ALBERT

Description: A marine ecosystem model, that had previously been calibrated in a one-dimensional (1D) mode against observations at three time-series and process-study sites simultaneously, is coupled to a three-dimensional (3D) circulation model of the North and Equatorial Atlantic. Compared to an experiment with a previously employed subjectively tuned ecosystem model, the new 3D-model does not only reduce the model-data misfit at those locations at which observations entered the 1D optimization procedure, but also at an oligotrophic site in the subtropics that had not been considered in the 1D calibration. Basin-scale gridded climatological data sets of nitrate, surface chlorophyll, and satellite-derived primary production also reveal a generally lower model-data misfit for the optimized model. The most significant improvement is found in terms of simulated primary production: on average, primary production is about 2.5 times higher in the optimized model which primarily results from the inclusion of a phytoplankton recycling pathway back to dissolved inorganic nitrogen. This recycling pathway also allows for a successful reproduction of nonvanishing surface nitrate concentrations over large parts of the subpolar North Atlantic. Apart from primary production, the parameter optimization reduces root-mean-square misfits by merely 10–25% and remaining misfits are still much larger than observational error estimates. These residual misfits can be attributed both to errors in the physical model component and to errors in the structure of the ecosystem model, which an objective estimation of ecosystem model parameters by data assimilation alone cannot resolve.

Description: Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models' performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways.

Description: The influence of seawater CO2 concentration on the abundance and size of suspended particles (2-40 μm) was investigated during a mesocosm experiment at the large scale facility (LFS) in Bergen. In nine outdoor enclosures the partial pressure of CO2 (pCO2) in the seawater was modified by an aeration system. The triplicate mesocosm treatments simulated low (~190 parts per million by volume (ppmV) CO2), present day (~370 ppmV CO2) and high (~700 ppmV CO2) CO2 conditions. The inorganic nutrients nitrate and phosphate were added initially to the mesocosms to induce phytoplankton blooms. Samples for suspended particles were collected daily over a period of 19 days and analysed with the Coulter Counter and by Flow Cytometry. First results indicate that the CO2 treatment significantly affected the size distribution of solid particles, and led to larger surface to volume ratios at lower pCO2. Size is important for diffusion-limited exchange processes at the cell surface as well as for gravitational settling of the solid particles. The observed changes in particle size distribution are therefore discussed with respect to organic matter production and potential sedimentation in the mesocosms during the bloom. An outlook on possible implications of our findings for the future carbon cycling and export in the ocean will be presented.

Description: During phytoplankton growth a fraction of dissolved inorganic carbon (DIC) assimilated by phytoplankton is exuded in the form of dissolved organic carbon (DOC), which can be transformed into extracellular particulate organic carbon (POC). A major fraction of extracellular POC is associated with carbon of transparent exopolymer particles (TEP; carbon content = TEPC) that form from dissolved polysaccharides (PCHO). The exudation of PCHO is linked to an excessive uptake of DIC that is not directly quantifiable from utilisation of dissolved inorganic nitrogen (DIN), called carbon overconsumption. Given these conditions, the concept of assuming a constant stoichiometric carbon-to-nitrogen (C:N) ratio for estimating new production of POC from DIN uptake becomes inappropriate. Here, a model of carbon overconsumption is analysed, combining phytoplankton growth with TEPC formation. The model describes two modes of carbon overconsumption. The first mode is associated with DOC exudation during phytoplankton biomass accumulation. The second mode is decoupled from algal growth, but leads to a continuous rise in POC while particulate organic nitrogen (PON) remains constant. While including PCHO coagulation, the model goes beyond a purely physiological explanation of building up carbon rich particulate organic matter (POM). The model is validated against observations from a mesocosm study. Maximum likelihood estimates of model parameters, such as nitrogen- and carbon loss rates of phytoplankton, are determined. The optimisation yields results with higher rates for carbon exudation than for the loss of organic nitrogen. It also suggests that the PCHO fraction of exuded DOC was 63±20% during the mesocosm experiment. Optimal estimates are obtained for coagulation kernels for PCHO transformation into TEPC. Model state estimates are consistent with observations, where 30% of the POC increase was attributed to TEPC formation. The proposed model is of low complexity and is applicable for large-scale biogeochemical simulations.

