Impact of bio‐optical data assimilation on short‐term coupled physical, bio‐optical model predictions

Abstract

Data assimilation experiments with the coupled physical, bio‐optical model of Monterey Bay are presented. The objective of this study is to investigate whether the assimilation of satellite‐derived bio‐optical properties can improve the model predictions (phytoplankton population, chlorophyll) in a coastal ocean on time scales of 1–5 days. The Monterey Bay model consists of a physical model based on the Navy Coastal Ocean Model and a biochemical model which includes three nutrients, two phytoplankton groups (diatoms and small phytoplankton), two groups of zooplankton grazers, and two detrital pools. The Navy Coupled Ocean Data Assimilation system is used for the assimilation of physical observations. For the assimilation of bio‐optical observations, we used reduced‐order Kalman filter with a stationary forecast error covariance. The forecast error covariance is specified in the subspace of the multivariate (bio‐optical, physical) empirical orthogonal functions estimated from a monthlong model run. With the assimilation of satellite‐derived bio‐optical properties (chlorophyll a or absorption due to phytoplankton), the model was able to reproduce intensity and tendencies in subsurface chlorophyll distributions observed at water sample locations in the Monterey Bay, CA. Data assimilation also improved agreement between the observed and model‐predicted ratios between diatoms and small phytoplankton populations. Model runs with or without assimilation of satellite‐derived bio‐optical observations show underestimated values of nitrate as compared to the water sample observations. We found that an instantaneous update of nitrate based on statistical relations between temperature and nitrate corrected the model underestimation of the nitrate fields during the multivariate update.