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Increased heat risk in wet climate induced by urban humid heat

Nature volume 617, pages 738–742 (2023)Cite this article

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Abstract

Cities are generally warmer than their adjacent rural land, a phenomenon known as the urban heat island (UHI). Often accompanying the UHI effect is another phenomenon called the urban dry island (UDI), whereby the humidity of urban land is lower than that of the surrounding rural land1,2,3. The UHI exacerbates heat stress on urban residents4,5, whereas the UDI may instead provide relief because the human body can cope with hot conditions better at lower humidity through perspiration6,7. The relative balance between the UHI and the UDI—as measured by changes in the wet-bulb temperature (Tw)—is a key yet largely unknown determinant of human heat stress in urban climates. Here we show that Tw is reduced in cities in dry and moderately wet climates, where the UDI more than offsets the UHI, but increased in wet climates (summer precipitation of more than 570 millimetres). Our results arise from analysis of urban and rural weather station data across the world and calculations with an urban climate model. In wet climates, the urban daytime Tw is 0.17 ± 0.14 degrees Celsius (mean ± 1 standard deviation) higher than rural Tw in the summer, primarily because of a weaker dynamic mixing in urban air. This Tw increment is small, but because of the high background Tw in wet climates, it is enough to cause two to six extra dangerous heat-stress days per summer for urban residents under current climate conditions. The risk of extreme humid heat is projected to increase in the future, and these urban effects may further amplify the risk.

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Fig. 1: The urban wet-bulb island depends on time of the day and on climate wetness.
Fig. 2: The urban wet-bulb island is controlled by dynamic mixing and diabatic heating.
Fig. 3: The daytime urban wet-bulb island is strongest in the wet climate.
Fig. 4: The modelled daytime UDI component is more negative (stronger UDI) with decreasing summer precipitation.
Fig. 5: Urban residents in the wet climate experience more heat-stressed days than rural residents.

Data availability

The ERA5-Land hourly data are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview. The GHS built-up grid data are available at https://ghsl.jrc.ec.europa.eu/download.php. The ISD data are available at https://www.ncei.noaa.gov/access/search/data-search/global-hourly. The observation data from Arizona mesonet are available at https://cals.arizona.edu/AZMET/az-data.htm. The observation data from Birmingham Urban Climate Laboratory are available at https://catalogue.ceda.ac.uk/uuid/e448a957fc53401794e48a23c265c25f. The observation data from Trans-African Hydro-Meteorological Observatory (TAHMO) are available at https://tahmo.org/climate-data/. The observation data obtained from open data portals provided by the National Meteorological Service of different countries are available at https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html (Germany); https://en.ilmatieteenlaitos.fi/download-observations (Finland); https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer#param=airtemperatureInstant,stations=core (Sweden); https://www.met.no/en/free-meteorological-data (Norway); https://climate.weather.gc.ca/index_e.html (Canada); https://www.smn.gob.ar/descarga-de-datos (Argentina); https://www.data.jma.go.jp/gmd/risk/obsdl/index.php (Japan); https://portal.inmet.gov.br/dadoshistoricos (Brazil); https://climatologia.meteochile.gob.cl/application/requerimiento/producto/RE3003 (Chile). The data on observed daytime and nighttime Tw and the UHI and UDI components are available on Figshare. The hourly model outputs are available from the corresponding author upon request.

Code availability

The Community Earth System Model Version 2 is available at https://www.cesm.ucar.edu/models/cesm2/. The Python code used to produce the figures in this paper is available on Figshare.

