, Volume 192, Issue 12, pp 3979–4008

An assessment of the foundational assumptions in high-resolution climate projections: the case of UKCP09

  • Roman Frigg
  • Leonard A. Smith
  • David A. Stainforth

DOI: 10.1007/s11229-015-0739-8

Cite this article as:
Frigg, R., Smith, L.A. & Stainforth, D.A. Synthese (2015) 192: 3979. doi:10.1007/s11229-015-0739-8


The United Kingdom Climate Impacts Programme’s UKCP09 project makes high-resolution projections of the climate out to 2100 by post-processing the outputs of a large-scale global climate model. The aim of this paper is to describe and analyse the methodology used and then urge some caution. Given the acknowledged systematic, shared errors of all current climate models, treating model outputs as decision-relevant projections can be significantly misleading. In extrapolatory situations, such as projections of future climate change, there is little reason to expect that post-processing of model outputs can correct for the consequences of such errors. This casts doubt on our ability, today, to make trustworthy probabilistic projections at high resolution out to the end of the century.


Climate change Prediction Projection Simulation Model Probability Reliability Emulation Systematic error Decision-making  Structural model error 

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Roman Frigg
    • 1
    • 2
  • Leonard A. Smith
    • 3
    • 4
    • 6
  • David A. Stainforth
    • 3
    • 5
    • 7
    • 8
  1. 1.Department of Philosophy, Logic and Scientific MethodLSELondonUK
  2. 2.Centre for Philosophy of Natural and Social Science (CPNSS)LSELondonUK
  3. 3.Centre for the Analysis of Time Series (CATS)LSELondonUK
  4. 4.Pembroke College OxfordOxfordUK
  5. 5.The Grantham Research Institute on Climate Change and the EnvironmentLSELondonUK
  6. 6.Department of StatisticsUniversity of ChicagoChicagoUSA
  7. 7.Department of PhysicsUniversity of WarwickCoventryUK
  8. 8.Environmental Change Institute, University of OxfordOxfordUK

Personalised recommendations