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Elevated urban energy risks due to climate-driven biophysical feedbacks

Nature Climate Change volume 14, pages 1056–1063 (2024)Cite this article

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Abstract

Climate-driven impacts on future urban heating and cooling (H&C) energy demand are critical to sustainable energy planning. Existing global H&C projections are predominantly made without accounting for future two-way biophysical feedbacks between urban climate and H&C use. Here, using a hybrid modelling framework we show that the prevalent degree-days methods misrepresent the magnitude, nonlinearity and uncertainty in the climate-driven projections of H&C energy demand changes due to the missing two-way feedbacks. We find a 220% increase (47% decrease) in cooling (heating) energy demand with amplified uncertainty by 2099 under a very high emission scenario, roughly twice that projected by previous methods. The spatially diverse H&C demand responses to the warming climates highlight the disparate challenges faced by individual cities and necessitate urban energy planning accounting for local climate–energy interactions. Our study underscores the critical necessity of explicit and dynamic modelling of urban H&C energy use for climate-sensitive energy planning.

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Fig. 1: Climate-driven biophysical and biogeochemical feedbacks between a warmer climate and H&C energy use.
Fig. 2: Nonlinear H&C energy demand responses under climate-driven temperature increases.
Fig. 3: Intensified nonlinearity and uncertainty in H&C energy demand projections are captured when biophysical feedbacks are modelled.
Fig. 4: Projected changes in cooling and heating energy demand show more distinct spatial patterns and higher uncertainties than projected temperature changes.
Fig. 5: Cities with similar background climates or urban characteristics may experience highly different H&C energy demand changes.

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Data availability

The CMIP6 data used for emulator training, testing and application are available via the CMIP6 archive operated by the Earth System Grid Federation (ESGF) at https://esgf-node.llnl.gov/projects/cmip6/. The degree-days data used for Fig. 3 and Extended Data Fig. 2 are available through the IPCC WGI Interactive Atlas at https://interactive-atlas.ipcc.ch/. All output data from the emulators necessary to reproduce the main results are archived and publicly available via Zenodo at https://doi.org/10.5281/zenodo.12659826 (ref. 80). The source of the base maps is available from https://scitools.org.uk/cartopy/docs/latest/index.html. Source data are provided with this paper.

Code availability

The Python code necessary to train and apply the urban climate–energy emulators to reproduce the main results are available on the NSF NCAR Cheyenne cluster (ref. 81) and publicly available from GitHub at https://github.com/cathyxinchangli/urban_climate_energy_emulators and via Zenodo at https://doi.org/10.5281/zenodo.12785004 (ref. 82). The CESM2 (used to perform the simulations) source code releases are publicly available on GitHub at https://github.com/ESCOMP/CESM.

References

  1. International Energy Outlook 2019 (EIA, 2020); www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf

  2. Yalew, S. G. et al. Impacts of climate change on energy systems in global and regional scenarios. Nat. Energy 5, 794–802 (2020).

    Article  Google Scholar 

  3. van Ruijven, B. J., De Cian, E. & Sue Wing, I. Amplification of future energy demand growth due to climate change. Nat. Commun. 10, 2762 (2019).

    Article  Google Scholar 

  4. Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. & Vandenbergh, M. P. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc. Natl Acad. Sci. USA 106, 18452–18456 (2009).

    Article  CAS  Google Scholar 

  5. Labriet, M. et al. Worldwide impacts of climate change on energy for heating and cooling. Mitig. Adapt. Strateg. Glob. Change 20, 1111–1136 (2015).

    Article  Google Scholar 

  6. Mastrucci, A., van Ruijven, B., Byers, E., Poblete-Cazenave, M. & Pachauri, S. Global scenarios of residential heating and cooling energy demand and CO2 emissions. Clim. Change 168, 14 (2021).

    Article  CAS  Google Scholar 

  7. Hadley, S. W., Erickson III, D. J., Hernandez, J. L., Broniak, C. T. & Blasing, T. J. Responses of energy use to climate change: a climate modeling study. Geophys. Res. Lett. https://doi.org/10.1029/2006GL026652 (2006).

  8. Salamanca, F., Georgescu, M., Mahalov, A., Moustaoui, M. & Wang, M. Anthropogenic heating of the urban environment due to air conditioning. J. Geophys. Res. Atmos. 119, 5949–5965 (2014).

    Article  Google Scholar 

  9. Takane, Y., Kikegawa, Y., Hara, M. & Grimmond, C. S. B. Urban warming and future air-conditioning use in an Asian megacity: importance of positive feedback. npj Clim. Atmos. Sci. 2, 39 (2019).

