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Predicting the ambient environmental conditions in the coming several years to one decade is of key relevance for elucidating how deep-sea habitats, like for example sponge habitats, in the North Atlantic will evolve under near-future climate change. However, it is still not well known to what extent the deep-sea environmental properties can be predicted in advance. A regional downscaling prediction system is developed to assess the potential predictability of the North Atlantic deep-sea environmental factors. The large-scale climate variability predicted with the coupled Max Planck Institute Earth System Model with low-resolution configuration (MPI-ESM-LR) is dynamically downscaled to the North Atlantic by providing surface and lateral boundary conditions to the regional coupled physical-ecosystem model HYCOM-ECOSMO. Model results of two physical fields (temperature and salinity) and two biogeochemical fields (concentrations of silicate and oxygen) over 21 sponge habitats are taken as an example to assess the ability of the downscaling system to predict the interannual to decadal variations of the environmental properties based on ensembles of retrospective predictions over the period from 1985 to 2014. The ensemble simulations reveal skillful predictions of the environmental conditions several years in advance with distinct regional differences. In areas closely tied to large-scale climate variability and ice dynamics, both the physical and biogeochemical fields can be skillfully predicted more than 4 years ahead, while in areas under strong influence of upper oceans or open boundaries, the predictive skill for both fields is limited to a maximum of 2 years. The simulations suggest higher predictability for the biogeochemical fields than for the physical fields, which can be partly attributed to the longer persistence of the former fields. Predictability is improved by initialization in areas away from the influence of Mediterranean outflow and areas with weak cou

The aphorism, ‘All models are wrong, but some models are useful’, originally referred to statistical models, but is now used for scientific models in general. When presenting results from a marine simulation model, this statement effectively stops discussions about the quality of the model, as there is always another observation to mismatch, and thereby another confirmation why the model cannot be trusted. It is common that observations are less challenged and are often viewed as a ‘gold standard’ for judging models, whereas proper interpretations and the true value of models are often overlooked. Models are not perfect, and there are many examples where models are used improperly to provide misleading answers with great confidence, but to what extent does an observation represent the truth? The precision of the observational gear may be high, but what about representativeness? The interpretation of observations is simply another model, but this time not coded in a computer language but rather formed by the individual observer. We submit that it would be more productive to initiate a process where the norm is that models and observations are joined to strengthen both. In the end, neither method is the goal, but both are useful tools for disclosing the truth. Biased views on either observational or modeling approaches would limit us from achieving this goal.

Different fish species and life stages depend not only on food abundance, but also on the size of planktonic prey, and (mis-)matches in time and space with suitable prey may influence the growth and survival of fish during their lifetime. We explored the sensitivity of a fish community to spatial-temporal differences in plankton prey fields. Data from 5 different lower trophic level models in the North Sea (Delft3D-WAQ, ECOHAM, ECOSMO, HBM-ERGOM and NORWECOM) were used to force the food web model OSMOSE which simulates spatially and temporally explicit higher trophic level fish dynamics. The estimated fish biomass levels were clearly and positively linked to zooplankton biomass, and sensitivity studies varying zooplankton biomass revealed that spatial and temporal variation in zooplankton drives the differences in absolute fish biomass. More zooplankton size bins resulted in less fish biomass due to size-based foraging constraints (i.e. a smaller proportion of bins falls within the prey size range of a fish, resulting in a decrease in available food). Nevertheless, we found a consistent response across models in the relative biomass contribution and spatial patterns of selected fish groups, indicating low sensitivity of the composition of the simulated fish community to the zooplankton input. The robustness of the outcome will aid model acceptance and implementation into management action. Relative, not absolute, changes in primary and secondary production may therefore be used to study the effects of management scenarios on fish communities.

The aphorism, ‘All models are wrong, but some models are useful’, originally referred to statistical models, but is now used for scientific models in general. When presenting results from a marine simulation model, this statement effectively stops discussions about the quality of the model, as there is always another observation to mismatch, and thereby another confirmation why the model cannot be trusted. It is common that observations are less challenged and are often viewed as a ‘gold standard’ for judging models, whereas proper interpretations and the true value of models are often overlooked. Models are not perfect, and there are many examples where models are used improperly to provide misleading answers with great confidence, but to what extent does an observation represent the truth? The precision of the observational gear may be high, but what about representativeness? The interpretation of observations is simply another model, but this time not coded in a computer language but rather formed by the individual observer. We submit that it would be more productive to initiate a process where the norm is that models and observations are joined to strengthen both. In the end, neither method is the goal, but both are useful tools for disclosing the truth. Biased views on either observational or modeling approaches would limit us from achieving this goal.