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Acknowledgements

C.C. acknowledges support by the National Key R&D Program of China (grant 2019YFA0607202), X.L. and L.Z. acknowledge support by the US National Science Foundation (grants 1933630 and 2145362), L.Z. acknowledges support by the Institute for Sustainability, Energy and Environment, and K.Z. acknowledges support by a Yale Graduate Fellowship. High-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) was provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the US National Science Foundation. We thank the following institutions and network operators for providing observation data: US National Centers for Environmental Information, Oklahoma mesonet, Arizona mesonet, T. Hawkins, US Environmental Protection Agency, DWD Climate Data Center of Germany, Reliable Prognosis, Trans-African Hydro-Meteorological Observatory (TAHMO), Birmingham Urban Climate Lab, and The National Meteorological Service of Switzerland, France, United Kingdom, Finland, Sweden, Austria, Spain, Norway, Canada, South Africa, Argentina, Japan, Brazil, Mexico, Chile, China and Thailand.

Author information

Authors and Affiliations

  1. School of the Environment, Yale University, New Haven, CT, USA

    Keer Zhang & Xuhui Lee

  2. Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing, China

    Chang Cao, Haoran Chu & Jiayu Zhao

  3. Key Laboratory of Meteorological Disaster, Ministry of Education and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

    Chang Cao & Haoran Chu

  4. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Lei Zhao

  5. College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China

    Jiayu Zhao

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Contributions

X.L. designed the research and developed the theory. K.Z. conducted the model simulations and data analysis. K.Z., C.C., H.C. and J.Z. contributed to the observation data collection. L.Z. contributed ideas to the model simulation and data analysis. X.L. and K.Z. drafted the manuscript. All authors edited and revised the manuscript.

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Correspondence to Xuhui Lee.

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Nature thanks Vimal Mishra, Athanasios Paschalis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Distribution of urban-rural station pairs.

Base map shows summer precipitation. Map was made with the Python software.

Extended Data Fig. 2 The urban wet-bulb island and its UHI and UDI components.

a,c,e, Daytime distributions; b, d, f, Nighttime distributions. Zonal mean values are also shown. Maps were made with the Python software.

Extended Data Fig. 3 The urban wet-bulb island calculated with the diagnostic analysis agrees with modelled results.

Comparison of modelled and calculated daytime (a) and nighttime (b) urban wet-bulb island. The calculated ∆Tw is the sum of all component contributions. Each data point represents one grid-cell mean value. Colour indicates data density. The black dotted line is 1:1. The solid line is linear regression with regression statistics noted.

Extended Data Fig. 4 The daytime ∆Tw increases and the nighttime ∆Tw decreases with precipitation.

a, Daytime; b Nighttime. Data are bin averages. Each bin consists of 1819 grids.

Extended Data Fig. 5 The heat storage term dominates the diabatic heating contribution to the urban wet-bulb island.

The four components of diabatic heating term during the daytime (a) and nighttime (b). Box plots show the median (line), 25–75% range (box), 5–95% range (whiskers), and the mean value (cross).

Extended Data Fig. 6 Comparison of observed and modelled diurnal patterns of wet-bulb temperature, the urban wet-bulb island, and its UHI and UDI components.

a–d, Berlin; e,f, Phoenix. Red filled areas denote one standard deviation of all urban-rural combinations of site pairing.

Extended Data Fig. 7 Comparison of observed and modelled diurnal patterns of wet-bulb temperature, the urban wet-bulb island, and its UHI and UDI components in the wet climate zone.

Gray areas denote one standard deviation of 11 model grids. Error bars denote one standard deviation of 11 site pairs.

Extended Data Fig. 8 Regional patterns of the urban wet-bulb island and its UHI and UDI components calculated from daily maximum Tw.

a, observed; b, modelled. Box plots show the median (line), 25–75% range (box), 5–95% range (whiskers), and the mean value (cross).

Extended Data Fig. 9 Statistics from random omission of site pairs.

a, observed daytime; c, observed nighttime; b, modelled daytime; d, modelled nighttime ∆Tw and its components. Box plots show the median (line), 25–75% range (box), 5–95% range (whiskers), and the mean value (cross).

Extended Data Table 1 Empirical heat indices weigh temperature more heavily than humidity
Full size table

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Zhang, K., Cao, C., Chu, H. et al. Increased heat risk in wet climate induced by urban humid heat. Nature 617, 738–742 (2023). https://doi.org/10.1038/s41586-023-05911-1

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