Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms

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1 This article was downloaded by: [Deltares], [Y.F. Friocourt] On: 27 September 2012, At: 07:08 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Food Additives & Contaminants: Part A Publication details, including instructions for authors and subscription information: Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms Y.F. Friocourt a, M. Skogen b, W. Stolte a & J. Albretsen c a Marine and Coastal Systems, Deltares, Delft, The Netherlands b Institute of Marine Research, Bergen, Norway c Institute of Marine Research, Flødevigen, Norway Accepted author version posted online: 15 Aug 2012.Version of record first published: 24 Aug To cite this article: Y.F. Friocourt, M. Skogen, W. Stolte & J. Albretsen (2012): Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms, Food Additives & Contaminants: Part A, 29:10, To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Food Additives & Contaminants: Part A Vol. 29, No. 10, October 2012, Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms Y.F. Friocourt a *, M. Skogen b, W. Stolte a and J. Albretsen c a Marine and Coastal Systems, Deltares, Delft, The Netherlands; b Institute of Marine Research, Bergen, Norway; c Institute of Marine Research, Flødevigen, Norway (Received 1 June 2011; final version received 16 July 2012) Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Two hydrodynamic and ecological models were used to investigate the effects of climate change according to the IPCC A1b emission scenario on the primary productivity of the North Sea and on harmful algal blooms. Both models were forced with atmospheric fields from a regional downscaling of General Circulation Models to compare two sets of 20-year simulations representative of present climate ( ) conditions and of the 2040s. Both models indicated a general warming of the North Sea by up to 0.8 C and a slight freshening by the 2040s. The models suggested that the eastern North Sea would be subjected to more temperature and salinity changes than the western part. In addition, the ecological modules of the models indicated that the warming up of the sea would result in a slightly earlier spring bloom. The one model that also computes the distribution of four different phytoplankton groups suggests an increase in the abundance of dinoflagellates, whereas the abundance of diatoms, flagellates and Phaeocystis sp. remains comparable to current levels, or decrease. Assuming that Dinophysis spp. would experience a similar increase in abundance as the modelled group of dinoflagellates, it is hypothesised that blooms of Dinophysis spp. may occur more frequently in the North Sea by However, implications for shellfish toxicity remain unclear. Keywords: climate change; harmful algal blooms; North Sea; Dinophysis; DSP; modelling; shellfish toxicity Introduction Certain types of microscopic algae found in marine and fresh water may cause problems when they accumulate in sufficient numbers, due to their production of endogenous toxins, or due to their sheer biomass or even their physical shape. These algae are referred to as harmful algae or harmful algal blooms (HABs), when their numbers become significant (Glibert et al. 2005). Harmful algae species comprise only a small component of the phytoplankton community, and their individual responses to climate variation and change can differ from that of the phytoplankton community as a whole. The primary human health implications associated with marine phytoplankton result from eating toxin-contaminated seafood (primarily shellfish). In marine waters, the most important toxin-producing harmful algae are diatoms from the genus Pseudo-nitzschia (causing amnesic shellfish poisoning, ASP; EFSA- CONTAM 2009a), and species of dinoflagellates from the genera Alexandrium, Pyrodinium, and Gymnodinium (related to paralytic shellfish poisoning, PSP; EFSA- CONTAM 2009b), Karenia (causing neurotoxic shellfish poisoning, NSP; EFSA-CONTAM 2010a), and aerosolised Florida red tide respiratory syndrome), Dinophysis and Prorocentrum (causing diarrhetic shellfish poisoning, DSP; EFSA-CONTAM 2008b), Azadinium spinosum (causing azaspiracid shellfish poisoning, AZP; EFSA-CONTAM 2008a), and Gambierdiscus (related to ciguatera fish poisoning; EFSA-CONTAM 2010b). In brackish and fresh water, the most important HABs are caused by certain species of cyanobacteria (blue green algae) from the genera Anabaena, Microcystis, Nodularia and Aphanizomenon (related to neurotoxic and hepatotoxic cyanobacterial poisoning). The relationship between phytoplankton biomass and HAB species toxicity is extremely complex. HABs can have harmful effects to human even if the species is not dominant. Toxin production may be related to hydro-climatic conditions and can vary among strains within a species, even during the course of one bloom event (Babin et al. 2005; Trainer and Suddleson 2005). Some species can cause shellfish toxicity even when present at very low abundances (Babin et al. 2005) while others are only toxic at high concentrations (Anderson et al. 2002). Besides effects on health, the presence of toxins or toxic algae in water or shellfish also leads to economic losses (costs and losses of income) estimated at up to several tens to *Corresponding author. yann.friocourt@deltares.nl ISSN print/issn online ß 2012 Taylor & Francis

