Articles | Volume 17, issue 5
https://doi.org/10.5194/essd-17-2147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 May 2025
Data description paper |
| 21 May 2025
| 21 May 2025
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Institute for Sustainability, Energy, and Environment (iSEE), University of Illinois Urbana-Champaign, Urbana, IL, USA
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Keith Oleson
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Matthias Demuzere
B-Kode VOF, Ghent, Belgium
Xiaoping Liu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yangzi Che
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yuyu Zhou
Department of Geography, The University of Hong Kong, 999077, Hong Kong SAR, China
Xinchang “Cathy” Li
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Related authors
No articles found.
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data, 17, 4005–4022, https://doi.org/10.5194/essd-17-4005-2025, https://doi.org/10.5194/essd-17-4005-2025, 2025
Short summary
Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycles. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982–2020 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
EGUsphere, https://doi.org/10.5194/egusphere-2025-3408, https://doi.org/10.5194/egusphere-2025-3408, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025, https://doi.org/10.5194/hess-29-2551-2025, 2025
Short summary
Short summary
Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Fengxiang Guo, Fan Dai, Peng Gong, and Yuyu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-178, https://doi.org/10.5194/essd-2025-178, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
China, the world’s largest methane emitter, faces challenges in accurately tracking. CHN-CH4, a map of anthropogenic methane emissions was created by combining satellite data, national statistics, and climate guidelines. Over 30 years, China emitted 1157 Tg of methane, peaking in the 2010s. Shanxi province had the highest emissions. CHN-CH4 helps improve tracking, informs global climate models, and strengthens collaboration between science and policy to combat climate change.
Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-96, https://doi.org/10.5194/essd-2025-96, 2025
Preprint under review for ESSD
Short summary
Short summary
China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term, high-resolution data on their biomass have been limited. We developed a 30-m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
Short summary
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
Short summary
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022, https://doi.org/10.5194/hess-26-2319-2022, 2022
Short summary
Short summary
An important step in projecting future climate is the bias adjustment of the climatological and hydrological variables. In this paper, we illustrate how bias adjustment can be impaired by bias nonstationarity. Two univariate and four multivariate methods are compared, and for both types bias nonstationarity can be linked with less robust adjustment.
Chandan Sarangi, TC Chakraborty, Sachchidanand Tripathi, Mithun Krishnan, Ross Morrison, Jonathan Evans, and Lina M. Mercado
Atmos. Chem. Phys., 22, 3615–3629, https://doi.org/10.5194/acp-22-3615-2022, https://doi.org/10.5194/acp-22-3615-2022, 2022
Short summary
Short summary
Transpiration fluxes by vegetation are reduced under heat stress to conserve water. However, in situ observations over northern India show that the strength of the inverse association between transpiration and atmospheric vapor pressure deficit is weakening in the presence of heavy aerosol loading. This finding not only implicates the significant role of aerosols in modifying the evaporative fraction (EF) but also warrants an in-depth analysis of the aerosol–plant–temperature–EF continuum.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
Short summary
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
Short summary
Short summary
Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Cited articles
Ajjur, S. B. and Al-Ghamdi, S. G.: Evapotranspiration and water availability response to climate change in the Middle East and North Africa, Climatic Change, 166, 28, https://doi.org/10.1007/s10584-021-03122-z, 2021.
Baklanov, A., Grimmond, C. S. B., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R. K., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Climate, 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Best, M. J. and Grimmond, C. S. B.: Importance of initial state and atmospheric conditions for urban land surface models' performance, Urban Climate, 10, 387–406, https://doi.org/10.1016/j.uclim.2013.10.006, 2014.
Bonafoni, S. and Sekertekin, A.: Albedo Retrieval From Sentinel-2 by New Narrow-to-Broadband Conversion Coefficients, IEEE Geosci. Remote S., 17, 1618–1622, https://doi.org/10.1109/LGRS.2020.2967085, 2020.
Cai, Z., Demuzere, M., Tang, Y., and Wan, Y.: The characteristic and transformation of 3D urban morphology in three Chinese mega-cities, Cities, 131, 103988, https://doi.org/10.1016/j.cities.2022.103988, 2022.
Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., and Zhao, L.: Urban heat islands in China enhanced by haze pollution, Nat. Commun., 7, 12509, https://doi.org/10.1038/ncomms12509, 2016.
Chajaei, F. and Bagheri, H.: Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features, Urban Climate, 57, 102102, https://doi.org/10.1016/j.uclim.2024.102102, 2024.
Chakraborty, T. and Lee, X.: A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability, I. J. Appl. Earth Obs., 74, 269–280, https://doi.org/10.1016/j.jag.2018.09.015, 2019.
Chakraborty, T. and Qian, Y.: Urbanization exacerbates continental- to regional-scale warming, One Earth, 7, 1387–1401, https://doi.org/10.1016/j.oneear.2024.05.005, 2024.
Chakraborty, T., Lee, X., Ermida, S., and Zhan, W.: On the land emissivity assumption and Landsat-derived surface urban heat islands: A global analysis, Remote Sens. Environ., 265, 112682, https://doi.org/10.1016/j.rse.2021.112682, 2021.
