TopoCLIM: Rapid topography-based downscaling of regional climate model output in complex terrain v.1.0

Abstract

Abstract. This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope scale atmospheric forcing time-series for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. It achieves this by utilising the previously published TopoSCALE scheme (Fiddes et al. 2014) to generate a synthetic observation of current climate at hillslope scale while accounting for a broad range of surface-atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical-statistical methods, namely speed of use while avoiding the need for ground data, which is often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high elevation stations across the Swiss Alps and a test application of modelling climate change impacts on Alpine snow cover is given.

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

“…Meteorological forcing data could account for a large proportion of turbulent heat flux in some cases (Jason Scot et al., 2021). Some atmospheric forcing downscaling methods for mountainous areas (Fiddes & Gruber, 2014; Fiddes et al., 2021; Tesfa et al., 2020) and high‐resolution forcing data (e.g., Daymet) (Thornton et al., 2016) make it possible to further consider the topography‐induced sub‐grid heterogeneity of atmospheric forcings. Especially, Tesfa et al.…”

“…Meteorological forcing data could account for a large proportion of turbulent heat flux in some cases (Jason Scot et al, 2021). Some atmospheric forcing downscaling methods for mountainous areas (Fiddes & Gruber, 2014;Fiddes et al, 2021;Tesfa et al, 2020) and high-resolution forcing data (e.g., Daymet) (Thornton et al, 2016) make it possible to further consider the topography-induced sub-grid heterogeneity of atmospheric forcings. Especially, Tesfa et al (2020) developed topography-based methods for downscaling grid-scale precipitation to sub-grid scales, which are essentially coupled with the topounit-based sub-grid structure in ELM for improving land surface modeling over mountainous areas.…”