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pH variability and CO2 induced acidification in the North Sea
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 Atmospheric levels of carbon dioxide have increased from pre-industrial levels of 280 ppm to around 380 ppm today as a consequence of human activities. Atmospheric CO2 is predicted to increase further to between 700 and 1000 ppm by the end of the century as fossil fuel reserves are consumed (IPCC, 2001; Caldeira and Wickett, 2003). A clear evidence-based scientific consensus has emerged that these increases are responsible for global warming (e.g., Hansen et al., 2005). Whilst the oceanic uptake of about 48% of the anthropogenic CO2 (Sabine et al., ⁎ Corresponding author. E-mail address: [email protected] (J.C. Blackford). 0924-7963/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2006.03.016
2004) has significantly buffered the rate of global warming, a secondary consequence, ocean acidification, a result of the dissociation of CO2 in solution, has recently emerged as a serious concern (e.g., Caldeira and Wickett, 2003; Raven et al., 2005). The chemistry and equilibria of the oceanic carbonate system are well known (e.g., Zeebe and WolfGladrow, 2001); thus, the rate of oceanic acidification is very predictable given atmospheric loadings. Caldeira and Wickett (2003) have shown that the oceans have already experienced a 0.1 pH unit reduction (a 30% increase in [H+]) since pre-industrial times and may experience a total reduction of over 0.7 pH units as fossil fuel reserves are depleted. The oceans are predicted to remain in this acidified state for hundreds if not thousands of years.
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Whilst it has been reported that ancient oceans experienced pH levels of about 0.6 units lower than today, proxy analysis suggests that oceanic pH has remained above 8.0 for over 20 million years (Pearson and Palmer, 2000). Much of the concern about presentday acidification relates to the rate at which it is predicted to occur, about 100 times faster than natural fluctuations over geological time scales. Whilst the oceanic sediments can buffer slow increases in CO2 this is not possible for the extreme rates of change currently seen, as oceanic mixing rates are too slow. It seems likely that human activity will provoke changes over a few hundred years that usually take millennia to occur (Fig. 1). A degree of natural spatial and temporal variability in marine pH is observed in today's oceans. On oceanic scales these are largely driven by temperature variation (higher temperatures lower CO2 solubility in the water and increase pH) and upwelling of cold deep water, supersaturated with respect to atmospheric CO2, which lowers pH. These factors are thought to give a global range of about 0.3 pH units (Skirrow, 1975). However, in eutrophic, highly productive shelf and coastal environments, the biological demands on dissolved CO2, coupled with the chemical dynamics of riverine inputs, combine to produce variations of as much as 1 pH unit (Hinga, 2002). In particular, the total dissolved inorganic carbon and total alkalinity of river systems are important drivers of coastal pH distributions. Thus, the consequences of acidification of shelf seas such as the North Sea may be modified by the high natural background variability of such a system. The impact of acidification on the marine ecosystem is not well known. A wide variety of ecosystem processes and species are thought to be vulnerable to changing pH. Whilst some progress has been made in investigating these in isolation, little is known about the net affect on the whole system (Riebesell, 2004). Useful
reviews of potential ecosystem effects include Turley et al. (2004), Raven et al. (2005) and Turley et al. (2006). Without reproducing the details contained in the above documents, some of the more important effects on systems such as the North Sea may be summarised as follows. Nutrient speciation is impacted by pH, for example, both the proportion of NH3 (to NH4+) and that of PO43− (to HPO42−) are very sensitive to small variations in pH around 8.0 (e.g., Zeebe and Wolf-Gladrow, 2001). There is evidence that pelagic nitrification rates are sensitive to [NH3] and hence pH. Huesemann et al. (2002) have shown that nitrification rates may decrease to zero at a pH around 6.0–6.5 as the NH3 substrate disappears from the system. Consequently, we may see increased ratios of NH4+ to NO3− with acidification. This has implications for the energetics of nitrogen acquisition by phytoplankton. Phytoplankton species differ in their response to pH. Hinga (2002) provides a review demonstrating that some coastal species are relatively tolerant to a wide range of pH, whilst some have very specific pH requirements which nevertheless are spread between pH 7.0–9.0. Species that utilise CO2 as their carbon source are likely to benefit from increased [CO2], whilst those that utilise the bicarbonate ion may be competitively disadvantaged. In terms of metabolism, diatom species are at or near saturation at present-day CO2 levels whilst coccolithophores are well below saturation and would presumably benefit from increased CO2 (Riebesell, 2004) . However, the ability of coccolithophores to calcify is significantly inhibited by the decreased carbonate saturation state (Riebesell et al., 2000) implying that calcifying strains of Emiliania huxleyi may be replaced by non-calcifying strains. If phytoplankton species composition is sensitive to perturbation, one may expect impacts on the grazer community and the higher trophic levels that have 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.
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may then expect decreased sequestration via sinking calcium carbonate liths. Conversely high [CO 2] enhances the release of dissolved organic carbon from phytoplankton cells (Riebesell, 2004); resulting aggregation (Engel et al., 2004) may increase the flux of carbon to the sediments. Decreased carbonate saturation states have been found to significantly increase mortality of settling bivalves (Green et al., 2004) which may affect cohort size. Pörtner et al. (2004) state that “Sensitivity to CO2 is hypothesized to be related to the organizational level of an animal, its energy requirements and mode of life”. For example, vertebrates are less sensitive than invertebrates. Such unequal effects on system components may disrupt the functional balance of the ecosystem. Evidently the marine ecosystem can function at a wide range of pH values but how it may function is unknown. There are both positive and negative effects and feedback mechanisms in response to high CO2, but quantifying their spatio-temporal balance remains a huge task. Modelling approaches, as described here, give us a possibility to investigate how changes in processes may interact and give us a predictive capability. Whilst the models depend entirely on a good grounding in process understanding, it is also possible to simulate uncertainties and produce probabilistic information. Here a modelling approach is used to quantify the potential acidification of the southern North Sea in response to atmospheric CO2 levels as predicted by the IPCC (2001). We quantify four scenarios corresponding to atmospheric CO2 levels of 375 ppm (~ year 2000), 500 (~ 2050), 700 (~ 2100) and 1000 (the current worst case scenario for 2100). Further, by including a detailed ecosystem model, the influence of the biota's production– respiration balance on the CO2 content of the water and hence pH can be accounted for. The model system will also form the basis of future work that attempts to integrate the effects of a wider range of pH modified processes. In this paper the aim is to quantify the seasonal and spatial variability in pH due to primary production and depletion of dissolved CO2; predict the change in pH likely to occur in the next 100 years due to increased atmospheric CO2 loadings; and show that there is some basis for expecting ecosystem consequences resulting from acidification. We choose the inhibition of nitrification, quantified by Huesemann et al. (2002), for further investigation to see if significant biogeochemical changes are possible in a shelf ecosystem within the next hundred years or so.
