Climatic Change

, Volume 115, Issue 3–4, pp 433–442 | Cite as

Use of Representative Climate Futures in impact and adaptation assessment

  • Penny Whetton
  • Kevin Hennessy
  • John Clarke
  • Kathleen McInnes
  • David Kent
Article

Abstract

A key challenge for climate projection science is to serve the rapidly growing needs of impact and adaptation assessments (hereafter risk assessments) in an environment where there are substantial differences in the regional projections of climate models, an expanding number of potentially relevant climate model results, and a desire amongst many users to limit the number of future climate scenarios in their assessments. While it may be attractive to select a small number of climate models based on their ability to replicate current climate, there is no robust method for doing this. We outline and illustrate a method that addresses this challenge in a different way. The range of plausible future climates simulated by climate models is classified into a small set of Representative Climate Futures (RCFs) and the relative likelihood of these estimated. For each region, the RCFs are then used as a framework in which to classify more detailed information, such as available climate model and downscaled data sets. Researchers wishing to apply the RCFs in risk assessments can then choose to use a subset of RCFs, such as the “most likely”, “high risk” and “least change” cases for their impact system. Preparation and analysis of future climate data sets can therefore be confined to those models whose simulations best represent the selected RCFs. This significantly reduces the number of models involved, and potentially the effort required to undertake the risk assessment. Consistently applied within a region, RCFs, rather than individual climate models, can become the boundary objects which anchor discussion between the climate science and risk assessment communities, simplifying communication. Since the RCF descriptions need not change as new climate model results emerge, they can also provide a stable framework for assimilating risk assessments undertaken at different times with different sets of climate models. Systematic application of this approach requires various challenges to be addressed, such as robustly classifying future regional climates into a small set and estimating likelihoods.

