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


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.

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Fig. 1


  1. The existence of a wide-spread a consensus is documented in Oreskes (2007); the evidence for the warming being anthropogenic is documented in the most recent IPCC report (Stocker et al. 2013); a shorter summary is Dessler (2011, Chap. 3).

  2. Knowing even roughly what is likely to happen may be reason enough not to go there.

  3. ‘UKCP’ stands for United Kingdom Climate Projections and ‘09’ indicates that it was launched for public use in 2009. The project’s broad outline is documented in the briefing report (Jenkins et al. 2009) (a revised version has been published in 2010); the science report (Murphy et al. 2010) and two recent papers (Sexton et al. 2012; Sexton and Murphy 2012) provide a detailed exposition.

  4. A ‘projection’ is the ‘response of the climate system to emission or concentration scenarios of greenhouse gases and aerosols, or radiative forcing scenarios [...]’ (Solomon et al. 2007, 943}. Unlike predictions or forecasts, projections ‘depend upon the emission/concentration/radiative forcing scenario used, which are based on assumptions concerning, for example, future socioeconomic and technological developments that may or may not be realised and are therefore subject to substantial uncertainty’ (ibid.).

  5. ‘IPCC’ refers to the Intergovernmental Panel on Climate Change, the international body for the assessment of climate change established by the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) in 1988. The panel’s findings are documented in its assessment reports. The 4th assessment report was published in 2007 (Solomon et al. 2007), and the 5th has been released in phases from September 2013 to October 2014.

  6. Similar projects include: Cal Adap (, Climate Wizard (, ClimateimpactsOnline (

  7. In this paper we shall use the word ‘trustworthy’ to denote probability forecasts, which one might rationally employ for decision-making purposes using probability theory in the standard way. Such probability forecasts are expected to be robust and reliable, the kind a good Bayesian would make. There may be many justifiable and interesting scientific reasons to construct probability forecasts; our criticism of them in this paper is only in regard to their direct use in decision support (as, for instance, illustrated in the worked examples of UKCP09).

  8. Uncertainty in climate modelling has been given considerable attention, among others, by Parker (2010a), Parker (2013), Winsberg and Biddle (2010), and Winsberg (2012). Our discussion has a different focus in that it deals specifically with local climate projection and concentrates on post-processing.

  9. We take this phrase to refer to the median of the probability distribution.

  10. The full set of UKCP09 predictions is at

  11. Our account of the method is based on Murphy et al. (2010, Chap. 3) and Sexton et al. (2012).

  12. See (information retrieved on 23 March 2014). Further information about HadCM3 can be found at

  13. A ‘run’ is the calculation of the value of \(x\) at some particular future instant of time given a certain \(x_0\) and a set of specific values for \(\alpha \). It is synonymous with the term ‘simulation’. With today’s climate models a run of a 100 years may take between hours and months depending on the model’s complexity and resolution and on the computing hardware utilised.

  14. Going from HadCM3 to HadSM3 roughly doubles the speed of the model.

  15. We note that the notion of a vector representing the world’s climate raises many serious questions. Which variables ought to be included? At what time and length scales should its components be defined? And more fundamental, how is climate (as opposed to weather) to be defined in the first place. The documentation of UKCP09 provides little information about how these issues have been resolved. Since nothing in our discussion depends on the definition of \(y\) we don’t pursue this issue further.

  16. Given our provisos about the definition of \(y\), selecting observations as indicators of the climate vector is an equally difficult task. UKCP09 use so-called ‘pseudo-observations’: ‘We obtain these by using two or three alternative data sets for each observed quantity, from which we generate 100 pseudo-observations made by adding random linear combinations (where coefficients sum to one) of the different data sources [...] regridded onto the HadSM3 grid’ (Sexton et al. 2012, p. 2517). Again, nothing in the discussion to follow depends on how exactly observations are treated and so we set this issue aside.

  17. Of course these distributional assumptions are often questionable; for example they cannot hold for precipitation which is positive definite.

  18. Arguably parameters of an imperfect model are not uncertain but rather indeterminate, as there is no ideal set of parameter values which will make the model adequate for all predictive purposes, as would be the case if the model structure was perfect and the values of the parameters were well defined but simply unknown (Du and Smith 2012).

  19. This assumption is controversial. Smith (2006) argues that for imperfect models appropriate values (leading to trustworthy forecasts) may not exist. For want of space we set these worries aside and proceed as if the question was one of uncertainty not indeterminacy; for more on this point see Smith and Stern (2011).

  20. We note in passing the lack of unanimity on whether the second ‘P’ of PPE stands for ‘parameter’, ‘parameterization’, or ‘physics’.

