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Global multi-model projections of local urban climates

Nature Climate Change volume 11, pages 152–157 (2021)Cite this article

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Abstract

Effective urban planning for climate-driven risks relies on robust climate projections specific to built landscapes. Such projections are absent because of a near-universal lack of urban representation in global-scale Earth system models. Here, we combine climate modelling and data-driven approaches to provide global multi-model projections of urban climates over the twenty-first century. The results demonstrate the inter-model robustness of specific levels of urban warming over certain regions under climate change. Under a high-emissions scenario, cities in the United States, Middle East, northern Central Asia, northeastern China and inland South America and Africa are estimated to experience substantial warming of more than 4 K—larger than regional warming—by the end of the century, with high inter-model confidence. Our findings highlight the critical need for multi-model global projections of local urban climates for climate-sensitive development and support green infrastructure intervention as an effective means of reducing urban heat stress on large scales.

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Fig. 1: Difference between projected urban warming and background regional warming for JJA.
Fig. 2: Multi-model ensemble mean urban warming for JJA under RCP 8.5.
Fig. 3: Multi-model ensemble mean urban warming for JJA under RCP 4.5.
Fig. 4: Multi-model mean urban relative humidity change for JJA.
Fig. 5: Multi-model mean urban evaporative cooling efficiency.

Data availability

All CMIP5 data used in this study are available at the CMIP5 archive via https://esgfnode.llnl.gov/projects/cmip5/ and the Climate Data Gateway at NCAR via https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CESM_CAM5_BGC_LE.html for RCP 8.5 and https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CESM_CAM5_BGC_ME.html for RCP 4.5. The output data from the emulator are available in the public repository—‘Illinois Data Bank’—via https://doi.org/10.13012/B2IDB-4585244_V1. Source data are provided with this paper.

Code availability

The R (https://www.R-project.org/) and NCL (The NCAR Command Language, https://doi.org/10.5065/D6WD3XH5) codes of the urban climate emulator are available on the NCAR Cheyenne cluster (https://doi.org/10.5065/D6RX99HX) and on Github (https://github.com/zhao-research-lab/urban_climate_emulator; https://doi.org/10.5281/zenodo.3893401). The CESM (used to perform the simulations) source code releases are available through the public GitHub repository (https://github.com/ESCOMP/CESM).

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Acknowledgements

We thank the National Center for Atmospheric Research (NCAR) for supercomputing and data storage resources, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), which were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. L.Z. and M.O. acknowledge the support from the High Meadows Foundation. K.O. acknowledges support by the US National Science Foundation (NSF) under grant no. AGS-1243095, and by NCAR, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. E.B.Z. acknowledges support by the Army Research Office under contract no. W911NF2010216 (program manager J. Barzyk), and the NSF under grant no. ICER 1664091 and SRN cooperative agreement no. 1444758. Q.Z. is supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area, Office of Biological and Environmental Research of the U.S. Department of Energy Office of Science.

Author information

Authors and Affiliations

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

    Lei Zhao & Zhonghua Zheng

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

    Lei Zhao

  3. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Keith Oleson

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

    Elie Bou-Zeid

  5. School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada

    E. Scott Krayenhoff

  6. Department of Mathematics, Reed College, Portland, OR, USA

    Andrew Bray

  7. Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Qing Zhu

  8. Centre for Climate Research, Singapore, Singapore

    Chen Chen

  9. School of Public and International Affairs and Department of Geosciences, Princeton University, Princeton, NJ, USA

    Michael Oppenheimer

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  1. Lei Zhao
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Contributions

L.Z. conceptualized the research and designed the emulator modelling framework; L.Z. developed the reduced-order emulator with contributions from K.O., A.B. and C.C.; K.O. and L.Z. conducted the CESM simulations; L.Z. performed the data analysis; K.O., E.B.Z. and M.O. contributed ideas to the data analysis; L.Z., K.O. and E.S.K. performed the model validation; L.Z. drafted the manuscript, with discussions and contributions from K.O., E.B.Z., E.S.K., M.O. and other co-authors.

Corresponding author

Correspondence to Lei Zhao.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Joshua Hacker, Gaby Langendijk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of 2000–2009 JJA (June – August) distributions of diurnal average (Ta), minimum (Tmin) and maximum (Tmax) 2 m urban air temperature between CLMU (red), WRF (blue) and PRISM (green).

