Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1 degrees & times; 1 degrees resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000-2020 emissions through 2025. In the test data (similar to 30 % of the total dataset), the emulator achieved a global R-2 of 0.65 +/- 0.003 (mean +/- 95 % CI, hereafter) and an RMSE of 5.49 +/- 0.12 & times; 10(-3) Tg CH4 yr(-1). The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021-2025 (157.8 +/- 2.4 Tg CH4 yr(-1)) are not significantly higher (similar to 0.05 Tg CH4 yr(-1)) than the 2000-2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021-2025, with mid- and high-latitudes increasing by 0.76 +/- 0.07 and 0.35 +/- 0.03 Tg CH4 yr(-1), respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (-0.95 +/- 0.19 and -0.11 +/- 0.02 Tg CH4 yr(-1), respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Ni & ntilde;o-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Abstract The carbon budget of floodplain lakes is regulated by natural hydrological processes, yet the role of human interventions, including cascade river‐lake hydraulic projects, remains unclear. We evaluated the combined effects of the Three Gorges Dam (TGD) located on the Yangtze River and the proposed lake‐specific hydraulic projects (LHPs) on the carbon budgets of the respective Dongting and Poyang Lakes downstream of the TGD. As a result of TGD's operation, Dongting Lake saw a weakened carbon sink, with carbon dioxide (CO2) uptake reduced by 6% and methane (CH4) emissions increasing by 4%. The opposite is true for the downstream Poyang Lake, with CO2 uptake increasing by 8% and CH4 emissions decreasing by 1%. This divergence stems from the lake‐specific vegetation composition and TGD's regulation strategy. The Phragmites in Dongting Lake is sensitive to hydrological regulation during the pre‐flooding seasons, while the Carex in Poyang Lake is sensitive to hydrological regulation during the post‐flooding seasons. In contrast, the operation of LHPs will uniformly reduce the carbon sink due to elevated lake water levels, leading to a sharp decline in CO2 uptake in Poyang Lake (36.98 gC m−2 yr−1) and Dongting Lake (11.87 gC m−2 yr−1). A cascade operation of these projects will increase the global warming potential (GWP) by 0.88 Tg CO2eq yr−1. Our findings underscore the necessity of integrating ecological consequences into water resources management to reconcile water security with climate stability goals.

Carnegie-Ames-Stanford Approach (CASA) is a widely used light-use-efficiency (LUE) model for estimating terrestrial carbon sequestration, yet its accuracy remains limited in climatically heterogeneous regions. We present a regionally adapted CASA framework that (i) calibrates maximum LUE (epsilon max) for six vegetation types in Northeast China using site-level observations, (ii) incorporates a nitrogen-limitation scalar (N epsilon), (iii) couples the revised model (CASA-N-Opt) with GSMSR to quantify carbon sink strength and trends, (iv) improves the spatial resolution of the carbon-sink product from 500 m to 30 m via a mass-conserving refinement guided by highresolution proxies. Key findings include: (1) epsilon max values (g C & sdot;MJ- 1) range from 0.326 (deciduous needle-leaf forest) to 0.677 (cropland) with the N epsilon marginally increasing for natural vegetation but considerably decreasing for cropland. (2) CASA-N-Opt alleviates the systematic underestimation of the original CASA, reducing RMSE by 36% and increasing R2 from 0.807 to 0.873. Forests, croplands, and wetlands saw RMSE drop by 22-46%; summer and autumn RMSE fall by 53% and 30%, respectively. Cross-validation confirmed robust spatial transferability, with extrapolation RMSE well-controlled for croplands and wetlands. (3) Mean annual regional sequestration is 147 Tg C & sdot;yr- 1 (130.4 g C & sdot;m- 2 & sdot;yr- 1), with forests (63.9%) and croplands (36.4%) as dominant contributors, while wetlands (0.2%) act as a weak sink and grasslands (-0.5%) as a weak source. From 2003 to 2020, forest sink strength declined, cropland remained stable, while wetlands and grasslands shifted toward stronger sequestration. The proposed CASA-N-Opt framework eliminates systematic bias and provides a reliable tool for regional carbon accounting and low-carbon planning in temperate-boreal landscapes.