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The domain we explore is the southern part of the North Sea (below 56°N), an area showing significant nutrient enrichment and riverine influence. 2. The models The model system used here is a coupling involving four well-established model codes, covering the carbonate system (HALTAFALL; Ingri et al., 1967), the marine ecosystem (ERSEM; Baretta et al., 1995; Blackford et al., 2004) and either POLCOMS (Holt and James, 2001) giving a 3D hydrodynamic system or GOTM (Burchard et al., 1999) which provides 1D, water column turbulence routines. All of these model codes have been used previously in combination (HALTAFALL and ERSEM (Blackford and Burkill, 2002); ERSEM and POLCOMS (for example, Allen et al., 2001; Holt et al., 2005) and ERSEM and GOTM (Blackford et al., 2004; Allen et al., 2004). The HALTAFALL-ERSEM-GOTM system provides a desktop simulation tool that integrates all but horizontal transfers and boundaries. As well as being a development engine, this system provides a good approximation to areas away from direct coastal influence whose seasonal dynamics are derived from vertical processes and exchange across the pelagic benthic interface. HALTAFALL-ERSEM-POLCOMS is a parallelised software tool that, for an annual cycle of the 3D SNS domain, requires several hours computation on a multiprocessor high-performance computer. 2.1. Carbonate system model The carbonate system is modelled by software based on HALTAFALL (Ingri et al., 1967), which provides an iterative method to determine chemical speciation. In this case the calculation is parameterised by total inorganic carbon (CT, a state variable in the ERSEM ecosystem model) and total alkalinity (TA), which we parameterise from salinity fields. The products are partial pressure of CO2 in the water, pH and the concentrations of the carbonate system components, H2CO3, HCO3− and CO32−. CT is initialised at a ballpark value; it achieves its true (dis-) equilibrium value dynamically, via air–sea exchange, within the first few weeks of the model's spin-up year. The parameterisation of TA is problematic in this domain with its complex mix of water masses and significant riverine influences; no single empirical relationship with salinity is appropriate. Data reported in Borges and Frankignoulle (1999) indicate an inverse relationship between salinity and TA in the vicinity of the Schede and Belgian coast,
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with TA values of ~ 2500 μmol/kg associated with salinities of ~ 30. Pätsch and Lenhart (2004) report TA concentrations in the major European mainland rivers ranging from 2231 to 3832 μmol/kg with a median value of 2580. TA data from the CANOBA project (2001/ 2002, Schiettecatte, personal communication) confirm TA maxima associated with the mainland European coast and a TA minima in the central North Sea with increasing values northward into the North Atlantic waters. Thus, we have used two regime-dependent relationships to derive TA from salinity. For salinity >34.65, we use the relationship reported by Bellerby et al. (2005) for North Atlantic waters, (TA = 66.96S − 36.803); for salinity <34.65 we approximate from Borges and Frankignoulle (1999) that an estuarine salinity of 30.0 corresponds with a TA = 2500 μmol/kg; hence, TA = 3887.0 − 46.25S. This creates a visual accord with the CANOBA data; however, the large TA variability in low-salinity regions is not represented. We have chosen to take this simplistic approach to the parameterisation of total alkalinity (TA) because firstly of the difficulty of calculating TA from concentrations of component ions in this complex region and secondly because we have no data to describe future TA loads (from rivers). These assumptions should be borne in mind when interpreting the model predictions. We use the sea water pH scale, with coefficients according to Weiss (1974), Dickson and Millero (1987), Hansson (1973) and Millero (1979). Air–sea exchange of CO2 is calculated using the parameterisation of Nightingale et al. (2000) acting on the HALTAFALL derived partial pressure of CO2 in the water (pCO2w) and the parameterised atmospheric concentration. 2.2. Ecosystem model The European Regional Seas Ecosystem Model (ERSEM) is a plankton functional type (PFT) model developed in the context of the North Sea but is now finding wider application (Baretta et al., 1995; Blackford et al., 2004). It resolves four phytoplankton groups, three consumer groups, bacteria, four macronutrients, dissolved and particulate organics and dissolved inorganics (Fig. 2). The model state variables are defined by carbon, nitrogen, phosphorus and silicon content, as appropriate, and not constrained by Redfield ratios. Decoupling carbon and nutrients, allowing for luxury uptake by phytoplankton, is an important quality of the model (Baretta-Bekker et al., 1997), which, in combination with simulating N, P and Si as controlling nutrients, allows for the model to
reproduce the spatio-temporal variability and diatom specific nutrient control observed in the region. The model also allows for variable carbon to chlorophyll ratios, depending on the light climate. Thus, the model is well (but not perfectly) adapted to the complex optical properties of the North Sea. The pelagic aspects of the ERSEM model as applied here are fully described in Blackford et al. (2004) and show some skill in reproducing regional observations (Allen et al., 2006-this issue). Benthic ecosystem and chemistry are described by Blackford (1997), Ebenhöh et al. (1995) and Ruardij and Van Raaphorst (1995). Whilst we do not analyse the plankton community composition further in this paper, we justify the use of this relatively complex ecosystem model in that it will give a more realistic representation of the production/ respiration budget and hence CO2 fluxes which impact on the carbonate cycle, and that it is the basis for future, more detailed process studies. 2.3. Hydrodynamic models The General Ocean Turbulence Model (GOTM; Burchard et al., 1999) is a 1-D physical model system that provides a menu of momentum and tracer equations and turbulence parameterisations. Its setup and coupling to ERSEM is described in Allen et al. (2004). Here we have used it to simulate a central North Sea station (CS) of 85 m depth, resolved at 5 m intervals (Fig. 3). The POLCOMS hydrodynamic model is a threedimensional baroclinic circulation model in this case set up for the southern part of the North Sea, taking boundary conditions from wider area versions of the same model. It is described in detail in Holt and James (2001) and reviewed with respect to performance in this region in Holt et al. (2005). 2.4. Forcing Dissolved inorganic carbon (DIC) concentrations for the main regional rivers (Fig. 3) are taken from Pätsch and Lenhart (2004). For rivers with no specific data, we use the budget calculations in Thomas et al. (2005) to derive a DIC load. For the future scenarios, we assume that riverine DIC is in equilibrium with the prescribed atmospheric conditions and scaled accordingly. Riverine nutrient and flow rates are taken from various sources and are assumed not to change in the future scenarios. Although the usual practice is to take boundary conditions from wider area applications of the same model system, in the absence of such simulations for the
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Fig. 2. Schematic of the ERSEM model as applied in this study.
future scenarios, we use reflective boundaries throughout this study. We prescribe atmospheric CO2 levels according to IPCC (2001) estimates, omitting the insignificant seasonality signal for simplicity (Table 1). We keep the meteorological and climate forcing constant between runs, to concentrate on changes due to acidification only. Simulations are spun up for two years before analysis, allowing the CO2 concentrations to equilibrate. 2.5. Nitrification parameterisation The effect of pH on nitrification rate is parameterised from Huesemann et al. (2002) by a linear fit to observations over the pH range simulated. We derive a relative nitrification rate (RNR = 0.61 · pH − 3.89), which modifies the nitrification rate parameter with variations in pH (Fig. 4).
