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Magnitude of urban heat islands largely explained by climate and population

Nature volume 573, pages 55–60 (2019)Cite this article

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Abstract

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔTs) worldwide and find a nonlinear increase in ΔTs with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔTs with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban–rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

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Fig. 1: Effect of background climate and population size on urban warming and its components.
Fig. 2: Urban warming and green spaces in Europe and South East Asia.
Fig. 3: Impact of background climate on the efficiency of heat mitigation strategies.

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Data availability

The Global Urban Heat Island Data Set 2013 is available at https://doi.org/10.7927/H4H70CRF (accessed on 7 December 2017). MERRA data were retrieved from https://disc.gsfc.nasa.gov/daac-bin/FTPSubset2.pl (downloaded on 4 March 2018) while GPCC data are available at https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html (accessed on 13 September 2016). MODIS albedo data are available at https://gcmd.nasa.gov/records/GCMD_MCD43B3.html (accessed on 15 July 2018). Urban green cover data for EU and SEA cities are available, respectively, at https://ec.europa.eu/eurostat/statistics-explained/index.php/Urban_Europe_-_statistics_on_cities,_towns_and_suburbs_-_green_cities#Further_Eurostat_information (accessed on 14 June 2017) and https://doi.org/10.1016/j.landurbplan.2016.09.005 (accessed on 29 September 2017). A summary table containing the urban and climate characteristics of the cities analysed is also available on Code Ocean (https://doi.org/10.24433/CO.9808462.v1).

Code availability

The MATLAB code (https://www.mathworks.com/products/matlab.html) of the coarse-grained UHI model is available on Code Ocean (https://doi.org/10.24433/CO.9808462.v1).

References

  1. Oke, T. R. City size and the urban heat island. Atmos. Environ. 7, 769–779 (1973).

    Article  ADS  Google Scholar 

  2. Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).

    Article  ADS  CAS  Google Scholar 

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

  4. Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).

    Article  ADS  CAS  Google Scholar 

  5. Rydin, Y. et al. Shaping cities for health: complexity and the planning of urban environments in the 21st century. Lancet 379, 2079–2108 (2012).

    Article  Google Scholar 

  6. Mora, C. et al. Global risk of deadly heat. Nat. Clim. Chang. 7, 501–506 (2017).

    Article  ADS  Google Scholar 

  7. Zhao, L. et al. Interactions between urban heat islands and heat waves. Environ. Res. Lett. 13, 034003 (2018).

    Article  ADS  Google Scholar 

  8. Imhoff, M. L., Zhang, P., Wolfe, R. E. & Bounoua, L. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens. Environ. 114, 504–513 (2010).

    Article  ADS  Google Scholar 

  9. Zhou, B., Rybski, D. & Kropp, J. P. On the statistics of urban heat island intensity. Geophys. Res. Lett. 40, 5486–5491 (2013).

    Article  ADS  Google Scholar 

  10. Zhou, D., Zhang, L., Li, D., Huang, D. & Zhu, C. Climate–vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environ. Res. Lett. 11, 074009 (2016).

    Article  ADS  Google Scholar 

  11. Liao, W. et al. Stronger contributions of urbanization to heat wave trends in wet climates. Geophys. Res. Lett. 45, 11310–11317 (2018).

    ADS  Google Scholar 

  12. Peng, S. et al. Surface urban heat island across 419 global big cities. Environ. Sci. Technol. 46, 696–703 (2012).

    Article  ADS  CAS  Google Scholar 

  13. Clinton, N. & Gong, P. MODIS detected surface urban heat islands and sinks: global locations and controls. Remote Sens. Environ. 134, 294–304 (2013).

    Article  ADS  Google Scholar 

  14. Li, D. et al. Urban heat island: aerodynamics or imperviousness? Sci. Adv. 5, eaau4299 (2019).

    Article  ADS  Google Scholar 

  15. Bai, X. et al. Six research priorities for cities and climate change. Nature 555, 23–25 (2018).

    Article  ADS  CAS  Google Scholar 

  16. Gu, Y. & Li, D. A modeling study of the sensitivity of urban heat islands to precipitation at climate scales. Urban Clim. 24, 982–993 (2018).

