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 UAW2008, Tokyo, Jul. 2, 2008

OutlineStudy motivationData and methodologyAnalysis resultsClimatology of CRF* in AMR*Annual cycle of CRF around East AsiaConclusion

CRF*: Cloud Radiative ForcingAMR*: Asian Monsoon Region

Study motivation (1)Clouds are important modulator of climate. The concept of CRF has been used extensively to study the impact of clouds on climate (Ramanathan, 1989).

In the current climate, clouds exert a cooling effect on climate corresponding to the global warming. Meanwhile, Cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates (IPCC AR4, 2007).IPCC AR4, 2007

Study motivation (2)There exists significant difference for circulation, precipitation and cloud radiative process in different areas of AMR.

So far most AOGCMs do not simulate the spatial or intra-seasonal variation of monsoon precipitation accurately.Bin Wang, 2002Could most AGCMs from IPCC AR4 reproduce the basic features of CRF in AMR?What are the main deficiencies for CRF simulation?

DataERBE data (Barkstrom et al, 1990)Monthly data from 1985 to 1989Resolution is 2.52.5and uncertainty is 5 Wm-2IPCC AR4 AMIP dataMonthly data from 1979 to 1993Interpolation into ERBE gridsCMAP precipitationMethodologyCRFLong-wave CRFShort-wave CRFStudy area60-150E and 0-50N including main AMRArea mean bias and RMSETaylor diagram analysis (Taylor, 2001)

IPCC AR4 AMIP models

Climatology of CRF* simulated by AMIP models in AMR*0-50N , 60-150EAnalysis results (1)

Observational climatology of CRF in AMRDJFJJAIn 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 DJFFour models reproduce the weak LWCF between Indian Byland and Bengal Bay.No model capture the strong LWCF over TP*.Positive LWCF simulated by most of models is lower than observation between East China and Japan. TP*: Tibet Plateau GFDL-CM2.1MIROC3.2(medres)MRI-CGCM2.3.2UKMO-HadGEM1

SWCF by AMIP models in DJFFour models capture the strong SWCF in East of TP in DJF .MME10 failed to reproduce the SWCF in East of TP, which is caused by the biases of most of models in this region. GISS-ERMPI-ECHAM5MRI-CGCM2.3.2UKMO-HadGEM1

LWCF by AMIP models in JJAIn active convective regions, the location and intensity of LWCF by most models have larger biases.

SWCF by AMIP models in JJAIn active convective regions, the location and intensity of SWCF by most models have larger biases.The same difficulty of CRF simulation also lies in Southwest of China downstream of TP.The LWCF and SWCF simulated by AMIP models are correlated well with simulated rainfall.

Rainfall by AMIP models in JJACompared to the spatial pattern of simulated CRF, particularly SWCF, simulated rainfall shows the similar spatial pattern. This is more clear in MME10 results

The relationship between CRF and rainfall in AMR in JJAISMEASMWNPSMISM: 5-20N70-100E EASM: 5-20N110-140E WNPSM: 20-35N100-130E

Correlation between rainfall and LWCFR=0.585R=0.455R=0.143R=0.242R=0.625R=0.889

Correlation between rainfall and SWCFR=-0.673R=-0.408R=0.342R=-0.742R=-0.826R=-0.493

The spatial patterns of observational and simulated CRF have good correlation with corresponding rainfall, which very likely indicates two questions as following: Generally, the simulated rainfall is directly connected with cumulus parameterization process in model, which affects rainfall, cloud physical process and CRF(Zhang, 2006). Hence, the larger biases of CRF is very likely to related to cumulus parameterization scheme in model. Many studies (Bin Wang et al, 2004, 2005) showed that AGCMs are unable to realistically reproduce Asian-Pacific summer monsoon rainfall due to neglecting the atmospheric feedback on SST , but AOGCMs have better performance in rainfall simulation, SST and their variability in AMR. a. Comparison CRF by coupled model with that by AGCM b. Relation between CRF and rainfall in coupled models

Area mean bias and RMSELWCFSWCFDJFJJAUnits: Wm-2

Taylor diagram analysis for CRFThere 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.

