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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
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(Cess et al. 1990)
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(moist adiabat)
T(z)
RH Fixed
Warm Pool Cold Tongue
T(z)
(Zhang and Bretherton, 2008)
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The objectives:
1. To understand the models
2. To understand the climate feedbacks in the models.
3. To compare SCM with LES/CRM simulations
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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
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Some Philosophical Questions
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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?
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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?
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CAM-SP
DLWP
DSWCF
DCRF
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CAM3.5
DLWP
DSWCF
DCRF
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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?
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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
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T(p) at two latitudes. Stars are from ECMWF analysis
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(moist adiabat)
T(z)
RH Fixed
Warm Pool Cold Tongue
T(z)T
Rh Rh_ec
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u
v
(control)
(p2k)
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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
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S11
S6
http://atmgcm.msrc.sunysb.edu/cfmips
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Preliminary Results
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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
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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
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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)
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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
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Cloud Amount at S11 from the Control Simulation in Different Models
Next: Only Results from S11 Are Presented
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ECMWF
SAM-LESUKMOL38
SNUKNMIGFDL GSFC
ECHAMCSIROCAM3.5
UKMO-LESLaRC/UCLA-LES
Cloud Amount in Control Simulation at s11
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Vertical Profiles of Cloud Amount at S11 from Control Simulation in Different Models
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Vertical Profiles of Cloud Liquid Water Content at S11 from Control Simulation in Different Models
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Comparison of Vertical Profiles of Cloud Amount at S11 Between Control (ctl, solid) and Perturbed (p2k, dashed)
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Comparison of Vertical Profiles of Cloud Liquid Water at S11 Between Control (ctl, solid) and Perturbed (p2k, dashed)
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Change of Net Cloud Radiative Forcing (p2k minus ctl) at s11
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Cloud Change for Some Selected Models with LargeNegative and Positive Feedbacks
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Negative Feedback in CAM4
ctl p2k
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Negative Feedback in CSIRO
ctl p2k
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Positive Feedback in GFDL
ctl p2k
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Positive Feedback in UKMO L38
ctl p2k
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Positive Feedback in UKMO L63
ctl p2k
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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.
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The interactions among PBL mixing, convection
(shallow and deep), and cloud scheme lead to
the unique cloud behaviors in each model
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1000mb
900mb
950mb
800mb
1010mb
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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
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http://atmgcm.msrc.sunysb.edu/cfmip
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Liquid Water Path in Control Simulation at s11
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Change of Liquid Water Path (p2k minus ctl) at s11
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Cloud Liquid Water at S11 from the Control Simulation in Different Models
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Cloud Liquid in Control Simulation at s11
ECMWF
SAM-LESUKMOL38
SNUKNMIGFDL GSFC
ECHAMCSIROCAM3.5
UKMO-LESLaRC/UCLA-LES