Cloud Radiative Forcing in Asian Monsoon Region Simulated by IPCC AR4 AMIP models. Jiandong Li, Yimin Liu, Guoxiong Wu State Key Laboratory of Atmospheric Science and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing.
Jiandong Li, Yimin Liu, Guoxiong Wu
State Key Laboratory of Atmospheric Science and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing
UAW2008, Tokyo, Jul. 2, 2008
CRF*: Cloud Radiative Forcing
AMR*: Asian Monsoon Region
IPCC AR4, 2007
Bin Wang, 2002
Could most AGCMs from IPCC AR4 reproduce the basic features of CRF in AMR?
What are the main deficiencies for CRF simulation?
Analysis results (1) IPCC AR4
0-50°N , 60-150°E
In observation data, there is a near cancellation between LWCF and SWCF at TOA in tropical deep convective regions. However, the net CRF is very large in AMR (M.Rajeevan et al, 2000), and the SWCF in the East of TP is very strong(Yu et al, 2001, 2004).
What about the performance of model?
LWCF by AMIP models in DJF IPCC AR4
TP*: Tibet Plateau
SWCF by AMIP models in DJF IPCC AR4
LWCF by AMIP models in JJA IPCC AR4
SWCF by AMIP models in JJA IPCC AR4
Rainfall by AMIP models in JJA IPCC AR4
The spatial patterns of observational and simulated CRF IPCC AR4 have good correlation with corresponding rainfall, which very likely indicates two questions as following:
a. Comparison CRF by coupled model with that by AGCM b. Relation between CRF and rainfall in coupled models
There are large diversity and biases of CRF by models.
The diversity and biases of SWCF is larger than that of LWCF especially in JJA.
GFDL-CM2.1, MPI-ECHAM5, UKMO_HadGEM1 and MME10 perform well in CRF simulation.
Analysis results (2) IPCC AR4
EA*: East Asian
0-50°N , 100-145°E