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TA Theoretical and Observational Study of the Impact of Longwave Radiation on Snowmelt and Sublimation using SAIL/SPLASH Field Observations

Active Dates 9/1/2023-8/31/2026
Program Area Atmospheric System Research
Project Description
Mountain snowpack in the western U.S. is an essential water resource. Earth system models (ESMs) for forecast and management of water resources are facing a major challenge to represent dynamics of mountain snowpack in the western U.S. and to forecast the peak snowpack timing and spring snowmelt rate within the California Sierra Nevada and Colorado Rocky Mountains. Improving model prediction of snowpack conditions raises fundamental scientific questions about sublimation (loss of snow through change of snow into water vapor) and snowmelt: what are the sublimation rates before and during snowmelt? what forcing drives sublimation and snowmelt? The progress in monitoring and modeling sublimation and snowmelt is also hampered by difficulties in field observations over complex terrain. The objective of this project is to improve the understanding of the physical mechanisms underlying sublimation and snowmelt by seeking answers to the above questions. New theoretical analysis predicts two snowmelt mechanisms, while observations suggest insignificant sublimation during melting season. The proposed research is guided by the validation and confirmation of the theoretical predictions and current understanding of sublimation and snowmelt process made possible by new observations from the Atmospheric Radiation Measurement (ARM) Surface Atmosphere Integrated Field Laboratory (SAIL) and the NOAA Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) field campaigns. The expected findings of this project will open new opportunities of enhancing Earth system modeling capability of not only forecasting and simulating snowpack dynamics over mountainous regions but also modeling snow-related climate trends and variability at global scales through advancing fundamental science.
Award Recipient(s)
  • Georgia Tech Research Corporation (PI: Wang, Jingfeng)