3 hundreds of million dollars due to shellfish harvesting closures and negative effects on tourism and recreation (Hoagland and Scatasta 2006). Over the past four decades, the frequency and global distribution of incidents involving toxic algae appear to have increased, together with human intoxications (Hallegraeff 1993; Van Dolah 2000; Glibert et al. 2005; Tirado et al. 2010). Although this global increase also relates to improvements in detection methods, numbers of observers and identified toxins, (part of) the expansion of HABs is also often hypothetically related to large-scale weather patterns and climate change. In the North-East Atlantic, for instance, an increase of dinoflagellates to the detriment of diatom populations, has been linked to regional increases in sea surface temperature attributed to the North Atlantic Oscillation (Edwards et al. 2006). More generally, climate ultimately controls fundamental parameters that regulate algal growth, such as water temperature, nutrients and light, and can therefore be expected to cause changes in species composition, trophic structure, and function of marine ecosystems (Glibert et al. 2005), but relationships are poorly understood. To date, relatively little work has been done to characterise these links (Haines and Patz 2004; Patz et al. 2005), primarily due to the difficulty of separating the influence of climate change (natural and anthropogenic) from other anthropogenic impacts (such as eutrophication and ballast water introduction) that are also known to contribute to the occurrence of some HABs (Anderson et al. 2002). In Northern Europe, the dinoflagellate genus Dinophysis contributes most to shellfish DSP toxicity. Its feeding mechanisms and life cycle are complex, which makes it hard to predict its growth mechanistically. In the North Sea coastal waters, Dinophysis acuminata is like many other dinoflagellates often associated with relatively high temperatures, low surface salinity and/or relatively strong stratification, and conditions unfavourable for diatoms (low silicate availability), circumstances that usually occur during late summer/early autumn (Klo pper et al. 2003). In UK and Irish waters, no clear relationship between Dinophysis abundance and nutrient loads or concentrations has been found (Gowen et al. 2010). As of today, knowledge of the relationships between HABs, food toxicity, and climate change remain very unclear. Previous studies highlighted the need for development of models for forecasting blooms of toxin-producing microalgae in time and space, also in relation to climate change. With respect to the latter, it was also stressed that these models should not only consider the role of temperature, but also the influence of other parameters, such as irradiation, precipitation and currents (Miraglia et al. 2009). A technical difficulty for such modelling studies was identified by Moore et al. (2008). Currently, typical IPCC global Food Additives & Contaminants: Part A 1631 climate models provide information on a grid with km resolution, which is not detailed enough to be used for local scale (1 10 km) HAB forecasting and risk assessments. In this respect, models for local HAB forecasting should rely on a downscaling of future climate change projections that also include the driving mechanisms that are both important environmental links to HAB risks, themselves resulting from climate model outputs (Moore et al. 2008). Besides having been studied for more than a century, the North Sea (Figure 1) is also one of the coastal seas for which monitoring programmes for the presence of (potentially) toxic phytoplankton species have been enforced for several years (if not decades) by the authorities of the bordering countries (Gowen et al. 2010; Naustvoll et al. 2012; van der Fels-Klerx et al. 2012). In several of these countries, shellfish consumption is also relatively important and, consequently, the risk of HABs cannot be neglected. The most common occurrences of toxic species in the Dutch, Danish, or Norwegian seas are related to Dinophysis spp., leading to DSP. Compared to the other seafood intoxications, DSP is a somewhat milder intoxication that consists of rapid onset (3 h) of gastrointestinal symptoms (vomiting and diarrhoea) that generally resolve within 2 3 days. The first incidence of human shellfish-related illness identified as DSP occurred in Japan in the late 1970s (Yasumoto et al. 1980), but since then occurrences have been documented all over the world (see, for instance, Van Dolah (2000) for a more extensive description). Longterm health effects cannot be ignored since increased numbers of digestive cancers were found in areas with DSP poisonings in France (Cordier et al. 2000). The main aim of the study was to investigate possible changes in dinoflagellate abundances in the North Sea in a future climate. Using two state-of-theart biophysical models, a control of present day conditions ( ) was compared to a forecast scenario (of ). The atmospheric forcing fields for the biophysical models were obtained by dynamically downscaling results of a global scale General Circulation Model (GCM) to ensure they were specific to the region of interest. The models simulate phytoplankton species composition at the group level. Dinoflagellates, the group that includes most toxic species, are included as a separate group in one of the models. The comparison was then used to discuss possible changes on HABs. Material and methods The computations of the effect of climate change on the functioning of the North Sea are carried out with the numerical hydrodynamic and ecological models, Delft3D and NORWECOM. Three simulations are

