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Collaborative Research: Characterizing the Roles of Atmospheric Structure and Clouds on the Radiation and Precipitation Budgets at Summit, Greenland


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Weather, climate & atmosphere
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Funds are provided to characterize the interactions among the atmospheric state, cloud properties, radiation, and precipitation at Summit, Greenland. The objective is to investigate a number of important cloud-related processes, how these interact with the Arctic climate system, and their impact on the surface energy and mass budgets. Specific foci will include: 1) Low-cloud persistence mechanisms that lead to long-lived Arctic stratiform clouds, which interact strongly with the atmospheric structure and surface energy budget; 2) Cloud-phase partitioning, which determines the cloud microphysical composition and, ultimately, the effects that clouds have on atmospheric radiation and the hydrologic cycle; and 3) Precipitation partitioning, in order to understand the different modes of precipitation at Summit and how these impact the total surface accumulation. To address these topics, this project would utilize detailed observations from a suite of ground-based remote sensors deployed at Summit as part of the NSF/AON-funded ICECAPS project in combination with data from satellite-borne active remote sensors. High-resolution numerical modeling will also be used to investigate many of the fine-scale cloud processes and their mesoscale influences. These studies over the Greenland Ice Sheet will also be considered within the context of similar measurements and model studies made at other Arctic locations in order to understand these important processes over Summit and, in a more general sense, across the Arctic. Atmospheric water vapor, clouds, and precipitation greatly affect the surface energy and cryospheric mass balances in the Arctic, and are responsible for much of the variability in these balances. It is thought that recent rapid melting of Arctic sea ice may be driven, in part, by changes in cloud cover and radiation. Cloud-related processes and feedbacks are known to be one of the greatest sources of uncertainty in global climate models, and these shortcomings have been clearly identified in model simulations over the Arctic. Thus, the results of this project should improve our understanding of Arctic cloud processes and their inclusion in climate models, which, in turn, will improve predictability. As broader impacts of this research, the project will provide important data analysis and integration experience for four new graduate students at the participating universities. In addition, data and results from this study will be integrated into undergraduate coursework and summer workshops for high school students and teachers.