Assessing Impacts of PBL and Surface Layer Schemes in Simulating the Surface–Atmosphere Interactions and Precipitation over the Tropical Ocean Using Observations from AMIE/DYNAMO

Abstract

Abstract Accuracy of turbulence parameterization in representing planetary boundary layer (PBL) processes and surface–atmosphere interactions in climate models is critical for predicting the initiation and development of clouds. This study 1) evaluates WRF Model–simulated spatial patterns and vertical profiles of atmospheric variables at various spatial resolutions and with different PBL, surface layer, and shallow convection schemes against measurements; 2) identifies model biases by examining the moisture tendency terms contributed by PBL and convection processes through nudging experiments; and 3) investigates the main causes of these biases by analyzing the dependence of modeled surface fluxes on PBL and surface layer schemes over the tropical ocean. The results show that PBL and surface parameterizations have surprisingly large impacts on precipitation and surface moisture fluxes over tropical oceans. All of the parameterizations tested tend to overpredict moisture in the PBL and free atmosphere and consequently result in larger moist static energy and precipitation. Moisture nudging tends to suppress the initiation of convection and reduces the excess precipitation. The reduction in precipitation bias in turn reduces the surface wind and latent heat (LH) flux biases, which suggests the positive feedback between precipitation and surface fluxes is responsible, at least in part, for the model drifts. The updated Kain–Fritsch cumulus potential (KF-CuP) shallow convection scheme tends to suppress the deep convection, consequently decreasing precipitation. The Eta Model surface layer scheme predicts more reasonable LH fluxes and LH–wind speed relationship than those for the MM5 scheme. The results help us identify sources of biases of current parameterization schemes in reproducing PBL processes, the initiation of convection, and intraseasonal variability of precipitation.