Chlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%. Next to theimprovement of the estimated chlorophyll concentrations, the estimatedbiases indicate regions with systemic errors in the model-represenedchlorophyll.