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The role of climate models in adaptation decision-making: the case of the UK climate projections 2009


When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to its probabilistic treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale.

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Fig. 1


  1. 1.

    Examples include KlimZug in Germany and Knowledge for Climate in the Netherlands (Hegger et al. 2012, 53). A regularly updated list of European initiatives on climate change adaptation is maintained here: (accessed January 2015).

  2. 2.

    Only the three A2 regional model runs were used for the pattern-scaling process as the modellers had 90 years of data from these three runs as compared to the 30 years of data from the single B2 regional model run. The uncertainties associated with this process are discussed in the accompanying scientific report (Hulme et al. 2002, 83).

  3. 3.

    For instance, the ENSEMBLES project created a multi-model ensemble of regional climate models (RCMs) to create a series of high-resolution probabilistic climate projections (with 50 and 25 km grid boxes) for Europe (van der Linden and Mitchell 2009, 47).

  4. 4.

    Lenhard and Winsberg (2010) are sceptical about the prospects for localising the sources of error in climate models, due to the limited modularity of the models, the continued use of techniques such as ‘tuning’ and flux adjustments, and the evolutionary-like development of their code.

  5. 5.

    For a full technical explanation of how UKCP09 accounts for structural uncertainty, using the CFMIP models and a multivariate emulator, see Sexton et al. (2012).

  6. 6.

    For a much more complete description of downscaling methods see Fowler et al. (2007) and Winkler et al. (2011).

  7. 7.

    The regional 25 km data is based on 11 model variants of HadRM3, out of a total of 17 runs of which 6 were discarded due to a deficiency in their capacity to capture storms, precipitation and variability (Murphy et al. 2009, 75).

  8. 8.

    See the UKCP09 website for a full overview of available products: (accessed 25th January 2015), or (Murphy et al. 2009, 129) for a comparison between the 11-member RCM model data and the outputs from the weather generator.

  9. 9.

    This selection process for the user panel was noted here: (accessed July 2014).

  10. 10.

    This interview and the others referenced afterwards were part of a larger fieldwork study on the links between climate modelling and adaptation decision-making in English cities. Six interviewees are cited in this paper, out of a total of 31 semi-structured interviews with researchers and modellers, boundary organisations such as UKCIP, and policy-makers at the city region and local authority levels in the cities of London and Manchester. All interviews were transcribed and analysed in Atlas.ti.

  11. 11.

    Nevertheless, it is possible to directly use dynamically downscaled data for impacts assessment (cf. Moriondo et al. 2011). In the case of UKCP09, this is possible by using the externally hosted spatially coherent data from the RCM run, which also includes wind data.

  12. 12.

    The ARCC research projects discussed above will be discussed in forthcoming papers in terms of the salience of their findings and research tools for city planners.

  13. 13.

    This is the case, for example, with the Climate Ready Support Service led by the Environment Agency, and the Climate UK network of regional organisations. See the Defra website for more information: (accessed March 2015).



Adaptation and resilience in cities: analysis and decision-making using integrated assessment


Adaptation and resilience in a changing climate


Climate change risk assessment


Cloud feedback model intercomparison project


Department for environment food, and rural affairs


ENSEMBLE-based predictions of climate changes and their impacts (full title)


General circulation model


Hadley centre climate model


Hadley centre regional model


Hadley centre slab model


Intergovernmental panel on climate change


Multi-model ensemble


National adaptation programme


Perturbed physics ensemble


Prediction of regional scenarios and uncertainties for defining european climate change risks and effects


Regional climate model


Special report on emissions scenarios


Thames estuary 2100


UK climate impacts programme


UKCIP 2002 climate change scenarios for the United Kingdom


UK climate projections 2009


  1. Betz, G. (2007). Probabilities in climate policy advice: a critical comment. Climatic Change, 85, 1–9.

  2. Capon, R., and G. Oakley. (2012). Climate Change Risk Assessment for the Built Environment Sector. Defra. PB13698.

  3. Collins, M., Chandler, R. E., Cox, P. M., Huthnance, J. M., Rougier, J., & Stephenson, D. B. (2012). Quantifying future climate change. Nature Climate Change, 2, 403–409.

  4. Dahan Dalmedico, A., & Guillemot, H. (2006). Changement climatique: dynamiques scientifiques, expertise, enjeux géopolitiques. Sociologie du travail, 48, 412–432.

  5. Dawson, R. J. (2007). Re-engineering cities: a framework for adaptation to global change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 3085–3098.

  6. Dawson, R. J., Ball, T., Werritty, J., Werritty, A., Hall, J. W., & Roche, N. (2011). Assessing the effectiveness of non-structural flood management measures in the Thames estuary under conditions of socio-economic and environmental change. Global Environmental Change, 21, 628–646.

  7. Defra. (2012). UK Climate Change Risk Assessment: Government Report. PB13698. London: UK Government.

  8. Defra. (2013). The National Adaptation Programme: Making the country resilient to a changing climate. London: UK Government.