4 1632 Y.F. Friocourt et al. Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure 1. Map of the North Sea with the coastal (NW010) and offshore stations (TS235) used in the present study. performed with each model. The first one is an ordinary hindcast simulation of present day conditions ( ) using real forcing. This simulation is used to assess the overall ability of the model to reproduce observed weather patterns, therefore named hindcast. The latter two simulations include a control run representative of the climate of the 1990s ( ) and a forecast of a future climate for using forcing fields from a climate model obtained through a regional downscaling of atmospheric fields computed by the ECHAM5 GCM (Roeckner et al. 2003). This approach is further described below. Delft3D Delft3D is an open source modelling suite that allows one to compute hydrodynamic transport fields as well as water quality for marine and estuarine environments. The advective and diffusive transport fields are computed with the hydrodynamic module Delft3D- FLOW of the modelling suite Delft3D. The Delft3D- FLOW module solves the unsteady shallow-water equations in three dimensions. The model takes into account a large number of processes (wind shear, wave forces, tidal forces, density-driven flows and stratification due to salinity and/or temperature gradients, atmospheric pressure changes, drying and flooding of intertidal flats, etc.) making it suitable for application to a wide range of river, estuarine, and coastal situations (Lesser et al. 2004). The primary production is modelled with the module BLOOM/GEM of the Delft3D modelling suite (Blauw et al. 2008). It is a generic ecological modelling instrument that calculates the transport of model substances (state variables) in the water column as a function of the advective and dispersive transport provided by a hydrodynamic model, such as Delft3D- FLOW, and calculates the water quality and ecological processes affecting the concentrations of the state variables. BLOOM/GEM includes descriptions of physical, biological and/or chemical reactions that cause one or more state variables of the model to appear, to disappear or to change into another state variable. These processes are related to algae growth and mortality, mineralisation of organic matter, nutrient uptake and release, and oxygen production and consumption. The BLOOM/GEM modelling instrument considers three nutrient cycles: nitrogen, phosphorus and silica. The carbon cycle is partially modelled, and a mass-balance of organic carbon is made. The model

5 Table 1. Main characteristics of the modelled phytoplankton groups as used in the Delft3D model. Modelled group Diatoms Phaeocystis spp. Toxicity/nuisance Usually non-toxic (only one toxic genus known causing ASP) Non-toxic, nuisance foam-producer Main modelled characteristics Silica dependent, optimal growth at relatively low temperature Optimal growth at relatively low temperature and light Flagellates Non-toxic Higher light requirement for optimal growth Dinoflagellates Many toxic species (leading to e.g. DSP, PSP) Optimal growth at relatively high temperature assumes that the availability of inorganic carbon for uptake by algae is unlimited. Furthermore, different groups of phytoplankton are considered as well as oxygen, suspended detritus, and inorganic particulate matter (Table 1). Light availability for phytoplankton growth is calculated based on the light irradiance and extinction, due to suspended sediment as well as phytoplankton and other organic matter. A detailed description of the module is presented by Blauw et al. (2008). The North Sea GEM-BLOOM model simulates diatoms, flagellates, dinoflagellates and Phaeocystis spp. as separate groups. Within each group, three types exist that are acclimated to nitrogen-, phosphorus- or light-limitation. In the model, dinoflagellates are only able to successfully compete with other algae at relatively high temperatures. Thus, stratification as such is not directly promoting dinoflagellate net growth rate in the model, but indirectly via higher surface water temperatures. Moreover, dinoflagellates have lower sedimentation rates than diatoms in the model and, therefore, their biomass will be retained in the surface layers to a greater extent during stratification. The model domain covers the southern North Sea from 1 Wto9 E and from 49 Nto57 N. The grid is curvilinear and follows the Dutch coastline to the best possible, with a horizontal resolution varying from about 2 2 km in the Dutch coastal zone to up to km in the most north-westerly part of the domain. The version used in this study consists of 4350 active elements horizontally and 12 vertical sigma layers. The hydrodynamic transport and primary production are computed onto the same grid. To absorb inconsistencies between the initial field, the forcing and the open boundaries a 4-year spin-up Food Additives & Contaminants: Part A 1633 is used. This implies that the first model year (either 1985 or 2031) was run four times initially. On the fifth repetition of this first year the simulation continued to run through the whole period (until 2004 or 2050). These results were then used for the analysis. NORWECOM The NORWegian ECOlogical Model system (NORWECOM) is a coupled physical, chemical, biological model system (Skogen et al. 1995; Skogen and Søiland 1998) applied to study primary production, nutrient budgets and dispersion of particles such as fish larvae and pollution. The model has been validated by comparison with field data in the North Sea/Skagerrak (e.g. Søiland and Skogen 2000; Skogen et al. 2004; Hjøllo et al. 2009; Skogen and Mathisen 2009). In general the model captures the main features of the hydrography, although the southern North Sea salinity values can be too low. For the volume transports, the Skagerrak and English Channel and northern North Sea inflows are comparable with observations, except the flow over the Shetland Shelf which is weaker than observations suggest. Heat content is well represented by the model. The modelled primary production is within other estimates, and there are significant relationships between modelled and measured nutrients in the southern North Sea. Generally, the model does fit better for nutrients than for chlorophyll, and model performance is better off the coast then at the coast. The physical model is based on the three-dimensional, primitive equation, time-dependent, wind- and density-driven Princeton Ocean Model (POM). In the present study, the model is used with a horizontal resolution of 10 km in an area covering an extended North Sea. In the vertical, 20 bottom following sigma layers are used. The chemical biological model is coupled to the physical model through the subsurface light, the hydrography, and the horizontal and the vertical movement of the water masses. The prognostic variables are dissolved inorganic nitrogen (DIN), phosphorus (PHO) and silicate (SI), two different types of phytoplankton (diatoms and flagellates), two detritus (dead organic matter) pools (N and P), diatom skeletals (biogenic silica) and oxygen. Two types of zooplankton (meso- and micro-zooplankton) are included based on a module taken from the ECOHAM4 model (Moll and Stegert 2007; Stegert et al. 2009; Pätsch et al. 2010). The micro-zooplankton feed on flagellates, while the meso-zooplankton feed on diatoms and micro zooplankton. In addition the phytoplankton experience a low (1% day 1 ) background mortality. The hindcast run uses the ERA40 operational surface forcing produced by the European Centre for Medium-Range Weather Forecasts (ECMWF;

