For two future scenarios on the expansion of offshore wind power in the Baltic Sea and the North Sea, SMHI has investigated how the hydrography, i.e. temperatures, salinities, currents and stratification, may be affected. Effects were induced by wind stress reductions on the sea surface and by the increased friction and turbulence in the water from wind turbine foundations.

The results show that an expansion of wind power in the Baltic Sea in general will cause a shallowed halocline, and increased deep water salinities and temperatures, due to decreasing winds behind the wind farms that lead to decreasing vertical mixing in the Baltic Sea. However, the magnitude of changes shows a strong sensitivity to assumptions about the wind stress reduction at the sea surface, and the size of wind power expansion.

The wind farm scenarios are prepared in collaboration with the Swedish Agency for Marine and Water Management (SwAM) and are based on marine plans from Sweden’s neighbouring countries as well as new proposals for suitable wind power areas that SwAM will present to the government in 2024. In one scenario, Scenario 1, it is assumed that there will be offshore wind in all proposed areas, while in the second scenario, Scenario 2, it is assumed that only 50% of areas will be developed. Both scenarios represent large offshore wind power developments that will probably not be realized in reality. The scenarios have been investigated by running an ocean model for the Baltic Sea and the North Sea with and without wind power for the period 1985 – 2016 to evaluate how different the sea would have looked if the wind power had been built in 1985 according to the scenarios.

There is still lack of knowledge about how wind farms affect the wind at the sea surface, so this work is based on studies of existing wind farms in the North Sea, where studies show a reduction of the wind by around 8% and an area that extends about 30 km behind the wind farm under stable atmospheric conditions. When the atmosphere is unstable, which it often is in winter, the reduction is less. In order to get an estimate of the largest and smallest possible impact of wind power on the sea, we have therefore, for both scenarios, assumed that the reduction of wind only exists in summer and no reduction during winter (minimum possible impact), or that the reduction exists all year round (upper limit of impact).

The magnitude of expected changes is very dependent on the assumptions on the wind wakes, and the response is much smaller for the minimum possible impact than for the upper limit impact. The real response for these scenarios probably lays somewhere in between these estimates.

For the scenario with less wind farms in Swedish waters (Scenario 2), the influences on salinity, temperature, and halocline are reduced relative to Scenario 1 in a manner that may be expected from the difference in total wind farm areas in the Baltic Sea in the two scenarios.

The model results also show that the wind power foundations (modelled as bottom mounted) cause a salinity decrease in the Baltic Sea deep water, probably due to increased friction and mixing in the entrance region to the Baltic Sea. This effect is much smaller than the wind wake effect when it is active during the whole year.

The Baltic Sea surface salinity, surface temperature, and currents show much smaller and less robust changes than the salinity and temperature changes in the deepwater.

Phytoplankton constitute the base of the marine food web and is used all over theworld to assess the quality status of marine and freshwater systems. Phytoplanktonare for example used in the EU Marine Strategy Framework Directive and in theEU Water Framework Directive. There are long time series of data from monitoringprograms, where phytoplankton are analyzed using microscopy. However, presentlyDNA methods are quickly emerging. The aim of this project was to develop a practical and robust method to analyze phytoplankton that can be implemented inSwedish marine monitoring programs.The project was performed by joining the regular marine monitoring cruisesfor a period of one year (2019–2020). We collected parallel seawater samples forDNA metabarcoding of phytoplankton. Nineteen stations were sampled, all theway from the Bothnian Bay in the northern Baltic Sea to the Skagerrak adjacent tothe North Sea, with a sampling frequency of ~1 sampling per month. Best practiceswere developed for field sampling, DNA extraction, sequencing, bioinformatics andtaxonomic annotation. We also produced a system for data handling at the nationalhost, and provided a new data type for national data host for marine biology andoceanography, the National Oceanographic Data Centre (https://sharkweb.smhi.se/hamta-data/).An important part of the project was to compare results from DNA metabarcoding and microscopy. In general, results of DNA metabarcoding gives about twiceas high biodiversity measures than microscopy, even though it varies betweendifferent phytoplankton groups. We found that the reference databases have shortcomings and need to be further developed. To find out if DNA metabarcoding canbe used quantitatively, we added an internal standard to the samples, composedof synthetic DNA strings. The analysis showed varying results, and therefore furtherdevelopment is needed. The distribution of common eukaryotic groups showedrelatively good agreement between DNA metabarcoding and the carbon biomassmicroscopy metric, while biovolume and abundance showed larger deviations.With DNA metabarcoding we were able to produce detailed maps of the occurrenceof potentially harmful algae, for example the genus Prymnesium.By using physicochemical data measured in the monitoring programs wehave looked for environmental drivers of phytoplankton diversity and communitycomposition. Both salinity and nutrients were shown to have significant impactson the phytoplankton communities.In summary, we think that the developed DNA metabarcoding method wouldbe a valuable complement to the established microscopy-based phytoplanktonmonitoring.