Chakraborty, T., Biswas, T., Campbell, L. S., Franklin, B., Parker, S. S., and Tukman, M.: Feasibility of afforestation as an equitable nature-based solution in urban areas, Sustain. Cities Soc., 81, 103826, https://doi.org/10.1016/j.scs.2022.103826, 2022.
Chakraborty, T. C., Newman, A. J., Qian, Y., Hsu, A., and Sheriff, G.: Residential segregation and outdoor urban moist heat stress disparities in the United States, One Earth, 6, 738–750, https://doi.org/10.1016/j.oneear.2023.05.016, 2023.
Chakraborty, T. C., Venter, Z. S., Demuzere, M., Zhan, W., Gao, J., Zhao, L., and Qian, Y.: Large disagreements in estimates of urban land across scales and their implications, Nat. Commun., 15, 9165, https://doi.org/10.1038/s41467-024-52241-5, 2024.
Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Zhang, H., Yuan, H., and Dai, Y.: 3D-GloBFP: the first global three-dimensional building footprint dataset, Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, 2024.
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011.
Chen, F., Yang, S., Su, Z., and Wang, K.: Effect of emissivity uncertainty on surface temperature retrieval over urban areas: Investigations based on spectral libraries, ISPRS J. Photogramm., 114, 53–65, https://doi.org/10.1016/j.isprsjprs.2016.01.007, 2016.
Chen, K., Boomsma, J., and Holmes, H. A.: A multiscale analysis of heatwaves and urban heat islands in the western U.S. during the summer of 2021, Sci. Rep., 13, 9570, https://doi.org/10.1038/s41598-023-35621-7, 2023.
Cheng, Y.: 1km urban density class based on Jackson et al. (2010), Figshare [data set], https://doi.org/10.6084/m9.figshare.28169324.v1, 2025a.
Cheng, Y.: Global 1 km Urban Surface Property Dataset, Google Earth Engine Apps [data set], https://ycheng1891.users.earthengine.app/view/global-1km-urban-surface-property-dataset, last access: 8 May 2025b.
Cheng, Y., Zhao, L., Chakraborty, T., Oleson, K., Demuzere, M., Liu, X., Che, Y., Liao, W., Zhou, Y., and Li, X.: U-Surf: A global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling, Zenodo [data set], https://doi.org/10.5281/zenodo.11247598, 2024.
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C., Brousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I., Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D., Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J., Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell, N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwes, N.: WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene, B. Am. Meteorol. Soc., 99, 1907–1924, https://doi.org/10.1175/BAMS-D-16-0236.1, 2018.
Conigliaro, E., Monti, P., Leuzzi, G., and Cantelli, A.: A three-dimensional urban canopy model for mesoscale atmospheric simulations and its comparison with a two-dimensional urban canopy model in an idealized case, Urban Climate, 37, 100831, https://doi.org/10.1016/j.uclim.2021.100831, 2021.
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Demuzere, M., De Ridder, K., and Van Lipzig, N. P. M.: Modeling the energy balance in Marseille: Sensitivity to roughness length parameterizations and thermal admittance, J. Geophys. Res.-Atmos., 113, D16120, https://doi.org/10.1029/2007JD009113, 2008.
Demuzere, M., Oleson, K., Coutts, A. M., Pigeon, G., and 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, https://doi.org/10.1002/joc.3656, 2013.
Demuzere, M., Coutts, A. M., Göhler, M., Broadbent, A. M., Wouters, H., van Lipzig, N. P. M., and Gebert, L.: The implementation of biofiltration systems, rainwater tanks and urban irrigation in a single-layer urban canopy model, Urban Climate, 10, 148–170, https://doi.org/10.1016/j.uclim.2014.10.012, 2014.
Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C. S. B., Masson, V., Oleson, K. W., Velasco, E., and Wouters, H.: Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017.
Demuzere, M., Kittner, J., and Bechtel, B.: LCZ Generator: A Web Application to Create Local Climate Zone Maps, Front. Environ. Sci., 9, 637455, https://doi.org/10.3389/fenvs.2021.637455, 2021.
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B.: A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, 2022a.
Demuzere, M., Argüeso, D., Zonato, A., and Kittner, J.: W2W: A Python package that injects WUDAPT's Local Climate Zone information in WRF, Journal of Open Source Software, 7, 4432, https://doi.org/10.21105/joss.04432, 2022b.
Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D., Metz-Marconcini, A., Marconcini, M., Roth, A., and Zeidler, J.: World Settlement Footprint 3D – A first three-dimensional survey of the global building stock, Remote Sens. Environ., 270, 112877, https://doi.org/10.1016/j.rse.2021.112877, 2022.
Fang, B., Zhao, L., Oleson, K. W., Zhang, K., Lawrence, P. J., Sacks, B., Cao, C., He, C., Huang, Q., Liu, Z., and Lee, X.: Representing dynamic urban land change in the Community Earth System Model (CESM), ESS Open Archive [preprint], https://doi.org/10.22541/essoar.168676909.95382628/v1, 14 June 2023.
