Sea ice, or frozen ocean water, freezes and melts every year in the Arctic. Forecasts of where sea ice will be located weeks to months in advance have become more important as the amount of sea ice declines due to climate change, for maritime planning and other uses. Typical sea ice forecasts are made with ensemble models, physics-based models of sea ice and the surrounding ocean and atmosphere. This paper introduces Mixture Contour Forecasting, a method to forecast sea ice probabilistically using a mixture of two distributions, one based on postprocessed output from ensembles and the other on observed sea ice patterns in recent years. At short lead times, these forecasts are better calibrated than unadjusted dynamic ensemble forecasts and other statistical reference forecasts. To produce these forecasts, a statistical technique is introduced that directly models the sea ice edge contour, the boundary around the region that is ice-covered. Mixture Contour Forecasting and reference methods are evaluated for monthly sea ice forecasts for 2008–2016 at lead times ranging from 0.5–6.5 months using one of the European Centre for Medium-Range Weather Forecasts ensembles.
This work was supported by the National Oceanic and Atmospheric Administration’s Climate Program Office, Climate Variability and Predictability Program through grant NA15OAR4310161.
The first author was partially supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1256082.
We thank Nicholas Wayand for assistance with data processing and Arlan Dirkson for addressing questions on Trend-Adjusted Quantile Mapping. We also thank the Associate Editor and two anonymous reviewers for suggestions on the manuscript. Results were produced using the IceCast R package available in Supplementary Material B (Director, Raftery and Bitz (2021)) or online https://github.com/hdirector/IceCast. The IceCast R package relies on the rgeos (Bivand and Rundel (2020)), sp (Pebesma and Bivand (2005)) and Rcpp (Eddelbuettel et al. (2011)) R packages, among others. Relevant code is also available in Supplementary Material B (Director, Raftery and Bitz (2021)) and online at https://github.com/hdirector/ProbSeaIce. The figures were primarily made with the Tidyverse R packages (Wickham et al. (2019)). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
"Probabilistic forecasting of the Arctic sea ice edge with contour modeling." Ann. Appl. Stat. 15 (2) 711 - 726, June 2021. https://doi.org/10.1214/20-AOAS1405