Climatic Change

, Volume 124, Issue 1–2, pp 105–117 | Cite as

Probabilistic spatial risk assessment of heat impacts and adaptations for London

  • Katie Jenkins
  • Jim Hall
  • Vassilis Glenis
  • Chris Kilsby
  • Mark McCarthy
  • Clare Goodess
  • Duncan Smith
  • Nick Malleson
  • Mark Birkin


High temperatures and heatwaves can cause large societal impacts by increasing health risks, mortality rates, and personal discomfort. These impacts are exacerbated in cities because of the Urban Heat Island (UHI) effect, and the high and increasing concentrations of people, assets and economic activities. Risks from high temperatures are now widely recognised but motivation and implementation of proportionate policy responses is inhibited by inadequate quantification of the benefits of adaptation options, and associated uncertainties. This study utilises high spatial resolution probabilistic projections of urban temperatures along with projections of demographic change, to provide a probabilistic risk assessment of heat impacts on urban society. The study focuses on Greater London and the surrounding region, assessing mortality risk, thermal discomfort in residential buildings, and adaptation options within an integrated framework. Climate change is projected to increase future heat-related mortality and residential discomfort. However, adjusting the temperature response function by 1–2 °C, to simulate adaptation and acclimatisation, reduced annual heat related mortality by 32–69 % across the scenarios tested, relative to a no adaptation scenario. Similar benefits of adaptation were seen for residential discomfort. The study also highlights additional benefits in terms of reduced mortality and residential discomfort that mitigating the urban heat island, by reducing albedo and anthropogenic heat emissions, could have.


Urban Land Urban Heat Island Adaptation Option Building Type Weather Generator 
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 paper has benefited from research undertaken as part of the ARCADIA Project (Adaptation and Resilience in Cities: Analysis and Decisions-making using Integrated Assessment), funded by the Engineering and Physical Sciences Research Council, award number EP/G060983/1.

Supplementary material

10584_2014_1105_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 19 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Katie Jenkins
    • 1
    • 7
  • Jim Hall
    • 1
  • Vassilis Glenis
    • 2
  • Chris Kilsby
    • 2
  • Mark McCarthy
    • 3
  • Clare Goodess
    • 4
  • Duncan Smith
    • 5
  • Nick Malleson
    • 6
  • Mark Birkin
    • 6
  1. 1.Environmental Change Institute (ECI)University of OxfordOxfordUK
  2. 2.School of Civil Engineering and GeosciencesNewcastle UniversityNewcastle upon TyneUK
  3. 3.Met Office, Hadley CentreExeterUK
  4. 4.Climatic Research Unit, School of Environmental SciencesUniversity of East AngliaNorfolkUK
  5. 5.LSE Cities, London School of Economics and Political ScienceLondonUK
  6. 6.School of GeographyUniversity of LeedsLeedsUK
  7. 7.Environmental Change Institute (ECI), School of Geography and the Environment and Tyndall Centre for Climate Change ResearchUniversity of OxfordOxfordUK

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