Feng, B., Zhang, Y., and Bourke, R.: Urbanization impacts on flood risks based on urban growth data and coupled flood models, Nat. Hazards, 106, 613–627, https://doi.org/10.1007/s11069-020-04480-0, 2021.
Fitria, R., Kim, D., Baik, J., and Choi, M.: Impact of Biophysical Mechanisms on Urban Heat Island Associated with Climate Variation and Urban Morphology, Sci. Rep., 9, 19503, https://doi.org/10.1038/s41598-019-55847-8, 2019.
Furuya, M. T. G., Furuya, D. E. G., de Oliveira, L. Y. D., da Silva, P. A., Cicerelli, R. E., Gonçalves, W. N., Junior, J. M., Osco, L. P., and Ramos, A. P. M.: A machine learning approach for mapping surface urban heat island using environmental and socioeconomic variables: a case study in a medium-sized Brazilian city, Environ. Earth Sci., 82, 325, https://doi.org/10.1007/s12665-023-11017-8, 2023.
Gao, J. and Bukovsky, M. S.: Urban land patterns can moderate population exposures to climate extremes over the 21st century, Nat. Commun., 14, 6536, https://doi.org/10.1038/s41467-023-42084-x, 2023.
Gao, J. and O'Neill, B. C.: Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways, Nat. Commun., 11, 2302, https://doi.org/10.1038/s41467-020-15788-7, 2020.
Georgescu, M.: Challenges Associated with Adaptation to Future Urban Expansion, J. Climate, 28, 2544–2563, https://doi.org/10.1175/JCLI-D-14-00290.1, 2015.
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Grimmond, C. S. B., Blackett, M., Best, M. J., Baik, J.-J., Belcher, S. E., Beringer, J., Bohnenstengel, S. I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Steeneveld, G. j., Tombrou, M., Voogt, J. A., Young, D. T., and Zhang, N.: Initial results from Phase 2 of the international urban energy balance model comparison, Int. J. Climatol., 31, 244–272, https://doi.org/10.1002/joc.2227, 2011.
Harman, I. N., Best, M. J., and Belcher, S. E.: Radiative Exchange in an Urban Street Canyon, Bound.-Lay. Meteorol., 110, 301–316, https://doi.org/10.1023/A:1026029822517, 2004.
He, W., Li, X., Zhou, Y., Shi, Z., Yu, G., Hu, T., Wang, Y., Huang, J., Bai, T., Sun, Z., Liu, X., and Gong, P.: Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, 2023.
Hertwig, D., Ng, M., Grimmond, S., Vidale, P. L., and McGuire, P. C.: High-resolution global climate simulations: Representation of cities, Int. J. Climatol., 41, 3266–3285, https://doi.org/10.1002/joc.7018, 2021.
Hidalgo, J., Dumas, G., Masson, V., Petit, G., Bechtel, B., Bocher, E., Foley, M., Schoetter, R., and Mills, G.: Comparison between local climate zones maps derived from administrative datasets and satellite observations, Urban Climate, 27, 64–89, https://doi.org/10.1016/j.uclim.2018.10.004, 2019.
Huang, X., Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: An evaluation of the variable-resolution CESM for modeling California's climate, J. Adv. Model. Earth Sy., 8, 345–369, https://doi.org/10.1002/2015MS000559, 2016.
Huang, X., Liu, A., and Li, J.: Mapping and analyzing the local climate zones in China's 32 major cities using Landsat imagery based on a novel convolutional neural network, Geo-spatial Information Science, 24, 528–557, https://doi.org/10.1080/10095020.2021.1892459, 2021.
Hulley, G. C., Hook, S. J., Abbott, E., Malakar, N., Islam, T., and Abrams, M.: The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth's emissivity at 100 meter spatial scale, Geophys. Res. Lett., 42, 7966–7976, https://doi.org/10.1002/2015GL065564, 2015.
Intergovernmental Panel on Climate Change: Climate Change 2014: Mitigation of Climate Change – Working Group III Contribution to the IPCC Fifth Assessment Report, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107415416, 2015.
Intergovernmental Panel On Climate Change: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023.
Islam, S. N. and Winkel, J.: Climate Change and Social Inequality, Working Papers 152, United Nations, Department of Economics and Social Affairs, https://digitallibrary.un.org/record/3859027 (last access: 9 August 2024), 2017.
Jackson, T. L., Feddema, J. J., Oleson, K. W., Bonan, G. B., and Bauer, J. T.: Parameterization of Urban Characteristics for Global Climate Modeling, Ann. Assoc. Am. Geogr., 100, 848–865, https://doi.org/10.1080/00045608.2010.497328, 2010.
Jia, S., Weng, Q., Yoo, C., Xiao, H., and Zhong, Q.: Building energy savings by green roofs and cool roofs in current and future climates, npj Urban Sustain., 4, 1–13, https://doi.org/10.1038/s42949-024-00159-8, 2024.
