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Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates. Using global flux tower observations, the authors show that atmospheric dryness always reduces photosynthesis, whereas soil dryness can increase photosynthesis if soil water stores are sufficient.

Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential. The fraction of leaf nitrogen allocated to RuBisCO indicates differing nitrogen use strategies of plants and varies considerably. Here the authors show that this variation is largely driven by leaf thickness and phosphorus content with light intensity, atmospheric dryness and soil pH also having considerable influence.

The terrestrial carbon sink slows the accumulation of carbon dioxide (CO 2 ) in the atmosphere by absorbing roughly 30% of anthropogenic CO 2 emissions, but varies greatly from year to year. The resulting variations in the atmospheric CO 2 growth rate (CGR) have been related to tropical temperature and water availability. The apparent sensitivity of CGR to tropical temperature ( γ CGR T ) has changed markedly over the past six decades, however, the drivers of the observation to date remains unidentified. Here, we use atmospheric observations, multiple global vegetation models and machine learning products to analyze the cause of the sensitivity change. We found that a threefold increase in γ CGR T emerged due to the long-term changes in the magnitude of CGR variability (i.e., indicated by one standard deviation of CGR; STD CGR ), which increased 34.7% from 1960-1979 to 1985-2004 and subsequently decreased 14.4% in 1997-2016. We found a close relationship (r 2  = 0.75, p < 0.01) between STD CGR and the tropical vegetated area (23°S – 23°N) affected by extreme droughts, which influenced 6-9% of the tropical vegetated surface. A 1% increase in the tropical area affected by extreme droughts led to about 0.14 Pg C yr −1 increase in STD CGR . The historical changes in STD CGR were dominated by extreme drought-affected areas in tropical Africa and Asia, and semi-arid ecosystems. The outsized influence of extreme droughts over a small fraction of vegetated surface amplified the interannual variability in CGR and explained the observed long-term dynamics of γ CGR T . The apparent temperature sensitivity of atmospheric CO 2 growth rate has increased markedly over the past six decades, however, the increase remains unexplained. Here we show that tropical extreme droughts amplified the interannual variability in atmospheric CO 2 growth rate and drove the sensitivity change.

Water availability plays a critical role in shaping terrestrial ecosystems, particularly in low- and mid-latitude regions. The sensitivity of vegetation growth to precipitation strongly regulates global vegetation dynamics and their responses to drought, yet sensitivity changes in response to climate change remain poorly understood. Here we use long-term satellite observations combined with a dynamic statistical learning approach to examine changes in the sensitivity of vegetation greenness to precipitation over the past four decades. We observe a robust increase in precipitation sensitivity (0.624% yr −1 ) for drylands, and a decrease (−0.618% yr −1 ) for wet regions. Using model simulations, we show that the contrasting trends between dry and wet regions are caused by elevated atmospheric CO 2 (eCO 2 ). eCO 2 universally decreases the precipitation sensitivity by reducing leaf-level transpiration, particularly in wet regions. However, in drylands, this leaf-level transpiration reduction is overridden at the canopy scale by a large proportional increase in leaf area. The increased sensitivity for global drylands implies a potential decrease in ecosystem stability and greater impacts of droughts in these vulnerable ecosystems under continued global change. Changes in vegetation responses to precipitation may be hydroclimate dependent. Here the authors reveal contrasting trends of vegetation sensitivity to precipitation in drylands vs. wetter ecosystems over the last 4 decades and identify increased CO2 as a major contributing factor.

Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months PPFD ¯ . Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. Here, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m −2  s −1 increase in PPFD ¯ 10 (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m −2  s −1 increase in the maximum photosynthetic assimilation rate ( A max ), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the PPFD ¯ 10 – A max relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models. Combining global eddy covariance observations and photosynthesis estimates from terrestrial biosphere models, the authors demonstrate widespread acclimation of photosynthesis to light in natural environments, with croplands showing stronger acclimation rates than forests or grasslands.