  10. Oleson, K. W. & Feddema, J. Parameterization and surface data improvements and new capabilities for the Community Land Model Urban (CLMU). J. Adv. Model. Earth Syst. 12, e2018MS001586 (2020).

  11. Ginzburg, A. S. & Demchenko, P. F. Anthropogenic meso-meteorological feedbacks: a review of a recent research. Izv. Atmos. Ocean. Phys. 55, 573–590 (2019).

    Article  Google Scholar 

  12. Wenz, L., Levermann, A. & Auffhammer, M. North–south polarization of European electricity consumption under future warming. Proc. Natl Acad. Sci. USA 114, E7910–E7918 (2017).

    Article  CAS  Google Scholar 

  13. Rastogi, D., Holladay, J. S., Evans, K. J., Preston, B. L. & Ashfaq, M. Shift in seasonal climate patterns likely to impact residential energy consumption in the United States. Environ. Res. Lett. 14, 074006 (2019).

    Article  Google Scholar 

  14. Isaac, M. & van Vuuren, D. P. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 37, 507–521 (2009).

    Article  Google Scholar 

  15. Auffhammer, M., Baylis, P. & Hausman, C. H. Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proc. Natl Acad. Sci. USA 114, 1886–1891 (2017).

    Article  CAS  Google Scholar 

  16. Levesque, A. et al. How much energy will buildings consume in 2100? A global perspective within a scenario framework. Energy 148, 514–527 (2018).

    Article  Google Scholar 

  17. Standard 169-2020, Climatic Data for Building Design Standards (ASHRAE, 2020).

  18. IPCC. Summary for policymakers. In Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  19. Reidmiller, D. R. et al. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment Vol. II (US Global Change Research Program, 2018); https://nca2018.globalchange.gov/

  20. Deroubaix, A. et al. Large uncertainties in trends of energy demand for heating and cooling under climate change. Nat. Commun. 12, 5197 (2021).

    Article  CAS  Google Scholar 

  21. Zhou, Y. et al. Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework. Appl. Energy 113, 1077–1088 (2014).

    Article  Google Scholar 

  22. Zhao, L. et al. Global multi-model projections of local urban climates. Nat. Clim. Change 11, 152–157 (2021).

  23. Tang, J. et al. Statistical downscaling and dynamical downscaling of regional climate in China: present climate evaluations and future climate projections. J. Geophys. Res. Atmos. 121, 2110–2129 (2016).

    Article  Google Scholar 

  24. Qian, Y. et al. Urbanization impact on regional climate and extreme weather: current understanding, uncertainties, and future research directions. Adv. Atmos. Sci. 39, 819–860 (2022).

    Article  Google Scholar 

  25. Herrera, M. et al. A review of current and future weather data for building simulation. Build. Serv. Eng. Res. Technol. 38, 602–627 (2017).

    Article  Google Scholar 

  26. 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  CAS  Google Scholar 

  27. Li, W. et al. Modeling urban building energy use: a review of modeling approaches and procedures. Energy 141, 2445–2457 (2017).

    Article  Google Scholar 

  28. Lauzet, N. et al. How building energy models take the local climate into account in an urban context—a review. Renew. Sustain. Energy Rev. 116, 109390 (2019).

    Article  Google Scholar 

  29. Hsieh, C.-M., Aramaki, T. & Hanaki, K. The feedback of heat rejection to air conditioning load during the nighttime in subtropical climate. Energy Build. 39, 1175–1182 (2007).

    Article  Google Scholar 

  30. Kikegawa, Y., Nakajima, K., Takane, Y., Ohashi, Y. & Ihara, T. A quantification of classic but unquantified positive feedback effects in the urban-building–energy–climate system. Appl. Energy 307, 118227 (2022).

    Article  Google Scholar 

  31. Vahmani, P., Luo, X., Jones, A. & Hong, T. Anthropogenic heating of the urban environment: an investigation of feedback dynamics between urban micro-climate and decomposed anthropogenic heating from buildings. Build. Environ. 213, 108841 (2022).

    Article  Google Scholar 

  32. Ortiz, L., González, J. E. & Lin, W. Climate change impacts on peak building cooling energy demand in a coastal megacity. Environ. Res. Lett. 13, 094008 (2018).

