKMO L38 p2k

ctl Positive Feedback in UKMO L63 p2k

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.

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

800mb 900mb 950mb 1000mb 1010mb

(Albrecht 1996)

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

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

Liquid Water Path in Control Simulation at s11

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

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

ECMWF Cloud Liquid in Control Simulation at s11 CAM3.5

CSIRO ECHAM GFDL GSFC KNMI SNU UKMOL38 LaRC/UCLA-LES SAM-LES UKMO-LES