A dynamical geography of observed trends in the global ocean

TL;DR

This study uses an empirical stochastic model to analyze 1993-2018 ocean data, revealing faster surface warming and key factors influencing marine trends, advancing understanding of ocean dynamics and climate change impacts.

Contribution

Introduces a data-driven stochastic modeling approach that enhances understanding of ocean trend patterns and their driving factors over recent decades.

Findings

Surface warming is >60% faster when accounting for Pacific oscillations.

Deep reshaping of ocean seascape is observed.

Main drivers of chlorophyll-a trend patterns are identified.

Abstract

Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. Yet, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993-2018 period. We found that including temporal derivatives into the state vector enhances the description of the ocean's dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll-a concentration. This data-driven approach opens new perspectives in empirical climate modelling.