Review article: Parameterizations of snow-related physical processes in land surface models

Abstract

Abstract. Snow on land surface 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. Since the snow cover affects the snow albedo and the ground and soil heat fluxes, it is crucial to detect snow cover changes accurately. It is challenging to acquire observation data for snow cover, snow albedo, and snow depth; thus, an excellent alternative is to use the simulation data produced by the LSMs that 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. Thus, the synthetic intercomparisons of the snow physics in the LSMs will help the improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, snow albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the Earth System Model 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), 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 (CLM 5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). First, we identified that BATS, Noah-MP, JULES, and ISBA reflect the snow depth and roughness length to parameterize snow cover fraction, and CLM 5 accounts for the standard deviation of the elevation value for the snow cover decay function. Second, CLM 5 and BATS are relatively complex, so that they explicitly take into account the solar zenith angle, black carbon, mineral dust, organic carbon, and ice grain size for the determinations of snow albedo. Besides, JULES and ISBA are also complicated model which concerns ice grain size, solar zenith angle, new snow depth, fresh snowfall rate, and surface temperature for the albedo scheme. Third, HTESSEL, CLM 5, and ISBA considered the effects of both wind and temperature in the determinations of the new snow density. Especially, ISBA and JULES considered internal snow characteristics such as snow viscosity, snow temperature, and vertical stress for parameterizing new snow density. The future outlook discussed geomorphic and vegetation-related variables for the further improvement of the LSMs. Previous studies clearly show that spatio-temporal variation of snow is due to the influence of altitude, slope, and vegetation condition. Therefore, we recommended applying geomorphic and vegetation factors such as elevation, slope, time-varying roughness length, vegetation indexes, or optimized parameters according to the land surface type to parameterize snow-related physical processes.

Investigating Catchment‐Scale Daily Snow Depths of CMIP6 in Canada

“…MIROC6 employs MATSIRO, a comprehensive LSM that includes processes like snowfall and melt but assumes constant snow density, which might limit accuracy (Takata et al., 2003). UKESM uses JULES, considering sub‐grid land types, but lacks vegetation‐snow interactions, while CESM2's CLM5 focuses on sub‐grid topography but is limited by being a single‐layer model (Lee et al., 2021). Despite these limitations, MIROC6, UKESM, and CESM2 generally perform better in simulating peak SD, highlighting the importance of accounting for geomorphological factors (Clark et al., 2011).…”

“…By enhancing the accuracy of SD simulations in Global Climate Models (GCMs), we can gain deeper insights into global climate variability and improve global climate projections. The latest generation of climate models, the Coupled Model Intercomparison Project Phase 6 (CMIP6), demonstrates considerable improvements in simulating snow processes compared to previous generations (e.g., Burke et al., 2020; Druel et al., 2017; Lee et al., 2021). However, parametrization schemes in GCMs often oversimplify sub‐grid heterogeneity and neglect various drivers, leading to inaccurate simulations of SD (Clark et al., 2011).…”

Evaluation of E3SM land model snow simulations over the western United States

“…The 1.9 • × 2.5 • climatological aerosol deposition data used in the ELM simulations are too coarse to capture the finescale spatial variations of BC and dust, which limits the accuracy of simulated R sno and thus α sno . The model structures used in different LSMs have different complexities, assumptions, and simplifications (Lee et al, 2021;Magnusson et al, 2015). In ELM, some snow processes are modeled empirically, and some parameters were set empirically or from the literature, which may contain large uncertainties.…”

“…Numerous parameterizations and models with various degrees of complexity have been developed to simulate seasonal snow dynamics and improve our understanding of snow processes (Krinner et al, 2018;Lee et al, 2021;Magnusson et al, 2015). These parameterizations/models have been coupled to land surface models (LSMs) (Krinner et al, 2018) to represent snow grain particles (Räisänen et al, 2017), snow cover (Swenson and Lawrence, 2012), snow albedo (Flanner et al, 2007), snowpack compaction (Decharme et al, 2016), and snow interception by vegetation (Lundquist et al, 2021).…”