The Concentration of Particulate Organic Carbon Investigation Using One-Dimensional Vertical Computational Fluid Dynamic Coupling Biochemical Model

As the largest carbon sink, the ocean plays a crucial role in achieving carbon neutrality. Phytoplankton, which thrive in the euphotic zone, drive primary productivity by providing energy for marine ecosystems and serving as a vital component of marine carbon storage. Among the acquisition methods of oceanographic data, in situ observations, remote sensing, and numerical models, the data from in situ observation is valuable but limited. Remote sensing provides rapid, wide-range imagery, however, lacking information on subsurface distributions. The numerical models involve numerous parameters but vary depending on the specific region. To facilitate easier estimation of particulate organic carbon (POC) concentrations across the region surrounding Taiwan, we revised and simplified the one-dimensional vertical model proposed by Omstedt [1]. By omitting time evolution effects and seabed terrain distribution, the major input parameters for the current model are reduced to sea surface temperature and wind speed. The results from this numerical model show qualitative trends and scales comparable to those of in situ measurements. Additionally, to leverage the strengths of both remote sensing and numerical modeling, we present a mixing model that combines results from both approaches. Comparisons of euphotic zone depth between in situ measurements and estimates from various methods (remote sensing, numerical model, and mixing model) suggest that the mixing model offers potential for improved POC estimation accuracy.