Front. Mar. Sci.
Sec. Marine Biogeochemistry
doi: 10.3389/fmars.2022.984236

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

 Nabir Mamnun1*,  Christoph Voelker1, Mihalis Vrekoussis2, 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
Provisionally accepted:
The final, formatted version of the article will be published soon.

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.

Keywords: Marine biogeochemical model, uncertainty quantification, Ensemble Kalman filter, parameter estimation, chlorophyll-a concentration, Net Primary Production

Received: 01 Jul 2022; Accepted: 02 Sep 2022.

Copyright: © 2022 Mamnun, Voelker, Vrekoussis and Nerger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Nabir Mamnun, Alfred-Wegener-Institut (AWI), Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany