Ph Variability and CO2 Induced Acidification in the North Sea ⁎ J.C

Journal of Marine Systems 64 (2007) 229–241 www.elsevier.com/locate/jmarsys

pH variability and CO2 induced acidification in the North Sea ⁎ J.C. Blackford , F.J. Gilbert

Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, UK Received 30 September 2005; received in revised form 1 March 2006; accepted 13 March 2006 Available online 7 July 2006

Abstract

A coupled carbonate system–marine ecosystem–hydrodynamic model is used to simulate the temporal and spatial variability in pH across the southern North Sea as it relates to the environmental and biological processes affecting CO2, namely, photosynthesis and respiration, riverine boundary conditions and atmospheric CO2 concentrations. Annual pH ranges are found to vary from <0.2 in areas of low biological activity to >1.0 in areas influenced by riverine signals, consistent with observations and previous studies. It is shown that benthic, as well as pelagic, activity is an important factor in this variability. The acidification of the region due to increased fluxes of atmospheric CO2 into the marine system is calculated and shown to exceed, on average, 0.1 pH units over the next 50 years and result in a total acidification of 0.5 pH units below pre-industrial levels at atmospheric CO2 concentrations of 1000 ppm. The potential for measurable changes in biogeochemistry are demonstrated by simulating the observed inhibition of pelagic nitrification with decreasing pH. However, we conclude that there is a lack of knowledge of how acidification might affect the complex interaction of processes that govern marine biogeochemical cycles and a consequent need for further research and observations. © 2006 Elsevier B.V. All rights reserved.

Keywords: Acidification; CO2; Production; Respiration; North Sea; Nitrification

1. Introduction 2004) has significantly buffered the rate of global warming, a secondary consequence, ocean acidifica- Atmospheric levels of carbon dioxide have in- tion, a result of the dissociation of CO2 in solution, creased from pre-industrial levels of 280 ppm to has recently emerged as a serious concern (e.g., around 380 ppm today as a consequence of human Caldeira and Wickett, 2003; Raven et al., 2005). The activities. Atmospheric CO2 is predicted to increase chemistry and equilibria of the oceanic carbonate further to between 700 and 1000 ppm by the end of system are well known (e.g., Zeebe and Wolf- the century as fossil fuel reserves are consumed Gladrow, 2001); thus, the rate of oceanic acidification (IPCC, 2001; Caldeira and Wickett, 2003). A clear is very predictable given atmospheric loadings. evidence-based scientific consensus has emerged that Caldeira and Wickett (2003) have shown that the these increases are responsible for global warming oceans have already experienced a 0.1 pH unit (e.g., Hansen et al., 2005). Whilst the oceanic uptake reduction (a 30% increase in [H+]) since pre-industrial of about 48% of the anthropogenic CO2 (Sabine et al., times and may experience a total reduction of over 0.7 pH units as fossil fuel reserves are depleted. The ⁎ Corresponding author. oceans are predicted to remain in this acidified state E-mail address: [email protected] (J.C. Blackford). for hundreds if not thousands of years.

