Climatic Change

, Volume 113, Issue 3–4, pp 949–963 | Cite as

How much information is lost by using global-mean climate metrics? an example using the transport sector

  • M. T. LundEmail author
  • T. Berntsen
  • J. S. Fuglestvedt
  • M. Ponater
  • K. P. Shine


Metrics are often used to compare the climate impacts of emissions from various sources, sectors or nations. These are usually based on global-mean input, and so there is the potential that important information on smaller scales is lost. Assuming a non-linear dependence of the climate impact on local surface temperature change, we explore the loss of information about regional variability that results from using global-mean input in the specific case of heterogeneous changes in ozone, methane and aerosol concentrations resulting from emissions from road traffic, aviation and shipping. Results from equilibrium simulations with two general circulation models are used. An alternative metric for capturing the regional climate impacts is investigated. We find that the application of a metric that is first calculated locally and then averaged globally captures a more complete and informative signal of climate impact than one that uses global-mean input. The loss of information when heterogeneity is ignored is largest in the case of aviation. Further investigation of the spatial distribution of temperature change indicates that although the pattern of temperature response does not closely match the pattern of the forcing, the forcing pattern still influences the response pattern on a hemispheric scale. When the short-lived transport forcing is superimposed on present-day anthropogenic CO2 forcing, the heterogeneity in the temperature response to CO2 dominates. This suggests that the importance of including regional climate impacts in global metrics depends on whether small sectors are considered in isolation or as part of the overall climate change.


Road Traffic Global Warming Potential Climate Impact Radiative Force Transport Sector 
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.



This research was supported by the European Union’s Sixth Framework Integrated Project QUANTIFY Contract No 003893 and the TEMPO project funded by the Norwegian Research Council. We thank Nicola Stuber for making available the results of the HadSM3 simulations.

Supplementary material

10584_2011_391_MOESM1_ESM.pdf (256 kb)
ESM 1 (PDF 255 kb)


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • M. T. Lund
    • 1
    Email author
  • T. Berntsen
    • 1
    • 2
  • J. S. Fuglestvedt
    • 1
  • M. Ponater
    • 3
  • K. P. Shine
    • 4
  1. 1.CICERO – Center for International Climate and Environmental ResearchOsloNorway
  2. 2.Department of GeosciencesUniversity of OsloOsloNorway
  3. 3.Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der AtmosphäreOberpfaffenhofenGermany
  4. 4.Department of MeteorologyUniversity of ReadingReadingUK

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