Previous studies have shown that methane emissions from East Africa are globally significant, yet their environmental controls remain poorly constrained. In this study, we use satellite observations of enhanced vegetation index (EVI), land surface temperature (LST), and rainfall to interpret methane emission estimates inferred from Greenhouse gases Observing SATellite (GOSAT) observations over 2010-2020, with a focus on the anomalous emission pulse during 2018-2020. We identify a pronounced seasonal contrast in the relationships between methane emissions and environmental variables. During the long rains, methane emissions are strongly correlated with contemporaneous EVI, LST, and rainfall across both the regional and basin scales. In contrast, during the short rains, cumulative EVI explains more variability in methane emissions ( r = 0.74) than seasonal EVI ( r = 0.35), indicating the importance of lagged ecosystem processes such as vegetation senescence and organic matter decomposition. We interpret this delayed emission peak as likely reflecting the combined influence of vegetation-driven carbon inputs and catchment-scale hydrological transport, whereby biomass production and water redistribution during the long rains contribute to enhanced methane emissions in the subsequent season. Periods of anomalously high EVI (2018-2020) coincide with elevated methane emissions, particularly over the Juba, Tana, Nile, and Rift basins. While these relationships are empirical, our results suggest that vegetation dynamics provide a stronger large-scale empirical indicator of methane variability than rainfall alone, and highlight the importance of accounting for lagged biogeochemical processes when interpreting wetland methane emissions.

Black carbon (BC) from biomass burning (BCbb) constitutes a major fraction of global BC's climate impact. Yet, the contribution of BCbb to atmospheric BC concentrations estimated by emission inventory-based models remains highly uncertain due to the lack of source-diagnostic observational constraints. Here, we present year-round, 14C-based measurements of BCbb from two regional and one background site in southern China. By comparing these measurements with tailored simulations from an atmospheric transport model, we find that although the model reproduces the seasonal variation reasonably well, simulated BCbb remains subject to considerable uncertainty (NMB: -68% to +28%). Unmonitored biomass burning (BB) in mainland China can result in a substantial model-observation discrepancy. Our results reveal that residential BB across East Asia is the main contributor to BCbb in southern China. Transport of BCbb from Southeast Asia is also important in summer. Notably, transboundary BB pollution from Southeast Asia, facilitated by free-tropospheric transport, cannot be ignored even in winter, when continental air masses prevail. Our findings highlight that the transboundary contribution of BCbb may exceed prior estimates. These results underscore the need to update emission inventories by incorporating overlooked BB sources.