Whilst it has been reported that ancient oceans reviews of potential ecosystem effects include Turley et experienced pH levels of about 0.6 units lower than al. (2004), Raven et al. (2005) and Turley et al. (2006). today, proxy analysis suggests that oceanic pH has Without reproducing the details contained in the above remained above 8.0 for over 20 million years (Pearson documents, some of the more important effects on and Palmer, 2000). Much of the concern about present- systems such as the North Sea may be summarised as day acidification relates to the rate at which it is follows. predicted to occur, about 100 times faster than natural Nutrient speciation is impacted by pH, for example, + 3− fluctuations over geological time scales. Whilst the both the proportion of NH3 (to NH4) and that of PO4 2− oceanic sediments can buffer slow increases in CO2 this (to HPO4 ) are very sensitive to small variations in pH is not possible for the extreme rates of change currently around 8.0 (e.g., Zeebe and Wolf-Gladrow, 2001). seen, as oceanic mixing rates are too slow. It seems There is evidence that pelagic nitrification rates are likely that human activity will provoke changes over a sensitive to [NH3] and hence pH. Huesemann et al. few hundred years that usually take millennia to occur (2002) have shown that nitrification rates may decrease (Fig. 1). to zero at a pH around 6.0–6.5 as the NH3 substrate A degree of natural spatial and temporal variability disappears from the system. Consequently, we may see + − in marine pH is observed in today's oceans. On increased ratios of NH4 to NO3 with acidification. oceanic scales these are largely driven by temperature This has implications for the energetics of nitrogen variation (higher temperatures lower CO2 solubility in acquisition by phytoplankton. the water and increase pH) and upwelling of cold deep Phytoplankton species differ in their response to pH. water, supersaturated with respect to atmospheric CO2, Hinga (2002) provides a review demonstrating that which lowers pH. These factors are thought to give a some coastal species are relatively tolerant to a wide global range of about 0.3 pH units (Skirrow, 1975). range of pH, whilst some have very specific pH However, in eutrophic, highly productive shelf and requirements which nevertheless are spread between coastal environments, the biological demands on pH 7.0–9.0. Species that utilise CO2 as their carbon dissolved CO2, coupled with the chemical dynamics source are likely to benefit from increased [CO2], whilst of riverine inputs, combine to produce variations of as those that utilise the bicarbonate ion may be competi- much as 1 pH unit (Hinga, 2002). In particular, the tively disadvantaged. In terms of metabolism, diatom total dissolved inorganic carbon and total alkalinity of species are at or near saturation at present-day CO2 river systems are important drivers of coastal pH levels whilst coccolithophores are well below saturation distributions. Thus, the consequences of acidification and would presumably benefit from increased CO2 of shelf seas such as the North Sea may be modified (Riebesell, 2004) . However, the ability of coccolitho- by the high natural background variability of such a phores to calcify is significantly inhibited by the system. decreased carbonate saturation state (Riebesell et al., The impact of acidification on the marine ecosystem 2000) implying that calcifying strains of Emiliania is not well known. A wide variety of ecosystem huxleyi may be replaced by non-calcifying strains. If processes and species are thought to be vulnerable to phytoplankton species composition is sensitive to changing pH. Whilst some progress has been made in perturbation, one may expect impacts on the grazer investigating these in isolation, little is known about the community and the higher trophic levels that have net affect on the whole system (Riebesell, 2004). Useful specific trophic links. In terms of carbon cycling, we

