By Clare Heaviside (University College London – UCL), Jonathon Taylor (UCL & Tampere U), Oscar Brousse (UCL), Charles Simpson (UCL)

Current and projected temperatures simulated by global climate models are typically output at a coarse resolution of 30–100 km. This is unhelpful for identifying climate-related public health risks in cities. New mapping is needed at higher resolution to better characterize hazards and prepare location-specific adaptation plans.

The range of negative health impacts of exposure to heat is widely researched and acknowledged (Basu, 2009). In the UK, around 2,000 deaths per year are attributed to heat (Hajat et al, 2014). Heat exposure is of particular concern due to: i) global observations of rising temperatures; ii) more frequent heatwaves associated with climate change, and iii) rising urban populations, who are exposed to the urban heat island (UHI) effect (Field et al., 2012; Melorose et al., 2015).

Risk assessments use relationships between ambient temperature and health outcomes such as risk of hospitalization or mortality to estimate health burdens for specific populations, e.g. the fraction of all-cause mortality which can be attributed to heat. By using projected temperatures derived from climate models, it is also possible to assess the potential change in health impacts we might expect in future (Gasparrini et al., 2017). These risk estimates are informative to policy makers for planning health and social care provisions, particularly in anticipation of heatwave events (Public Health England, 2015), or for designing mitigation and adaptation actions to protect health in future.

One source of uncertainty in climate change risk assessment relates to the type and resolution of exposure (temperature) data input to the risk assessment model, for example global-scale climate models are not designed to capture local or regional scale features, due to their coarse resolution (~30-100 km) (Goodess et al., 2021). This has particular importance for the UHI, because global climate model outputs do not represent the urban impact on the local climate, and therefore underestimate population heat exposure, and related public health impacts.

One solution is to use higher resolution climate or meteorological modelling. This can be computationally expensive and difficult to set up and run over large areas, but it can be effectively employed for specific cities or regions to better represent temperatures across urban areas (e.g. London, Figure 1). Previous simulations using a 1x1 km resolution meteorological model for Birmingham in the UK estimated that the UHI contributes up to 40% of heat-related mortality over a summer, (Macintyre & Heaviside, 2019) and up to 50% during a heatwave (Heaviside et al., 2016). Estimates of heat-related risks in the future without such highly resolved data might therefore be underestimated by a half.

Action

Climate change risk assessment models are an important component of local adaptation strategies. They need to be used by policy makers, strategists, planners and stakeholders to assess potential future impacts of climate change, and to develop actions that will reduce risks. Quantitative estimates of societal risk are essential for long term planning, but local and city scale analysis provides necessary detail to reveal the extent to which a population is exposed or is vulnerable to the effects of heat. This vulnerability depends on multiple factors, including local climate, geography, housing and social and economic factors (Macintyre et al., 2018; Taylor et al., 2018).

Additional city-scale modelling and spatial analysis (based on high-resolution climate modelling) must add demographic data to 1) identify vulnerable population groups or geographical areas; 2) investigate the impacts of potential city-level and neighbourhood-level interventions, and 3) reduce health inequalities and the potential for unintended consequences from adaptation measures at local scales. This will enable strategists, planners and others in central and local government to more effectively prepare appropriate adaptation responses and focus efforts where they can have the greatest benefit to health and wellbeing, and to society as a whole.

Acknowledgements

CH is supported by a NERC fellowship (NE/R01440X/1) and acknowledges funding from the Wellcome Trust HEROIC project (216035/Z/19/Z), which funds OB and CS.

References

Basu, R. (2009). High ambient temperature and mortality: A review of epidemiologic studies from 2001 to 2008. Environmental Health: A Global Access Science Source, 8(1). https://doi.org/10.1186/1476-069X-8-40

Field, C. B., Barros, V. R., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., … Midgley, P. M. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, 1–594. https://doi.org/10.1017/CBO9781139177245

Gasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., … Armstrong, B. (2017). Projections of temperature-related excess mortality under climate change scenarios. The Lancet Planetary Health, 1(9), e360–e367. https://doi.org/10.1016/S2542-5196(17)30156-0

Goodess, C., Berk, S., Ratna, S. B., Brousse, O., Davies, M., Heaviside, C., Moore, C. & Pineo, H. (2021). Climate change projections for sustainable and healthy cities. Buildings and Cities, 2(1), 812-836. https://doi.org/10.5334/bc.111

Hajat, S., Vardoulakis, S., Heaviside, C., & Eggen, B. (2014). Climate change effects on human health: Projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. Journal of Epidemiology and Community Health, 68(7), 641–648. https://doi.org/10.1136/jech-2013-202449

Heaviside, C., Vardoulakis, S., & Cai, X.-M. (2016). Attribution of mortality to the urban heat island during heatwaves in the West Midlands, UK. Environmental Health: A Global Access Science Source, 15. https://doi.org/10.1186/s12940-016-0100-9

Macintyre, H. L., & Heaviside, C. (2019). Potential benefits of cool roofs in reducing heat-related mortality during heatwaves in a European city. Environment International, 127, 430–441. https://doi.org/10.1016/J.ENVINT.2019.02.065

Macintyre, H. L., Heaviside, C., Taylor, J., Picetti, R., Symonds, P., Cai, X.-M., & Vardoulakis, S. (2018). Assessing urban population vulnerability and environmental risks across an urban area during heatwaves – Implications for health protection. Science of the Total Environment, 610–611, 678–690. https://doi.org/10.1016/j.scitotenv.2017.08.062

Melorose, J., Perroy, R., & Careas, S. (2015). World population prospects. United Nations, 1(6042), 587–592. https://doi.org/10.1017/CBO9781107415324.004

Taylor, J., Symonds, P., Wilkinson, P., Heaviside, C., Macintyre, H., Davies, M., … Hutchinson, E. (2018). Estimating the influence of housing energy efficiency and overheating adaptations on heat-related mortality in the West Midlands, UK. Atmosphere, 9(5). https://doi.org/10.3390/atmos9050190

Share:

Email

Read also

• COP26 & Beyond: What Role for Cities?
• Why Building Regulations Must Incorporate Embodied Carbon
• COP-26: A Commitment to Regulate Embodied Carbon
• COP-26: Engaging Built Environment Professionals to Support Climate Justice
• COP-26: Make Nature-Based Solutions a Top Adaptation Priority