Increasing drought frequency and severity in a warming climate threaten forest ecosystems with widespread tree deaths. Canopy structure is important in regulating tree mortality during drought, but how it functions remains controversial. Here, we show that the interplay between tree size and forest structure explains drought-induced tree mortality during the 2012-2016 California drought. Through an analysis of over one million trees, we find that tree mortality rate follows a “negative-positive-negative” piecewise relationship with tree height, and maintains a consistent negative relationship with neighborhood canopy structure (a measure of tree competition). Trees overshadowed by tall neighboring trees experienced lower mortality, likely due to reduced exposure to solar radiation load and lower water demand from evapotranspiration. Our findings demonstrate the significance of neighborhood canopy structure in influencing tree mortality and suggest that re-establishing heterogeneity in canopy structure could improve drought resiliency. Our study also indicates the potential of advances in remote-sensing technologies for silvicultural design, supporting the transition to multi-benefit forest management. Tree height and forest structure may both determine forest responses to drought. Here, the authors analyse highresolution airborne LIDAR data on <1 million trees during the 2012-2016 California drought and find that presence of both tall trees and structurally complex stands reduces tree mortality under drought.

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. Croplands alter land surface temperature, with 60% warming and 40% cooling effects globally. Temperature changes are driven by convection, which is influenced by leaf area index differences. The warming is strongest in temperate dry regions surrounded by trees.

Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle. Due to fundamental anatomical and biochemical differences, C 3 and C 4 plant species tend to differ in their biogeography and response to climate change. Here, the authors use global observations and optimality theory to map patterns and temporal trends in C 4 species distribution and the contribution of C 4 plants to global photosynthesis.

Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change. Plain Language Summary Vegetation greenness changes year to year in response to climate variability and reflects the stability of ecosystems. How the interannual variability (IAV) of vegetation greenness has changed in the past decades, however, remained uncertain with recent studies reporting conflicting IAV trends using different satellite remote sensing products. Here, we investigated the greenness IAV trends of global vegetation using multiple mainstream satellite remote sensing products. We found that the changes in greenness IAV are conflicting on half of the global vegetated surface, while the differences in background climate, greening trends and nitrogen deposition rates account for either positive or negative trends in greenness IAV on the remaining half of the vegetated surface. Key Points On half of the global vegetated surface, the changes in the vegetation greenness interannual variability (IAV) are conflicting 22.20% and 28.20% of the global vegetated surface show significant positive and negative trends of vegetation greenness IAV, respectively Warmer and drier places lead to greater greenness IAV whereas greater greening trend and higher nitrogen deposition make IAV smaller

Understanding the spontaneous water imbibition mechanism in coal of varying ranks has substantial implications for hydraulic operations and the safe, efficient extraction of coalbed methane. During spontaneous imbibition, low-field nuclear magnetic resonance techniques were used to test the T 2 spectra and imaging (MRI) of coal samples, enabling the determination of water signal distribution in the samples at different time intervals. By combining this with the pore structure, we explored the water migration characteristics of the coal samples during spontaneous imbibition from a microscopic perspective. Additionally, we deeply investigated the relationship between the MRI pixel eigenvalues, the capillary absorption coefficients, and the degree of pore development. The results demonstrated a positive correlation between the capillary water absorption coefficient and the degree of pore development in the coal samples, while revealing a negative correlation with both pore diameter and tortuosity. In the process of spontaneous imbibition, micropores exert a dominant influence and achieve saturation first, followed by a gradual increase in the contribution of mesopores and macropores (including microfractures) to spontaneous imbibition. The MRI results demonstrate that water migration primarily occurs within the interior of the coal samples before extending towards the exterior. The pixel values obtained from MRI follow a normal distribution, with the mean value reflecting the extent of pore development and the entropy representing the randomness in pore distribution. The higher the pixel eigenvalue, the greater the pore content of the coal sample, indicating a more random distribution of pores and enhanced connectivity among various types of pores, which results in an increased capillary water absorption coefficient.