Quantifying the Impact of Dynamic Lapse Regimes on Snow Simulations

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for the accurate simulation of snowpack dynamics. This becomes increasingly difficult at finer spatial resolutions that require spatially complete gridded forcing datasets, due to the sparsity of long-term temperature measurements and the influence of local factors like cool air pooling and inversions. Near-surface air temperatures (T_a) are instead often assumed to decrease with elevation at a constant rate of 6.5^oC km^-1, which can lead to large model errors in snow evolution and other processes key to water resource management. This study evaluates the impact of local dynamical adjustments to downscaled T_a on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization (NoahMP) land surface model (LSM). Forcings are derived from remote sensing and reanalysis precipitation products and the (Modern-Era Retrospective Analysis for Research and Applications, version 2) MERRA-2 atmospheric products (including T_a) at the downscaled 1-km resolution. Hourly lapse rates at each grid cell are calculated by applying linear regression to T_a and elevation from surrounding grid cells (within one grid lengths in the x or y direction) at the T_a native MERRA-2 resolution and applied to the downscaled 1-km T_a product. We will present the quantification of the impact of dynamic lapse rate considerations on the snow (e.g., snow water equivalent, snow cover, snow depth) estimations by comparing LSM simulation experiments that are forced with the downscaled T_a (1) without lapse rate correction, (2) corrected with a constant lapse rate (6.5^oC km^-1), or (3) corrected with the dynamic hourly lapse rate.