The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980-2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China's National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a-1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a-1 averaged through 2010-2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China's terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010-2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

Global patterns of leaf construction traits and their covariation along climate and soil environmental gradients.

Leaf functional traits and their covariation underlie plant ecological adaptations along environmental gradients, but there is limited information on the global covariation patterns of key leaf construction traits. To explore how leaf construction traits co-vary across diverse climate and soil environmental conditions, we compiled a global dataset including cell wall mass per unit leaf mass (CWmass ), leaf carbon (C) and calcium (Ca) concentrations, and specific leaf area (SLA) for 2348 angiosperm species from 340 sites world-wide. Our results demonstrated negative correlations between leaf C and Ca concentrations and between leaf C and SLA across diverse nongraminoid angiosperms. Leaf C concentration increased with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) and with decreasing soil pH and calcium carbonate (CaCO3 ) concentration, whereas leaf Ca concentration and SLA exhibited the opposite responses to these environmental variables. The covariations of leaf Ca-C and of leaf SLA-C were stronger in habitats with lower MAT and MAP, and/or higher soil CaCO3 content. This global-scale analysis demonstrates that the leaf C and Ca concentrations and SLA together govern the C and biomass investment strategies in leaves of nongraminoids. We conclude that environmental conditions strongly shape leaf construction traits and their covariation patterns.

Adaptive Carbon Allocation by Plants Enhances the Terrestrial Carbon Sink.

Carbon allocation is one of the most important physiological processes to optimize the plant growth, which exerts a strong influence on ecosystem structure and function, with potentially large implications for the global carbon budget. However, it remains unclear how the carbon allocation pattern has changed at global scale and impacted terrestrial carbon uptake. Based on the Community Atmosphere Biosphere Land Exchange (CABLE) model, this study shows the increasing partitioning ratios to leaf and wood and reducing ratio to root globally from 1979 to 2014. The results imply the plant optimizes carbon allocation and reaches its maximum growth by allocating more newly acquired photosynthate to leaves and wood tissues. Thus, terrestrial vegetation has absorbed 16% more carbon averagely between 1979 and 2014 through adjusting their carbon allocation process. Compared with the fixed carbon allocation simulation, the trend of terrestrial carbon sink from 1979 to 2014 increased by 34% in the adaptive carbon allocation simulation. Our study highlights carbon allocation, associated with climate change, needs to be mapped and incorporated into terrestrial carbon cycle estimates.

Global validation of a process-based model on vegetation gross primary production using eddy covariance observations.

Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year(-1) (mean value ± standard deviation) across the vegetated area for the period 2000-2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year(-1)). To evaluate the uncertainty introduced by the parameter Vcmax, which represents the maximum photosynthetic capacity, we inversed Vcmax using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed Vcmax values, the simulated global GPP increased by 16.5 Pg C year(-1), indicating that IBIS model is sensitive to Vcmax, and large uncertainty exists in model parameterization.

Satellite-based analysis of evapotranspiration and water balance in the grassland ecosystems of Dryland East Asia.

The regression tree method is used to upscale evapotranspiration (ET) measurements at eddy-covariance (EC) towers to the grassland ecosystems over the Dryland East Asia (DEA). The regression tree model was driven by satellite and meteorology datasets, and explained 82% and 76% of the variations of ET observations in the calibration and validation datasets, respectively. The annual ET estimates ranged from 222.6 to 269.1 mm yr(-1) over the DEA region with an average of 245.8 mm yr(-1) from 1982 through 2009. Ecosystem ET showed decreased trends over 61% of the DEA region during this period, especially in most regions of Mongolia and eastern Inner Mongolia due to decreased precipitation. The increased ET occurred primarily in the western and southern DEA region. Over the entire study area, water balance (the difference between precipitation and ecosystem ET) decreased substantially during the summer and growing season. Precipitation reduction was an important cause for the severe water deficits. The drying trend occurring in the grassland ecosystems of the DEA region can exert profound impacts on a variety of terrestrial ecosystem processes and functions.