Fig. 1. Past (white diamonds, data from Pearson and Palmer, 2000) and contemporary variability of marine pH (grey diamonds with dates). Future predictions are model derived values based on IPCC mean scenarios. J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 231 may then expect decreased sequestration via sinking The domain we explore is the southern part of the calcium carbonate liths. Conversely high [CO2] North Sea (below 56°N), an area showing significant enhances the release of dissolved organic carbon from nutrient enrichment and riverine influence. phytoplankton cells (Riebesell, 2004); resulting aggregation (Engel et al., 2004) may increase the flux of 2. The models carbon to the sediments. Decreased carbonate saturation states have been The model system used here is a coupling involving found to significantly increase mortality of settling four well-established model codes, covering the car- bivalves (Green et al., 2004) which may affect cohort bonate system (HALTAFALL; Ingri et al., 1967), the size. Pörtner et al. (2004) state that “Sensitivity to CO2 is marine ecosystem (ERSEM; Baretta et al., 1995; hypothesized to be related to the organizational level of Blackford et al., 2004) and either POLCOMS (Holt an animal, its energy requirements and mode of life”. and James, 2001) giving a 3D hydrodynamic system or For example, vertebrates are less sensitive than GOTM (Burchard et al., 1999) which provides 1D, invertebrates. Such unequal effects on system compo- water column turbulence routines. All of these model nents may disrupt the functional balance of the codes have been used previously in combination ecosystem. (HALTAFALL and ERSEM (Blackford and Burkill, Evidently the marine ecosystem can function at a 2002); ERSEM and POLCOMS (for example, Allen et wide range of pH values but how it may function is al., 2001; Holt et al., 2005) and ERSEM and GOTM unknown. There are both positive and negative effects (Blackford et al., 2004; Allen et al., 2004). The and feedback mechanisms in response to high CO2, HALTAFALL-ERSEM-GOTM system provides a desk- but quantifying their spatio-temporal balance remains top simulation tool that integrates all but horizontal a huge task. Modelling approaches, as described here, transfers and boundaries. As well as being a develop- give us a possibility to investigate how changes in ment engine, this system provides a good approximation processes may interact and give us a predictive to areas away from direct coastal influence whose capability. Whilst the models depend entirely on a seasonal dynamics are derived from vertical processes good grounding in process understanding, it is also and exchange across the pelagic benthic interface. possible to simulate uncertainties and produce prob- HALTAFALL-ERSEM-POLCOMS is a parallelised abilistic information. Here a modelling approach is software tool that, for an annual cycle of the 3D SNS used to quantify the potential acidification of the domain, requires several hours computation on a multi- southern North Sea in response to atmospheric CO2 processor high-performance computer. levels as predicted by the IPCC (2001). We quantify four scenarios corresponding to atmospheric CO2 2.1. Carbonate system model levels of 375 ppm (~year 2000), 500 (~2050), 700 (~2100) and 1000 (the current worst case scenario for The carbonate system is modelled by software based 2100). Further, by including a detailed ecosystem on HALTAFALL (Ingri et al., 1967), which provides an model, the influence of the biota's production– iterative method to determine chemical speciation. In respiration balance on the CO2 content of the water this case the calculation is parameterised by total and hence pH can be accounted for. The model system inorganic carbon (CT, a state variable in the ERSEM will also form the basis of future work that attempts to ecosystem model) and total alkalinity (TA), which we integrate the effects of a wider range of pH modified parameterise from salinity fields. The products are processes. partial pressure of CO2 in the water, pH and the In this paper the aim is to quantify the seasonal and concentrations of the carbonate system components, − 2− spatial variability in pH due to primary production and H2CO3, HCO3 and CO3 . CT is initialised at a ballpark depletion of dissolved CO2; predict the change in pH value; it achieves its true (dis-) equilibrium value likely to occur in the next 100 years due to increased dynamically, via air–sea exchange, within the first few atmospheric CO2 loadings; and show that there is some weeks of the model's spin-up year. The parameterisation basis for expecting ecosystem consequences resulting of TA is problematic in this domain with its complex from acidification. We choose the inhibition of nitrifi- mix of water masses and significant riverine influences; cation, quantified by Huesemann et al. (2002), for no single empirical relationship with salinity is further investigation to see if significant biogeochemical appropriate. Data reported in Borges and Frankignoulle changes are possible in a shelf ecosystem within the next (1999) indicate an inverse relationship between salinity hundred years or so. and TA in the vicinity of the Schede and Belgian coast, 232 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 with TA values of ~2500 μmol/kg associated with reproduce the spatio-temporal variability and diatom salinities of ~30. Pätsch and Lenhart (2004) report TA specific nutrient control observed in the region. The concentrations in the major European mainland rivers model also allows for variable carbon to chlorophyll ranging from 2231 to 3832 μmol/kg with a median value ratios, depending on the light climate. Thus, the model of 2580. TA data from the CANOBA project (2001/ is well (but not perfectly) adapted to the complex 2002, Schiettecatte, personal communication) confirm optical properties of the North Sea. The pelagic aspects TA maxima associated with the mainland European of the ERSEM model as applied here are fully coast and a TA minima in the central North Sea with described in Blackford et al. (2004) and show some increasing values northward into the North Atlantic skill in reproducing regional observations (Allen et al., waters. Thus, we have used two regime-dependent 2006-this issue). Benthic ecosystem and chemistry are relationships to derive TA from salinity. For salinity described by Blackford (1997), Ebenhöh et al. (1995) >34.65, we use the relationship reported by Bellerby et and Ruardij and Van Raaphorst (1995). Whilst we do al. (2005) for North Atlantic waters, (TA=66.96S not analyse the plankton community composition −36.803); for salinity <34.65 we approximate from further in this paper, we justify the use of this Borges and Frankignoulle (1999) that an estuarine relatively complex ecosystem model in that it will salinity of 30.0 corresponds with a TA=2500 μmol/kg; give a more realistic representation of the production/ hence, TA=3887.0−46.25S. This creates a visual respiration budget and hence CO2 fluxes which impact accord with the CANOBA data; however, the large on the carbonate cycle, and that it is the basis for TA variability in low-salinity regions is not repre- future, more detailed process studies. sented. We have chosen to take this simplistic approach to the parameterisation of total alkalinity 2.3. Hydrodynamic models (TA) because firstly of the difficulty of calculating TA from concentrations of component ions in this complex The General Ocean Turbulence Model (GOTM; region and secondly because we have no data to Burchard et al., 1999) is a 1-D physical model system describe future TA loads (from rivers). These assump- that provides a menu of momentum and tracer equations tions should be borne in mind when interpreting the and turbulence parameterisations. Its setup and coupling model predictions. We use the sea water pH scale, with to ERSEM is described in Allen et al. (2004). Here we coefficients according to Weiss (1974), Dickson and have used it to simulate a central North Sea station (CS) Millero (1987), Hansson (1973) and Millero (1979). of 85 m depth, resolved at 5 m intervals (Fig. 3). Air–sea exchange of CO2 is calculated using the The POLCOMS hydrodynamic model is a three- parameterisation of Nightingale et al. (2000) acting on dimensional baroclinic circulation model in this case set the HALTAFALL derived partial pressure of CO2 in the up for the southern part of the North Sea, taking water (pCO2w) and the parameterised atmospheric boundary conditions from wider area versions of the concentration. same model. It is described in detail in Holt and James (2001) and reviewed with respect to performance in this 2.2. Ecosystem model region in Holt et al. (2005).

