The Surface Energy Budget Computed at the Grid‐Scale of a Climate Model Challenged by Station Data in West Africa. (23rd November 2017) - Diallo, F. B.; Hourdin, F.; Rio, C.; Traore, A.‐K.; Mellul, L.; Guichard, F.; Kergoat, L.
Abstract: In most state‐of‐the‐art climate models, systematic errors persist in the representation of the rainfall seasonality, near surface air temperature, and surface energy budget over West Africa, even during the dry season. Most biases are related to an incorrect latitudinal position of the monsoon structures. To disentangle the role of the large‐scale dynamics from that of the physical processes in these biases, simulations are performed with the LMDZ general circulation model in which the horizontal winds are nudged toward reanalysis. Wind nudging greatly improves the position of the ITCZ as well as the representation of the components of the surface energy budget directly impacted by the water budget and hence facilitates a more systematic analysis of remaining biases associated with the physics in the model. The great potential of wind nudging to compare the energetics of the atmospheric column in climate models at grid cell scale with station observations, even for coarse grid models, is then shown. Despite the improved water advection and rainfall seasonality in the nudged simulations, errors consisting in a cold bias during the dry season over Sahel, an underestimated seasonal variation of surface albedo, and an overestimation of the solar incoming flux remain. The origin of these remaining biases is further investigated by conducting a series of dedicated sensitivity experiments. Results highlight the key role of the soil thermal inertia, the turbulent mixing efficiency, the surface albedo, and the aerosols and clouds radiative effects in the representation of meteorological 2m‐variables and surface energy budget. Key Points: Nudging horizontal winds toward reanalysis enables the comparison of a GCM grid cell with station data under same synoptic conditions Nudging allows to disentangle biases related to a bad positioning of the WAM from those due to the misrepresentation of physical processes Soil thermal inertia, turbulent mixing, surface albedo, aerosols and clouds are all key for the representation of the surface energy budge