Parameterizations of Snow Cover, Snow Albedo and Snow Density in Land Surface Models: A Comparative Review

Abstract

Snow plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. While snow plays a crucial role as a boundary condition in meteorological applications and serves as a vital water resource in certain regions, the acquisition of its observational data poses significant challenges. An effective alternative lies in utilizing simulation data generated by Land Surface Models (LSMs), which accurately calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. In this regard, the synthetic intercomparisons of the snow physics in the LSMs can give insight for further improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the earth system models or operational forecasting systems. We examined single-layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), the University of Torino land surface Process Interaction model in Atmosphere (UTOPIA), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). Through the comparison analysis, we emphasized that inclusion of geomorphic and vegetation-related variables such as elevation, slope, time-varying roughness length, and vegetation indexes as well as optimized parameters for specific regions, in the snow-related physical processes, are crucial for further improvement of the LSMs.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

“…Apart from the sources of uncertainties discussed above, the parametrisation of snow density also adds uncertainty (Essery et al, 2013). The density of newly fallen snow is usually in the range of 20 to 300 kg m −3 and depends on whether the air temperature induces dry to wet snow characteristics and on the wind speed (Lee et al, 2023). Here, all models parametrise the density of fresh snow differently depending on the temperature, with only ERA5L additionally including the near-surface wind speed.…”

“…Single-layer snow schemes are common in the atmospheric community for numerical weather prediction (NWP) models and reanalyses (Arduini et al, 2019). Lee et al (2023) give an overview of various snow parametrisations within nine land-surface models.…”

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

“…In the cool season, more solar radiation is reflected back into space due to the higher albedo of surface cover (e.g., bare soil, snow and ice, etc.) [35][36][37], resulting in less heat being absorbed by the surface as in Figure 10d, but at night, less heat is also released from these areas, and thus the nighttime heat island effect may be stronger. During the hot season, areas of lower surface albedo (e.g., vegetated areas) absorb more solar radiation and release heat during the day, leading to a stronger daytime heat island effect.…”