| Abstrakt: |
The quality of snow simulations in land surface models (LSMs) largely depends on the accuracy of the atmospheric forcing data, especially precipitation and air temperature. To investigate the sensitivities of snow simulations to atmosphere forcing, historical simulations from 1981–2010 were conducted using the Chinese Academy of Sciences land surface model (CAS‐LSM) with four atmospheric forcing data sets: third Global Soil Wetness Projects (GSWP3), the Water and Global Change (WATCH) Forcing Data (WFD/WFDEI), the Climate Research Unit ‐ National Centers for Environmental Prediction (CRU‐NCEP), and Princeton. A sensitivity index (Ψ) is utilized to quantify the sensitivity of the simulated snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SDP) to the uncertainties in forcing data. By comparing the simulated results with satellite‐based products and in situ observations, we find that CAS‐LSM generally captured the spatial and seasonal variations of the SCF, SWE, and SDP. The simulation based on GSWP3 produced more reasonable estimates than the other three simulations, particularly for the SCF and SDP. The sensitivity analysis suggested that the SWE and SDP suffered the most from the uncertainties in the atmospheric forcing data sets. The sensitivities of the SCF, SWE, and SDP to precipitation uncertainties had various seasonal cycles depending on the climate regime. The highest sensitivity in boreal climates appeared during January–March, while in warm temperate climates the highest sensitivity existed during April–June. On average, the strongest precipitation sensitivity was found for temperate climates with cold, dry winters. The sensitivities to uncertainties in air temperature showed similar patterns; however, air temperature sensitivity was generally dominant over precipitation sensitivity in boreal climates, while in warm temperate climates both of them were high. Plain Language Summary: We conducted four historical simulations using the land surface model Chinese Academy of Sciences land surface model (CAS‐LSM) with four state‐of‐the‐art atmospheric forcing data sets, that is, third Global Soil Wetness Project (GSWP3), the Water and Global Change (WATCH) Forcing Data (WFD/WFDEI), the Climate Research Unit ‐ National Centers for Environmental Prediction (CRU‐NCEP), and Princeton, to investigate the sensitivities of snow simulations to different atmosphere forcing data sets. The land surface model demonstrated its capability in reproducing temporal and spatial variations of historical snowpack over the Northern Hemisphere. Compared with the other three simulations, the simulation based on GSWP3 produced more reasonable estimates, particularly for the snow cover fraction and snow depth. Sensitivity analysis suggested that snow mass and snow depth suffered the most from the uncertainties in the atmospheric forcing data sets. The simulated snowpack is more sensitive to the uncertainties in precipitation and air temperature in temperate climates with cold, dry winters. This work highlighted that reliable forcing data, especially precipitation data, are of great significance to the accuracy of snow simulations. Key Points: Snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SDP) from simulations using different forcing data were evaluatedA sensitivity index, Ψ, was used to quantify the sensitivities of the SCF, SWE, and SDP to the uncertainties in atmospheric forcing dataSWE and SDP showed stronger sensitivities than SCF, with the highest sensitivity appearing for temperate climates with cold, dry winters [ABSTRACT FROM AUTHOR] |
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