Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach

TL;DR

This paper develops a dynamic factor model to optimally combine multiple satellite-based measures of Arctic sea ice extent, improving the reliability of this key climate change indicator.

Contribution

It introduces a novel dynamic factor modeling approach to integrate different Arctic sea ice measures, accounting for their volatility and correlations.

Findings

Almost all weight is assigned to the NSIDC Sea Ice Index.

The combined measure enhances confidence in the Sea Ice Index.

The method confirms the reliability of the NASA Team algorithm.

Abstract

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.