Abstract Applying the four‐dimensional variational (4DVar) method in nonlinear systems faces conflicts between a lack of long‐term dynamical information using a short assimilation window and a trap of local minima using a long window. Here, an adjusted analysis approach that incorporates a thought of pseudo‐orbit analysis adjusting into 4DVar was proposed, resulting in the enhanced consistency between the initial condition (IC) and the long‐term dynamics of model, and the reduced risk of falling into local minima. This approach tries to find a truer orbit of weather state by minimizing the distance from the 4DVar‐based pseudo‐orbit to the long‐term model trajectory. Sensitivity experiments based on observing system simulation experiment reveal that the new approach with a proper iterative number can produce more reasonable analysis than 4DVar. Real observation experiments further exhibit that this approach can help 4DVar produce ICs that are more consistent with the long‐term dynamics and benefit the forecast skills.

Free amino acids (FAAs) in ambient PM2.5 samples were investigated at a forest site in the Qinling Mountains region of central China to explore their chemical characteristics, potential atmospheric processes, and sources across four seasons of 2021-2022. A total of 14 FAAs were quantitatively characterized using ultrahigh performance liquid chromatograph coupled with a high-resolution Orbitrap mass spectrometer. The mass concentrations of total FAAs varied from 6.4 to 48.5 ng m-3 (mean 23 f 11 ng m-3) over the study periods, with higher levels in spring and summer. The compositional characteristics of total FAAs varied in different seasons. Among them, valine was mostly abundant, accounting for 39 f 5 % of total FAAs, followed by alanine (14 f 5 %) and glycine (14 f 6 %). The correlations of FAAs with oxidants O3 and NO2 suggest that atmospheric oxidation capacity promoted their production, but the effects on individual ones varied. The total FAAs correlated negatively with ambient relative humidity but positively with temperature, indicating that meteorology was important factors governing the FAAs formation. Seven sources for PM2.5 and FAAs, including biomass burning, gasoline vehicles exhaust, biological emissions, coal combustion, secondary formation, dust, and agricultural activities, were resolved from positive matrix factorization analysis. Biological emissions and agricultural activities were found to be the dominant sources of FAAs in this forest atmosphere. The contributions of these sources to individual FAAs varied substantially across different seasons. This study highlights the seasonal variability in the compositional characteristics of FAAs affected by the environmental factors and sources.

The health risks of atmospheric particulate matter (PM) are intrinsically linked to its oxidative potential (OP), while OP exhibits significant seasonal variations, the underlying mechanisms remain unclear. This study investigates the interactions between transition metals and organic components in water-soluble PM using Xi'an, a representative megacity in Northwest China, as a case study to decipher the mechanisms behind its toxicity. We found that while summer PM2.5 concentrations were one-third to one-half of winter levels, its oxidative toxicity exceeded winter levels by more than twofold. This indicates that health risks fundamentally depend on chemical composition rather than mass concentration alone. In summer, total OP from water-soluble matter (OPWSM) primarily consists of highly polar components, mainly soluble metals. Secondary formation of non-oxidizing organic compounds (e.g., organic sulfates and carboxylic acids) enhances aerosol acidity, further promoting metal complexation and generating an antagonistic effect that suppresses OPWSM. In contrast, winter PM exhibits a synergistic mechanism. When atmospheric oxidation capacity weakens, primary combustion derived humic-like substances (HULIS), especially nitrogen-containing aromatic compounds, significantly contribute to OPWSM (∼ 30%) and synergistically enhance OPWSM with soluble metals. This study reveals that metal-organic coupling is a key mechanism driving the seasonal dependence of PM2.5 oxidative toxicity. This finding provides crucial scientific evidence for formulating air quality management policies, promoting a shift from traditional "concentration reduction" to a more targeted "toxicity-oriented" approach. It also establishes a theoretical foundation for implementing seasonally differentiated precision control strategies to more effectively mitigate the health impacts of PM.