    Article  Google Scholar 

  33. Craig, M. T. et al. Overcoming the disconnect between energy system and climate modeling. Joule 6, 1405–1417 (2022).

    Article  Google Scholar 

  34. McCarthy, M. P., Best, M. J. & Betts, R. A. Climate change in cities due to global warming and urban effects. Geophys. Res. Lett. https://doi.org/10.1029/2010GL042845 (2010).

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

    Article  Google Scholar 

  36. Danabasoglu, G. et al. The Community Earth System Model Version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019MS001916 (2020).

    Article  Google Scholar 

  37. 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).

    Article  Google Scholar 

  38. O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model. Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  39. Iturbide, M. et al. Repository supporting the implementation of FAIR principles in the IPCC-WG1 Atlas. Zenodo https://doi.org/10.5281/zenodo.3691646 (2021).

  40. IPCC. Summary for policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  41. Davis, L., Gertler, P., Jarvis, S. & Wolfram, C. Air conditioning and global inequality. Glob. Environ. Change 69, 102299 (2021).

    Article  Google Scholar 

  42. Debnath, R., Bardhan, R. & Sunikka-Blank, M. How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers. Energy Policy 132, 418–428 (2019).

    Article  Google Scholar 

  43. Ju, S. R. Southeast Asian Houses: Expanding Tradition (Seoul Selection, 2017).

  44. Li, X. ‘Cathy’ et al. Enhancing urban climate–energy modeling in the Community Earth System Model (CESM) through explicit representation of urban air-conditioning adoption. J. Adv. Model. Earth Syst. 16, e2023MS004107 (2024).

    Article  Google Scholar 

  45. Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).

    Article  Google Scholar 

  46. Lawrence, D. et al. Technical Description of Version 5.0 of the Community Land Model (CLM) (NSF National Center for Atmospheric Research, 2020).

  47. Demuzere, M. et al. Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city. Q. J. R. Meteorol. Soc. 143, 1581–1596 (2017).

    Article  Google Scholar 

  48. Karsisto, P. et al. Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki. Q. J. R. Meteorol. Soc. 142, 401–417 (2016).

    Article  Google Scholar 

  49. Oleson, K. W., Bonan, G. B., Feddema, J., Vertenstein, M. & Grimmond, C. S. B. An urban parameterization for a global climate model. Part I: formulation and evaluation for two cities. J. Appl. Meteorol. Climatol. 47, 1038–1060 (2008).

    Article  Google Scholar 

  50. Demuzere, M., Oleson, K., Coutts, A. M., Pigeon, G. & van Lipzig, N. P. M. Simulating the surface energy balance over two contrasting urban environments using the Community Land Model Urban. Int. J. Climatol. 33, 3182–3205 (2013).

    Article  Google Scholar 

  51. 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).

    Article  CAS  Google Scholar 

  52. Lin, S., Feng, J., Wang, J. & Hu, Y. Modeling the contribution of long-term urbanization to temperature increase in three extensive urban agglomerations in China. J. Geophys. Res. Atmos. 121, 1683–1697 (2016).

    Article  Google Scholar 

  53. Demuzere, M. et al. The implementation of biofiltration systems, rainwater tanks and urban irrigation in a single-layer urban canopy model. Urban Clim. 10, 148–170 (2014).

    Article  Google Scholar 

  54. Harmay, N. S. M. & Choi, M. Effects of heat waves on urban warming across different urban morphologies and climate zones. Build. Environ. 209, 108677 (2022).

    Article  Google Scholar 

  55. Lipson, M. J. et al. Evaluation of 30 urban land surface models in the Urban-PLUMBER project: phase 1 results. Q. J. R. Meteorol.Soc. 150, 126–169 (2024).

    Article  Google Scholar 

  56. Flanner, M. G. Integrating anthropogenic heat flux with global climate models. Geophys. Res. Lett. https://doi.org/10.1029/2008GL036465 (2009).

  57. Varquez, A. C. G., Kiyomoto, S., Khanh, D. N. & Kanda, M. Global 1-km present and future hourly anthropogenic heat flux. Sci. Data 8, 64 (2021).

    Article  Google Scholar 

  58. Technology Roadmap, Energy-Efficient Buildings: Heating and Cooling Equipment (IEA, 2011); www.iea.org/reports/technology-roadmap-energy-efficient-buildings-heating-and-cooling-equipment

  59. Transition to Sustainable Buildings: Strategies and Opportunities to 2050 (OECD/IEA, 2013).

  60. International Energy Outlook 2008 (IEA, 2008); www.eia.gov/outlooks/archive/ieo08/index.html

  61. Nakajima, K., Takane, Y., Kikegawa, Y. & Yamaguchi, K. Improvement of WRF–CM–BEM and its application to high-resolution hindcasting of summertime urban electricity consumption. Energy Build. 296, 113336 (2023).

