Spatially Varying Biogeochemical Parameter Estimation in a Global Ocean Model

Abstract Ocean biogeochemical (BGC) models are key tools for investigating ocean biogeochemistry and the global carbon cycle. These models contain many uncertain and often poorly known process parameters that are treated as constant values. This study addresses this limitation by estimating spatially and temporally varying parameters in the Regulated Ecosystem Model 2 (REcoM2) through the assimilation of satellite‐derived chlorophyll‐a data using an ensemble Kalman filter. Nine key BGC parameters were optimized, significantly improving the model's performance. Utilizing the optimized parameters in the model results in a 26% reduction in root mean square error for surface chlorophyll‐a concentrations compared to simulations with uniform parameters, with the spatial patterns of parameter estimates aligning well with observed distributions. These findings underscore the benefits of incorporating spatially and temporally varying parameters for enhancing model accuracy and understanding BGC variability.