    Article  Google Scholar 

  17. Schläpfer, M., Lee, J. & Bettencourt, L. Urban skylines: building heights and shapes as measures of city size. Preprint at https://arxiv.org/abs/1512.00946 (2015).

  18. Grimmond, S. & Oke, T. R. Aerodynamic properties of urban areas derived from analysis of surface form. J. Appl. Meteorol. 38, 1262–1292 (1999).

    Article  ADS  Google Scholar 

  19. Gunawardena, K. R., Wells, M. J. & Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584-585, 1040–1055 (2017).

    Article  ADS  CAS  Google Scholar 

  20. Eurostat. Urban Europe—Statistics on Cities, Towns and Suburbs https://doi.org/10.2785/91120 (Publications Office of the European Union, Luxembourg, 2016).

  21. CIESIN. Global Urban Heat Island (UHI) Data Set, 2013 https://doi.org/10.7927/H4H70CRF (Center for International Earth Science Information Network, 2016).

  22. Richards, D. R., Passy, P. & Oh, R. Impacts of population density and wealth on the quantity and structure of urban green space in tropical Southeast Asia. Landsc. Urban Plan. 157, 553–560 (2017).

    Article  Google Scholar 

  23. Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C. & West, G. B. Growth, innovation, scaling, and the pace of life in cities. Proc. Natl Acad. Sci. USA 104, 7301–7306 (2007).

    Article  ADS  CAS  Google Scholar 

  24. Chrysoulakis, N. et al. Urban energy exchanges monitoring from space. Sci. Rep. 8, 11498 (2018).

    Article  ADS  Google Scholar 

  25. Sobstyl, J. M., Emig, T., Qomi, M. J. A., Ulm, F. J. & Pellenq, R. J. Role of city texture in urban heat islands at nighttime. Phys. Rev. Lett. 120, 108701 (2018).

    Article  ADS  CAS  Google Scholar 

  26. Gill, S. E., Handley, J. F., Ennos, A. R. & Pauleit, S. Adapting cities for climate change: the role of the green infrastructure. Built Environ. 33, 115–133 (2007).

    Article  Google Scholar 

  27. Scott, A. A., Waugh, D. W. & Zaitchik, B. F. Reduced urban heat island intensity under warmer conditions. Environ. Res. Lett. 13, 064003 (2018).

    Article  ADS  Google Scholar 

  28. Imamura, I. R. Role of soil moisture in the determination of urban heat island intensity in different climate regimes. WIT Trans. Ecol. Envir. 1, 395–402 (1970).

    Google Scholar 

  29. Lee, X. et al. Observed increase in local cooling effect of deforestation at higher latitudes. Nature 479, 384–387 (2011).

    Article  ADS  CAS  Google Scholar 

  30. Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).

    ADS  Google Scholar 

  31. Shashua-Bar, L., Pearlmutter, D. & Erell, E. The cooling efficiency of urban landscape strategies in a hot dry climate. Landsc. Urban Plan. 92, 179–186 (2009).

    Article  Google Scholar 

  32. Kumar, R. et al. Dominant control of agriculture and irrigation on urban heat island in India. Sci. Rep. 7, 14054 (2017).

    Article  ADS  Google Scholar 

  33. Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).

    Article  ADS  Google Scholar 

  34. Lim, Y. K., Cai, M., Kalnay, E. & Zhou, L. Observational evidence of sensitivity of surface climate changes to land types and urbanization. Geophys. Res. Lett. 32, L22712 (2005).

    Article  ADS  Google Scholar 

  35. Shastri, H., Barik, B., Ghosh, S., Venkataraman, C. & Sadavarte, P. Flip flop of day-night and summer-winter surface urban heat island intensity in India. Sci. Rep. 7, 40178 (2017).

    Article  ADS  CAS  Google Scholar 

  36. Juang, J. Y., Katul, G., Siqueira, M., Stoy, P. & Novick, K. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett. 34, L21408 (2007).

