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The criterion for stratification is that squared buoyancy frequency N 2 remains higher than 0. These changes mainly concern the river discharge and nutrient load data sources. An updated set of river runoff and nutrient load data was applied with more complete river forcing data coverage for the North Sea and Baltic Sea.

Water quality data for Baltic rivers were provided by the University of Stockholm and the Baltic Nest. Furthermore, nutrient status and freshwater runoff information in the southern and eastern Baltic Sea was supplemented by data from the Balt-HYPE model Arheimer et al. These data stem from a national monitoring program Windolf et al.

We selected a relatively short time period — for our analysis to assure a long enough spin-up time that accounts for the characteristic long timescales of the North Sea—Baltic Sea system Daewel and Schrum, The period from to will hereafter be called the analyzed period. Tidal cycles with long periods, such as the nodal and elliptical cycles, although considered in the forcing via nodal corrections of partial tide amplitudes and phases see Sect.

In addition, tidal elevations were calculated from tidal constituents provided by the German Federal Maritime and Hydrographic Agency Federal Maritime and Hydrographic Agency, Deutsches Hydrographisches Institut, Nodal corrections were implemented in the calculation of tides to represent the long-term variation in lunar nodes. For the standard tidal scenario, partial M 2 tide principle lunar tide and S 2 tide principle solar tide Thomson and Emery, were considered; we hereafter call this scenario the tidal scenario.

To evaluate the contribution of the spring—neap tidal cycle, a tidal scenario using only the M 2 partial tide, called the M 2 scenario, was simulated and discussed in comparison to the tidal scenario. To quantify the overall impact of tidal forcing, a scenario without tidal forcing at the open boundary was simulated to yield the non-tidal reference state of the system non-tidal scenario.

The responses of ecosystem productivity to tidal forcing were assessed by comparing the annual mean NPP during the analyzed period between the tidal and non-tidal scenarios tidal scenario minus non-tidal scenario. Furthermore, we disentangled processes that might contribute to variations in NPP, such as the seasonality of spatial patterns in limitation factors nutrients vs. We quantified these processes using subdomains and further made comparisons between scenarios, emphasizing spatial variability and the seasonal cycle.

The pre-division of the area into subdomains is based on a combination of geographic location, bathymetry and the local responses of NPP to tidal forcing increase, decrease.

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Based on this pre-division of subdomains, we identified the most representative grid cell within each subdomain using correlation analysis Eliasen et al. To identify the most representative grid cell location in each subdomain, we first produced a time series of the NPP differences between the non-tidal scenario and tidal scenario for each grid cell. Subsequently, we estimated, for each of the grid cells, the correlation to the time series of the other grid cells within the same pre-divided subdomain.

The grid cell with the highest correlation coefficient to all other grid cells in each subdomain was selected as the most representative point for further analysis. The peak amplitude and the onset time of the spring bloom for the different scenarios were compared. The onset of the spring bloom is defined here as the day when the daily vertically integrated NPP reaches its maximum prior to the spring maximum in diatom biomass Fig. A2 Sharples et al.

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To further disentangle mechanisms resulting in spring bloom phenology differences among the scenarios, we quantified potentially related biological and physical factors relevant for spring bloom dynamics, such as the zooplankton biomass prior to the onset of the spring bloom, light conditions and development of stratification, for each grid cell.

To quantify the time when the light was sufficient for phytoplankton growth in each year, we estimated the date when the integrated light-limiting term exceeded 0. Similar methods have been used in many other studies Gong et al. The mixed layer depth is defined as the thickness of the surface mixed layer, ranging from surface to pycnocline. The onset of the spring bloom, the first day of the year with stratification and the first day of the year with sufficient light conditions were identified for each grid point for every simulated year; subsequently, the percentage of years in which those time identifiers were advanced or delayed in the tidal scenario compared to that in the non-tidal scenario as a response to tidal forcing was estimated for every grid cell.

Furthermore, we studied the changes in the spring bloom phenology in response to the spring—neap tidal cycle i. Considering that several spring—neap cycles may take place during the spring bloom development, we studied the NPP difference during of spring bloom development between the tidal scenario and M 2 scenario in relation to the spring—neap tidal phase.


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The period of spring bloom development was defined as the time period with an increase in NPP from This enabled us to evaluate the impact of spring—neap tidal cycles on spring bloom phenology. The limiting value quantifies the availability of growth resources with a range of 0—1.

The closer the value is to 1, the more sufficient the resource is. We analyzed the limiting value to represent the environmental conditions of phytoplankton growth and the spatial and temporal dynamics of the most limiting factor. The mixing intensity in the water column controls the distribution of phytoplankton and nutrients.

As suggested by previous studies, phytoplankton may develop high subsurface concentrations in layers of low turbulence such as the pycnocline; production continues locally in low-turbulent zones as long as the growth requirements of nutrients and light are balanced Cullen, The SBM was defined by its width, which was small compared to the water depth, and was persistent in both time and space Dekshenieks et al. As an SBM necessarily includes local peaks, we first selected the depth at which the first-order derivative of biomass changed from positive to negative in the vertical biomass profile as a potential location for an SBM peak.

