Arctic Processes Under Ice: Structures in a Changing Climate

The Arctic region is undergoing unprecedented transformations due to Arctic amplification, warming at twice the global average rate. This warming has led to a drastic reduction in sea ice, with predictions of ice-free Arctic summers before 2050. Such profound changes signal a shift to a new climatic regime, posing significant risks to regional communities, industries, and ecosystems.

This research addresses the urgent need to understand the evolving Arctic environment by harnessing machine learning (ML) to analyse sparse oceanic data. Utilising nearly two decades of Ice Tethered Profilers (ITP) data, complemented by ship-based (U-DASH), and ARGO profiles, this study aims to investigate the structure and dynamics of the Arctic Ocean.

We fit a Gaussian Mixture Model (GMM) to our observations, assigning each data point into a different cluster or class. Despite no spatial information being provided to the model, we find coherent classes emerge. We analyse the properties of each class, compare them with standard water masses from the literature, and look at decadal trends in properties such as oxygen saturation. This approach promises to enhance our understanding of Arctic water masses and their evolving role in a changing environment.