Biophysical impacts of Earth greening largely controlled by aerodynamic resistance

Biophysical effects of croplands on land surface temperature

Converting natural vegetation to croplands alters the local land surface energy budget. Here, we use two decades of satellite data and a physics-based framework to analyse the biophysical mechanisms by which croplands influence daily mean land surface temperature (LST). Globally, 60% of croplands exhibit an annual warming effect, while 40% have a cooling effect compared to their surrounding natural ecosystems. Aerodynamic resistance is identified as the dominant biophysical factor impacting LST by adjusting latent heat flux. The magnitude of cropland-induced LST change is negatively correlated with the difference in leaf area index between croplands and their surrounding biome types. The strongest warming occurs in temperate dry regions where croplands are surrounded by savannas. However, a lower-than-expected LST disturbance is seen in hot and wet regions where croplands are surrounded by rainforests, attributed to lower cropland fraction and energy limitations. These findings highlight the complex interplay of land use, vegetation, and regional climate, providing valuable insights into sustainable agriculture and land-based climate change mitigation.

Quantifying the Cooling Effect of Urban Greening Driven by Ecological Restoration Projects in China

Urban greening (UG) affects local climate by altering surface energy balance, while long-term UG cooling potential, patterns, and contribution to curbing urban warming remain unclear. Here, we designed an novel statistical model to evaluate the cooling potential of UG (CPUG) and created the first CPUG map for China. By exploring the trends in observed and simulated urban surface temperatures (UST), we quantified the CPUG of 0.20 K over the past two decades, which slowed down the warming trend by 14.17% in Chinese cities. We found that the CPUG varied significantly between the urban core and sprawl areas. Specifically, the CPUG in the urban core was approximately 1.01 K, and it contributed to curbing urban warming by 56.08%, which was more than 7.2 times higher than in the sprawl areas, where the CPUG was only 0.14 K and contributed to curbing urban warming by 9.93%. We further revealed that urbanization and major ecological restoration projects are the key factors influencing CPUG, emphasizing the need for anthropogenic vegetation management to curb urban warming. The proposed model in this study provides a powerful tool for quantitatively assessing the impact of long-term UG trends on urban warming. The results of the study are an important reference for building climate-adaptive cities.

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (-0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (-0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

Satellite-based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015

Tree planting is a prevalent strategy to mitigate urban heat. Tree cooling efficiency (TCE), defined as the temperature reduction for a 1% tree cover increase, plays an important role in urban climate as it regulates the capacity of trees to alter the surface energy and water budget. However, the spatial variation and more importantly, temporal heterogeneity of TCE in global cities are not fully explored. Here, we used Landsat-based tree cover and land surface temperature (LST) to compare TCEs at a reference air temperature and tree cover level across 806 global cities and to explore their potential drivers with a boosted regression tree (BRT) machine learning model. From the results, we found that TCE is spatially regulated by not only leaf area index (LAI) but climate variables and anthropogenic factors especially city albedo, without a specific variable dominating the others. However, such spatial difference is attenuated by the decrease of TCE with tree cover, most pronounced in midlatitude cities. During the period 2000-2015, more than 90% of analyzed cities showed an increasing trend in TCE, which is likely explained by a combined result of the increase in LAI, intensified solar radiation due to decreased aerosol content, increase in urban vapor pressure deficit (VPD) and decrease of city albedo. Concurrently, significant urban afforestation occurred across many cities showing a global city-scale mean tree cover increase of 5.3 ± 3.8% from 2000 to 2015. Over the growing season, such increases combined with an increasing TCE were estimated to on average yield a midday surface cooling of 1.5 ± 1.3°C in tree-covered urban areas. These results are offering new insights into the use of urban afforestation as an adaptation to global warming and urban planners may leverage them to provide more cooling benefits if trees are primarily planted for this purpose.

Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming

Vegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001-2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of -0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies.

Tropical surface temperature response to vegetation cover changes and the role of drylands

Vegetation cover creates competing effects on land surface temperature: it typically cools through enhancing energy dissipation and warms via decreasing surface albedo. Global vegetation has been previously found to overall net cool land surfaces with cooling contributions from temperate and tropical vegetation and warming contributions from boreal vegetation. Recent studies suggest that dryland vegetation across the tropics strongly contributes to this global net cooling feedback. However, observation-based vegetation-temperature interaction studies have been limited in the tropics, especially in their widespread drylands. Theoretical considerations also call into question the ability of dryland vegetation to strongly cool the surface under low water availability. Here, we use satellite observations to investigate how tropical vegetation cover influences the surface energy balance. We find that while increased vegetation cover would impart net cooling feedbacks across the tropics, net vegetal cooling effects are subdued in drylands. Using observations, we determine that dryland plants have less ability to cool the surface due to their cooling pathways being reduced by aridity, overall less efficient dissipation of turbulent energy, and their tendency to strongly increase solar radiation absorption. As a result, while proportional greening across the tropics would create an overall biophysical cooling feedback, dryland tropical vegetation reduces the overall tropical surface cooling magnitude by at least 14%, instead of enhancing cooling as suggested by previous global studies.

