Climatic Change

, Volume 112, Issue 2, pp 325–337 | Cite as

Intensification of seasonal extremes given a 2°C global warming target



Current international efforts to reduce greenhouse gas emissions and limit human-induced global-mean near-surface temperature increases to 2°C, relative to the pre-industrial era, are intended to avoid possibly significant and dangerous impacts to physical, biological, and socio-economic systems. However, it is unknown how these various systems will respond to such a temperature increase because their relevant spatial scales are much different than those represented by numerical global climate models—the standard tool for climate change studies. This deficiency can be addressed by using higher-resolution regional climate models, but at great computational expense. The research presented here seeks to determine how a 2°C global-mean temperature increase might change the frequency of seasonal temperature extremes, both in the United States and around the globe, without necessarily resorting to these computationally-intensive model experiments. Results indicate that in many locations the regional temperature increases that accompany a 2°C increase in global mean temperatures are significantly larger than the interannual-to-decadal variations in seasonal-mean temperatures; in these locations a 2°C global mean temperature increase results in seasonal-mean temperatures that consistently exceed the most extreme values experienced during the second half of the 20th Century. Further, results indicate that many tropical regions, despite having relatively modest overall temperature increases, will have the most substantial increase in number of hot extremes. These results highlight that extremes very well could become the norm, even given the 2°C temperature increase target.

Supplementary material

10584_2011_213_MOESM1_ESM.doc (674 kb)
Esm 1Intensification of seasonal extremes given a 2°C global warming target (DOC 674  kb)


