Eutrophication status of the North Sea, Skagerrak, Kattegat and the Baltic Sea in present and future climates: A model study

Skogen, Morten D.; Eilola, Kari; Hansen, Jørgen L.S.; Meier, H. E. Markus; Molchanov, Mikhail; Ryabchenko, Vladimir

doi:10.1016/j.jmarsys.2014.02.004

Cited by 57 publications

(49 citation statements)

References 42 publications

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“…The POLCOMS, ECOSMO, HAMSOM, Delft3D, NORWECOM and MPIOM simulations were equipped with a lower trophic level model (Alheit et al 2012;Holt et al 2012Holt et al , 2014Holt et al , 2016Wakelin et al 2012a;Gröger et al 2013;Chust et al 2014;Skogen et al 2014;Pushpadas et al 2015). Some of these downscaling scenarios also considered carbonate chemistry, but published estimates of future ocean acidification are available only from two regional models: POLCOM-ERSEM and ECOSMO (Wakelin et al 2012a;Artioli et al 2013Artioli et al , 2014.…”

Section: Changes In Transport and Circulationmentioning

confidence: 99%

“…Future changes in eutrophication in the North Sea as a consequence of climate change have been investigated through scenario simulations with NORWECOM for the end of the century (Eilola et al 2013;Skogen et al 2014). To assess eutrophication impacts in downscaling scenarios the OSPAR Commission Common Procedure was applied (OSPAR 2005), which distinguishes between parameters in four categories (see Almroth and Skogen 2010): the degree of nutrient enrichment (Cat.…”

Section: Primary Production and Eutrophicationmentioning

confidence: 99%

“…A slight increase is also seen in summer chlorophyll levels in the German Bight and Kattegat, while the North Sea oxygen minimum is almost unchanged. Using these indicators, Skogen et al (2014) concluded from one scenario that the overall eutrophication status of the North Sea would remain unchanged under a future climate. However, an increase in the river nutrient load caused by increased runoff, which has the potential to increase winter nutrient levels and eutrophication status near the coast (Zavatarelli et al 2013a) was not considered in their assessment.…”

Section: Primary Production and Eutrophicationmentioning

confidence: 99%

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Projected Change—North Sea

Schrum

Lowe

Meier

et al. 2016

North Sea Region Climate Change Assessment

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Increasing numbers of regional climate change scenario assessments have become available for the North Sea. A critical review of the regional studies has helped identify robust changes, challenges, uncertainties and specific recommendations for future research. Coherent findings from the climate change impact studies reviewed in this chapter include overall increases in sea level and ocean temperature, a freshening of the North Sea, an increase in ocean acidification and a decrease in primary production. However, findings from multi-model ensembles show the amplitude and spatial pattern of the projected changes in sea level, temperature, salinity and primary production are not consistent among the various regional projections and remain uncertain. Different approaches are used to downscale global climate change impacts, each with advantages and disadvantages. Regardless of the downscaling method employed, the regional studies are ultimately affected by the forcing global climate models. Projecting regional climate change impacts on biogeochemistry and primary production is currently limited by a lack of consistent downscaling approaches for marine and terrestrial impacts. Substantial natural variability in the North Sea region from annual to multi-decadal time scales is a particular challenge for projecting regional climate change impacts. Natural variability dominates long-term trends in wind fields and strongly wind-influenced characteristics like local sea level, storm C. Schrum

