Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO₂ fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and −28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle.

The maximum carboxylation rate (Vcmax) is a key parameter in determining the plant photosynthesis rate per unit leaf area. However, most terrestrial biosphere models currently treat Vcmax as constants changing only with plant functional types, leading to large uncertainties in modeled carbon fluxes. Vcmax is tightly linked with Ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco). Here we investigated the relationship between leaf chlorophyll and Rubisco (Chl‐Rub) contents within a winter wheat paddock. With chlorophyll as a proxy of Rubisco, a semimechanistic model was developed to model Vcmax₂₅ (Vcmax normalized to 25°C)_. The Chl‐Rub relationship was validated using measurements in a temperate mixed deciduous forest in Canada. The results showed that Rubisco was strongly correlated with chlorophyll (R² = 0.96, p < 0.001) for winter wheat since the absorption of light energy by chlorophyll and the amount of CO₂ catalyzed by Rubisco are tightly coupled. Incorporating the Chl‐Rub relationship into the semimechanistic model, the root mean square error of modeled Vcmax₂₅ was the lowest among all estimation models. The slopes of Chl‐Rub relations were almost consistent in the winter wheat and temperate forest, demonstrating the potential for using leaf chlorophyll content as a proxy of leaf Rubisco in modeling Vcmax₂₅ at large spatial scales. We anticipate that improving Vcmax₂₅ estimates over time and space will reduce uncertainties in global carbon budgets simulated by terrestrial biosphere models.

The maximum rate of carboxylation (Vcmax), a key parameter indicating photosynthetic capacity, is commonly fixed as a constant by vegetation types and/or varies according to empirical scaling functions in Earth system models (ESMs). As such, the setting of Vcmax results in uncertainties of estimated carbon assimilation. It is known that the coupling between leaf chlorophyll and Rubisco (ribulose-1,5-biphosphate carboxylase-oxygenase) contents can be applied to estimate Vcmax. However, how this coupling is affected by environmental changes and varies among plant functional types (PFTs) has not been well investigated yet. The effect of varying coupling between chlorophyll and Rubisco contents on the estimation of Vcmax is still not clear. In this study, we compiled data from 76 previous studies to investigate the coupling between Chlorophyll (Chl) and Rubisco (Rub), in different PFTs and under different environmental conditions. We also assessed the ability of a Rub-based semi-mechanistic model to estimate Vcmax normalized to 25 °C (Vcmax₂₅) based on the Rub–Chl relationship. Our results revealed strong, linear Rub-Chl relationships for different PFTs (R ² = 0.73, 0.67, 0.54 and 0.72 for forest, crop, grass and shrub, and C4 plants, respectively). The Rub–Chl slope of natural C3 PFTs was consistent and significantly different from those of crops and C4 plants. A meta-analysis indicated that reduced light intensity, elevated CO₂, and nitrogen addition strongly altered Rub/Chl. A semi-mechanistic model based on PFT-specific Rub–Chl relationships was able to estimate Vcmax₂₅ with high confidence. Our findings have important implications for improving global carbon cycle modeling by ESMs through the improved parameterization of Vcmax₂₅ using remotely sensed Chl content.

Atmospheric methane (CH₄) is an important greenhouse gas that can reflect variations of CH₄ emissions and sinks. This study aimed to detect spatial and temporal variations of atmospheric CH₄ concentrations in China during 2003–2021 based on CH₄ column-averaged dry-air mole fraction (XCH₄) products from three satellites, namely, Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT), and Copernicus Sentinel-5 Precursor (S5P). The results revealed that XCH₄ observed from three satellites showed high agreement in spatiotemporal variations and demonstrated good consistency with ground station measurements. The correlation coefficients (r) between the three satellites were 0.72 and 0.73, and the correlation coefficients for the ground stations were 0.79, 0.66, 0.03, 0.21, 0.70, and 0.80. The spatial distribution of XCH₄ in China was generally high in the east and low in the west and close to that of CH₄ emissions, indicating that CH₄ emission sources dominated the spatial variations of atmospheric XCH₄. From 2003 to 2006, XCH₄ remained stable with an annual growth rate of 0.51 ppb·yr⁻¹ and then abruptly increased with an overall growth rate of 6.96 ppb·yr⁻¹. There were obvious seasonal changes in XCH₄, with peaks in autumn and summer and nadir in winter and spring. These seasonal variations of XCH₄ were related to CH₄ emissions from rice planting. Rice cultivation areas generally had high XCH₄ concentrations, and the growth cycle of rice plants significantly contributed to seasonal variations of XCH₄ in the main rice planting areas. These results provide scientific data that could encourage decision-makers to enact policies and processes to reduce methane emissions.

Precipitation, a key determinant of soil moisture variations, plays an important role in regulating terrestrial carbon fluxes on multiple time scales. It is a critical meteorological forcing to drive terrestrial biosphere model (TBM), however, with a large uncertainty itself. We here investigated to what extent precipitation alone can cause uncertainties of model‐simulated carbon flux from terrestrial ecosystems to atmosphere (F_TA), based on eight precipitation products and a TBM, VEGAS. We find that the pattern of uncertainties in simulated F_TA obviously differs from the pattern of discrepancies in precipitation, owing to divergent water sensitivities of vegetation over different regions. Globally, the uncertainty in F_TA can be up to approximately 40.73% of the uncertainty in TRENDYv6 multi‐model simulated F_TA which is caused by model structural and parameter uncertainty. A good linear relationship emerges between global area‐averaged land climatological annual precipitation and simulated total F_TA with the slope of −0.0040 PgC yr⁻¹ per mm yr⁻¹ ( = 0.03; negative for carbon sink), where 70% is explained by the sensitivity over extra‐tropical Northern Hemisphere (NH). For seasonal cycle, compared to nearly constant inter‐precipitation spreads over tropics plus extra‐tropical southern hemisphere (Trop + SH), uncertainties in corresponding simulated F_TA show obvious seasonal differences with the relatively larger uncertainties in March‐April‐May (MAM) and August‐September‐October (ASO). For interannual variability, uncertainties in simulated total F_TA are, albeit smaller, nonnegligible, which are 40.61% (global), 38.17% (Trop + SH), and 29.63% (NH) of the TRENDYv6 inter‐model uncertainty, respectively. Therefore, generating better global precipitation product is important for reducing the uncertainty in simulating terrestrial carbon sinks.