6 1634 Y.F. Friocourt et al. Uppala et al. 2005), four tidal constituents at the lateral boundaries and freshwater runoff. Initial values and open boundary conditions are taken from monthly climatologies (Martinsen et al. 1992). Also here a 4-year spin-up was used. Regional downscaling of climate scenarios In the framework of the ENSEMBLES project ( several regional climate models (RCM) were employed for a downscaling of a GCM (van der Linden and Mitchell 2009; Kjellstro m and Giorgi 2010). First the RCMs were compared to the ERA-40 reanalysis (Uppala et al. 2005) to assess each RCM s performance, and then the RCMs were used for a downscaling of the IPCC SRES A1b emission scenario (Nakicenovic et al. 2000) to compute the regional effect of this climate change scenario onto Europe. The computing time required for the hydrodynamic and ecological simulations in the present study only allowed the use of input from one single RCM. Therefore, the RCM that performed best when compared to the ERA-40 reanalysis was chosen (Christensen et al. 2010), i.e. the RACMO model (van Meijgaard et al. 2008). In the case of RACMO, the GCM at the basis of the regional downscaling is the ECHAM5 atmosphere model (Roeckner et al. 2003). In a preliminary phase, both models are also forced with a regional downscaling of the ERA-40 reanalysis (Uppala et al. 2005) for the years This simulation is referred to as hindcast in this study. This baseline situation is used to assess any systematic bias of either model in reproducing the observed hydrodynamic and ecological state of the North Sea. For that period, river discharges computed from measurements from a compilation by the UK Centre for Environment, Fisheries and Agriculture Science (ICG-EMO, 2007) were used. The regional model output was used for two periods: a control run representative of the climate of the 1990s (period ), and a future run representative of the projected climate change around 2040 (period ). Both periods were chosen to be 20-year long to include inter-annual variability. One aspect not covered in the ENSEMBLES project relates to possible changes in the state of the North Eastern Atlantic Ocean and neighbouring seas as a result of the projected climate change. For a shallow and semi-enclosed sea such as the North Sea, those changes were considered important enough that a work-around needed to be found. In an earlier climate change-related project, ESSENCE, an ocean model (MPI-OM, Marsland et al. 2003) was coupled to the atmosphere model ECHAM5 to compute the expected climate change at a global level under the SRES A1b climate scenario for the period (Sterl et al. 2007, 2008). Considering the common aspects shared by these two projects (including some models), the lateral boundary conditions for the hydrodynamic model Delft3D-FLOW were obtained from the model output of the ESSENCE project. Since the temperature fields for the North Eastern Atlantic Ocean off Norway seemed incorrect (also with respect to measured temperature values for the present), the lateral boundary conditions for the NORWECOM model were kept identical to those of the present climate. Similarly, although possible changes in precipitation patterns over Europe are an output of the regional downscaling of the ENSEMBLES project, the consequences on the hydrological cycle of the European rivers was not computed. However, several studies pertaining to the expected effect of climate change on European river discharges are available for the period In particular, Vellinga et al. (2009) reported an expected increase of average winter flows of the Rhine by 5 15% and a decrease of average summer flows by 0 35% by 2050, depending on the climate scenario. In their study, the largest changes were obtained for climate change scenarios that seem more extreme than the SRES A1b scenario. Similar studies for British and French rivers indicated increased winter discharges and decreased summer discharges by 2050 with varying ranges per river and per climate scenario (Arnell 2003; Ducharne et al. 2009). As more rivers are included in the models than the ones for which some model studies could be found, a general 10% winter (JFM) increase and 10% summer (JAS) decrease in river discharges was applied to all rivers in the Delft3D- FLOW and NORWECOM models. For the other months, the scaling factor was interpolated linearly between those values to eliminate any abrupt change in the scaling. To include the effect of inter-annual variability in the river discharges, the period was modelled with the scaled discharges of the period The nutrient concentrations were kept constant for present-day and future simulations, so that the total nutrient loads were scaled similarly to the river discharges. For both models (Delft3D and NORWECOM) and both climate projections (presentday and future), a 4-year spin-up was used prior to the analysis. Results A discussion of the hydrodynamic and ecological model performance of the hindcast simulation can be found in a series of studies (Søiland and Skogen 2000; Skogen et al. 2004; Los and Wijsman 2007; Los et al. 2008; Hjøllo et al. 2009; Skogen and Mathisen 2009; Los and Blaas 2010), but for completeness a short intercomparison of the models is presented.

