Article | Published:

Diurnal interaction between urban expansion, climate change and adaptation in US cities

Nature Climate Changevolume 8pages10971103 (2018) | Download Citation

Abstract

Climate change and urban development are projected to substantially warm US cities, yet dynamic interaction between these two drivers of urban heat may modify the warming. Here, we show that business-as-usual GHG-induced warming and corresponding urban expansion would interact nonlinearly, reducing summer night-time warming by 0.5 K over the twenty-first century in most US regions. Nevertheless, large projected warming remains, particularly at night when the degree of urban expansion warming approaches that of climate change. Joint, high-intensity implementation of adaptation strategies, including cool and evaporative roofs and street trees, decreases projected daytime mean and extreme heat, but region- and emissions scenario-dependent nocturnal warming of 2–7 K persists. A novel adaptation strategy—lightweight urban materials—yields ~1 K night-time cooling and minor daytime warming in denser areas. Our findings highlight the diurnal interplay of urban warming and adaptation cooling, and underscore the inability of infrastructure-based adaptation to offset projected night-time warming, and the consequent necessity for simultaneous emissions reductions.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

Regional climate simulation output supporting the findings of this study is accessible at https://erams.com/UWIN/asu-conus-urban-and-climate-change-assessment-data/

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Gallo, K. P., Owen, T. W., Easterling, D. R. & Jamason, P. F. Temperature trends of the US historical climatology network based on satellite-designated land use/land cover. J. Clim. 12, 1344–1348 (1999).

  2. 2.

    Qu, M., Wan, J. & Hao, X. Analysis of diurnal air temperature range change in the continental United States. Weather Clim. Extremes 4, 86–95 (2014).

  3. 3.

    Revi, A. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 535–612 (IPCC, Cambridge Univ. Press, 2014).

  4. 4.

    Alder, J. R. & Hostetler, S. W. CMIP5 Global Climate Change Viewer (US Geological Survey, 2013); http://regclim.coas.oregonstate.edu/gccv/index.html

  5. 5.

    Bierwagen, B. G. et al. National housing and impervious surface scenarios for integrated climate impact assessments. Proc. Natl Acad. Sci. USA 107, 20887–20892 (2010).

  6. 6.

    Georgescu, M., Moustaoui, M., Mahalov, A. & Dudhia, J. Summer-time climate impacts of projected megapolitan expansion in Arizona. Nat. Clim. Change 3, 37–41 (2013).

  7. 7.

    Georgescu, M., Morefield, P. E., Bierwagen, B. G. & Weaver, C. P. Urban adaptation can roll back warming of emerging megapolitan regions. Proc. Natl Acad. Sci. USA 111, 2909–2914 (2014).

  8. 8.

    Wouters, H. et al. Heat stress increase under climate change twice as large in cities as in rural areas: a study for a densely populated midlatitude maritime region. Geophys. Res. Lett. 44, 8997–9007 (2017).

  9. 9.

    Hondula, D. M., Balling, R. C., Vanos, J. K. & Georgescu, M. Rising temperatures, human health, and the role of adaptation. Curr. Clim. Change Rep. 1, 144–154 (2015).

  10. 10.

    Li, D. H., Yang, L. & Lam, J. C. Impact of climate change on energy use in the built environment in different climate zones—a review. Energy 42, 103–112 (2012).

  11. 11.

    Oleson, K. W., Bonan, G. B., Feddema, J. & Jackson, T. An examination of urban heat island characteristics in a global climate model. Int. J. Climatol. 31, 1848–1865 (2011).

  12. 12.

    Oleson, K. Contrasts between urban and rural climate in CCSM4 CMIP5 climate change scenarios. J. Clim. 25, 1390–1412 (2012).

  13. 13.

    Argüeso, D., Evans, J. P., Fita, L. & Bormann, K. J. Temperature response to future urbanization and climate change. Clim. Dynam. 42, 2183–2199 (2014).

  14. 14.

    Zhao, L., Lee, X. & Schultz, N. M. A wedge strategy for mitigation of urban warming in future climate scenarios. Atmos. Chem. Phys. 17, 9067–9080 (2017).

  15. 15.

    Stein, U. & Alpert, P. Factor separation in numerical simulations. J. Atmos. Sci. 50, 2107–2115 (1993).

  16. 16.

    Bowler, D. E., Buyung-Ali, L., Knight, T. M. & Pullin, A. S. Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc. Urban Plan. 97, 147–155 (2010).

  17. 17.

    Krayenhoff, E. S. & Voogt, J. A. Impacts of urban albedo increase on local air temperature at daily–annual time scales: model results and synthesis of previous work. J. Appl. Meteorol. Climatol. 49, 1634–1648 (2010).

  18. 18.

    Skamarock, W. C. & Klemp, J. B. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys. 227, 3465–3485 (2008).

  19. 19.

    Kusaka, H., Kondo, H., Kikegawa, Y. & Kimura, F . A simple single-layer urban canopy model for atmospheric models: comparison with multi-layer and slab models. Bound. Layer Meteorol. 101, 329–358 (2001).

  20. 20.

    Chapman, S., Watson, J. E., Salazar, A., Thatcher, M. & McAlpine, C. A. The impact of urbanization and climate change on urban temperatures: a systematic review. Landsc. Ecol. 32, 1921–1935 (2017).

  21. 21.

    Höppe, P. The physiological equivalent temperature—a universal index for the biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43, 71–75 (1999).

  22. 22.

    Jendritzky, G., de Dear, R. & Havenith, G. UTCI—why another thermal index? Int. J. Biometeorol. 56, 421–428 (2012).

  23. 23.

    Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional climate change on human health. Nature 438, 310–317 (2005).

