Probabilistic Forecasts of Arctic Sea Ice Thickness View Full Text


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Article Info

DATE

2021-11-09

AUTHORS

Peter A. Gao, Hannah M. Director, Cecilia M. Bitz, Adrian E. Raftery

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. More... »

PAGES

280-302

References to SciGraph publications

  • 2020-06-23. Vecchia Approximations of Gaussian-Process Predictions in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2016-01-20. Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project in CLIMATE DYNAMICS
  • 2018-12-14. A Case Study Competition Among Methods for Analyzing Large Spatial Data in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2018-09-26. The Trajectory Towards a Seasonally Ice-Free Arctic Ocean in CURRENT CLIMATE CHANGE REPORTS
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    http://scigraph.springernature.com/pub.10.1007/s13253-021-00480-0

    DOI

    http://dx.doi.org/10.1007/s13253-021-00480-0

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