Comparing reconstructed past variations and future projections of the Baltic Sea ecosystem—first results from multi-model ensemble simulations

Following Meier et al (2011), four climate scenario simulations using regionalized data from two general circulation models (GCMs) and two greenhouse gas emission scenarios (A2, A1B) were used to force three state-of-the-art coupled physical–biogeochemical models in the period 1961–2098. For the dynamical downscaling, a regional, high-resolution coupled atmosphere–ice–ocean–land surface model (the Rossby Centre Atmosphere Ocean model, RCAO; Döscher et al 2002) with lateral boundary data from GCMs was applied. Runoff is calculated using a statistical model (Meier et al 2012). In the investigated four scenario simulations, annual mean air temperature in the Baltic Sea Region (BSR) is projected to be 2.7–3.8 K higher in 2070–99 relative to 1969–98 (Meier et al 2012). Due to an increase of net precipitation over the Baltic Sea catchment area, river runoff is projected to increase between 15 and 22%. Over the Baltic proper (comprising the Arkona, Bornholm and Gotland sub-basins) small, but statistically significant wind speed changes are only found in two out of four scenario simulations.

Three nutrient load scenarios, ranging from a pessimistic 'business-as-usual' to a more optimistic case, were used in the models: a reference case with a continuation of present loads (REF), the implementation of abatements according to the BSAP (BSAP), and a 'business-as-usual' case (BAU) with increasing loads due to increased use of fertilizers in transitional Baltic Sea countries (Gustafsson et al 2011). Atmospheric deposition changes vary between a 50% reduction in BSAP and no change in BAU, compared to present conditions. In addition, the food web simulations presented here employ two cod fishery scenarios ('business-as-usual' and 'cod recovery plan' with a fishing mortality F_cod = 0.3 following EC (2007)). Note that cod is the main predatory fish in the Baltic Sea and has a high value for fisheries.

For 1850–2006, multivariate high-resolution atmospheric forcing fields were reconstructed (Schenk and Zorita 2012). The daily fields are homogeneous and physically consistent by making use of both long European historical station data since 1850, and simulated atmospheric fields from RCAO over Northern Europe in the period 1958–2006, driven by re-analysis data at the lateral boundaries (Samuelsson et al 2011). The reconstruction of monthly nutrient loads from rivers and point sources and of atmospheric nitrogen deposition for 1850–2006 is based on available historical data (Savchuk et al 2012).

Three coupled physical–biogeochemical models are used to calculate either historical climate variations in the period 1850–2006 (driven by reconstructed data), or changing climate in the period 1961–2098 (driven by regionalized GCM data): the BAltic sea Long-Term large-Scale Eutrophication Model (BALTSEM) (Gustafsson 2003, Savchuk 2002), the Ecological ReGional Ocean Model (ERGOM) (Neumann et al 2002), and the Swedish Coastal and Ocean BIogeochemical model coupled to the Rossby Centre Ocean circulation model (RCO-SCOBI) (Eilola et al 2009, Meier et al 2012). To calculate appropriate initial conditions, customized spin-up strategies for each of the models were developed.

A new Baltic proper Ecopath with Ecosim food web model (BaltProWeb) is used to analyze climate induced changes in marine food webs and the implications on ecosystem services (Tomczak et al 2012). The model contains 22 functional groups, from primary producers to seals and fishery, and was calibrated with data across trophic levels. BaltProWeb is forced by model outputs (primary production, temperature, salinity, hypoxic area and cod reproductive volume) from the three biogeochemical models. In addition, statistical single- and multi-species models are used to link climatic forcing and lower trophic level processes to fish dynamics. Furthermore, an assessment of plausible impacts of ocean acidification on key functional groups using available food web data is carried out to better understand the response of Baltic Sea organisms.

Assessments of the impact of climate change on regional and local developments and a cross-country analysis of stakeholder perceptions in eight Baltic Sea countries are performed. As the awareness of stakeholders will ultimately affect their actions, the results of the questionnaires are important for the implementation of climate change adaptation strategies in long-term coastal management.