  21. For a discussion of what ‘best’ might mean see also Parker (2010b).

  22. Note the difference between the range of reasonable model-parameter values within the model and the uncertainty in the value of the corresponding physical parameter, when such a thing exists.

  23. There are a host of challenges here, as climate is a distribution, the model climate and the real climate are not in the same state space, whatever notion of ‘best’ is taken the simulation model will not be ‘best’ for all target variants, and so on. These points raise important questions about how the distance is measured and what the discrepancy is intended to represent in practice in the climate case.

  24. UKCP09 expresses this by writing (in our notion) \(y=\varphi (a^{*}) + \varepsilon \); see Eq. (1) in (Sexton et al. 2012, p. 2521). However this is not a correct formal expression of the concept of a discrepancy term because, as noted above, \(y\) and \(x\) are not members of the same vector space and hence cannot be added. For a discussion of ‘subtractability’, see Smith (2006) and references therein.

  25. See also Murphy et al. (2010, pp. 63–64).

  26. For details see Sexton et al. (2012, pp. 2525–2527).

  27. This is Eq. (5) in Sexton et al. (2012, p. 2523); for a discussion of the derivation see Rougier (2007).

  28. Note that emulators may elsewhere be called ‘surrogate models’, ‘meta-models’, and ‘models of models’. A general introduction to emulation (unrelated to UKCP09) can be found at

  29. UKCP09 does so indirectly in the sense that it emulates the coefficients of a set of basis vectors for the output space in question. Nothing in the discussion that follow depends on this.

  30. Furthermore, there are limitations to our scientific understanding of the climate system and there may be relevant factors and process that we are simply unaware of—there may be unknown unknowns which would lead us to alter the equations of the model even under our current computational constraints.

  31. Even today’s best climate models do not simulate blocking realistically. For a discussion of this point see Smith and Stern (2011) and Hoskins’ review of UKCP09 (available at

  32. The question of what good science should report on the lead times beyond those on which quantitative guidance is informative is a separate issue.

  33. An example is Murphy et al. (2010, pp. 63–69).

  34. The challenges to smoothness posed by computations on a digital computer are ignored below. We note in passing that such challenges exist when, for example, a change in the least significant bit of \(x_0\) yield significant changes in the target variable (Lorenz 1968).

  35. The principle and its problems are well documented in the philosophical literature on probability; see, for instance, Salmon et al. (1992, pp. 74–77).

  36. See Stainforth et al. (2005, (2007) for a discussion of the ice fall rate.

  37. Initial conditions are mentioned again on p. 129, but no information beyond what has been said on pp. 26–27 is provided.

  38. Initial conditions are briefly mentioned but not discussed in Sexton et al. (2012) and in the Briefing Report (Jenkins et al. 2009). The Science Report (Murphy et al. 2010), a document of over 190 pages, dedicates three pages in the introductory part to the problem of initial condition uncertainty.

  39. The multi-years averages in (2007) were 8 years long compared to 30 years in UKPC09. However, the problem pinpointed by Stainforth et al. is unlikely to disappear by moving from 8 year averages to 30 year averages.

  40. A summary of the review is available at The above quotation has been retrieved on 7 March 2014.

  41. We thank an anonymous referee for raising this concern.

  42. Sometimes this observation comes in the guise of there being a cascade of uncertainty, with moderate confidence at the continental scale and less confidence at the local scale; see for instance (Jenkins et al. 2009, pp. 6 and 22). How exactly the point that local projections are uncertain is expressed is immaterial to the dialectic in this section.

  43. These alternative readings are also discussed in Parker (2014).

  44. See for details.

  45. Discussion of the translation between model variables and real world variables with similar names can be found in Smith (2000) and Smith (2002).

  46. Expected to change even without a deeper scientific understanding of the phenomena, or new observations. Scientific projects are always subject to change when our basic understanding of science changes, the question is whether they are mature conditioned on everything we know today.


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Work for this paper has been supported by the LSE’s Grantham Research Institute on Climate Change and the Environment and the Centre for Climate Change Economics and Policy funded by the Economics and Social Science Research Council and Munich Re. Frigg further acknowledges financial support from the AHRC-funded ‘Managing Severe Uncertainty’ Project and Grant FFI2012-37354 of the Spanish Ministry of Science and Innovation (MICINN). Smith would like to acknowledge continuing support from Pembroke College, Oxford, from the Blue-Green Cities Research Consortium funded by EPSRC (Grant EP/K013661/1), and from RDCEP via NSF grant No. 0951576. We would like to thank Wendy Parker, Erica Thompson, and Charlotte Werndl for comments on earlier drafts and/or helpful discussions.

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Frigg, R., Smith, L.A. & Stainforth, D.A. An assessment of the foundational assumptions in high-resolution climate projections: the case of UKCP09. Synthese 192, 3979–4008 (2015).

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