The European Regional Seas Ecosystem Model 2.4. Forcing (ERSEM) is a plankton functional type (PFT) model developed in the context of the North Sea but is now Dissolved inorganic carbon (DIC) concentrations finding wider application (Baretta et al., 1995; Black- for the main regional rivers (Fig. 3) are taken from ford et al., 2004). It resolves four phytoplankton Pätsch and Lenhart (2004). For rivers with no specific groups, three consumer groups, bacteria, four macro- data, we use the budget calculations in Thomas et al. nutrients, dissolved and particulate organics and (2005) to derive a DIC load. For the future scenarios, dissolved inorganics (Fig. 2). The model state variables we assume that riverine DIC is in equilibrium with the are defined by carbon, nitrogen, phosphorus and prescribed atmospheric conditions and scaled accord- silicon content, as appropriate, and not constrained ingly. Riverine nutrient and flow rates are taken from by Redfield ratios. Decoupling carbon and nutrients, various sources and are assumed not to change in the allowing for luxury uptake by phytoplankton, is an future scenarios. important quality of the model (Baretta-Bekker et al., Although the usual practice is to take boundary 1997), which, in combination with simulating N, P and conditions from wider area applications of the same Si as controlling nutrients, allows for the model to model system, in the absence of such simulations for the J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 233

Fig. 2. Schematic of the ERSEM model as applied in this study.

future scenarios, we use reflective boundaries throughout 3. Results and discussion this study. We prescribe atmospheric CO2 levels according to IPCC (2001) estimates, omitting the insignificant 3.1. General model validation seasonality signal for simplicity (Table 1). We keep the meteorological and climate forcing constant between runs, Detailed attempts to validate the ERSEM-POL- to concentrate on changes due to acidification only. COMS North Sea simulations show a generally good Simulations are spun up for two years before analysis, representation of the physical environment, skill in allowing the CO2 concentrations to equilibrate. resolving seasonal dynamics but a limited ability to resolve daily variability in harsh like-for-like comparisons 2.5. Nitrification parameterisation with data (Holt et al., 2005; Allen et al., 2006-this issue). In stratified and offshore regions the model The effect of pH on nitrification rate is parameterised generally reproduces the correct partitioning of from Huesemann et al. (2002) by a linear fit to biomass between functional units but tends to observations over the pH range simulated. We derive a underestimate the thermocline depth during summer. relative nitrification rate (RNR=0.61·pH−3.89), which In coastal environments the model does not produce modifies the nitrification rate parameter with variations completely the observed drawdown in nutrients during in pH (Fig. 4). the spring bloom, although chlorophyll levels are 234 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241