Jongen, H. J., Lipson, M., Teuling, A. J., Grimmond, S., Baik, J.-J., Best, M., Demuzere, M., Fortuniak, K., Huang, Y., De Kauwe, M. G., Li, R., McNorton, J., Meili, N., Oleson, K., Park, S.-B., Sun, T., Tsiringakis, A., Varentsov, M., Wang, C., Wang, Z.-H., and Steeneveld, G. J.: The Water Balance Representation in Urban-PLUMBER Land Surface Models, J. Adv. Model. Earth Sy., 16, e2024MS004231, https://doi.org/10.1029/2024MS004231, 2024.
Kim, S. K., Bennett, M. M., van Gevelt, T., and Joosse, P.: Urban agglomeration worsens spatial disparities in climate adaptation, Sci. Rep., 11, 8446, https://doi.org/10.1038/s41598-021-87739-1, 2021.
Krayenhoff, E. S., Moustaoui, M., Broadbent, A. M., Gupta, V., and Georgescu, M.: Diurnal interaction between urban expansion, climate change and adaptation in US cities, Nat. Clim. Change, 8, 1097–1103, https://doi.org/10.1038/s41558-018-0320-9, 2018.
Krayenhoff, E. S., Broadbent, A. M., Zhao, L., Georgescu, M., Middel, A., Voogt, J. A., Martilli, A., Sailor, D. J., and Erell, E.: Cooling hot cities: a systematic and critical review of the numerical modelling literature, Environ. Res. Lett., 16, 053007, https://doi.org/10.1088/1748-9326/abdcf1, 2021.
Langendijk, G. S., Halenka, T., Hoffmann, P., Adinolfi, M., Aldama Campino, A., Asselin, O., Bastin, S., Bechtel, B., Belda, M., Bushenkova, A., Campanale, A., Chun, K. P., Constantinidou, K., Coppola, E., Demuzere, M., Doan, Q.-V., Evans, J., Feldmann, H., Fernandez, J., Fita, L., Hadjinicolaou, P., Hamdi, R., Hundhausen, M., Grawe, D., Johannsen, F., Milovac, J., Katragkou, E., Kerroumi, N. E. I., Kotlarski, S., Le Roy, B., Lemonsu, A., Lennard, C., Lipson, M., Mandal, S., Muñoz Pabón, L. E., Pavlidis, V., Pietikäinen, J.-P., Raffa, M., Raluy-López, E., Rechid, D., Ito, R., Schulz, J.-P., Soares, P. M. M., Takane, Y., Teichmann, C., Thatcher, M., Top, S., Van Schaeybroeck, B., Wang, F., and Yuan, J.: Towards better understanding the urban environment and its interactions with regional climate change - The WCRP CORDEX Flagship Pilot Study URB-RCC, Urban Climate, 58, 102165, https://doi.org/10.1016/j.uclim.2024.102165, 2024.
Lawrance, D., Fisher, R., Koven, C., Oleson, K., Swenson, S., Vertenstein, M., Andre, B., Bonan, G., Ghimire, B., Kampenhout, L. van, Kennedy, D., Kluzek, E., Knox, R., Lawrence, P., Li, F., Li, H., Lombardozzi, D., Lu, Y., Perket, J., Riley, W., Sacks, W., Shi, M., Wieder, W., Xu, C., Ali, A., Badger, A., Bisht, G., Broxton, P., Brunke, M., Buzan, J., Clark, M., Craig, T., Dahlin, K., Drewniak, B., Emmons, L., Fisher, J., Flanner, M., Gentine, P., Lenaerts, J., Levis, S., Leung, L. R., Lipscomb, W., Pelletier, J., Ricciuto, D. M., Sanderson, B., Shuman, J., Slater, A., Subin, Z., Tang, J., Tawfik, A., Thomas, Q., Tilmes, S., Vitt, F., and Zeng, X.: Technical Description of version 5.0 of the Community Land Model (CLM), National Center for Atmospheric Research, Boulder, CO, https://www.researchgate.net/publication/328792350_CLM50_Technical_Description (last access: 10 September 2024), 2018.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Li, D. and Bou-Zeid, E. R.: Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts, J. Appl. Meteorol. Clim., 52, 2051–2064, https://doi.org/10.1175/JAMC-D-13-02.1, 2013.
Li, M., Wang, Y., Rosier, J. F., Verburg, P. H., and van Vliet, J.: Global maps of 3D built-up patterns for urban morphological analysis, Int. J. Appl. Earth Obs., 114, 103048, https://doi.org/10.1016/j.jag.2022.103048, 2022.
Li, X., Zhou, Y., Yu, S., Jia, G., Li, H., and Li, W.: Urban heat island impacts on building energy consumption: A review of approaches and findings, Energy, 174, 407–419, https://doi.org/10.1016/j.energy.2019.02.183, 2019.
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu, B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X., Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global urban boundaries from the global artificial impervious area (GAIA) data, Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3, 2020a.
