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 < 0.01) in tropical forests and decreases by 1.4% (p < 0.01) and 1.8% (p < 0.01) in evergreen needleleaf and deciduous needleleaf boreal forests, respectively, suggesting an ecological niche of certain canopy structure for ecosystems to achieve maximum GPP. Unlike transpiration from sunlit leaves that has a turning point in the trend in 2003, global transpiration from shaded leaves steadily increased at the rate of 34 km3/year (p < 0.0001) during 1982–2016. Our study therefore suggests that shaded leaves have an increasing role in buffering the adverse impact of climate change and extremes. Further studies are still needed to reduce the uncertainties in reported trends arisen from climate forcing data, leaf area index, and land cover and land change products.

The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO₂ emissions through photosynthesis. The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation rate; Vcmax25 ). A lack of an effective means to derive spatially and temporally explicit Vcmax25 has long hampered efforts towards estimating global photosynthesis accurately. Recent work suggests that leaf chlorophyll content (Chl_leaf ) is strongly related to Vcmax25 , since Chl_leaf and Vcmax25 are both correlated with photosynthetic nitrogen content. We used medium resolution satellite images to derive spatially and temporally explicit Chl_leaf , which we then used to parameterize Vcmax25 within a terrestrial biosphere model. Modelled photosynthesis estimates were evaluated against measured photosynthesis at 124 eddy covariance sites. The inclusion of Chl_leaf in a terrestrial biosphere model improved the spatial and temporal variability of photosynthesis estimates, reducing biases at eddy covariance sites by 8% on average, with the largest improvements occurring for croplands (21% bias reduction) and deciduous forests (15% bias reduction). At the global scale, the inclusion of Chl_leaf reduced terrestrial photosynthesis estimates by 9 PgC/year and improved the correlations with a reconstructed solar-induced fluorescence product and a gridded photosynthesis product upscaled from tower measurements. We found positive impacts of Chl_leaf on modelled photosynthesis for deciduous forests, croplands, grasslands, savannas and wetlands, but mixed impacts for shrublands and evergreen broadleaf forests and negative impacts for evergreen needleleaf forests and mixed forests. Our results highlight the potential of Chl_leaf to reduce the uncertainty of global photosynthesis but identify challenges for incorporating Chl_leaf in future terrestrial biosphere models.

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chlleaf) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25°C (Vmax25), for the purpose of improving carbon and water flux estimation. Measurements of Chlleaf and Vmax25 were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chlleaf-based Vmax25 in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant Vmax25, leaf area index (LAI)-based Vmax25 or specific leaf area (SLA)-based Vmax25. The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8gCm−2d−1 in spring and from between 2.1–2.8 to 1.8gCm−2 d−1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8mmd−1 to 0.6mmd−1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled Vmax25 in sunlit leaves is 34% larger than in the shaded leaves of the same Chlleaf, which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for Vmax25 in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain Vmax25 and reduce the uncertainties in GPP and ET simulations.

In this study, high-quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process-based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun-induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment-2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single-cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater-stressed areas are included.

Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO₂ levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013-2015), approximately every 10 days for leaf chlorophyll content (Chl_Leaf ) and canopy structure. Leaf nitrogen (N_Area ) was also measured during 2014. Leaf photosynthesis was measured during 2014-2015 using a Li-6400 gas-exchange system, with A-Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and N_Area (R²  = 0.62, P < 0.001), whereas Chl_Leaf demonstrated a much stronger correlation with Vcmax25 (R²  = 0.78, P < 0.001). The relationship between Chl_Leaf and N_Area was also weak (R²  = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive Chl_Leaf . TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple N_Area -Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that Chl_Leaf provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

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.

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.

Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO₂ and climate change. By extension, it has been suggested that declining carboxylation capacity (V_cmax ) and leaf N content in enhanced-CO₂ experiments and satellite records signify increasing N limitation of primary production. We predicted V_cmax using the coordination hypothesis and estimated changes in leaf-level photosynthetic N for 1982-2016 assuming proportionality with leaf-level V_cmax at 25°C. The whole-canopy photosynthetic N was derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for V_cmax , and converted to annual N demand using estimated leaf turnover times. The predicted spatial pattern of V_cmax shares key features with an independent reconstruction from remotely sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.27% yr^-1 , while observed leaf (total) N declined by 0.2-0.25% yr^-1 . Predicted global canopy N (and N demand) declined from 1996 onwards, despite increasing LAI. Leaf-level responses to rising CO₂ , and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7 ± 1.2 PgC in 2015, but a slight positive anomaly of 0.05 ± 0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1 ± 0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22 ± 0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33 ± 0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most. We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

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.