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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.

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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

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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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