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

Abstract

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

Notes

  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: http://www.eea.europa.eu/themes/climate/national-adaptation-strategies (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: http://ukclimateprojections.metoffice.gov.uk/22533 (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: http://ukclimateprojections.metoffice.gov.uk/23225 (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: https://www.gov.uk/government/policies/adapting-to-climate-change (accessed March 2015).

Abbreviations

ARCADIA:

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

ARCC:

Adaptation and resilience in a changing climate

CCRA:

Climate change risk assessment

CFMIP:

Cloud feedback model intercomparison project

DEFRA:

Department for environment food, and rural affairs

ENSEMBLES:

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

GCM:

General circulation model

HadCM:

Hadley centre climate model

HadRM:

Hadley centre regional model

HadSM:

Hadley centre slab model

IPCC:

Intergovernmental panel on climate change

MME:

Multi-model ensemble

NAP:

National adaptation programme

PPE:

Perturbed physics ensemble

PRUDENCE:

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

RCM:

Regional climate model

SRES:

Special report on emissions scenarios

TE2100:

Thames estuary 2100

UKCIP:

UK climate impacts programme

UKCIP02:

UKCIP 2002 climate change scenarios for the United Kingdom

UKCP09:

UK climate projections 2009

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Acknowledgments

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). https://doi.org/10.1007/s13194-015-0114-0

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Keywords

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