Description: Highlights: • We examine the role of marine particle for regulating trace element distribution. • We review the state of the art for modelling the oceanic distribution of specific tracers: Thorium, Protactinium, Iron, and Aluminium. • We review the state of the art for modelling particle distribution in large scale ocean biogeochemical model. The distribution of trace elements in the ocean is governed by the combined effects of various processes, and by exchanges with external sources. Modelling these represents an opportunity to better understand and quantify the mechanisms that regulate the oceanic tracer cycles. Observations collected during the GEOTRACES program provide an opportunity to improve our knowledge regarding processes that should be considered in biogeochemical models to adequately represent the distributions of trace elements in the ocean. Here we present a synthesis about the state of the art for simulating selected trace elements in biogeochemical models: Protactinium, Thorium, Iron and Aluminium. In this contribution we pay particular attention on the role of particles in the cycling of these tracers and how they may provide additional constraints on the transfer of matter in the ocean.

Description: We present a suite of experiments with a hierarchy of biogeochemical models of increasing complexity coupled to an offline global ocean circulation model based on the “transport matrix method”. Biogeochemical model structures range from simple nutrient models to more complex nutrient-phytoplankton–zooplankton-detritus-DOP models. The models’ skill is assessed by various misfit functions with respect to observed phosphate and oxygen distributions. While there is generally good agreement between the different metrics employed, an exception is a cost function based on the relative model-data misfit. We show that alterations in parameters and/or structure of the models – especially those that change particle export or remineralization profile – affect subsurface and mesopelagic phosphate and oxygen, particularly in the upwelling regions. Visual inspection of simulated biogeochemical tracer distributions as well as the evaluation of different cost functions suggest that increasing complexity of untuned, unoptimized models, simulated with parameters commonly used in large-scale model studies does not necessarily improve performance. Instead, variations in individual model parameters may be of equal, if not greater, importance.

Description: Amodel is presented that simulates the formation of marine aggregates from particles of different origin inside amodel of pelagic biological processes. Experiments are carried out with parameterizations appropriate for different types of aggregates, using different kinds of physical forcing, and compared to observations of dissolved inorganic nitrogen (DIN), particulate organic nitrogen (PON), marinesnow concentration, and sedimentation. The occurrence of large, macroscopically visible aggregates (marinesnow) can best be simulated with parameterizations that have been derived from in situ observations of marinesnow, but not with aparameterization sufficient for dense particles. The parameterization strongly determines the amount and timing of deep export, as well as the post-bloom development of the food web in the upper layers. Detritus in aggregates plays a role mainly during times when zooplankton are abundant, as e.g. in the western Arabian Sea during Southwest Monsoon. Then the large aggregates as fast sinking vehicles may remove detritus quickly from shallow and mid-water depth, preventing the accumulation of nutrients that are produced via detritus decomposition. In this region, detritus contributes strongly to deep sedimentation. The nitrogenbudget at this location with regard to the observations cannot be closed: depending on model type, either the model simulates too high sedimentation, or too high DIN. Possible causes for this mismatch include undercollection by sediment traps, inaccurate representation of physical processes in the model and the neglect of biological processes, such as production of dissolved organic matter or denitrification.

Description: We investigate the effects of different vertical grid resolutions and algorithms for the calculation of particle sinking on the sedimentation and remineralization of particulate organic matter. Simulations carried out with an idealized 1D model of detritus sinking show that a coarse vertical resolution, such as used in many global biogeochemical models, tends to enhance the particle flux through numerical mixing within the vertical boxes, and thereby simulates deeper remineralization, compared to a model with a fine vertical resolution. This effect can be ameliorated by assuming a distribution of detritus within the individual grid boxes that corresponds to the prescribed sinking and remineralization parameters. Experiments of the different flux algorithms, carried out with 3D global biogeochemical models of different vertical grid resolution reveal impacts on simulated biogeochemical tracer distributions that are similar to those obtained by substantial variations in biogeochemical model parameters. Our results indicate that numerical schemes have to be considered when comparing biogeochemical parameter values of different models and also when porting biogeochemical models among different circulation models.

Description: The effect of phytoplankton cell size on the variation of nutrient uptake and exudation rates is examined: we first present an overview of the relationship between the variation of the growth and loss parameters and cell size. We then investigate the effect of cell-size-dependent parameters on the development of an entire phytoplankton community by means of a numerical, vertically resolved nutrient phytoplankton model. The model represents phytoplankton size distributions in three different ways, namely one configuration with explicit representation of 14 size classes, one configuration with constant-slope power-law spectral representation, and one configuration with variable-slope power-law spectral representation. The size-dependent configurations are further compared to a size-independent configuration. Consistent with theory, the explicit and variable-slope spectral model simulations predict increased importance of larger cells, or "flat" size distribution under conditions of low light and high nutrients, while smaller cells ("steep" size distributions) may dominate in oligotrophic, well-lit regimes. In some situations the variable-slope spectral model seems to be sufficient to reflect the phytoplankton size distribution; however, especially in the deep phytoplankton maximum a unimodal rather than power-law spectral description might be more appropriate to reproduce results of the explicit 14-size-class model. The assumption of a fixed spectral slope, according to which larger size classes gain importance especially during bloom periods, is not consistent with the underlying theory, and does not agree with the results of the size-discrete model. The comparison of model predictions with variations of phytoplankton size distribution observed in the field is hampered by the sparsity of data, especially for the winter season. A half-saturation constant that represents the nutrient uptake of the entire phytoplankton community (K*) compares well to published values. (C) 2007 Elsevier Ltd. All rights reserved.