Simulated water temperature, salinity, oxygen and nutrients are evaluated using long observational records at Gotland Deep, a monitoring station which characterizes typical hydrographic properties of the Baltic proper (figure 2). Although during the period 1961–90 sea surface temperatures (SSTs) in GCM driven simulations are usually warmer compared to SSTs in the reconstructions, SSTs in the 1990s and 2000s are relatively close in both datasets (figure 2(a)). Note that annual mean, observed SSTs might be biased because the seasonal cycle during some years is not well resolved. For instance, the lightships did not operate during winter.

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Observations of water temperature at 200 m depth at Gotland Deep suggest that reconstructions and GCM driven simulations have cold and warm biases, respectively (figure 2(b)). Nevertheless, temperature variations in reconstructions and observations are similar.

Reconstructed sea surface salinities (SSSs) show realistic long-term variations but are slightly underestimated compared to observations, in particular prior to 1930. In the GCM driven simulations mean SSS values, averaged for the period 1961–2006, are slightly overestimated compared to observations (figure 2(c)).

Although many of the saltwater inflows are reproduced using the reconstructed atmospheric forcing, salinities at 200 m depth are considerably underestimated in two out of three models, whereas mean deep water salinities in the GCM driven simulations are close to observations (figure 2(d)). However, the chronologies of natural variations differ among the climate realizations due to the chaotic behavior of the Earth system. Hence, the temporal evolution of the ensemble mean of the GCM driven simulations does not include observed decadal variations, like the stagnation period 1983–92.

Although oxygen concentrations at 200 m depth are overestimated in the reconstructions compared to observations, in particular prior to 1930, the negative trend since the 1950s caused by eutrophication is well captured (figure 2(e)). The too high deep water oxygen concentrations during the early period might be caused partly by a too weak vertical stratification (due to the too low salinities in the deep water) and partly by shortcomings in simulated oxygen consumption in oligotrophic ecosystems. Note that the temperature offset plays a minor role only. Also, mean oxygen concentrations in GCM driven simulations are slightly overestimated compared to observations.

Furthermore, surface phosphate concentrations in the reconstructions reflect the history of eutrophication since the 1950s satisfactorily (figure 2(f)). However, in the GCM driven simulations, the positive trend in the ensemble mean surface phosphate concentration is somewhat too small. This might be explained by the fact that at least in one out of three models, external nutrient loads do not represent the observed temporal evolution correctly. In this case, riverine nutrient concentrations are assumed to be constant during 1961–2006.

For surface nitrate concentrations, the discrepancies among the models during 1961–2006 are even larger than for surface phosphate concentrations (figure 2(g)). During the 1980s and 1990s ensemble average, surface nitrate concentrations are in both, the reconstructions and GCM driven simulations, too small compared to observations.

Figure 2 also shows results from scenario simulations of future climate in the central Gotland Basin. As a consequence of increased air temperature and freshwater supply, water temperatures are projected to increase and salinities to decrease (figures 2(a)–(d)), in accordance with results by Meier et al (2011). At the end of the 21st century, both surface and deep water temperature and salinity changes in the A1B/A2 scenarios are larger than all decadal variations ever observed or reconstructed since 1850.

Warmer water changes the oxygen saturation concentrations and turnover rates of biogeochemical processes, enhancing eutrophication (Meier et al 2011). Hence, depending on the nutrient load scenario, oxygen concentrations in the Baltic deep water are projected to decrease, compared to the 2000s (figure 2(e)). Only in the BSAP nutrient load scenario, oxygen concentrations during the 21st century remain more or less the same. However, compared to the observed, exceptionally low oxygen concentrations during the beginning of the 1990s and 2000s, projected oxygen concentrations reach only lower values by 2100 in the lower range of the BAU and REF scenarios (ensemble mean minus one standard deviation). Both the reconstructions and scenario simulations suggest that the oxygen concentrations during the beginning of the 1990s and 2000s are unusually low. These extremes are explained by the coincidence of a 10 yr long stagnation period and the impact of eutrophication caused by the excessive nutrient loads from land that reached a maximum during the beginning of the 1980s (Savchuk et al 2012).

