An integrated study of economic effects of and vulnerabilities to global warming on the Barents Sea cod fisheries
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.
KeywordsManagement Regime Stock Biomass Precautionary Approach Total Allowable Catch Limited Entry
Unable to display preview. Download preview PDF.
- Anon (2000) Lønnsomhetsundersøkelser for helårsdrevne fiskefartøy 8 meter største lengde og over. (In Norwegian). The Norwegian Fisheries Directorate. Bergen, NorwayGoogle Scholar
- Åsnes M (2005) Prost users guide, internal note, Norwegian Institute of Marine Research (IMR). Bergen, NorwayGoogle Scholar
- Eide A (2007) Economic impacts of global warming – the case of the Barents Sea fisheries. Natural Resource Modeling 20(2):199–221Google Scholar
- Eide A, Flaaten O (1998) Bioeconomic multispecies models of the Barents Sea fisheries. In: Rødseth T (ed.) models for multispecies management. Physica-Verlag, Heidelberg, New York, pp 141–17, ISBN 3-7908-1001-0Google Scholar
- Jacob D, Andrae U, Elgered G, Fortelius C, Graham LP, Jackson SD, Karstens U, Koepken C, Lindau R, Podzun R, Rockel B, Rubel F, Sass HB, Smith RND, van den Hurk BJJM, Yang X (2001) A comprehensive model intercomparison study investigating the water budget during the BALTEX-PIDCAP Period. Meteorol Atmos Phys 77:19–43CrossRefGoogle Scholar
- Nakícenovíc N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, Emilio la Rovere E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger ME, Shukla PR, Smith S, Swart RJ, van Rooyen S, Victor N, Dadi Z (2000) Special report on emissions scenarios. Cambridge University Press, Cambridge, pp 599Google Scholar
- Parry MP, Canziani OF, Palutikof J (eds) (2007) Climate Change 2007. Impacts, adaptation and vulnerability. Cambridge University Press, Cambridge, (in press).Google Scholar
- Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM4: Model description and simulation of present-day climate. Max-Planck-Institut für Meteorologie, Hamburg, Report No. 218.Google Scholar
- Thompson G (1999) Optimizing harvest control rules. In: presence of natural variability and parameter uncertainty. Proceedings, 5th NMFS NSAW. NOAA Tech. Memo. NMFSF/SPO-40.Google Scholar
- Tjelmeland S, Bogstad B (1998) Biological modelling. In: Models for multispecies management. Physica-Verlag, Heidelberg, New York, pp 69–92 ISBN 3-7908-1001-0Google Scholar
- Ursin E (1967) A mathematical model of some aspects of fish growth, respiration, and mortality. J Fish Res Board Can 24(11):2355–2453Google Scholar