Use of A-train satellite observations (CALIPSO-PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM.

The evaluation of key cloud properties such as cloud cover, vertical profile and optical depth as well as the analysis of their intercorrelation lead to greater confidence in climate change projections. In addition, the comparison between observations and parameterizations of clouds in climate models is improved by using collocated and instantaneous data of cloud properties. Simultaneous and independent observations of the cloud cover and its three-dimensional structure at high spatial and temporal resolutions are made possible by the new space-borne multi-instruments observations collected with the A-train. The cloud cover and its vertical structure observed by CALIPSO and the visible directional reflectance (a surrogate for the cloud optical depth) observed by PARASOL, are used to evaluate the representation of cloudiness in two versions of the atmospheric component of the IPSL-CM5 climate model (LMDZ5). A model-to-satellite approach, applying the CFMIP Observation Simulation Package (COSP), is used to allow a quantitative comparison between model results and observations. The representation of clouds in the two model versions is first evaluated using monthly mean data. This classical approach reveals biases of different magnitudes in the two model versions. These biases consist of (1) an underestimation of cloud cover associated to an overestimation of cloud optical depth, (2) an underestimation of low- and mid-level tropical clouds and (3) an overestimation of high clouds. The difference in the magnitude of these biases between the two model versions clearly highlights the improvement of the amount of boundary layer clouds, the improvement of the properties of high-level clouds, and the improvement of the simulated mid-level clouds in the tropics in LMDZ5B compared to LMDZ5A, due to the new convective, boundary layer, and cloud parametrizations implemented in LMDZ5B. The correlation between instantaneous cloud properties allows for a process-oriented evaluation of tropical oceanic clouds. This process-oriented evaluation shows that the cloud population characterized by intermediate values of cloud cover and cloud reflectance can be split in two groups of clouds when using monthly mean values of cloud cover and cloud reflectance: one group with low to intermediate values of the cloud cover, and one group with cloud cover close to one. The precise determination of cloud height allows us to focus on specific types of clouds (i.e. boundary layer clouds, high clouds, low-level clouds with no clouds above). For low-level clouds over the tropical oceans, the relationship between instantaneous values of the cloud cover and of the cloud reflectance reveals a major bias in the simulated liquid water content for both model versions. The origin of this bias is identified and possible improvements, such as considering the sub-grid heterogeneity of cloud properties, are investigated using sensitivity experiments. In summary, the analysis of the relationship between different instantaneous and collocated variables allows for process-oriented evaluations. These evaluations may in turn help to improve model parameterizations, and may also help to bridge the gap between model evaluation and model development. [ABSTRACT FROM AUTHOR]