Center bar represent the median, box edges the 25th and 75th percentiles, and error bars the 1st and 99th percentiles. PHX = Phoenix; LAX = Los Angeles; CHI = Chicago; DNV = Denver; POR = Portland; BAL = Baltimore; MIA = Miami; DAL = Dallas; BOS = Boston; ATL = Atlanta.

Source data

Extended Data Fig. 2 Comparison of urban warming projection of diurnal average temperature between CESM and WRF-SLUCM simulation for season JJA (June – August) in 2090 – 2099 relative to 2000 – 2009 under RCP 8.5.

WRF-SLUCM dynamic downscaling simulation (forced by a CESM-RCP8.5 meteorology) was conducted in ref. 11. a, WRF-SLUCM projected urban warming in WRF’s original 20 km grids; b, CESM projected urban warming in the CESM grids (0.9° latitude × 1.25° longitude); c, WRF-SLUCM projected urban warming in the aggregated CESM grids (0.9° latitude × 1.25° longitude); d, difference in projected urban warming between CESM and WRF-SLUCM in the CESM grids.

Source data

Extended Data Fig. 3 Average root-mean-square-error (RMSE) in urban temperature and RH validation of the emulator across 5 CESM ensemble member runs (member #2 - #6).

The ‘error’ in RMSE denotes the difference between monthly temperatures or RH dynamically modeled by the CESM ensemble member and the ones modeled by the emulator. The average RMSE was calculated based on the 5 CESM ensemble member runs from 2006 to 2100 (that is 95 years). a-d: RCP 8.5; e-h: RCP 4.5. a and e: RMSE in diurnal average temperature Ta; b and f: RMSE in diurnal maximum temperature Tmax; c and g: RMSE in diurnal minimum temperature Tmin; d and h: RMSE in urban relative humidity RH.

Source data

Extended Data Fig. 4 Comparison of CMIP5 multi-model mean urban warming (ΔTurban) and background regional warming (ΔTbackground) for season JJA (June – August) in 2091 – 2100 relative to 2006 – 2015.

Δ refers to changes between end of the century and beginning of the century, that is (2091 to 2100) − (2006 to 2015); a, RCP 8.5; b, RCP 4.5.

Source data

Extended Data Fig. 5 Multi-model mean urban warming for season DJF (December – February) in 2091 – 2100 relative to 2006 – 2015.

a–c: RCP 8.5; d-f: RCP 4.5. a and d: urban warming in diurnal average temperature Ta; b and e: in diurnal maximum temperature Tmax; c and f: in diurnal minimum temperature Tmin. Stippling indicates substantial change (ΔT ≥ 4K under RCP 8.5 or ΔT ≥ 1.5K under RCP 4.5) with high inter-model robustness (SNR > 2.5).

Source data

Extended Data Fig. 6 Inter-model robustness of urban temperature projections measured by signal-to-noise ratio (SNR).

a–d, RCP 8.5; e-h, RCP 4.5. a, b, e and f: season JJA (June – August); c, d, g and h: season DJF (December – February); a, c, e and g: projection in diurnal maximum temperature Tmax; b, d, f and h: projection in diurnal minimum temperature Tmin.

Source data

Extended Data Fig. 7 Multi-model mean urban specific humidity (Q) change for season JJA (June – August) in 2091 – 2100 relative to 2006 – 2015.

a, RCP 8.5; b, RCP 4.5.

Source data

Extended Data Fig. 8 Multi-model mean urban relative humidity (RH) change for season DJF (December – February) in 2091 – 2100 relative to 2006 – 2015.

a, RCP 8.5; b, RCP 4.5.

Source data

Extended Data Fig. 9 Inter-model robustness of urban RH projections measured by signal-to-noise ratio (SNR).

a,b: RCP 8.5; c,d: RCP 4.5. a and c: season JJA (June – August); b and d: season DJF (December – February).

Source data

Extended Data Fig. 10 Multi-model mean of urban Δ(HI−Ta) for season JJA (June – August) in 2091 – 2100 relative to 2006 – 2015.

a: RCP 8.5; b: RCP 4.5. Stippling indicates substantial change (Δ(HI − Ta) > 3 K under RCP8.5 or Δ(HI − Ta) > 1.5 K under RCP4.5) with high inter-model robustness (SNR>2.5).

Source data

Supplementary information

Supplementary Information

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Zhao, L., Oleson, K., Bou-Zeid, E. et al. Global multi-model projections of local urban climates. Nat. Clim. Chang. 11, 152–157 (2021). https://doi.org/10.1038/s41558-020-00958-8

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