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Cloud Radiative Forcing in Asian Monsoon Region Simulated by IPCC AR4 AMIP models

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.

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Cloud Radiative Forcing in Asian Monsoon Region Simulated by IPCC AR4 AMIP models

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  1. 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

  2. Outline • Study motivation • Data and methodology • Analysis results • Climatology of CRF* in AMR* • Annual cycle of CRF around East Asia • Conclusion CRF*: Cloud Radiative Forcing AMR*: Asian Monsoon Region

  3. Study motivation (1) IPCC AR4, 2007 • 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).

  4. Study motivation (2) Bin Wang, 2002 • 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. Could most AGCMs from IPCC AR4 reproduce the basic features of CRF in AMR? What are the main deficiencies for CRF simulation?

  5. Data • ERBE data (Barkstrom et al, 1990) • Monthly data from 1985 to 1989 • Resolution is 2.5°×2.5°and uncertainty is ±5 Wm-2 • IPCC AR4 AMIP data • Monthly data from 1979 to 1993 • Interpolation into ERBE grids • CMAP precipitation Methodology • CRF • Long-wave CRF • Short-wave CRF • Study area • 60-150°E and 0-50°N including main AMR • Area mean bias and RMSE • Taylor diagram analysis (Taylor, 2001)

  6. IPCC AR4 AMIP models

  7. Analysis results (1) Climatology of CRF* simulated by AMIP models in AMR* 0-50°N , 60-150°E

  8. Observational climatology of CRF in AMR DJF 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? JJA

  9. LWCF by AMIP models in DJF GFDL-CM2.1 MIROC3.2(medres) MRI-CGCM2.3.2 UKMO-HadGEM1 • Four 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

  10. SWCF by AMIP models in DJF GISS-ER MPI-ECHAM5 MRI-CGCM2.3.2 UKMO-HadGEM1 • Four 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.

  11. LWCF by AMIP models in JJA • In active convective regions, the location and intensity of LWCF by most models have larger biases.

  12. SWCF by AMIP models in JJA • In 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.

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

  14. The relationship between CRF and rainfall in AMR in JJA EASM ISM WNPSM ISM: 5-20°N，70-100°E EASM: 5-20°N，110-140°E WNPSM: 20-35°N，100-130°E

  15. Correlation between rainfall and LWCF R=0.585 R=0.455 R=0.143 R=0.242 R=0.625 R=0.889

  16. Correlation between rainfall and SWCF R=-0.673 R=-0.408 R=0.342 R=-0.742 R=-0.826 R=-0.493

  17. 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

  18. Area mean bias and RMSE LWCF SWCF DJF JJA Units: Wm-2

  19. Taylor diagram analysis for CRF 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.

  20. Analysis results (2) Annual cycle of CRF* simulated by AMIP models around EA* EA*: East Asian 0-50°N , 100-145°E

  21. Annual cycle of observational CRF • In tropical area (south to 20°N) ，the variation of CRF is consistent with that of rain season。 • In East of TP (between 25°and 40°N), stronger CRF appears since February and lasts until late May, when CRF evolves north with the rain season.

  22. Annual cycle of CRF by AMIP models GISS-ER GFDL-CM2.1 MPI-ECHAM5 UKMO-HadGEM1

  23. Conclusion • There 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.

  24. Thank you谢谢 lijd@mail.iap.ac.cn

  25. Centered RMSE Units: Wm-2

  26. Annual cycle of rainfall by AMIP models

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