Climatic Change

, Volume 112, Issue 3–4, pp 1037–1058 | Cite as

Identifying climatic analogs for Wisconsin under 21st-century climate-change scenarios

  • Samuel Veloz
  • John W. Williams
  • David Lorenz
  • Michael Notaro
  • Steve Vavrus
  • Daniel J. Vimont
Article

Abstract

There is a deep disconnect between scientific and public concern about climate change. One reason is that global climate change is a fairly abstract concept with little perceived relevance, so a key challenge is to translate climate-change projections into locally concrete examples of potential impacts. Here we use climate analog analyses as an alternative method for identifying and communicating climate-change impacts. Our analysis uses multiple downscaled general circulation models for the state of Wisconsin, at 0.1 decimal degree resolution, and identifies contemporary locations in North America that are the most similar to the projected future climates for Wisconsin. We assess the uncertainties inherent in climate-change projections among greenhouse gas emission scenarios, time windows (mid-21st century vs. late 21st-century) and different combinations of climate variables. For all future scenarios and simulations, contemporary climatic analogs within North America were found for Wisconsin’s future climate. Closest analogs are primarily 200–500 km to the south-southwest of their Wisconsin reference location. Temperature has the largest effect on choice of climatic analog, but precipitation is the greatest source of uncertainty. Under the higher-end emission scenarios, the contemporary climatic analogs for Wisconsin’s end-21st-century climates are almost entirely outside the state. Climate-analog analyses offer a place-based means of assessing climate impacts that is complementary to the species-based approaches of species distributional models, and carries no assumptions about the characterization and conservatism of species niches. The analog method is simple and flexible, and can be readily extended to other regions and other environmental variables.

Supplementary material

10584_2011_261_MOESM1_ESM.doc (206 kb)
ESM 1(DOC 206 kb)
10584_2011_261_MOESM2_ESM.doc (758 kb)
ESM 2(DOC 757 kb)
10584_2011_261_MOESM3_ESM.doc (5.5 mb)
ESM 3(DOC 5.52 mb)
10584_2011_261_MOESM4_ESM.doc (154 kb)
ESM 4(DOC 153 kb)