Organic aerosol in the Yangtze River Basin arises from a complex mixture of primary emissions and secondary formation processes, yet diurnal variations in its molecular composition remain poorly understood in this populous region. This study used ultra-high performance liquid chromatography coupled with Orbitrap mass spectrometry (UHPLC-Orbitrap MS) and electrospray ionization to investigate the diurnal variations in the molecular composition of PM2.5 collected from Chongqing and Huai'an, Jiangsu Province, in the Yangtze River Basin. Chongqing exhibited higher molecular formula richness than Huai'an in both ionization modes, suggesting greater molecular complexity and compositional heterogeneity of PM2.5 organics in Chongqing. CHO and CHON compounds dominated the assigned formulas at both sites (54.9%-84.9%) in terms of both formula number and intensity. The CHO fraction showed an afternoon minimum in Huai'an, which may reflect the combined influence of daytime photochemical processing and dilution or mixing associated with boundary layer development. In Huai'an, opposite evening trends of CHOS and CHONS suggested different sensitivities to humid particle-phase processing and NOx-related nitrogen incorporation. In Chongqing, daytime CHO/CHOS enrichment and nighttime CHONS enhancement indicated distinct daytime oxidation and nocturnal multiphase pathways. Huai'an was more affected by primary sources such as coal combustion and traffic emissions, whereas Chongqing was more strongly influenced by secondary processes. This study improves our understanding of source influences on processed organics in the Yangtze River Basin and supports the evaluation of their health and climate impacts.

Abstract Machine learning (ML)‐based schemes face challenges in online stability and biases because of the accumulated errors during long‐term integrations. Here, we propose a new ML training strategy for the convection process that provides online corrections to tendencies in a more systematic way with significant improvements in stability and bias. The new training strategy employs simulations nudged toward reanalysis dynamical field as targets, while non‐nudging simulations serve as inputs. The results from the low‐resolution hybrid ML‐physics model show that the new ML scheme performs substantially better, as it strengthens heavy stratiform and total precipitation and reduces temperature bias, acting as an effective online bias correction mechanism. In addition, the resulting hybrid model alleviates the long‐standing overestimation of the tropical convective‐to‐total precipitation ratio. Lastly, the hybrid model can run stably for over a year, highlighting the effectiveness and generalizability of the new training strategy.

The formation and scavenging of secondary aerosol under extremely high humidity conditions (relative humidity >80 %) remained as an issue in subtropical urban areas. In order to evaluate the effects of aqueous-phase oxidation and wet removal on secondary organic aerosol (SOA) composition and dynamics, a field campaign, which mainly used a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) to characterized the non-refractory PM2.5 (NR-PM2.5) composition, was carried out during autumn fog-rain periods (FRPs) in Chongqing, Southwestern China. The chemical composition of PM2.5 was characterized, and the factors impacting SOA formation were analyzed. The effects of regional transport on SOA were also investigated. The results showed that SOA, which included less oxidized oxygenated organic aerosol (LO-OOA) and more oxidized oxygenated organic aerosol (MO-OOA), dominated OA, contributing 80 % to total OA and 36 % to PM2.5. LO-OOA and MO-OOA concentrations increased with liquid water content (LWC) and oxidant levels (Ox), indicating aqueous-phase and photochemical pathways jointly promoted SOA formation. Compared to PreFRPs and PostFRPs, MO-OOA concentration decreased by 62 % and 1 % during FRPs, highlighting efficient wet scavenging. Notably, SOA exhibited higher removal efficiency than primary organic aerosol (POA), and MO-OOA tended to be more easily scavenged by fog and rain droplets than LO-OOA. Diurnal O/C ratios revealed enhanced nighttime oxidation under high humidity, while backward trajectories identified southern Chongqing as a key SOA source region. This study quantified the distinct scavenging efficiencies and formation processes of MO-OOA and LO-OOA under fog-rain conditions, bridging gaps in understanding SOA evolution in subtropical high-humidity environments.