7 Model evaluation on the basis of the hindcast scenario Both models perform generally well at reproducing observed surface temperatures, albeit a better performance from NORWECOM compared to Delft3D. In particular, the Delft3D model tends to underestimate summer temperatures by 1 or 2 C. Both models reproduce winter temperatures properly, except that temperatures lower than 5 C tend to be slightly overestimated (Figure 2). In general, the Delft3D model performs better in the coastal zone than in the offshore areas of the North Sea, as this particular model was originally set up for coastal applications along the Dutch coast. Both models reproduce measured salinities satisfyingly. Looking into the details of comparisons at individual stations (see Figure 1 for the station locations) reveals that both models tend to slightly underestimate the range of salinity variability compared to the measurements (Figure 2). In general, the low frequency of the measurements does not allow one to assess whether or not the amplitude captured by measurements is realistic and what exactly causes the variations. The area of riverine influence in the Dutch, German, and Danish coastal zones is much larger in Food Additives & Contaminants: Part A 1635 Delft3D than in NORWECOM, probably as a result of the finer resolution in the coastal zone. Water quality and ecology Model results were compared with measurements from the Dutch national (MWTL) monitoring programmes (see Figure 1 for the station locations). Delft3D describes light extinction, oxygen concentrations and the various inorganic nutrient concentrations well (Figure 3). The biomass in (gc/m 3 ) of individual phytoplankton groups is generally overestimated by the model compared to the carbon estimates from phytoplankton counts (available from 1991 now). Consequently, total phytoplankton carbon is often overestimated by the model. Due to the relatively low frequency of phytoplankton, it is not possible to judge the models capability to estimate the timing of the spring bloom peak at the offshore station TS235. At the coastal station NW010, the Delft3D model performs well in this respect. The average chlorophyll a concentration a derived variable in the model is reproduced well at both stations, although the variability in measured concentrations is not completely Figure 2. Scatter plots of modelled versus observed temperature at the coastal station NW010 (left) and salinity at the offshore station TS235 (right) for the hindcast scenario. Results from Delft3D (resp. NORWECOM) are indicated with þ signs (resp. o signs). A model value is indicated whenever a measurement is available. Temperature measurements are carried out daily whereas salinity measurements only occur fortnightly, hence differences in the density of data between both plots.

8 1636 Y.F. Friocourt et al. Figure 3. Time series of modelled nutrients, chlorophyll and phytoplankton carbon at coastal station NW010 (Delft3D). Measured concentrations are also indicated.

