ALBERT

Description: Highlights: • Elemental C:N:P variations of organic matter are simulated at monitoring site BY15. • No N2 fixation needed to explain observed PO4PO4 and pCO2pCO2 levels after spring bloom. • Model features relevance of DOP production and remineralization for N2 fixation. • Model estimates of annual N2 fixation are View the MathML source297±24mmolNm-2a-1. • Model estimates of annual total production are View the MathML source14.16±0.71molCm-2a-1. Abstract: For most marine ecosystems the growth of diazotrophic cyanobacteria and the associated amount of nitrogen fixation are regulated by the availability of phosphorus. The intensity of summer blooms of nitrogen (N2) fixing algae in the Baltic Sea is assumed to be determinable from a surplus of dissolved inorganic phosphorus (DIP) that remains after the spring bloom has ended. But this surplus DIP concentration is observed to continuously decrease at times when no appreciable nitrogen fixation is measured. This peculiarity is currently discussed and has afforded different model interpretations for the Baltic Sea. In our study we propose a dynamical model solution that explains these observations with variations of the elemental carbon-to-nitrogen-to-phosphorus (C:N:P) ratio during distinct periods of organic matter production and remineralization. The biogeochemical model resolves seasonal C, N and P fluxes with depth at the Baltic Sea monitoring site BY15, based on three assumptions: (1) DIP is utilized by algae though not needed for immediate growth, (2) the uptake of dissolved inorganic nitrogen (DIN) is hampered when the algae׳s phosphorus (P) quota is low, and (3) carbon assimilation continues at times of nutrient depletion. Model results describe observed temporal variations of DIN, DIP and chlorophyll-a concentrations along with partial pressure of carbon dioxide (pCO2)(pCO2). In contrast to other model studies, our solution does not require N2 fixation to occur shortly after the spring bloom to explain DIP drawdown and pCO2pCO2 levels. Model estimates of annual N2 fixation are View the MathML source297±24mmolNm-2a-1. Estimates of total production are View the MathML source14200±700mmolCm-2a-1, View the MathML source1400±70mmolNm-2a-1, and View the MathML source114±5mmolPm-2a-1 for the upper 50 m. The models C, N and P fluxes disclose preferential remineralization of P and of organic N that was introduced via N2 fixation. Our results are in support of the idea that P uptake by phytoplankton during the spring bloom contributes to the consecutive availability of labile dissolved organic phosphorus (LDOP). The LDOP is retained within upper layers and its remineralization affects algal growth in summer, during periods of noticeable N2 fixation.

Description: Highlights: • Sensitivities of annual carbon (C), nitrogen (N) and phosphorus (P) flux estimates to parameter variations are determined. • Model parameters that specify annual inventories are compared with those that determine timing and magnitude of bloom events. • Seven model parameters are of primary importance, affecting C, N and P budgets simultaneously. • Nine parameters have negligible effects on annual budget estimates and on seasonal trajectories. • Parameter categorization provides important prior information for parameter optimization in the central Baltic Sea. Abstract: This study describes a sensitivity analysis that allows the parameters of a one-dimensional ecosystem model to be ranked according to their specificity in determining biochemical key fluxes. Key fluxes of interest are annual (a) total production (TP), (b) remineralization above the halocline (RM), and (c) export at 50 m (EX) at the Baltic Sea monitoring site BY15 located in the Gotland Deep basin. The model resolves mass flux of carbon (C), nitrogen (N), and phosphorous (P), while considering nitrogen fixation explicitly. Our first null hypothesis is that the variation of the value of every single model parameter affects each annual C, N, and P budget simultaneously. Our second null hypothesis states that the variation of every parameter value induces changes at least in either of the annual C, N or P budgets. Our analyses falsify both null hypotheses and reveal that 8 out of 36 parameters must be regarded redundant, as their variation neither alter annual key fluxes nor produce considerable time-shifts in model trajectories at the respective site. Seven parameters were found to induce substantial changes in annual C, N, and P flux estimates simultaneously. The assimilation efficiency of zooplankton turned out to be of vital importance. This parameter discriminates between the assimilation and destruction of algal prey during grazing. The fraction of unassimilated dead algal cells is critical for the amount of organic matter exported out of the euphotic zone. The maximum cellular N:C quota of diazotrophs and the degradation/hydrolysis rate of detrital carbon are two parameters that will likely remain unconstrained by time series data, but both affect the annual C budget considerably. Overall, our detailed specification of model sensitivities to parameter variations will facilitate the formulation of a well-posed inverse problem for the estimation of C, N and P fluxes from stock observations at the Gotland Deep.

Description: Global biogeochemical ocean models are used to predict the future evolution of so-called oxygen minimum zones (OMZ), and the associated environmental and possible socio-economic impacts. Different models give different results and vary largely in their biogeochemical, physical and numerical setup. In order to assess the ability of the models to describe the present state as a necessary condition for skillful predictions into the future, they are usually compared against observed distributions of oxygen and other variables, such as thickness of oxygen minimum zones, nutrients, tracers for circulation and/or water mass age. We here examine different metrics for skill evaluation particularly of model representations of oxygen (and OMZs), for a wide range of global biogeochemical models. Among the metrics considered are Taylor plots, volume distributions of oxygen, volume of OMZ, preformed oxygen, and metrics that combine various diagnostic biogeochemical tracers. We finally investigate the impact these metrics may have for the “choice” of any best model, and discuss their applicability for different research or societal questions.

Description: 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.