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Collaborative Research: Arctic sea ice variability: Remote drivers and local processes


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Weather, climate & atmosphere
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Arctic sea ice loss is an important concern for global society affecting both Arctic and global environments. At present, global climate models do a poor job of predicting recent observed declines in Arctic sea ice, and the causes of this discrepancy remain unclear. One interpretation is that models may not be sensitive enough to capture the response of sea ice to greenhouse warming. However, another possibility is that natural cycles have accelerated the recent Arctic sea ice loss. Solving this problem has important implications not only for the interpretation of recent Arctic climate change but also the future projection for the Arctic, including the question as to when we will see the first ice free summer in the Arctic. The study will conduct targeted simulations and place these simulations in the context of observations over the last 117 years. As a result, we expect to better understand how well models capture naturally occurring variations of the Arctic climate and sea ice, and how we can improve models to yield more confident predictions. In addition, the study will train a graduate student and a postdoctoral scientist in polar aspects of climate science and general climate modeling and will provide outreach activities targeting K-12 students and teachers in Santa Barbara. This project will help us understand and quantify how atmospheric circulation in the Arctic influences winds, cloudiness, water vapor, radiation and thereby sea ice variability. It will lead to better understanding of the relative contribution of forced and internal variabilities in recent Arctic warming and sea ice loss. First, the investigators will use data assimilation techniques in a global earth system model and explore how tropical sea surface temperature contributes to the recent sea ice decline via teleconnections to the high latitudes. The investigators will then examine dynamical mechanisms that link Arctic circulation to remote drivers in the tropics and put circulation changes over the last 40 years into the context of longer centennial term changes. Finally, results will help evaluate model skill for Coupled Model Intercomparison Project Phase 5 (CMIP5) and determine whether the tropical drivers represent a main source of modeled internal variability.