Chenyu Bian

The CO ₂ fertilization effect, quantified as the sensitivity of global photosynthesis to elevated atmospheric CO ₂ concentration ( β _GPP ), is likely overestimated in the current generation of Earth system models. An emerging hypothesis is that coordinated acclimation of leaf structural and physiological traits, represented by specific leaf area (SLA) and area‐based leaf nitrogen content ( N _a ), can reduce photosynthetic sensitivity to elevated CO ₂ , thereby lowering simulated β _GPP in these models. This study integrated experimental data with the Community Atmosphere Biosphere Land Exchange (CABLE) model to investigate how coordinated trait acclimation modifies β _GPP . The global meta‐analysis revealed that elevated CO ₂ decreases the intercept of the relationship between SLA and N _a . Incorporating this relationship into the CABLE model reduced simulated β _GPP by 7.1% globally, with substantial spatial variation. The regional variation was primarily driven by spatial differences in aridity (48.2%), leaf area index (41.4%), and maximum carboxylation rate ( v _cmax , 5.5%), with the coordinated trait effect on gross primary production decreasing along the aridity gradient. Our findings identify coordinated leaf trait acclimation as an important mechanism for refining simulated β _GPP , highlighting the need for trait‐based mechanisms in Earth system models to better predict ecosystem responses to global change.

Peatlands store about one‐third of total global soil carbon. Vegetation composition strongly regulates peatland carbon dynamics. Global warming and climate‐driven ecohydrological changes are expected to alter peatland vegetation composition, necessitating accurate simulation of vegetation dynamics to predict future fate of peatland carbon. We incorporated six plant functional types (PFTs) into the ORCHIDEE‐PEAT model to represent bryophytes (mosses), C3 graminoids (sedges and grasses), boreal broadleaf deciduous shrubs, boreal needleleaf evergreen trees, tropical evergreen and raingreen (water‐driven deciduous) trees growing in peatlands. The introduction and elimination of each PFT in response to bioclimatic conditions, as well as sapling establishment, growth, mortality, and competition among PFTs, are explicitly modeled. Simulated vegetation distributions align well with site‐level observations from West Siberian wetlands, where extensive vegetation composition measurements are available for model evaluation. The model slightly overestimated gross primary productivity (GPP) across 60 sites. Evaluation using global satellite‐derived land cover, leaf area index and GPP data was encouraging, though challenges lie in the lack of observational data specific to peatlands. From 1901 to 2020, simulated tropical peatland vegetation composition remains relatively stable. In northern peatlands, as a result of warming and declining water table, bryophyte and C3 graminoid cover decrease by 0.2 (13%) and 0.1 (13%) million km ² , respectively, while shrub and tree cover increase by 0.3 (75%) and 0.03 (2%) million km ² , respectively. The impacts of these vegetation shift on peatland carbon balance can be explored in future studies using the model, which integrates peatland vegetation dynamics with peatland‐specific hydrology and carbon cycling.

Subtropical forests play a crucial role in the global carbon cycle, yet their carbon sink capacity is significantly constrained by phosphorus availability. Models that omit phosphorus dynamics risk overestimating carbon sinks, potentially undermining the scientific basis for carbon neutrality strategies. In this study, we developed TECO-CNP Sv1.0, a coupled carbon-nitrogen-phosphorus model based on the Terrestrial ECOsystem (TECO) model, which explicitly captures key biogeochemical interactions and nutrient-regulated carbon cycling. The model simulates how plant growth and carbon partitioning respond to both external soil nutrient availability and internal physiological constraints, enabling plant acclimation to varying nutrient conditions. Using observations from a phosphorus-limited subtropical forest in East China, we first evaluated the model’s performance in estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model more accurately reproduced the observed pools of plant and soil C, N, and P. To systematically optimize model parameters and reduce uncertainties in predictions, we further incorporated a built-in data assimilation framework for parameter optimization. The CNP model with optimized parameters significantly improved carbon flux estimates, reducing root mean square errors and enhancing concordance correlation coefficients for gross primary productivity, ecosystem respiration, and net ecosystem exchange. By explicitly incorporating phosphorus dynamics and data assimilation, this study provides a more accurate and robust framework for predicting carbon sequestration in phosphorus-limited subtropical forests.

Ecosystem respiration (ER) is the second‐largest terrestrial carbon flux, yet ecosystem models often fail to capture its variability under climatic extremes. The increasing frequency and severity of precipitation and drought extremes pose substantial challenges to accurately predicting ER. It remains unclear whether parameters calibrated under normal climates can reliably predict ER under extreme events. Here, we used long‐term eddy covariance data from a semi‐arid grassland to investigate the predictability of conventional linear and non‐linear microbial models. Both models were parameterized using a Monte Carlo Markov Chain assimilation approach based on data from normal climatic years. However, both exhibited poor performance in simulating daily ER during extreme drought and wet years, due to significant parameter divergence between normal and extreme years. We derived model parameters for extreme drought and wet years, revealing pronounced divergence: all parameters in the linear and microbial model varied significantly between normal and extreme years, with ∼29% displaying high variability (coefficient of variation >0.3). Furthermore, principal component analysis revealed substantial parameter divergence among hydrological regimes. Sensitivity analysis showed 93% of parameters exhibit asymmetric responses in extreme drought and wet years. These results indicate that fixed parameters calibrated under normal climatic conditions cannot represent the emergent properties of ecosystems during extreme events. Our findings highlight that varying parameters are not merely a technical adjustment but a fundamental requirement for improving the predictability of ER under climate extremes.

