ALBERT

Description: 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.

Description: The Baltic Sea is a seasonally ice-covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6-based ocean–sea-ice setup for the North Sea and Baltic Sea region (NEMO-Nordic). The setup includes a new depth-based fast-ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. We show that NEMO-Nordic is well suited for simulating the mean sea-ice extent, concentration, and thickness as compared to the best available observational data set. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations, but the 1961–2006 trend is underestimated. Capturing the correct ice thickness distribution is more challenging. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations.

Description: Deoxygenation in the Baltic Sea endangers fish yields and favours noxious algal blooms. Yet, vertical transport processes ventilating the oxygen-deprived waters at depth and replenishing nutrient-deprived surface waters (thereby fuelling export of organic matter to depth), are not comprehensively understood. Here, we investigate the effects of the interaction between surface currents and winds (also referred to as eddy/wind effects) on upwelling in an eddy-rich general ocean circulation model of the Baltic Sea. Contrary to expectations we find that accounting for current/wind effects does inhibit the overall vertical exchange between oxygenated surface waters and oxygen-deprived water at depth. At major upwelling sites, however, as e.g. off the south coast of Sweden and Finland, the reverse holds: the interaction between topographically steered surface currents with winds blowing over the sea results in a climatological sea surface temperature cooling of 0.5 K. This implies that current/wind effects drive substantial local upwelling of cold and nutrient-replete waters.

Description: In a changing climate, marine pelagic biogeochemistry may modulate the atmospheric concentrations of climate-relevant species such as CO2 and N2O. To date, projections rely on earth system models, featuring simple pelagic biogeochemical model components, embedded into 3-D ocean circulation models. Most of these biogeochemical model components rely on the hyperbolic Michaelis–Menten (MM) formulation which specifies the limiting effect of light and nutrients on carbon assimilation by autotrophic phytoplankton. The respective MM constants, along with other model parameters, of 3-D coupled biogeochemical ocean-circulation models are usually tuned; the parameters are changed until a "reasonable" similarity to observed standing stocks is achieved. Here, we explore with twin experiments (or synthetic "observations") the demands on observations that allow for a more objective estimation of model parameters. We start with parameter retrieval experiments based on "perfect" (synthetic) observations which we distort, step by step, by low-frequency noise to approach realistic conditions. Finally, we confirm our findings with real-world observations. In summary, we find that MM constants are especially hard to constrain because even modest noise (10 %) inherent to observations may hinder the parameter retrieval already. This is of concern since the MM parameters are key to the model's sensitivity to anticipated changes in the external conditions. Furthermore, we illustrate problems caused by high-order parameter dependencies when parameter estimation is based on sparse observations of standing stocks. Somewhat counter to intuition, we find that more observational data can sometimes degrade the ability to constrain certain parameters.

Description: To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time- and space-dependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation

Description: The Southern Ocean is a major sink for anthropogenic carbon. Yet, there is no quantitative consensus about how this sink will change when surface winds increase (as they are anticipated to do). Among the tools employed to quantify carbon uptake are global coupled ocean-circulation biogeochemical models. Because of computational limitations these models still fail to resolve potentially-important spatial scales. Instead, processes on these scales are parameterized. There is concern that deficiencies in these so-called eddy-parameterizations might imprint wrong sensitivities of projected oceanic carbon uptake. Here, we compare natural carbon uptake in the Southern Ocean simulated with contemporary eddy-parameterizations. We find that very differing parameterizations yield surprisingly similar oceanic carbon in response to strengthening winds. In contrast, we find (in an additional simulation) that the carbon uptake does differ substantially when the supply of bioavailable iron is altered within its envelope of uncertainty. We conclude that a more comprehensive understanding of bioavailable iron dynamics will substantially reduce the uncertainty of model-based projections of oceanic carbon uptake.

Description: The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62–67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian Sea during the severe winter 2011 and employs 15 to 25 min averages of ship speed.

Description: The Baltic Sea is a seasonally ice-covered, marginal sea in central northern Europe. It is an essential waterway connecting highly industrialised countries. Because ship traffic is intermittently hindered by sea ice, the local weather services have been monitoring sea ice conditions for decades. In the present study we revisit a historical monitoring data set, covering the winters 1960/1961 to 1978/1979. This data set, dubbed Data Bank for Baltic Sea Ice and Sea Surface Temperatures (BASIS) ice, is based on hand-drawn maps that were collected and then digitised in 1981 in a joint project of the Finnish Institute of Marine Research (today the Finnish Meteorological Institute (FMI)) and the Swedish Meteorological and Hydrological Institute (SMHI). BASIS ice was designed for storage on punch cards and all ice information is encoded by five digits. This makes the data hard to access. Here we present a post-processed product based on the original five-digit code. Specifically, we convert to standard ice quantities (including information on ice types), which we distribute in the current and free Network Common Data Format (NetCDF). Our post-processed data set will help to assess numerical ice models and provide easy-to-access unique historical reference material for sea ice in the Baltic Sea. In addition we provide statistics showcasing the data quality. The website http://www.baltic-ocean.org hosts the post-processed data and the conversion code. The data are also archived at the Data Publisher for Earth & Environmental Science, PANGAEA (doi:10.1594/PANGAEA.832353)

Description: Nitrogen fixers, or diazotrophs, play a key role in the carbon and nitrogen cycle of the world oceans, but the controlling mechanisms are not comprehensively understood yet. The present study compares two paradigms on the ecological niche of diazotrophs in an Earth System Model (ESM). In our standard model configuration, which is representative for most of the state-of-the-art pelagic ecosystem models, diazotrophs take advantage of zooplankton featuring a lower food preference for diazotrophs than for ordinary phytoplankton. We compare this paradigm with the idea that diazotrophs are more competitive under oligotrophic conditions, characterized by low (dissolved, particulate, organic and inorganic) phosphorous availability. Both paradigms are supported by observational evidence and lead to a similar good agreement to the most recent and advanced observation-based nitrogen fixation estimate in our ESM framework. Further, we illustrate that the similarity between the two paradigms breaks in a RCP 8.5 anthropogenic emission scenario. We conclude that a more advanced understanding of the ecological niche of diazotrophs is mandatory for assessing the cycling of essential nutrients, especially under changing environmental conditions. Our results call for more in-situ measurements of cyanobacteria biomass if major controls of nitrogen fixation in the oceans are to be dissected.

Description: We present Nemo-Nordic, a Baltic and North Sea model based on the NEMO ocean engine. Surrounded by highly industrialized countries, the Baltic and North seas and their assets associated with shipping, fishing and tourism are vulnerable to anthropogenic pressure and climate change. Ocean models providing reliable forecasts and enabling climatic studies are important tools for the shipping infrastructure and to get a better understanding of the effects of climate change on the marine ecosystems. Nemo-Nordic is intended to be a tool for both short-term and long-term simulations and to be used for ocean forecasting as well as process and climatic studies. Here, the scientific and technical choices within Nemo-Nordic are introduced, and the reasons behind the design of the model and its domain and the inclusion of the two seas are explained. The model's ability to represent barotropic and baroclinic dynamics, as well as the vertical structure of the water column, is presented. Biases are shown and discussed. The short-term capabilities of the model are presented, especially its capabilities to represent sea level on an hourly timescale with a high degree of accuracy. We also show that the model can represent longer timescales, with a focus on the major Baltic inflows and the variability in deep-water salinity in the Baltic Sea.