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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

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


Outline
Outline IPCC AR4

  • 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


Study motivation 1
Study motivation (1) IPCC AR4

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).


Study motivation 2
Study motivation (2) IPCC AR4

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?


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models
Data IPCC AR4

  • 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)



Climatology of crf simulated by amip models in amr

Analysis results (1) IPCC AR4

Climatology of CRF* simulated by AMIP models in AMR*

0-50°N , 60-150°E


Observational climatology of crf in amr
Observational climatology of CRF in AMR IPCC AR4

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


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

LWCF by AMIP models in DJF IPCC AR4

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


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

SWCF by AMIP models in DJF IPCC AR4

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.


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

LWCF by AMIP models in JJA IPCC AR4

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


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

SWCF by AMIP models in JJA IPCC AR4

  • 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.


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

Rainfall by AMIP models in JJA IPCC AR4

  • Compared 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 jja
The relationship between CRF and rainfall in AMR in JJA IPCC AR4

EASM

ISM

WNPSM

ISM: 5-20°N,70-100°E

EASM: 5-20°N,110-140°E

WNPSM: 20-35°N,100-130°E


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

Correlation between rainfall and LWCF IPCC AR4

R=0.585

R=0.455

R=0.143

R=0.242

R=0.625

R=0.889


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

Correlation between rainfall and SWCF IPCC AR4

R=-0.673

R=-0.408

R=0.342

R=-0.742

R=-0.826

R=-0.493


Cloud radiative forcing in asian monsoon region simulated by ipcc ar4 amip models

The spatial patterns of observational and simulated CRF IPCC AR4 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 rmse
Area mean bias and RMSE IPCC AR4

LWCF

SWCF

DJF

JJA

Units: Wm-2


Taylor diagram analysis for crf
Taylor diagram analysis for CRF IPCC AR4

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.


Annual cycle of crf simulated by amip models around ea

Analysis results (2) IPCC AR4

Annual cycle of CRF* simulated by AMIP models around EA*

EA*: East Asian

0-50°N , 100-145°E


Annual cycle of observational crf
Annual cycle of observational CRF IPCC AR4

  • 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.


Annual cycle of crf by amip models
Annual cycle of CRF by AMIP models IPCC AR4

GISS-ER

GFDL-CM2.1

MPI-ECHAM5

UKMO-HadGEM1


Conclusion
Conclusion IPCC AR4

  • 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.


Thank you

Thank you IPCC AR4 谢谢

lijd@mail.iap.ac.cn


Centered rmse
Centered RMSE IPCC AR4

Units: Wm-2