Physiological constraints on the global distribution of <i>Trichodesmium</i> ? effect of temperature on diazotrophy

International audienceThe cyanobacterium Trichodesmium is an important link in the global nitrogen cycle due to its significant input of atmospheric nitrogen to the ocean. Attempts to incorporate Trichodesmium in ocean biogeochemical circulation models have, so far, relied on the observed correlation between temperature and Trichodesmium abundance. This correlation may result in part from a direct effect of temperature on Trichodesmium growth rates through the control of cellular biochemical processes, or indirectly through temperature influence on mixed layer depth, light and nutrient regimes. Here we present results indicating that the observed correlation of Trichodesmium with temperature in the field reflects primarily the direct physiological effects of temperature on diazotrophic growth of Trichodesmium. Trichodesmium IMS-101 (an isolate of Trichodesmium) could acclimate and grow at temperatures ranging from 20 to 34°C. Maximum growth rates (?max=0.25 day?1) and maximum nitrogen fixation rates (0.13 mmol N mol POC?1 h?1) were measured within 24 to 30°C. Combining this empirical relationship with global warming scenarios derived from state-of-the-art climate models sets a physiological constraint on the future distribution of Trichodesmium that could significantly affect the future nitrogen input into oligotrophic waters by this diazotroph

Isotopic constraints on the pre-industrial oceanic nitrogen budget

The size of the bio-available (i.e. "fixed") nitrogen inventory in the ocean influences global marine productivity and the biological carbon pump. Despite its importance, the pre-industrial rates for the major source and sink terms of the oceanic fixed nitrogen budget, N2 fixation and denitrification, respectively, are not well known. However, these processes leave distinguishable imprints on the ratio of stable nitrogen isotopes, δ15N, which can therefore help to infer their patterns and rates. Here we use δ15N observations from the water column and a new database of seafloor measurements to constrain rates of N2 fixation and denitrification predicted by a global three-dimensional Model of Ocean Biogeochemistry and Isotopes (MOBI). Sensitivity experiments were performed to quantify uncertainties associated with the isotope effect of denitrification in the water column and sediments. They show that the level of nitrate utilization in suboxic zones, that is the balance between nitrate consumption by denitrification and nitrate replenishment by mixing (dilution effect), significantly affects the isotope effect of water column denitrification and thus global mean δ15NO3−. Experiments with lower levels of nitrate utilization within the suboxic zone (i.e. higher residual water column nitrate concentrations, ranging from 20–32 μM) require higher ratios of benthic to water column denitrification (BD:WCD = 0.75–1.4, respectively), to satisfy the global mean NO3− and δ15NO3− constraints in the modern ocean. This suggests that nitrate utilization in suboxic zones play an important role in global nitrogen isotope cycling. Increasing the net fractionation factor for benthic denitrification (ϵBD = 0–4‰) requires even higher ratios of benthic to water column denitrification (BD:WCD = 1.4–3.5, respectively). The model experiments that best reproduce observed seafloor δ15N support the middle to high-end estimates for the net fractionation factor of benthic denitrification (ϵBD = 2–4‰). Assuming a balanced fixed nitrogen budget, we estimate that pre-industrial rates of N2 fixation, water column denitrification, and benthic denitrification were approximately 195–345, 65–75, and 130–270 Tg N yr−1, respectively. Although uncertainties still exist, these results suggest that previous estimates of N2 fixation have been significantly underestimated and the residence time for oceanic fixed nitrogen is between ~ 1500–3000 yr

Split-domain calibration of an ecosystem model using satellite ocean colour data

The application of satellite ocean colour data to the calibration of plankton ecosystem models for large geographic domains, over which their ideal parameters cannot be assumed to be invariant, is investigated. A method is presented for seeking the number and geographic scope of parameter sets which allows the best fit to validation data to be achieved. These are independent data not used in the parameter estimation process. The goodness-of-fit of the optimally calibrated model to the validation data is an objective measure of merit for the model, together with its external forcing data. Importantly, this is a statistic which can be used for comparative evaluation of different models. The method makes use of observations from multiple locations, referred to as stations, distributed across the geographic domain. It relies on a technique for finding groups of stations which can be aggregated for parameter estimation purposes with minimal increase in the resulting misfit between model and observations.The results of testing this split-domain calibration method for a simple zero dimensional model, using observations from 30 stations in the North Atlantic, are presented. The stations are divided into separate calibration and validation sets. One year of ocean colour data from each station were used in conjunction with a climatological estimate of the station’s annual nitrate maximum. The results demonstrate the practical utility of the method and imply that an optimal fit of the model to the validation data would be given by two parameter sets. The corresponding division of the North Atlantic domain into two provinces allows a misfit-based cost to be achieved which is 25% lower than that for the single parameter set obtained using all of the calibration stations. In general, parameters are poorly constrained, contributing to a high degree of uncertainty in model output for unobserved variables. This suggests that limited progress towards a definitive model calibration can be made without including other types of observations

