Wu et al. (2025) Significant sensitivity of global vegetation productivity to terrestrial surface wind speed changes

Identification

• Journal: Nature Communications
• Year: 2025
• Date: 2025-10-21
• Authors: Haohao Wu, Congsheng Fu, Lingling Zhang, Z. A. Mekonnen, Qing Zhu, Kailiang Yu, Philippe Ciais, Jianyao Chen, Dagang Wang, Huawu Wu, Guishan Yang
• DOI: 10.1038/s41467-025-65000-x

Research Groups

• Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
• School of Geographic Information and Tourism, Chuzhou University, Chuzhou, China
• Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
• High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
• Le Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCECEA/CNRS/UVSQ Saclay, Gif-sur-Yvette, France
• School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
• College of Geography and Remote Sensing, Hohai University, Nanjing, China

Short Summary

This study systematically investigates the global impact of terrestrial surface wind speed changes on gross primary production (GPP). It finds a significant negative sensitivity of GPP to wind speed, primarily due to reduced atmospheric dryness and soil drying, making wind speed decline the second most important factor after rising CO2 concentrations in driving GPP increases.

Objective

• Determine the sensitivity of global terrestrial gross primary production (GPP) to changes in surface wind speed (∂GPP/∂Wind).
• Explore the underlying biophysical mechanisms and influence pathways driving this sensitivity.
• Evaluate the contribution of wind speed changes to the global trend in terrestrial GPP under historical and future climate scenarios.

Study Configuration

• Spatial Scale: Global terrestrial ecosystems.
• Temporal Scale: Centennial scale (1960s-2100), with detailed analysis for 1983-2100, divided into historical decline (1983–2010), reversal (2011–2030), and projected decline (2031–2100) phases.

Methodology and Data

• Models used:
  • Coupled Model Intercomparison Project Phase 6 (CMIP6) models (ACCESS-ESM1-5, CanESM5, CESM2, CMCC-ESM2, GFDL-ESM4, INM-CM5-0)
  • Community Land Model version 5 (CLM5)
• Data sources:
  • Satellite-derived GPP datasets: NIRV GPP, EC-LUE GPP, P-model GPP.
  • Flux tower observations: FLUXNET Tier 1 database (101 sites).
  • Reanalysis datasets: Climatic Research Unit (CRU), ECMWF Reanalysis v5 (ERA5), Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) for wind speed; CRU Time-Series (CRU TS) version 4.0.4 for air temperature, air pressure, precipitation; CRU and Japanese reanalysis (CRU JRA) version 2.2 for wind speed, shortwave radiation, specific humidity.
  • Atmospheric carbon dioxide concentration: Cheng et al. (2022).
  • Global phenological indicators: Start of Season (SOS) and End of Season (EOS) from AVHRR data.
  • Normalized Difference Vegetation Index (NDVI): PKU GIMMS NDVI product (1982–2020).
  • Ancillary data: Global Human Footprint Dataset (HFI), Global Fire Emissions Database version 4, Historical Land-Cover Change and Land-Use Conversions Global Dataset (for Plant Functional Types).
  • Statistical methods: Principal Components Regression (PCR), Thiel-Sen slope estimator, Mediation analysis, Structural Equation Modeling (SEM).

Main Results

• Terrestrial GPP exhibits a negative sensitivity to wind speed change, ranging from −156.67 to −65.82 g C m−2 yr−1 (m s−1)−1 across different data sources from 1983 to 2100.
• This negative sensitivity is primarily driven by wind speed decline reducing atmospheric dryness (vapor pressure deficit, VPD) and soil drying (increasing soil water content, SWC), which subsequently enhances stomatal conductance.
• During 1983–2010, wind speed decline was the second most important factor (contributing 6.0%–7.8%) after rising atmospheric CO2 concentrations to the increasing global GPP trend.
• Wind speed's contribution to GPP is projected to rank between second and third during the future decline period of 2031–2100.
• Grasslands contribute most (26.8%–73.3%) to the wind-induced GPP trend changes across both historical and projected periods.
• CLM5 simulations show that a 1% decrease in wind speed leads to a 0.41 g C m−2 yr−1 increase in average GPP, mainly by enhancing stomatal conductance (median effect: 0.07%) despite increasing boundary layer resistance (median effect: 0.50%).
• Mediation analysis indicates that reduced atmospheric aridity (17% mediation effect) has a greater influence than increased soil moisture deficit in modulating ∂GPP/∂Wind.
• Projected future wind speed declines (2031–2100) are expected to result in increasing GPP trends (0.15 to 0.35 g C m−2 yr−2), potentially larger than historical trends, due to a projected higher GPP sensitivity to wind speed.

Contributions

• Provides the first systematic, multi-data source, and multi-model exploration of the mechanism and extent of terrestrial surface wind speed impacts on global ecosystem productivity.
• Quantifies the significant negative sensitivity of GPP to wind speed changes, offering a robust estimate across various datasets and models.
• Identifies stomatal conductance, primarily mediated by reduced atmospheric dryness and soil drying, as the predominant biophysical mechanism linking wind speed changes to GPP.
• Establishes wind speed decline as the second most important factor, after rising atmospheric CO2 concentrations, in driving historical GPP increases and highlights its continued significance in future projections.
• Offers quantitative evidence to reduce uncertainties in Earth system models' predictions regarding future climate change impacts on ecosystem carbon sequestration capacity.

Funding

• National Natural Science Foundation of China (U2240219, 42371046, 41971044, 42571120, 42101112)
• National Key Research and Development Program of China (2019YFA0607100)
• Natural Science Foundation of Jiangsu Province (BK20220018, BK20191099)
• Pioneer Hundred Talent Program, Chinese Academy of Sciences (Y7BR021001)
• NIGLAS startup project for introducing talents (Y7SL041001)
• Science and Technology Planning Project of NIGLAS (2022NIGLASTJ15)

Citation

@article{Wu2025Significant,
  author = {Wu, Haohao and Fu, Congsheng and Zhang, Lingling and Mekonnen, Z. A. and Zhu, Qing and Yu, Kailiang and Ciais, Philippe and Chen, Jianyao and Wang, Dagang and Wu, Huawu and Yang, Guishan},
  title = {Significant sensitivity of global vegetation productivity to terrestrial surface wind speed changes},
  journal = {Nature Communications},
  year = {2025},
  doi = {10.1038/s41467-025-65000-x},
  url = {https://doi.org/10.1038/s41467-025-65000-x}
}