References

  1. AGO (2006) Climate change impacts & risk management a guide for business and government. Australian Greenhouse Office in the Department of the Environment and Heritage, Canberra, AustraliaGoogle Scholar
  2. Barron E (2009) Editorial: beyond climate science. Science 326(5953):643CrossRefGoogle Scholar
  3. Clarke J, Heady C (2010) Temperature and dry-days projections for 2030 for the shires of Dubbo, Gunnedah and Liverpool Plains, NSW. A report prepared for Marsden Jacobs Associates. Tech rep, CSIRO Marine and Atmopsheric Research, Aspendale, VictoriaGoogle Scholar
  4. Clarke J, Whetton P, Hennessy K (2011) Providing application-specific climate projections datasets: CSIROs climate futures framework. In: Proceedings of MODSIM, international congress on modelling and simulation, modelling and simulation society of Australia and New Zealand, 12–16 December 2011, Perth, AustraliaGoogle Scholar
  5. Dessai S, Hulme M, Lempert R, Pielke RJ (2009) Climate prediction: a limit to adaptation? Cambridge University Press, Cambridge, pp 64–78Google Scholar
  6. Hennessy K, Clarke J, Whetton P, Kent D (2012) Internally consistent projections for Victoria. CAWCR Technical Report, 10 p. http://www.climatechangeinaustralia.gov.au/resources.php. Accessed 11 May 2012
  7. Hulme M, Dessai S (2008) Negotiating future climates for public policy: a critical assessment of the development of climate scenarios for the UK. Environ Sci Policy 11(1):54–70CrossRefGoogle Scholar
  8. KNMI (2009) Climate change in the Netherlands: supplements to the KNMI 06 scenarios. Royal Netherlands Meteorological Institute, Ministry of Transport, Public Works, and Water ManagementGoogle Scholar
  9. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Climate 23(10):2739–2758CrossRefGoogle Scholar
  10. Lucas C, Hennessy K, Bathols J (2007) Bushfire weather in Southeast Australia recent trends and projected climate change impacts. Bushfire CRC, Melbourne, AustraliaGoogle Scholar
  11. Mastrandrea M, Field C, Stocker T, Edenhofer O, Ebi K, Frame D, Held H, Kriegler E, Mach K, Matschoss P, Plattner GK, Yohe G, Zwiers F (2010) Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. IPCC, GenevaGoogle Scholar
  12. McInnes K, Abbs D, O’Farrell S, Macadam I, O’Grady J, Ranasinghe R (2007) Projected changes in climatological forcing conditions for coastal erosion in NSW. Tech rep, CSIRO Marine and Atmospheric Research, Department of Environment and Climate ChangeGoogle Scholar
  13. McMichael A, Woodruff R, Whetton P, Hennessy K, Nicholls N, Hales S, Woodward A, Kjellstrom T (2003) Human health and climate change in oceania: a risk assessment 2002. Tech rep, Department of Health and Ageing, Canberra, AustraliaGoogle Scholar
  14. Mearns LO (2010) The drama of uncertainty. Clim Change 100(1):77–85CrossRefGoogle Scholar
  15. Meehl G, Covey C, Delworth T, Latif M, McAvaney B, Mitchell J, Stouffer R, Taylor K (2007) The WCRP CMIP3 multimodel dataset—a new era in climate change research. Bull Am Meteorol Soc 88(9):1383–1394CrossRefGoogle Scholar
  16. Mitchell TD (2003) Pattern scaling: an examination of the accuracy of the technique for describing future climates. Clim Change 60(3):217–242CrossRefGoogle Scholar
  17. Morton F (1983) Operational estimates of areal evapo-transpiration and their significance to the science and practice of hydrology. J Hydrol 66(1–4):1–76CrossRefGoogle Scholar
  18. Murdock T, Spittlehouse D (2011) Selecting and using climate change scenarios for british columbia. Tech rep, Pacific Climate Impacts Consortium, University of Victoria, Victoria, BCGoogle Scholar
  19. Parry M, Canziani O, Palutikof J, van der Linden P, Hanson C (2007) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  20. Pitman A, Perkins S (2008) Regional projections of future seasonal and annual changes in rainfall and temperature over australia based on skill-selected AR(4) models. Earth Interact 12:1–50CrossRefGoogle Scholar
  21. Ricketts J, Hennessy K (2009) Climate change in southern South Australia and western Victoria. CSIRO technical report for Maunsel/AECOM, Aspendale, Australia. Available at http://www.climatechangeinaustralia.gov.au/resources.php. Accessed 11 May 2012
  22. Ruosteenoja K, Carter T, Jylh K, Tuomenvirt H (2003) Future climate in world regions: an intercomparison of model-based projections for the new ipcc emissions scenarios. Tech rep, The Finnish Environment 644, Finnish Environment Institute, HelsinkiGoogle Scholar
  23. Schoemaker P (1995) Scenario planning: a tool for strategic thinking. In: Sloan management review winter: 1995, pp 25–40Google Scholar
  24. Smith I, Chandler E (2010) Refining rainfall projections for the Murray Darling Basin of south-east Australia—the effect of sampling model results based on performance. Clim Change 102(3–4):377–393CrossRefGoogle Scholar
  25. Taylor K, Stouffer R, Meehl G (2011) A summary of the CMIP5 experiment design. PCMDI design document, PCMDIGoogle Scholar
  26. van den Hurk B, Tank A, Lenderink G, van Ulden A, van Oldenborgh G, Katsman C, van den Brink H, Keller F, Bessembinder J, Burgers G, Komen G, Hazeleger W, Drijfhout S (2006) KNMI climate change scenarios 2006 for the Netherlands. Royal Netherlands Meteorological Institute, Ministry of Transport, Public Works, and Water ManagementGoogle Scholar
  27. van Vuuren D, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt G, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith S, Rose S (2011) The representative concentration pathways: an overview. Clim Change 109(1):5–31CrossRefGoogle Scholar
  28. Watterson I (2012) Understanding and partioning future climates for Australian regions from CMIP3 using ocean warming indices. Clim Change 111:903–922CrossRefGoogle Scholar
  29. Webb LB, Whetton PH, Barlow EWR (2008) Climate change and winegrape quality in Australia. Clim Res 36(2):99–111CrossRefGoogle Scholar
  30. Wilby R, Troni J, Biot Y, Tedd L, Hewitson B, Smith D, Sutton R (2009) A review of climate risk information for adaptation and development planning. Int J Climatol 29(9):1193–1215CrossRefGoogle Scholar
  31. Wilkinson A, Eidinow E (2008) Evolving practices in environmental scenarios: a new scenario typology. Environ Res Lett 3(4):045017CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Penny Whetton
    • 1
  • Kevin Hennessy
    • 1
  • John Clarke
    • 1
  • Kathleen McInnes
    • 1
  • David Kent
    • 1
  1. 1.CSIRO Marine and Atmospheric ResearchAspendaleAustralia

Personalised recommendations