Nexus of heat-vulnerable chronic diseases and heatwave mediated through tri-environmental interactions: A nationwide fine-grained study in Australia

The warming trend over recent decades has already contributed to the increased prevalence of heat-vulnerable chronic diseases in many regions of the world. However, understanding the relationship between heat-vulnerable chronic diseases and heatwaves remains incomplete due to the complexity of such a relationship mingling with human society, urban and natural environments. Our study extends the Social Ecological Theory by constructing a tri-environmental conceptual framework (i.e., across social, built, and natural environments) and contributes to the first nationwide study of the relationship between heat-vulnerable chronic diseases and heatwaves in Australia. We utilize the random forest regression model to explore the importance of heatwaves and 48 tri-environmental variables that contribute to the prevalence of six types of heat-vulnerable diseases. We further apply the local interpretable model-agnostic explanations and the accumulated local effects analysis to interpret how the heat-disease nexus is mediated through tri-environments and varied across urban and rural space. The overall effect of heatwaves on diseases varies across disease types and geographical contexts (latitudes; inland versus coast). The local heat-disease nexus follows a J-shape function-becoming sharply positive after a certain threshold of heatwaves-reflecting that people with the onset of different diseases have various sensitivity and tolerance to heatwaves. However, such effects are relatively marginal compared to tri-environmental variables. We propose a number of policy implications on reducing urban-rural disparity in healthcare access and service distribution, delineating areas, and identifying the variations of sensitivity to heatwaves across urban/rural space and disease types. Our conceptual framework can be further applied to examine the relationship between other environmental problems and health outcomes.

Protected areas provide thermal buffer against climate change

Climate change is pushing temperatures beyond the thermal tolerance of many species. Whether protected areas (PAs) can serve as climate change refugia for biodiversity has not yet been explored. We find that PAs of natural (seminatural) vegetation effectively cool the land surface temperature, particularly the daily maximum temperature in the tropics, and reduce diurnal and seasonal temperature ranges in boreal and temperate regions, as compared to nonprotected areas that are often disturbed or converted to various land uses. Moreover, protected forests slow the rate of warming more at higher latitudes. The warming rate in protected boreal forests is up to 20% lower than in their surroundings, which is particularly important for species in the boreal where warming is more pronounced. The fact that nonprotected areas with the same type of vegetation as PAs show reduced warming buffer capacity highlights the importance of conservation to stabilize the local climate and safeguard biodiversity.

High-resolution mapping of the global silicate weathering carbon sink and its long-term changes

Climatic and non-climatic factors affect the chemical weathering of silicate rocks, which in turn affects the CO₂ concentration in the atmosphere on a long-term scale. However, the coupling effects of these factors prevent us from clearly understanding of the global weathering carbon sink of silicate rocks. Here, using the improved first-order model with correlated factors and non-parametric methods, we produced spatiotemporal data sets (0.25° × 0.25°) of the global silicate weathering carbon-sink flux (SCSF_α ) under different scenarios (SSPs) in present (1950-2014) and future (2015-2100) periods based on the Global River Chemistry Database and CMIP6 data sets. Then, we analyzed and identified the key regions in space where climatic and non-climatic factors affect the SCSF_α . We found that the total SCSF_α was 155.80 ± 90 Tg C yr^-1 in present period, which was expected to increase by 18.90 ± 11 Tg C yr^-1 (12.13%) by the end of this century. Although the SCSF_α in more than half of the world was showing an upward trend, about 43% of the regions were still showing a clear downward trend, especially under the SSP2-4.5 scenario. Among the main factors related to this, the relative contribution rate of runoff to the global SCSF_α was close to 1/3 (32.11%), and the main control regions of runoff and precipitation factors in space accounted for about 49% of the area. There was a significant negative partial correlation between leaf area index and silicate weathering carbon sink flux due to the difference between the vegetation types. We have emphasized quantitative analysis the sensitivity of SCSF_α to critical factors on a spatial grid scale, which is valuable for understanding the role of silicate chemical weathering in the global carbon cycle.

