Forest and bioenergy expansion amplifies climate warming by accelerating regional cloud loss
- 1Institute of Geographic Sciences and Natural Resources Research (Beijing, China)
- 2University of Chinese Academy of Sciences (Beijing, China)
Abstract
Land use and cover change (LUCC) can exacerbate cloud-mediated climate warming, yet long-term cloud responses to contrasting LUCC pathways remain poorly quantified. Here, we perform transient simulations based on the fully coupled Community Earth System Model (CESM) under the SSP1-2.6 scenario to quantify how long-term cloud trends respond to large-scale idealized afforestation (AF50, conversion of grassland or shrubland to forest), idealized bioenergy expansion (BE50, sugarcane expansion), and realistic afforestation (REAL). AF50 drives a pronounced vertical restructuring of cloud trends, steepening mid-level cloud decline and accelerating low-level cloud loss from −0.0153 ± 0.0030 % yr ⁻¹ in the control simulation (CTL) to −0.0175 ± 0.0036 % yr ⁻¹ , corresponding to 1.14 times the CTL rate globally and 1.52 times over land. Concurrently, it amplifies the global increase in high-level clouds by a factor of 1.5 and accelerates total cloud cover loss over land. Bioenergy expansion induces a similar but weaker vertical shift, whereas realistic afforestation slows the loss rate of low- and mid-level clouds and suppresses regional warming. Interpretable machine learning identifies changes in relative-humidity trends as the highest-ranked predictor of LUCC-induced changes in low-level cloud trends, with relative importance values of 20.4 %, 20.4 %, and 15.3 % under AF50, BE50, and REAL, respectively. The simulated low-level cloud response is consistent with surface darkening reducing albedo, increasing sensible heat flux, deepening the planetary boundary layer, enhancing dry-air entrainment, and raising the lifting condensation level relative to the boundary-layer top, thereby suppressing low-level cloud formation. The cloud response is also modulated by remote atmospheric adjustment. The accelerated depletion of low-level clouds strengthens positive shortwave cloud radiative forcing, while the concurrent increase in high-level clouds enhances longwave warming, together amplifying regional warming hotspots. These results demonstrate that larger-scale forest expansion does not necessarily provide greater climate cooling benefits; the sign and magnitude of the outcome depend not only on the latitude band of land conversion but, more critically, on the type of land conversion.
Versions
Related
- Influence of historical urban expansion on the regional climate: A case study in the Paris region with CNRM-AROME
- Warming since the Last Glacial Maximum amplifies Northern Hemisphere drought expansion driven by the Pacific Decadal Oscillation
- Climate warming amplifies coastal hypoxia response to extreme river discharge
- Evidence from 2024 Storm Boris indicates anthropogenic climate change amplifies floods more than extreme precipitation
- Climate-driven water stress in the United States is seasonal, regional, and shaped by adaptation
BibTeX
@article{liu2026forest,
title = {{Forest and bioenergy expansion amplifies climate warming by accelerating regional cloud loss}},
author = {Nanjian Liu and Zhixin Hao and Siyou Xia and Peng Zhao},
journal = {Atmospheric Chemistry and Physics},
year = {2026},
month = oct,
doi = {10.5194/acp-26-14133-2026},
url = {https://acp.copernicus.org/articles/26/14133/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Forest and bioenergy expansion amplifies climate warming by accelerating regional cloud loss
AU - Nanjian Liu
AU - Zhixin Hao
AU - Siyou Xia
AU - Peng Zhao
PY - 2026
DA - 2026/10/09
JO - Atmospheric Chemistry and Physics
DO - 10.5194/acp-26-14133-2026
UR - https://acp.copernicus.org/articles/26/14133/2026/
AB - Land use and cover change (LUCC) can exacerbate cloud-mediated climate warming, yet long-term cloud responses to contrasting LUCC pathways remain poorly quantified. Here, we perform transient simulations based on the fully coupled Community Earth System Model (CESM) under the SSP1-2.6 scenario to quantify how long-term cloud trends respond to large-scale idealized afforestation (AF50, conversion of grassland or shrubland to forest), idealized bioenergy expansion (BE50, sugarcane expansion), and realistic afforestation (REAL). AF50 drives a pronounced vertical restructuring of cloud trends, steepening mid-level cloud decline and accelerating low-level cloud loss from −0.0153 ± 0.0030 % yr ⁻¹ in the control simulation (CTL) to −0.0175 ± 0.0036 % yr ⁻¹ , corresponding to 1.14 times the CTL rate globally and 1.52 times over land. Concurrently, it amplifies the global increase in high-level clouds by a factor of 1.5 and accelerates total cloud cover loss over land. Bioenergy expansion induces a similar but weaker vertical shift, whereas realistic afforestation slows the loss rate of low- and mid-level clouds and suppresses regional warming. Interpretable machine learning identifies changes in relative-humidity trends as the highest-ranked predictor of LUCC-induced changes in low-level cloud trends, with relative importance values of 20.4 %, 20.4 %, and 15.3 % under AF50, BE50, and REAL, respectively. The simulated low-level cloud response is consistent with surface darkening reducing albedo, increasing sensible heat flux, deepening the planetary boundary layer, enhancing dry-air entrainment, and raising the lifting condensation level relative to the boundary-layer top, thereby suppressing low-level cloud formation. The cloud response is also modulated by remote atmospheric adjustment. The accelerated depletion of low-level clouds strengthens positive shortwave cloud radiative forcing, while the concurrent increase in high-level clouds enhances longwave warming, together amplifying regional warming hotspots. These results demonstrate that larger-scale forest expansion does not necessarily provide greater climate cooling benefits; the sign and magnitude of the outcome depend not only on the latitude band of land conversion but, more critically, on the type of land conversion.
SN - 1680-7324
ER -