A critical examination of the role of marine snow and zooplankton faecal pellets in removing ocean surface microplastic

Numerical simulations and emissions estimates of plastic in and to the ocean consistently over-predict the surface inventory, particularly in the case of microplastic (MP), i.e. fragments less than 5 mm in length. Sequestration in the sediments has been both predicted and, to a limited extent, observed. It has been hypothesized that biology may be exporting a significant fraction of surface MP by way of marine snow aggregation and zooplankton faecal pellets. We apply previously published data on MP concentrations in the surface ocean to an earth system model of intermediate complexity to produce a first estimate of the potential global sequestration of MP by marine aggregates, including faecal pellets. We find a MP seafloor export potential of between 7.3E3-4.2E5 metric tons per year, or about 0.06-8.8% of estimated total annual plastic ocean pollution rates. We find that presently, aggregates alone would have the potential to remove most existing surface ocean MP to the seafloor within less than 2 years if pollution ceases. However, the observed accumulation of MP in the surface ocean, despite this high potential rate of removal, suggests that detrital export is an ineffective pathway for permanent MP removal. We theorize a prominent role of MP biological fouling and de-fouling in the rapid recycling of aggregate-associated MP in the upper ocean. We also present an estimate of how the potential detrital MP sink might change into the future, as climate change (and projected increasing MP pollution) alters the marine habitat. The polar regions, and the Arctic in particular, are projected to experience increasing removal rates as export production increases faster than MP pollution. Northern hemisphere subtropical gyres are projected to experience slowing removal rates as stratification and warming decrease export production, and MP pollution increases. However, significant uncertainty accompanies these results

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part I: Implementation and Model Behaviour

Uncertainties in projections from Earth system models (ESMs) are associated to a large degree with the imperfect representation of the marine plankton ecosystem, in particular the physiology of primary and secondary producers. Here we describe the implementation of an optimality-based plankton-ecosystem model (OPEM) with variable C:N:P stoichiometry in the University of Victoria ESM (UVic) and the behaviour of two calibrated reference configurations, which differ in the assumed temperature dependence of diazotrophs. Predicted tracer distributions of oxygen and dissolved inorganic nutrients are similar to those of an earlier fixed-stoichiometry model (Keller et al., 2012). Compared to the classic fixed-stoichiometry model, OPEM is closer to recent satellite-based estimates of net community production (NCP), despite overestimating net primary production (NPP), can better reproduce deep-ocean gradients in the NO3:PO43− ratio, and partially explains observed patterns of particulate C:N:P in the surface ocean. Allowing diazotrophs to grow (but not necessarily fix N2) at similar temperatures as other phytoplankton results in a better representation of surface Chl and NPP in the Arctic and Antarctic Oceans. Deficiencies of our calibrated OPEM configurations may serve as a magnifying glass for shortcomings in global biogeochemical models and hence guide future model development. The overestimation of NPP at low latitudes indicates the need for improved representations of temperature effects on biotic processes, as well as phytoplankton community composition, which may be represented by locally-varying parameters based on suitable trade-offs. Discrepancies between observed and predicted vertical gradients in particulate C:N:P ratios suggest the need to include preferential P remineralisation, which could also benefit the representation of N2 fixation. While OPEM yields a much improved distribution of surface N* (NO3− 16·PO43− + 2.9 mmol m−3), it still fails to reproduce observed N* in the Arctic, possibly related to a mis-representation of the phytoplankton community there and the lack of benthic denitrification in the model. Coexisting ordinary and diazotrophic phytoplankton can exert strong control on N* in our simulations, which questions the interpretation of N* as reflecting the balance of N2 fixation and denitrification

Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0)

