Understanding Sea Ice Melting via Functional Data Analysis

TL;DR

This study uses functional data analysis on satellite microwave data to show significant recent decline in Arctic sea ice, while Antarctic sea ice remains statistically unchanged, providing new insights into polar melting patterns.

Contribution

The paper introduces a distribution-free functional data analysis approach to assess long-term sea ice changes using satellite data, highlighting significant Arctic melting.

Findings

Significant decline in Arctic sea ice in recent decades.

No statistical evidence of change in Antarctic sea ice.

Arctic sea ice is about 30% smaller in recent summers compared to the decade of the first observed period.

Abstract

In this article, we considered the problem of sea ice cover is melting. Considering the `satellite passive microwave remote sensing data' as functional data, we studied daily observation of sea ice cover of each year as a smooth continuous function of time. We investigated the mean function for the sea ice area for following decades and computed the corresponding $95\%$ bootstrap confidence interval for the both Arctic and Antarctic Oceans. We found the mean function for the sea ice area dropped statistically significantly in recent decades for the Arctic Ocean. However, no such statistical evidence was found for the Antarctic ocean. Essentially, the mean function for sea ice area in the Antarctic Ocean is unchanged. Additional evidence of the melting of sea ice area in the Arctic Ocean is provided by three types of phase curve (namely, Area vs. Velocity, Area vs. Acceleration, and Velocity Vs. Acceleration). In the Arctic Ocean, during the summer, the current decades is observing the size of the sea ice area about $30\%$ less, than what it used to be during the first decade. In this article, we have taken a distribution-free approach for our analysis, except the data generating process, belongs to the Hilbert space.