  9. Environment Agency. (2012). Thames Estuary 2100: Managing risks through London and the Thames Estuary. TE2100 Plan. Charlton: Environment Agency.

  10. Flato, G., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., et al. (2013). Evaluation of Climate Models. In T. F. Stocker, M. Tignor, J. Marotzke, S. K. Allen, A. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC (pp. 741–866). Cambridge, UK: Cambridge University Press.

  11. Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27, 1547–1578.

  12. Gill, S., Handley, J. F., Ennos, A. R., & Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment, 33, 115–133.

  13. Goodess, C. M., Hall, J. W., Best, M., Betts, R., Cabantous, L., Jones, P. D., Kilsby, C. G., Pearman, A., & Wallace, C. J. (2007). Climate scenarios and decision making under uncertainty. Built Environment, 33, 10–30.

  14. Hall, J. M., Handley, J. F., & Roland Ennos, A. (2012). The potential of tree planting to climate-proof high density residential areas in Manchester, UK. Landscape and Urban Planning, 104, 410–417.

  15. Hedger, M. M. K., Connell, R., & Bramwell, P. (2006). Bridging the gap: empowering decision-making for adaptation through the UK climate impacts programme. Climate Policy, 6, 201–215.

  16. Hegger, D., Lamers, M., Van Zeijl-Rozema, A., & Dieperink, C. (2012). Conceptualising joint knowledge production in regional climate change adaptation projects: success conditions and levers for action. Environmental Science & Policy, 18, 52–65.

  17. Hewitt, C., Mason, S., & Walland, D. (2012). The global framework for climate services. Nature Climate Change, 2, 831–832.

  18. Hulme, M., & Dessai, S. (2008). Negotiating future climates for public policy: a critical assessment of the development of climate scenarios for the UK. Environmental Science & Policy, 11, 54–70.

  19. Hulme, M., Lu, X., & Turnpenny, J. (2002). Climate change scenarios for the United Kingdom The UKCIP02 scientific report. UK Climate Impacts Programme: Oxford University.

  20. Hulme, M., & Turnpenny, J. (2004). Understanding and managing climate change: the UK experience. The Geographical Journal, 170, 105–115.

  21. IPCC. (2013). In S. Thomas, D. Qin, P. Gian-Kasper, M. Tignor, S. K. Allen, A. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

  22. Jones, P.D., Kilsby, C.G., Harpham, C., Glenis, V., Burton, A. (2009). UK Climate Projections science report: Projections of future daily climate for the UK from the Weather Generator. University of Newcastle, Defra.

  23. Keller, K., Yohe, G., & Schlesinger, M. (2007). Managing the risks of climate thresholds: uncertainties and information needs. Climatic Change, 91, 5–10.

  24. Kilsby, C. G., Burton, P. D., Ford, A. C., Fowler, H. J., Harpham, C., James, P., Smith, A., Wilby, R. L. (2007). A daily weather generator for use in climate change studies. Environmental Monitoring and Modelling Software: 1705–1719.

  25. Knutti, R., & Sedláček, J. (2012). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3, 369–373.

  26. Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52, 514–528.

  27. Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41, 253–262.

  28. Van der Linden, P., & Mitchell, J. F. B. (2009). ENSEMBLES: Climate change and its impacts at seasonal, decadal and centennial timescales. Exeter: Met Office Hadley Centre.

  29. Lloyd, E. A., & Schweizer, V. J. (2014). Objectivity and a comparison of methodological scenario approaches for climate change research. Synthese, 191, 2049–2088.

  30. Lonsdale, K. G., Megan, G., Johnstone, K., Street, R., West, C., & Brown, A. D. (2010). Attributes of Well-Adapting Organisations. UK Climate Impacts Programme: Oxford University.

  31. Lowe, J., Howard, T., Pardaens, A., Tinker, J., Holt, J., Wakelin, S., Milne, G., et al. (2009). UK Climate Projections science report: Marine and coastal projections. Exeter: Met Office Hadley Centre.

  32. McAvaney, B. J., Le Treut, H. (2003). The cloud feedback intercomparison project (CFMIP). CLIVAR Exchanges—supplementary contributions 26.

  33. McCarthy, M. P., Harpham, C., Goodess, C. M., & Jones, P. D. (2011). Simulating climate change in UK cities using a regional climate model, HadRM3. International Journal of Climatology, 32(12),1875--1888.

  34. Moriondo, M., Giannakopoulos, C., & Bindi, M. (2011). Climate change impact assessment: the role of climate extremes in crop yield simulation. Climatic Change, 104, 679–701.

  35. Murphy, J. M., Ben, B. B., Booth, M. C., Harris, G. R., Sexton, D. M. H., & Webb, M. J. (2007). A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 1993–2028.