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Section: Changes In Transport and Circulationmentioning

confidence: 99%

Section: Primary Production and Eutrophicationmentioning

confidence: 99%

Section: Primary Production and Eutrophicationmentioning

confidence: 99%

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Projected Change—North Sea

Schrum

Lowe

Meier

et al. 2016

North Sea Region Climate Change Assessment

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“…This demand for detailed information applies to the North Sea, with users and policy makers requiring information about topics including eutrophication and nutrient ratios (Skogen et al, 2014), productivity in relation to fisheries (Chassot et al, 2007), harmful and nuisance algal blooms (Blauw et al, 2010;Kurekin et al, 2014), water clarity (Dupont and Aksnes, 2013;Capuzzo et al, 2015), biodiversity (Brandsma et al, 2013), effects of climate change (van der Wakelin et al, 2015a), effects of trawling (Allen and Clarke, 2007; , and impacts of marine renewable energy generation (van der Molen et al, 2014. In particular, indicators of Good Environmental Status (GES) are required in the context of the Marine Strategy Framework Directive (MSFD; Borja et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Observing and modelling phytoplankton community structure in the North Sea

et al. 2017

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Abstract. Phytoplankton form the base of the marine food chain, and knowledge of phytoplankton community structure is fundamental when assessing marine biodiversity. Policy makers and other users require information on marine biodiversity and other aspects of the marine environment for the North Sea, a highly productive European shelf sea. This information must come from a combination of observations and models, but currently the coastal ocean is greatly under-sampled for phytoplankton data, and outputs of phytoplankton community structure from models are therefore not yet frequently validated. This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea using accessory pigment analysis. The observations allow a good understanding of the patterns of surface phytoplankton biomass and community structure in the North Sea for the observed months of August 2010 and 2011. Two physical-biogeochemical ocean models, the biogeochemical components of which are different variants of the widely used European Regional Seas Ecosystem Model (ERSEM), were then validated against these and other observations. Both models were a good match for sea surface temperature observations, and a reasonable match for remotely sensed ocean colour observations. However, the two models displayed very different phytoplankton community structures, with one better matching the in situ observations than the other. Nonetheless, both models shared some similarities with the observations in terms of spatial features and interannual variability. An initial comparison of the formulations and parameterizations of the two models suggests that diversity between the parameter settings of model phytoplankton functional types, along with formulations which promote a greater sensitivity to changes in light and nutrients, is key to capturing the observed phytoplankton community structure. These findings will help inform future model development, which should be coupled with detailed validation studies, in order to help facilitate the wider application of marine biogeochemical modelling to user and policy needs.

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“…Most previous ecosystem model inter-comparisons have focused on changes in environmental drivers such as nutrient loads and climate change on water quality (e.g. Lenhart et al 2010, Meier et al 2012a, Skogen et al 2014. A few studies have focused on the sensitivity of lower trophic levels to different formulations of predator-prey interactions, and the strength and complexity of zooplankton grazing dynamics (Anderson et al 2013, Le Quéré et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling

Maar

Butenschön

Daewel

et al. 2018

Ecological Modelling

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The present study describes the responses of summer phytoplankton biomass to changes in top-down forcing (expressed as zooplankton mortality) in three ecosystems (the North Sea, the Baltic Sea and the Nordic Seas) across different 3D ecosystem models. In each of the model set-ups, we applied the same changes in the magnitude of mortality (±20%) of the highest trophic zooplankton level (Z1). Model results showed overall dampened responses of phytoplankton relative to Z1 biomass. Phytoplankton responses varied depending on the food web structure and trophic coupling represented in the models.Hence, a priori model assumptions were found to influence cascades and pathways in model estimates and, thus, become highly relevant when examining ecosystem pressures such as fishing and climate change. Especially, the different roles and parameterizations of additional zooplankton groups grazed by Z1, and their importance for the outcome, emphasized the need for better calibration data. Spatial variability was high within each model indicating that physics (hydrodynamics and temperature) and nutrient dynamics also play vital roles for ecosystem responses to top-down effects. In conclusion, the model comparison indicated that changes in top-down forcing in combination with the modelled foodweb structure affect summer phytoplankton biomass and, thereby, indirectly influence water quality of the systems.3

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Eutrophication status of the North Sea, Skagerrak, Kattegat and the Baltic Sea in present and future climates: A model study

Cited by 57 publications

References 42 publications

Projected Change—North Sea

Projected Change—North Sea

Observing and modelling phytoplankton community structure in the North Sea

Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling

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