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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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.
References
Oleson, K. W. et al. Interactions between urbanization, heat stress, and climate change. Climatic Change 129, 525–541 (2015).
Luo, M. & Lau, N. C. Urban expansion and drying climate in an urban agglomeration of East China. Geophys. Res. Lett. 46, 6868–6877 (2019).
Meili, N., Paschalis, A., Manoli, G. & Fatichi, S. Diurnal and seasonal patterns of global urban dry islands. Environ. Res. Lett. 17, 054044 (2022).
Santamouris, M. Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy Build. 207, 109482 (2020).
Ward, K., Lauf, S., Kleinschmit, B. & Endlicher, W. Heat waves and urban heat islands in Europe: a review of relevant drivers. Sci. Total Environ. 569–570, 527–539 (2016).
Epstein, Y. & Moran, D. S. Thermal comfort and the heat stress indices. Ind. Health 44, 388–398 (2006).
Sherwood, S. C. & Huber, M. An adaptability limit to climate change due to heat stress. Proc. Natl Acad. Sci. USA 107, 9552–9555 (2010).
Borg, F. H. et al. Climate change and health in urban informal settlements in low- and middle-income countries–a scoping review of health impacts and adaptation strategies. Glob. Health Action 14, 1908064 (2021).
Ramsay, E. E. et al. Chronic heat stress in tropical urban informal settlements. iScience 24, 103248 (2021).
Revi, A. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 535–612 (Cambridge Univ. Press, 2014).
Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).
Im, E.-S., Pal, J. S. & Eltahir, E. A. B. Deadly heat waves projected in the densely populated agricultural regions of South Asia. Sci. Adv. 3, e1603322 (2017).
Du, H. et al. Simultaneous investigation of surface and canopy urban heat islands over global cities. ISPRS J. Photogram. Remote Sens. 181, 67–83 (2021). 2021.
Stewart, I. D. A systematic review and scientific critique of methodology in modern urban heat island literature. Int. J. Climatol. 31, 200–217 (2011).
Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).
Manoli, G. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573, 55–60 (2019).
Fitria, R., Kim, D., Baik, J. & Choi, M. Impact of biophysical mechanisms on urban heat island associated with climate variation and urban morphology. Sci Rep. 9, 19503 (2019).
Lee, D. O. Urban–rural humidity differences in London. Int. J. Climatol. 11, 577–582 (1991).
Holmer, B. & Eliasson, I. Urban–rural vapour pressure differences and their role in the development of urban heat islands. Int. J. Climatol. 19, 989–1009 (1999).
Kuttler, W., Weber, S., Schonnefeld, J. & Hesselschwerdt, A. Urban/rural atmospheric water vapour pressure differences and urban moisture excess in Krefeld, Germany. Int. J. Climatol. 27, 2005–2015 (2007).
Wang, Z., Song, J., Chan, P. W. & Li, Y. The urban moisture island phenomenon and its mechanisms in a high-rise high-density city. Int. J. Climatol. 41, E150–E170 (2021).
Shi, Y. & Zhang, Y. Urban morphological indicators of urban heat and moisture islands under various sky conditions in a humid subtropical region. Build. Environ. 214, 108906 (2022).
Zhang, Y. & Fueglistaler, S. How tropical convection couples high moist static energy over land and ocean. Geophys. Res. Lett. 47, e2019GL086387 (2020).
Zhang, Y., Held, I. & Fueglistaler, S. Projections of tropical heat stress constrained by atmospheric dynamics. Nat. Geosci. 14, 133–137 (2021).
Dupont, E., Menut, L., Carissimo, B., Pelon, J. & Flamant, P. Comparison between the atmospheric boundary layer in Paris and its rural suburbs during the ECLAP experiment. Atmos. Environ. 33, 979–994 (1999).
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
Gómez-Navarro, C., Pataki, D. E., Pardyjak, E. R. & Bowling, D. R. Effects of vegetation on the spatial and temporal variation of microclimate in the urbanized Salt Lake Valley. Agric. For. Meteorol. 296, 108211 (2021).
Mishra, V. et al. Moist heat stress extremes in India enhanced by irrigation. Nat. Geosci. 13, 722–728 (2020).
Krakauer, N. Y., Cook, B. I. & Puma, M. J. Effect of irrigation on humid heat extremes. Environ. Res. Lett. 15, 094010 (2020).