  36. Murphy, J. M., Sexton, D. M. H., Jenkins, G. J., Booth, B. B. B., Brown, C. C., Clark, R. T., Collins, M., et al. (2009). UK Climate Projections science report: Climate change projections. UK Gov.

  37. Nakićenović, N., Alcamo J., Davis, G, de Vries, B., Fenhann, J. Gaffin, S. Gregory, K. et al. (2000). IPCC Special Report on Emissions Scenarios (SRES). Cambridge University Press.

  38. Parker, W. S. (2009). II—confirmation and adequacy-for-purpose in climate modelling. Aristotelian Society Supplementary Volume, 83, 233–249.

  39. Parker, W. S. (2011). When climate models agree: the significance of robust model predictions. Philosophy of Science, 78, 579–600.

  40. Parker, W. S. (2013). Ensemble modeling, uncertainty and robust predictions. Wiley Interdisciplinary Reviews: Climate Change, 4, 213–223.

  41. Petersen, A. C. (2006). Simulating nature: a philosophical study of computer-simulation uncertainties and their role in climate science and policy advice. Apeldoorn: Het Spinhuis.

  42. Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V. M., et al. (2007). Climate Models and Their Evaluation. In S. Solomon, Q. Din, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, & H. L. Miller (Eds.), Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge: Cambridge University Press.

  43. Ranger, N., Reeder, T., & Lowe, J. (2013). Addressing “deep” uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 Project. EURO Journal on Decision Processes, 1, 233–262.

  44. Van Renssen, S. (2013). EU adaptation policy sputters and starts. Nature Climate Change, 3, 614–615.

  45. Rougier, J. (2007). Probabilistic inference for future climate using an ensemble of climate model evaluations. Climatic Change, 81, 247–264.

  46. Sanderson, B., Knutti, Dr. R. (2012). Climate change projections: characterizing uncertainty using climate models. In P. J. Rasch (Ed.), Climate Change Modeling Methodology (pp. 235–259). New York: Springer.

  47. Schneider, S. H. (1983). CO2, climate and society: a brief overview. In Social Science Research and Climate Change, (pp. 9–15). Springer.

  48. Sexton, D. M. H., Murphy, J. M., Collins, M., & Webb, M. J. (2012). Multivariate probabilistic projections using imperfect climate models part I: outline of methodology. Climate Dynamics, 38, 2513–2542.

  49. Smith, C., Lindley, S., & Levermore, G. (2009). Estimating spatial and temporal patterns of urban anthropogenic heat fluxes for UK cities: the case of Manchester. Theoretical and Applied Climatology, 98, 19–35.

  50. Steynor, A., Gawith M., Street R. (2012). Engaging users in the development and delivery of climate projections: the UKCIP experience of UKCP09. Oxford University, UK Climate Impacts Programme.

  51. Tang, S., & Dessai, S. (2012). Usable science? The U.K. Climate projections 2009 and decision support for adaptation planning. Weather, Climate, and Society, 4, 300–313.

  52. Walsh, C. L., Hall, J. W., Roger, S., Blanksby, J., Cassar, M., Ekins, P., Glendinning, S., et al. (2007). Building knowledge for a changing climate: collaborative research to understand and adapt to the impacts of climate change on infrastructure, the built environment and utilities.. England: Newcastle University.

  53. Wilby, R. L., & Dessai, S. (2010). Robust adaptation to climate change. Weather, 65, 180–185.

  54. Wilks, D. S. (2010). Use of stochastic weathergenerators for precipitation downscaling. Wiley Interdisciplinary Reviews: Climate Change, 1, 898–907.

  55. Willows, R., Connell R. (Eds.) (2003). Climate adaptation: Risk, uncertainty and decision-making. UKCIP Technical Report. Oxford University, UK Climate Impacts Programme.

  56. Winkler, J. A., Guentchev, G. S., Perdinan, Tan, P.‐. N., Zhong, S., Liszewska, M., Abraham, Z., Niedźwiedź, T., & Ustrnul, Z. (2011). Climate scenario development and applications for local/regional climate change impact assessments: an overview for the non‐climate scientist. Geography Compass, 5, 275–300.

  57. Winsberg, E. (2012). Values and uncertainties in the predictions of global climate models. Kennedy Institute of Ethics Journal, 22, 111–137.

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My thanks to Wendy Parker, Joel Katzav, and the two anonymous reviewers for their very helpful comments and critiques, and to Suraje Dessai, for recommending my research to the guest editors for this special issue. This research was made possible through the financial and directive support of the Sustainable Consumption Institute’s former Centre for Doctoral Training (CDT) at the University of Manchester, who funded the thesis on which this article is based.

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Correspondence to Liam James Heaphy.

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Heaphy, L.J. The role of climate models in adaptation decision-making: the case of the UK climate projections 2009. Euro Jnl Phil Sci 5, 233–257 (2015).

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  • Climate models
  • Uncertainty
  • Decision-making
  • Climate adaptation
  • Built environment
  • Urban heat island
  • Downscaling