  24. 24.

    Chen, F. et al. The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int. J. Climatol. 31, 273–288 (2011).

  25. 25.

    Wang, M. et al. On the long-term hydroclimatic sustainability of perennial bioenergy crop expansion over the United States. J. Clim. 30, 2535–2557 (2017).

  26. 26.

    European Centre for Medium-Range Weather Forecasts ERA-Interim Project 2009 (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 29 November 2016); https://doi.org/10.5065/D6CR5RD9

  27. 27.

    Monaghan, A. J., Steinhoff, D. F., Bruyere, C. L. and Yates, D. M NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research 2014 (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 23 June 2016); https://doi.org/10.5065/D6DJ5CN4

  28. 28.

    CMIP5 Data Availability (Geophysical Fluid Dynamics Laboratory, accessed 4 November 2016); http://nomads.gfdl.noaa.gov:8080/DataPortal/cmip5.jsp

  29. 29.

    Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

  30. 30.

    ICLUS Tools and Datasets ( Version 1.3.2) (US Environmental Protection Agency, 2010); https://go.nature.com/2S8homy

  31. 31.

    IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).

  32. 32.

    US Environmental Protection Agency ICLUS v1.3 User’s Manual: ArcGIS Tools and Datasets for Modeling US Housing Density Growth (Global Change Research Program, National Center for Environmental Assessment, 2010).

  33. 33.

    Monaghan, A. J., Hu, L., Brunsell, N. A., Barlage, M. & Wilhelmi, O. V. Evaluating the impact of urban morphology configurations on the accuracy of urban canopy model temperature simulations with MODIS. J. Geophys. Res. Atmos. 119, 6376–6392 (2014).

  34. 34.

    Stewart, I. D., Oke, T. R. & Krayenhoff, E. S. Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations. Int. J. Climatol. 34, 1062–1080 (2014).

  35. 35.

    ENERGY STAR Roof Product List (Energy Star, 2013); https://go.nature.com/2CuhGPn

  36. 36.

    Scherba, A., Sailor, D. J., Rosenstiel, T. N. & Wamser, C. C. Modeling impacts of roof reflectivity, integrated photovoltaic panels and green roof systems on sensible heat flux into the urban environment. Build. Environ. 46, 2542–2551 (2011).

  37. 37.

    Krayenhoff, E. S., Christen, A., Martilli, A. & Oke, T. R . A multi-layer radiation model for urban neighbourhoods with trees. Bound. Layer Meteorol. 151, 139–178 (2014).

  38. 38.

    Kenney, W. A. in Ecology, Planning, and Management of Urban Forests 336–345 (Springer, New York, 2008).

  39. 39.

    Upreti, R., Wang, Z. H. & Yang, J. Radiative shading effect of urban trees on cooling the regional built environment. Urban For. Urban Green. 26, 18–24 (2017).

  40. 40.

    Yaghoobian, N., Kleissl, J. & Krayenhoff, E. S. Modeling the thermal effects of artificial turf on the urban environment. J. Appl. Meteorol. Climatol. 49, 332–345 (2010).

  41. 41.

    Oke, T. R., Mills, G., Christen, A. & Voogt, J. A. Urban Climates (Cambridge Univ. Press, Cambridge, 2017).

  42. 42.

    Oleson, K. W., Bonan, G. B. & Feddema, J. Effects of white roofs on urban temperature in a global climate model. Geophys. Res. Lett. 37, L03701 (2010).

  43. 43.

    McCarthy, M. P., Best, M. J. & Betts, R. A. Climate change in cities due to global warming and urban effects. Geophys. Res. Lett. 37, L09705 (2010).

  44. 44.

    Janssen, E., Wuebbles, D. J., Kunkel, K. E., Olsen, S. C. & Goodman, A. Observational‐ and model‐based trends and projections of extreme precipitation over the contiguous United States. Earth’s Future 2, 99–113 (2014).

Download references

Acknowledgements

This work was supported by National Science Foundation Sustainability Research Network Cooperative Agreement 1444758, the Urban Water Innovation Network, and NSF grants SES-1520803 and EAR‐1204774. The authors acknowledge support from Research Computing at Arizona State University for the provision of high-performance supercomputing services. We also thank A. Martilli for helpful discussions.

Author information

Affiliations

  1. Urban Climate Research Center, Arizona State University, Tempe, AZ, USA

    • E. Scott Krayenhoff
    • , Mohamed Moustaoui
    • , Ashley M. Broadbent
    • , Vishesh Gupta
    •  & Matei Georgescu
  2. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

    • E. Scott Krayenhoff
    • , Ashley M. Broadbent
    • , Vishesh Gupta
    •  & Matei Georgescu
  3. School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada

    • E. Scott Krayenhoff
  4. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA

    • Mohamed Moustaoui
    •  & Matei Georgescu
  5. Julie Ann Wrigley Global Institute of Sustainability, Arizona State University, Tempe, AZ, USA

    • Mohamed Moustaoui
    •  & Matei Georgescu

Authors

  1. Search for E. Scott Krayenhoff in:

  2. Search for Mohamed Moustaoui in:

  3. Search for Ashley M. Broadbent in:

  4. Search for Vishesh Gupta in:

  5. Search for Matei Georgescu in:

Contributions

E.S.K., M.M. and M.G. designed the research. E.S.K., M.M., A.M.B. and M.G. performed the model simulations. E.S.K., A.M.B. and V.G. analysed the model output. All authors contributed to the writing of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to E. Scott Krayenhoff or Matei Georgescu.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Figures 1–22, Supplementary Tables 1–3, Supplementary References

  2. Reporting Summary

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41558-018-0320-9

Further reading