Annual cycle of CRF* simulated by AMIP models around EA*EA*: East Asian0-50N , 100-145EAnalysis results (2)

Annual cycle of observational CRF In tropical area (south to 20N) the variation of CRF is consistent with that of rain season

In East of TP (between 25and 40N), stronger CRF appears since February and lasts until late May, when CRF evolves north with the rain season.

Annual cycle of CRF by AMIP modelsGISS-ERGFDL-CM2.1MPI-ECHAM5UKMO-HadGEM1

ConclusionThere still exists a lot of difficulty in simulating the CRF in AMR. Our study shows that the lee slide of TP in DJF and JJA and active convective regions in JJA, such as Bengal Bay, are the major bias regions.

Further analysis indicates the biases and diversity of SWCF are larger than that of LWCF. As a whole, GFDL-CM2.1, MPI-ECHAM5, UKMO-HadGEM1 and MIROC3.2(medres) perform well in CRF simulation in AMR.

It is suggested that strengthening the study of physical parameterization involved in TP, improving cumulus convective process and ameliorating model experiment design will be crucial to the CRF simulation in AMR.

Thank youlijd@mail.iap.ac.cn

Centered RMSEUnits: Wm-2

Annual cycle of rainfall by AMIP models

Good afternoon, everyone. My name is Jiandong Li and from LASG/IAP. The co-authors are Prof. Liu and Prof. Wu. Today my report is Cloud ..My report has four parts. Part 1 is Study; Part 2 is Data ; Part 3 is Analysis results, and 2 aspects will be shown; The last part is Conclusion. Note that CRF means Cloud and AMR means Asian .From the schematic figure of climate system, we can see that cloud physical properties and their distribution have profound impacts on radiation process, precipitation and general circulation. As we all know that in Asian monsoon region, topography and vegetation are very complex, and there have strong air-land-sea interaction in this region. At the same time,just as shown by the right figure, we also know that In our report, some basic answers will be given out.DataMethodology

Based on radiation output in clear-sky, we choose 10 models from IPCC AR4 AMIP models. The table shows their names, developing groups and resolution.The upper two figures show CRF in DJF. From them, we can see that:In equatorial regions, such as Maritime Continent and tropical western Pacific, there are Strong positive LWCF and negative SWCF. The magnitude of LWCF and SWCF are very close. Between Indian byland and Bengal Bay, there are weak LWCF and SWCF. However, in East Asia, there are also strong SWCF in eastern TP and its magnitude is close to that of SWCF in equator. Some studies indicate this is mainly caused by dynamical effect of TP.The below two figures show CRF in JJA. We can see that in strong convective regions, the spatial pattern of CRF is tightly connected with that of precipitation. In western Pacific, Indian byland and Bengal Bay, there exist strong LWCF and SWCF. Note that there is a near cancellation between LWCF and SWCF in western Pacific region, but Between Bengal Bay and eastern TP, SWCF is larger than LWCF, especially in the southwest China.The character is also related to the effect of TP.

Now, lets look at the performance of AMIP models.The figure is What about the CRF simulation in JJA? In active convective regions, there are stronger LWCF and also in these regions, Especially in Bengal Bay.Except in active convective regions, there are also large SWCF in East of TP, especially in SouthWest of China, such as Sichuan Basin.From above results, we can find the relationship between simulated CRF and rainfall is very close. To further analyze this relationship in more details.now we choose 3 regions, referring to Bin Wangs study, These scatter diagrams show the correlation between rainfall and LWCF. The upper row is for observation.The bottom row is for Multi-Model-Ensemble.The left figure is in DJF. The right figure is in JJA. The red symbol is for SWCF and the blue is for LWCF.From the two figures, we can find No correlation coefficient can exceed 0.9 and the SWCF has lower correlation.