Vegetation carbon turnover time (τveg) dominates the uncertainty in terrestrial carbon cycle dynamics. Reports have shown that the τveg of mature or old-growth forests in North America and Europe has decreased because of faster carbon loss under global climate change. However, the temporal trend of τveg in widespread younger forests, which exhibit different growth patterns, remains inconclusive. Here, we consistently revealed a significant overall increase in τveg (0.025 ± 0.002 years per year) across China’s forests that are characterized by a relatively young forest age structure, using multisource data-model assimilation and long-term network observations. In young forests that receive high nitrogen deposition, increasing levels of CO2 accelerate vegetation growth, causing it to grow faster than it dies or decomposes, leading to increases in the τveg. The effects of forest age on τveg dynamics and the high sensitivity of enhanced carbon sinks to τveg should be incorporated into future land surface models to ensure that the τveg dynamics and their impacts on terrestrial carbon cycling and climate mitigation are accurately assessed. Our results also provide valuable insights for forest management aimed at enhancing carbon retention and sequestration through optimizing age-related stand dynamics.

Human activities substantially reduce net ecosystem productivity (NEP) globally, yet debates remain over the contributions of land-use and land-cover change (LUCC), such as afforestation (afforestation and reforestation), versus non-LUCC ecosystem management (EM; e.g., forest tending, mountain forest restoration, and fire control). Here, we developed an analytical framework by harmonizing structurally consistent remote sensing-driven and climate-driven ecological process models to quantify the dynamic effects of LUCC and eight EM types on NEP from 2001 to 2021 in China by isolating anthropogenic effects from global change factors. We found that the NEP, which averaged 327 Tg C yr–1 across the 9.6×106 km2 country, increased at a rate of 16.1 Tg C yr–2. Forest management, including forest tending (5.53 Tg C yr–2) and mountain forest restoration (2.7 Tg C yr–2), primarily drove carbon sink increases. Although afforestation induced greater NEP growth per unit area, the total effect of forest management—due to its much greater coverage—was 4.14 times greater than that of afforestation (1.6 Tg C yr–2). Notably, the acceleration of China’s NEP after 2010 was closely associated with intensified forest management efforts. Moreover, the rate of NEP gains caused by forest tending investment (8.54 kg C yr–2 $–1) was much greater than that caused by afforestation investment (0.25 kg C yr–2 $–1). Our findings highlight the critical role of forest management in cost-effectively enhancing the carbon sink. This has important implications for global forest management strategies and achieving net-zero emissions. Forest management primarily drives the carbon sink enhancement in China from 2001 to 2021, achieving carbon gains at a rate over 4 times higher than afforestation, according to an integrated framework combining remote sensing-driven and climate-driven process models.

Abstract The terrestrial carbon sink depends not only on how much biomass forests accumulate, but also on how long that carbon remains stored in vegetation. This is known as vegetation carbon turnover time ( τ veg ), which varies with forest age, structure and climate. Furthermore, τ veg is widely expected to decline under climate change as warming and disturbance accelerate biomass loss. However, most evidence comes from mature forests, even as global forest demographics shift due to reforestation, disturbance and land‐use change. Younger forests differ in growth rates, mortality patterns and stand structure, which may create distinct turnover trajectories with rapid biomass accumulation potentially slowing τ veg . Here, we evaluated this possibility using 17 years of in situ data from a network of temperate, subtropical and tropical forest plots across China. We found that τ veg declined over time in mature forests but increased in younger ones. These opposing trends were shaped by age‐specific trade‐offs between growth and mortality, mediated by stand structure and environmental conditions. Synthesis . Our results demonstrate that forest age strongly influences vegetation carbon turnover and must be integrated into models to improve predictions of terrestrial carbon sink dynamics.

Vegetation productivity is vulnerable to climate change in Central Asia, where ecosystems are projected to experience a stronger warming trend than global mean and more severe climate extremes. However, how these future climate changes influence the interannual variation of net primary production (NPP) in Central Asia is still limited. In this study, we used nine Coupled Model Intercomparison Project models to investigate the interannual variability (IAV) of NPP in Central Asia and its attributions to region, climatic factors, and climate extremes in the future (2021-2100) under four shared socioeconomic pathways scenarios (SSPs). We found that the NPP interannual variation would be larger under higher emissions scenarios with larger extremes in precipitation. The standard deviation of NPP IAV would increase by 21% under SSP5-8.5 compared to that under SSP1-2.6. Precipitation IAV exerts a larger contribution than temperature IAV to NPP IAV in Central Asia, due to the strong response of NPP to precipitation. Around 92% of the future NPP IAV can be associated with NPP extremes over Central Asia. Dry and wet conditions are the main climate extremes causing NPP extremes in Central Asia, especially in north and southeast of Central Asia. Dry conditions cause 33% of negative NPP extremes and wet conditions cause 37% of positive NPP extremes under SSP5-8.5. Our findings underscore the critical role of future climate extremes, especially the water conditions, in causing the risk of ecosystem stability posed by future climate changes in Central Asia.

