Validating and improving the uncertainty assumptions for the assimilation of ocean-colour-derived chlorophyll a into a marine biogeochemistry model of the Northwest European Shelf Seas

The correct specification of all sources of uncertainty is critical to the success of data assimilation (DA) in improving the realism and accuracy of forecasts and reanalyses. This work focuses on improving the uncertainty assumptions made during the assimilation of ocean-colour-derived chlorophyll a into an operational marine coupled physical-biogeochemical DA system, which produces daily biogeochemistry forecasts on the Northwest European Shelf Seas. Analysis of the observation-model misfits shows significant biases in chlorophyll a, which vary strongly with season. The behaviour of these misfits agrees well with previous studies and can be attributed to systematic errors within the coupled model. Diagnostic metrics, used frequently within numerical weather prediction, are applied to separate out the random component of the observation and model errors, allowing for the derivation of new error covariance matrices. These new error covariance matrices are then modified to account for the biases in the model that cannot be treated explicitly within the operational DA system. This has the effect of inflating both the error variances and the correlation length-scales. Experiments show that the new error covariances can result in significant improvements in the accuracy of the analysis and forecast. In particular, the new error covariance matrices reduce the bias in the spring phytoplankton bloom present when using the previous error covariances. Validation against independent glider observations in the North Sea also shows reductions in bias in chlorophyll a and oxygen that extend below the surface to the depth of the mixed layer. Accounting for the biases in the model in the error correlations can lead to much larger improvements than not accounting for them; however, there are also regions where large degradations are seen that may indicate model instabilities. This may be improved by estimating the bias separately for the different regions on the shelf.