Fig. 3. The model domain showing latitude, longitude and depth contours. Modelled riverine inputs are indicated by name, the transect used for validation is shown by the dashed line. The position of station CS, referred to in the text, is indicated. broadly correct. It is assumed that the complex optical duces the mean of the data, the spatial trend and in properties of the strongly case II waters are not yet coastal regions, the range of the seasonal signal, well represented in the model. Despite some specific although the timing of the main productivity signal in drawbacks, the ERSEM-POLCOMS North Sea models coastal regions is incorrect (Fig. 5). This relates to the are generally considered fit-for-purpose and cited as poor representation of case II water optical properties, the best validated of all regional modelling attempts rather than a misrepresentation of the physiological (Moll and Radach, 2003). processes. Offshore the model seems to be under- estimating the range of the seasonal signal for these 3.2. Seasonal succession and pH variability particular stations in Waterbase, with a tendency not to simulate the minima observed, although an annual Fig. 5 presents a seasonal validation of modelled variability of ~0.4 units is represented in some pH against data available from the Dutch Waterbase offshore domains (Fig. 6). It is worth noting that database (www.waterbase.nl) for a transect stretching there is a considerable downward trend in pH as from the Dutch coast to the Dogger bank (see Fig. 3). recorded in Waterbase, over the period 1997–2004 of Because of the high variation, especially in peak about 0.02 pH units year− 1, about 6 times greater than timing, between years in the data and because predicted from increases in atmospheric pH over this ERSEM's skill lies in the seasonal rather than short- term prediction (Allen et al., 2006-this issue), we have calculated monthly mean, maximum and minimum pH values for the period 1997–2004. The model repro-

Table 1 Mean pH across the modelled domain for each scenario Atmospheric Approximate Mean Standard Difference from a CO2 (ppm) year pH deviation pre-industrial pH 375 2000 8.06 0.06 0.10 500 2050 7.95 0.06 0.21 700 2100 7.82 0.06 0.35 1000 2100-wcs 7.67 0.06 0.49 Fig. 4. Plot of the relative nitrification rate (RNR, from Huesemann et a The total change from pre-industrial marine pH is given assuming al.) against pH. Dashed line shows the linear interpolation used in the that there has been a 0.1 pH unit reduction between 1800 and 2000. model over the simulated pH range. (RNR=0.6111×pH−3.8889). J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 235

Fig. 5. Comparison of modelled pH values with measured data. The data is published on the Waterbase database (www.waterbase.nl) from a transect running from the Dutch coast to the Dogger bank. Distances are those from the Dutch coast. ‘Error’ bars indicate the maximum and minimum recorded values for each month between 1998 and 2004. 236 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241

Fig. 6. Map of the modelled annual pH range simulated across the southern North Sea domain. period. In our analysis we have de-trended the data (up to 0.15 units above background levels) tracks the around the year 2000 mean, consistent with our production maxima from the surface spring bloom and atmospheric CO2 parameterisation. The model results continuing along the deep chlorophyll layer throughout concur with ‘Ferrybox’ pH measurements from the summer. This coincides with the distribution of net Cuxhaven–Harwich transect (Wilhelm Petersen, per- positive pelagic community production and CO2 uptake sonal communication), which report a general back- (Fig. 7c). Secondly there is a pronounced pH minima (as ground pH above 8.0 with maxima over 8.6 associated much as 0.15 pH units below background levels) with regions of high chlorophyll and presumably associated with the deeper waters under the thermocline production. Ranges approaching and sometimes ex- that build up during the stratified period. Here benthic ceeding 1 pH unit are associated only with the major respiration and the subsequent diffusion of CO2 into the river plumes (Fig. 6), consistent with observations in pelagic are causing elevated CO2 levels that are trapped the region (from Waterbase) and observations of in the stratified system. The flux of CO2 across the air– similar systems (Hinga, 2002). It is suspected that, sea interface (Fig. 7a) is shown to be a net uptake during in the immediate vicinity of some of the major April and May, coincident with high primary produc- riverine inputs, the accuracy of the pH derivation is tion. During the summer months, the out-gassing is affected by the lack of accurate parameterisation of the driven by the net community respiration in the surface riverine chemistry, particularly the simplification that stratified layer. This reduces with the weakening of the total alkalinity is solely derived from salinity. thermocline and resultant increase in surface production Evidence (Borges and Frankignoulle, 1999; Pätsch during the autumn. A strong out-gassing signal is seen and Lenhart, 2004) suggests that, apart from between late in the year as the system finally overturns and the river variation, river plumes exhibit significant sea- benthic CO2 is exposed to the atmosphere. sonal trends. Offshore, away from riverine influence, the annual pH range is much reduced, generally 3.3. Acidification <0.4 pH units. A more detailed examination of the biologically Table 1 details the domain mean annual pH and mediated CO2 fluxes over a seasonal cycle sheds light standard deviation for each of the four scenarios on the natural controls of pH variability in the context of simulated. Clear reductions of pH exceeding 0.1 pH a stratified water column (Fig. 7). We choose station CS unit between scenarios contrast with standard deviations as it is away from direct riverine influence, stratifies of the order of 0.06, demonstrating that pH changes due strongly during the summer months and, in terms of its to significant atmospheric CO2 increase exceed the biogeochemistry, well validated. Two broad features, in seasonal variability. The model simulations suggest that terms of pH (Fig. 7b), are apparent. Firstly a pH maxima North Sea pH will be 0.2 pH units lower than pre- J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 237

