Analysis of Arctic Buoy Dynamics using the Discrete Fourier Transform and Principal Component Analysis

TL;DR

This study analyzes Arctic sea-ice drift dynamics using Fourier and Principal Component Analysis on drift data, revealing frequency characteristics and suggesting deployment optimizations for better modeling and monitoring.

Contribution

It introduces a combined Fourier and PCA approach to analyze sea-ice drift, highlighting the dual nature of drift processes and proposing deployment optimizations.

Findings

Drift exhibits both slow and high-frequency dynamics.

High correlation between deployment locations suggests optimization potential.

Analysis supports the dual-process nature of sea-ice drift.

Abstract

Sea-Ice drift affects various global processes including the air-sea-ice energy system, numerical ocean modelling, and maritime activity in the polar regions. Drift has been investigated via various technologies ranging from satellite based systems to ship or ice-borne processes. This paper analyses the dynamics of sea-drift in the Arctic over 2019-2021 by Fourier Analysis and Principal Component Analysis of displacement data generated from the drift tracks of Ice-Tethered Profilers. We show that the frequency characteristics of drift support the notion that it is a function of both slowly varying processes, and higher frequency, random, forcing. In addition, we show that displacement data features high correlation between deployment locations and, consequently, suggest that there is scope for the optimisation of profiler deployment locations and for the reduction in number of instruments required to capture the displacement characteristics of drift.