Thermal and moisture response to land surface changes across different ecosystems over Heilong-Amur River Basin

The Heilong-Amur River Basin (HARB) in Northeast Asia has experienced distinct land surface changes during the past 40 years due to extensive ecological restoration programs, agricultural management, and grassland grazing in different ecosystems. However, the regional climate impact caused by the long-term spatially heterogeneous land surface changes in this mid-high latitude region is not well documented. Therefore, this study used multi-source satellite measurements records and a high-resolution land-atmosphere coupled regional climate model (WRF) to investigate the land surface changes and their associated thermal and moisture impacts across three main ecosystems over the Heilong-Amur River basin from 1982 to 2018. Firstly, satellite observations indicated an overall greening in HARB, with variations across ecosystems. The significant summer farmland greening is the most representative, with the farmland green vegetation fraction (GVF) remarkably increasing by 7.78% in summer. The forest greening magnitude is stronger in spring (3.42%) than in summer (2.85%), while the grassland vegetation showed some local browning signals in summer. Secondly, our simulated results showed the summer farmland greening accelerated evapotranspiration (ET) by 0.161 mm/d and significantly cools the surface temperature by 0.508 °C averaged at the ecosystem scale, which was highly correlated with the satellite observations but with lower cooling magnitude. The forest greening brought less surface cooling in spring than summer due to the stronger albedo feedback, despite with greater increase in GVF and ET. While with the opposite process, the local grassland browning leads to consistent warming effects, which can be detected from both satellite observations and our simulation results. Finally, our results also found that rainfall increasing averagely at the ecosystem scale can't fully compensate the water emission from enhanced ET due to the surface greening, contributing to soil moisture decline in both farmland and relative dry forests.

CO2 fertilization of terrestrial photosynthesis inferred from site to global scales

The magnitude of the CO₂ fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO₂ fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO₂ fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO₂ fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

Global photosynthesis is increasing with elevated atmospheric CO₂ concentrations, a response known as the CO₂ fertilization effect (CFE), but the key processes of CFE are not constrained and therefore remain uncertain. Here, we quantify CFE by combining observations from a globally distributed network of eddy covariance measurements with an analytical framework based on three well-established photosynthetic optimization theories. We report a strong enhancement of photosynthesis across the observational network (9.1 gC m⁻² year⁻²) and show that the CFE is responsible for 44% of the gross primary production (GPP) enhancement since the 2000s, with additional contributions primarily from warming (28%). Soil moisture and specific humidity are the two largest contributors to GPP interannual variation through their influences on plant hydraulics. Applying our framework to satellite observations and meteorological reanalysis data, we diagnose a global CO₂-induced GPP trend of 4.4 gC m⁻² year⁻², which is at least one-third stronger than the median trends of 13 dynamic global vegetation models and eight satellite-derived GPP products, mainly because of their differences in the magnitude of CFE in evergreen broadleaf forests. These results highlight the critical role that CFE has played in the global carbon cycle in recent decades.

A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory

Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs.

Vegetation-based climate mitigation in a warmer and greener World

The mitigation potential of vegetation-driven biophysical effects is strongly influenced by the background climate and will therefore be influenced by global warming. Based on an ensemble of remote sensing datasets, here we first estimate the temperature sensitivities to changes in leaf area over the period 2003-2014 as a function of key environmental drivers. These sensitivities are then used to predict temperature changes induced by future leaf area dynamics under four scenarios. Results show that by 2100, under high-emission scenario, greening will likely mitigate land warming by 0.71 ± 0.40 °C, and 83% of such effect (0.59 ± 0.41 °C) is driven by the increase in plant carbon sequestration, while the remaining cooling (0.12 ± 0.05 °C) is due to biophysical land-atmosphere interactions. In addition, our results show a large potential of vegetation to reduce future land warming in the very-stringent scenario (35 ± 20% of the overall warming signal), whereas this effect is limited to 11 ± 6% under the high-emission scenario.

Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth

Vegetation productivity dynamics are closely related to climate change, and water availability determines vegetation growth in water-limited ecosystems. Nevertheless, how changes in the interactions between climatic factors and vegetation activity variation regulate the relationship between their trends remains unclear. The Normalized Difference Vegetation Index (NDVI) is an effective proxy of vegetation growth. First, we investigated the NDVI trends, and the results revealed a vegetation activity with weaker greening and greater spatial heterogeneity after an obvious land-cover breakpoint in 1999 compared with that before 1999 in northwest China. Notably, the Loess Plateau greatly led the greenness trends, but the Tibet Plateau showed mean browning after 1999, which implied that the coupling of climate change and vegetation trends varied with spatio-temporal changes. Subsequently, using the Geographical Detector Method (GDM), we quantified and compared the association between climate change and the interannual variability of NDVI in the two stages. Vegetation productivity variation is more closely related to changes in climatic factors after 1999 compared with that before 1999. Precipitation (PPT) and vapor pressure deficit (VPD) are the primary constraints to vegetation growth in both stages. Patterns in NDVI trend increases are consistent with those of increased PPT and decreased VPD and vice versa after 1999. However, the same patterns were not observed before 1999 because of the weak association between climate change and NDVI variation. This implicated a great significance of the association between climate change and changes in vegetation activity for the prediction of potential carbon sequestration due to the shift of dominant factors and their trends under future climate change.