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 Atmosph...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 20-year 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.展开更多
Experiments were conducted to test the impact of a cloud diagnosis scheme in place of prescribed zonal average cloud on medium and long range integrations with the Australian Bureau of Meteorology Research Centre(BMRC...Experiments were conducted to test the impact of a cloud diagnosis scheme in place of prescribed zonal average cloud on medium and long range integrations with the Australian Bureau of Meteorology Research Centre(BMRC)global atmosphere model.The cloud scheme was shown to improve the temperature bias in the lower troposphere but there was deterioration in the upper troposphere,especially in the tropics,asso- ciated with underestimation of high cloud amount. Thirty day mean fields in a January integration showed greater amplitude in the Northern Hemisphere planetary waves and a deeper Antarctic circumpolar trough than the control experiment or a simulation with no cloud.The results for the diagnosed cloud case agree more closely with corresponding observed fields. There was also some reduction in the zonal average zonal wind component reflecting the additional zonal asymmetry introduced by the diagnostic cloud scheme.Similar trends were also noted in medium and long range forecasts for January and July conditions,although the impact on predictive skill was slight and in some cases detrimental. Areas for improving the diagnostic cloud scheme are noted.展开更多
文摘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 20-year 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.
文摘Experiments were conducted to test the impact of a cloud diagnosis scheme in place of prescribed zonal average cloud on medium and long range integrations with the Australian Bureau of Meteorology Research Centre(BMRC)global atmosphere model.The cloud scheme was shown to improve the temperature bias in the lower troposphere but there was deterioration in the upper troposphere,especially in the tropics,asso- ciated with underestimation of high cloud amount. Thirty day mean fields in a January integration showed greater amplitude in the Northern Hemisphere planetary waves and a deeper Antarctic circumpolar trough than the control experiment or a simulation with no cloud.The results for the diagnosed cloud case agree more closely with corresponding observed fields. There was also some reduction in the zonal average zonal wind component reflecting the additional zonal asymmetry introduced by the diagnostic cloud scheme.Similar trends were also noted in medium and long range forecasts for January and July conditions,although the impact on predictive skill was slight and in some cases detrimental. Areas for improving the diagnostic cloud scheme are noted.
