Earth system models (ESMs) project a global increase in Leaf Area Index (LAI) throughout the 21st century, a trend also observed via satellite data. Changes in plant physiology and structure associated with this greening affect surface temperatures by altering land-atmosphere energy and water exchanges. The biophysical impact on climate is increasingly recognized, potentially enhancing or counteracting the climate benefits of land-based carbon sequestration. However, this effect is uncertain and inconsistent in sign and magnitude, depending heavily on the background climate (e.g., local warming at northern latitudes, cooling in tropical and temperate regions). The projected increase in vegetation density and concurrent climate changes could further amplify or dampen these interactions, making the net global biophysical effect highly uncertain. The increasing relevance of nature-based solutions in climate targets underscores the critical need to quantify the evolution of biophysical and biochemical mitigation potentials of vegetation under future climate conditions. Previous assessments suggest an overall evaporation-driven cooling effect from increased LAI, particularly in water-limited environments. Conversely, observations indicate that increased LAI reduced surface albedo in boreal regions, resulting in local warming. The net effect remains controversial, with model simulations showing contrasting results. The key role of background climate conditions in modulating biophysical processes, especially the relative importance of radiative versus non-radiative processes, is widely acknowledged. Projected declines in key drivers like snow cover and soil moisture are expected to substantially influence land-atmosphere interactions in future climates. Progressive climate warming might enhance non-radiative biophysical effects, leading to amplified mitigation associated with vegetation greening. However, rising atmospheric CO2 could have an opposite effect by reducing water loss through transpiration and increasing water-use efficiency (WUE) via stomatal closure. This increased WUE might dampen or offset the cooling associated with increased evaporative surfaces and changes in vapor pressure deficits. The lack of robust experimental evidence on the sensitivities of biophysical effects to background climate hinders predictions on their future evolution and accurate representation in dynamic vegetation models. The net effect of future combined changes in vegetation density and climate remains highly uncertain and requires robust quantification.

The introduction section extensively reviews existing literature on the topic of vegetation's impact on climate. Studies highlighting the contrasting effects of greening on regional climate are cited (Forzieri et al., 2017; Zeng et al., 2017; Duveiller et al., 2018), along with research emphasizing the importance of background climate conditions in determining the sign and magnitude of these effects (Pitman et al., 2011). The role of radiative versus non-radiative processes is discussed in the context of various studies (Bright et al., 2017; Zeng et al., 2018), alongside the complexities introduced by changes in water-use efficiency due to rising CO2 concentrations (Keenan et al., 2013; Cheng et al., 2017; Berry et al., 2010). The limitations of current vegetation models in accurately capturing these complex interactions and the uncertainty surrounding future projections are also highlighted, emphasizing the need for the current study.

This study uses a combination of Earth observations and Earth system modeling to investigate the global biophysical impacts of future LAI changes on surface temperature under different climate warming and CO2 scenarios. First, satellite retrievals are used to quantify the baseline (2003-2014) monthly sensitivity of temperature (T) to LAI changes as a function of snow cover, solar radiation, and evaporation rates. This baseline period is chosen based on the availability of complete MODIS AQUA records for land surface temperature and the end of historical CMIP6 simulations. The sensitivity dT/dLAI is expressed as a function of these drivers to account for future ecosystem adaptation. Future evaporation data is derived from CMIP6 simulations, which already incorporate plant adaptation to climate change and increasing atmospheric CO2. To map climate sensitivities of biophysical effects from Earth observations, the large-scale climate signal on surface temperature is disentangled from the local effect of vegetation dynamics. For each grid cell, adjacent areas within a 50 km radius with stable vegetation cover (LAI variation < 0.1 m²/m²) are identified as reference areas. Temperature changes in these reference areas are attributed to climate variability. Variations in T due to LAI changes are then obtained by subtracting the large-scale T signal from the total observed signal at the target grid cell. The remaining signal reflects the unidirectional control of vegetation on surface temperature for the baseline period. The observed sensitivity dT/dLAI is then applied to snow cover, solar radiation, and evapotranspiration predicted by an ensemble of historical (2003-2014) CMIP6 simulations. The resulting map is compared to observations to validate the methodology. Finally, the data-driven estimates of dT/dLAI are combined with different trajectories of vegetation, snow cover, solar radiation, and evapotranspiration consistent with four Shared Socioeconomic Pathways (SSPs) (SSP126, SSP245, SSP370, and SSP585) simulated from 2015 to 2100 from an ensemble of CMIP6 ESMs to assess the future evolution of biophysical mitigation driven by vegetation. The sensitivity analysis incorporates the relationship between water use efficiency and atmospheric CO2 concentration by using evaporation rates (already accounting for CO2 fertilization effects on stomatal conductance in climate models) instead of soil moisture as a driver of dT/dLAI. Specific equations are used to model the sensitivity dT/dLAI as a function of snow cover, solar radiation, and evaporation, accounting for both radiative and non-radiative effects. The Köppen-Geiger climate classification is used to define climate zones for analysis. The GLASS LAI product is selected for its robustness and performance over other available datasets, with additional tests performed using Copernicus LAI to ensure robustness. CMIP6 simulations provide data on LAI, solar radiation, snow cover, evapotranspiration, and vegetation carbon stock under different SSPs. A method is used to separate the contributions of LAI changes and climate change effects on temperature variations. Biochemical effects are estimated using a linear relationship between atmospheric carbon concentration and regional surface air temperature and simulated variations in vegetation carbon stock from CMIP6 models.

