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Climatic Change

, Volume 131, Issue 4, pp 677–689 | Cite as

Objective estimate of future climate analogues projected by an ensemble AGCM experiment under the SRES A1B scenario

  • Kenshi HibinoEmail author
  • Izuru Takayabu
  • Tosiyuki Nakaegawa
Article

Abstract

Climate analogues (CAs), regions whose present climates are similar to the future climate of a target place, are identified to assess the effects of climate change towards the end of the 21st century. The location of CAs and their present climates yield information that may be used to mitigate the harmful impacts of climate change. Present (1979–2003) and future (2075–2099) climates are projected in an ensemble experiment using the atmospheric general circulation model of the Meteorological Research Institute in Japan. The ensemble members consist of combinations of four distributions of sea surface temperature, three convection schemes, and two initial conditions. The emission scenario for greenhouse gases is A1B of the Special Report on Emissions Scenarios. A new method to identify the location of CAs is introduced, in which the uncertainty in the climate projection is taken into account. CAs for all land regions of the world are presented and those of four capital cities are analyzed in detail. The CAs are generally distributed equator-ward from the target places, consistent with the global warming. It is also found that ensemble experiments that encompass the uncertainty of climate projections can yield robust results for the CA and lead to reliable assessments of climate change.

Keywords

Future Climate Root Mean Square Difference Similarity Score Ensemble Member Convection Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We are thankful to Drs Mizuta and Yoshida of the Meteorological Research Institute for providing the results of the AGCMs analyzed in this paper. This research is supported by the SOUSEI program of MEXT, Japan.

References

  1. Boberg F, Christensen JH (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nat Clim Chang 2(6):433–436CrossRefGoogle Scholar
  2. Burrows MT, Schoeman DS, Richardson AJ, Molinos JG, Hoffmann A, Buckley LB, Moore PJ, Brown CJ, Bruno JF, Duarte CM et al (2014) Geographical limits to species-range shifts are suggested by climate velocity. Nature 507(7493):492–495CrossRefGoogle Scholar
  3. Buser CM, Künsch H, Lüthi D, Wild M, Schär C (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim Dyn 33(6):849–868CrossRefGoogle Scholar
  4. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3-4):527–546CrossRefGoogle Scholar
  5. Endo H, Kitoh A, Ose T, Mizuta R, Kusunoki S (2012) Future changes and uncertainties in Asian precipitation simulated by multiphysics and multi–sea surface temperature ensemble experiments with high-resolution meteorological research institute atmospheric general circulation models (MRI-AGCMs). J Geophys Res Atmos (1984–2012) 117(D16)Google Scholar
  6. Hallegatte S, Hourcade JC, Ambrosi P (2007) Using climate analogues for assessing climate change economic impacts in urban areas. Clim Chang 82(1-2):47–60CrossRefGoogle Scholar
  7. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme. In: Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models, Meteorol Monogr, vol 24, no 46. American Meteorological Society, Boston, pp 165– 170Google Scholar
  8. Kopf S, Ha-Duong M, Hallegatte S et al (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
  9. Koven CD (2013) Boreal carbon loss due to poleward shift in low-carbon ecosystems. Nat Geosci 6(6):452–456CrossRefGoogle Scholar
  10. Meehl GA, Covey C, Taylor KE, Delworth T, Stouffer RJ, Latif M, McAvaney B, Mitchell JF (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88(9):1383–1394CrossRefGoogle Scholar
  11. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25 (6):693–712CrossRefGoogle Scholar
  12. Mizuta R, Yoshimura H, Murakami H (2012) Climate simulations using MRI-AGCM3. 2 with 20-km grid (special issue on recent development on climate models and future climate projections). J Meteorol Soc Jpn 90:233–258CrossRefGoogle Scholar
  13. Nakaegawa T, Kitoh A, Hosaka M (2013) Discharge of major global rivers in the late 21st century climate projected with the high horizontal resolution MRI-AGCMs. Hydrol Process 27(23):3301– 3318Google Scholar
  14. Nakicenovic N, Alcamo J, Davis G, Vries Bd, Fenhann JV, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T et al (2000) Special report on emissions scenarios: special report of working group iii of the intergovernmental panel on climate changeGoogle Scholar
  15. Ordonez A, Williams JW (2013) Climatic and biotic velocities for woody Taxa distributions over the last 16 000 years in eastern North America. Ecol Lett 16(6):773–781CrossRefGoogle Scholar
  16. Parry ML, Carter T (1989) An assessment of the effects of climatic change on agriculture. Clim Chang 15(1-2):95–116CrossRefGoogle Scholar
  17. Ramírez-Villegas J, Lau C, Kohler A, Jarvis A, Arnell N, Osborne T, Hooker J (2011) Climate analogues: finding tomorrow’s agriculture todayGoogle Scholar
  18. Randall D, Pan D (1993) Implementation of the Arakawa-Schubert cumulus parameterization with a prognostic closure. In: Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models, Meteor Monogr, vol 24, no 46. American Meteorological Society, Boston , pp 137–144Google Scholar
  19. Rayner N, Parker DE, Horton E, Folland C, Alexander L, Rowell D, Kent E, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res Atmos (1984–2012) 108(D14)Google Scholar
  20. Veloz SD, Williams JW, Blois JL, He F, Otto-Bliesner B, Liu Z (2012) No-analog climates and shifting realized niches during the late quaternary: implications for 21st-century predictions by species distribution models. Glob Chang Biol 18(5):1698–1713Google Scholar
  21. Wilby R, Charles S, Zorita E, Timbal B, Whetton P, Mearns L (2004) Guidelines for use of climate scenarios developed from statistical downscaling methodsGoogle Scholar
  22. Williams JW, Jackson ST, Kutzbach JE (2007) Projected distributions of novel and disappearing climates by 2100 ad. Proc Natl Acad Sci 104(14):5738–5742CrossRefGoogle Scholar
  23. Yukimoto S, Yoshimura H, Hosaka M, Sakami T, Tsujino H, Hirabara M, Tanaka TY, Deushi M, Obata A, Nakano H, Adachi Y, Shindo E, Yabu S, Ose T, Kitoh A (2011) Meteorological research institute earth system model version 1 (MRI-ESM1): model descriptionGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Kenshi Hibino
    • 1
    Email author
  • Izuru Takayabu
    • 2
  • Tosiyuki Nakaegawa
    • 2
  1. 1.Faculty of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan
  2. 2.Meteorological Research InstituteTsukubaJapan

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