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Global environmental changes have implications for the terrestrial ecosystem functioning, but disentangling individual effects remains elusive. The impact of vegetation responses to increasing atmospheric CO2 concentrations is particularly poorly understood. As the atmospheric CO2 concentration increases, the CO2 acts as a fertilizer for plant growth. An increase in atmospheric CO2 reduces the amount of water needed to produce an equivalent amount of biomass due to closing or a narrowing of the stomata that reduces the amount of water that is transpired by plants. To study the impacts of climate change and CO2 fertilization on plant growth, we analyzed the growing season sensitivity of plant growth to climatic forcing from alpine to semi-desert eco-climatic zones of Ethiopia for various plant functional types over the period of 1982–2011. Growing season 3rd generation Normalized Difference Vegetation Index of Global Inventory Modeling and Mapping Studies (NDVI) was used as a proxy of plant growth, while mean growing season precipitation (prec), temperature (temp), and solar radiation (sr) as the climate forcing. The sensitivities of plant growth are calculated as a partial correlation, and a derivative of NDVI with respect to prec, temp and sr for earliest and recent 15-year periods of the satellite records, and using a moving window of 15-year. Our results show increasing trends of plant growth that are not explained by any climate variables. We also find that an equivalent increase in prec leads to a larger increase in NDVI since the 1980s. This result implies a given amount of prec has sustained greater amounts of plant foliage materials over time due to decreasing transpiration with increasing CO2 concentration as expected from the CO2 fertilization effect on water use efficiency and plant growth. Increasing trends of growth in shallow-rooted vegetation tend to be associated with woody vegetation encroachment.

Climate‐vegetation interaction assessments often focus on vegetation response to concurrent climatic perturbations, seldom on the time‐lag effect of climate. Here we employ global satellite observations, climate data records and CO2 flux measurements to calculate the time‐lag of vegetation response to climate. We analyze the time‐lags of various climate variables under distinct environmental conditions to gain insight into how the long‐term climatic regimes and tree cover influence the time‐lag effects. Our findings reveal that terrestrial ecosystems characterized by arid and cold climates show more concurrent climate‐vegetation interactions than other ecosystems. Whereas areas with higher tree cover and humid ecosystems with both high mean annual temperature and precipitation show substantial time‐lag response of vegetation to climate by up to 6 months. Since the global climate‐vegetation interaction is dominated by time‐lag effects, incorporating these effects is paramount to improve our understanding of vegetation dynamics under a changing climate. Plain Language Summary: When studying how climate affects vegetation, many studies usually focus on immediate plant responses, without considering the long‐term effects of climate. In our study, we used satellite data to look at how plant photosynthesis and growth changed over time in response to concurrent and past climates. We found that in dry and cold areas, plants respond quickly to changes in climate. But in regions with high tree cover and humid climate, plant responses to climate can take up to 6 months. Understanding these delays is crucial for predicting how vegetation will respond as the climate changes around the world. Key Points: Terrestrial ecosystems with higher tree cover respond to climate perturbation more slowly than grasslands and croplandsTemperature consistently has more significant impacts on vegetation in the longer term than VPD and soil moistureArid and cold ecosystems show shorter time‐lag responses of vegetation to climate [ABSTRACT FROM AUTHOR]

Globally shaded leaves contribute to more than a half of the total increase in gross primary production (GPP; 7.6 Pg C) for 1982–2016. During 1982–2016, the fraction of shaded GPP increases by 1.1% (p

Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

The magnitude and variability of the terrestrial CO2 sink remain uncertain, partly due to limited global information on ecosystem nitrogen (N) and its cycle. Without N constraint in ecosystem models, the simulated benefits from CO2 fertilization and CO2-induced increases in water use efficiency (WUE) may be overestimated. In this study, satellite observations of a relative measure of chlorophyll content are used as a proxy for leaf photosynthetic N content globally for 2003–2011. Global gross primary productivity (GPP) and evapotranspiration are estimated under elevated CO2 and N-constrained model scenarios. Results suggest that the rate of global GPP increase is overestimated by 85% during 2000–2015 without N limitation. This limitation is found to occur in many tropical and boreal forests, where a negative leaf N trend indicates a reduction in photosynthetic capacity, thereby suppressing the positive vegetation response to enhanced CO2 fertilization. Based on our carbon-water coupled simulations, enhanced CO2 concentration decreased stomatal conductance and hence increased WUE by 10% globally over the 1982 to 2015 time frame. Due to increased anthropogenic N application, GPP in croplands continues to grow and offset the weak negative trend in forests due to N limitation. Our results also show that the improved WUE is unlikely to ease regional droughts in croplands because of increases in evapotranspiration, which are associated with the enhanced GPP. Although the N limitation on GPP increase is large, its associated confidence interval is still wide, suggesting an urgent need for better understanding and quantification of N limitation from satellite observations.

Global Ecosystem Dynamics Investigation (GEDI); a full waveform lidar sensor, acquires samples of the terrain and vegetation structures. To derive a spatially continuous estimate, the canopy height and cover metrics retrieved from GEDI L2A and L2B, respectively, were extrapolated using support vector regression models and explanatory variables derived from multiple remotely sensed datasets. Explanatory variables extrapolated GEDI canopy height with RMSE = 3.83 m (R2 = 0.84) as the performance was validated using a subset of GEDI data. However, the accuracy decreased to RMSE = 7.98 m (R2 = 0.65) as validated with field-measured canopy heights. The accuracy of the prediction of forest canopy covers retrieved from GEDI L2B generally remains poor; RMSE = 0.14 (R2 = 0.53). [ABSTRACT FROM AUTHOR]

Ecosystem restoration is a fundamental way of delivering nature‐based solutions to improve resilience in a changing climate and sustain biodiversity. Spatial analyses to identify where ecosystem restoration would yield targeted environmental benefits are critical to inform, and coordinate restoration initiatives at multiple scales to achieve national commitments and global goals. Here, we provide an optimization analysis for restoration potential of converted terrestrial ecosystems in Canada by integrating carbon storage and biodiversity benefits as key considerations. Our results show that converted landscapes are prevalent in southern anthropic regions of Canada, with the greatest potential for biodiversity benefits through forest and grassland restoration. At national scales, carbon density (tonnes C/km2) and total carbon storage (tonnes C) potential were greatest for wetland and forest restoration, respectively. When biodiversity and carbon were both included in an optimization framework, consistent priorities across all three restoration targets (50,000; 100,000; and 150,000 km2) comprised forest restoration in the St. Lawrence and Lake Erie Lowlands, with the Lake Manitoba Plains, Interlake Plains, and Manitoulin‐Lake Simcoe ecoregions also frequently identified. Our analysis will help decision‐makers identify where restoration of converted lands may support considerable gains in simultaneously achieving climate and biodiversity goals in Canada. [ABSTRACT FROM AUTHOR]