Herbivorous insects and nitrogen deposition are key drivers of ecosystem productivity under global change. However, their combined effects on plant photosynthesis, particularly during insect outbreaks, remain poorly understood in natural ecosystems. We conducted a decade‐long, multi‐level nitrogen‐addition experiment in an alpine meadow on the Qinghai–Tibet Plateau, with insect‐herbivory outbreak treatments nested within each nitrogen level. Our results showed that insect outbreaks consistently suppressed leaf photosynthesis in dominant species, but this effect was strongly modulated by nitrogen level. Under low nitrogen supply (2–8 g N m −2 year −1 ), herbivory significantly reduced net photosynthetic rates by 45.4%–60.6%, whereas this reduction was roughly halved at higher nitrogen levels. Across the nitrogen gradient, the decline in photosynthesis scaled linearly with increasing nitrogen availability, suggesting a nitrogen‐dependent reduction of herbivore impact. This pattern likely resulted from a trade‐off of nitrogen resource allocation between reduced investment in alkaloid synthesis and increased photosynthesis in facing insect herbivory and nitrogen enrichment, reflecting a shift from resistance‐based to tolerance‐based defence strategies. Overall, these findings reveal the non‐linear nature of the insect herbivory effect in natural ecosystems when experiencing variable nitrogen levels, providing new insights into understanding and predicting grassland productivity responses to the interactions of nitrogen enrichment and insect outbreaks. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Mangroves are critical blue carbon ecosystems, essential for coastal preservation and carbon sequestration. Despite widespread attention to mangrove deforestation and reforestation driven by land-use/land-cover changes (LULCCs), integrated assessments of CO2 and CH4 fluxes across diverse transitions under the same spatiotemporal context remain scarce, hindering advances in restoration planning and land-use emission modeling. This study investigated five LULCC types on Qi’ao Island, China: mudflat, native Kandelia obovata, exotic Sonneratia apetala, S. apetala deforestation areas, and abandoned fish ponds. Combining one year of in situ carbon flux measurements with remote sensing data (1980–2020), we assessed changes in carbon sink dynamics and CH4 fluxes at both site and regional scales. At the site level, mudflats acted as weak carbon sinks (2.13 ± 1.70 t CO2 hm-2 a-1). By contrast, the exotic S. apetala exhibited a carbon sink 3-4 times stronger (60.55 ± 5.83 t CO2 hm-2 a-1) than the native K. obovata (16.79 ± 1.98 t CO2 hm-2 a-1). Abandoned fish ponds (7.73 ± 1.26 t CO2 hm–2 a–1) retained a moderate carbon sink due to abundant aquatic vegetation, whereas deforestation resulted in a carbon source (–1.09 ± 0.93 t CO2 hm–2 a–1). Notably, sites with higher CO2 uptake also emitted higher CH4 emissions, which indicated a climate mitigation trade-off. CH4 emissions offset ∼12% of the climate benefit from CO2 uptake. Regionally, LULCCs, driven largely by mangrove reforestation, shifted the region from a net carbon source to a net carbon sink around 1990. These findings highlight the impacts of LULCC in shaping carbon sinks, and they offer valuable insights for optimizing future restoration strategies with greater climate benefits.

Abstract Rice paddies are among the largest anthropogenic sources of methane (CH4), yet substantial uncertainties persist in the long-term magnitude and spatiotemporal dynamics of the emissions. Here we show a spatially explicit global estimate of rice CH4 emissions from 1961 to 2020, leveraging a new high-resolution dataset and two Tier 3 modeling approaches (process-based and machine-learning-based). We find that global emissions have more than tripled over the past six decades, reaching a record high of 38.8 Tg CH4 yr−1 in 2020 based on the mean of the two Tier 3 estimates, both the rate and magnitude are substantially higher than conventional emission-factor-based inventories. Traditional rice-producing regions (e.g. Asia) dominated the increase due to expanded cultivation and intensified organic inputs, while Africa emerged as a rapidly growing source. Beyond trends in absolute emissions, the CH4 emission intensity (per unit of rice yield) declined across 70% of the global paddy area, mainly reflecting yield-driven efficiency gains. Notably, 44% of global rice production in the 2010 s occurs in countries with below-average emission intensity, with China being the largest contributor. By resolving long-term spatial heterogeneity with new data and models, this study provides a robust reassessment of the global rice methane budget and highlights pathways to reconcile food production with climate mitigation.