References

  1. Araujo MB, Thuiller W, Pearson RG (2006) Climate warming and the decline of amphibians and reptiles in Europe. J Biogeogr 33:1712–1728CrossRefGoogle Scholar
  2. Brankovic C, Srnec L, Patarcic M (2010) An assessment of global and regional climate change based on the EH5OM climate model ensemble. Clim Chang 98:21–49CrossRefGoogle Scholar
  3. Burke MB, Lobell DB, Guarino L (2009) Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation. Global Environ Chang Hum Policy Dimens 19:317–325CrossRefGoogle Scholar
  4. Coetzee BWT, Robertson MP, Erasmus BFN, van Rensburg BJ, Thuiller W (2009) Ensemble models predict Important Bird Areas in southern Africa will become less effective for conserving endemic birds under climate change. Glob Ecol Biogeogr 18:701–710CrossRefGoogle Scholar
  5. Curtis JT (1959) The vegetation of Wisconsin. University of Wisconsin Press, MadisonGoogle Scholar
  6. Diffenbaugh NS, Giorgi F, Pal JS (2008) Climate change hotspots in the United States. Geophys Res Lett 35:L16709CrossRefGoogle Scholar
  7. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  8. Gowda MVR, Fox JC, Magelky RD (1997) Students’ understanding of climate change: Insights for scientists and educators. Bull Am Meteorol Soc 78:2232–2240Google Scholar
  9. Hallegatte S, Hourcade JC, Ambrosi P (2007) Using climate analogues for assessing climate change economic impacts in urban areas. Clim Chang 82:47–60CrossRefGoogle Scholar
  10. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107CrossRefGoogle Scholar
  11. Hayhoe K, VanDorn J, Croley T II, Schlegal N, Wuebbles D (2010) Regional climate change projections for Chicago and the US Great Lakes. J Great Lakes Res 36:7–21CrossRefGoogle Scholar
  12. Hitch AT, Leberg PL (2007) Breeding distributions of north American bird species moving north as a result of climate change. Conserv Biol 21:534–539CrossRefGoogle Scholar
  13. Hobbs RJ, Arico S, Aronson J, Baron JS, Bridgewater P, Cramer VA, Epstein PR, Ewel JJ, Klink CA, Lugo AE, Norton D, Ojima D, Richardson DM, Sanderson EW, Valladares F, Vila M, Zamora R, Zobel M (2006) Novel ecosystems: theoretical and management aspects of the new ecological world order. Glob Ecol Biogeogr 15:1–7CrossRefGoogle Scholar
  14. Homer C, Huang CQ, Yang LM, Wylie B, Coan M (2004) Development of a 2001 national land-cover database for the United States. Photogramm Eng Remote Sens 70:829–840Google Scholar
  15. IPCC (2007a) Climate Change 2007—Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  16. IPCC (2007b) 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, United Kingdom and New York, NY, USAGoogle Scholar
  17. Iverson LR, Prasad AM, Matthews SN, Peters M (2008) Estimating potential habitat for 134 eastern US tree species under six climate scenarios. For Ecol Manag 254:390–406CrossRefGoogle Scholar
  18. Jackson ST, Betancourt JL, Booth RK, Gray ST (2009) Ecology and the ratchet of events: Climate variability, niche dimensions, and species distributions. Proc Natl Acad Sci USA 106:19685–19692CrossRefGoogle Scholar
  19. Johnson RM, Henderson S, Gardiner L, Russell R, Ward D, Foster S, Meymaris K, Hatheway B, Carbone L, Eastburn T (2008) Lessons learned through our climate change professional development program for middle and high school teachers. Phys Geogr 29:500–511CrossRefGoogle Scholar
  20. Jylha K, Tuomenvirta H, Ruosteenoja K, Niemi-Hugaerts H, Keisu K, Karhu JA (2010) Observed and projected shifts of climatic zones in Europe, and their use to visualize climate change information. Weather, Climate, and Society 2:148–167Google Scholar
  21. Kling G, Hayhoe K, Johnson L, Lindroth R, Magnuson J, Moser S, Polasky S, Robinson S, Shuter B, Wander M, Wilson M, Wuebbles D, Zak D (2003) Confronting climate change in the Great Lakes: impacts on our communities and ecosystems. A Report to the Ecological Society of America and the Union of Concerned ScientistsGoogle Scholar
  22. Kopf S, Ha-Duong M, Hallegatte S (2008) Using maps of city analogues to display and interpret climate change scenarios and their uncertainty. Nat Hazards Earth Syst Sci 8:905–918CrossRefGoogle Scholar
  23. Krosnick JA, Holbrook AL, Lowe L, Visser PS (2006) The origins and consequences of democratic citizens’ policy agendas: a study of popular concern about global warming. Clim Chang 77:7–43CrossRefGoogle Scholar
  24. Leary N, Averyt K, Hewitson B, Marengo J (2009) Crossing thresholds in regional climate research: synthesis of the IPCC expert meeting on regional impacts, adaptation, vulnerability, and mitigation introduction. Clim Res 40:121–131CrossRefGoogle Scholar
  25. Leathwick JR (2002) Intra-generic competition among Nothofagus in New Zealand’s primary indigenous forests. Biodivers Conserv 11:2177–2187CrossRefGoogle Scholar
  26. Lorenz DJ, Vavrus SJ, Vimont DJ, Williams JW, Notaro M, Young JA, Deweaver ET, Hopkins EJ (2009a) Wisconsin’s changing climate: Hydrological cycle. In: Pryor SC (ed) Understanding Climate Change: Climate variability, predictability and change in the Midwestern United States. Indiana University Press, Bloomington, pp 135–144Google Scholar
