Snow Process Studies and Modeling to Improve Arctic Climate Prediction

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Bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic uniquely susceptible to the radiatively-induced effects of surface light absorbing carbon (LAC, sometimes referred to as black carbon) and dust, such as ice-albedo feedback amplification. Such feedbacks may make dirty snow more efficacious than greenhouse gases in driving atmospheric temperature change in the Arctic. Dirty snow feedbacks change throughout the aerosol lifecycle in the complex Arctic environment of snowfall, snowpack aging, snow-melt, drainage, and analogous sea-ice processes. The goal of this project is to assess light absorbing aerosol interactions in the coupled Arctic climate system using models which represent the complex surface lifecycles of Arctic snow, LAC, and dust, and which have been evaluated against satellite, in-situ, and laboratory measurements.Funds are provided to use International Polar Year (IPY) field and laboratory measurements to improve the representation of snowpack microphysical processes; to implement and/or refine these processes in arctic land, atmosphere, and sea-ice components of an Earth System Model (ESM); and to use the ESM to upscale and improve quantification of the efficacy of and response to arctic climate forcing in the 20th and 21st centuries.