CAREER: Reducing the projection uncertainty for Arctic sea ice loss

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Arctic summer sea ice cover has been declining rapidly over the past few decades. Scientists estimate that future warming will lead to even greater losses, with implications not just for the Arctic but for the global climate system as a whole. Currently, different climate models exhibit a wide spread in their predictions of sea ice loss. In this CAREER award, the investigator will assess the causes of these discrepancies with the ultimate goal of reducing the uncertainty of Arctic sea ice projections. She will also develop a course based undergraduate research experience (CURE) class in climate science involving 80 students over 4 years at her home institution. CURE classes are effective in increasing student retention and self-efficacy in other disciplines, with particularly positive benefits for diverse students. Class materials will be made available on CUREnet for other faculty interested in implementing a CURE research experience in Atmospheric and Oceanic Sciences. Finally, the project will support training for a postdoctoral scientist and graduate student and will further the career of a female, tenure track investigator.This project will evaluate the physical causes of the large spread in Arctic sea ice model projections and mean states between climate models, with the goal of enabling process-based model selection to refine sea ice projections. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models provide the first opportunity for this type of process-based sea ice analysis, due to the new output requested by the Sea-Ice Model Inter-comparison Project (SIMIP) for CMIP6 and spearheaded by the investigator as SIMIP co-chair. This CAREER award will provide insights into the contributions of sea ice dynamics, sea ice thermodynamics, and ocean mixed layer processes to the spread in sea ice predictions. It will also provide an assessment of the ability of models to simulate realistic sea ice internal variability and facilitate the improvement of future climate models by providing information to modeling groups on important processes that can explain model biases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.