  27. Lorenz DJ, Vavrus SJ, Vimont DJ, Williams JW, Notaro M, Young JA, Deweaver ET, Hopkins EJ (2009b) Wisconsin’s changing climate: temperature. In: Pryor SC (ed) Understanding climate change: Climate variability, predictability and change in the Midwestern United States. Indiana University Press, Bloomington, pp 76–87Google Scholar
  28. Lorenzoni I, Leiserowitz A, Doria MD, Poortinga W, Pidgeon NF (2006) Cross-national comparisons of image associations with “global warming” and “climate change” among laypeople in the United States of America and Great Britain. J Risk Res 9:265–281CrossRefGoogle Scholar
  29. Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J Clim 15:3237–3251CrossRefGoogle Scholar
  30. McCaffrey MS, Buhr SM (2008) Clarifying climate confusion: addressing systemic holes, cognitive gaps, and misconceptions through climate literacy. Phys Geogr 29:512–528CrossRefGoogle Scholar
  31. Moran JM, Hopkins EJ (2002) Wisconsin’s weather and climate. The University of Wisconsin Press, MadisonGoogle Scholar
  32. Niepold F, Herring D, McConville D (2008) The role of narrative and geospatial visualization in fostering climate literate citizens. Phys Geogr 29:529–544CrossRefGoogle Scholar
  33. Nogues-Bravo D (2009) Predicting the past distribution of species climatic niches. Glob Ecol Biogeogr 18:521–531CrossRefGoogle Scholar
  34. Parry ML, Carter TR (1988) The assessment of climatic variations on agriculture. In: Parry ML, Carter TR, Konijn NT (eds) The impact of climatic variations on agriculture, vol 1, Assessments in cool temperate and cold regions. Kluwer Academic Publishers, Dordrecht, pp 11–95CrossRefGoogle Scholar
  35. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (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 589–662Google Scholar
  36. Randin CF, Engler R, Normand S, Zappa M, Zimmermann NE, Pearman PB, Vittoz P, Thuiller W, Guisan A (2009) Climate change and plant distribution: local models predict high-elevation persistence. Glob Chang Biol 15:1557–1569CrossRefGoogle Scholar
  37. Root T, Price J, Hall K, Schnelder S, Rosenzwelg C, Pounds A (2003) Fingerprints of global warming on wild animals and plants. Nature 412:57–60CrossRefGoogle Scholar
  38. Saxon E, Baker B, Hargrove W, Hoffman F, Zganjar C (2005) Mapping environments at risk under different global climate change scenarios. Ecol Lett 8:53–60CrossRefGoogle Scholar
  39. Serbin SP, Kucharik CJ (2009) Spatiotemporal mapping of temperature and precipitation for the development of a multidecadal climatic dataset for Wisconsin. J Appl Meteorol Climatol 48:742–757CrossRefGoogle Scholar
  40. Steel RGD, Torrie JH, Dickey DA (1997) Principles and procedures of statistics A biometircal approach. WCB McGraw-HillGoogle Scholar
  41. Stephenson NL (1998) Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. J Biogeogr 25:855–870CrossRefGoogle Scholar
  42. Stroeve JC, Serreze MC, Fetterer F, Arbetter T, Meier W, Maslanik J, Knowles K (2005) Tracking the Arctic’s shrinking ice cover: another extreme September minimum in 2004. Geophysical Research Letters 32Google Scholar
  43. Svenning JC, Normand S, Skov F (2008) Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31:316–326CrossRefGoogle Scholar
  44. Team NAS (2000) Climate change impacts on the United States: The potential consquences of climate variability and change. Washington DCGoogle Scholar
  45. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, Ferreira de Siqueira M, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–148CrossRefGoogle Scholar
  46. Thompson LG, Brecher HH, Mosley-Thompson E, Hardy DR, Mark BG (2009) Glacier loss on Kilimanjaro continues unabated. Proc Natl Acad Sci USA 106:19770–19775CrossRefGoogle Scholar
  47. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in Europe. Proc Natl Acad Sci 102:8245–8250CrossRefGoogle Scholar
  48. Thuiller W, Lavorel S, Sykes MT, Araujo MB (2006) Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe. Divers Distrib 12:49–60CrossRefGoogle Scholar
  49. WICCI Wisconsin’s Initiative on Climate Change Impacts (2011) Wisconsin’s changing climate: impacts and adaptation. Nelson Institute of the Environment, University of Wisconsin, Madison, Wisconsin Department of Natural ResourcesGoogle Scholar
  50. Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Front Ecol Environ 5:475–482CrossRefGoogle Scholar
  51. Williams JW, Jackson ST, Kutzbach JE (2007) Projected distributions of novel and disappearing climates by 2100AD. Proc Natl Acad Sci 104:5738–5742CrossRefGoogle Scholar
  52. Woodward FI (1987) Climate and plant distribution. Cambridge University Press, CambridgeGoogle Scholar
  53. Wuebbles D, Hayhoe K (2004) Climate change projections for the United States Midwest mitigation and adaptation strategies for global change. pp. 335–363Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Samuel Veloz
    • 1
    • 2
  • John W. Williams
    • 1
  • David Lorenz
    • 3
  • Michael Notaro
    • 3
  • Steve Vavrus
    • 3
  • Daniel J. Vimont
    • 4
  1. 1.Department of Geography, Center for Climatic ResearchUniversity of WisconsinMadisonUSA
  2. 2.PRBO Conservation SciencePetalumaUSA
  3. 3.Center for Climatic ResearchUniversity of WisconsinMadisonUSA
  4. 4.Department of Atmospheric and Oceanic Sciences, Center for Climatic ResearchUniversity of WisconsinMadisonUSA

Personalised recommendations