Enhancing net ecosystem productivity (NEP) and water yield (WY) services is critical for sustainable ecosystem management and water security. In 2010, China established National Key Ecological Function Zones (NKEFZs) to restore ecosystems. However, their impacts on carbon-water services dynamics remains poorly quantified. Using a calibrated process-based model (CEVSA-ES), we assessed the effects of vegetation restoration (greening and vegetation type changes) and global climate changes (climate change, elevated CO2, and nitrogen deposition) on the shifts in NEP and WY trends relative to NKEFZ implementation. Over 2001-2021, both NEP and WY exhibited increasing trends (5.1 Tg C yr(-2) and 0.3 mm yr(-1), respectively), and were the most evident in the water and soil conservation zones, biodiversity maintenance zones, and water conservation zones, respectively. Notably, following the NKEFZs establishment, NEP growth accelerated remarkably from 1.9 Tg C yr(-2) (2001-2010) to 5.6 Tg C yr(-2) (2011-2021), particularly within water conservation zones, whereas WY trends reversed from a decline (-0.5 mm yr(-1)) to an increase (0.9 mm yr(-1)). While greening drove NEP growth and precipitation governed WY changes during 2001-2021, the post-2010 NEP acceleration was jointly controlled by vegetation restoration and global climate change. Conversely, the WY trend reversal was primarily attributed to shifts in precipitation trends. These findings provide critical insights into how ecological policies can synergistically enhance carbon and water services under a changing climate, offering important implications for sustainable ecological restoration and natural climate solutions.

Comprehensive, reliable, and long-term radiation data are imperative for ecological model development and improvement. However, the existing radiation datasets either lack comprehensiveness or have relatively short temporal coverage, and few provide information regarding their uncertainties. In the present study, we generated a comprehensive radiation dataset for China from 1981 to 2020 by combining in situ observations from the China Meteorological Administration (CMA) and the Chinese Ecosystem Research Network (CERN) with an advanced Gaussian process regression (GPR) approach, covering global radiation, diffuse radiation, photosynthetically active radiation (PAR), and diffuse PAR, along with corresponding uncertainty estimates, and independently validated the robustness of the dataset. The results indicated that the generated radiation dataset effectively captured the interannual and seasonal dynamics of the observations, with R2 values of 0.69 and 0.89, respectively, and the spatial uncertainties of the dataset were relatively low, around 1%. The dataset exhibited evident spatial heterogeneity, higher in the southwest and lower in the northeast, and the annual mean values of global radiation, diffuse radiation, PAR, and diffuse PAR were 5355.63, 2411.11, 2075.78, and 1018.99 MJ m-2 yr, respectively. From 1981 to 2020, global radiation and PAR exhibited decreasing trends primarily driven by water vapor, with decline rates of-3.62 and-1.37 MJ m-2 yr, respectively, whereas changes in their diffuse components were modest, at-0.39 and-0.21 MJ m-2 yr, mainly attributable to the combined influence of aerosol optical depth (AOD) and cloud cover. Our study provides a comprehensive and consistent spatiotemporal dataset, which is of great value for investigating the impact of radiation on terrestrial ecosystems and for conducting ecosystem modeling and uncertainty analyses.

Plant spring phenology advancements have been broadly observed, but the change in autumn phenology has varied greatly among different regions and species under global warming. Moreover, how plant phenology responds to climate change in grasslands and deserts is not well understood compared with that in forests. Here, we used long-term (2005 2020) phenological and climatic in situ observation data from six grassland and desert sites in China to analyze temporal trends in the start (SOS) and end (EOS) of the growing season for 27 herbaceous and shrub species and their responses to climatic factors. The results demonstrated that 70

