  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.
