A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting

TL;DR

This paper introduces a simple benchmark model for predicting Arctic sea ice extent at fixed future dates, providing a baseline for evaluating more complex models and demonstrating its real-time forecasting performance for September 2020.

Contribution

The paper presents a reduced-form benchmark predictive model for fixed-target Arctic sea ice forecasting and illustrates its real-time performance, aiding model comparison and selection.

Findings

The benchmark model effectively predicts sea ice extent for September 2020.

Visual analysis of forecast evolution enhances understanding of prediction dynamics.

The model serves as a useful baseline for more complex dynamical models.

Abstract

We propose a reduced-form benchmark predictive model (BPM) for fixed-target forecasting of Arctic sea ice extent, and we provide a case study of its real-time performance for target date September 2020. We visually detail the evolution of the statistically-optimal point, interval, and density forecasts as time passes, new information arrives, and the end of September approaches. Comparison to the BPM may prove useful for evaluating and selecting among various more sophisticated dynamical sea ice models, which are widely used to quantify the likely future evolution of Arctic conditions and their two-way interaction with economic activity.