Anchovy Engraulis encrasicolus distribution in European waters spans from the Mediterranean Sea to the North Sea, and is expected to expand further north with global warming. Observations from the eastern Mediterranean (North Aegean Sea), the Bay of Biscay and the North Sea reveal latitudinal differences in growth, maximum size, fecundity and timing of reproduction. We set up a mechanistic framework combining a bioenergetics model with regional physical-biogeochemical models providing temperature and zooplankton biomass to investigate the underlying mechanisms of variation in these traits. The bioenergetics model, based on the Dynamic Energy Budget theory and initially calibrated in the Bay of Biscay, was used to simulate growth and reproduction patterns. Environment partly explained the increased growth rate and larger body size towards the north. However, regional calibration of the maximum assimilation rate was necessary to obtain the best model fit. This suggests a genetic adaptation, with a pattern of cogradient variation with increasing resource towards the north, in addition to a countergradient thermal adaptation. Overall, the seasonal energy dynamics supports the pattern of body-size scaling with latitude, i.e. food-limited growth but low maintenance costs in the warm Aegean Sea, and larger size in the North Sea allowing sufficient storage capacity for overwintering. Further, the model suggests a synchronisation of reproductive timing with environmental seasonality as a trade-off between thresholds of temperature and reserves for spawning and overwintering, respectively. Finally, low temperature, short productive and spawning seasons, and insufficient reserves for overwintering appear to be current limitations for an expansion of anchovy to the Norwegian Sea.

Hydrodynamic Ocean Circulation Models and Lagrangian particle tracking models are valuable tools e.g. in coastal ecology to identify the connectivity between offshore spawning and coastal nursery areas of commercially important fish, for risk assessment and more for defining or evaluating marine protected areas. Most studies are based on only one model and do not provide levels of uncertainty. Here this uncertainty was addressed by applying a suite of 11 North Sea models to test what variability can be expected concerning connectivity. Different notional test cases were calculated related to three important and well-studied North Sea fish species: herring (Clupea harengus), and the flatfishes sole (Solea solea) and plaice (Pleuronectes platessa). For sole and plaice we determined which fraction of particles released in the respective spawning areas would reach a coastal marine protected area. For herring we determined the fraction located in a wind park after a predefined time span. As temperature is more and more a focus especially in biological and global change studies, furthermore inter-model variability in temperatures experienced by the virtual particles was determined. The main focus was on the transport variability originating from the physical models and thus biological behavior was not included. Depending on the scenario, median experienced temperatures differed by 3 °C between years. The range between the different models in one year was comparable to this temperature range observed between modelled years. Connectivity between flatfish spawning areas and the coastal protected area was highly dependent on the release location and spawning time. No particles released in the English Channel in the sole scenario reached the protected area while up to 20% of the particles released in the plaice scenario did. Interannual trends in transport directions and connectivity rates were comparable between models but absolute values displayed high variations. Most model

Regional seas are exceptionally vulnerable to climate change, yet are the most directly societally important regions of the marine environment. The combination of widely varying conditions of mixing, forcing, geography (coastline and bathymetry) and exposure to the open-ocean makes these seas subject to a wide range of physical processes that mediates how large scale climate change impacts on these seas' ecosystems. In this paper we explore these physical processes and their biophysical interactions, and the effects of atmospheric, oceanic and terrestrial change on them. Our aim is to elucidate the controlling dynamical processes and how these vary between and within regional seas. We focus on primary production and consider the potential climatic impacts: on long term changes in elemental budgets, on seasonal and mesoscale processes that control phytoplankton's exposure to light and nutrients, and briefly on direct temperature response. We draw examples from the MEECE FP7 project and five regional models systems using ECOSMO, POLCOMS-ERSEM and BIMS_ECO. These cover the Barents Sea, Black Sea, Baltic Sea, North Sea, Celtic Seas, and a region of the Northeast Atlantic, using a common global ocean-atmosphere model as forcing. We consider a common analysis approach, and a more detailed analysis of the POLCOMS-ERSEM model.

We employed a coupled biological–physical, individual-based model (IBM) to estimate spatial and temporal changes in larval fish habitat suitability (the potential for areas to support survival and high rates of growth) of the German Bight, southern North Sea. In this Lagrangian approach, larvae were released into a size-structured prey field that was constructed from in situ measurements of the abundance and prosome lengths of stages of three copepods (Acartia spp., Temora longicornis, Pseudocalanus elongatus) collected on a station grid repeatedly sampled from February to October 2004. The choice of prey species and the model parameterisations for larval fish foraging and growth were based on field data collected for sprat (Sprattus sprattus) and other clupeid larvae. A series of 10-day simulations were conducted using 20 release locations to quantify spatial–temporal differences in projected larval sprat growth rates (mm d− 1) for mid-April, mid-May and mid-June 2004. Based upon an optimal foraging approach, modeled sprat growth rates agreed well with those measured in situ using larval fish ototliths. On the German GLOBEC station grid, our model predicted areas that were mostly unsuitable habitats (areas of low growth potential), e.g. north of the Frisian Islands, and others that were consistently suitable habitats (areas that had high growth potential), e.g. in the inner German Bight. In some instances, modelled larvae responded rapidly (~ 5 days) to changing environmental characteristics experienced along their drift trajectory, a result that appears reasonable given the dynamic nature of frontal regions such as our study area in the southern North Sea.