How to Perform Primary Productivity Measurements

To further identify the boundaries of the potential SBM, different strategies were applied depending on the number of vertical layers on either side of the potential SBM peak. If there were more than five vertical layers on either side of the potential SBM peak, the vertical layer with the local maximum in the second-order derivative on each side of the potential SBM peak was recognized as the boundary of the SBM layer Benoit-Bird et al. Otherwise, the adjacent layers were assumed to confine the potential SBM.

We estimated the local background biomass value by linearly interpolating the biomass values of the upper and lower edges to the depth where the peak in biomass emerged. If the peak maximum biomass exceeded a value 1.

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In addition to tidal forcing, atmospheric forcing and bathymetry modulate stratification e. Consequently, tidal impacts on stratification and primary production are subject to spatial—temporal variability. Although we preliminarily estimated the influence of the spring—neap tidal cycle via the difference in NPP between the tidal scenario and the M 2 scenario, related responses would not necessarily be visible in a fortnightly cycle. To better associate the variation in NPP with the spring—neap tidal cycle, we identified specific grid cells where both currents and biochemical factors displayed a distinguishable spring—neap cycle.

By adopting the SCPS method, we were able to select representative grid cells where both NPP and velocity showed obvious spring—neap cycles. The area-averaged NPP increases slightly from In the non-tidal scenario, high productivity is restricted to the near-shore shallow regions along the British coast and the European continental coast Fig.

Tides cause a significant reduction in stratification in the shallow near-coastal areas of the North Sea and in the EC at Dogger Bank and south of Dogger Bank and foster the development of tidal mixing fronts. Consequently, the production pattern changes notably when tidal forcing is considered. The primary production maximum is shifted further offshore towards the frontal region Fig.

The shallow near-coastal areas in the south and the deeper areas in the NNS show a negative response of NPP to tidal forcing. A stronger negative response is observed in the highly dynamic EC Fig. The tidally induced change in NPP is associated with variations in the spatial distribution of the main limiting resources limiting pattern Fig. Generally, in the tidal scenario, the area experiencing nutrient limitation decreases due to the enhanced mixing of inorganic nutrients into the euphotic zone, especially in the shallow North Sea where the bottom and surface mixed layer interact with each other.

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Simultaneously, light limitation increases. The predominantly light-limited regions, which are restricted to the shallow coastal regions in the non-tidal scenario Fig. In contrast, in the surface layers of the stratified area, summer nutrient limitation is predominant, and the limiting value remains below 0. The limiting value derived from Liebig's law is indicated by dashed contour lines.

Stratified and unstratified areas are separated by black lines for definition, see Fig. The subdomain-division method described in Sect. Based on the division and the point-wise correlation of NPP variations in each subdomain Fig. A1 , representative grid cells were selected to study the mechanisms underlying the spatial variability of tidal responses in detail.

Areas with correlation coefficients higher than 0. This indicates that the division effectively explains the spatial diversity of the system with respect to the tidally induced changes in NPP and the predominantly inherent similarity within each subdomain. The seven identified subdomains are listed below Fig. The English Channel EC; dark blue. This area is characterized by an early onset of the spring bloom, strong mixing due to tidal stirring and shallow bathymetry.

The EC is the most productive area in the non-tidal scenario Fig. Negatively responding southern North Sea neg. SNS; blue. The neg. SNS characterizes the permanently mixed area in the shallow water near the coast. Positively responding southern North Sea pos. SNS; light blue. This area includes the frontal regions that were identified as the areas with the highest responses in NPP Fig.

Eastern British coast BC; green. This area is a highly productive, positively responding inshore region of the eastern British coast. In this area, a slight decrease in NPP is estimated when tidal forcing is considered. The Norwegian Trench NT; orange. This represents the area off the Norwegian coast which is strongly impacted by the low saline outflow from the Baltic Sea. Low-sensitivity area in the northern North Sea low-sen. This subdomain is influenced by two amphidromic points in the eastern North Sea, with tidal amplitudes of the M 2 partial tide generally below 0.

Some narrow transient zones between the positively responding areas and negatively responding areas are shown in white in Fig. SNS: negatively responding southern North Sea; pos. NNS: low-sensitivity northern North Sea. NNS areas. The subdomain division corresponds well with the regional characteristics of M 2 tidal energy dissipation rates, as suggested by the simulation study of Davies et al.

In most of the neg. In the pos. The low-sen.


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The strong tidal energy in the SNS destabilizes stratification, as also revealed by the subdivision based on stratification patterns presented by Van Leeuwen et al. Our neg. SNS and EC subdomains coincide with permanently mixed regions defined in the above study; in addition, the defined BC correlates with mixed or temporally stratified belts along the eastern British coast, as suggested by Van Leeuwen et al.