Description: Global biogeochemical ocean models rely on many parameters, which govern the interaction between individual components, and their response to the physical environment. They are often assessed/calibrated against quasi-synoptic data sets of dissolved inorganic tracers. However, a good fit to one observation might not necessarily imply a good match to another. We investigate whether two different metrics—the root-mean-square error to nutrients and oxygen and a metric measuring the overlap between simulated and observed oxygen minimum zones (OMZs)—help to constrain a global biogeochemical model in different aspects of performance. Three global model optimizations are carried out. Two single-objective optimizations target the root-mean-square metric and a sum of both metrics, respectively. We then present and explore multiobjective optimization, which results in a set of compromise solutions. Our results suggest that optimal parameters for denitrification and nitrogen fixation differ when applying different metrics. Optimization against observed OMZs leads to parameters that enhance fixed nitrogen cycling; this causes too low nitrate concentrations and a too high global pelagic denitrification rate. Optimization against nutrient and oxygen concentrations leads to different parameters and a lower global fixed nitrogen turnover; this results in a worse fit to OMZs. Multiobjective optimization resolves this antagonistic effect and provides an ensemble of parameter sets, which help to address different research questions. We finally discuss how systematic model calibration can help to improve models used for projecting climate change and its effect on fisheries and climate gas emissions.

Description: Southern Ocean (SO) physical and biological processes are known to have a large impact on global biogeochemistry. However, the role that SO biology plays in determining ocean oxygen concentrations is not completely understood. These dynamics are investigated here by shutting off SO biology in two marine biogeochemical models. The results suggest that SO biological processes reduce the ocean's oxygen content, mainly in the deep ocean, by 14 to 19%. However, since these processes also trap nutrients that would otherwise be transported northward to fuel productivity and subsequent organic matter export, consumption, and the accompanying oxygen consumption in midlatitude to low-latitude waters, SO biology helps to maintain higher oxygen concentrations in these subsurface waters. Thereby, SO biology can influence the size of the tropical oxygen minimum zones. As a result of ocean circulation the link between SO biological processes and remote oxygen changes operates on decadal to centennial time scales.

Description: We show how to represent changes in the distribution of size and sinking speed of marine particles by a two-parameter model. In contrast to fully size-resolved models, this representation holds promise for constructing ocean biogeochemical models with detailed spatial resolution and seasonally varying sinking speed. We treat the mass and number of particles as separate state variables, each obeying its own conservation law. Average size and sinking speed of particles change as particles aggregate or the largest particles sink out. The distribution of particle sizes is assumed to follow a power law, whose exponent changes as a function of average particle size. Compared to biogeochemical models with constant particle sinking speed, our approach imposes a modest increase in computational cost and produces important effects like more rapid sinking immediately following a phytoplankton bloom. Compared to models that use hundreds of size classes to represent the detailed evolution of particle size distribution, our approach offers a major reduction in computational cost, while maintaining realistic behaviour like the sudden onset of significant aggregation when particles are sufficiently abundant.

Description: In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real-world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. Different optimization tools have been successfully applied to address this task of model calibration. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real-world processes which cannot be fully resolved, it might be preferable to consider associated model parameters as random parameters. In this paper, a random parameter is drawn from a wide probability distribution for every singe model simulation. We developed an optimization approach that allows us to declare certain parameters random while optimizing those that are assumed to take fixed values. We designed a corresponding variant of the well known Covariance Matrix Adaption Evolution Strategy (CMA-ES). The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA-ES, our new method converges to a solution that better suits the credible range of the corresponding random parameter with less computational effort.

Description: Highlights: • Modelled fish biomass was affected by interannual variability in the plankton food. • The effects were small compared with the high variability in observations. • Fish were highly affected by changes in the larval mortality of anchovy. Abstract: The Northern Humboldt Current System is the most productive eastern boundary upwelling system, generating about 10 % of the global fish production, mainly coming from small pelagic fish. It is bottom-up and top-down affected by environmental and anthropogenic variability, such as El-Niño Southern Oscillation and fishing pressure, respectively. The high variability of small pelagic fish in this system, as well as their economic importance, call for a careful management aided by the use of end-to-end models. This type of models represent the ecosystem as a whole, from the physics, through plankton up to fish dynamics. In this study, we utilised an end-to-end model consisting of a physical–biogeochemical model (CROCO-BioEBUS) coupled one-way with an individual-based fish model (OSMOSE). We investigated how time-variability in plankton food production affects fish populations in OSMOSE and contrasted it against the sensitivity of the model to two parameters with high uncertainty: the plankton accessibility to fish and fish larval mortality. Relative interannual variability in the modelled fish is similar to plankton variability. It is, however, small compared with the high variability seen in fish observations in this productive ecosystem. In contrast, changes in larval mortality have a strong effect on anchovies. In OSMOSE, it is a common practice to scale plankton food for fish, accounting for processes that may make part of the total plankton in the water column unavailable. We suggest that this scaling should be done constant across all plankton groups when previous knowledge on the different availabilities is lacking. In addition, end-to-end modelling systems should consider environmental impacts on other biological processes such as larval mortality in order to better capture the interactions between environmental processes, plankton and fish.