Climatic Change

, Volume 99, Issue 3–4, pp 515–534

Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia

  • Stefan Fronzek
  • Timothy R. Carter
  • Jouni Räisänen
  • Leena Ruokolainen
  • Miska Luoto
Article

Abstract

A comparison of two approaches for determining probabilistic climate change impacts is presented. In the first approach, ensemble climate projections are applied directly as inputs to an impact model and the risk of impact is computed from the resulting ensemble of outcomes. As this can involve large numbers of projections, the approach may prove to be impractical when applied to complex impact models with demanding input requirements. The second approach is to construct an impact response surface based on a sensitivity analysis of the impact model with respect to changes in key climatic variables, and then to superimpose probabilistic projections of future climate onto the response surface to assess the risk of impact. To illustrate this comparison, an impact model describing the spatial distribution of palsas in Fennoscandia was applied to estimate the risk of palsa disappearance. Palsas are northern mire complexes with permanently frozen peat hummocks, located at the outer limit of the permafrost zone and susceptible to rapid decline due to regional warming. Probabilities of climate changes were derived from an ensemble of coupled atmosphere–ocean general circulation model (AOGCM) projections using a re-sampling method. Results indicated that the response surface approach, though introducing additional uncertainty, gave risk estimates of area decline for palsa suitability that were comparable to those obtained using multiple simulations with the original palsa model. It was estimated as very likely (>90% probability) that a decline of area suitable for palsas to less than half of the baseline distribution will occur by the 2030s and likely (>66%) that all suitable areas will disappear by the end of the twenty-first century under scenarios of medium (A1B) and moderately high (A2) emissions. For a low emissions (B1) scenario, it was more likely than not (>50%) that conditions over a small fraction of the current palsa distribution would remain suitable until the end of the twenty-first century.