Li, X., Zhou, Y., Hejazi, M., Wise, M., Vernon, C., Iyer, G., and Chen, W.: Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling, Commun. Earth Environ., 2, 1–10, https://doi.org/10.1038/s43247-021-00273-w, 2021.
Li, X., Feng, M., Ran, Y., Su, Y., Liu, F., Huang, C., Shen, H., Xiao, Q., Su, J., Yuan, S., and Guo, H.: Big Data in Earth system science and progress towards a digital twin, Nat. Rev. Earth Environ., 4, 319–332, https://doi.org/10.1038/s43017-023-00409-w, 2023a.
Li, X., Yang, B., Liang, F., Zhang, H., Xu, Y., and Dong, Z.: Modeling urban canopy air temperature at city-block scale based on urban 3D morphology parameters – A study in Tianjin, North China, Build. Environ., 230, 110000, https://doi.org/10.1016/j.buildenv.2023.110000, 2023b.
Li, X. “Cathy”, Zhao, L., Qin, Y., Oleson, K., and Zhang, Y.: Elevated urban energy risks due to climate-driven biophysical feedbacks, Nat. Clim. Change, 14, 1056–1063, https://doi.org/10.1038/s41558-024-02108-w, 2024a.
Li, X. “Cathy,” Zhao, L., Oleson, K., Zhou, Y., Qin, Y., Zhang, K., and Fang, B.: Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) Through Explicit Representation of Urban Air-Conditioning Adoption, J. Adv. Model. Earth Sy., 16, e2023MS004107, https://doi.org/10.1029/2023MS004107, 2024b.
Li, Y., Schubert, S., Kropp, J. P., and Rybski, D.: On the influence of density and morphology on the Urban Heat Island intensity, Nat. Commun., 11, 2647, https://doi.org/10.1038/s41467-020-16461-9, 2020b.
Liang, S., Strahler, A. H., and Walthall, C.: Retrieval of Land Surface Albedo from Satellite Observations: A Simulation Study, J. Appl. Meteorol., 38, 712–725, https://doi.org/10.1175/1520-0450(1999)038<0712:ROLSAF>2.0.CO;2, 1999.
Lin, S., Feng, J., Wang, J., and 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, https://doi.org/10.1002/2015JD024227, 2016.
Lin, X., Wu, S., Chen, B., Lin, Z., Yan, Z., Chen, X., Yin, G., You, D., Wen, J., Liu, Q., Xiao, Q., Liu, Q., and Lafortezza, R.: Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine, ISPRS J. Photogramm., 194, 1–20, https://doi.org/10.1016/j.isprsjprs.2022.09.016, 2022.
Lipson, M., Grimmond, S., Best, M., Chow, W. T. L., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B. G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J. P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E., and Ward, H. C.: Harmonized gap-filled datasets from 20 urban flux tower sites, Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, 2022.
Lipson, M. J., Grimmond, S., Best, M., Abramowitz, G., Coutts, A., Tapper, N., Baik, J.-J., Beyers, M., Blunn, L., Boussetta, S., Bou-Zeid, E., De Kauwe, M. G., de Munck, C., Demuzere, M., Fatichi, S., Fortuniak, K., Han, B.-S., Hendry, M. A., Kikegawa, Y., Kondo, H., Lee, D.-I., Lee, S.-H., Lemonsu, A., Machado, T., Manoli, G., Martilli, A., Masson, V., McNorton, J., Meili, N., Meyer, D., Nice, K. A., Oleson, K. W., Park, S.-B., Roth, M., Schoetter, R., Simón-Moral, A., Steeneveld, G.-J., Sun, T., Takane, Y., Thatcher, M., Tsiringakis, A., Varentsov, M., Wang, C., Wang, Z.-H., and Pitman, A. J.: Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results, Q. J. Roy. Meteor. Soc., 150, 126–169, https://doi.org/10.1002/qj.4589, 2024.
Liu, S., Han, Y., Wang, P., Zhang, G. J., Wang, B., and Wang, Y.: More Heavy Precipitation in World Urban Regions Captured Through a Two-Way Subgrid Land-Atmosphere Coupling Framework in the NCAR CESM2, Geophys. Res. Lett., 51, e2024GL108747, https://doi.org/10.1029/2024GL108747, 2024.
Lobo, J., Aggarwal, R. M., Alberti, M., Allen-Dumas, M., Bettencourt, L. M. A., Boone, C., Brelsford, C., Broto, V. C., Eakin, H., Bagchi-Sen, S., Meerow, S., D'Cruz, C., Revi, A., Roberts, D. C., Smith, M. E., York, A., Lin, T., Bai, X., Solecki, W., Pataki, D., Tapia, L. B., Rockman, M., Wolfram, M., Schlosser, P., and Gauthier, N.: Integration of urban science and urban climate adaptation research: opportunities to advance climate action, npj Urban Sustain., 3, 1–9, https://doi.org/10.1038/s42949-023-00113-0, 2023.
Ma, J. and Mostafavi, A.: Urban form and structure explain variability in spatial inequality of property flood risk among US counties, Commun. Earth Environ., 5, 1–12, https://doi.org/10.1038/s43247-024-01337-3, 2024.