Abstract. Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by several years, preventing a timely understanding of CH4 sources, sinks, and their trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates and applying the framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record through 2025 at monthly 1°x1° resolution. We developed a machine-learning emulator to reconstruct spatially explicit monthly emission fields (global R2 =0.65 ± 0.003 (mean ± 95 % CI, hereafter) and RMSE=5.49 ± 0.12 ×10-3 Tg CH4/year in test data which is ~30 % of the total data). The emulator is trained on 35 GMB model estimates (22 process-based model estimates and 13 atmospheric inversion estimates) paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. While the global mean predicted wetland CH4 emissions for 2021–2025 (157.83 ± 2.38 Tg CH4/year) are only marginally higher (~0.05 Tg CH4/year) than the 2000–2020 baseline, this stability masks a significant hemispheric redistribution of emissions. We detect a surge in Northern Hemisphere emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 (z-score: 2.21) and 0.35 ± 0.03 Tg/year (z-score:1.01), respectively, while the tropics and Southern Hemisphere extratropics show offsetting negative trends (-0.95 ± 0.19 and -0.11 ± 0.02 Tg/year with z-scores of -2.81 and -0.34, respectively). The predicted emissions capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Post-2020 growth rates of emission anomalies are a magnitude higher than that in 2000–2025, suggesting an intensification of emission variability. Furthermore, we identify a distinct seasonal amplification of global emission growth peaking in late boreal summer. This new dataset and operational framework bridge the gap between latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Coastal salt marshes (CSMs) are vital blue carbon (BC) reservoirs, yet accurately quantifying their gross primary productivity (GPP) remains challenging due to limitations in terrestrial biosphere models (TBMs), which often overlook coastal-specific processes. Here, we present SAL-GPP, a process-based model that incorporates coastal-specific modules to capture the effects of salinity and temperature stress on photosynthesis, as well as light-use efficiency across salinity gradients in diverse CSM plant species. Model validation showed strong agreement with observations, with R2 of 0.82 and model efficiencies of 0.82 and 0.74 for daily and seasonal GPP, respectively. Driven with global inputs, SAL-GPP produced high-resolution global simulations, yielding a mean annual GPP of 66.89 ± 11.68 TgC yr-1 (2011-2020), with 64% concentrated in key hotspots across the southeastern United States, western Europe, southeastern China, and Australia. From 2011 to 2016, global CSM GPP increased by 1.56 TgC yr-1, then declined, rebounded after 2018, and peaked at 71.45 ± 12.02 TgC yr-1 in 2020. Model evaluation showed that SAL-GPP outperformed existing remote sensing-based GPP products and TBMs at both site and grid levels. By explicitly incorporating coastal ecosystem dynamics, SAL-GPP supports global BC accounting and climate mitigation strategies aligned with nature-based solutions for carbon neutrality.

Following record-breaking surges in 2020 and 2021 and highly elevated growth in 2022, atmospheric methane (CH4) growth decelerated in 2023 and 2024, returning to pre-2020 levels. Here, using the Global ObservatioN-based system for monitoring Greenhouse Gases (GONGGA) inversion that assimilates a blended and bias-corrected TROPOMI + GOSAT XCH4 dataset, we estimated global CH4 budgets for 2019-2024 and partitioned the drivers of the observed growth-rate anomalies. We find that reduced hydroxyl radical (OH) concentrations were a primary driver of the highly elevated growth during 2020-2022, reducing the atmospheric sink by an average of 14.3 Tg CH4 yr-1, while OH recovery and higher CH4 abundance subsequently strengthened the sink in 2023-2024 relative to 2019. Despite this strengthened sink, wetland emissions rebounded strongly in 2024 and offset elevated sink, producing an atmospheric growth rate near 2019 levels. Partial correlation analysis indicates precipitation anomalies as the dominant driver of wetland variability. However, process-based wetland models diverged from the inversions in key regions, underscoring the need to reconcile bottom-up and top-down estimates. Our findings indicate that combined variability of natural sources and sinks (12.6 Tg CH4 yr-1) is comparable to the pledged reductions, highlighting the importance of accounting for natural variability in methane monitoring.

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

Biochar-derived dissolved organic matter (BDOM) is a significant carbon component released from biochar, characterized by high reactivity and mobility. It has substantial potential in soil remediation, agriculture, and environmental protection. BDOM plays a crucial role in shaping microbial community structures, enhancing soil fertility, and improving water quality. However, the release of BDOM may also worsen environmental issues, such as pollutant mobilization, shifts in microbial community composition, and water eutrophication, impacting ecosystem stability. Despite BDOM is importance, the specific mechanisms underlying its environmental applications are not well understood. Thus, investigating BDOM's environmental behavior and optimizing biochar preparation and application strategies are essential for maintaining healthy soil and aquatic ecosystems. This article reviews the factors influencing BDOM, its characterization methods, environmental behavior, and mechanisms. Furthermore, we emphasize the necessity of establishing standardized biochar regulatory frameworks and quantifying BDOM's ecological thresholds to balance its benefits and risks. These insights provide a scientific basis for optimizing biochar applications and mitigating potential environmental concerns.

