ALBERT

Description: 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.

Description: The dynamics of biogeochemical models are determined by the mathematical structure used for 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 an intermediately complex biogeochemical model (MEDUSA) by modelling the chlorophyll and nitrogen concentrations at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain over a 10-year timescale. A 1-D MEDUSA ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe 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 other statistical measures. The spread of the ensemble as a measure of uncertainty is assessed against observations using the Normalised 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 concentrations are between 0.14–3.69 mg m−3 at coastal stations, 0.43–1.11 mg m−3 on the abyssal plain, and 0.004–0.16 mg m−3 at the oligotrophic stations. Changing mortality and grazing functions have the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range and using the ensemble properties such as the mean and median, the errors are mostly reduced compared to the default model output. The NRRs for monthly variability suggest the ensemble spread is generally narrow (NRR 1.21–1.39 for nitrogen 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 ensemble are obtained for the oligotrophic station at ALOHA (for the surface and integrated chlorophyll 10-year time series and bloom peak height), coastal station L4 (for inter-annual mean), and abyssal plain station PAP (for bloom peak height). Overall our studies provided a novel way to generate ensemble spread by perturbing the model structure/parameterizations, and reliable ensemble means and spreads may be generated.

Description: Coupled physical–biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The instantaneous acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modeled grid cell, at each time step), such that they can be resolved as diagnostic variables. Here, we present the first tests of IA in an idealized 1-D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM) and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatiotemporal dynamics in a 1-D water column. We compare the IA model against a fully dynamic, otherwise equivalently acclimative (dynamic acclimation; DA) variant with an additional state variable and a third, non-acclimative and fixed-stoichiometry (FS) variant. We find that the IA and DA variants, which require the same parameter set, behave similarly in many respects, although some differences do emerge especially during the winter–spring and autumn–winter transitions. These differences however are relatively small in comparison to the differences between the DA and FS variants, suggesting that the IA approach can be used as a cost-effective improvement over a fixed-stoichiometry approach. Our analysis provides insights into the roles of acclimative flexibilities in simulated primary production and nutrient drawdown rates, seasonal and vertical distribution of phytoplankton biomass, formation of thin chlorophyll layers and stoichiometry of detrital material.

Description: The acclimative response of phytoplankton, which adjusts their nutrient and pigment content in response to changes in ambient light, nutrient levels, and temperature, is an important determinant of observed chlorophyll distributions and biogeochemistry. Acclimative models typically capture this response and its impact on the C : nutrient : Chl ratios of phytoplankton by explicitly resolving the dynamics of these constituents of phytoplankton biomass. The instantaneous acclimation (IA) approach only requires resolving the dynamics of a single tracer and calculates the elemental composition assuming instantaneous local equilibrium. IA can capture the acclimative response without substantial loss of accuracy in both 0D box models and spatially explicit 1D models. A major drawback of IA so far has been its inability to maintain mass balance for the elements with unresolved dynamics. Here we extend the IA model to capture both C and N cycles in a 0D setup, which requires analytical derivation of additional flux terms to account for the temporal changes in cellular N quota, Q. We present extensive tests of this model, with regard to the conservation of total C an N and its behavior in comparison to an otherwise equivalent, fully explicit dynamic acclimation (DA) variant under idealized conditions with variable light and temperature. We also demonstrate a modular implementation of this model in the Framework for Aquatic Biogeochemical Modelling (FABM), which facilitates modeling competition between an arbitrary number of different acclimative phytoplankton types. In a 0D setup, we did not find evidence for computational advantages of the IA approach over the DA variant. In a spatially explicit setup, performance gains may be possible but would require modifying the physical-flux calculations to account for spatial differences in Q between model grid cells.