Mackey, C. W., Lee, X., and Smith, R. B.: Remotely sensing the cooling effects of city scale efforts to reduce urban heat island, Build. Environ., 49, 348–358, https://doi.org/10.1016/j.buildenv.2011.08.004, 2012.
Malakar, N. K., Hulley, G. C., Hook, S. J., Laraby, K., Cook, M., and Schott, J. R.: An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation, IEEE T. Geosci. Remote, 56, 5717–5735, https://doi.org/10.1109/TGRS.2018.2824828, 2018.
Masson, V., Heldens, W., Bocher, E., Bonhomme, M., Bucher, B., Burmeister, C., De Munck, C., Esch, T., Hidalgo, J., Kanani-Sühring, F., Kwok, Y.-T., Lemonsu, A., Lévy, J.-P., Maronga, B., Pavlik, D., Petit, G., See, L., Schoetter, R., Tornay, N., Votsis, A., and Zeidler, J.: City-descriptive input data for urban climate models: Model requirements, data sources and challenges, Urban Climate, 31, 100536, https://doi.org/10.1016/j.uclim.2019.100536, 2020.
Microsoft: Worldwide building footprints derived from satellite imagery, GitHub [data set], https://github.com/microsoft/GlobalMLBuildingFootprints/tree/main (last access: 30 January 2024), 2022.
NCAR: CTSM – Community Terrestrial Systems Model, GitHub [code], https://github.com/ESCOMP/CTSM, last access: 8 May 2025.
Oak Ridge National Laboratory: LandScan 2004 Global Population Database, Oak Ridge National Laboratory [data set], https://landscan.ornl.gov/ (last access: 31 August 2024), 2005.
Ogawa, K., Schmugge, T., and Rokugawa, S.: Estimating Broadband Emissivity of Arid Regions and Its Seasonal Variations Using Thermal Infrared Remote Sensing, IEEE T. Geosci. Remote, 46, 334–343, https://doi.org/10.1109/TGRS.2007.913213, 2008.
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781139016476, 2017.
Oleson, K. W. and Feddema, J.: Parameterization and Surface Data Improvements and New Capabilities for the Community Land Model Urban (CLMU), J. Adv. Model. Earth Sy., 12, e2018MS001586, https://doi.org/10.1029/2018MS001586, 2020.
Oleson, K. W., Bonan, G. B., Feddema, J., Vertenstein, M., and Grimmond, C. S. B.: An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities, J. Appl. Meteorol. Clim., 47, 1038–1060, https://doi.org/10.1175/2007JAMC1597.1, 2008a.
Oleson, K. W., Bonan, G. B., Feddema, J., and Vertenstein, M.: An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations, J. Appl. Meteorol. Clim., 47, 1061–1076, https://doi.org/10.1175/2007JAMC1598.1, 2008b.
Oleson, K. W., Bonan, G. B., Feddema, J. J., Vertenstein, M., and Kluzek, E.: Technical description of an urban parameterization for the Community Land Model (CLMU), National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/D6K35RM9, 2010.
Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J.: Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, 17–24 June 2023, 2125–2135, https://doi.org/10.1109/CVPRW59228.2023.00206, 2023.
Pfister, G. G., Eastham, S. D., Arellano, A. F., Aumont, B., Barsanti, K. C., Barth, M. C., Conley, A., Davis, N. A., Emmons, L. K., Fast, J. D., Fiore, A. M., Gaubert, B., Goldhaber, S., Granier, C., Grell, G. A., Guevara, M., Henze, D. K., Hodzic, A., Liu, X., Marsh, D. R., Orlando, J. J., Plane, J. M. C., Polvani, L. M., Rosenlof, K. H., Steiner, A. L., Jacob, D. J., and Brasseur, G. P.: The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), B. Am. Meteorol. Soc., 101, E1743–E1760, https://doi.org/10.1175/BAMS-D-19-0331.1, 2020
Qi, M., Xu, C., Zhang, W., Demuzere, M., Hystad, P., Lu, T., James, P., Bechtel, B., and Hankey, S.: Mapping urban form into local climate zones for the continental US from 1986–2020, Sci. Data, 11, 195, https://doi.org/10.1038/s41597-024-03042-4, 2024.
Reinhart, C. F. and Cerezo Davila, C.: Urban building energy modeling – A review of a nascent field, Build. Environ., 97, 196–202, https://doi.org/10.1016/j.buildenv.2015.12.001, 2016.
Robinson, A., Lehmann, J., Barriopedro, D., Rahmstorf, S., and Coumou, D.: Increasing heat and rainfall extremes now far outside the historical climate, npj Clim. Atmos. Sci., 4, 1–4, https://doi.org/10.1038/s41612-021-00202-w, 2021.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Girolamo, S. D., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects and Challenges, B. Am. Meteorol. Soc., 101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T., Sprenger, M., Ubbiali, S., and Wernli, H.: Prospects for Kilometer-Scale Climate Models, B. Am. Meteorol. Soc., 102, 47–52, 2021.
