the cfmip-gcss scm/les case study: overview and preliminary results

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)

RH Fixed

Warm Pool Cold Tongue

T(z)

(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)CSIROECHAM1ECHAM2ECMWFGFDLGSFCKNMILaRC/UCLA*LMDSAM*SNUUKMO*UKMO-L38UKMO-L63

Yet to Submit

CCCMeteo-FranceGISSUUtah*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?

Control GCM Output at S9 SST=2K GCM Output at S9

Control SCM Output at S9Under idealized forcing

SST=2K SCM Output at S9Under idealized forcing

Cloud Amount from CAM3.5

Forcing Data

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

(moist adiabat)

T(z)

RH Fixed

Warm Pool Cold Tongue

T(z)T

Rh Rh_ec

u

v

(control)

(p2k)

GPCI

S6

S11 s12

S6Shallow

Cu

S11Stratocum

ulus

S12Stratus

Latitude (Degrees North)

17oN 32oN 35oN

Longitude (Degrees)

149oW 129oW 125oW

SLP (mb) 1014.1 1020.8 1018.6

SST (oC) 25.6 19.3 17.8

Tair_surface (oC) 24.1 17.8 16.3

U_surface (m/s) -7.4 -1.8 2.1

V_surface (m/s) -2.7 -6.5 -8.0

RH_surface (m/s) 80% 80% 80%

Mean TOA insolation (w/m2)

448.1 471.5 473.1

Mean daytime solar zenith angle

51.0 52.0 52.7

Daytime fraction on July 15

0.539 0.580 0.590

Eccentricity on July 15

0.967 0.967 0.967

Surface Albedo 0.07 0.07 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 (1st column), GFDL (2nd Column),UKMO L38 (3rd Column)LaRC/UCLA LES (4th Column)

In all 2-D plots that follow, the ordinate is pressure, the abscissa is time in days

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

Sample of Simulated Cloud Liquid Water from Control Case at s6 (top row), s11 (middle row), and s12 (bottom row)

CAM3.5 (1st column), GFDL (2nd Column),UKMO L38 (3rd Column)LaRC/UCLA LES (4th Column)

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

Cloud Amount at S11 from the Control Simulation in Different Models

Next: Only Results from S11 Are Presented

ECMWF

SAM-LESUKMOL38

SNUKNMIGFDL GSFC

ECHAMCSIROCAM3.5

UKMO-LESLaRC/UCLA-LES

Cloud Amount in Control Simulation at s11

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 LargeNegative and Positive Feedbacks

Negative Feedback in CAM4

ctl p2k

Negative Feedback in CSIRO

ctl p2k

Positive Feedback in GFDL

ctl p2k

Positive Feedback in UKMO L38

ctl p2k

Positive Feedback in UKMO L63

ctl 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

1000mb

900mb

950mb

800mb

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

Cloud Liquid in Control Simulation at s11

ECMWF

SAM-LESUKMOL38

SNUKNMIGFDL GSFC

ECHAMCSIROCAM3.5

UKMO-LESLaRC/UCLA-LES