Subgrid-Scale Variability for Thermodynamic Variables in an Offline Land Surface Prediction System

Abstract

his study presents a numerical analysis of the impact of the horizontal resolution on the forecast capability of the Canadian offline land surface prediction system (SPS; formerly known as GEM-Surf) forced by the 15-km Global Environmental Multiscale (GEM) atmospheric model. This system is used to quantify on a statistical basis the subgrid-scale variability of (near-)surface variables for 25-km grid spacing based on the 2.5- or 10-km SPS run at regional scale over the 2012 summer season. The model bias and the distributions characterizing the subgrid-scale variability drastically depend on the geographic areas as well as on the diurnal cycle. These results show the benefits of high-resolution land surface simulations to account for length scales that are more consistent with the scales at which the actual land surface balance is affected by the heterogeneous geophysical fields (i.e., roughness length, land?water mask, glacier mask, and soil texture). The model bias results highlight the potential of an SPS?GEM two-way coupling strategy for refining predictions near the surface through the upscaling of high-resolution surface heat fluxes to the coarser atmospheric grid spacing, with these fluxes being significantly different from those explicitly resolved at 25 km and featuring nonlinear behavior with respect to the horizontal resolution. Since the computational power of meteorological operational centers progressively increases, making it possible to run high-resolution limited-area models, solving the surface at high resolution in a surface?atmosphere fully coupled system becomes a key aspect for improving numerical weather and environmental forecast performance.