OPP-PRF: The predictive capacity of ocean heat gain and autumn freeze up dates on seasonal sea ice extent from three reanalyses and the CESM2 Large Ensemble

Colorado State University, Fort Collins CO

Investigators

Abstract

This project focuses on improving sea ice extent predictability in the Arctic. Using freely available data from a number of simulations, this research will use ocean heat patterns to assess how the timing of autumn sea ice freeze up relates to seasonal predictability. The researcher expects this project to contribute significantly to the understanding of environmental influences on sea ice change across the Arctic Ocean, by applying statistics to evaluate which environmental variables are the most significant for sea ice loss. Over the last 40 years, sea ice has declined in all months, leaving thinner and smaller sea ice floes, and such changes are expected to continue due to climate warming. These changes produce not only climate impacts, but also economic impacts, which is why accurately forecasting sea ice extent on seasonal scales is important. As the Arctic loses sea ice cover, it becomes more accessible to shipping and resource extraction but also less reliable for Indigenous communities and threatened species that depend on the ice. Sea ice can grow to be multiple meters thick causing regions of the Arctic to be impassible, unless the entity has access to an icebreaker ship. Better knowing the factors contributing to sea ice change will improve models and inform the public so they can plan more efficiently. This project aims to engage minority groups and encourage the development of greater scientific understanding of the Arctic climate for people at many educational levels. This will be done by creating a short course for high school and middle school students called a Data Puzzle (https://datapuzzles.org/) and employing an undergraduate intern. While basic physical processes impacting sea ice loss in the Arctic are well recognized, little quantitative information exists on the magnitudes, variability, and trends in seasonal Arctic Ocean heat uptake and release. This project will make novel use of ocean heat gain data from three retrospective models in conjunction with output from the newly available Community Earth System Model version 2 Large Ensemble (CESM2-LE). Using sea ice concentration (SIC) data from the combined Passive Microwave sea ice record, the total days with open water at each grid cell will be calculated to assess how open water days and the date of autumn freeze up have varied and changed. Lastly, a seasonal autoregressive integrated moving average model (SARIMA) will be used to predict seasonal SIC at lead times of one, two and three months. Total ocean heat gain, average cloud cover, total column water, and number of open water days will be used as predictors in the multivariate model. The researcher will use statistical models to estimate the influence of various Arctic climate characteristics on sea ice extent and future seasonal predictions. This project will significantly contribute to the understanding of the Arctic climate and aid in model improvement. 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.