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References

  1. Alcamo J, Döll P, Henrichs T, Kaspar F, Lehner B, Rösch T, Siebert S (2003) Development and testing of the WaterGAP 2 global model of water use and availability. Hydrol Sci J 48:317–337CrossRefGoogle Scholar
  2. Alcamo J, Moreno JM, Novaky B, Bindi M, Corobov R, Devoy R, Giannakopoulos C, Martin E, Olesen JE, Shvidenko A (2007) Europe. In: Parry M, Canziani O, Palutikof J, Hanson C, van der Linden P (eds) Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 541–580Google Scholar
  3. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419:224–232CrossRefGoogle Scholar
  4. An W, Allard M (1995) A mathematical approach to modelling palsa formation: insights on processes and growth conditions. Cold Reg Sci Technol 23:231–244CrossRefGoogle Scholar
  5. Anisimov OA (2001) Predicting patterns of near-surface air temperature using empirical data. Clim Change 50:297–315CrossRefGoogle Scholar
  6. Anon (2007) Interpretation manual of European Union habitats—EUR 27. European Commission, DG Environment, nature and biodiversity, Brussels, Belgium. http://ec.europa.eu/environment/nature/legislation/habitatsdirective/docs/2007_07_im.pdf. Accessed 4 Dec 2007, pp 144
  7. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47CrossRefGoogle Scholar
  8. Arnell NW (2003) Effects of IPCC SRES emissions scenarios on river runoff: a global perspective. Hydrol Earth Syst Sci 7:619–641CrossRefGoogle Scholar
  9. Downing TE, Harrison PA, Butterfield RE, Lonsdale KG (eds) (2000) Climate change, climatic variability and agriculture in Europe: an integrated assessment. Environmental Change Unit, University of Oxford, Oxford, 445 ppGoogle Scholar
  10. EEA (2008) Impacts of Europe’s changing climate—2008 indicator-based assessment. EEA report no. 4/2008, European Environment Agency, 246 ppGoogle Scholar
  11. Fronzek S, Carter TR (2007) Assessing uncertainties in climate change impacts on resource potential for Europe based on projections from RCMs and GCMs. Clim Change 81(Supplement 1):357–371CrossRefGoogle Scholar
  12. Fronzek S, Luoto M, Carter TR (2006) Potential effect of climate change on the distribution of palsa mires in subarctic Fennoscandia. Clim Res 32:1–12CrossRefGoogle Scholar
  13. Giorgi F (2008) A simple equation for regional climate change and associated uncertainty. J Clim 21:1589–1604CrossRefGoogle Scholar
  14. Graham L, Hagemann S, Jaun S, Beniston M (2007) On interpreting hydrological change from regional climate models. Clim Change 81(Supplement 1):97–122CrossRefGoogle Scholar
  15. Hall J (2007) Probabilistic climate scenarios may misrepresent uncertainty and lead to bad adaptation decisions. Hydrol Process 21:1127–1129CrossRefGoogle Scholar
  16. Harvey LDD (2004) Characterizing the annual-mean climatic effect of anthropogenic CO2 and aerosol emissions in eight coupled atmosphere–ocean GCMs. Clim Dyn 23:569–599CrossRefGoogle Scholar
  17. Huntingford C, Cox PM (2000) An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim Dyn 16:575–586CrossRefGoogle Scholar
  18. Jones RN (2000a) Analysing the risk of climate change using an irrigation demand model. Clim Res 14:89–100CrossRefGoogle Scholar
  19. Jones RN (2000b) Managing uncertainty in climate change projections—issues for impact assessment. Clim Change 45:403–419CrossRefGoogle Scholar
  20. Kauppi P, Posch M (1985) Sensitivity of boreal forests to possible climatic warming. Clim Change 7:45–54CrossRefGoogle Scholar
  21. Kellomäki S, Leinonen S (eds) (2005) Management of European forests under changing climatic conditions. final report of the project “silvicultural response strategies to climatic change in managed European forests” funded by the European Union under the contract EVK2-2000-00723 (SilviStrat). University of Joensuu, Faculty of Forestry. Research Notes 163, Joensuu, FinlandGoogle Scholar
  22. Kundzewicz ZW, Parry ML, Cramer W, Holten J, Kaczmarek Z, Martens P, Nicholls RJ, Öquist M, Rounsevell MDA, Szolgay J (2001) Europe. Chapter 13. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation, and vulnerability. Contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 641–692Google Scholar
  23. Luo Q, Bellotti W, Williams M, Cooper I, Bryan B (2007) Risk analysis of possible impacts of climate change on South Australian wheat production. Clim Change 85:89–101CrossRefGoogle Scholar
  24. Luoto M, Fronzek S, Zuidhoff FS (2004a) Spatial modelling of palsa mires in relation to climate in Northern Europe. Earth Surf Process Landf 29:1373–1387CrossRefGoogle Scholar
  25. Luoto M, Heikkinen RK, Carter TR (2004b) Loss of palsa mires in Europe and biological consequences. Environ Conserv 31:1–8CrossRefGoogle Scholar
  26. Mearns LO, Hulme M, Carter TR, Leemans R, Lal M, Whetton P (2001) Climate scenario development. In: McCarthy J, Canziani O, Leary N, Dokken D, White K (eds) Climate change, 2001. Impacts, adaptation and vulnerability. Contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 739–768Google Scholar
  27. Meehl G, Stocker T, Collins W, Friedlingstein P, Gaye A, Gregory J, Kitoh A, Knutti R, Murphy J, Noda A, Raper S, Watterson I, Weaver A, Zhao Z-C (2007a) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 748–845Google Scholar
  28. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007b) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  29. Mitchell TD (2003) Pattern scaling. An examination of the accuracy of the technique for describing future climates. Clim Change 60:217–242CrossRefGoogle Scholar
  30. Mitchell TD, Carter TR, Jones PD, Hulme M, New M (2003) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901–2000) and 16 scenarios (2001–2100). Tyndall Centre Working Paper 55:29Google Scholar
  31. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, et al (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  32. Murphy JM, Booth BBB, Collins M, Harris GR, Sexton DMH, Webb MJ (2007) A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philos Trans R Soc A Math Phys Eng Sci 365:1993–2028CrossRefGoogle Scholar
  33. Nakićenović N, Alcamo J, Davis G, de Fries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, von Rooijen S, Victor N, Dadi Z (eds) (2000) Emissions scenarios. A special report of working group III of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 599 ppGoogle Scholar
  34. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Clim Res 21:1–25CrossRefGoogle Scholar
  35. New M, Lopez A, Dessai S, Wilby R (2007) Challenges in using probabilistic climate change information for impact assessments: an example from the water sector. Philos Trans R Soc A Math Phys Eng Sci 365:2117–2131CrossRefGoogle Scholar
  36. Olesen J, Carter T, Díaz-Ambrona C, Fronzek S, Heidmann T, Hickler T, Holt T, Minguez M, Morales P, Palutikof J, Quemada M, Ruiz-Ramos M, Rubæk G, Sau F, Smith B, Sykes M (2007) Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim Change 81(Supplement 1):123–143CrossRefGoogle Scholar
  37. Parry ML (ed) (2000) Assessment of potential effects and adaptations for climate change in Europe: The Europe ACACIA Project. Jackson Environment Institute, University of East Anglia, Norwich, 320 ppGoogle Scholar
  38. Räisänen J, Ruokolainen L (2006) Probabilistic forecasts of near-term climate change based on a resampling ensemble technique. Tellus 58A:461–472Google Scholar
  39. Ruosteenoja K, Carter TR, Jylhä K, Tuomenvirta H (2003) Future climate in world regions: an intercomparison of model-based projections for the new IPCC emissions scenarios. The Finnish Environment, 644, Finnish Environment Institute, 83 ppGoogle Scholar
  40. Schröter D, Cramer W, Leemans R, Prentice IC, Araujo MB, Arnell NW, Bondeau A, Bugmann H, Carter TR, Gracia CA, de la Vega-Leinert AC, Erhard M, Ewert F, Glendining M, House JI, Kankaanpaa S, Klein RJT, Lavorel S, Lindner M, Metzger MJ, Meyer J, Mitchell TD, Reginster I, Rounsevell M, Sabate S, Sitch S, Smith B, Smith J, Smith P, Sykes MT, Thonicke K, Thuiller W, Tuck G, Zaehle S, Zierl B (2005) Ecosystem service supply and vulnerability to global change in Europe. Science 310:1333–1337CrossRefGoogle Scholar
  41. Seppälä M (1986) The origin of palsas. Geografiska Annaler 68A:141–147CrossRefGoogle Scholar
  42. Seppälä M (1988) Palsas and related forms. In: Clark MJ (ed) Advances in periglacial geomorphology. Wiley, Chichester, pp 247–278Google Scholar
  43. Sollid JL, Sørbel L (1998) Palsa bogs as a climatic indicator—examples from Dovrefjell, Southern Norway. Ambio 27:287–291Google Scholar
  44. Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
  45. Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resource Res 42:W02419CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Stefan Fronzek
    • 1
  • Timothy R. Carter
    • 1
  • Jouni Räisänen
    • 2
  • Leena Ruokolainen
    • 2
  • Miska Luoto
    • 3
  1. 1.Finnish Environment Institute (SYKE)HelsinkiFinland
  2. 2.Department of PhysicsUniversity of HelsinkiHelsinkiFinland
  3. 3.Department of GeographyUniversity of OuluOuluFinland

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