Abstract
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 3.69 mg m−3 at coastal stations, 0.43 and 1.11 mg m−3 on the abyssal plain, and 0.004 and 0.16 mg m−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.
Highlights
Major changes in ocean biogeochemistry have been driven by anthropogenic activities, leading to ocean acidification, eutrophication, and increased levels of dissolved inorganic carbon (DIC) (Gehlen et al, 2015; Bopp et al, 2013; Doney, 2010)
We find that small perturbations in model structure can produce a wide range of results regarding chlorophyll and nutrient concentration as well as phytoplankton phenology
We have explored the structural sensitivity of the 1-D version of MEDUSA, the ocean biogeochemistry component of UK-ESM1
Summary
Major changes in ocean biogeochemistry have been driven by anthropogenic activities, leading to ocean acidification, eutrophication, and increased levels of dissolved inorganic carbon (DIC) (Gehlen et al, 2015; Bopp et al, 2013; Doney, 2010). Anugerahanti et al.: A perturbed biogeochemistry model ensemble these changes, marine biogeochemical models have been developed The majority of these models focus on the lower trophic food webs and explicitly represent dissolved nutrients, phytoplankton, zooplankton, and detritus (NPZD). These models are coupled with physical general circulation models to address and predict the impact of climate change in the ocean ecosystems (Doney et al, 2012; Yool et al, 2013; Butenschön et al, 2016), to assess the impact of anthropogenic input on biogeochemical cycles in the marine ecosystem (Bopp et al, 2005), and to produce decadal reanalyses (Ford et al, 2012)
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