Forests, as the largest terrestrial carbon sink, play a critical role in mitigating climate change. Accurately estimating forest vegetation carbon storage and identifying its drivers are essential for evaluating regional carbon sink functions and supporting carbon neutrality policies. However, long-term carbon storage estimation that simultaneously captures spatial non-stationarity and separately quantifies aboveground and belowground carbon pools at the provincial scale remains limited, and the spatial differentiation drivers and the temporal change drivers of carbon storage have rarely been disentangled through pixel-wise attribution. This study aimed to estimate forest vegetation carbon storage in Jiangxi Province, China, from 1990 to 2024, and to separately quantify the drivers of its spatial differentiation and the contributions of climate change and human activities to its temporal changes. A geographically weighted regression (GWR) model was constructed using field measurements and multi-source remote sensing data; the geographical detector and partial correlation analysis were applied for spatial differentiation attribution, and pixel-wise residual analysis was used for temporal change attribution. The results showed that: (1) total carbon storage fluctuated between 553.95 and 839.78 Tg C over the 35-year period and exhibited a significant increasing trend, with a cumulative carbon sequestration of approximately 122 Tg C; (2) the belowground carbon pool increased disproportionately (net gain 79.32 Tg C) compared with the aboveground pool (42.20 Tg C); (3) precipitation and solar radiation were the dominant drivers of the spatial differentiation of carbon storage; and (4) climate change contributed approximately 60% and human activities approximately 43% to the temporal changes in total carbon storage. These findings provide a scientific basis for delineating forest carbon sink conservation zones and formulating differentiated forest management strategies in subtropical China.

Abstract. Controlled gasoline tailpipe profiles may not fully represent traffic particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) under real road conditions. The molecular compositions of tunnel and controlled tailpipe PM2.5 were compared using high-resolution mass spectrometry. Formulas common to both profiles were concentrated at low m/z and low to intermediate carbon numbers and were dominated by CHO and CHON. In contrast, molecular formulas unique to the tunnel showed greater formula richness and had higher proportions of CHOS and CHONS formulas than those unique to the controlled tailpipe profile. Tailpipe coverage ratios were high for CHO and CHON formulas in both ionization modes (0.77–0.82 and 0.84–0.92, respectively) but much lower for the combined CHOS and CHONS class (0.10–0.15). Hierarchical clustering resolved a dominant shared CHO/CHON baseline and two smaller domains concentrated in tunnel samples, characterized respectively by organosulfur formulas and by candidate formulas associated with reported tire wear chemicals together with a low molecular mass CHN response in ESI+. Thus, the controlled gasoline tailpipe profile captured the dominant shared CHO/CHON molecular baseline but incompletely represented sulfur-rich and other chemically distinct molecular domains retained in tunnel aerosol. Comprehensive molecular characterization of traffic PM2.5 therefore requires controlled tailpipe measurements together with molecular profiles representing non-tailpipe vehicle materials and near-road processing.

Biomass burning organic aerosol (BBOA) is a major source of atmospheric brown carbon (BrC), which contributes to climate through solar radiation absorption. Chemical aging (bleaching) of BrC diminishes light absorption over time. Although recent modeling studies have emphasized the need to account for bleaching when assessing BrC’s climate impacts, a parameterization representing the entire BBOA matrix has been lacking. Here, we develop a bleaching parameterization for the whole BrC in BBOA based on laboratory experiments that systematically varied temperature and relative humidity. The bleaching timescale increases under low-humidity and low-temperature conditions, likely due to enhanced aerosol viscosity. Implementing this parameterization in a global model increases the simulated direct radiative effect (DRE) of BrC by 1.5 to 2 times relative to previous estimates, with associated uncertainties exceeding 10% of the total organic aerosol DRE. The present scheme particularly shows elevated fresh BrC concentrations in boreal regions, suggesting that bleaching dynamics may influence not only radiative forcing but also snow darkening effects.

Based on the Grid-point Atmospheric Model of the Institute of Atmospheric Physics LASG version 3 (GAMIL3) with 2 degrees horizontal resolution, the whole-atmospheric model named W-GAMIL1.0, with a high top (approximately 0.01 hPa) and 137 vertical levels, was developed through modifying the standard stratification profile and interpolation method, incorporating non-orographic gravity wave (NGW) parameterization schemes, and adjusting the convective processes for strong variability. Twenty-six-year Atmospheric Model Intercomparison Project integrations indicated that W-GAMIL1.0 markedly improves the stratospheric dynamical variables owing to its high model lid and increased vertical resolution, including reductions in cold biases and dry biases in the tropic. When the frequency and accumulation of convective precipitation are increased through changing the convection scheme, the Madden-Julian Oscillation (MJO) convection, Quasi-Biennial Oscillation (QBO) westerlies and downward propagation are strengthened substantially. The mean period, height of maximum amplitude, and lowest level of the QBO are also well reproduced. Under the background of strong QBO westerlies, the Semi-Annual Oscillation (SAO) westerly and the frequency of sudden stratospheric warming events are obviously overestimated in W-GAMIL1.0, mainly because of (convective) gravity wave forcing. However, the eastward propagation of MJO convection, the vertical and latitudinal extents as well as the amplitude and descent rate of the QBO, and the SAO easterly are all underestimated, indicating the importance of compatibility between the vertical and horizontal resolutions, and between the convective process and convective NGW process in the development of a whole-atmospheric model.

