Limited Role of Absolute Humidity in Intraurban Heat Variability

Darryn W. Waugh aDepartment of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland

Search for other papers by Darryn W. Waugh in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-7692-2798
,
Benjamin Zaitchik aDepartment of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland

Search for other papers by Benjamin Zaitchik in
Current site
Google Scholar
PubMed
Close
,
Anna A. Scott aDepartment of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland

Search for other papers by Anna A. Scott in
Current site
Google Scholar
PubMed
Close
,
Peter C. Ibsen bGeosciences and Environmental Change Science Center, U.S. Geological Survey, Denver, Colorado

Search for other papers by Peter C. Ibsen in
Current site
Google Scholar
PubMed
Close
,
G. Darrel Jenerette cDepartment of Botany and Plant Sciences, University of California, Riverside, Riverside, California

Search for other papers by G. Darrel Jenerette in
Current site
Google Scholar
PubMed
Close
,
Jason Schatz dSkyTruth, Santa Fe, New Mexico

Search for other papers by Jason Schatz in
Current site
Google Scholar
PubMed
Close
, and
Christopher J. Kucharik eNelson Institute Center for Sustainability and the Global Environment, University of Wisconsin–Madison, Madison, Wisconsin
fDepartment of Agronomy, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Christopher J. Kucharik in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Significance Statement

Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Darryn Waugh, waugh@jhu.edu

Abstract

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Significance Statement

Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Darryn Waugh, waugh@jhu.edu
Save
  • Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus’s form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601609, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Alonzo, M., M. E. Baker, Y. Gao, and V. Shandas, 2021: Spatial configuration and time of day impact the magnitude of urban tree canopy cooling. Environ. Res. Lett., 16, 084028, https://doi.org/10.1088/1748-9326/ac12f2.

    • Search Google Scholar
    • Export Citation
  • Anderson, G. B., M. L. Bell, and R. D. Peng, 2013: Methods to calculate the heat index as an exposure metric in environmental health research. Environ. Health Perspect., 121, 11111119, https://doi.org/10.1289/ehp.1206273.

    • Search Google Scholar
    • Export Citation
  • Błażejczyk, K., G. Jendritzky, P. Bröde, D. Fiala, G. Havenith, Y. Epstein, A. Psikuta, and B. Kampmann, 2013: An introduction to the universal thermal climate index (UTCI). Geogr. Pol., 86, 510, https://doi.org/10.7163/GPol.2013.1.

    • Search Google Scholar
    • Export Citation
  • Budd, G. M., 2008: Wet-bulb globe temperature (WBGT)—Its history and its limitations. J. Sci. Med. Sport, 11, 2032, https://doi.org/10.1016/j.jsams.2007.07.003.

    • Search Google Scholar
    • Export Citation
  • Copernicus, 2023: Sentinel-2 LC-1 data. Accessed 30 August 2023, https://developers.google.com/earth-engine/datasets/catalog/sentinel-2.

  • Farquhar, G. D., and T. D. Sharkey, 1982: Stomatal conductance and photosynthesis. Annu. Rev. Plant Physiol., 33, 317345, https://doi.org/10.1146/annurev.pp.33.060182.001533.

    • Search Google Scholar
    • Export Citation
  • Fenner, D., A. Holtmann, F. Meier, I. Langer, and D. Scherer, 2019: Contrasting changes of urban heat island intensity during hot weather episodes. Environ. Res. Lett., 14, 124013, https://doi.org/10.1088/1748-9326/ab506b.

    • Search Google Scholar
    • Export Citation
  • Gosling, S. N., and Coauthors, 2014: A glossary for biometeorology. Int. J. Biometeor., 58, 277308, https://doi.org/10.1007/s00484-013-0729-9.

    • Search Google Scholar
    • Export Citation
  • Grossiord, C., T. N. Buckley, L. A. Cernusak, K. A. Novick, B. Poulter, R. T. W. Siegwolf, J. S. Sperry, and N. G. McDowell, 2020: Plant responses to rising vapor pressure deficit. New Phytol., 226, 15501566, https://doi.org/10.1111/nph.16485.

    • Search Google Scholar
    • Export Citation
  • Hall, S. J., and Coauthors, 2016: Convergence of microclimate in residential landscapes across diverse cities in the United States. Landscape Ecol., 31, 101117, https://doi.org/10.1007/s10980-015-0297-y.

    • Search Google Scholar
    • Export Citation
  • Hoffman, J. S., V. Shandas, and N. Pendleton, 2020: The effects of historical housing policies on resident exposure to intra-urban heat: A study of 108 US urban areas. Climate, 8, 12, https://doi.org/10.3390/cli8010012.

    • Search Google Scholar
    • Export Citation
  • Ibsen, P. C., and Coauthors, 2021: Greater aridity increases the magnitude of urban nighttime vegetation-derived air cooling. Environ. Res. Lett., 16, 034011, https://doi.org/10.1088/1748-9326/abdf8a.

    • Search Google Scholar
    • Export Citation
  • Lu, Y.-C., and D. M. Romps, 2022: Extending the heat index. J. Appl. Meteor. Climatol., 61, 13671383, https://doi.org/10.1175/JAMC-D-22-0021.1.

    • Search Google Scholar
    • Export Citation
  • Novick, K. A., and Coauthors, 2016: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Climate Change, 6, 10231027, https://doi.org/10.1038/nclimate3114.

    • Search Google Scholar
    • Export Citation
  • Phiri, D., M. Simwanda, S. Salekin, V. R. Nyirenda, Y. Murayama, and M. Ranagalage, 2020: Sentinel-2 data for land cover/use mapping: A review. Remote Sens., 12, 2291, https://doi.org/10.3390/rs12142291.