3. Results and discussion 3.1. General model validation Detailed attempts to validate the ERSEM-POLCOMS North Sea simulations show a generally good representation of the physical environment, skill in resolving seasonal dynamics but a limited ability to resolve daily variability in harsh like-for-like comparisons with data (Holt et al., 2005; Allen et al., 2006-this issue). In stratified and offshore regions the model generally reproduces the correct partitioning of biomass between functional units but tends to underestimate the thermocline depth during summer. In coastal environments the model does not produce completely the observed drawdown in nutrients during the spring bloom, although chlorophyll levels are
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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 properties of the strongly case II waters are not yet well represented in the model. Despite some specific drawbacks, the ERSEM-POLCOMS North Sea models are generally considered fit-for-purpose and cited as the best validated of all regional modelling attempts (Moll and Radach, 2003). 3.2. Seasonal succession and pH variability Fig. 5 presents a seasonal validation of modelled pH against data available from the Dutch Waterbase database (www.waterbase.nl) for a transect stretching from the Dutch coast to the Dogger bank (see Fig. 3). Because of the high variation, especially in peak timing, between years in the data and because ERSEM's skill lies in the seasonal rather than shortterm 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-
duces the mean of the data, the spatial trend and in coastal regions, the range of the seasonal signal, although the timing of the main productivity signal in coastal regions is incorrect (Fig. 5). This relates to the poor representation of case II water optical properties, rather than a misrepresentation of the physiological processes. Offshore the model seems to be underestimating the range of the seasonal signal for these particular stations in Waterbase, with a tendency not to simulate the minima observed, although an annual variability of ~ 0.4 units is represented in some offshore domains (Fig. 6). It is worth noting that there is a considerable downward trend in pH as recorded in Waterbase, over the period 1997–2004 of about 0.02 pH units year− 1, about 6 times greater than predicted from increases in atmospheric pH over this
Table 1 Mean pH across the modelled domain for each scenario Atmospheric CO2 (ppm)
Approximate year
Mean pH
Standard deviation
Difference from pre-industrial pHa
375 500 700 1000
2000 2050 2100 2100-wcs
8.06 7.95 7.82 7.67
0.06 0.06 0.06 0.06
0.10 0.21 0.35 0.49
a
The total change from pre-industrial marine pH is given assuming that there has been a 0.1 pH unit reduction between 1800 and 2000.
Fig. 4. Plot of the relative nitrification rate (RNR, from Huesemann et al.) against pH. Dashed line shows the linear interpolation used in the model over the simulated pH range. (RNR = 0.6111 × pH − 3.8889).
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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.
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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 around the year 2000 mean, consistent with our atmospheric CO2 parameterisation. The model results concur with ‘Ferrybox’ pH measurements from the Cuxhaven–Harwich transect (Wilhelm Petersen, personal communication), which report a general background pH above 8.0 with maxima over 8.6 associated with regions of high chlorophyll and presumably production. Ranges approaching and sometimes exceeding 1 pH unit are associated only with the major river plumes (Fig. 6), consistent with observations in the region (from Waterbase) and observations of similar systems (Hinga, 2002). It is suspected that, in the immediate vicinity of some of the major riverine inputs, the accuracy of the pH derivation is affected by the lack of accurate parameterisation of the riverine chemistry, particularly the simplification that total alkalinity is solely derived from salinity. Evidence (Borges and Frankignoulle, 1999; Pätsch and Lenhart, 2004) suggests that, apart from between river variation, river plumes exhibit significant seasonal trends. Offshore, away from riverine influence, the annual pH range is much reduced, generally <0.4 pH units. A more detailed examination of the biologically mediated CO2 fluxes over a seasonal cycle sheds light on the natural controls of pH variability in the context of a stratified water column (Fig. 7). We choose station CS as it is away from direct riverine influence, stratifies strongly during the summer months and, in terms of its biogeochemistry, well validated. Two broad features, in terms of pH (Fig. 7b), are apparent. Firstly a pH maxima
(up to 0.15 units above background levels) tracks the production maxima from the surface spring bloom and continuing along the deep chlorophyll layer throughout summer. This coincides with the distribution of net positive pelagic community production and CO2 uptake (Fig. 7c). Secondly there is a pronounced pH minima (as much as 0.15 pH units below background levels) associated with the deeper waters under the thermocline that build up during the stratified period. Here benthic respiration and the subsequent diffusion of CO2 into the pelagic are causing elevated CO2 levels that are trapped in the stratified system. The flux of CO2 across the air– sea interface (Fig. 7a) is shown to be a net uptake during April and May, coincident with high primary production. During the summer months, the out-gassing is driven by the net community respiration in the surface stratified layer. This reduces with the weakening of the thermocline and resultant increase in surface production during the autumn. A strong out-gassing signal is seen late in the year as the system finally overturns and the benthic CO2 is exposed to the atmosphere. 3.3. Acidification Table 1 details the domain mean annual pH and standard deviation for each of the four scenarios simulated. Clear reductions of pH exceeding 0.1 pH unit between scenarios contrast with standard deviations of the order of 0.06, demonstrating that pH changes due to significant atmospheric CO2 increase exceed the seasonal variability. The model simulations suggest that North Sea pH will be 0.2 pH units lower than pre-
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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−2, negative values represent fluxes into the water column. (b) Depth resolved pH evolution. (c) Primary production–pelagic community respiration in mg C m−3. (d) Out-gassing of CO2 from the benthos in mg C m−2.
industrial by 2050 and may decrease by a further 0.13– 0.28 pH units by 2100, depending on emissions. These estimates are consistent with modelled oceanic acidification rates (e.g., Caldeira and Wickett, 2003). The seasonality of surface pH within the four scenarios is illustrated in Fig. 8. Surface pH is near uniform in winter with the system well mixed, equilibrated and with little biological activity. The only perturbations are detected in the vicinity of riverine inputs. During April and June the surface signal is driven
by the distribution of modelled surface production. The four scenarios suggest a consistent degree of acidification across the domain. This follows from two assumptions made in the model, that atmospheric CO2 is spatially homogeneous and that river DIC loads increase proportionately with future atmospheric CO2. The other contributory factor is that the model does not (yet) represent any biological consequences of changing pH; thus, the community production–respiration balance is identical in each simulation.