Scheuer, S., Haase, D., and Volk, M.: Integrative assessment of climate change for fast-growing urban areas: Measurement and recommendations for future research, PLOS ONE, 12, e0189451, https://doi.org/10.1371/journal.pone.0189451, 2017.
Sezer, N., Yoonus, H., Zhan, D., Wang, L. (Leon), Hassan, I. G., and Rahman, M. A.: Urban microclimate and building energy models: A review of the latest progress in coupling strategies, Renew. Sust. Energ. Rev., 184, 113577, https://doi.org/10.1016/j.rser.2023.113577, 2023.
Sharma, A., Fernando, H. J. S., Hamlet, A. F., Hellmann, J. J., Barlage, M., and Chen, F.: Urban meteorological modeling using WRF: a sensitivity study, Int. J. Climatol., 37, 1885–1900, https://doi.org/10.1002/joc.4819, 2017.
Sharma, A., Wuebbles, D. J., and Kotamarthi, R.: The Need for Urban-Resolving Climate Modeling Across Scales, AGU Advances, 2, e2020AV000271, https://doi.org/10.1029/2020AV000271, 2021.
Shi, Q., Zhu, J., Liu, Z., Guo, H., Gao, S., Liu, M., Liu, Z., and Liu, X.: The Last Puzzle of Global Building Footprints – Mapping 280 Million Buildings in East Asia Based on VHR Images, Journal of Remote Sensing, 4, 0138, https://doi.org/10.34133/remotesensing.0138, 2024.
Shu, E. G., Porter, J. R., Hauer, M. E., Sandoval Olascoaga, S., Gourevitch, J., Wilson, B., Pope, M., Melecio-Vazquez, D., and Kearns, E.: Integrating climate change induced flood risk into future population projections, Nat. Commun., 14, 7870, https://doi.org/10.1038/s41467-023-43493-8, 2023.
Sjöstrand, K.: Urbanization impacts on floods, Nat. Rev. Earth Environ., 3, 738–738, https://doi.org/10.1038/s43017-022-00367-9, 2022.
Stewart, I. D. and Oke, T. R.: Local Climate Zones for Urban Temperature Studies, B. Am. Meteorol. Soc., 93, 1879–1900, https://doi.org/10.1175/BAMS-D-11-00019.1, 2012.
Sun, Y., Zhang, N., Miao, S., Kong, F., Zhang, Y., and Li, N.: Urban Morphological Parameters of the Main Cities in China and Their Application in the WRF Model, J. Adv. Model. Earth Sy., 13, e2020MS002382, https://doi.org/10.1029/2020MS002382, 2021.
Tabari, H.: Climate change impact on flood and extreme precipitation increases with water availability, Sci. Rep., 10, 13768, https://doi.org/10.1038/s41598-020-70816-2, 2020.
Tang, Q., Golaz, J.-C., Van Roekel, L. P., Taylor, M. A., Lin, W., Hillman, B. R., Ullrich, P. A., Bradley, A. M., Guba, O., Wolfe, J. D., Zhou, T., Zhang, K., Zheng, X., Zhang, Y., Zhang, M., Wu, M., Wang, H., Tao, C., Singh, B., Rhoades, A. M., Qin, Y., Li, H.-Y., Feng, Y., Zhang, Y., Zhang, C., Zender, C. S., Xie, S., Roesler, E. L., Roberts, A. F., Mametjanov, A., Maltrud, M. E., Keen, N. D., Jacob, R. L., Jablonowski, C., Hughes, O. K., Forsyth, R. M., Di Vittorio, A. V., Caldwell, P. M., Bisht, G., McCoy, R. B., Leung, L. R., and Bader, D. C.: The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results, Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, 2023a.
Tang, W., Pfister, G. G., Kumar, R., Barth, M., Edwards, D. P., Emmons, L. K., and Tilmes, S.: Capturing High-Resolution Air Pollution Features Using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) Global Modeling System, J. Geophys. Res.-Atmos., 128, e2022JD038345, https://doi.org/10.1029/2022JD038345, 2023b.
United Nations, Department of Economic and Social Affairs, Population Division: World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420), United Nations, New York, https://doi.org/10.18356/b9e995fe-en, 2019.
Vardoulakis, S., Fisher, B. E. A., Pericleous, K., and Gonzalez-Flesca, N.: Modelling air quality in street canyons: a review, Atmos. Environ., 37, 155–182, https://doi.org/10.1016/S1352-2310(02)00857-9, 2003.
Wang, D., Schwartz, P., Yuan, F., Thornton, P., and Zheng, W.: Toward Ultrahigh-Resolution E3SM Land Modeling on Exascale Computers, Comput. Sci. Eng., 24, 44–53, https://doi.org/10.1109/MCSE.2022.3218990, 2022.
