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Abstract. 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.

[1] We analyze an extensive set of global coupled biogeochemical ocean circulation models. The focus is on the equatorial Pacific. In all simulations, which are consistent with observed standing stocks of relevant biogeochemical species at the surface, we find spuriously enhanced (reduced) macronutrient (oxygen) concentrations in the deep eastern equatorial Pacific. This modeling problem, apparently endemic to global coupled biogeochemical ocean circulation models, was coined "nutrient trapping" by Najjar et al. (1992). In contrast to Aumont et al. (1999), we argue that "nutrient trapping" is still a persistent problem, even in eddy-permitting models and, further, that the scale of the problem retards model projections of nitrogen cycling. In line with previous work, our results indicate that a deficient circulation is at the core of the problem rather than an admittedly poor quantitative understanding of biogeochemical cycles. More specifically, we present indications that "nutrient trapping" in models is a result of a spuriously damped Equatorial Intermediate (zonal) Current System and Equatorial Deep Jets-phenomenon which await a comprehensive understanding and have, to date, not been successfully simulated.

Abstract. 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 spacedependent 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.