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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 the North Sea will be experiencing a pH range completely distinct from current levels (Fig. 9a), although the majority of the region retains some degree of range overlap. By 2100 much of the region will have a distinct range (Fig. 9b).Under an atmospheric CO2 concentration of 1000 ppm, the pH range for the majority of the southern North Sea will be completely distinct from the current pH range (Fig. 9c). Exceptions are restricted to near-shore environments which are predominantly forced by riverine inputs and experience ranges of ~ 1.0 pH unit. The degree of overlap in these near-river zones is probably exaggerated for two reasons; the riverine biogeochemistry, particularly the TA loadings, is not well represented and the river loads have been kept constant between each scenario. There is
also a tendency for excessive diffusion and transport of river plumes by the 3D hydrodynamics, arising from the 7 km horizontal resolution. Comparison with presumed pre-industrial pH levels (not shown) indicates that the domain will have a mostly distinct range by 2050. 3.4. Nitrification Fig. 10 shows the proportional change (S) of the ratio of nitrate to nitrate + ammonium between the year 2000/ 375 ppm simulation and the year 2100 wcs/1000 ppm simulation, e.g.,
S¼
N1 =ðN1 þ A1 Þ−N2 =ðN2 þ A2 Þ N1 =ðN1 þ A1 Þ
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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 mesocosm 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
Fig. 9. The proportion of overlap in pH ranges comparing the year 2000/375 ppm CO2 simulation with (a) the year 2050/500 ppm CO2 simulation, (b) the year 2100/700 ppm CO2 simulation and (c) the year 2100 wcs/1000 ppm CO2 simulation.
where N represents nitrate, A ammonium and the subscript differentiates between simulations (1: 375 ppm, 2: 1000 ppm). The model predicts that a 5–10% difference in the ratio could be expected as atmospheric CO2 approaches 1000 ppm. The lowest response in the coastal waters is due to the masking of the nitrification signal by the riverine inputs which do not change between model
The coupled complex ERSEM-POLCOMS-HALTAFALL 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 the capacity of biological processes to influence pH and its range in the southern North Sea. Whilst this range can be large it is shown that predicted CO2 emissions will provoke acidification that for the most part would create a distinct pH profile from the pre-industrial baseline. It is also shown that measurable biogeochemical consequences of pH reduction can be predicted in the chemical speciation of the key limiting nutrient, nitrate. However no claim can be made yet about the ecosystem consequences of such a process effect. The model shows that pH variability can only be understood in terms of a
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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 effect of pH change will only be predicted with any degree of certainty if an integration of individual process affects is considered. This study is scratching at the surface of a complex question; future work is planned to increase the domain of the model and improve open boundary conditions, include more of the affects of pH on individual processes and to link the physical effects of climate change to those of acidification. This would be facilitated by increased observations of basic carbonate parameters across the region and high-frequency sampling at time series stations. Acknowledgements This work was part funded by the UK Government Department of the Environment, Food and Rural Affairs, the Department of Trade and Industry and by the Plymouth Marine Laboratory Core Research Programme funded by the Natural Environment Research Council. The authors thank Laure-Sophie Schiettecatte of the University of Liège and Dr. Wilhelm Petersen of the GKSS Research Centre, Germany, for providing useful observational data and the referees for their helpful comments. References Allen, J.I., Blackford, J.C., Ashworth, M.I., Proctor, R., Holt, J.T., Siddorn, J.R., 2001. A highly spatially resolved ecosystem model for the north-west European continental shelf. Sarsia 86, 423–440.
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J.C. Blackford, F.J. Gilbert / Journal of Marine Systems 64 (2007) 229–241 Caldeira, K., Wickett, M.E., 2003. Anthropogenic carbon and ocean pH. Nature 425, 365. Dickson, A.G., Millero, F.J., 1987. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep Sea Research 34, 1733–1743. Ebenhöh, W., Kohlmeier, C., Radford, P.J., 1995. The benthic biological model in the European regional seas ecosystem model. Netherlands Journal of Sea Research 33, 423–452. Engel, A., Thoms, S., Riebesell, U., Rochelle-Newall, E., Zondervan, I., 2004. Polysaccharide aggregation as a potential sink for marine dissolved organic carbon. Nature 428, 929–931. Green, M.A., Jones, M.E., Boudreau, C.L., Moore, R.L., Westman, B.A., 2004. Dissolution mortality of juvenile bivalves in coastal marine deposits. Limnology and Oceanography 49 (3), 727–734. Hansson, I., 1973. A new set of acidity constants for carbonic acid and boric acid in sea water. Deep Sea Research 20, 461–478. Hansen, J., Nazarenko, L., Ruedy, R., Sato, M., Willis, J., Del Genio, A., Koch, D., Lacis, A., Lo, K., Menon, S., Novakov, T., Perlwitz, J., Russell, G., Schmidt, G.A., Tausnev, N., 2005. Earth's energy imbalance: confirmation and implications. Science 308 (5727), 1431–1435. Hinga, K.R., 2002. Effects of pH on coastal marine phytoplankton. Marine Ecology Progress Series 238, 281–300. Holt, J.T., James, I.D., 2001. An s-coordinate model of the North West European Continental Shelf: Part 1. Model description and density structure. J. Geophys. Res. 106 (C7), 14015–14034. Holt, J.T., Allen, J.I., Proctor, R., Gilbert, F.G., 2005. Error quantification of a high resolution coupled hydrodynamicecosystem coastal ocean model: Part 1. Model overview and hydrodynamics. Journal of Marine Systems 57, 167–188. Huesemann, M.H., Skillman, A.D., Creclius, E.A., 2002. The inhibition of marine nitrification by ocean disposal of carbon dioxide. Marine Pollution Bulletin 44 (2), 142–148. Ingri, N., Kakolowicz, W., Sillén, L.G., Warnqvist, B., 1967. Highspeed computere as a supplement to graphical methods: V. HALTAFALL, a general program for calculating the composition of equilibrium mixtures. Talanta 14, 1261. IPCC, 2001. Climate change 2001: the scientific basis. In: Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (Eds.), Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press Cambridge, United Kingdom. 881 pp. Jacobson, M.Z., 2005. Studying ocean acidification with conservative, stable numerical schemes for nonequilibrium air–ocean exchange and ocean equilibrium chemistry. Journal of Geophysical Research 110, D07302. Millero, F.J., 1979. The thermodynamics of the carbonate system in seawater. Geochimica et Cosmochimica Acta 43, 1651–1661. Moll, A., Radach, G., 2003. Review of three-dimensional ecological modelling related to the North Sea shelf system: Part 1. Models and their results. Progress in Oceanography 57 (2), 175–217. Nightingale, P.D., Malin, G., Law, C.S., Watson, A.J., Liss, P.S., Liddicoat, M.I., Boutin, J., Upstill-Goddard, R.C., 2000. In situ evaluation of air–sea gas exchange parameterizations using novel conservative and volatile tracers. Global Biogeochemical Cycles 14, 373–388. Orr, J.C., Fabry, V.J., Aumont, O., Bopp, L., Doney, S.C., Feely, R.A., Gnanadesikan, A., Gruber, N., Ishida, A., Joos, F., Key, R.M.,