The study's key findings revolve around the quantification of vegetation's biophysical and biochemical climate mitigation potential under future climate scenarios. Analysis of satellite data (2003-2014) reveals that the sensitivity of temperature to LAI changes (dT/dLAI) is strongly influenced by snow cover, solar radiation, and evaporation. In snow-covered areas, radiative processes dominate, while in snow-free areas, non-radiative processes (evaporative cooling) are more significant. The sensitivity is higher in arid regions with low evaporation rates and under high radiation levels. Analysis of CMIP6 simulations under four SSPs (SSP126, SSP245, SSP370, and SSP585) shows a significant increase in LAI by 2100, with the largest increase under the high-emission scenario (SSP585). This increase in LAI is generally associated with increased evaporation, except in water-scarce regions and the Amazon. Snow cover is predicted to decrease under all scenarios, particularly under SSP585. Changes in dT/dLAI sensitivity are most pronounced in boreal zones due to snow cover reduction. Elsewhere, the trend is more complex, with decreasing sensitivity linked to reduced snow cover or evaporation, and increasing sensitivity in regions with increased evaporation. Future greening under SSP585 results in a biophysical cooling effect almost everywhere, particularly over African savannas. Half of this cooling is attributed to greening itself (with future LAI under current climate conditions), while the other half is due to synergistic changes in future climate conditions that amplify the cooling effect. This amplification is caused by reduced radiative warming due to decreased snow cover and enhanced non-radiative cooling due to increased evapotranspiration. These mechanisms are also observed under more ambitious mitigation scenarios (SSP126, SSP245, SSP370), although with a lower magnitude. The largest temperature mitigation from greening under SSP585 is observed during boreal summer at high latitudes, partially offset by slight winter warming due to radiative effects of winter greening. The study finds that biochemical mitigation (from increased plant carbon sequestration) is about five times larger than biophysical mitigation. However, the magnitude of biophysical mitigation varies depending on the combined dynamics of LAI and background climate. Notably, biophysical mitigation is maximized during warmest months and in arid regions. In absolute terms, both biophysical and biochemical effects are larger under warmer SSPs (higher CO2). Relatively speaking, land-based climate mitigation is more significant under more ambitious mitigation plans.

The findings highlight how projected climate changes amplify the cooling effect of land greening through two primary mechanisms: reduced radiative warming due to declining snow cover and enhanced non-radiative cooling due to increased evapotranspiration. The study acknowledges limitations, including the potential underestimation of non-local biophysical effects (driven by large-scale teleconnections) due to the relatively short observation period (2003-2014) and uncertainties in high-latitude winter observations. The potential overestimation of future LAI increases in ESMs is also noted, suggesting that the median mitigation estimates might be overestimated. Despite these limitations, the study's evidence-driven analysis significantly improves understanding of present and future biophysical climate impacts of vegetation dynamics under combined greening and warming. While the absolute mitigation potential of predicted LAI dynamics is limited, the study suggests that afforestation and restoration programs could enhance this effect by increasing vegetation area and density. This is in addition to the biochemical effect of absorbing atmospheric CO2. The biophysical mitigation signal peaks during the warmest months and in arid regions, where future warming will be most challenging.

This study provides robust evidence, based on a combination of earth observations and climate modeling, on the biophysical and biochemical effects of vegetation on climate mitigation. Although the mitigation potential of projected LAI increases is limited, the results emphasize the importance of considering afforestation and restoration programs to enhance vegetation density and area. The study highlights the heterogeneous nature of biophysical mitigation effects, particularly the maximized cooling in arid regions during warmer months. Future research should focus on refining models to better capture non-local effects and improving the accuracy of future LAI projections.

The study acknowledges several limitations. Non-local biophysical effects, driven by large-scale teleconnections, might not be fully captured due to the relatively short observation period (2003–2014) and the limited changes in LAI during this period. Uncertainties in high-latitude winter observations due to lack of daylight hours and high solar zenith angles are also acknowledged. Additionally, the CMIP6 models used do not simulate natural shifts in plant species due to climate change, which could affect the accuracy of LAI predictions. Finally, the future projection of LAI increases might be overstated in ESMs, potentially leading to an overestimation of the median mitigation estimates.