    • Search Google Scholar
    • Export Citation
  • Richard, Y., and Coauthors, 2021: Is urban heat island intensity higher during hot spells and heat waves (Dijon, France, 2014–2019)? Urban Climate, 35, 100747, https://doi.org/10.1016/j.uclim.2020.100747.

    • Search Google Scholar
    • Export Citation
  • Saverino, K. C., E. Routman, T. R. Lookingbill, A. M. Eanes, J. S. Hoffman, and R. Bao, 2021: Thermal inequity in Richmond, VA: The effect of an unjust evolution of the urban landscape on urban heat islands. Sustainability, 13, 1511, https://doi.org/10.3390/su13031511.

    • Search Google Scholar
    • Export Citation
  • Schatz, J., and C. J. Kucharik, 2014: Seasonality of the urban heat island effect in Madison, Wisconsin. J. Appl. Meteor. Climatol., 53, 23712386, https://doi.org/10.1175/JAMC-D-14-0107.1.

    • Search Google Scholar
    • Export Citation
  • Schatz, J., and C. J. Kucharik, 2015: Urban climate effects on extreme temperatures in Madison, Wisconsin, USA. Environ. Res. Lett., 10, 094024, https://doi.org/10.1088/1748-9326/10/9/094024.

    • Search Google Scholar
    • Export Citation
  • Schatz, J., C. Ziter, and C. Kucharik, 2021: WSC—Temperature and relative humidity data from 150 locations in and around Madison, Wisconsin from 2012–2020 ver 21. Environmental Data Initiative, accessed 30 September 2021, https://doi.org/10.6073/pasta/c1322bd2fb3e6eac0749a83033d24ab6.

  • Scott, A. A., B. Zaitchik, D. W. Waugh, and K. O’Meara, 2017: Intraurban temperature variability in Baltimore. J. Appl. Meteor. Climatol., 56, 159171, https://doi.org/10.1175/JAMC-D-16-0232.1.

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and Coauthors, 2021: Weather and climate extreme events in a changing climate. Climate Change 2021: The Physical Science Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 1513–1766, https://doi.org/10.1017/9781009157896.013.

  • Shandas, V., J. Voelkel, J. Williams, and J. Hoffman, 2019: Integrating satellite and ground measurements for predicting locations of extreme urban heat. Climate, 7, 5, https://doi.org/10.3390/cli7010005.

    • Search Google Scholar
    • Export Citation
  • Shi, R., B. F. Hobbs, B. F. Zaitchik, D. W. Waugh, A. A. Scott, and Y. Zhang, 2021: Monitoring intra-urban temperature with dense sensor networks: Fixed or mobile? An empirical study in Baltimore, MD. Urban Climate, 39, 100979, https://doi.org/10.1016/j.uclim.2021.100979.

    • Search Google Scholar
    • Export Citation
  • Steadman, R. G., 1984: A universal scale of apparent temperature. J. Climate Appl. Meteor., 23, 16741687, https://doi.org/10.1175/1520-0450(1984)023<1674:AUSOAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taleghani, M., D. Sailor, and G. A. Ban-Weiss, 2016: Micrometeorological simulations to predict the impacts of heat mitigation strategies on pedestrian thermal comfort in a Los Angeles neighborhood. Environ. Res. Lett., 11, 024003, https://doi.org/10.1088/1748-9326/11/2/024003.

    • Search Google Scholar
    • Export Citation
  • Terando, A. J., E. Youngsteadt, E. K. Meineke, and S. G. Prado, 2017: Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements. Ecol. Evol., 7, 98909904, https://doi.org/10.1002/ece3.3499.

    • Search Google Scholar
    • Export Citation
  • Tuholske, C., K. Caylor, C. Funk, A. Verdin, S. Sweeney, K. Grace, P. Peterson, and T. Evans, 2021: Global urban population exposure to extreme heat. Proc. Natl. Acad. Sci. USA, 118, e2024792118, https://doi.org/10.1073/pnas.2024792118.

    • Search Google Scholar
    • Export Citation
  • Waugh, D. W., B. Zaitchik, and A. A. Scott, 2022: JHU Baltimore iButton data. Zenodo, accessed 19 November 2022, https://doi.org/10.5281/zenodo.7336263.

  • Yang, P., G. Ren, and W. Liu, 2013: Spatial and temporal characteristics of Beijing urban heat island intensity. J. Appl. Meteor. Climatol., 52, 18031816, https://doi.org/10.1175/JAMC-D-12-0125.1.

    • Search Google Scholar
    • Export Citation
  • Yang, P., G. Ren, and W. Hou, 2017: Temporal–spatial patterns of relative humidity and the urban dryness island effect in Beijing City. J. Appl. Meteor. Climatol., 56, 22212237, https://doi.org/10.1175/JAMC-D-16-0338.1.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., K. O’Meara, K. Baja, A. A. Scott, D. W. Waugh, and M. C. McCormack, 2016: B’more cool: Monitoring the urban heat island at high density for health and urban design. Earthzine, https://earthzine.org/bmore-cool-monitoring-the-urban-heat-island-at-high-density-for-health-and-urban-design/.

  • Zipper, S. C., J. Schatz, C. J. Kucharik, and S. P. Loheide II, 2017: Urban heat island induced increases in evapotranspirative demand. Geophys. Res. Lett., 44, 873881, https://doi.org/10.1002/2016GL072190.

    • Search Google Scholar
    • Export Citation
  • Ziter, C. D., E. J. Pedersen, C. J. Kucharik, and M. G. Turner, 2019: Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. USA, 116, 75757580, https://doi.org/10.1073/pnas.1817561116.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 409 409 32
Full Text Views 105 105 7
PDF Downloads 114 114 15