Wang, Y., Sun, G., Wu, Y., and Rosenberg, M. W.: Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China, Sci. Rep., 14, 12887, https://doi.org/10.1038/s41598-024-63698-1, 2024.
van der Wiel, K. and Bintanja, R.: Contribution of climatic changes in mean and variability to monthly temperature and precipitation extremes, Commun. Earth Environ., 2, 1–11, https://doi.org/10.1038/s43247-020-00077-4, 2021.
Wu, S., Lin, X., Bian, Z., Lipson, M., Lafortezza, R., Liu, Q., Grimmond, S., Velasco, E., Christen, A., Masson, V., Crawford, B., Ward, H. C., Chrysoulakis, N., Fortuniak, K., Parlow, E., Pawlak, W., Tapper, N., Hong, J., Hong, J.-W., Roth, M., An, J., Lin, C., and Chen, B.: Satellite observations reveal a decreasing albedo trend of global cities over the past 35 years, Remote Sens. Environ., 303, 114003, https://doi.org/10.1016/j.rse.2024.114003, 2024.
Yang, J., Zhao, L., and Oleson, K.: Large humidity effects on urban heat exposure and cooling challenges under climate change, Environ. Res. Lett., 18, 044024, https://doi.org/10.1088/1748-9326/acc475, 2023.
Yuan, F., Wang, D., Kao, S.-C., Thornton, M., Ricciuto, D., Salmon, V., Iversen, C., Schwartz, P., and Thornton, P.: An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska, J. Comput. Sci., 73, 102145, https://doi.org/10.1016/j.jocs.2023.102145, 2023.
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F., and Arino, O.: ESA WorldCover 10m 2021 v200 (v200), Zenodo [data set], https://doi.org/10.5281/zenodo.7254221, 2022
Zhan, Y., Yao, Z., Groffman, P. M., Xie, J., Wang, Y., Li, G., Zheng, X., and Butterbach-Bahl, K.: Urbanization can accelerate climate change by increasing soil N2O emission while reducing CH4 uptake, Glob. Change Biol., 29, 3489–3502, https://doi.org/10.1111/gcb.16652, 2023.
Zhang, K., Cao, C., Chu, H., Zhao, L., Zhao, J., and Lee, X.: Increased heat risk in wet climate induced by urban humid heat, Nature, 617, 738–742, https://doi.org/10.1038/s41586-023-05911-1, 2023a.
Zhang, W., Cui, R., Li, C., Ge, H., Zhang, Z., and Tang, X.: Impact of urban agglomeration construction on urban air quality – empirical test based on PSM-DID model, Sci. Rep., 13, 15099, https://doi.org/10.1038/s41598-023-42314-8, 2023b.
Zhang, Y. and Gu, Z.: Air quality by urban design, Nat. Geosci., 6, 506–506, https://doi.org/10.1038/ngeo1869, 2013.
Zhang, Z., Qian, Z., Zhong, T., Chen, M., Zhang, K., Yang, Y., Zhu, R., Zhang, F., Zhang, H., Zhou, F., Yu, J., Zhang, B., Lü, G., and Yan, J.: Vectorized rooftop area data for 90 cities in China, Sci. Data, 9, 66, https://doi.org/10.1038/s41597-022-01168-x, 2022.
Zhao, L.: Urban growth and climate adaptation, Nat. Clim. Change, 8, 1034–1034, https://doi.org/10.1038/s41558-018-0348-x, 2018.
Zhao, L., Lee, X., Smith, R. B., and Oleson, K.: Strong contributions of local background climate to urban heat islands, Nature, 511, 216–219, https://doi.org/10.1038/nature13462, 2014.
Zhao, L., Lee, X., and Schultz, N. M.: A wedge strategy for mitigation of urban warming in future climate scenarios, Atmos. Chem. Phys., 17, 9067–9080, https://doi.org/10.5194/acp-17-9067-2017, 2017.
Zhao, L., Oppenheimer, M., Zhu, Q., Baldwin, J. W., Ebi, K. L., Bou-Zeid, E., Guan, K., and Liu, X.: Interactions between urban heat islands and heat waves, Environ. Res. Lett., 13, 034003, https://doi.org/10.1088/1748-9326/aa9f73, 2018.
Zhao, L., Oleson, K., Bou-Zeid, E., Krayenhoff, E. S., Bray, A., Zhu, Q., Zheng, Z., Chen, C., and Oppenheimer, M.: Global multi-model projections of local urban climates, Nat. Clim. Change, 11, 152–157, https://doi.org/10.1038/s41558-020-00958-8, 2021.
Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., and Song, C.: A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights, Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, 2022.
Zheng, Z., Zhao, L., and Oleson, K. W.: Large model structural uncertainty in global projections of urban heat waves, Nat. Commun., 12, 3736, https://doi.org/10.1038/s41467-021-24113-9, 2021.
Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B., Asrar, G. R., Zhang, X., He, C., and Elvidge, C. D.: A global map of urban extent from nightlights, Environ. Res. Lett., 10, 054011, https://doi.org/10.1088/1748-9326/10/5/054011, 2015.
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
The absence of globally consistent and spatially continuous urban surface input has long...
Altmetrics
Final-revised paper
Preprint