9 covered (Figure 3). For example, dinoflagellate concentrations are, on average, reasonably well reproduced, but the model tends to overestimate the peak of abundance. Generally, the agreement between the modelled and the measured phytoplankton concentration is better at the end of the time-series than at the start (Figure 4). In general Delft3D performs better than NORWECOM in the Dutch coastal zone as it benefits from a finer horizontal resolution and subsequent better description of transport processes (Figure 5). Future climate Both models show a mean increase in temperature of 0.4 to 0.8 C between the control run and the future climate in the southern North Sea. Temperatures increase more along the eastern side of the North Sea than along the western side (Figure 6). In addition, NORWECOM indicates an increase of almost 1 C of the annual mean temperature in the Skagerrak. Delft3D shows the largest temperature increase in summer, whereas it occurs in winter in NORWECOM. The details of time series indicate that maxima and minima below present-day climate maxima and minima may still be possible in the future but, in general, both models indicate that summers and winters will become warmer. The warming is also not homogeneous spatially (Figure 6). In winter and spring, the central North Sea warms up slightly more than the coastal areas, whereas in summer and fall the warming is more concentrated in the coastal areas. This differential warming probably relates to coastal (and therefore shallower) seas heating up faster than the deeper, open areas in summer, and to winter and spring storms mixing the deeper, cooler waters more efficiently in the shallower areas near the coast than in the deeper areas of the central North Sea. The model indicates that the North Sea warms up evenly over its whole depth. However, the surface layers in the central part heat up slightly earlier than the deeper layers, and inversely cool down also slightly later. This only occurs in the part of the southern North Sea that becomes stratified in summer. This delay results from the fact that the sea is heated up from above and that only diffusion and mixing can allow the heat to descend into the deeper layers. Finally, the effect of the boundaries on temperature can be noted in the results of Delft3D but it should be stressed that these disappear within a few grid cells and do not influence the results much inside the domain. Both models indicate a salinity decrease of psu in the future climate compared to the present climate (Figure 7). This decrease shows little seasonal variability, except along the Belgian, Dutch, German, and Danish coasts, where salinity decreases in future Food Additives & Contaminants: Part A 1637 winter and spring compared to the present-day climate, and remains about the same as (or even slightly higher than) in the present-day climate in summer and fall. This seasonal salinity change is mostly concentrated near the surface, with a more homogeneous salinity change, and is related to the late winter river discharge increase and late summer decrease. The other area with a strong seasonal variability is the Skagerrak, with a salinity decrease of 0.5 psu in summer and a salinity increase in winter. The effects of the combination of these temperature and salinity changes on stratification remain moderate. In general, the central North Sea (which is already stratified in spring and summer in the current climate) becomes slightly more stable in spring and especially in summer. The change is particularly noticeable in the German Bight. In winter, the central North Sea (for the parts that remain stratified) becomes generally less stable, with the exception of a few locations in the coastal river. In fall, the change is more variable in space; some areas become more stable whereas others become less stable. However, it should be noted that the variations remain very small. The model results also do not show any temporal shift in the onset or in the disappearance of stratification in the future climate scenario. The inflow from the Atlantic has a large impact on the North Sea climate, as it is a major source of both nutrients and heat (Brockmann et al. 1990; Hjøllo et al. 2009). In Figure 8, the difference in this inflow between the different climate change projections across a section from the Orkneys to Utsira (Norway along N; see Figure 1 for the location) is shown using estimates from the NORWECOM model. The simulation results show that the water transports in the control run are too large compared to the hindcast all through the year (about 20% too high). Comparing the control run and the future climate, there will be a decrease in this inflow of about 5%. The largest decrease is seen in spring, with the maximum found in June, while there is a slight increase in Atlantic inflow during fall. Water quality and ecology results In general, the seasonal variation in average chlorophyll concentrations does not differ much between the control run and the future climate scenario (Figure 9) in either model. Whereas the NORWECOM model shows a slight decrease in chlorophyll most of the year, this is not obvious for the Delft3D model. In this latter model, the onset of the spring increase in chlorophyll, and in offshore stations also the autumn increase, occurs slightly, but consistently earlier for the future climate scenario compared to the control run. Integrated over the whole North Sea, this pattern

10 1638 Y.F. Friocourt et al. Figure 4. Time series of modelled nutrients, chlorophyll and phytoplankton carbon at offshore station TS235 (Delft3D). Measured concentrations are also indicated.

11 Food Additives & Contaminants: Part A 1639 Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure 5. Scatter plots of modelled versus observed phosphate (left) and silicate (right) concentrations at the coastal station NW010 for the hindcast scenario. Results from Delft3D (resp. NORWECOM) are indicated with þ signs (resp. o signs). Figure 6. (Colour online). Annual mean temperature change (in C) in the surface layer of Delft3D (left) and NORWECOM (right) in a future climate scenario compared to the present climate scenario. emerges together with a slightly earlier decay of the chlorophyll levels in a future climate scenario. In the NORWECOM model, the onset of spring increase is unchanged in the future scenario while this model, like the Delft3D model, indicates a slight earlier decay in fall. The Delft3D model distinguishes four different phytoplankton groups (Table 1). Even if total chlorophyll remains almost unchanged between the control and future climate, there are substantial differences in the average distribution of the different phytoplankton groups over the year. The spring diatom bloom occurs only slightly, but consistently earlier in the future climate scenario. The general trend is that the abundance of dinoflagellates as a group increases in the future climate scenario compared to the control run using the Delft3D model. Also, the onset of the growth of this group occurs consistently earlier in the future climate (Figure 10). The earlier onset of dinoflagellate growth and a higher maximum biomass also results in longer periods with dinoflagellate biomasses larger than 0.1 mgc/l (arbitrarily chosen and approximately corresponding to 1 2 mg Chl-a/l) in a future climate scenario. The Delft3D model also enables one to compute an indication of nutrient limitation. The number of years

12 1640 Y.F. Friocourt et al. Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure 7. (Colour online). Annual mean salinity change (in psu) in the surface layer of Delft3D (left) and NORWECOM (right) in a future climate scenario compared to the present climate scenario. Figure 8. (Colour online). Mean seasonal cycle of modelled water transports along the Orkneys Utsira section in Sverdrups (1 Sv ¼ 10 6 m 3 /s) for the hindcast, control and future climate simulations from the NORWECOM model. that the modelled dinoflagellates exceeded 0.1 mgc/l is presented in Figure 11 for the three different types (light-, nitrogen- and phosphorus-limited) and for three coastal areas along the Dutch coast. For offshore stations, this threshold was seldom reached (Figure 11). These results suggest that bloom probability and duration for the future climate scenario are higher than present day irrespective of the type of limitation for growth. However, the relative increase is the largest for the nitrogen-limited type of dinoflagellate, i.e. the dinoflagellate type that is best adapted to nitrogen limitation (Figure 11). Discussion The present results have been compared to the results from another climate study of the North Sea.