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Lindsay, K., Maier-Reimer, E., Matear, R., Monfray, P., Mouchet, A., Najjar, R.G., Plattner, G.-K., Rodgers, K.B., Sabine, C.L., Sarmiento, L.J., Schlitzer, R., Slater, R.D., Totterdell, I.J., Weirig, M.-F., Yamanaka, Y., Yool, A., 2005. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686. Pätsch, J., Lenhart, H.J., 2004. Daily loads of nutrients, total alkalinity, dissolved inorganic carbon and dissolved organic carbon of the European continental rivers for the years 1977–2002. Berichte aus dem Zentrum für Meeres-und Klimaforschung, Reihe B: Ozeanographie. Pearson, P.N., Palmer, M.R., 2000. Atmospheric carbon dioxide concentrations over the past 60 millions years. Nature 6797, 695–699. Pörtner, H.O., Langenbuch, M., Reipschlager, A., 2004. Biological impact of elevated ocean CO2 concentrations: lessons from animal physiology and Earth history. Journal of Oceanography 60 (4), 705–718. Raven, J., et al., 2005. Ocean Acidification Due to Increasing Atmospheric Carbon Dioxide. The Royal Society0 85403 617 2. Riebesell, U., 2004. Effects of CO2 enrichment on marine phytoplankton. Journal of Oceanography 60, 719–729. Riebesell, U., Zondervan, I., Rost, B., Tortell, P.D., Zeebe, R.E., Morel, F.M.M., 2000. Reduced calcification of marine plankton in response to increased atmospheric CO2. Nature 407 (6802), 364–367. Ruardij, P., Van Raaphorst, W., 1995. Benthic nutrient regeneration in the ERSEM ecosystem model of the North Sea. Netherlands Journal of Sea Research 33 (3–4), 453–483. Sabine, C.L., Feely, R.A., Gruber, N., Key, R.M., Lee, K., Bullister, J.L., Wanninkhof, R., Wong, C.S., Wallace, D.W.R., Tilbrook, B., Millero, F.J., Peng, T.H., Kozyr, A., Ono, T., Rios, A.F., 2004. The oceanic sink for anthropogenic CO2. Science 305, 367–371. Skirrow, G., 1975. The dissolved gases—carbon dioxide. In: Riley, J.P., Skirrow, G. (Eds.), Chemical Oceanography. Academic Press New York. Thomas, H., Bozec, Y., de Baar, H.J.W., Elkalay, K., Frankignoulle, M., Schiettecatte, L.-S., Kattner, G., Borges, A.V., 2005. The carbon budget of the North Sea. Biogeosciences 2, 87–96. Turley, C., Nightingale, P., Riley, N., Widdicombe, S., Joint, I., Gallienne, C., Lowe, D., Goldson, L., Beaumont, N., Mariotte, P., Groom, S., Smerdon, G., Rees, A., Blackford, J., Owens, N., West, J Land, P., Woodason, E., 2004. Literature Review: Environmental impacts of a gradual or catastrophic release of CO2 into the marine environment following carbon dioxide capture. (DEFRA: MARP 30 (ME2104) 31 March 2004. Turley, C., Blackford, J.C., Widdicombe, S., Lowe, D., Nightingale, P.D., 2006. Reviewing the impact of increased atmospheric CO2 on oceanic pH and the marine ecosystem. In: Schellnhuber, H., Cramer, J., Nakicenovic, W., Wigley, N., Yohe, T. (Eds.), Avoiding Dangerous Climate Change. InCambridge University Press. Weiss, R.F., 1974. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Marine Chemistry 2, 203–215. Zeebe, R.E., Wolf-Gladrow, D.A., 2001. CO2 in seawater: equilibrium, kinetics and isotopes. Elsevier Oceanography Series 65, 346.
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 Atmospheric levels of carbon dioxide have increased from pre-industrial levels of 280 ppm to around 380 ppm today as a consequence of human activities. Atmospheric CO2 is predicted to increase further to between 700 and 1000 ppm by the end of the century as fossil fuel reserves are consumed (IPCC, 2001; Caldeira and Wickett, 2003). A clear evidence-based scientific consensus has emerged that these increases are responsible for global warming (e.g., Hansen et al., 2005). Whilst the oceanic uptake of about 48% of the anthropogenic CO2 (Sabine et al., ⁎ Corresponding author. E-mail address: [email protected] (J.C. Blackford). 0924-7963/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2006.03.016
2004) has significantly buffered the rate of global warming, a secondary consequence, ocean acidification, a result of the dissociation of CO2 in solution, has recently emerged as a serious concern (e.g., Caldeira and Wickett, 2003; Raven et al., 2005). The chemistry and equilibria of the oceanic carbonate system are well known (e.g., Zeebe and WolfGladrow, 2001); thus, the rate of oceanic acidification is very predictable given atmospheric loadings. Caldeira and Wickett (2003) have shown that the oceans have already experienced a 0.1 pH unit reduction (a 30% increase in [H+]) since pre-industrial times and may experience a total reduction of over 0.7 pH units as fossil fuel reserves are depleted. The oceans are predicted to remain in this acidified state for hundreds if not thousands of years.
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Whilst it has been reported that ancient oceans experienced pH levels of about 0.6 units lower than today, proxy analysis suggests that oceanic pH has remained above 8.0 for over 20 million years (Pearson and Palmer, 2000). Much of the concern about presentday acidification relates to the rate at which it is predicted to occur, about 100 times faster than natural fluctuations over geological time scales. Whilst the oceanic sediments can buffer slow increases in CO2 this is not possible for the extreme rates of change currently seen, as oceanic mixing rates are too slow. It seems likely that human activity will provoke changes over a few hundred years that usually take millennia to occur (Fig. 1). A degree of natural spatial and temporal variability in marine pH is observed in today's oceans. On oceanic scales these are largely driven by temperature variation (higher temperatures lower CO2 solubility in the water and increase pH) and upwelling of cold deep water, supersaturated with respect to atmospheric CO2, which lowers pH. These factors are thought to give a global range of about 0.3 pH units (Skirrow, 1975). However, in eutrophic, highly productive shelf and coastal environments, the biological demands on dissolved CO2, coupled with the chemical dynamics of riverine inputs, combine to produce variations of as much as 1 pH unit (Hinga, 2002). In particular, the total dissolved inorganic carbon and total alkalinity of river systems are important drivers of coastal pH distributions. Thus, the consequences of acidification of shelf seas such as the North Sea may be modified by the high natural background variability of such a system. The impact of acidification on the marine ecosystem is not well known. A wide variety of ecosystem processes and species are thought to be vulnerable to changing pH. Whilst some progress has been made in investigating these in isolation, little is known about the net affect on the whole system (Riebesell, 2004). Useful
reviews of potential ecosystem effects include Turley et al. (2004), Raven et al. (2005) and Turley et al. (2006). Without reproducing the details contained in the above documents, some of the more important effects on systems such as the North Sea may be summarised as follows. Nutrient speciation is impacted by pH, for example, both the proportion of NH3 (to NH4+) and that of PO43− (to HPO42−) are very sensitive to small variations in pH around 8.0 (e.g., Zeebe and Wolf-Gladrow, 2001). There is evidence that pelagic nitrification rates are sensitive to [NH3] and hence pH. Huesemann et al. (2002) have shown that nitrification rates may decrease to zero at a pH around 6.0–6.5 as the NH3 substrate disappears from the system. Consequently, we may see increased ratios of NH4+ to NO3− with acidification. This has implications for the energetics of nitrogen acquisition by phytoplankton. Phytoplankton species differ in their response to pH. Hinga (2002) provides a review demonstrating that some coastal species are relatively tolerant to a wide range of pH, whilst some have very specific pH requirements which nevertheless are spread between pH 7.0–9.0. Species that utilise CO2 as their carbon source are likely to benefit from increased [CO2], whilst those that utilise the bicarbonate ion may be competitively disadvantaged. In terms of metabolism, diatom species are at or near saturation at present-day CO2 levels whilst coccolithophores are well below saturation and would presumably benefit from increased CO2 (Riebesell, 2004) . However, the ability of coccolithophores to calcify is significantly inhibited by the decreased carbonate saturation state (Riebesell et al., 2000) implying that calcifying strains of Emiliania huxleyi may be replaced by non-calcifying strains. If phytoplankton species composition is sensitive to perturbation, one may expect impacts on the grazer community and the higher trophic levels that have 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.
