Are changes in the phytoplankton community structure altering the flux of CO2 in regions of the North Atlantic?

The North Atlantic Ocean is a globally important sink of carbon dioxide (CO₂). However, the strength of the sink varies temporally and regionally. This study uses a neural network method to map the surface ocean pCO₂ (partial pressure of CO₂) and flux of CO₂from the atmosphere to the ocean alongside measurements of plankton abundance collected from the Continuous Plankton Recorder (CPR) survey to determine the relationship between regional changes in phytoplankton community structure and regional differences in carbon flux.

Despite increasing sea surface temperatures, the Grand Banks of Newfoundland show a decrease in sea surface pCO₂ of ~-2 µatm yr^-1 from 1993 to 2011. The carbon flux in the North Sea is variable over the same period. This is in contrast to most of the open ocean within the North Atlantic, where increases in sea surface pCO₂ follow the trend of increasing CO₂ in the atmosphere, i.e. the flux or sink remains constant. The increasing CO₂ sink in the Grand Banks of Newfoundland and the variable sink in the North Sea correlate with changes in phytoplankton community composition. This study investigates the biogeochemical and oceanographic mechanisms potentially linking increasing sea surface temperature, changes in phytoplankton community structure and the changing carbon sink in these two important regions of the Atlantic Ocean.

The use of volunteer ships to concurrently collect these datasets demonstrates the potential to investigate relationships between plankton community structure and carbon flux in a cost-effective way. These results not only have implications for plankton-dynamic biogeochemical models, but also likely influence carbon export, as different phytoplankton communities have different carbon export efficiencies. Extending and maintaining such datasets is critical to improving our understanding of and monitoring carbon cycling in the surface ocean and improving climate model accuracy.