Liu et al. (2025) Urbanization is projected to increase local surface temperature by 2100

Identification

• Journal: Communications Earth & Environment
• Year: 2025
• Date: 2025-12-02
• Authors: Shirao Liu, Xuecao Li, Zitong Shi, Mengqing Geng, Guojiang Yu, Tengyun Hu
• DOI: 10.1038/s43247-025-02947-1

Research Groups

• College of Land Science and Technology, China Agricultural University, Beijing, China
• National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China
• School of Architecture, Tsinghua University, Beijing, China
• Beijing Municipal Institute of City Planning and Design, Beijing, China

Short Summary

This study developed a 1-km resolution global land surface temperature dataset for 2020–2100, integrating climate change and urbanization effects. It projects that urbanization will contribute an average local warming of 0.1 °C by 2100, with 10–16% of urban areas experiencing extreme warming exceeding 1 °C.

Objective

• To develop a 1-km resolution global land surface temperature dataset for 2020–2100 that integrates the combined effects of climate change-induced global warming and urbanization-driven local warming, addressing the gap in detailed future urban heat pattern projections.

Study Configuration

• Spatial Scale: Global, with a resolution of 1 km. Analysis conducted at pixel, administrative unit (level-2), and city scales.
• Temporal Scale: Projections for 2020–2100 at five-year intervals. Historical data from 2003–2020 used for model calibration and validation.

Methodology and Data

• Models used:
  • Multi-model ensemble projections from 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for climate change-induced global warming.
  • Dynamic regression model (ordinary least squares regression) to establish the relationship between impervious surface area (ISA) and local land surface temperature (LST) for urbanization-induced warming.
  • S-shaped urban growth model combined with the Logistic–Trend–CA model for projecting future ISA.
• Data sources:
  • Satellite observations: Gap-filled Moderate-Resolution Imaging Spectroradiometer (MODIS) global daily LST data (Terra, Aqua) from 2003–2020.
  • Observation/Derived: Global Artificial Impervious Area (GAIA) data (30 m aggregated to 1 km) for historical (2003–2020) and future (2015–2100) Impervious Surface Area (ISA) fractions.
  • Model outputs: Surface temperature outputs from 26 CMIP6 models (2015–2100) under Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, and SSP5-RCP8.5) scenarios.
  • Auxiliary datasets: Database of Global Administrative Areas Level-2, Global Urban Boundary (SSP5-RCP8.5 for 2100), and Köppen–Geiger climate classification.

Main Results

• By 2100, urbanization is projected to contribute an average local warming of 0.1 °C globally.
• Approximately 10–16% of urban areas are projected to experience urbanization-induced extreme warming exceeding 1 °C by 2100 across all SSP-RCP scenarios.
• Urban areas consistently remain warmer than the global mean (historically ~4 °C warmer), but their warming rates are projected to be 0.5–8% lower than the global average by 2100 under all scenarios, indicating a gradual slowdown in relative warming.
• The historical relationship between LST change (ΔLST) and ISA change (ΔISA) exhibits pronounced global spatial heterogeneity, with slopes predominantly ranging from -0.04 to 0.04 °C per percent ISA change. Tropical and temperate regions show stronger warming responses (average slopes of 0.0187 °C per % and 0.0125 °C per %, respectively).
• Cold regions are projected to experience the greatest absolute warming (1–5 °C increase by 2100), with relative warming rates exceeding 170% and 240% under SSP3-RCP7.0 and SSP5-RCP8.5, respectively, despite having lower baseline temperatures.
• Urbanization emerges as the dominant driver of future Surface Urban Heat Island (SUHI) trends, intensifying SUHI by approximately 0.013 °C per decade (from 1.56 °C in 2020 to ~1.66 °C in 2100).
• The developed 1 km LST dataset for 2020-2100 demonstrates strong spatial consistency (R² > 0.9 globally with MODIS-observed LST in 2020) and temporal continuity.

Contributions

• Developed the first 1-km resolution global land surface temperature dataset (2020–2100) that explicitly integrates both climate change-induced global warming and urbanization-driven local warming.
• Provides improved assessments of urban heat risks and supports climate-resilient urban planning by capturing both large-scale climate impacts and localized urban heat amplification.
• Quantifies the distinct contributions of urbanization and climate change to future LST, highlighting the significant local impact of urbanization despite its modest global average effect.
• Offers a scalable and interpretable framework for global applications of urbanization-temperature relationships, utilizing a dynamic regression model that adjusts to the maturity of urban expansion.

Funding

• National Natural Science Foundation of China (42371413, 42205043, and 42571476)
• NSFC Funds for International Cooperation and Exchange (42361164614)
• The Chinese Universities Scientific Fund
• The 2115 Talent Development Program of China Agricultural University

Citation

@article{Liu2025Urbanization,
  author = {Liu, Shirao and Li, Xuecao and Shi, Zitong and Geng, Mengqing and Yu, Guojiang and Hu, Tengyun},
  title = {Urbanization is projected to increase local surface temperature by 2100},
  journal = {Communications Earth & Environment},
  year = {2025},
  doi = {10.1038/s43247-025-02947-1},
  url = {https://doi.org/10.1038/s43247-025-02947-1}
}