    Article  ADS  Google Scholar 

  37. Willis, K. J. & Petrokofsky, G. The natural capital of city trees. Science 356, 374–376 (2017).

    Article  ADS  CAS  Google Scholar 

  38. Manickathan, L., Defraeye, T., Allegrini, J., Derome, D. & Carmeliet, J. Parametric study of the influence of environmental factors and tree properties on the transpirative cooling effect of trees. Agric. For. Meteorol. 248, 259–274 (2018).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  40. Llaguno-Munitxa, M. & Bou-Zeid, E. Shaping buildings to promote street ventilation: a large-eddy simulation study. Urban Clim. 26, 76–94 (2018).

    Article  Google Scholar 

  41. Yang, J. & Bou-Zeid, E. Should cities embrace their heat islands as shields from extreme cold? J. Appl. Meteorol. Climatol. 57, 1309–1320 (2018).

    Article  ADS  Google Scholar 

  42. Seino, N., Aoyagi, T. & Tsuguti, H. Numerical simulation of urban impact on precipitation in Tokyo: how does urban temperature rise affect precipitation? Urban Clim. 23, 8–35 (2018).

    Article  Google Scholar 

  43. Endreny, T. A. Strategically growing the urban forest will improve our world. Nat. Commun. 9, 1160 (2018).

    Article  ADS  Google Scholar 

  44. Emmanuel, R., Rosenlund, H. & Johansson, E. Urban shading—a design option for the tropics? A study in Colombo, Sri Lanka. Int. J. Climatol. 27, 1995–2004 (2007).

    Article  Google Scholar 

  45. Wong, M. S., Nichol, J. E., To, P. H. & Wang, J. A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Build. Environ. 45, 1880–1889 (2010).

    Article  Google Scholar 

  46. Akbari, H., Menon, S. & Rosenfeld, A. Global cooling: increasing world-wide urban albedos to offset CO2. Clim. Change 94, 275–286 (2009).

    Article  ADS  CAS  Google Scholar 

  47. 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).

    Article  ADS  CAS  Google Scholar 

  48. Estrada, F., Botzen, W. W. J. & Tol, R. S. J. A global economic assessment of city policies to reduce climate change impacts. Nat. Clim. Chang. 7, 403–406 (2017).

    Article  ADS  Google Scholar 

  49. Mayor of London. London Environment Strategy https://www.london.gov.uk/what-we-do/environment/london-environment-strategy (Mayor of London, 2018).

  50. Bastin, J.-F. et al. Understanding climate change from a global analysis of city analogues. PloS One 14, e0217592 (2019).

    Article  Google Scholar 

  51. Bettencourt, L. M. & Lobo, J. Urban scaling in Europe. J. R. Soc. Interface 13, 20160005 (2016).

    Article  Google Scholar 

  52. Fuller, R. A. & Gaston, K. J. The scaling of green space coverage in European cities. Biol. Lett. 5, 352–355 (2009).

    Article  Google Scholar 

  53. Fang, Y. & Jawitz, J. W. High-resolution reconstruction of the United States human population distribution, 1790 to 2010. Sci. Data 5, 180067 (2018).

    Article  Google Scholar 

  54. Gasparrini, A. et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 386, 369–375 (2015).

    Article  Google Scholar 

  55. Clarke, J. F. Some effects of the urban structure on heat mortality. Environ. Res. 5, 93–104 (1972).

    Article  CAS  Google Scholar 

  56. Li, Y. et al. Evaluating biases in simulated land surface albedo from CMIP5 global climate models. J. Geophys. Res. Atmos. 121, 6178–6190 (2016).

    Article  ADS  Google Scholar 

  57. Chen, D., Loboda, T. V., He, T., Zhang, Y. & Liang, S. Strong cooling induced by stand-replacing fires through albedo in Siberian larch forests. Sci. Rep. 8, 4821 (2018).