Accurate simulation of terrestrial ecological carbon cycles is crucial for global climate change and ecosystem management. Process-based carbon models have high interpretability, but suffer from insufficient accuracy and slow computation due to fixed parameters. In contrast, deep-learning carbon models achieve high accuracy, but disregard physical principles, which prevents ecologists from explaining ecosystem dynamics. We propose NeuroDALEC, an interpretable framework that embeds the DALEC carbon-cycle model within a neural network, enabling differentiable computation of ecological processes. Key parameters and ensemble learning strategies are designed, and mass-conserving carbon pool state transition equations are introduced to ensure physical consistency. Experiments show NeuroDALEC outperforms existing models in both accuracy and efficiency. Moreover, it provides sufficient interpretability by predicting all components of the carbon cycle. Deployed in a real-time carbon assimilation system, NeuroDALEC supports daily carbon forecasting and decision-making. This work contributes to the United Nations' Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land). The source code is available at: https://osf.io/ubcv4/overview?view_only=ac8753c98677438180e82926ae898aba.

Carbon (C), nitrogen (N), and phosphorus (P) are fundamental elements for plant growth and metabolism. Leaf C-N-P stoichiometry provides critical insights into plant nutrient use efficiency and ecosystem-scale carbon-nutrient interactions, particularly under global change. However, comprehensive long-term records of leaf C, N, and P concentrations remain scarce. Here we compiled and publicly released the Leaf Carbon-Nitrogen-Phosphorus Concentrations in China's Forests (CNP-China) dataset, containing 628 standardized records from 52 dominant tree species across 11 representative China's forest ecosystems from 2005 to 2020. All samples were collected and analyzed following the Chinese Ecosystem Research Network (CERN) protocols, with accompanying plot-level geographic and environmental data, species taxonomy and functional traits, and surface soil properties (0-20 cm depth). The dataset underwent rigorous quality control, including unit harmonization, error checking, and outlier detection, and is provided in accessible CSV format. CNP-China enables critical assessments of stoichiometric responses to environmental change, supports improved ecosystem modeling, and facilitates cross-biome comparisons of nutrient cycling dynamics, providing an essential foundation for understanding forest carbon-nutrient feedback under global change.

Ecosystem engineering-driven land-use change (LUCC) and global changes (climate change, elevated CO2, and nitrogen deposition) profoundly affect net ecosystem productivity (NEP), yet their temporal dynamics remain unclear. Using a process-based model, we evaluated their effects on NEP in China's ecosystem engineering zones from 2001 to 2021. Results showed an average NEP of 256 Tg C yr-1, increasing at a rate of 11.3 Tg C yr-2, primarily driven by afforestation (effect increasing at 2.9 Tg C yr-2) and elevated CO2 (effect increasing at 5.6 Tg C yr-2)-higher than that of climate change (0.4 Tg C yr-2). LUCC-global change interactions strengthened, contributing 27 % (2001-2010) to 32.1 % (2011-2021) of the LUCC effect, primarily due to increasing CO2 and shifting precipitation. Furthermore, we identified optimal climatic spaces (precipitation: 750-2600 mm, temperature: 10-25 degrees C) that enhanced interactions. Restricting afforestation to these areas could enhance NEP by 65 %. Our findings highlight afforestation's persistent carbon sink potential under global change, providing key insights for optimizing sustainable land management.

The aboveground net primary production (ANPP) and precipitation use efficiency (PUE) play essential roles in governing the carbon and water cycles of grassland ecosystems, but the associations among the ANPP, PUE and climate variation remain a subject of controversy in the grasslands of Central Asia. In this study, the variability in grassland ANPP and PUE in the Central Asian region was investigated based on ANPP (2002-2019) estimated by a machine learning model. In Central Asian grasslands, the average values of ANPP and PUE were 92.76 g m(-2) yr(-1) and 0.42 g m(-2) mm(-1), respectively. A positive relationship was observed between ANPP and precipitation in the Central Asian region, indicating the role of precipitation in grassland productivity improvement; however, the relationship is not a simple linear relationship. Compared with the semiarid region, grasslands in the arid region (precipitation <200 mm) had a higher PUE and showed a lower sensitivity of ANPP to precipitation. The analysis of interannual variability in PUE and ANPP resistance suggested that the ability to regulate PUE will significantly impact grassland resistance to environmental changes in arid and semiarid ecosystems. This study provides new perspectives for comprehending the variations in ANPP and PUE among arid and semiarid grasslands of the Central Asian region.

