Probabilistic Forecasts of Arctic Sea Ice Thickness

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...

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Bibliographic Details
Published in:Journal of Agricultural, Biological and Environmental Statistics
Main Authors: Gao, Peter A., Director, Hannah M., Bitz, Cecilia M., Raftery, Adrian E.
Other Authors: Climate Program Office
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2021
Subjects:
Applied Mathematics
Statistics, Probability and Uncertainty
General Agricultural and Biological Sciences
Agricultural and Biological Sciences (miscellaneous)
General Environmental Science
Statistics and Probability
Arctic
Arctic Ocean
Sea ice
Online Access:http://dx.doi.org/10.1007/s13253-021-00480-0
https://link.springer.com/content/pdf/10.1007/s13253-021-00480-0.pdf
https://link.springer.com/article/10.1007/s13253-021-00480-0/fulltext.html
Description
Summary: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.

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