    Article  ADS  Google Scholar 

  58. Oke, T. R. The urban energy balance. Prog. Phys. Geogr. 12, 471–508 (1988).

    Article  Google Scholar 

  59. Taha, H., Akbari, H., Rosenfeld, A. & Huang, J. Residential cooling loads and the urban heat island—the effects of albedo. Build. Environ. 23, 271–283 (1988).

    Article  Google Scholar 

  60. Akbari, H., Rosenfeld, A. & Taha, H. in Proc. American Society of Heating, Refrigeration, and Air-Conditioning Engineers, Lawrence Berkeley National Laboratory Report LBNL-28308 (Atlanta, Georgia, 1990)

  61. Yang, X. & Li, Y. The impact of building density and building height heterogeneity on average urban albedo and street surface temperature. Build. Environ. 90, 146–156 (2015).

    Article  Google Scholar 

  62. Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA–2). J. Clim. 30, 5419–5454 (2017).

    Article  ADS  Google Scholar 

  63. Schneider, U. et al. GPCC full data reanalysis version 7.0 at 0.5: monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050 (Global Precipitation Climatology Centre, 2015).

  64. Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res. Atmos. 117, D15114 (2012).

    ADS  Google Scholar 

  65. Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).

    Article  ADS  CAS  Google Scholar 

  66. Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).

    Article  ADS  CAS  Google Scholar 

  67. Bettencourt, L. M. The origins of scaling in cities. Science 340, 1438–1441 (2013).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  68. Shepherd, J. M. A review of current investigations of urban-induced rainfall and recommendations for the future. Earth Interact. 9, 1–27 (2005).

    Article  Google Scholar 

  69. Taleghani, M., Tenpierik, M., van den Dobbelsteen, A. & Sailor, D. J. Heat mitigation strategies in winter and summer: field measurements in temperate climates. Build. Environ. 81, 309–319 (2014).

    Article  Google Scholar 

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Acknowledgements

G.M. was supported by the The Branco Weiss Fellowship—Society in Science administered by ETH Zurich. E.B.-Z. acknowledges support by the US National Science Foundation under grant no. ICER 1664091, the SRN under cooperative agreement no. 1444758, and the Army Research Office under contract W911NF-15-1-0003 (program manager J. Barzyk). M.S. was supported by the Future Cities Laboratory at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016), under its Campus for Research Excellence and Technological Enterprise programme. We thank P. Edwards, J. Carmeliet, C. Küffer, and D. Richards for help and discussions at the beginning of this research.

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  1. Gabriele Manoli

    Present address: Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK

Authors and Affiliations

  1. Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland

    Gabriele Manoli, Simone Fatichi, Naika Meili & Paolo Burlando

  2. Future Cities Laboratory, Singapore-ETH Centre, ETH Zurich, Singapore, Singapore

    Markus Schläpfer & Naika Meili

  3. Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    Kailiang Yu & Thomas W. Crowther

  4. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Gabriel G. Katul

  5. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Elie Bou-Zeid

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Contributions

G.M. designed the study, developed the model and conducted the analysis with contributions from S.F., G.G.K. and E.B.-Z. K.Y. and T.W.C. analysed albedo remote sensing observations. G.M. wrote the original draft of the manuscript with input from S.F., G.G.K. and E.B.-Z. M.S., K.Y., T.W.C., N.M. and P.B. reviewed and edited the manuscript. All authors discussed the results and contributed to the final version of the manuscript.

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Correspondence to Gabriele Manoli.

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Peer review information Nature thanks Lahouari Bounoua, Ben Crawford and Qihao Weng for their contribution to the peer review of this work.

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Supplementary Methods, Supplementary Tables 1–6, Supplementary Figs 1–25 and Supplementary References.

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Manoli, G., Fatichi, S., Schläpfer, M. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573, 55–60 (2019). https://doi.org/10.1038/s41586-019-1512-9

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Comments

Commenting on this article is now closed.