Accurate soil moisture simulation is essential for understanding regional hydroclimate variability and improving climate predictions. This study evaluates the performance of FGOALS-g3 under the Atmospheric Model Intercomparison Project (AMIP) configuration in simulating surface soil moisture (SSM) over southeastern China during 1980–2014. Compared to the ERA5 and ESA-CCI reference datasets, the model exhibits a consistent dry bias across spatial distribution, seasonal cycle, and interannual variability. Replacing the model’s default soil texture with the Global Soil Dataset for Earth System Modeling (GSDE) significantly reduces this bias. The improvement stems from a shift toward finer soil texture, characterized by a 41.25

Orbitrap MS achieves mass accuracy below 1 ppm and mass resolution over 240,000 for organic aerosol characterization. Employing different inlet systems offers extensive insights into aerosol chemistry and meets specific analytical needs. Future applications and directions are proposed to address atmospheric research challenge and advance current knowledge. Investigation on the chemical characteristics, formation and transformation pathways, and origins of organic aerosols is of great importance to understand their impacts on climate and environment in the atmospheric research field. Ambient soft-ionization methods coupled with high-resolution Orbitrap mass spectrometry (MS) techniques are capable to provide extensive information on the compositional and structural characteristics of organic aerosols based on accurate mass determination. The Orbitrap MS has high accuracy to separate adjacent peaks, large dynamic range of analyte concentration levels, wide mass-to-charge ratio range, and better tandem MS analysis performance to fulfill different research purposes. In this review, the development of Orbitrap MS is briefly introduced for a better understanding on its core working principles. The current research progresses on the molecular characterization of organic aerosols using Orbitrap MS are presented through a detailed literature survey on previous laboratory and field works. Organic aerosol studies using offline direct-infusion approach, gas and liquid chromatography coupled with Orbitrap MS, and real-time measurements are thoroughly examined and discussed. Finally, the remaining challenges and future directions in the characterization of atmospheric organic aerosol chemistry using Orbitrap MS are proposed.

Reducing the double Intertropical Convergence Zone (ITCZ) bias has long been the subject of intensive research among the climate modeling community; however, the role of uncertain moist physical parameters from the atmospheric component model in causing the double ITCZ bias has not been analyzed systematically. This study investigated the potential for reducing the double ITCZ bias through perturbing nine moist physical parameters, using uniform sampling and Latin hypercube sampling methods, and quantified the parametric uncertainty and effects of two‐way interactions and overall nonlinear interaction between parameters on ITCZ precipitation in the Grid‐Point Atmospheric Model of the IAP/LASG Version 2. Results showed that the double ITCZ bias with excessive precipitation over the central and eastern Pacific south of the equator can be significantly reduced by suppressing deep convection intensity via multiple‐parameter perturbation. The uncertainty ranges of the double ITCZ bias associated with multiple‐parameter perturbation are apparently larger than those reconstructed from linear superposition of the single‐parameter perturbations, thereby highlighting the profound influences of interactions between parameters on ITCZ precipitation. The two‐way interaction terms are the primary contributors to the total variance of ITCZ precipitation. The effects of efficiency of precipitation and evaporation for deep convection are highly dependent on the threshold value for relative humidity for deep convection. The overall nonlinear effect among all the parameters tends to sharply exacerbate the bias of ITCZ heavy precipitation and reduce the bias of light precipitation. This strong overall nonlinear interaction among parameters in extreme precipitation simulation plays an important role in causing the double ITCZ bias, and it is closely associated with its impact on the intensity of the total moist processes. These findings validate the feasibility of reducing the double ITCZ bias by tuning moist parameters, and highlight the impact of interactions between parameters on simulation of ITCZ precipitation.

