| 30 Nov 2021
Review article: Parameterizations of snow-related physical processes in land surface models
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.
Lee, Gim and Park review the representation of snow cover fraction, snow albedo and snow density in eight land surface models used for climate modelling. This will have been a useful exercise for informing development of the Korean Integrated Model. With regrets, however, I do not think that this manuscript meets the requirements for a The Cryosphere review article to “summarize the status of knowledge and outline future directions of research within the journal scope”. The observation by Menard et al. (2021) that model documentation can be missing or inaccurate is presented as a motivation for this review but is not addressed. Instead, the authors present very dense lists of variables and equations from existing and openly available model documentation. For the reader to really understand what these models are doing, it will be easier (and in fact essential) for them to consult the original documentation. The claim in the Conclusions that this review allowed the authors “to find each parameterization’s vulnerabilities” is not demonstrated. Two directions for future research are identified: influences of topographic features and vegetation on snow. The processes of variability listed in Table 9, reproduced from Clark et al. (2011), all operate on spatial scales much finer than climate model grids on which the reviewed models are applied; assessments of which processes have significant influences on coarser resolutions and how they can be parametrized are missing. All of the reviewed models already take account of “vegetation-related factors such as vegetation density, vegetation type, vegetation cover fraction, LAI and SAI” to some extent; exactly what the authors think needs to and can be done in the future is not stated.