  1. Alberto Martilli

    We believe there are several fundamental problems with this article. It lacks methodological rigor in several places, but most importantly there are several key mismatches between the model’s capabilities and its scope of application. We present our arguments in full detail in the following archive: https://osf.io/8gnbf/. In brief, the three most important problems we identify are as follows:
    (a) The scale adopted is too coarse to resolve critical features that control urban climates (e.g. urban structure, form and fabric), as evidenced by decades of research by the urban climate research community (Urban Climates, Oke et al., 2017). At the chosen scale, only vague and general statements can be made, most of which are already well known. For example, the reduced efficiency of vegetation in the provision of evaporative cooling in humid climates can be derived from basic principles (reduced water vapour pressure deficit), and it is one of the few tangible results that come out of the analysis given the coarse resolution of the model used (e. g. without considering irrigation, vegetation type, etc.). Hence, this article adds little new knowledge that is useful for improving the thermal environment of cities.
    (b) The sections on "heat mitigation strategies" and "climate sensitive urban design" use the assumption that the SUHI magnitude is the relevant measure to assess a city’s potential for heat mitigation (HM). We disagree. The mitigation potential of HM strategies depends on the maximum Ts reduction that they can provide (if we consider mitigation of only Ts, not air temperature – see point c below). It does not depend on the SUHI magnitude. In reality, the cooling benefit can be larger, smaller, or equal to the SUHI magnitude and it can vary between these categories over the diurnal cycle. While the outcome is certainly influenced by the background synoptic climate and the climate of the surrounding rural areas, it is not directly related to SUHI magnitude. A city with a relatively small SUHI magnitude can still significantly modify its Ts (and micro-climate) through proper application of HM strategies. This is corroborated by the examples and data presented in the paper: vegetation and albedo are more effective strategies for cities in arid climates, which are precisely the cities where the daytime SUHI is generally smaller. This point becomes important when cities in completely different climates are compared, as is the case in this article. In summary, the HM-related portions of the article are based on what we consider to be a research question of little practical relevance (how do we mitigate the urban heat island?), instead of what we consider a more useful one (how do we improve a city's thermal environment?). A more complete discussion of this point is found in Martilli et al (Urban Climate, 2020).
    (c) Perhaps most critically, this study relies on seasonally-averaged Ts only, neglecting air temperature and its diurnal variation. Both, however, are essential for a first order characterization of the urban climate and the associated need for heat reduction.
    A number of other inaccurate assumptions related to the treatment of the satellite data used to calibrate and validate the model, numerous incompatibilities between variables in the input data and those included in the model, as well as inaccuracies in key parameterizations used in the modeling, are also highlighted in the archive document.
    We recognize that the authors describe and present their methods with adequate clarity, and in many cases acknowledge the limitations inherent in their coarse-grained approach. However, their results nevertheless lack practical utility and their conclusions are inappropriate relative to the capabilities of the model and the context in which it is applied.
    Alberto Martilli, Matthias Roth, Scott Krayenhoff, Winston Chow, Andreas Christen, Negin Nazarian, Melissa Hart, Matthias Demuzere, Ariane Middel, Benjamin Bechtel, Jamie Voogt

  2. Alberto Martilli

    We believe there are several fundamental problems with this article. It lacks methodological rigor in several places, but most importantly there are several key mismatches between the model’s capabilities and its scope of application. We present our arguments in full detail in the following archive: https://osf.io/8gnbf/. In brief, the three most important problems we identify are as follows:
    (a) The scale adopted is too coarse to resolve critical features that control urban climates (e.g. urban structure, form and fabric), as evidenced by decades of research by the urban climate research community (Urban Climates, Oke et al., 2017). At the chosen scale, only vague and general statements can be made, most of which are already well known. For example, the reduced efficiency of vegetation in the provision of evaporative cooling in humid climates can be derived from basic principles (reduced water vapour pressure deficit), and it is one of the few tangible results that come out of the analysis given the coarse resolution of the model used (e. g. without considering irrigation, vegetation type, etc.). Hence, this article adds little new knowledge that is useful for improving the thermal environment of cities.
    (b) The sections on "heat mitigation strategies" and "climate sensitive urban design" use the assumption that the SUHI magnitude is the relevant measure to assess a city’s potential for heat mitigation (HM). We disagree. The mitigation potential of HM strategies depends on the maximum Ts reduction that they can provide (if we consider mitigation of only Ts, not air temperature – see point c below). It does not depend on the SUHI magnitude. In reality, the cooling benefit can be larger, smaller, or equal to the SUHI magnitude and it can vary between these categories over the diurnal cycle. While the outcome is certainly influenced by the background synoptic climate and the climate of the surrounding rural areas, it is not directly related to SUHI magnitude. A city with a relatively small SUHI magnitude can still significantly modify its Ts (and micro-climate) through proper application of HM strategies. This is corroborated by the examples and data presented in the paper: vegetation and albedo are more effective strategies for cities in arid climates, which are precisely the cities where the daytime SUHI is generally smaller. This point becomes important when cities in completely different climates are compared, as is the case in this article. In summary, the HM-related portions of the article are based on what we consider to be a research question of little practical relevance (how do we mitigate the urban heat island?), instead of what we consider a more useful one (how do we improve a city's thermal environment?). A more complete discussion of this point is found in Martilli et al (Urban Climate, 2020).

