Probabilistic Forecasting of the Arctic Sea Ice Edge with Contour Modeling

TL;DR

This paper presents Mixture Contour Forecasting, a probabilistic method for predicting the Arctic sea ice edge by combining ensemble outputs and observed patterns, improving calibration over traditional models.

Contribution

It introduces a novel statistical technique that directly models the sea ice edge contour using a mixture of distributions, enhancing forecast accuracy.

Findings

Better calibration at short lead times compared to existing methods

Effective modeling of the sea ice edge contour

Improved forecast performance over multiple months

Abstract

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 post-processed 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.