    Article  Google Scholar 

  62. Gamarro, H. & González-Cruz, J. E. On the electrification of winter season in cold climate megacities—the case of New York City. ASME J. Eng. Sustain. Build. Cities 4, 031006 (2023).

  63. Chen, M., Ban-Weiss, G. A. & Sanders, K. T. Utilizing smart-meter data to project impacts of urban warming on residential electricity use for vulnerable populations in Southern California. Environ. Res. Lett. 15, 064001 (2020).

    Article  Google Scholar 

  64. Ichinose, T., Shimodozono, K. & Hanaki, K. Impact of anthropogenic heat on urban climate in Tokyo. Atmos. Environ. 33, 3897–3909 (1999).

    Article  CAS  Google Scholar 

  65. Wang, L., Sun, T., Zhou, W., Liu, M. & Li, D. Deciphering the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework. Environ. Res. Lett. 18, 094005 (2023).

    Article  Google Scholar 

  66. Fan, H. & Sailor, D. J. Modeling the impacts of anthropogenic heating on the urban climate of Philadelphia: a comparison of implementations in two PBL schemes. Atmos. Environ. 39, 73–84 (2005).

    Article  CAS  Google Scholar 

  67. Narumi, D., Kondo, A. & Shimoda, Y. Effects of anthropogenic heat release upon the urban climate in a Japanese megacity. Environ. Res. 109, 421–431 (2009).

    Article  CAS  Google Scholar 

  68. Feng, J.-M., Wang, Y.-L., Ma, Z.-G. & Liu, Y.-H. Simulating the regional impacts of urbanization and anthropogenic heat release on climate across China. J. Clim. 25, 7187–7203 (2012).

    Article  Google Scholar 

  69. de Munck, C. et al. How much can air conditioning increase air temperatures for a city like Paris, France?. Int. J. Climatol. 33, 210–227 (2013).

    Article  Google Scholar 

  70. Bohnenstengel, S. I., Hamilton, I., Davies, M. & Belcher, S. E. Impact of anthropogenic heat emissions on London’s temperatures. Q. J. R. Meteorol. Soc. 140, 687–698 (2014).

    Article  Google Scholar 

  71. Kikegawa, Y., Tanaka, A., Ohashi, Y., Ihara, T. & Shigeta, Y. Observed and simulated sensitivities of summertime urban surface air temperatures to anthropogenic heat in downtown areas of two Japanese Major Cities, Tokyo and Osaka. Theor. Appl. Climatol. 117, 175–193 (2014).

    Article  Google Scholar 

  72. Feng, J., Wang, J. & Yan, Z. Impact of anthropogenic heat release on regional climate in three vast urban agglomerations in China. Adv. Atmos. Sci. 31, 363–373 (2014).

    Article  CAS  Google Scholar 

  73. Wang, X., Sun, X., Tang, J. & Yang, X. Urbanization-induced regional warming in Yangtze River Delta: potential role of anthropogenic heat release. Int. J. Climatol. 35, 4417–4430 (2015).

    Article  Google Scholar 

  74. Zhang, N., Wang, X., Chen, Y., Dai, W. & Wang, X. Numerical simulations on influence of urban land cover expansion and anthropogenic heat release on urban meteorological environment in Pearl River Delta. Theor. Appl. Climatol. 126, 469–479 (2016).

    Article  Google Scholar 

  75. Ma, S. et al. The impact of an urban canopy and anthropogenic heat fluxes on Sydney’s climate. Int. J. Climatol. 37, 255–270 (2017).

    Article  Google Scholar 

  76. Yang, B. et al. Modeling the impacts of urbanization on summer thermal comfort: the role of urban land use and anthropogenic heat. J. Geophys. Res. Atmos. 124, 6681–6697 (2019).

    Article  Google Scholar 

  77. Molnár, G., Kovács, A. & Gál, T. How does anthropogenic heating affect the thermal environment in a medium-sized Central European city? A case study in Szeged. Hung. Urban Clim. 34, 100673 (2020).

    Article  Google Scholar 

  78. Mei, S.-J. & Yuan, C. Analytical and numerical study on transient urban street air warming induced by anthropogenic heat emission. Energy Build. 231, 110613 (2021).

    Article  Google Scholar 

  79. Zheng, Z., Zhao, L. & Oleson, K. W. Large model structural uncertainty in global projections of urban heat waves. Nat. Commun. 12, 3736 (2021).

