The Coupling Model Intercomparison Program organized by the World Climate Research Programme (WCRP) has been advanced to CMIP6. The climate system model CAMS-CSM developed by the Chinese Academy of Meteorological Sciences is one of the registered models to participate in CMIP6. In addition to the Diagnostic, Evaluation and Characterization of Klima (DECK) expreiment and Historical simulation required by CMIP6, CAMS-CSM also plans to participate in four Model Intercomparison Projects (MIPs) such as Scenario Model Intercomparison Project (ScenarioMIP), Cloud Feedback Model Intercomparison Project (CFMIP), Global Monsoons Model Intercomparison Project (GMMIP) and High Resolution Model Intercomparison Project (HighResMIP). This paper provides a reference for model data users by introducing the basic configuration of CAMS-CSM model, its basic simulation performance, as well as the CMIP6 experiments and MIPs it plans to participate.

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contributed at 2020-06-29


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The Chinese Academy of Meteorological Sciences (CAMS) has been devoted to developing a climate system model (CSM) to meet demand for climate simulation and prediction for the East Asian region. In this study, we evaluated the performance of CAMS-CSM in regard to sensible heat flux (H), latent heat flux (LE), surface temperature, soil moisture, and snow depth, focusing on the Atmospheric Model Intercomparison Project experiment, with the aim of participating in the Coupled Model Intercomparison Project phase 6. We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations, including the FLUXNET model tree ensembles H and LE data, Climate Prediction Center soil moisture data, snow depth climatology data, and Chinese ground observations of snow depth and winter surface temperature. We compared these results with data from the ECMWF Interim reanalysis (ERA-Interim) and Global Land Data Assimilation System (GLDAS). Our results indicated that CAMS-CSM simulations were better than or comparable to ERAInterim reanalysis for snow depth and winter surface temperature at regional scales, but slightly worse when simulating total column soil moisture. The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis, but less than or comparable to those from GLDAS. The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions, except for North America. CAMS-CSM LE produced the lowest bias in Siberia, North America, and South America, but with the lowest spatial correlation coefficients. Therefore, there are still scopes for improving H and LE simulations in CAMS-CSM, particularly for LE.

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