Profile Data Reconstruction for Deep Chl<inline-formula><tex-math notation="LaTeX">$a$</tex-math></inline-formula> Maxima in Mediterranean Sea via Improved-MLP Networks

Abstract

Deep chlorophyll maximum (DCM) is a common oceanographic phenomenon characterized by a significant peak in chlorophyll concentration at a specific depth below the ocean surface. DCM formation is closely related to factors, such as light availability, nutrient distribution, and ocean circulation, making it an important indicator for studying marine ecosystems and their changes. This study aims to estimate subsurface chlorophyll concentrations in the Mediterranean region using an improved multilayer perceptron model, bridging the gap between sparse observation data and dense sea surface data. We utilize Biogeochemical Argo and satellite data, including longitude, latitude, sea surface temperature, surface chlorophyll concentration, and month, as inputs to the model to estimate subsurface chlorophyll concentrations from 1 to 300 m depth. Through fitting and analyzing chlorophyll concentration data in the Mediterranean region, we explore DCM characteristics and their variations across different regions and seasons. The results indicate that the IMLP model performs excellently in estimating subsurface chlorophyll concentrations and effectively captures DCM features in various regions and seasons. By comparing the model estimations with observation data, we reveal patterns in DCM characteristics in the Mediterranean region, providing valuable data support for further research into marine ecosystems.

Keywords

• Biogeochemical Argo (BGC-Argo)
• deep chlorophyll maximum (DCM)
• Gaussian fitting
• IMLP
• satellite data