Simultaneous data-based optimization of a 1D-ecosystem model at three locations in the North Atlantic: Part II—Standing stocks and nitrogen fluxes

This study relates the performance of an optimized one-dimensional ecosystem model to observations at three sites in the North Atlantic Ocean: the Bermuda Atlantic Time Series Study (BATS, 31N 64W), the location of the North Atlantic Bloom Experiment (NABE, 47N 20W), and Ocean Weather Ship INDIA (OWS-INDIA, 59N 19W). The ecosystem model is based on nitrogen and resolves dissolved inorganic nitrogen (N), phytoplankton (P), zooplankton (Z) and detritus (D), therefore called the NPZD-model. Physical forcing, such as temperature and eddy diffusivities are taken from an eddy-permitting general circulation model of the North Atlantic Ocean, covering a period from 1989 through 1993. When an optimized parameter set is applied, the recycling of organic nitrogen becomes significantly enhanced, compared to previously published results of the NPZD model. The optimized model yields improved estimates of the annual ratio of regenerated to total primary production (f-ratio). The annual f-ratios are 0.09, 0.31, and 0.42 for the locations of BATS, NABE, and OWS-INDIA, respectively. Nevertheless, three major model deficiencies are identified. Most conspicuous are systematic discrepancies between measured 14C-fixation rates and modeled primary production under nutrient depleted conditions. This error is primarily attributed to the assumption of a constant carbon-to-nitrogen ratio for nutrient acquisition. Secondly, the initial period of the modeled phytoplankton blooms is hardly tracked by the model. That particular model deficiency becomes most apparent at the OWS-INDIA site. The interplay between algal growth and short-term alterations in stratification and mixing is believed to be insufficiently resolved by the physical model. Eventually, the model\u27s representation of the vertical nitrogen export appears to be too simple in order to match, at the same time, remineralization within the upper 300 meters and the biomass export to greater depths

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory

Potential sources of variability in mesocosm experiments on the response of phytoplankton to ocean acidification

Mesocosm experiments on phytoplankton dynamics under high CO2 concentrations mimic the response of marine primary producers to future ocean acidification. However, potential acidification effects can be hindered by the high standard deviation typically found in the replicates of the same CO2 treatment level. In experiments with multiple unresolved factors and a sub-optimal number of replicates, post-processing statistical inference tools might fail to detect an effect that is present. We propose that in such cases, data-based model analyses might be suitable tools to unearth potential responses to the treatment and identify the uncertainties that could produce the observed variability. As test cases, we used data from two independent mesocosm experiments. Both experiments showed high standard deviations and, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Conversely, our simulations showed earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggested that uncertainties in average cell size, phytoplankton biomass losses, and initial nutrient concentration potentially outweigh acidification effects by triggering strong variability during the bloom phase. We also estimated the thresholds below which uncertainties do not escalate to high variability. This information might help in designing future mesocosm experiments and interpreting controversial results on the effect of acidification or other pressures on ecosystem function

Density estimation of plankton size spectra: a reanalysis of IronEx II data

Many critical processes of ecosystem function, including trophic relationships between predators and prey and maximum rates of photosynthesis and growth, are size-dependent. Size spectral data are therefore precious to modellers because they can constrain model predictions of size-dependent processes. Here we illustrate a multi-step statistical approach to create size spectra based on a reanalysis of plankton size data from the IronEx II experiment, where iron was added to a marked patch of water and changes in productivity and community structure were followed. First, bootstrapping was applied to resample original size measurements and cell counts. Kernel density estimation was then used to provide nonparametric descriptions of density versus size. Finally, parametric distributions were used to obtain parameter estimates that can more easily be applied in models. A major advantage of this approach is that it provides confidence envelopes for the density distributions. Our analyses suggest three basic distributional patterns of cell concentration versus logarithm of equivalent spherical diameter for individual taxa. Composite size-densities of heterotrophs and photoautotrophs reveal important aspects of the coupling between protist grazing and the phytoplankton community

Marvelous Marine Microgels: On the Distribution and Impact of Gel-Like Particles in the Oceanic Water-Column

Three-dimensional hydrogels of organic polymers have been suggested to affect a variety of processes in the ocean, including element cycling, microbial ecology, food-web dynamics, and air-sea exchange. However, their abundance and distribution in the ocean are hardly known, strongly limiting an assessment of their global significance. As a consequence, marine gels are often disregarded in biogeochemical or ecosystem models. Here, we demonstrate the widespread abundance of microgels in the ocean, from the surface to the deep sea. We exhibit size spectra of two major classes of marine gels, transparent exopolymer particles (TEP) and Coomassie stainable particles (CSP) for three different ocean regimes: (a) Polar Seas, (b) Eastern Boundary Upwelling Systems, and (c) the oligotrophic open ocean. We show the variations of TEP and CSP over the water-column, and compare them to dissolved organic carbon (DOC). We also discuss how the observed distributional patterns inform about productivity and particle dynamics of these distinct oceanic regimes. Finally, we exploit current research topics, where consideration of microgels may give new insight into the role of organic matter for marine biogeochemical processes

Response patterns of phytoplankton growth to variations in resuspension in the German Bight revealed by daily MERIS data in 2003 and 2004

Chlorophyll (chl a) concentration in coastal seas exhibits variability on various spatial and temporal scales. Resuspension of particulate matter can somewhat limit algal growth, but can also enhance productivity because of the intrusion of nutrient-rich pore water from sediments or bottom water layers into the whole water column. This study investigates whether characteristic changes in net phytoplankton growth can be directly linked to resuspension events within the German Bight. Satellite-derived chl a were used to derive spatial patterns of net rates of chl a increase/decrease (NR) in 2003 and 2004. Spatial correlations between NR and mean water column irradiance were analysed. High correlations in space and time were found in most areas of the German Bight (R2 > 0.4), suggesting a tight coupling between light availability and algal growth during spring. These correlations were reduced within a distinct zone in the transition between shallow coastal areas and deeper offshore waters. In summer and autumn, a mismatch was found between phytoplankton blooms (chl a > 6 mg m−3) and spring-tidal induced resuspension events as indicated by bottom velocity, suggesting that there is no phytoplankton resuspension during spring tides. It is instead proposed here that frequent and recurrent spring-tidal resuspension events enhance algal growth by supplying remineralized nutrients. This hypothesis is corroborated by a lag correlation analysis between resuspension events and in-situ measured nutrient concentrations. This study outlines seasonally different patterns in phytoplankton productivity in response to variations in resuspension, which can serve as a reference for modelling coastal ecosystem dynamics