Probabilistic Forecasts of Arctic Sea Ice Thickness

Peter A. Gao (), Hannah M. Director (), Cecilia M. Bitz () and Adrian E. Raftery ()
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Peter A. Gao: University of Washington
Hannah M. Director: Colorado School of Mines
Cecilia M. Bitz: University of Washington
Adrian E. Raftery: University of Washington

Journal of Agricultural, Biological and Environmental Statistics, 2022, vol. 27, issue 2, No 5, 280-302

Abstract: Abstract In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

Keywords: Spatio-temporal forecasting; Gaussian process; Integrated nested Laplace approximation; Stochastic partial differential equation; Spatial statistics (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13253-021-00480-0

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