Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Nabir Mamnun^1*, Christoph Voelker¹, Mihalis Vrekoussis^{2, 3, 4} and Lars Nerger¹

• ¹Alfred-Wegener-Institut (AWI), Helmholtz Zentrum für Polar- und Meeresforschung, Germany
• ²Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Germany
• ³Center for Marine Environmental Sciences, University of Bremen, Germany
• ⁴Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Cyprus

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the biogeochemical model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-days satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for three years to jointly estimate ten preselected key biogeochemical parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.