The leaf nitrogen (N) to phosphorus (P) ratio (N:P) is a critical indicator of nutrient dynamics and ecosystem function. Investigating temporal variations in leaf N:P can provide valuable insights into how plants adapt to environmental changes and nutrient availability. However, limited research has been conducted on long-term temporal leaf N:P variation over a range of temperature zones. Using long-term monitoring data from the Chinese Ecosystem Research Network (CERN), we investigated temporal changes in leaf N and P stoichiometry for 50 dominant tree species from 10 typical forest sites across temperate and subtropical regions, and identified the underlying mechanisms driving these changes. For both regions combined, leaf P concentration of the 50 dominant tree species decreased (20.6%), whereas leaf N:P increased (52.0%) from 2005 to 2020. Leaf P decreased and leaf N:P increased in 67% and 69% of the tree species, respectively. The leaf N:P increase was primarily driven by the tree species in eastern subtropical forests, where global change factors and soil nutrients explained 68% of leaf N:P variation. The P limitation exhibited by tree species in eastern subtropical forest ecosystems intensified over time, and elevated temperature and CO2 levels, coupled with decreased soil available P concentrations, appear to be the main factors driving long-term leaf N:P increases in these forests. Investigating long-term variations in soil nutrients together with global change factors will improve our understanding of the nutrient status of forest ecosystems in the context of global change and will support effective forest ecosystem management.

Rare and endangered plants play an important role in the stability of ecosystems and genetic diversity,and their conservation is an essential component of biodiversity protection.The Chinese Ecosystem Research Network(CERN)covers a wide range of vegetation types,including a large number of rare and endangered plants.The protection and research of rare and endangered plant resources in the CERN are significant for economic development,maintaining ecological balance and biodiversity conservation in China.Based on the plant species list dataset of the CERN,this study compiled a list of rare and endangered vascular plants in the CERN,including 189 rare and endangered plants distributed across 14 ecological field stations.With reliable data sources and sound processing methods,this dataset ensures quality,and can serve as foundational data for the research on plant diversity at the national scale,and provide important reference for the ecological field stations in strengthening the protection,monitoring and utilization of rare and endangered plants.

Vegetation restoration in the Loess Plateau (LP) of China is driven by atmospheric environmental changes (climate change, rising CO _2 , and nitrogen deposition), land cover change (LCC) from ecological restoration projects (ERPs), and change in forest age. However, the dominant factors influencing vegetation restoration remain controversial. This study improved the Deep Crossing network by integrating bidirectional long short-term memory (Bi-LSTM) with embedding, creating the Deep Crossing LSTM Age (DC-LSTM-Age) network. It incorporates land cover type, forest age, and atmospheric environmental factors to reconstruct the leaf area index (LAI). We investigated the LAI increase (greening) driven by various factors and their dynamics in the Grain for Green Project (GGP) regions of the LP from 2001 to 2021. Results showed that DC-LSTM-Age network effectively simulated LAI values and its temporal dynamics in LCC regions, with superior validation performance ( R ^2 = 0.87) compared to the Deep Crossing LSTM network ( R ^2 = 0.84) that excluded forest age and the Bi-LSTM network ( R ^2 = 0.79) that excluded forest age and land cover type. The greening trend in afforested regions (GGP-Forest, 0.013 m ^2 m ^−2 yr ^−1 ) was much larger than in grass revegetation regions (GGP-Grass, 0.005 m ^2 m ^−2 yr ^−1 ). Dominant drivers varied by restoration strategy: in GGP-Forest, LCC was the primary driver (0.25 m ^2 m ^−2 , 52.9%), with an increasing impact over time. In GGP-Grass, atmospheric environmental changes dominated (0.127 m ^2 m ^−2 , 78.5%), led by climate change (0.064 m ^2 m ^−2 , 39.4%), CO _2 rising (0.056 m ^2 m ^−2 , 35%), and nitrogen deposition change (0.007 m ^2 m ^−2 , 4.1%). The CO _2 fertilization effect showed signs of saturation. This research highlights the crucial role of ERPs in LAI increase.