    (c) Perhaps most critically, this study relies on seasonally-averaged Ts only, neglecting air temperature and its diurnal variation. Both, however, are essential for a first order characterization of the urban climate and the associated need for heat reduction.

    A number of other inaccurate assumptions related to the treatment of the satellite data used to calibrate and validate the model, numerous incompatibilities between variables in the input data and those included in the model, as well as inaccuracies in key parameterizations used in the modeling, are also highlighted in the archive document.

    We recognize that the authors describe and present their methods with adequate clarity, and in many cases acknowledge the limitations inherent in their coarse-grained approach. However, their results nevertheless lack practical utility and their conclusions are inappropriate relative to the capabilities of the model and the context in which it is applied.

    Alberto Martilli, Matthias Roth, Scott Krayenhoff, Winston Chow, Andreas Christen, Negin Nazarian, Melissa Hart, Matthias Demuzere, Ariane Middel, Benjamin Bechtel, James Voogt

  3. Gabriele Manoli

    This comment was formally submitted by Martilli et al. (hereafter referred to as M20) to Nature referencing the work of Manoli and collaborators (Nature 573 p. 55-60) and, after consideration by the editorial office, it was declined for publication. For the sake of transparency, we have uploaded the detailed response to the comments raised in M20 that we shared with the authors in the following online repository: https://osf.io/mwpna/
    Briefly, the criticism in M20 originates from a misinterpretation of the scales and scope of our analysis: while we are aware of the complexity and heterogeneity of cities, the approach featured in the paper intentionally focuses on multi-city scale conditions averaged in time and over a global ensemble of urban areas with similar population and precipitation. That is, the focus remains on emergent global patterns and seasonal averages, a focus that is purposely distinct from most current canonical urban climate studies and parameterizations dealing with block/neighbourhood/single-city scale processes (that are important, but not the focus here). The limitations of such a global scale effort have been acknowledged and discussed in the original manuscript and it will be redundant to repeat them here. Inevitably some trade-offs between global analysis and fine-scale processes are necessary to make progress on general patterns in cities. This is unambiguously stated in the published manuscript, and we have no reason to suspect readers will not be mindful of these limitations when interpreting our conclusions.
    Regarding heat mitigation, we are keenly aware of the distinction between measures for broadly improving local microclimate versus mitigation measures for reducing the urban heat island (UHI). However, the local microclimate is not unrelated to the intensity of UHIs as M20 suggests. Given a constant rural reference, modifying the surface UHI modifies the surface, canopy, and boundary layer absolute temperatures over the entire city. Therefore, UHI remains a key indicator of urban climate studies: it measures how better or worse the modified city climate is relative to its background conditions (that are, in principle, not modified by anthropogenic intervention).
    In general, the results and conclusions of the published article are not affected by any of the issues raised in M20. They remain robust and appropriate at the scale of analysis and congruent with existing literature on urban climate and city analytics.
    G. Manoli (on behalf of all coauthors)

  4. Scott Krayenhoff Replied to Gabriele Manoli

    I’d like to thank Manoli and colleagues for their engagement, and I am optimistic that this dialogue will help further clarify several fundamental issues related to urban climate, urban heat mitigation, and study design. As the world population urbanizes and the climate warms, it is good to see urban climate-related research achieve this kind of visibility. In-depth
    discussions of these issues are crucial to the success of heat mitigation
    efforts.

