Why High-Resolution Climate Modelling Matters: Cities and Health

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.

<strong>Figure 1</strong>. Daily minimum air temperature at 2m height, averaged over 20-27th July 2018, over south-east England and London at increasing horizontal resolution: (A) the ERA5 reanalysis product at 30km, and modelled using the Weather Research Forecasting (WRF) model driven by ERA5 at (B) 12km,(C) 3km, and (D) 1km. Blue lines represent shores, black lines are areas with impervious surface fraction above 10%.
Figure 1. Daily minimum air temperature at 2m height, averaged over 20-27th July 2018, over south-east England and London at increasing horizontal resolution: (A) the ERA5 reanalysis product at 30km, and modelled using the Weather Research Forecasting (WRF) model driven by ERA5 at (B) 12km,(C) 3km, and (D) 1km. Blue lines represent shores, black lines are areas with impervious surface fraction above 10%.


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.


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.


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