Interannual variability (IAV) of terrestrial carbon uptake is a major contributor to the variation of atmospheric CO2. With the influence of the East Asian monsoon, future climate variability would significantly increase in China. However, how these future changes will modulate the IAV of China's terrestrial carbon sinks remains unclear. Here, we analyzed the IAV of China's terrestrial net ecosystem productivity (NEPIAV) and investigated the potential impacts of climate change under various scenarios during the 21st century using the outputs from nine Coupled Model Intercomparison Project Phase 6 models. The results reveal that China's terrestrial NEPIAV would be enhanced under higher emissions scenarios from 2015 to 2100. The standard deviation of national NEPIAV under the SSP585 scenario rises by 12% compared with SSP126. The most prominent contribution to this enhancement in the total NEPIAV comes from a larger NEP IAV in summer (10%), particularly in the subtropical-tropical monsoonal climate zone in China. Moreover, the enhancement is largely attributed to the intensified IAV in temperature and precipitation in the monsoonal climate zones as well as the heightened sensitivity to them, especially in the ecosystems of the subtropical-tropical monsoonal zone. Compared with monsoonal precipitation, IAV of temperature in the subtropical-tropical monsoonal climate zone also plays an important role in NEPIAV under higher emissions scenarios. Our results highlight the crucial influence of future fluctuations in monsoon climate systems on terrestrial carbon sink IAV and the urgency of reducing the uncertainties of Earth system models in predicting both climate in monsoon regions and the responses of carbon cycling processes to temperature.

China has experienced a shift in nitrogen (N) deposition from an upward trend since 1980s to stabilized since 2001–2005 and decline in recent years due to N management. Global atmospheric chemical transport models tend to underestimate the magnitude of N deposition in China and fail to reproduce such a shift. How do underestimation and trend shift in N deposition influence China's terrestrial carbon (C) uptake remains unclear. Here we used a new N deposition data set and three independent methods to investigate the effect of N deposition on terrestrial C uptake in China. We found that the magnitude and trend of China's terrestrial C sink induced by N deposition (∆C Ndep ) would be underestimated during 1990–2015 when using commonly used global atmospheric N deposition data sets. Despite the decrease in N deposition trend, the increasing rate of ∆C Ndep changed from 4.42 Tg C yr −2 in 1990–2005 to 5.64 Tg C yr −2 in 2006–2015, which was dominated by subtropical and tropical monsoon region. The interactive effect of N deposition with other environmental factors has a greater impact on the trend of ∆C Ndep than direct effect. Our results highlight the rising terrestrial C uptake as N deposition stabilizes and the crucial role of interaction mechanisms among global change factors in assessing the impact of declining reactive N inputs on China's future land C sinks under C neutrality targets.

Abstract The large variation in net ecosystem productivity (NEP) with forest age was dominated by the dynamics of net primary productivity (NPP)–which in turn was determined by the different response slopes of gross primary productivity (GPP) and autotrophic respiration (Ra) with forest age. However, only few models can comprehensively represent the impacts of forest age and global changes including land‐use change, climate change, nitrogen deposition, and atmospheric CO2 from the perspective of ecological processes. Based on a process‐based model (CEVSA‐ES) that included these global changes, we developed an ecosystem carbon sink assessment model considering forest age dynamics (CEVSA‐AgeD) using satellite‐based relationships between GPP (or Ra) and forest age to constrain photosynthesis and autotrophic respiration processes. Subsequently, we used a model data‐fusion framework combined with carbon flux observations to calibrate the model. The calibrated CEVSA‐AgeD model performed well in simulating seasonal (R2 values for GPP, ecosystem respiration, and NEP were 0.86, 0.79, and 0.66, respectively) and annual carbon flux changes (R2 of GPP, ecosystem respiration, and NEP were 0.83, 0.77, and 0.67, respectively). The magnitude of average NEP in China estimated using this model was 0.35 ± 0.005 TgC/yr from 2001 to 2021, which was close to previous estimates, and the dynamics of forests age increased NEP by 87–92 TgC/yr. These results indicate that the CEVSA‐AgeD model performed well in simulating carbon fluxes at the site and regional scales and that it was necessary to incorporate the effect of forest age dynamics on carbon cycling processes into process‐based models.

China's terrestrial ecosystem carbon sink (TCS) is crucial for the global carbon budget. However, little is known how the enhanced human disturbances and increased extreme climate events may potentially destabilize TCS under warming climate. Using three process-based ecosystem models, we simulated the spatiotemporal variations of China's terrestrial net ecosystem productivity (NEP) from 2000 to 2020. We found that 26.7 % of the land area exhibit simultaneous increases in NEP temporal variability and autocorrelation during this period, indicating an increasing risk of TCS destabilization. Particularly, the southeastern subtropical monsoon region in China emerged as a hot-spot of potentially increasing NEP instability, despite its high carbon sink capacity, both NEP temporal variability and autocorrelation in this area exhibit a notable upward trend. Climate change, notably increasing precipitation and its temporal variation, appeared to be the primary driver of this instability. This harbinger implies that a regime shift in carbon sink capacity may occur as the warming climate continues to push it to the verge of stability.