−2 Fig. 7. The modelled variation of the water column at CS over a seasonal cycle. (a) Air–sea flux of CO2 in mg C m , negative values represent fluxes −3 into the water column. (b) Depth resolved pH evolution. (c) Primary production–pelagic community respiration in mg C m . (d) Out-gassing of CO2 from the benthos in mg C m−2. industrial by 2050 and may decrease by a further 0.13– by the distribution of modelled surface production. The 0.28 pH units by 2100, depending on emissions. These four scenarios suggest a consistent degree of acidifica- estimates are consistent with modelled oceanic acidifi- tion across the domain. This follows from two assump- cation rates (e.g., Caldeira and Wickett, 2003). tions made in the model, that atmospheric CO2 is The seasonality of surface pH within the four spatially homogeneous and that river DIC loads increase scenarios is illustrated in Fig. 8. Surface pH is near proportionately with future atmospheric CO2. The other uniform in winter with the system well mixed, contributory factor is that the model does not (yet) equilibrated and with little biological activity. The only represent any biological consequences of changing pH; perturbations are detected in the vicinity of riverine thus, the community production–respiration balance is inputs. During April and June the surface signal is driven identical in each simulation. 238 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241

Fig. 8. Monthly mean surface pH values for (left to right) January, April and June and (top to bottom) simulations of 2000 (atmospheric

CO2 =375 ppm), 2050 (500 ppm), 2100 (700 ppm) and the 2100 worst case scenario (1000 ppm).

The simulations suggest that by 2050 some areas of also a tendency for excessive diffusion and transport of the North Sea will be experiencing a pH range river plumes by the 3D hydrodynamics, arising from the completely distinct from current levels (Fig. 9a), 7 km horizontal resolution. Comparison with presumed although the majority of the region retains some degree pre-industrial pH levels (not shown) indicates that the of range overlap. By 2100 much of the region will have domain will have a mostly distinct range by 2050. a distinct range (Fig. 9b).Under an atmospheric CO2 concentration of 1000 ppm, the pH range for the 3.4. Nitrification majority of the southern North Sea will be completely distinct from the current pH range (Fig. 9c). Exceptions Fig. 10 shows the proportional change (S)oftheratioof are restricted to near-shore environments which are nitrate to nitrate+ammonium between the year 2000/ predominantly forced by riverine inputs and experience 375 ppm simulation and the year 2100 wcs/1000 ppm ranges of ~1.0 pH unit. The degree of overlap in these simulation, e.g., near-river zones is probably exaggerated for two reasons; the riverine biogeochemistry, particularly the =ð þ Þ− =ð þ Þ TA loadings, is not well represented and the river loads ¼ N1 N1 A1 N2 N2 A2 S =ð þ Þ have been kept constant between each scenario. There is N1 N1 A1 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 239