    Article  CAS  Google Scholar 

  80. Li, X. ‘Cathy’, Zhao, L., Qin, Y., Oleson, K. & Zhang, Y. Data for ‘Elevated urban energy risks due to climate-driven biophysical feedbacks’. Zenodo https://doi.org/10.5281/ZENODO.12659826 (2024).

  81. Cheyenne: SGI ICE XA Cluster (NCAR, 2017); https://doi.org/10.5065/D6RX99HX

  82. Li, X. ‘Cathy’. cathyxinchangli/urban_climate_energy_emulators: v0.0.0. Zenodo https://doi.org/10.5281/ZENODO.12785004 (2024).

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Acknowledgements

L.Z. acknowledges the support by the US National Science Foundation (CAREER award grant no. 2145362) and the Institute for Sustainability, Energy, and Environment at the University of Illinois Urbana-Champaign. We acknowledge the high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NSF NCAR Computational and Information Systems Laboratory, sponsored by the US National Science Foundation.

Author information

Authors and Affiliations

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

    Xinchang ‘Cathy’ Li, Lei Zhao & Yiwen Zhang

  2. National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Lei Zhao

  3. Institute for Sustainability, Energy, and Environment (iSEE), University of Illinois Urbana-Champaign, Urbana, IL, USA

    Lei Zhao

  4. College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Yue Qin

  5. Institute of Carbon Neutrality, Peking University, Beijing, China

    Yue Qin

  6. Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research (NSF NCAR), Boulder, CO, USA

    Keith Oleson

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Contributions

L.Z. and Y.Q. conceptualized and designed the research. X.L. developed the AI-based urban climate–energy emulators with contributions from L.Z. X.L. performed the data modelling and analysis. L.Z., Y.Q. and K.O. contributed ideas to the data analysis. X.L. and Y.Z. processed CMIP6 model data. X.L. and K.O. performed model validation for CLMU-BEM. X.L. drafted the manuscript, with discussions and contributions from L.Z., Y.Q. and K.O.

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Correspondence to Lei Zhao or Yue Qin.

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Nature Climate Change thanks Jorge Gonzalez-Cruz, Yukihiro Kikegawa and Ting Sun for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Ignoring two-way biophysical feedbacks between urban climate and H&C energy use underestimate summer cooling energy demand and overestimate winter heating energy demand under present-day climate.

a and b, relative difference between no-feedback (NO_FB) and with-feedback (WI_FB) runs for average summer cooling (a) and average winter heating (b) energy demand, for 2005–2014 as simulated by CESM2. For the northern hemisphere, summer is assumed to be from June to August and winter from December to February; seasons are assumed to be flipped for the southern hemisphere. See Methods (Validation of the BEM) for more information on the simulations. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Source data

Extended Data Fig. 2 Intensified nonlinearity and uncertainty in H&C energy demand projections are captured when biophysical feedbacks are modeled.

a, e, time series of relative changes in global annual mean cooling energy demand (∆QC) and cooling degree-days (∆CDD) for SSP3-7.0 and SSP2-4.5, respectively. b, f, relative ∆QC and ∆CDD against projected changes in mean air temperature for SSP3-7.0 and SSP2-4.5, respectively. c, d, g, h, same as a, b and e, f but for relative changes in annual mean heating energy demand (∆QH) and heating degree-days (∆HDD) for SSP3-7.0 (c, d) and SSP2-4.5 (g, h). Relative ∆ refers to projected changes between the global annual mean of each year in the projection period (2015–2099) and that of 2015 divided by the 2015 value. In a, c, e, and g, multi-model means (thick lines) with ± one standard deviation on either side (shaded area) and individual models’ trajectories (thin lines) are presented. In b, d, f, and h, multi-model means (large dots) and individual models’ projections (small dots) of all years are presented. ∆QC and ∆QH are projected by our method for all urban areas within grid cells (4,241 such grid cells in total). ∆CDD and ∆HDD are calculated from near-surface air temperature (background temperature) projections made by CMIP6 models over all land areas. CDD and HDD data are extracted from IPCC WGI Interactive Atlas. ∆T represents changes in (b, f) mean urban 2 m air temperature (Tu) and (d, h) mean background temperature from 2015. A total of 19 and 17 CMIP6 models common to our study and Atlas are used for SSP3-7.0 (a-d) and SSP2-4.5 (e-h), respectively.