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may then expect decreased sequestration via sinking calcium carbonate liths. Conversely high [CO 2] enhances the release of dissolved organic carbon from phytoplankton cells (Riebesell, 2004); resulting aggregation (Engel et al., 2004) may increase the flux of carbon to the sediments. Decreased carbonate saturation states have been found to significantly increase mortality of settling bivalves (Green et al., 2004) which may affect cohort size. Pörtner et al. (2004) state that “Sensitivity to CO2 is hypothesized to be related to the organizational level of an animal, its energy requirements and mode of life”. For example, vertebrates are less sensitive than invertebrates. Such unequal effects on system components may disrupt the functional balance of the ecosystem. Evidently the marine ecosystem can function at a wide range of pH values but how it may function is unknown. There are both positive and negative effects and feedback mechanisms in response to high CO2, but quantifying their spatio-temporal balance remains a huge task. Modelling approaches, as described here, give us a possibility to investigate how changes in processes may interact and give us a predictive capability. Whilst the models depend entirely on a good grounding in process understanding, it is also possible to simulate uncertainties and produce probabilistic information. Here a modelling approach is used to quantify the potential acidification of the southern North Sea in response to atmospheric CO2 levels as predicted by the IPCC (2001). We quantify four scenarios corresponding to atmospheric CO2 levels of 375 ppm (~ year 2000), 500 (~ 2050), 700 (~ 2100) and 1000 (the current worst case scenario for 2100). Further, by including a detailed ecosystem model, the influence of the biota's production– respiration balance on the CO2 content of the water and hence pH can be accounted for. The model system will also form the basis of future work that attempts to integrate the effects of a wider range of pH modified processes. In this paper the aim is to quantify the seasonal and spatial variability in pH due to primary production and depletion of dissolved CO2; predict the change in pH likely to occur in the next 100 years due to increased atmospheric CO2 loadings; and show that there is some basis for expecting ecosystem consequences resulting from acidification. We choose the inhibition of nitrification, quantified by Huesemann et al. (2002), for further investigation to see if significant biogeochemical changes are possible in a shelf ecosystem within the next hundred years or so.
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The domain we explore is the southern part of the North Sea (below 56°N), an area showing significant nutrient enrichment and riverine influence. 2. The models The model system used here is a coupling involving four well-established model codes, covering the carbonate system (HALTAFALL; Ingri et al., 1967), the marine ecosystem (ERSEM; Baretta et al., 1995; Blackford et al., 2004) and either POLCOMS (Holt and James, 2001) giving a 3D hydrodynamic system or GOTM (Burchard et al., 1999) which provides 1D, water column turbulence routines. All of these model codes have been used previously in combination (HALTAFALL and ERSEM (Blackford and Burkill, 2002); ERSEM and POLCOMS (for example, Allen et al., 2001; Holt et al., 2005) and ERSEM and GOTM (Blackford et al., 2004; Allen et al., 2004). The HALTAFALL-ERSEM-GOTM system provides a desktop simulation tool that integrates all but horizontal transfers and boundaries. As well as being a development engine, this system provides a good approximation to areas away from direct coastal influence whose seasonal dynamics are derived from vertical processes and exchange across the pelagic benthic interface. HALTAFALL-ERSEM-POLCOMS is a parallelised software tool that, for an annual cycle of the 3D SNS domain, requires several hours computation on a multiprocessor high-performance computer. 2.1. Carbonate system model The carbonate system is modelled by software based on HALTAFALL (Ingri et al., 1967), which provides an iterative method to determine chemical speciation. In this case the calculation is parameterised by total inorganic carbon (CT, a state variable in the ERSEM ecosystem model) and total alkalinity (TA), which we parameterise from salinity fields. The products are partial pressure of CO2 in the water, pH and the concentrations of the carbonate system components, H2CO3, HCO3− and CO32−. CT is initialised at a ballpark value; it achieves its true (dis-) equilibrium value dynamically, via air–sea exchange, within the first few weeks of the model's spin-up year. The parameterisation of TA is problematic in this domain with its complex mix of water masses and significant riverine influences; no single empirical relationship with salinity is appropriate. Data reported in Borges and Frankignoulle (1999) indicate an inverse relationship between salinity and TA in the vicinity of the Schede and Belgian coast,
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with TA values of ~ 2500 μmol/kg associated with salinities of ~ 30. Pätsch and Lenhart (2004) report TA concentrations in the major European mainland rivers ranging from 2231 to 3832 μmol/kg with a median value of 2580. TA data from the CANOBA project (2001/ 2002, Schiettecatte, personal communication) confirm TA maxima associated with the mainland European coast and a TA minima in the central North Sea with increasing values northward into the North Atlantic waters. Thus, we have used two regime-dependent relationships to derive TA from salinity. For salinity >34.65, we use the relationship reported by Bellerby et al. (2005) for North Atlantic waters, (TA = 66.96S − 36.803); for salinity <34.65 we approximate from Borges and Frankignoulle (1999) that an estuarine salinity of 30.0 corresponds with a TA = 2500 μmol/kg; hence, TA = 3887.0 − 46.25S. This creates a visual accord with the CANOBA data; however, the large TA variability in low-salinity regions is not represented. We have chosen to take this simplistic approach to the parameterisation of total alkalinity (TA) because firstly of the difficulty of calculating TA from concentrations of component ions in this complex region and secondly because we have no data to describe future TA loads (from rivers). These assumptions should be borne in mind when interpreting the model predictions. We use the sea water pH scale, with coefficients according to Weiss (1974), Dickson and Millero (1987), Hansson (1973) and Millero (1979). Air–sea exchange of CO2 is calculated using the parameterisation of Nightingale et al. (2000) acting on the HALTAFALL derived partial pressure of CO2 in the water (pCO2w) and the parameterised atmospheric concentration. 2.2. Ecosystem model The European Regional Seas Ecosystem Model (ERSEM) is a plankton functional type (PFT) model developed in the context of the North Sea but is now finding wider application (Baretta et al., 1995; Blackford et al., 2004). It resolves four phytoplankton groups, three consumer groups, bacteria, four macronutrients, dissolved and particulate organics and dissolved inorganics (Fig. 2). The model state variables are defined by carbon, nitrogen, phosphorus and silicon content, as appropriate, and not constrained by Redfield ratios. Decoupling carbon and nutrients, allowing for luxury uptake by phytoplankton, is an important quality of the model (Baretta-Bekker et al., 1997), which, in combination with simulating N, P and Si as controlling nutrients, allows for the model to