Parameterization in climate models often involves parameters that are poorly constrained by observations or theoretical understanding alone. Manual tuning by experts can be time-consuming, subjective, and prone to underestimating uncertainties. Automated tuning methods offer a promising alternative, enabling faster, objective improvements in model performance and better uncertainty quantification. This study presents an automated parameter-tuning framework that employs a derivative-free optimization solver (DFO-LS) to simultaneously perturb and tune multiple convection-related and microphysics parameters. The framework explicitly accounts for observational and initial condition uncertainties (internal variability) to calibrate a 1° resolution atmospheric model (GAMIL3). To evaluate its performance, two main tuning experiments were conducted, targeting 10 and 20 parameters, respectively. In addition, three sensitivity experiments tested the effect of varying initial parameter values in the 10-parameter case. Both tuning experiments achieved a rapid reduction in the cost function. The 10-parameter optimization improved model accuracy for 24 of 34 key variables, while expanding to 20 parameters yielded improvement for 25 variables, though some structural model biases appeared. Ten-year AMIP simulations validated the robustness and stability of the tuning results, showing that the improvements persisted over extended simulations. Additionally, evaluations of the coupled model with optimized parameters showed, compared to the default parameters settings, reduced climate drift, a more stable climate system, and more realistic sea surface temperatures, despite a residual global energy imbalance of 2.0 W m−2 (about 1.4 W m−2 arising from the intrinsic imbalance of the atmospheric component) and some remaining regional biases. The sensitivity experiments further underscored the efficiency of the tuning algorithm and highlight the importance of expert judgment in selecting initial parameter values. This tuning framework is broadly applicable to other general circulation models (GCMs), supporting comprehensive parameter tuning and advancing model development.

Atmospheric brown carbon (BrC) aerosols were investigated at a forest site in the Qinling Mountains region of central China across the four seasons of 2021-2022. The molecular composition and optical properties of organic aerosols were characterized using an ultrahigh performance liquid chromatograph coupled with a diode array detector and an Orbitrap mass spectrometer. The light absorption of organic aerosols was relatively low in this forest environment, as revealed by their lower mass absorption coefficients at 365 nm wavelength (MAC365, mean 0.19 ± 0.13 m2 g-1) compared with those of worldwide locations (approximately 0.04-2.8 m2 g-1). The light absorption of BrC exhibited strong seasonal variabilities, with higher MAC365 values in fall and winter than those in spring and summer seasons. They were mostly classified as very weakly absorbing BrC, while the absorption capacities were increased with enhanced anthropogenic pollution. A total of 51 major BrC species were identified and mostly composed of CHO and CHON species (84 %), accounting for 14-39 % of the total absorbance of BrC at 300-450 nm. The optical properties of BrC largely depended on their chemical composition and molecular structures, with lignin-like compounds and condensed aromatics being the major BrC components. The lipid- and protein-like compounds with lower unsaturation degrees could be the potential BrC species mainly originating from biomass burning and biological emissions. This study advances our knowledge on the connections of optical properties of BrC aerosols with their molecular characteristics in anthropogenic-biogenic intersection atmosphere.

Global warming has led to the intensification and increased frequency of drought events. Determining the extent to which these events are influenced by human activities is critical for developing effective strategies to address climate change. However, detecting human impacts and providing robust attribution results remain key challenges in drought research. In the summer of 2022, the Yangtze River Valley of China experienced an unprecedented extreme drought, marked by record-high surface temperatures and record-low precipitation over the past 60 years. This event caused substantial socio-economic and ecological disruptions. To assess the role of anthropogenic climate change in the intensity and frequency of such events, this study established an attribution framework based on GAMIL3.0. This study evaluated anomalies in surface temperature, precipitation, and large-scale circulation patterns during the summer of 2022. Results indicate that human activities have intensified the Western North Pacific Subtropical High and South Asian High, increasing their strength and frequency and thereby amplifying the intensity and likelihood of extreme drought events in the Yangtze River Valley. Anthropogenic forcing contributed to an additional 0.8°C rise in surface temperature (95% confidence interval: 0.1–1.5°C) and a 7.9% reduction in precipitation (-24.1% to 7.8%) during the 2022 summer. The anthropogenic forcing increased the probability of surface temperature anomalies associated with such an extreme drought event like 2022 by 1300 times (range: 87–3,001) and precipitation anomalies by 65 times (range: 1–90). This study highlights the urgent need to strengthen adaptive capacities to mitigate the impacts of extreme drought in the Yangtze River Valley.

