Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 15, issue 21
Biogeosciences, 15, 6685-6711, 2018
https://doi.org/10.5194/bg-15-6685-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 15, 6685-6711, 2018
https://doi.org/10.5194/bg-15-6685-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Nov 2018

Research article | 12 Nov 2018

A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations

Prima Anugerahanti1, Shovonlal Roy1,2, and Keith Haines3 Prima Anugerahanti et al.
  • 1Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading, RG6 6AB, UK
  • 2School of Agriculture, Policy, and Development, University of Reading, Whiteknights, Reading, RG6 6AR, UK
  • 3Department of Meteorology and National Centre for Earth Observation, University of Reading, Whiteknights campus Early Gate, Reading, RG6 6BB, UK

Abstract. The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69mgm−3 at coastal stations, 0.43 and 1.11mgm−3 on the abyssal plain, and 0.004 and 0.16mgm−3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.

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Minor changes in the biogeochemical model equations lead to major dynamical changes. We assessed this structural sensitivity for the MEDUSA biogeochemical model on chlorophyll and nitrogen concentrations at five oceanographic stations over 10 years, using 1-D ensembles generated by combining different process equations. The ensemble performed better than the default model in most of the stations, suggesting that our approach is useful for generating a probabilistic biogeochemical ensemble model.
Minor changes in the biogeochemical model equations lead to major dynamical changes. We assessed...
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