reproduce the spatio-temporal variability and diatom specific nutrient control observed in the region. The model also allows for variable carbon to chlorophyll ratios, depending on the light climate. Thus, the model is well (but not perfectly) adapted to the complex optical properties of the North Sea. The pelagic aspects of the ERSEM model as applied here are fully described in Blackford et al. (2004) and show some skill in reproducing regional observations (Allen et al., 2006-this issue). Benthic ecosystem and chemistry are described by Blackford (1997), Ebenhöh et al. (1995) and Ruardij and Van Raaphorst (1995). Whilst we do not analyse the plankton community composition further in this paper, we justify the use of this relatively complex ecosystem model in that it will give a more realistic representation of the production/ respiration budget and hence CO2 fluxes which impact on the carbonate cycle, and that it is the basis for future, more detailed process studies. 2.3. Hydrodynamic models The General Ocean Turbulence Model (GOTM; Burchard et al., 1999) is a 1-D physical model system that provides a menu of momentum and tracer equations and turbulence parameterisations. Its setup and coupling to ERSEM is described in Allen et al. (2004). Here we have used it to simulate a central North Sea station (CS) of 85 m depth, resolved at 5 m intervals (Fig. 3). The POLCOMS hydrodynamic model is a threedimensional baroclinic circulation model in this case set up for the southern part of the North Sea, taking boundary conditions from wider area versions of the same model. It is described in detail in Holt and James (2001) and reviewed with respect to performance in this region in Holt et al. (2005). 2.4. Forcing Dissolved inorganic carbon (DIC) concentrations for the main regional rivers (Fig. 3) are taken from Pätsch and Lenhart (2004). For rivers with no specific data, we use the budget calculations in Thomas et al. (2005) to derive a DIC load. For the future scenarios, we assume that riverine DIC is in equilibrium with the prescribed atmospheric conditions and scaled accordingly. Riverine nutrient and flow rates are taken from various sources and are assumed not to change in the future scenarios. Although the usual practice is to take boundary conditions from wider area applications of the same model system, in the absence of such simulations for the
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Fig. 2. Schematic of the ERSEM model as applied in this study.
future scenarios, we use reflective boundaries throughout this study. We prescribe atmospheric CO2 levels according to IPCC (2001) estimates, omitting the insignificant seasonality signal for simplicity (Table 1). We keep the meteorological and climate forcing constant between runs, to concentrate on changes due to acidification only. Simulations are spun up for two years before analysis, allowing the CO2 concentrations to equilibrate. 2.5. Nitrification parameterisation The effect of pH on nitrification rate is parameterised from Huesemann et al. (2002) by a linear fit to observations over the pH range simulated. We derive a relative nitrification rate (RNR = 0.61 · pH − 3.89), which modifies the nitrification rate parameter with variations in pH (Fig. 4).
3. Results and discussion 3.1. General model validation Detailed attempts to validate the ERSEM-POLCOMS North Sea simulations show a generally good representation of the physical environment, skill in resolving seasonal dynamics but a limited ability to resolve daily variability in harsh like-for-like comparisons with data (Holt et al., 2005; Allen et al., 2006-this issue). In stratified and offshore regions the model generally reproduces the correct partitioning of biomass between functional units but tends to underestimate the thermocline depth during summer. In coastal environments the model does not produce completely the observed drawdown in nutrients during the spring bloom, although chlorophyll levels are
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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 properties of the strongly case II waters are not yet well represented in the model. Despite some specific drawbacks, the ERSEM-POLCOMS North Sea models are generally considered fit-for-purpose and cited as the best validated of all regional modelling attempts (Moll and Radach, 2003). 3.2. Seasonal succession and pH variability Fig. 5 presents a seasonal validation of modelled pH against data available from the Dutch Waterbase database (www.waterbase.nl) for a transect stretching from the Dutch coast to the Dogger bank (see Fig. 3). Because of the high variation, especially in peak timing, between years in the data and because ERSEM's skill lies in the seasonal rather than shortterm 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-
duces the mean of the data, the spatial trend and in coastal regions, the range of the seasonal signal, although the timing of the main productivity signal in coastal regions is incorrect (Fig. 5). This relates to the poor representation of case II water optical properties, rather than a misrepresentation of the physiological processes. Offshore the model seems to be underestimating the range of the seasonal signal for these particular stations in Waterbase, with a tendency not to simulate the minima observed, although an annual variability of ~ 0.4 units is represented in some offshore domains (Fig. 6). It is worth noting that there is a considerable downward trend in pH as recorded in Waterbase, over the period 1997–2004 of about 0.02 pH units year− 1, about 6 times greater than predicted from increases in atmospheric pH over this
Table 1 Mean pH across the modelled domain for each scenario Atmospheric CO2 (ppm)
Approximate year
Mean pH
Standard deviation
Difference from pre-industrial pHa
375 500 700 1000
2000 2050 2100 2100-wcs
8.06 7.95 7.82 7.67
0.06 0.06 0.06 0.06
0.10 0.21 0.35 0.49
a
The total change from pre-industrial marine pH is given assuming that there has been a 0.1 pH unit reduction between 1800 and 2000.
Fig. 4. Plot of the relative nitrification rate (RNR, from Huesemann et al.) against pH. Dashed line shows the linear interpolation used in the model over the simulated pH range. (RNR = 0.6111 × pH − 3.8889).
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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.