The resolution sensitivity of the Kain–Fritsch (KF) convection scheme and the role of interactions between the physics and dynamics within the gray zone (<10 km) were investigated using the Separate Physics and Dynamics Experiment (SPADE) framework. Two groups of experiments were conducted using the Weather Research and Forecasting (WRF) model via traditional (Tradition) runs and SPADE runs with resolutions of 1, 2, 4, and 8 km during the wet period of the Tropical Warm Pool–International Cloud Experiment (TWP‐ICE). Results show that the KF scheme simulates the weakened convective processes well as the resolution increases in both groups, and the changes in the convective variables with resolution in SPADE are smaller than in the Tradition group. This indicates the important effects of interactions between model components on convection parameterizations as the resolution changes. Additionally, the microphysics variables remain nearly unchanged with resolution in SPADE and weaken slightly in Tradition as the resolution decreases, suggesting the relatively weaker influences of model interactions for the resolved‐cloud parameterization. Therefore, the scale‐aware behavior of KF scheme is further strengthened in Tradition runs, primarily through inhibiting the strength of stratiform processes through physics–dynamics interactions and physical components.

To unravel the equilibrium climate sensitivity (ECS) changes of the models of the Coupled Model Intercomparison Project (CMIP) during the upgrade, 10 pairs of CMIP phase 5 (CMIP5) and phase 6 (CMIP6) models from different centers were categorized into high and low ECS groups according to their ECS and surface air temperature response to CO2. Results showed that the higher ECS of the CMIP6 multimodel mean is derived primarily from five models of the high group, and is contributed by both stronger positive cloud feedback (CF) and stronger albedo feedback relative to the corresponding values in the CMIP5 models, and the spread of CF is associated with that of the ECS. Positive albedo feedback in the Arctic may be related to the relationship between weakening of the Atlantic Meridional Overturning Circulation (AMOC) and diminishing sea ice area (SIA). For example, the stronger albedo feedback in the CMIP6 high group is linked to the strongly weakening AMOC and sharply reducing SIA, further associated with their mean states compared with those of the CMIP5 models. The higher CF in the CMIP6 high group results from the greater reduction in both cloud area fraction (CAF) and ice water path (IWP) and the weaker increase in the liquid water path (LWP), leading to enhanced shortwave CF and reduced longwave CF. Furthermore, when the total precipitation response is dominated by only the convective or stratiform component, it is prone to substantial increase by the model upgrade, thereby notably affecting the changes in CAF, IWP, and LWP and the variation in CF and ECS in the high group.

Understanding long-term carbon stock dynamics in coastal wetlands is essential for assessing their sequestration potential and informing climate mitigation strategies. However, the combined influences of land use and land cover change (LUCC) and tidal barriers on carbon stock dynamics in coastal wetlands remain unclear. We used Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model corrected by in-situ measurements to investigate carbon stock dynamics in response to LUCC and tidal barrier construction in the Yellow River Delta (YRD) for the period of 1980-2020. The results indicated that the corrected carbon stock increased by more than twofold. Soil inorganic carbon (SIC) stock significantly exceeded soil organic carbon (SOC) and even total ecosystem organic carbon (EOC, comprising SOC, aboveground, belowground and dead organic carbon) stock. Carbon stocks showed divergent trends over four decades, with inorganic carbon increasing steadily and organic carbon fluctuating in response to land use changes. Specifically, from 1980 to 2020, SIC continuously increased from 3.211 x 107 t/a to 3.335 x 107 t/a (+3.9 %). In contrast, EOC first increased by 0.222 x 107 t (+16.2 %) from 1.376 x 107 t/a to 1.598 x 107 t/a during 1980-1990, but then decreased by 0.141 x 107 t (-8.8 %) to 1.457 x 107 t/a during 1990-2020. The increase in SIC stock was principally owing to the seaward expansion of the YRD, whereas the decrease in EOC stock from 1990 to 2020 was largely attributed to the reduced grassland areas. The severe degradation of wetlands further reduced the carbon stock in the YRD. Large-scale tidal barriers mitigated SIC loss from coastal erosion but did not enhance organic carbon sequestration due to disrupted hydrological connectivity and vegetation shifts. Notably, SIC stock decreased while EOC stock increased with greater distance from tidal barriers. To enhance carbon sinks in large river deltas, future efforts should prioritize ecological restoration through strategic land use conversion.