Abstract
Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.
Highlights
The ocean plays a crucial role in the climate of our planet by regulating the amount of atmospheric carbon dioxide
The latter is governed by the global export of particulate organic carbon (POC) from surface waters to the deep ocean
We considered doing a Principal Components Analysis (PCA) transformation on the output depths to reduce the number of outputs, but instead we decided against it for the first pass as it adds a level of complexity
Summary
The ocean plays a crucial role in the climate of our planet by regulating the amount of atmospheric carbon dioxide. The magnitude of carbon sequestration in the ocean is driven by two different mechanisms: the so-called physical and biological carbon pumps The latter is governed by the global export of particulate organic carbon (POC) from surface waters to the deep ocean. Despite their importance, the processes involved in the biological carbon pump are still poorly constrained. In the context of the study mentioned above, a neural networkbased method was trained using the BGC-Argo floats database (∼4700 concurrent in situ temperature, salinity and bbp profiles) This method retrieves the bbp in the water column with an error of ∼20% at a global scale. It is timely to consider and evaluate a more powerful method that would allow to estimate bbp at higher resolution along the vertical dimension which is of great interest for carbon export applications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
