Climatic Change (2008) 87:251–262 DOI 10.1007/s10584-007-9338-0 An integrated study of economic effects of and vulnerabilities to global warming on the Barents Sea cod fisheries Arne Eide Received: 6 July 2006 /Accepted: 3 October 2007 / Published online: 27 November 2007 # Springer Science + Business Media B.V. 2007 Abstract The Barents Sea area is characterised by a highly fluctuating physical environment causing substantial variations in the ecosystems and fisheries depending upon this. Simulations assuming different management regimes have been carried out to study how physical and biological effects of global warming influence the Barents Sea cod fisheries. A regional, high-resolution representation of the B2 world region (OECD90) scenario from the Intergovernmental Panel on Climate Change was used to calculate water temperatures and plankton biomasses by hydrodynamic modelling. These results were included in simulations performed by a multi-fleet, multi-species model, by which a fully integrated model linking to the global circulation model to the Barents Sea fisheries through a regional downscaling to the Barents Sea area is constructed. One factor of particular importance for the natural annual biological variations is the occasional inflow of young herring into the Barents Sea area. The herring inflow is difficult to predict and links to dynamical systems outside the Barents Sea area, complex recruitment mechanisms and oceanographic conditions. These processes are in the study represented by a stochastic representation of herring inflow based on historical observations. According to the performed simulations the biomass fluctuations may slightly increase over the next 25 years, possibly caused by changes in temperature patterns. Six different management regimes have been included in the study and the results support earlier studies claiming that the choice of management regime potentially has a greater importance for biological and economic performance in the Barents Sea fisheries than impacts which derive from global warming over the next 25 years. A basic assumption for this conclusion is however that the Barents Sea ecosystem essentially preserves its structure and composition of today. Possible, unpredictable significant shifts in the ecosystem structure are not considered. 1 Introduction Impacts of global warming on biological and economic systems are a major concern in the world today (Parry et al. 2007). However, it is a complex task to identify long term A. Eide (*) Norwegian College of Fishery Science, University of Tromsø, Breivika, Tromsø 9037, Norway e-mail: [email protected] 252 Climatic Change (2008) 87:251–262 consequences of global warming on these systems. In particular local and regional effects are difficult to predict. Also normal variations in physical and biological systems, and in and between regions are substantial. Regional models, which have been developed to determine vulnerabilities of such systems, therefore include normal seasonal variations within and between years. Similarly to many other fisheries also the cod fisheries in the Barents Sea area closely links to seasonal variations. The modern history of fisheries management reflects a development of adaptation techniques to varying environmental situations. The new concept of Harvest Control Rules (HCR) opens for a more dynamic adaptation compared with the traditional more static regulation procedures (Thompson 1999). This study is a part of the European BALANCE project, which aims to assess vulnerabilities due to climate changes in the Barents region. Within the BALANCE project integrated vulnerability studies are carried out, based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES; Nakícenovíc et al. 2000) scenario B2, world region OECD90. The B2 scenario assumes effective local solutions to economic, social, and environmental sustainability issues and intermediate economic development (the regional/environmental scenario). As the other scenarios also the B2 scenario therefore includes assumptions related to the magnitude and distribution of different types of economic activities throughout the simulation period and management decisions related to these activities. The precautionary principle in fisheries management fits the basic assumptions of the B2 scenario. Physical variables are brought into the integrated model structure in a more straight forward manner, as indicated in Fig. 1. EconMult Stock unit biomasses Catches Management EconSimp2000 AggMult Temperatures Plankton biomasses SinMod Physical variables REMO 5.1 Physical variables Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B2 Physical Biological Economic Management environment environment activity decisions Fig. 1 Flow chart of the integrated model structure covering the physical and biological environment, economic activity and management. EconSimp2000 is shown as the combined model of AggMult and EconMult. The B2 scenario covers all sectors, including management decisions, but only the physical variables are direct inputs to the integrated model, while the management integration (indicated by a dashed arrow) exists in terms of consistency. Up to the fishing activity the model integration has only one direction, while feedback mechanisms are included in the combined model (EconSimp2000) Climatic Change (2008) 87:251–262 253 Physical conditions are downscaled to the Barents region by applying the REMO5.1 model (Jacob et al. 2001). REMO5.1 is a regional climate model with ½ degree resolution, driven by the global atmospheric general circulation model ECHAM4 (Roeckner et al. 1996). The REMO5.1 climate simulation data is used in calculating values on physical variables in the 3D ocean circulation model SinMod (for more details see Pedersen et al. 2003; Slagstad and McClimans 2005). In addition to the Barents Sea hydrodynamics, SinMod also covers primary and secondary biological production in Atlantic and Arctic waters of the Barents Sea. Temperatures obtained by SinMod simulations indicate a slight increase in average sea temperature in the 50 meter upper layer of the Atlantic water the next 25 years (Fig. 2). The quarterly trend graphs in Fig. 2 show rather moderate increases, about half a Celsius degree within each quarter, much less than the normal quarterly variation over a year. The SinMod results lay however well within the probability range from −2 to + 3 Celsius degrees (compared with current average level) assumed in previous studies of global warming impacts on the Barents Sea fisheries (Eide and Heen 2002; Eide 2007). The uncertainty reflected by the rather wide probability range relates mainly to the two counterworking effects of warmer Atlantic water flowing into the Barents Sea basin and the slight reduction in Atlantic water inflow. The B2/REMO5.1/SinMod simulations propose that the two effects almost level out each other. January - March April - June 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 8 8 C) 7 C) 7 ° ° 6 6 5 5 4 4 Temperature ( Temperature ( Temperature 3 3 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 Year Year July - September October - December 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 8 8 7 C) 7 C) ° ° 6 6 5 5 4 4 Temperature ( Temperature Temperature ( Temperature 3 3 2005 2010 2015 2020 2025 2030 2005 2010 2015 2020 2025 2030 Year Year Fig. 2 SinMod calculated average quarterly temperatures of the Atlantic water upper 50 m layer in the Barents Sea 2005–2030, based on simulations by the regional model Remo5.1 (points). The solid lines show the trends (linear regression model) while the dotted lines gives the corresponding 95% confidence intervals 254 Climatic Change (2008) 87:251–262 2 Materials and methods The SinMod results have been implemented in the EconSimp2000 model (Eide 2007), the combined model of AggMult (Tjelmeland and Bogstad 1998) and EconMult (Eide and Flaaten 1998). AggMult covers the essential parts of the Barents Sea ecosystem which relates to ongoing commercial fisheries, cod and capelin being the most important fish species. Herring also plays a role in the Barents Sea ecosystem, as described below. Individual growth and predation is modelled by the feeding level principle (Ursin 1967), length based maturation and Beverton and Holt recruitment at age 0 (see Tjelmeland and Bogstad 1998 for details). The time resolution in AggMult is a quarter of a year, similar to the time step in the fleet model, EconMult. In addition to the major fish populations, cod, capelin and herring, the Barents Sea ecosystem is represented in AggMult by three plankton biomasses, small plankton in the southern Barents Sea (food for capelin and herring), small plankton in the northern Barents Sea (food for capelin) and large plankton organisms (food for cod, herring and capelin). In this study, the plankton growth rates are calculated within the SinMod model. While assuming the essential ecosystem structure of today to prevail, the system is influenced by global warming through water temperature changes and growth changes in plankton biomasses. The latter also acts as a proxy of oceanographic changes in the combined model, reflecting changes in spatial distribution and production, influencing fish production and harvest. Essentially EconSimp2000 is a deterministic model. The AggMult module has however the possibility of including some stochastic processes, particularly related to the complex interaction