Future surface phosphate concentrations are projected to continue to increase in the REF and BAU nutrient load scenarios but decrease in BSAP, compared to the 2000s (figure 2(f)). Also, surface nitrate concentrations increase in BAU and (slightly) in REF, but remain approximately unchanged in BSAP (figure 2(g)). In none of the nutrient load scenarios could the environmental status of the 1950s be restored.

In summary, climate induced changes, together with present external nutrient loads (REF) will very likely affect the marine environment, inter alia, with enhanced eutrophication (figures 2(f) and (g)), increased oxygen depletion (figure 2(e)), and reduced water transparency (due to increased organic material in the sea, not shown). In addition, decreased salinity (figures 2(c) and (d)) may contribute to reduced biodiversity and increased atmospheric CO₂ concentrations may lead to increased risk of acidification.

Note that for biogeochemical variables, especially in BAU, the spread among ensemble members increases with time because the sensitivities of the models to nutrient load changes differ. Despite these uncertainties, the scenario simulations suggest that climate induced changes are considerable and may not easily be counteracted by reductions of future nutrient loads.

Although fish population/food web models have very different parameterizations, assumptions and sensitivities to environmental forcing, all applied models under currently defined sustainable fishing conditions indicate that the sprat spawning biomass will likely increase in the Baltic Sea during the 21st century.

BaltProWeb was used for an extensive combination of climate, nutrient loading and fishery scenarios, using outputs from all three biogeochemical models (figure 3). These simulations demonstrate the dominant impact of fishing on cod. However, a worsening of reproduction conditions, due to climate change in combination with high nutrient loading (the cod reproductive habitat based on oxygen concentration and salinity is projected to decrease), seems to limit the cod stock even at low fishing levels. Previously, it has been described that a decreased cod biomass and a consequent increase in sprat abundance may, via top-down trophic cascades, lead to a decreased zooplankton biomass and consequent increases in phytoplankton biomass (e.g., Casini et al 2008). Some top-down cascades to the zooplankton level are observed in BaltProWeb, but they do not reach the level of phytoplankton. Overall, the projections of lower trophic level groups are mainly affected by climate and nutrient loads rather than by fishing on cod (not shown). Other food web models show that a recovery of the population of gray seals (which prey on cod) is also likely to have a smaller impact on the development of cod biomass in the Baltic Sea during the 21st century than exploitation and climate change (MacKenzie et al 2011).

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Climate scenario simulations show a continuous acidification of the Baltic Sea, which is mainly controlled by the increasing atmospheric pCO₂. Changes in pH due to other factors such as increasing temperature and primary production are less important and differ between the regions. Future acidification may also affect Baltic Sea biota. Although available data suggest that many species and ecologically important groups in the Baltic Sea food web (zooplankton, macrozoobenthos, cod and sprat) will be robust to the expected changes in pH, a general conclusion cannot be drawn because most studies to date have only investigated the effects of single factors on single species, not multiple factors on multi-level ecosystems. A preliminary sensitivity analysis of the consequences of ocean acidification on the Baltic Sea ecosystem assuming 'worst case' impacts, suggests that ocean acidification may cause substantial (>50%) declines in some key fish populations of the Baltic Sea (herring, cod) and in biomass of other taxa (zooplankton, benthic filter feeders) because of increased mortality of target species.

BSR countries have different economic and social conditions, but these differences are not reflected in the attitudes towards climate change. When these attitudes were assessed by survey questions, the answers indicate in general that the effect of climate change is considered to be negative on many sectors of human activity. The respondents acknowledge that climate change may become increasingly pressing, but perceive these pressures to be distant in space and time. Because of the costs incurred by the actions necessary to mitigate the effects of climate change, the most prevailing view is that it is best to 'wait and see' and to take less costly soft actions, mainly related to education. The stakeholders declare to have knowledge about adaptation and mitigation but they do not consider them of primary importance. The respondents believe that climate change will not affect them personally. Their understanding of its consequences remains vague and abstract. Hence, our results suggest that there is a need for providing more information and knowledge about the importance of adequate adaptation and mitigation actions.