13 Food Additives & Contaminants: Part A 1641 Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure year average of weekly modelled chlorophyll a and standard deviations for coastal station NW010, offshore station TS235 and averaged for the North Sea area for the control run and the future climate. Especially at the offshore station, the onset of the spring and autumn diatom blooms is earlier in the future climate projection (see arrows). A dlandsvik (2008) downscaled the SRES A1b scenario from the Bergen Climate Model for the period in the North Sea, and compared results to a control downscaling simulation for the period (A dlandsvik and Bentsen 2007). His results showed a warming of the North Sea with a volume average of 1.4 C and a mean change of the sea surface temperature (SST) of 1.7 C, with the largest mean temperature increase in May (1.8 C) with a minimum (1.0 C) in November; the SST peak warming was found in June. The results described in the present study are in good agreement with that earlier study by Adlandsvik (2008), keeping in mind the different timewindows used. Ådlandsvik (2008) also reported changes in the North Sea inflow. Using a slightly different section (Orkney-Feie), the mean inflow was increased from 1.4 to 1.5 Sv (Sverdrup, 1 Sv ¼ 10 6 m 3 /s) from the control to the future scenario with a maximum (0.3) in May and a minimum ( 0.2) in October. These earlier findings contradict the results found in the present study, which suggest a decrease in North Sea inflow under a future climate projection, except for the autumn period (Figure 8). This discrepancy is probably due to the different climate models used, and it should be noted that a single regional projection cannot provide an adequate uncertainty measure. Therefore, an impact study based only on a single global model projection could be strongly biased (Ådlandsvik and Bentsen 2007). In a recent sensitivity study (Skogen et al. 2011), effects of changes in atmospheric forcing on the lower trophic levels of the North Sea were examined using typical projected climate change rates for a number of parameters. An increase in wind, temperature and short wave radiation would all result in an increased North Sea primary production. The influence of the wind was the largest (leading to a 10% increase in primary production), whereas an increase in air temperature of 3 C would only lead to an increase of the order of 1% in primary production. The sensitivities, where the atmospheric perturbations considered were

14 1642 Y.F. Friocourt et al. Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure year average of weekly modelled phytoplankton groups for coastal station NW010, offshore station TS235 and averaged for the southern North Sea area for the control run and the future climate projection. The onset of autumn dinoflagellate growth is earlier and maximum biomass higher for the future scenario. in the range of future climate change (IPCC 2007), seem to contradict the slight decrease in chlorophyll concentration found in the present study. However, the largest fraction of the reported increase is mainly in the northern North Sea and is explained from an increase in Atlantic inflow. In the present study, using the RACMO forcing, the NORWECOM model suggests a decrease in this inflow. One aspect ignored by the current study relates to potential changes in the nutrient and particulate matter loads of rivers in the future, in particular in the context of the European Water Framework Directive. Particulate matter relates directly to the penetration of light in the water column and, therefore, to the geographical distribution of primary production and nutrients. In addition, Europe is currently implementing the Water Framework Directive, the policy determining the water quality of water bodies, whereby the quantities of nutrients released in rivers are expected to decrease significantly by Effect of future climate on Dinoflagellates While some dinoflagellate species produce toxins and are, therefore, important for seafood safety, these toxic species usually correspond to a minor fraction of the total dinoflagellate community. The results of this study have to be interpreted in this context. The clear increase of dinoflagellate bloom frequency and average duration for the Dutch coastal waters indicates that the chemical physical conditions for dinoflagellate bloom formation might improve in a future climate with warmer water and changing river discharges. This is consistent with the findings of Edwards and Richardson (2004) that especially dinoflagellates seem to be influenced by warmer sea temperatures. Moreover, since Dinophysis abundance in the North Sea is often associated with deep thin layers under conditions of thermal stratification (Gentien et al. 2005), the implications of the future climate scenarios for this species are not clear. Duration and extent of