Grasslands, Earth’s most widespread terrestrial ecosystems, are vital for global carbon sequestration and food security. A key indicator of these functions is vegetation growth peak, reflecting maximum seasonal productivity. However, whether this growth peak consistently increases across global grasslands remains unknown. Here, using satellite-derived vegetation data from 1982 to 2021, we reveal a widespread increase in grassland growth peaks, with 71% (20/28) IPCC climate regions exhibiting significant trends. However, this trend reverses between 1998 and 2009 in 64% of regions, notably across the Tibetan Plateau and East Asia. The reversal is supported by a global gross primary productivity dataset generated via machine learning, trained on eddy covariance flux data. The interruption is primarily associated with a global-scale decadal drought. These findings demonstrate that persistent drought can disrupt the upward trajectory of grassland growth peaks, posing a spatially uneven but widespread threat to ecological functions of grasslands under climate change.

Subtropical forests play a crucial role in the global carbon cycle, yet their carbon sink capacity is significantly constrained by phosphorus availability. Models that omit phosphorus dynamics risk overestimating carbon sinks, potentially undermining the scientific basis for carbon neutrality strategies. In this study, we developed TECO-CNP Sv1.0, a coupled carbon-nitrogen-phosphorus model based on the Terrestrial ECOsystem (TECO) model, which explicitly captures key biogeochemical interactions and nutrient-regulated carbon cycling. The model simulates how plant growth and carbon partitioning respond to both external soil nutrient availability and internal physiological constraints, enabling plant acclimation to varying nutrient conditions. Using observations from a phosphorus-limited subtropical forest in East China, we first evaluated the model's performance in estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model more accurately reproduced the observed pools of plant and soil C, N, and P. To systematically optimize model parameters and reduce uncertainties in predictions, we further incorporated a built-in data assimilation framework for parameter optimization. The CNP model with optimized parameters significantly improved carbon flux estimates, reducing root mean square errors and enhancing concordance correlation coefficients for gross primary productivity, ecosystem respiration, and net ecosystem exchange. By explicitly incorporating phosphorus dynamics and data assimilation, this study provides a more accurate and robust framework for predicting carbon sequestration in phosphorus-limited subtropical forests.

Subtropical forests play a crucial role in global cycle, yet their carbon sink capacity is significantly constrained by phosphorus availability. Models that omit phosphorus dynamics risk overestimating carbon sinks, potentially undermining the scientific basis for carbon neutrality strategies. In this study, we developed TECO-CNP Sv1.0, a coupled carbon-nitrogen-phosphorus model based on the Terrestrial ECOsystem (TECO) model, explicitly capturing key biogeochemical interactions and nutrient-regulated carbon cycling. The model simulates how plant growth and carbon partitioning respond to both external soil nutrient availability and internal physiological constraints, enabling plant acclimation to varying nutrient conditions. Using observations from a phosphorus-limited subtropical forest in East China, we first evaluated model performance on estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model better reproduced observed plant and soil C, N, and P pools. To systematically optimize model parameters and reduce uncertainties in predictions, we further incorporated a built-in data assimilation framework for parameter optimization. The CNP model with optimized parameters significantly improved carbon flux estimates, reducing root mean square errors and enhancing concordance correlation coefficients for gross primary productivity, ecosystem respiration, and net ecosystem exchange. By explicitly incorporating phosphorus dynamics and data assimilation, this study provides a more accurate and robust framework for predicting carbon sequestration in phosphorus-limited subtropical forests.

Over the past four decades, global land greening has promoted carbon (C) storage in terrestrial ecosystems. However, whether the future trajectory of this positive greening effect on ecosystem C sequestration is sustainable under various climate scenarios remains uncertain. Here, using projections from ten Earth system models, we found divergent trends in the relationship between global land greening and C storage among three distinct shared socioeconomic pathways (SSPs). We identified global transition times for their positive relationships, which will occur in 2034, 2038, and 2048 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. We found a widespread decoupling trend of vegetation greening and ecosystem C storage under medium to high emission scenarios, particularly in the tundra, boreal forest, and grassland regions, by delving deeper into six transition modes. These findings underscore the highly uncertain role of vegetation greening in land C sequestration under prospective climate change scenarios.

Subtropical forests, recognized for their intricate vertical canopy stratification, exhibit high resistance to extreme drought. However, the response of leaf phenology to drought in the species‐rich understory remains poorly understood. In this study, we constructed a digital camera system, amassing over 360,000 images through a 70% throughfall exclusion experiment, to explore the drought response of understory leaf phenology. The results revealed a significant advancement in understory leaf senescence phenology under drought, with 11.75 and 15.76 days for the start and end of the leaf‐falling event, respectively. Pre‐season temperature primarily regulated leaf development phenology, whereas soil water dominated the variability in leaf senescence phenology. Under drought conditions, temperature sensitivities for the end of leaf emergence decreased from −13.72 to −11.06 days °C⁻¹, with insignificance observed for the start of leaf emergence. Consequently, drought treatment shortened both the length of the growing season (15.69 days) and the peak growth season (9.80 days) for understory plants. Moreover, this study identified diverse responses among intraspecies and interspecies to drought, particularly during the leaf development phase. These findings underscore the pivotal role of water availability in shaping understory phenology patterns, especially in subtropical forests.