Biogeochemical models, capturing the major feedbacks of the pelagic ecosystem of the world ocean, are today often embedded into Earth system models which are increasingly used for decision making regarding climate policies. These models contain poorly constrained parameters (e.g., maximum phytoplankton growth rate), which are typically adjusted until the model shows reasonable behavior. Systematic approaches determine these parameters by minimizing the misfit between the model and observational data. In most common model approaches, however, the underlying functions mimicking the biogeochemical processes are nonlinear and non-convex. Thus, systematic optimization algorithms are likely to get trapped in local minima and might lead to non-optimal results. To judge the quality of an obtained parameter estimate, we propose determining a preferably large lower bound for the global optimum that is relatively easy to obtain and that will help to assess the quality of an optimum, generated by an optimization algorithm. Due to the unavoidable noise component in all observations, such a lower bound is typically larger than zero. We suggest deriving such lower bounds based on typical properties of biogeochemical models (e.g., a limited number of extremes and a bounded time derivative). We illustrate the applicability of the method with two real-world examples. The first example uses real-world observations of the Baltic Sea in a box model setup. The second example considers a three-dimensional coupled ocean circulation model in combination with satellite chlorophyll a

Basin-scale pCO2 maps estimated from ARGO float data: A model study

A novel method for mapping surface pCO(2) on a basin scale using ARGO floats is presented and tested in the framework of an eddy-resolving biogeochemical model of the North Atlantic. Voluntary observing ship (VOS) and ARGO float coverage of the year 2005 is applied to the model to generate synthetic "observations." The model-generated VOS line &quot;observations'' of pCO(2), SST, and SSS form a training data set for a self-organizing neural network. The trained neural network is subsequently applied locally to estimate pCO(2) from the model-generated ARGO float SST and SSS data. The local pCO(2) estimates at the simulated float positions are extrapolated using objective mapping. The accuracy of the nearly basinwide pCO(2) estimates is assessed by comparing against the pCO(2) output of the model that serves as synthetic &quot;ground truth.'' For an ARGO float coverage of the year 2005, the resulting monthly mean pCO(2) maps cover 70% of the considered area (15 degrees N to 65 degrees N) with an RMS error of 15.9 mu atm. Compared to remote sensing-based estimates that suffer from large regional gaps in optical satellite data coverage, the RMS error in reproducing the annual cycle of pCO(2) can be reduced by 42% when the more evenly distributed ARGO float-based data are used

Mixed layer depth dominates over upwelling in regulating the seasonality of ecosystem functioning in the Peruvian Upwelling System

The Peruvian Upwelling System hosts an extremely high productive marine ecosystem. Observations show that the Peruvian Upwelling System is the only Eastern Boundary Upwelling Systems (EBUS) with an out-of-phase relationship of seasonal surface chlorophyll concentrations and upwelling intensity. This "seasonal paradox" triggers the questions: (1) what is the uniqueness of the Peruvian Upwelling System compared with other EBUS that leads to the out of phase relationship; (2) how does this uniqueness lead to low phytoplankton biomass in austral winter despite strong upwelling and ample nutrients? Using observational climatologies for four EBUS we diagnose that the Peruvian Upwelling System is unique in that intense upwelling coincides with deep mixed layers. We then apply a coupled regional ocean circulation-biogeochemical model (CROCO-BioEBUS) to assess how the interplay between mixed layer and upwelling is regulating the seasonality of surface chlorophyll in the Peruvian Upwelling System. The model recreates the "seasonal paradox" within 200 km off the Peruvian coast. We confirm previous findings that deep mixed layers, which cause vertical dilution and stronger light limitation, mostly drive the diametrical seasonality of chlorophyll relative to upwelling. In contrast to previous studies, reduced phytoplankton growth due to enhanced upwelling of cold waters and lateral advection are second-order drivers of low surface chlorophyll concentrations. This impact of deep mixed layers and upwelling propagates up the ecosystem, from primary production to export efficiency. Our findings emphasize the crucial role of the interplay of the mixed layer and upwelling and suggest that surface chlorophyll may increase along with a weakened seasonal paradox in response to shoaling mixed layers under climate change

Zooplankton grazing of microplastic can accelerate global loss of ocean oxygen

Global warming has driven a loss of dissolved oxygen in the ocean in recent decades. We demonstrate the potential for an additional anthropogenic driver of deoxygenation, in which zooplankton consumption of microplastic reduces the grazing on primary producers. In regions where primary production is not limited by macronutrient availability, the reduction of grazing pressure on primary producers causes export production to increase. Consequently, organic particle remineralisation in these regions increases. Employing a comprehensive Earth system model of intermediate complexity, we estimate this additional remineralisation could decrease water column oxygen inventory by as much as 10% in the North Pacific and accelerate global oxygen inventory loss by an extra 0.2–0.5% relative to 1960 values by the year 2020. Although significant uncertainty accompanies these estimates, the potential for physical pollution to have a globally significant biogeochemical signal that exacerbates the consequences of climate warming is a novel feedback not yet considered in climate research