Climatic Change

, Volume 117, Issue 4, pp 951–960 | Cite as

The use of the land-sea warming contrast under climate change to improve impact metrics

  • Manoj M. Joshi
  • Andrew G. Turner
  • Chris Hope


A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.


CMIP3 Model Integrate Assessment Model Surface Temperature Change PAGE09 Model CMIP3 Ensemble 
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.



AT is supported by a NERC Postdoctoral Fellowship, grant NE/H015655/1. We would like to thank D. Frame and R. Warren for useful discussions. We acknowledge the modelling groups, the PCMDI and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.


  1. Drost F, Karoly D, Braganza K (2012) Communicating global climate change using simple indices: an update. Clim Dyn 39:989–999CrossRefGoogle Scholar
  2. ESF (2009) Impacts of Ocean Acidification, European Science Foundation,
  3. Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywood J, Lean J, Lowe DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Van Dorland R (2007) Changes in atmospheric constituents and in radiative forcing. 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, CambridgeGoogle Scholar
  4. Gohar LK and Lowe A, 2009, “Summary of the Committee on Climate Change’s 2016 peak emission scenarios”, AV/WS1/D1/01,
  5. Hope C (2011) The Social Cost of CO2 from the PAGE09 model, Judge Business School Working paper 5/2011, submitted to the economics e-journal,
  6. Huntingford C, Cox PM (2000) An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim Dyn 16:575–586CrossRefGoogle Scholar
  7. Joshi MM, Gregory JM, Webb MJ, Sexton DMH, Johns TC (2008) Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Clim Dyn 30:455–465CrossRefGoogle Scholar
  8. Joshi MM, Lambert FH, Webb MJ (2012) An explanation for the difference between 20th and 21st century land-sea warming ratio in climate models. Clim Dyn (in press)Google Scholar
  9. Ju J, Slingo JM (1995) The Asian summer monsoon and ENSO. Q J R Meteorol Soc 121:1133–1168CrossRefGoogle Scholar
  10. Lambert FH, Chiang JCH (2007) Control of land-ocean temperature contrast by ocean heat uptake. Geophys Res Lett 34:L13704. doi: 10.1029/2007GL029755 CrossRefGoogle Scholar
  11. Lund MT, Berntsen T, Fuglestvedt JS, Ponater M, Shine KP (2011) How much information is lost by using global-mean climate metrics? An example using the transport sector. Clim Chang Lett 113:949–963CrossRefGoogle Scholar
  12. Manabe S, Stouffer RJ, Spelman MJ, Bryan K (1991) Transient responses of a coupled ocean–atmosphere model to gradual changes of atmospheric CO2 part I: annual mean response. J Clim 4:785–818CrossRefGoogle Scholar
  13. Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCP, Watterson IG, Weaver AG, Zhao Z-C (2007a) Global Climate Projections. 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
  14. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007b) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  15. Meinshausen M, Raper SCB, Wigley TML (2011) Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6—Part 1: model description and calibration. Atmos Chem Phys 11:1417–1456. doi: 10.5194/acp-11-1417-2011 CrossRefGoogle Scholar
  16. Pham M, Boucher O, Hauglustaine D (2005) Changes in atmospheric sulfur burdens and concentrations and resulting radiative forcings under IPCC SRES emission scenarios for 1990–2100. J Geophys Res 110 doi: 10.1029/2004JD005125
  17. Rowell DP, Jones RG (2006) Causes and uncertainty of future summer drying over Europe. Clim Dynam 27:281–299CrossRefGoogle Scholar
  18. Sutton RT, Dong B-W, Gregory JM (2007) Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys Res Lett 34:L02701. doi: 10.1029/2006GL028164 CrossRefGoogle Scholar
  19. van Vuuren DP, Batlle-Bayer L, Chuwah C, Ganzeveld L, Hazeleger W, van den Hurk B, van Noije T, O’Neill B, Strengers BJ (2012) A comprehensive view on climate change: coupling of earth system and integrated assesment models. Environ Res Lett 7:024102. doi: 10.1088/1748-9326/7/2/024012 Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Manoj M. Joshi
    • 1
  • Andrew G. Turner
    • 2
  • Chris Hope
    • 3
  1. 1.Climatic Research Unit and Tyndall Centre for Climate Change Research, School of Environmental SciencesUniversity of East AngliaNorwichUK
  2. 2.NCAS ClimateUniversity of ReadingReadingUK
  3. 3.Judge Business SchoolUniversity of CambridgeCambridgeUK

Personalised recommendations