15 Food Additives & Contaminants: Part A 1643 Downloaded by [Deltares], [Y.F. Friocourt] at 07:08 27 September 2012 Figure 11. (Colour online). Number of years within the 20-year model run-period that the biomass of dinoflagellates exceeded a threshold of 0.1 mg C/l for three coastal areas along the Dutch coast, where the Delft3D model has its highest resolution for the control run (solid) and future climate projection (dashed). Results are presented for light-limited (-E), phosphorus-limited (-P), nitrogen limited (-N) and total dinoflagellates (regardless of any limitation type). stratification, rather than a temperature increase itself, will influence the abundance of this toxic species in the North Sea. The slightly enforced stratification in future climate, as predicted by the models, may contribute to a more suitable habitat for species like Dinophysis spp. than at present. More specialised models capable of predicting such thin layer-forming species may give more insight in the future distribution of Dinophysis spp. Toxicity of Prorocentrum lima, a well-culturable dinoflagellate with the same toxin profile, accumulated most toxin at low phosphate availability (Varkitzi et al. 2010). The observed increase of the nitrogen-limited type of the modelled dinoflagellate can at the moment at best be related to the group of dinoflagellates, and not to any particular toxic species. For this reason, it is still highly speculative to draw any conclusions with respect to the toxicity of dinoflagellate blooms in a future scenario. Possible effect of future climate on dinoflagellate toxicity As of today, a clear relation between Dinophysis abundance and shellfish toxicity still remains to be found (e.g. van der Fels-Klerx et al. 2012). Among other factors, nutrient limitation could enhance specific toxicity of toxic algae due to an unbalance in toxin production and dilution through growth (e.g. Paz et al. 2009). Indeed, for D. acuminata, highest cellular toxin content have been related to nitrogen limitation in semi-laboratory experiments (Johansson et al. 1996). Future developments On the one hand, this study has proven the feasibility of combining state-of-the-art climate models and ecological models to investigate possible effects of climate change on the ecological status of coastal seas. On the other hand, drawing conclusions on possible implications for food safety remains hindered by the complexity of the processes at hand and the relative lack of understanding of the relations between (i) presence of phytoplanktonic species; (ii) occurrence of

16 1644 Y.F. Friocourt et al. HABs; and (iii) accumulation of toxins in shellfish. The complexity of such a task was already identified in an earlier study by Hallegraeff (2010), with the argument that climate change may lead to various strain-specific responses. More generally, the present study, together with several others published in this special issue, are in line with earlier studies (e.g. Hallegraeff 2010; Miraglia et al. 2009); they actually underline once more the importance for further research on these relations being encouraged through: (i) increased monitoring of water quality and harmful algal blooms; (ii) improved methods for toxin detection; (iii) better understanding of the relationships between cell numbers of toxinproducing microalgae and the accumulation of biotoxins in bivalve molluscs; and (iv) better understanding of the mechanism of various toxin groups. Summary and conclusions In the present study, two hydrodynamic and ecological models of the North Sea are used to investigate the effect of climate change on the primary productivity of the North Sea and on the likelihood of presence of dinoflagellate blooms. Both models are forced with atmospheric fields obtained from a regional downscaling of the SRES A1b scenario for emission of greenhouse gases. Both models are run for two periods of 20 years, one as a baseline for present climate conditions, and the other as representative of expected conditions in Both models indicate a warming up of the North Sea by about C, with the highest temperature in the eastern North Sea. In addition, both models also indicate a slight freshening of the North Sea salinity by 0.3 psu in the southern North Sea. According to the models, such changes in the temperature conditions would lead to the chlorophyll spring bloom to start and to decay slightly earlier than in the present climate. As a result, substantial differences in the average distribution of the phytoplankton groups could be expected. The model that is able to estimate different groups indicates that, whereas the spring diatom bloom would occur only slightly earlier, the general abundance of dinoflagellates would increase significantly in the future climate. Also, the onset of the growth of this phytoplankton group would occur consistently earlier. With the present knowledge, it can only be hypothesised upon the effect of the modelled future climate on toxic HABs and shellfish toxicity. Moreover, there are several other factors affected by climate change that influence shellfish toxicity but not taken into account in the present study, such as elevated CO 2 levels, changing concentrations of micronutrients, and small-scale wind-driven transport of Dinophysis spp., towards inshore shellfish beds. Although no clear answer could be given, the current combination of hydrodynamic and rather detailed primary production models is an encouraging step towards understanding how future climate may change the risk for HAB frequency and sea food toxicity. Acknowledgements The authors kindly thank the national funders of the EMTOX project from the Netherlands (the Dutch Ministry for Economic Affairs, Agriculture & Innovation), as well as the project Advisory Board, with representatives from (among others) the Dutch Food and Consumer Product Safety Authority. The authors would like to thank Anouk Blauw, Hans Los, and Xavier Desmit for their helpful suggestions throughout the course of the project. References A dlandsvik B Marine downscaling of a future climate scenario for the North Sea. Tellus A. 60(3): , doi: /j x. A dlandsvik B, Bentsen. M Downscaling a twentieth century global climate simulation to the North Sea. Ocean Dyn. 57(4 5): , doi: /s Anderson DM, Glibert PM, Burkholder. 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