    With regards to the comment by Manoli and colleagues above, I think three considerations could usefully be brought to the fore:

    1) “Given a constant rural reference, modifying the surface UHI modifies the surface, canopy, and boundary layer absolute temperatures over the entire city.” Indeed, this is correct, yet this observation simply indicates that rural temperature, and therefore the UHI intensity, is not relevant when assessing the need for urban heat mitigation strategies. “Therefore, UHI remains a key indicator of urban climate studies: it measures how better or worse the modified city climate is relative to its background conditions (that are, in principle, not modified by anthropogenic intervention).” The actual urban climate is the main determinant of the need for heat mitigation, not the surface UHI intensity. A simple example is illustrative: two cities with identical urban temperatures (and therefore the same heat stress and the same need for heat mitigation, all else being equal within the city), will have different surface UHI intensities if their surrounding rural temperatures differ. This fact limits the practical utility of the surface UHI intensity when considering the need for heat mitigation.

    2) “In general, the results and conclusions of the published article are not affected by any of the issues raised in [Martilli et al. 2020]”: The results and conclusions in Manoli et al. (2019; hereafter M19) related to urban heat mitigation rest in large part on the appropriateness of the metric that M19 applies – seasonal-average city-scale 2-D surface urban heat island (SUHI) intensity – to their stated objective, “geographically targeted guidelines for heat mitigation”. I have not seen any published evidence or coherent argumentation to indicate that this coarse-scale metric, targeted
    at surface temperature differences, is appropriate for heat mitigation assessment. My sense is that this kind of approach (“mitigate the UHI”) is delaying progress in the field of urban heat mitigation. While M19 is one among numerous such studies, it is a particularly high profile example.

    3) Manoli and colleagues recognize some of the points made in our archive (Martilli et al. 2020) in another recent publication (Manoli et al. 2020, Proc Natl Acad Soc USA, see Limitations and Perspectives section). However, in it they nevertheless state that “intensity of SUHI is a necessary but not sufficient metric to characterize heat stress.” I contend that characterization of the urban climate alone is sufficient to assess urban heat stress – comparison with a rural climate and therefore assessment of the surface UHI intensity are not necessary and in fact are potentially misleading. Secondly, they state that “[cities] can only influence the urban-induced perturbation from that background to improve their climatic condition.” However, the urban-induced perturbation, for example the surface UHI of a city, in no way sets an
    upper limit on the urban temperature reduction potential. A clear example is that an arid city with a negative daytime UHI can be further cooled by increasing the albedo of surface materials or the coverage of irrigated vegetation. Indeed, many such arid cities desperately need heat reduction measures. However, one would not reach this conclusion based on the metric used by M19, or any UHI-based metric. In other words, the goal is not to “[minimize] the urban-rural temperature differences” as stated in M19, but to cool hot cities, irrespective of the temperatures of nearby rural areas.

    While M19 include novel modelling and analysis that may help reveal dynamics of seasonal- and city-scale 2-D surface temperature differences between urban and rural areas globally, by virtue of its focus on this metric its results are not practically relevant to urban heat mitigation, which inherently requires a focus on absolute thermal conditions in the city (among other elements discussed by Martilli et al. 2020).

    Scott Krayenhoff
    Assistant Professor
    University of Guelph

    Reference:

    Martilli, A., Roth, M., Chow, W.T., Demuzere, M., Lipson, M., Krayenhoff, E.S., Sailor, D., Nazarian, N., Voogt, J., Wouters, H., Middel, A., Stewart, I.D., Bechtel, B., Christen, A., Hart, M.A., 2020. Summer average urban-rural surface temperature differences do not indicate the need for urban heat reduction. doi:10.31219/osf.io/8gnbf

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