scenarios (due to lack of data), although in reality, under high CO2 conditions some change might be expected. Other studies show the potential for significant affects within decades at the ecosystem process level (e.g., Riebesell et al., 2000; Orr et al., 2005). That a measurable impact can be seen by a ~20% decrease in pelagic nitrification after a relatively short spin up in a system whose nutrient dynamics are largely mediated by river loads, transport and benthic recycling hints at acidification's potential to affect ecosystems. It has been − hypothesised that a reduction in nitrification and NO3 could lead to a substrate-based reduction in denitrification, decreasing volatile nitrogen emissions and leading to eutrophication. Indeed the model suggests that a 10% reduction in nitrification would lead to a similar reduction in denitrification, although the consequences due to this alone on an already eutrophic North Sea system would be negligible. However, emerging meso- cosm studies suggest that acidification may have complex direct and indirect effects on sediment denitrification, via species-specific effects on benthic fauna; Jacobson (2005) reports the likelihood of a ‘nontrivial’ transfer of ammonia from atmosphere to ocean under future high CO2 scenarios and currently our model does not represent the biological impact of changing nitrate to ammonium ratios in terms of physiological energetics and differential species effects. Hence, not only does it seem that high CO2 can have a complex effect on nitrogen cycling, but significant model development is required to quantify it.

4. Conclusions

The coupled complex ERSEM-POLCOMS-HALTA- FALL model system is demonstrated to have utility for investigating the integrated effect of biology, physics and external drivers on marine pH and the consequences of pH change. These initial studies identify the improved treatment of coastal processes: river loads, optical properties and TA parameterisation, as the key model refinements required. This study demonstrates Fig. 9. The proportion of overlap in pH ranges comparing the year the capacity of biological processes to influence pH and 2000/375 ppm CO simulation with (a) the year 2050/500 ppm CO 2 2 its range in the southern North Sea. Whilst this range can simulation, (b) the year 2100/700 ppm CO2 simulation and (c) the year 2100 wcs/1000 ppm CO2 simulation. be large it is shown that predicted CO2 emissions will provoke acidification that for the most part would create where N represents nitrate, A ammonium and the subscript a distinct pH profile from the pre-industrial baseline. It differentiates between simulations (1: 375 ppm, 2: is also shown that measurable biogeochemical con- 1000 ppm). The model predicts that a 5–10% difference sequences of pH reduction can be predicted in the in the ratio could be expected as atmospheric CO2 chemical speciation of the key limiting nutrient, nitrate. approaches 1000 ppm. The lowest response in the coastal However no claim can be made yet about the ecosystem waters is due to the masking of the nitrification signal by consequences of such a process effect. The model shows the riverine inputs which do not change between model that pH variability can only be understood in terms of a 240 J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241

Fig. 10. The percentage change in the ratio of nitrate to total nitrogen (nitrate+ammonium) in the water column between the year 2000/375 ppm CO2 simulation and the year 2100 wcs/1000 ppm CO2 simulation. system-wide understanding. The converse is likely; the Allen, J.I., Siddorn, J.R., Blackford, J.C., Gilbert, F.J., 2004. effect of pH change will only be predicted with any Turbulence as a control on the microbial loop in a temperate seasonally stratified marine ecosystem. Journal of Sea Research degree of certainty if an integration of individual process 52, 1–20. affects is considered. Allen, J.I., Somerfield, P.J., Gilbert, F.J., 2006-this issue. Quantifying This study is scratching at the surface of a complex uncertainty in high-resolution coupled hydronamic-ecosystem question; future work is planned to increase the domain models. Netherlands Journal of Sea Research 64, 3–14, of the model and improve open boundary conditions, doi:10.1016/j.jmarsys.2006.02.010. Baretta, J.W., Ebenhöh, W., Ruardij, P., 1995. The European regional include more of the affects of pH on individual seas ecosystem model, a complex marine ecosystem model. processes and to link the physical effects of climate Netherlands Journal of Sea Research 33, 233–246. change to those of acidification. This would be Baretta-Bekker, J., Baretta, J.W., Ebenhöh, W., 1997. Microbial facilitated by increased observations of basic carbonate dynamics in the Marine Ecosystem model ERSEM II with parameters across the region and high-frequency decoupled carbon assimilation and nutrient uptake. Journal of Sea Research 38 (3–4), 195–212. sampling at time series stations. Bellerby, R.G.J., Olsen, A., Furevik, T., Anderson, L.A., 2005.

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