LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection
Based on a decade of research on cloud processes, a new version of the LMDZ atmospheric general circulation model has been developed that corresponds to a complete recasting of the parameterization of turbulence, convection and clouds. This LMDZ5B version includes a mass-flux representation of the thermal plumes or rolls of the convective boundary layer, coupled to a bi-Gaussian statistical cloud scheme, as well as a parameterization of the cold pools generated below cumulonimbus by re-evaporation of convective precipitation. The triggering and closure of deep convection are now controlled by lifting processes in the sub-cloud layer. An available lifting energy and lifting power are provided both by the thermal plumes and by the spread of cold pools. The individual parameterizations were carefully validated against the results of explicit high resolution simulations. Here we present the work done to go from those new concepts and developments to a full 3D atmospheric model, used in particular for climate change projections with the IPSL-CM5B coupled model. Based on a series of sensitivity experiments, we document the differences with the previous LMDZ5A version distinguishing the role of parameterization changes from that of model tuning. Improvements found previously in single-column simulations of case studies are confirmed in the 3D model: (1) the convective boundary layer and cumulus clouds are better represented and (2) the diurnal cycle of convective rainfall over continents is delayed by several hours, solving a longstanding problem in climate modeling. The variability of tropical rainfall is also larger in LMDZ5B at intraseasonal time-scales. Significant biases of the LMDZ5A model however remain, or are even sometimes amplified. The paper emphasizes the importance of parameterization improvements and model tuning in the frame of climate change studies as well as the new paradigm that represents the improvement of 3D climate models under the control of single-column case studies simulations.