Liu, Y. et al. Urban heat island effects of various urban morphologies under regional climate conditions. Sci. Total Environ. 743, 140589 (2020).
Wienert, U. & Kuttler, W. The dependence of the urban heat island intensity on latitude—a statistical approach. Meteorol. Z. 14, 677–686 (2005).
Zhang, Z. et al. A mechanistic assessment of urban heat island intensities and drivers across climates. Urban Climate 44, 101215 (2022).
Wong, N. H., Tan, C. L., Kolokotsa, D. D. & Takebayashi, H. Greenery as a mitigation and adaptation strategy to urban heat. Nat. Rev. Earth Environ. 2, 166–181 (2021).
Lee, S. H. et al. Impacts of in-canyon vegetation and canyon aspect ratio on the thermal environment of street canyons: numerical investigation using a coupled WRF-VUCM model. Q. J. R. Meteorol. Soc. 142, 2562–2578 (2016).
Meili, N. et al. An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0). Geosci. Model Dev. 13, 335–362 (2020).
Huang, X., Song, J., Wang, C., Chui, T. F. M. & Chan, P. W. The synergistic effect of urban heat and moisture islands in a compact high-rise city. Build. Environ. 205, 108274 (2021).
Meili, N. et al. Tree effects on urban microclimate: diurnal, seasonal, and climatic temperature differences explained by separating radiation, evapotranspiration, and roughness effects. Urban For. Urban Green. 58, 126970 (2021).
Voogt, J. A. & Grimmond, C. S. B. Modeling surface sensible heat flux using surface radiative temperatures in a simple urban area. J. Appl. Meteorol. 39, 1679–1699 (2000).
Li, Q., Bou-Zeid, E., Grimmond, S., Zilitinkevich, S. & Katul, G. Revisiting the relation between momentum and scalar roughness lengths of urban surfaces. Q. J. R. Meteorol. Soc. 146, 3144–3164 (2020).
Im, E. S., Kang, S. & Eltahir, E. A. B. Projections of rising heat stress over the western Maritime Continent from dynamically downscaled climate simulations. Glob. Planet. Change 165, 160–172 (2018).
Oke, T. R. Initial Guidance to Obtain Representative Meteorological Observations at Urban Sites Instruments and Observing Methods WMO/TD-No. 1250 (WMO, 2006).
Corbane, C., Florczyk, A., Pesaresi, M., Politis, P. & Syrris, V. GHS-BUILT R2018A—GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014). European Commission Joint Research Centre https://doi.org/10.2905/jrc-ghsl-10007 (2018).
Smith, A., Lott, N. & Vose, R. The integrated surface database: recent developments and partnerships. Bull. Am. Meteorol. Soc. 92, 704–708 (2011).
Danabasoglu, G. et al. The Community Earth System Model Version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019MS001916 (2020).
Jackson, T. L., Feddema, J. J., Oleson, K. W., Bonan, G. B. & Bauer, J. T. Parameterization of urban characteristics for global climate modeling. Ann. Assoc. Am. Geogr. 100, 848–865 (2010).
Byrne, M. P. & O’Gorman, P. A. Land–ocean warming contrast over a wide range of climates: convective quasi-equilibrium theory and idealized simulations. J. Clim. 26, 4000–4016 (2013).
Mahrt, L. The bulk aerodynamic formulation over heterogeneous surfaces. Bound Layer Meteorol. 78, 87–119 (1996).
Kumar, R. et al. Dominant control of agriculture and irrigation on urban heat island in India. Sci. Rep. 7, 14054 (2017).
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).
Zhao, L. et al. Global multi-model projections of local urban climates. Nat. Clim. Change 11, 152–157 (2021).
Lee, X. Fundamentals of Boundary-Layer Meteorology (Springer, 2018).
Fischer, E. M. & Knutti, R. Robust projections of combined humidity and temperature extremes. Nat. Clim. Change 3, 126–130 (2013).
Buzan, J. R., Oleson, K. & Huber, M. Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev. 8, 151–170 (2015).
Mora, C. et al. Global risk of deadly heat. Nat. Clim. Change 7, 501–506 (2017).
Armstrong, B. et al. The role of humidity in associations of high temperature with mortality: a multicountry, multicity study. Environ. Health Persp. 127, 097007 (2019).
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
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
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.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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).
Supplementary information
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-05911-1
This article is cited by
-
Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective
Science China Earth Sciences (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