  1. Adams RM, McCarl BA, Mearns LO (2003) The effects of spatial scale of climate scenarios on economic assessments: An example from US agriculture. Climatic Change 60:131–148CrossRefGoogle Scholar
  2. Battisti DS, Naylor RL (2009) Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323:240–244CrossRefGoogle Scholar
  3. Bauser CM et al (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim Dyn 33:849–868CrossRefGoogle Scholar
  4. Ben Ari T et al (2008) Human plague in the USA: the importance of regional and local climate. Biol Lett 4:737–740CrossRefGoogle Scholar
  5. Christidis N et al (2010) Probablistic estimates of recent changes in temperature: a multi-scale attribution analysis. Clim Dyn 34:1139–1156CrossRefGoogle Scholar
  6. Clark RT, Murphy JM, Brown SJ (2010) Do global warming targets limit heatwave risk? Geophys Res Lett 37:L17703CrossRefGoogle Scholar
  7. Collins WD et al (2006) The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). J Climate 19:2144–2161CrossRefGoogle Scholar
  8. Council of the European Union (2005) Presidency conclusions—Brussels 22 and 23 March 2005Google Scholar
  9. Diffenbaugh NS, Ashfaq M (2010) Intensification of hot extremes in the United States. Geophys Res Lett 37. doi:101029/2010GL043888
  10. Ebisuzaki W (1997) A method to estimate the statistical significance of a correlation when the data are serially correlated. J Climate 9:2147–2153CrossRefGoogle Scholar
  11. Fan Y, van den Dool H (2008) A global monthly land surface air temperature analysis for 1948-present. J Geophys Res 113:D01103CrossRefGoogle Scholar
  12. Giorgi F (1990) Simulation of regional climate using a limited area model nested in a general circulation model. J Clim 3:941–963CrossRefGoogle Scholar
  13. Giorgi F, Coppola E (2010) Does the model regional bias affect the projected regional climate change? An analysis of global model projections. Climatic Change 100:787–795CrossRefGoogle Scholar
  14. Intergovernmental Panel on Climate Change (IPCC) (2007a) 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, United Kingdom and New York, NY, USAGoogle Scholar
  15. Intergovernmental Panel on Climate Change (IPCC) (2007b) In: Parry ML, Canziani OF, Palutikof JP, der Linden PJvan, Hanson CE (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, United Kingdom and New York, NY, USAGoogle Scholar
  16. Jenkinson AF (1955) The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quart J Roy Meteor Soc 87:145–158Google Scholar
  17. Kiktev D et al (2007) Comparison of observed and multimodeled trends in annual extremes of temperature and precipitation. J Geophys Res 34:L10702Google Scholar
  18. Knutti R et al (2010) Challenges in combining projections from multiple climate models. J Climate 23:2739–2758CrossRefGoogle Scholar
  19. Kushnir Y (1994) Interdecadal variations in North Atlantic sea surface temperature and associated atmospheric conditions. J Climate 7:141–157CrossRefGoogle Scholar
  20. Liang X-Z et al (2008) Regional climate models downscaling analysis of general circulation models present climate biases propagation into future change projections. Geophys Res Lett 35:L08709CrossRefGoogle Scholar
  21. Lobell DB, Burke MB (2008) Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environ Res Lett 3. doi:10.1088/1748-9326/3/3/034007
  22. Lorenz R, Jaeger EB, Seneviratne SI (2010) Persistence of heat waves and its link to soil moisture memory. Geophys Res Lett 37:L09703CrossRefGoogle Scholar
  23. Mantua NJ et al (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteor Soc 78:1069–1079CrossRefGoogle Scholar
  24. Meehl GA et al (2009) Decadal prediction: can it be skillful? Bull Am Meteor Soc 90:1467–1485CrossRefGoogle Scholar
  25. Mitchell TD (2003) Pattern scaling: an examination of the accuracy of the technique for describing future climates. Climatic Change 60:217–242CrossRefGoogle Scholar
  26. Murphy JM et al (2004) Quantification of modeling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  27. Nakićenović N et al (2000) Special report on emissions scenarios: a special report of Working Group III on the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  28. Pan ZT et al (2004) Altered hydrologic feedback in a warming climate introduces a “warming hole”. Geophys Res Lett 31:L17109CrossRefGoogle Scholar
  29. Sanderson MG, Hemming DL, Betts RA (2011) Regional temperature and precipitation changes under high-end (>4°C) global warming. Phil Trans Roy Soc A 369:85–98CrossRefGoogle Scholar
  30. Schlenker W, Roberts MJ (2006) Nonlinear effects of weather on corn yields. Rev Ag Econ 28:391–398CrossRefGoogle Scholar
  31. Seneviratne SI et al (2006) Land-atmosphere coupling and climate change in Europe. Nature 443:205–209CrossRefGoogle Scholar
  32. Sherwood SC, Huber M (2010) An adaptability limit to climate change due to heat stress. Proc Nat Acad Sci 107:9552–9555CrossRefGoogle Scholar
  33. Smith JB et al (2009) Assessing dangerous climate change though an update on the International Panel on Climate Change (IPCC) “reasons for concern”. Proc Natl Acad Sci USA 106:4133–4137CrossRefGoogle Scholar
  34. Stenseth NC et al (2002) Ecological effects of climate fluctuations. Science 297:1292–1296CrossRefGoogle Scholar
  35. Tebaldi C, Hayhoe K, Arblaster J, Meehl G (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Climatic Change 7:9185–211Google Scholar
  36. United Nations Framework Convention on Climate Change (UNFCCC) (2009) The Copenhagen Accord 5 pp U N Geneva SwitzerlandGoogle Scholar
  37. Wang H et al (2009) Attribution of the seasonality and regionality in climate trends over the United States during 1950–2000. J Climate 22:2571–2590CrossRefGoogle Scholar
  38. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geog 21:530–548CrossRefGoogle Scholar
  39. Woollings T (2010) Dynamical influences on European climate: an uncertain future. Phil Trans Roy Soc A 368:3733–3756CrossRefGoogle Scholar
  40. Xu JC et al (2009) The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Cons Bio 23:520–530CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Geography and EnvironmentBoston UniversityBostonUSA

Personalised recommendations