Developing a Scale-Aware Snow Cover Parameterization in the Noah-MP Land Surface Model

Snow cover area is an important component of the Earth system because surface albedo feedback has a large impact on water supply in snow-dominated regions, regional weather systems, and global climate change. Thus, snow cover area and its interactions with water and energy systems are essential to model accurately within land surface components of hydrologic, weather, and climate models. This presentation will focus on recent Noah-MP land surface model enhancements pertaining to snow cover fraction (SCF). We will focus on an analysis that evaluates a new scale-aware SCF formulation derived using SNODAS snow depth and snow water equivalent and MODSCAG SCF. Evaluations are conducted to determine whether the enhanced scale-aware SCF parameterization is transferable to Noah-MP simulations conducted across a range of spatial resolutions commonly used by operational weather and climate prediction systems. These model evaluations show significant error reductions for Noah-MP simulations using the new scale-aware SCF parameterization, relative to the baseline Noah-MP simulations using the default SCF parameterization, across a range of land cover classifications and elevation bands for 1-25km resolution. The SCF improvement also shows potential to reduce cold bias in maximum daily surface temperature that persists over mountain snowpack in the coupled WRF/Noah-MP system.