Can carbon export in the North Atlantic Ocean be quantified by combining bio-optical Argo observations with a simple model?

The yearly phytoplankton spring bloom in the North Atlantic Ocean provides an important mechanism for carbon transport to the deep ocean through sinking aggregates, but to date this transport has been difficult to measure in situ. Our hope is that emerging bio-Argo observations combined with biological models will enable quantification of carbon export, and its inter-annual variations and trends. Argo is a global array of free-drifting profiling floats that measure temperature and salinity. More recently, some floats have been equipped with bio-optical and chemical sensors. The resulting bio-optical data not only give insights into the temporal dynamics of organic matter in the upper ocean, but can also be used to optimize and validate biological models. Here we use physical and bio-optical Argo data from the North Atlantic Ocean to constrain a 1D, float-following Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model. The model parameters are optimized with the aid of an evolutionary algorithm in order to replicate float-based chlorophyll and particulate organic matter estimates. We analyze the combined dataset and model with specific focus on carbon transport to the deep ocean and its relation to the spring bloom.