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Abstract. ECOSMO II is a fully coupled bio-physical model of 3D hydrodynamics with an intermediate-complexity NPZD (nutrient, phytoplankton, zooplankton, detritus) type biology including sediment-water column exchange processes originally formulated for the North Sea and Baltic Sea. Here we present an updated version of the model incorporating chlorophyll a as a prognostic state variable: ECOSMO II(CHL). The version presented here is online coupled to the HYCOM ocean model. The model is intended to be used for regional configurations for the North Atlantic and the Arctic incorporating coarse to high spatial resolutions for hind-casting and operational purposes. We provide the full descriptions of the changes in ECOSMO II(CHL) from ECOSMO II and provide the evaluation for the inorganic nutrients and chlorophyll a variables, present the modelled biogeochemistry of the Nordic Seas and the Arctic, and experiment on various parameterization sets as use cases targeting chlorophyll a dynamics. We document the performance of each parameter set objectively analysing the experiments against in situ, satellite and climatology data. The model evaluations for each experiment demonstrated that the simulations are consistent with the large-scale climatological nutrient setting and are capable of representing regional and seasonal changes. Explicitly resolving chlorophyll a allows for more dynamic seasonal and vertical variations in phytoplankton biomass to chlorophyll a ratio and improves model chlorophyll a performance near the surface. Through experimenting with the model performance, we document the general biogeochemisty of the Nordic Seas and the Arctic. The Norwegian and Barents seas primary production show distinct seasonal patterns with a pronounced spring bloom dominated by diatoms and low biomass during winter months. The Norwegian Sea annual primary production is around double that of the Barents Sea while also having an earlier spring bloom.

Abstract. ECOSMO II is a fully coupled bio-physical model of 3d-hydrodynamics with an intermediate complexity N(utrient) P(hytoplankton) Z(ooplankton) D(etritus) type biology including sediment-water column exchange processes originally formulated for the North Sea and Baltic Sea. Here we present an updated version of the model incorporating chlorophyll a as a prognostic state variable: ECOSMO II(CHL). The version presented here is online coupled to the HYCOM ocean model. The model is intended to be used for regional configurations for the North Atlantic and the Arctic incorporating coarse to high spatial resolutions for hind-casting and operational purposes. We provide the full descriptions of the changes in ECOSMO II(CHL) from ECOSMO II and provide the evaluation for the inorganic nutrients and chlorophyll variables, present the modeled biogeochemistry of the Nordic Seas and the Artic and experiments on various parameterization sets as use cases targeting chlorophyll a dynamics. The model evaluations demonstrated that the simulations are consistent with the large-scale climatological nutrient settings, and are capable of representing regional and seasonal changes. The Norwegian and Barents Seas primary production show distinct seasonal patterns with a pronounced spring bloom dominated by diatoms and low biomass during winter months. The Norwegian Sea annual primary production is around double that of the Barents Sea while also having an earlier spring bloom. The parameterization experiments showed that the representation of open ocean chlorophyll a benefits from using higher phytoplankton growth and zooplankton grazing rates with less photosynthesis efficiency compared to the original implementation of ECOSMO II, which was valid for the North Sea and the Baltic Sea representing coastal domains. Thus, for open ocean modeling studies, we suggest the use of the parameterization sets presented in this study.

Deep-sea sponges inhabit multiple areas of the deep North Atlantic at depths below 250 m. Living in the deep ocean, where environmental properties below the permanent thermocline generally change slowly, they may not easily acclimatize to abrupt changes in the environment. Until now consistent monitoring timeseries of the environment at deep sea sponge habitats are missing. Therefore, long-term simulation with coupled bio-physical models can shed light on the changes in environmental conditions sponges are exposed to. To investigate the variability of North Atlantic sponge habitats for the past half century, the deep-sea conditions have been simulated with a 67-year model hindcast from 1948 to 2014. The hindcast was generated using the ocean general circulation model HYCOM, coupled to the biogeochemical model ECOSMO. The model was validated at known sponge habitats with available observations of hydrography and nutrients from the deep ocean to evaluate the biases, errors, and drift in the model. Knowing the biases and uncertainties we proceed to study the longer-term (monthly to multi-decadal) environmental variability at selected sponge habitats in the North Atlantic and Arctic Ocean. On these timescales, these deep sponge habitats generally exhibit small variability in the water-mass properties. Three of the sponge habitats, the Flemish Cap, East Greenland Shelf and North Norwegian Shelf, had fluctuations of temperature and salinity in 4–6 year periods that indicate the dominance of different water masses during these periods. The fourth sponge habitat, the Reykjanes Ridge, showed a gradual warming of about 0.4°C over the simulation period. The flux of organic matter to the sea floor had a large interannual variability, that, compared to the 67-year mean, was larger than the variability of primary production in the surface waters. Lateral circulation is therefore likely an important control mechanism for the influx of organic material to the sponge habitats. Simulated oxygen varies interannually by less than 1.5 ml/l and none of the sponge habitats studied had oxygen concentrations below hypoxic levels. The present study establishes a baseline for the recent past deep conditions that future changes in deep sea conditions from observations and climate models can be evaluated against.

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