Source data

Extended Data Fig. 3 Projected changes in cooling and heating energy demand show more distinct spatial patterns and higher uncertainties than projected temperature changes.

a-c, global maps of changes in multi-model mean between the last decade (2090–2099) and the first decade (2015–2024) for Tu (a), QC (b), and QH (c) under SSP3-7.0. Note b and c are plotted in log-scale and are thus much more spatially variant than they appear. Stippling indicates substantial change (where change is larger than the 25th percentile of all grid cells) with high inter-model robustness (signal-to-noise ratio > 1; see Methods). The number in each subpanel represents the areal percentage of stippled area over all modeled urban areas. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Source data

Extended Data Fig. 4 Cities with similar background climates or urban characteristics may experience highly different H&C energy demand changes.

a-h, seasonal mean energy demand changes with respect to 2015 against seasonal mean Tu changes for cooling in the summer (orange) and heating in the winter (blue), under SSP3-7.0 (a-h) and SSP2-4.5 (i-p) for urban areas in the grid cells that contain Athens (a, i), Los Angeles (b, j), Jakarta (c, k), Miami (d, l), Tehran (e, m), Riyadh (f, n), Hong Kong (g, o), and Chongqing (h, p). For cities in the northern hemisphere (a, b, d-h, i, j, l-p), regardless of their latitudes, summer is assumed to be from June to August and winter from December to February; seasons are assumed to be flipped for c, k in the southern hemisphere. Each dot represents one model’s projection of one year. The steepness of the curves represents the city’s H&C demand sensitivity to temperature change, while the spread of the dots indicates the agreement between multi-model projections. The climate and urban characteristics of these cities are summarized in Supplementary Table 2. Note while we use the city names for the ease of discussion, given the resolution (~100 km at the equator), each grid cell that contains the city will likely also contain the surrounding districts or smaller towns, thus should be understood as the city and its surrounding ‘urban’ landscapes.

Source data

Extended Data Fig. 5 Schematic of CLMU-BEM.

Haircond, heat removed by air conditioning. Hwstht, waste heat from inefficiencies in the H&C equipment and in the conversion of primary to end use energy. They are returned as sensible heat to the canyon floor distributed to both pervious and impervious surfaces. Adapted from Fig. 3 in ref. 10.

Extended Data Fig. 6 CLMU-BEM-simulated anthropogenic heat flux (AHF) reproduces published AHF dataset reasonably well.

a, c, AHF due to H&C modeled by CLMU-BEM for U.S. (a) and Europe (c). b, d, AHF due to H&C estimated from Varquez et al. dataset57 for U.S. (b) and Europe (d). The observational total AHF from all sources has been multiplied by 16% (b) and 25% (d) to adjust it for energy due only to space heating and cooling in the U.S. and Europe, respectively, based on estimates for the proportions of H&C energy use in total energy consumption (see ref. 10 for more details). The modeled and the observational data have been masked for each other’s urban areas. R is the pattern correlation between the model simulations and observations. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Source data

Extended Data Fig. 7 Urban climate-energy emulator framework used in our study.

Tu, monthly average urban 2-meter air temperature. QH and QC, monthly average heating and cooling energy demand fluxes, respectively.

Extended Data Fig. 8 The emulators produce reasonably small errors when evaluated against the testing data.

a-c, RMSE for Tu under SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively. d-f, relative RMSE (normalized against the difference between the maximum and the minimum) for QC under three scenarios. g-i, relative RMSE for QH under three scenarios. The number in each subpanel represents the global average RMSE or relative RMSE. Because there are regions where both their minimum and maximum QC or QH values are close to zero, overflow problem (division by zero) can occur. To resolve this, we manually set the relative RMSE of a grid cell to zero when the maximum QC or QH is smaller than 1 W m−2 and the RMSE is less than 0.01 W m−2. Basemap from Natural Earth (https://www.naturalearthdata.com/).

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Extended Data Table 1 CESM2 modeled H&C energy-temperature (QE–Tu) sensitivities agree well with local to regional scale observations and models
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Extended Data Table 2 CESM2 modeled urban air temperature sensitivities to anthropogenic heat (Tu–QAH) agree well with reported estimates from local to regional scale observations and models
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Supplementary information

Supplementary Information

Supplementary Notes 1–5, Figs. 1–6, Tables 1 and 2 and references.

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Li, X.‘., Zhao, L., Qin, Y. et al. Elevated urban energy risks due to climate-driven biophysical feedbacks. Nat. Clim. Chang. 14, 1056–1063 (2024). https://doi.org/10.1038/s41558-024-02108-w

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