In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

Additional Metadata
Persistent URL dx.doi.org/10.5194/gmd-10-2905-2017
Journal Geoscientific Model Development
Citation
Cao, B. (Bin), Gruber, S, & Zhang, T. (Tingjun). (2017). REDCAPP (v1.0): Parameterizing valley inversions in air temperature data downscaled from reanalyses. Geoscientific Model Development, 10(8), 2905–2923. doi:10.5194/gmd-10-2905-2017