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An Evaluation of the Effects of Cloud Parameterization in the R42L9 GCM


doi: 10.1007/BF02915701

  • Cloud is one of the uncertainty factors influencing the performance of a general circulation model (GCM). Recently, the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics (LASG/IAP) has developed a new version of a GCM (R42L9). In this work, roles of cloud parameterization in the R42L9 are evaluated through a comparison between two 20year simulations using different cloud schemes. One scheme is that the cloud in the model is diagnosed from relative humidity and vertical velocity, and the other one is that diagnostic cloud is replaced by retrieved cloud amount from the International Satellite Cloud Climatology Project (ISCCP), combined with the amounts of high-, middle-, and low-cloud and heights of the cloud base and top from the NCEP. The boreal winter and summer seasonal means, as well as the annual mean, of the simulated top-of-atmosphere shortwave radiative flux, surface energy fluxes, and precipitation are analyzed in comparison with the observational estimates and NCEP reanalysis data. The results show that the scheme of diagnostic cloud parameterization greatly contributes to model biases of radiative budget and precipitation. When our derived cloud fractions are used to replace the diagnostic cloud amount, the top-of-atmosphere and surface radiation fields are better estimated as well as the spatial pattern of precipitation. The simulations of the regional precipitation, especially over the equatorial Indian Ocean in winter and the Asia-western Pacific region in summer, are obviously improved.
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Manuscript History

Manuscript received: 10 March 2004
Manuscript revised: 10 March 2004
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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An Evaluation of the Effects of Cloud Parameterization in the R42L9 GCM

  • 1. State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Laboratory for Climate Study, National Climate Center, Beijing 100081,State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: Cloud is one of the uncertainty factors influencing the performance of a general circulation model (GCM). Recently, the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics (LASG/IAP) has developed a new version of a GCM (R42L9). In this work, roles of cloud parameterization in the R42L9 are evaluated through a comparison between two 20year simulations using different cloud schemes. One scheme is that the cloud in the model is diagnosed from relative humidity and vertical velocity, and the other one is that diagnostic cloud is replaced by retrieved cloud amount from the International Satellite Cloud Climatology Project (ISCCP), combined with the amounts of high-, middle-, and low-cloud and heights of the cloud base and top from the NCEP. The boreal winter and summer seasonal means, as well as the annual mean, of the simulated top-of-atmosphere shortwave radiative flux, surface energy fluxes, and precipitation are analyzed in comparison with the observational estimates and NCEP reanalysis data. The results show that the scheme of diagnostic cloud parameterization greatly contributes to model biases of radiative budget and precipitation. When our derived cloud fractions are used to replace the diagnostic cloud amount, the top-of-atmosphere and surface radiation fields are better estimated as well as the spatial pattern of precipitation. The simulations of the regional precipitation, especially over the equatorial Indian Ocean in winter and the Asia-western Pacific region in summer, are obviously improved.

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