摘要
By using Comprehensive Land Surface Model (CLSM), three snow cases, i.e.,France Col de Porte 1993/1994, 1994/1995 and BOREAS SSA-OJP 1994/1995, were simulated. The simulatedresults were compared with the observations to examine the capability of the model to describe theevolutions of snow cover under two different land cover conditions. Several sensitivity experimentswere performed to investigate the effects of the parameterization schemes of some snow coverinternal processes and vegetation on the model results. Results suggest that the CLSM simulates thebasic processes of snow cover accurately and describes the features of snow cover evolutionsreasonably, indicating that the model has the potential to model the processes related to the snowcover evolution. It is also found that the different parameterization schemes of the snowfalldensity and snow water holding capacity have significant effects on the simulation of snow cover.The estimation of snowfall density mainly impacts the simulated snow depth, and the underestimation(overestimation) of the snowfall density increases (decreases) the snow depth simulatedsignificantly but with little effect on the simulated snow water equivalent (SWE). Theparameterization of the snow water holding capacity plays a crucial role in the evolution of snowcover, especially in the ablation of snow cover. Larger snow water holding capacity usually leads tolarger snow density and heat capacity by storing more liquid water in the snow layer, and makes thetemperature of snow cover and the snow ablation vary more slowly. To a smaller snow water holdingcapacity, contrary is the case. The results also show that the physical processes related to thesnow cover variation are different, which are dependent on the vegetation existed. Vegetation playsan important role in the evolution of soil-snow system by changing the energy balance at thesnow-soil surface. The existence of vegetation is favorable to the maintenance of snow cover anddelays the increase of underlying soil temperature.
By using Comprehensive Land Surface Model (CLSM), three snow cases, i.e.,France Col de Porte 1993/1994, 1994/1995 and BOREAS SSA-OJP 1994/1995, were simulated. The simulatedresults were compared with the observations to examine the capability of the model to describe theevolutions of snow cover under two different land cover conditions. Several sensitivity experimentswere performed to investigate the effects of the parameterization schemes of some snow coverinternal processes and vegetation on the model results. Results suggest that the CLSM simulates thebasic processes of snow cover accurately and describes the features of snow cover evolutionsreasonably, indicating that the model has the potential to model the processes related to the snowcover evolution. It is also found that the different parameterization schemes of the snowfalldensity and snow water holding capacity have significant effects on the simulation of snow cover.The estimation of snowfall density mainly impacts the simulated snow depth, and the underestimation(overestimation) of the snowfall density increases (decreases) the snow depth simulatedsignificantly but with little effect on the simulated snow water equivalent (SWE). Theparameterization of the snow water holding capacity plays a crucial role in the evolution of snowcover, especially in the ablation of snow cover. Larger snow water holding capacity usually leads tolarger snow density and heat capacity by storing more liquid water in the snow layer, and makes thetemperature of snow cover and the snow ablation vary more slowly. To a smaller snow water holdingcapacity, contrary is the case. The results also show that the physical processes related to thesnow cover variation are different, which are dependent on the vegetation existed. Vegetation playsan important role in the evolution of soil-snow system by changing the energy balance at thesnow-soil surface. The existence of vegetation is favorable to the maintenance of snow cover anddelays the increase of underlying soil temperature.
基金
Supported by the National Natural Science Foundation of China under Grant No. 40405018.
