Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

Abstract

Insufficient studies in characterizing vertical structure of Chlorophyll-a (Chl-a) in the ocean critically limit better understanding about marine ecosystem based on global climate change. In this study, we developed a Gaussian-activation deep neural network (Gaussian-DNN) model to assess vertical Chl-a structure in the upper ocean at high spatial resolution. Our Gaussian-DNN model used the input variables including satellite data of sea surface Chl-a and in-situ vertical physics profiles (temperature and salinity) in the northwestern Pacific Ocean (NWPO). After validation test based on two independent datasets of BGC-Argo and ship measurement, we applied the Gaussian-DNN model to reconstruct temporal evolution of 3-D Chl-a structure in the NWPO. Our modelling results successfully explain over 80% of the Chl-a vertical profiles in the NWPO at a horizontal resolution of 1° × 1° and 1 m vertical resolution within upper 300 meters during 2004 to 2022. Moreover, according to our modelling results, the Subsurface Chlorophyll Maxima (SCMs) and total Chl-a within 0-300 m depths were extracted and presented seasonal variability overlapping longer-time trends of spatial discrepancies all over the NWPO. In addition, our sensitivity testing suggested that sea-water temperatures predominantly control 3-D structures of the Chl-a in the tropical NWPO, while salinity played a key role in the temperate gyre of the NWPO. Here, our development of the Gaussian-DNN model may also be applied to craft long term, 3-D Chl-a products in the global ocean.