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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 around the year 2000 mean, consistent with our atmospheric CO2 parameterisation. The model results concur with ‘Ferrybox’ pH measurements from the Cuxhaven–Harwich transect (Wilhelm Petersen, personal communication), which report a general background pH above 8.0 with maxima over 8.6 associated with regions of high chlorophyll and presumably production. Ranges approaching and sometimes exceeding 1 pH unit are associated only with the major river plumes (Fig. 6), consistent with observations in the region (from Waterbase) and observations of similar systems (Hinga, 2002). It is suspected that, in the immediate vicinity of some of the major riverine inputs, the accuracy of the pH derivation is affected by the lack of accurate parameterisation of the riverine chemistry, particularly the simplification that total alkalinity is solely derived from salinity. Evidence (Borges and Frankignoulle, 1999; Pätsch and Lenhart, 2004) suggests that, apart from between river variation, river plumes exhibit significant seasonal trends. Offshore, away from riverine influence, the annual pH range is much reduced, generally <0.4 pH units. A more detailed examination of the biologically mediated CO2 fluxes over a seasonal cycle sheds light on the natural controls of pH variability in the context of a stratified water column (Fig. 7). We choose station CS as it is away from direct riverine influence, stratifies strongly during the summer months and, in terms of its biogeochemistry, well validated. Two broad features, in terms of pH (Fig. 7b), are apparent. Firstly a pH maxima
(up to 0.15 units above background levels) tracks the production maxima from the surface spring bloom and continuing along the deep chlorophyll layer throughout summer. This coincides with the distribution of net positive pelagic community production and CO2 uptake (Fig. 7c). Secondly there is a pronounced pH minima (as much as 0.15 pH units below background levels) associated with the deeper waters under the thermocline that build up during the stratified period. Here benthic respiration and the subsequent diffusion of CO2 into the pelagic are causing elevated CO2 levels that are trapped in the stratified system. The flux of CO2 across the air– sea interface (Fig. 7a) is shown to be a net uptake during April and May, coincident with high primary production. During the summer months, the out-gassing is driven by the net community respiration in the surface stratified layer. This reduces with the weakening of the thermocline and resultant increase in surface production during the autumn. A strong out-gassing signal is seen late in the year as the system finally overturns and the benthic CO2 is exposed to the atmosphere. 3.3. Acidification Table 1 details the domain mean annual pH and standard deviation for each of the four scenarios simulated. Clear reductions of pH exceeding 0.1 pH unit between scenarios contrast with standard deviations of the order of 0.06, demonstrating that pH changes due to significant atmospheric CO2 increase exceed the seasonal variability. The model simulations suggest that North Sea pH will be 0.2 pH units lower than pre-
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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−2, negative values represent fluxes into the water column. (b) Depth resolved pH evolution. (c) Primary production–pelagic community respiration in mg C m−3. (d) Out-gassing of CO2 from the benthos in mg C m−2.
industrial by 2050 and may decrease by a further 0.13– 0.28 pH units by 2100, depending on emissions. These estimates are consistent with modelled oceanic acidification rates (e.g., Caldeira and Wickett, 2003). The seasonality of surface pH within the four scenarios is illustrated in Fig. 8. Surface pH is near uniform in winter with the system well mixed, equilibrated and with little biological activity. The only perturbations are detected in the vicinity of riverine inputs. During April and June the surface signal is driven
by the distribution of modelled surface production. The four scenarios suggest a consistent degree of acidification across the domain. This follows from two assumptions made in the model, that atmospheric CO2 is spatially homogeneous and that river DIC loads increase proportionately with future atmospheric CO2. The other contributory factor is that the model does not (yet) represent any biological consequences of changing pH; thus, the community production–respiration balance is identical in each simulation.
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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 the North Sea will be experiencing a pH range completely distinct from current levels (Fig. 9a), although the majority of the region retains some degree of range overlap. By 2100 much of the region will have a distinct range (Fig. 9b).Under an atmospheric CO2 concentration of 1000 ppm, the pH range for the majority of the southern North Sea will be completely distinct from the current pH range (Fig. 9c). Exceptions are restricted to near-shore environments which are predominantly forced by riverine inputs and experience ranges of ~ 1.0 pH unit. The degree of overlap in these near-river zones is probably exaggerated for two reasons; the riverine biogeochemistry, particularly the TA loadings, is not well represented and the river loads have been kept constant between each scenario. There is
also a tendency for excessive diffusion and transport of river plumes by the 3D hydrodynamics, arising from the 7 km horizontal resolution. Comparison with presumed pre-industrial pH levels (not shown) indicates that the domain will have a mostly distinct range by 2050. 3.4. Nitrification Fig. 10 shows the proportional change (S) of the ratio of nitrate to nitrate + ammonium between the year 2000/ 375 ppm simulation and the year 2100 wcs/1000 ppm simulation, e.g.,
S¼
N1 =ðN1 þ A1 Þ−N2 =ðN2 þ A2 Þ N1 =ðN1 þ A1 Þ
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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 mesocosm 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
Fig. 9. The proportion of overlap in pH ranges comparing the year 2000/375 ppm CO2 simulation with (a) the year 2050/500 ppm CO2 simulation, (b) the year 2100/700 ppm CO2 simulation and (c) the year 2100 wcs/1000 ppm CO2 simulation.
where N represents nitrate, A ammonium and the subscript differentiates between simulations (1: 375 ppm, 2: 1000 ppm). The model predicts that a 5–10% difference in the ratio could be expected as atmospheric CO2 approaches 1000 ppm. The lowest response in the coastal waters is due to the masking of the nitrification signal by the riverine inputs which do not change between model
The coupled complex ERSEM-POLCOMS-HALTAFALL 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 the capacity of biological processes to influence pH and its range in the southern North Sea. Whilst this range can be large it is shown that predicted CO2 emissions will provoke acidification that for the most part would create a distinct pH profile from the pre-industrial baseline. It is also shown that measurable biogeochemical consequences of pH reduction can be predicted in the chemical speciation of the key limiting nutrient, nitrate. However no claim can be made yet about the ecosystem consequences of such a process effect. The model shows that pH variability can only be understood in terms of a
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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 effect of pH change will only be predicted with any degree of certainty if an integration of individual process affects is considered. This study is scratching at the surface of a complex question; future work is planned to increase the domain of the model and improve open boundary conditions, include more of the affects of pH on individual processes and to link the physical effects of climate change to those of acidification. This would be facilitated by increased observations of basic carbonate parameters across the region and high-frequency sampling at time series stations. Acknowledgements This work was part funded by the UK Government Department of the Environment, Food and Rural Affairs, the Department of Trade and Industry and by the Plymouth Marine Laboratory Core Research Programme funded by the Natural Environment Research Council. The authors thank Laure-Sophie Schiettecatte of the University of Liège and Dr. Wilhelm Petersen of the GKSS Research Centre, Germany, for providing useful observational data and the referees for their helpful comments. References Allen, J.I., Blackford, J.C., Ashworth, M.I., Proctor, R., Holt, J.T., Siddorn, J.R., 2001. A highly spatially resolved ecosystem model for the north-west European continental shelf. Sarsia 86, 423–440.
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