Description: The influence of seawater carbon dioxide (CO2) concentration on the size distribution of suspended particles (2–60 μm) and on phytoplankton abundance was investigated during a mesocosm experiment at the large scale facility (LFS) in Bergen, Norway, in the frame of the Pelagic Ecosystem CO2 Enrichment study (PeECE II). In nine outdoor enclosures the partial pressure of CO2 in seawater was modified by an aeration system to simulate past (~190 μatm CO2), present (~370 μatm CO2) and future (~700 μatm CO2) CO2 conditions in triplicates. Due to the initial addition of inorganic nutrients, phytoplankton blooms developed in all mesocosms and were monitored over a period of 19 days. Seawater samples were collected daily for analysing the abundance of suspended particles and phytoplankton with the Coulter Counter and with Flow Cytometry, respectively. During the bloom period, the abundance of small particles (〈4 μm) significantly increased at past, and decreased at future CO2 levels. At that time, a direct relationship between the total-surface-to-total-volume ratio of suspended particles and DIC concentration was determined for all mesocosms. Significant changes with respect to the CO2 treatment were also observed in the phytoplankton community structure. While some populations such as diatoms seemed to be insensitive to the CO2 treatment, others like Micromonas spp. increased with CO2, or showed maximum abundance at present day CO2 (i.e. Emiliania huxleyi). The strongest response to CO2 was observed in the abundance of small autotrophic nano-plankton that strongly increased during the bloom in the past CO2 mesocosms. Together, changes in particle size distribution and phytoplankton community indicate a complex interplay between the ability of the cells to physiologically respond to changes in CO2 and size selection. Size of cells is of general importance for a variety of processes in marine systems such as diffusion-limited uptake of substrates, resource allocation, predator-prey interaction, and gravitational settling. The observed changes in particle size distribution are therefore discussed with respect to biogeochemical cycling and ecosystem functioning.

Description: By means of numerical modeling, we analyze the cycling of iron between its various physical (dissolved, colloidal, particulate) and chemical (redox state and organic complexation) forms in the upper mixed layer. With our proposed model it is possible to obtain a first quantitative assessment of how this cycling influences iron uptake by phytoplankton and its loss via particle export. The model is forced with observed dust deposition rates, mixed layer depths, and solar radiation at the site of the Bermuda Atlantic Time-series Study (BATS). It contains an objectively optimized ecosystem model which yields results close to the observational data from BATS that has been used for the data-assimilation procedure. It is shown that the mixed layer cycle strongly influences the cycling of iron between its various forms. This is mainly due to the light dependency of photoreductive processes, and to the seasonality of primary production. The daily photochemical cycle is driven mainly by the production of superoxide, and its amplitude depends on the concentration and speciation of dissolved copper. Model results are almost insensitive to the dominant form of dissolved iron within dust deposition, and also to the form of iron that is taken up directly during algal growth. In our model solutions, the role of the colloidal pumping mechanism depends strongly on assumptions on the colloid aggregation and photoreduction rate.

Description: In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochemical cycles in the ocean will respond to global warming. We define the term “biogeochemical functional group” to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, “functional groups” have no phylogenetic meaning—these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E. huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E. huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent. When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future. It may be necessary to include explicit dynamic representations of nitrogen fixation, denitrification, silicification and calcification in our models if our goal is predicting the oceanic carbon cycle in the future, because these processes appear to play a very significant role in the carbon cycle of the present-day ocean and they are sensitive to climate change. Observations and models suggest that it may also be necessary to include the DMS cycle to predict future climate, though the effects are still highly uncertain. We have learned a tremendous amount about the distributions and biogeochemical impact of bacteria in the ocean in recent years, yet this improved understanding has not yet been incorporated into many of our models. All of these considerations lead us toward the development of increasingly complex models. However, recent quantitative model intercomparison studies suggest that continuing to add complexity and more functional groups to our ecosystem models may lead to decreases in predictive ability if the models are not properly constrained with available data. We also caution that capturing the present-day variability tells us little about how well a particular model can predict the future. If our goal is to develop models that can be used to predict how the oceans will respond to global warming, then we need to make more rigorous assessments of predictive skill using the available data.