Bottom RedOx Model (BROM v.1.1): a coupled benthic–pelagic model for simulation of water and sediment biogeochemistry

Interactions between seawater and benthic systems play an important role in global biogeochemical cycling. Benthic fluxes of some chemical elements (e.g., C, N, P, O, Si, Fe, Mn, S) alter the redox state and marine carbonate system (i.e., pH and carbonate saturation state), which in turn modulate the functioning of benthic and pelagic ecosystems. The redox state of the near-bottom layer in many regions can change with time, responding to the supply of organic matter, physical regime, and coastal discharge. We developed a model (BROM) to represent key biogeochemical processes in the water and sediments and to simulate changes occurring in the bottom boundary layer. BROM consists of a transport module (BROM-transport) and several biogeochemical modules that are fully compatible with the Framework for the Aquatic Biogeochemical Models, allowing independent coupling to hydrophysical models in 1-D, 2-D, or 3-D. We demonstrate that BROM is capable of simulating the seasonality in production and mineralization of organic matter as well as the mixing that leads to variations in redox conditions. BROM can be used for analyzing and interpreting data on sediment–water exchange, and for simulating the consequences of forcings such as climate change, external nutrient loading, ocean acidification, carbon storage leakage, and point-source metal pollution

Spatially implicit plankton population models: transient spatial variability

Ocean plankton models are useful tools for understanding and predicting the behaviour of planktonic ecosystems. However, when the regions represented by the model grid cells are not well mixed, the population dynamics of grid cell averages may differ from those of smaller scales (such as the laboratory scale). Here, the ‘mean field approximation’ fails due to ‘biological Reynolds fluxes’ arising from nonlinearity in the fine-scale biological interactions and unresolved spatial variability. We investigate the domain-scale behaviour of two-component, 2D reaction–diffusion plankton models producing transient dynamics, with spatial variability resulting only from the initial conditions. Failure of the mean field approximation can be quite significant for sub grid-scale mixing rates applicable to practical ocean models. To improve the approximation of domain-scale dynamics, we investigate implicit spatial resolution methods such as spatial moment closure. For weak and moderate strengths of biological nonlinearity, spatial moment closure models generally yield significant improvements on the mean field approximation, especially at low mixing rates. However, they are less accurate given weaker transience and stronger nonlinearity. In the latter case, an alternative ‘two-spike’ approximation is accurate at low mixing rates. We argue that, after suitable extension, these methods may be useful for understanding and skillfully predicting the large-scale behaviour of marine ecosystems.<br/

Accounting for unresolved spatial variability in marine ecosystems using time lags

The formulation and calibration of models is a vital method for probing and predicting the behavior of marine ecosystems. The ability to do this may suffer, however, if the calibrating data set is subject to significant spatial variability between samples that is not resolved in the model. We propose that some of this variability might be accounted for by variable time lags between sampled water masses which are otherwise assumed to follow a common pattern of ecosystem variability (dynamical trajectory). Using twin tests of fitting models to simulated data sets, we show that realistic levels of meso/sub-mesoscale variability in time lags may have significant distortion effects on the parameter fits from standard methods which do not account for it. The distortion is such as to 'smooth out' or underestimate the magnitude of temporal variability within sampled water masses, causing loss of accuracy and robustness of biological parameter estimates and functions thereof (e.g. gross primary production). A new method of model fitting is shown to avoid these effects, allowing improved estimates over a broad range of spatial time lag variability and measurement noise levels, assuming accurate estimation of the time lag variance, for which we also suggest a method

Skill assessment via cross-validation and Monte Carlo simulation: an application to Georges Bank plankton models

Better methods are required to assess the skill or uncertainty of plankton model predictions. A method is presented which combines cross-validation with simulated repeat samplings of the data (Monte Carlo simulation), in order to robustly estimate uncertainty in predictions beyond the calibration data (‘extra-sample’). The method is applied to compare two bulk models of chlorophyll on Georges Bank using the GLOBEC data set, accounting for data and forcing errors as well as prior uncertainty in all model parameters and initial conditions. The first model is a simple interpolation of chlorophyll data (‘inductive’ model), and serves as a baseline of predictive skill. The second is a simple process model forced by interannually-variable nutrient and mesozooplankton mean fields. Uncertainty in the process model forcings severely increases the extra-sample prediction variance (over repeat experiments). Although the process model can reproduce some of the interannual chlorophyll variability via top-down control by mesozooplankton, other predictions are strongly biased, possibly due to neglected boundary fluxes of chlorophyll. As a result, the new skill metrics generally favour the inductive model. By contrast, a standard skill metric based on calibration data misfit incorrectly favours the process model, mainly due to the neglect of extra-sample prediction variance.<br/

Efficient upscaling of ocean biogeochemistry

More accurate methods are needed to represent biogeochemistry in ocean models with coarse spatial resolution, in order to assess the response of marine ecosystems to global change. We use eddy-resolving simulations to test methods of upscaling biogeochemistry from 1 km to the 100 km scale of global model grid cells. The neglect of subgrid-scale variability results in serious errors which are not robustly corrected by retuning parameters in the model dynamics. Moment closure schemes provide accurate upscaling for modest computational investment, with broadly similar results obtained by second moment and conditional moment closure schemes. However, the conditional scheme gives clear improvement when variability is imposed on maximum uptake rates under Michaelis–Menten nutrient limitation, as this may invalidate second-order expansions of the mean field dynamics

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Presentation accepted to the Society of Health and Physical Educators National Conference. Postponed due to COVID-19 pandemic

The influence of mesoscale and submesoscale heterogeneity on ocean biogeochemical reactions

The oceanic circulation in the meso to submesoscale regime generates heterogeneity in the concentrations of biogeochemical components over these scales, horizontally between 1 and 100 km. Due to nonlinearities in the biogeochemical reactions, such as phytoplankton primary production and zooplankton grazing, this small-scale heterogeneity can lead to departure from the mean field approximation, whereby plankton reactions are evaluated from mean distributions at coarser scale. Here we explore the magnitude of these eddy reactions and compare their strength to those of the more widely studied eddy transports. We use the term eddy to denote effects arising from scales smaller than ∼ 100 km. This is done using a submesoscale permitting biogeochemical model, representative of the seasonally varying subtropical and subpolar gyres. We found that the eddy reactions associated with primary production and grazing account for ±5–30% of productivity and grazing, respectively, depending on location and time of year, and are scale dependent: two thirds are due to heterogeneities at scales 30–100 km and one third to those at scales below 30 km. Moreover, eddy productivities are systematically negative, implying that production tends to be reduced by nonlinear interactions at the mesoscale and smaller. The opposite result is found for eddy grazing, which is generally positive. The contrasting effects result from vertical advection, which negatively correlates phytoplankton and nutrients and positively correlates phytoplankton and zooplankton in the meso to submesoscale range. Moreover, our results highlight the central role played by eddy reactions for ecological aspects and the distribution of organisms and by eddy transport for biogeochemical aspects and nutrient budgets