Towards the detection of ocean carbon regimes

In the context of global climate change and environmental challenges, one research question is how different ocean regions take up carbon dioxide and which bio-physical drivers are responsible for these patterns. The carbon uptake at the sea surface is different in different areas. It depends on several drivers (sea surface temperature, the salinity of the water, alkalinity, dissolved inorganic carbon, phytoplankton, etc.), which enormously vary on both a spatial and seasonal time scale. We name a carbon regime a region having common relationships (on a seasonal and spatial scale) between carbon uptake and its drivers (sea surface temperature, etc.).

We are using the output of a global ocean biogeochemistry model providing surface fields of carbon uptake and its drivers on a monthly time scale. We aim to use spatial and seasonal correlations to detect the regimes.  We take advantage of both supervised and unsupervised machine learning methodologies to find different carbon states. The aim is to determine individual local correlations in each carbon state. We build a top-down grid-based algorithm that incorporates both regression and clustering algorithms. The technique divides the entire ocean surface into smaller grids. The regression model detects a linear relationship between carbon uptake and other ocean drivers in each grid box and over each of the twelve months in a year. The correlation clustering model provides clusters of carbon states that have a distinct connection between carbon uptake and different ocean drivers. While the detection of clusters that exhibit correlations relies on static data, here, the aim is to include both the spatial and temporal dimensions, which will reveal temporal trajectories of changes in correlations.