Paddy rice, one of the world’s primary cereals, plays a critical role in food security and environmental sustainability. Existing global rice distribution datasets provide valuable insights but are often constrained by time and coverage. In this study, we developed the GloRice (I), a long-term global gridded dataset mapping rice harvested and physical areas at a 5-arcminute resolution for 1961–2021. The dataset consists of three components: (1) GloRice-2000hvst: four types of rice harvested area maps for the year 2000, using multiple established datasets (M3, SPAM, GAEZ); (2) GloRice-hvst: annual harvested area maps (1961–2021) integrating national and subnational statistics; and (3) GloRice-phsc: annual physical area maps (1961–2021) derived from the rice multiple cropping index (MCI) data. Validation demonstrated strong agreement at global and regional levels, with consistent accuracy in subnational comparisons for major rice-producing regions, including China, South Asia, and Southeast Asia. The GloRice (I) provides essential data input for regional and global modelling and assessments, with broad applicability in agriculture, climate, and environmental research.

China's natural terrestrial ecosystems（NTEs） are significant sources and sinks of methane（CH 4 ） and nitrous oxide（N 2 O）,two potent non-CO 2 greenhouse gases.This article reviews CH 4 and N 2 O inventories for China' s NTEs,derived from site-specific extrapolation and models,to elucidate their spatiotemporal emission patterns.D espite progress,significant gaps remain,including large uncertainties due to model limitations and inconsistent driving data,insufficient assessments of integrated global warming potential（GWP） under long-term land-use and climate changes,the lack of freshwater emission inventories,and the need for more observations,refined prior sectoral contributions,and novel methods like isotopic signature applications in machine-learning and inversion techniques.This review offers a new perspective by compiling a new CH 4 and N 2 O inventory and evaluating their integrated GWP for 1980-2020,developed using multi-model approaches to assess climate and land-use impacts.The review underscores the importance of CH 4 and N 2 O sources and sinks,offering recommendations to enhance carbon sequestration and reduce emissions.

High ammonium (NH4+) levels inhibit primary root (PR) elongation in plants, but the underlying regulatory mechanisms remain poorly understood. In this study, we screened the Arabidopsis (Arabidopsis thaliana) PSKI015 activation-tagged mutant library and identified a dominant mutant, named Ammonium Sensitive 3D (amos3D), which shows increased sensitivity to high NH4+ in terms of PR elongation. Gene cloning revealed that amos3D overexpresses IPT3, a gene involved in cytokinin biosynthesis. Pharmacological and genetic analyses revealed that the PR sensitivity to high NH4+ in amos3D is due to elevated levels of the active cytokinins iP and tZ. Furthermore, we identified the type-B ARRs ARR10 and ARR12 as key transcription factors in the cytokinin-mediated inhibition of PR elongation under high-NH4+ stress. Using CUT&RUN (Cleavage Under Targets & Release Using Nuclease), yeast 1-hybrid, and dual-luciferase assays, we showed that ARR10 and ARR12 directly bind to the promoter of CAP1, a tonoplast-localized kinase, repressing its transcription. This repression reduces NH4+ transport from the cytosol to the vacuole, leading to increased Gln/Glu ratios and enhanced NH4+ toxicity. Collectively, our identification of AMOS3 as a key inhibitor of PR growth under high NH4+ through the cytokinin-dependent ARR10/ARR12-CAP1 pathway not only reveals an NH4+-sensing mechanism but also offers promising agronomic potential for optimizing root architecture and improving nitrogen-acquisition efficiency in crops under ammonium-based fertilization systems.

Northern wetlands are considered to be one of the most significant natural sources of methane (CH4) emissions. The default wetland CH4 emission scheme in JULES, a current state-of-art land surface model, only takes into account the CH4 emissions from inundated wetland areas in a simple manner based on soil temperature and substrate availability. In this work, a process-based peatland CH4 emission model HIMMELI was integrated with JULES, and the HIMMELI parameters were optimized with measured CH4 flux at six northern wetland sites for each site separately or multi-sites simultaneously. The simulated CH4 emission was significantly improved when using the optimized parameter values, with the bias of 54.88 mg m-2 d-1 averaged across all the studied sites in the simulation using the default parameter (DPR) values being reduced to -0.70 mg m-2 d-1 in the simulations using parameters values derived from the single site optimization (SSO) for each site. In the simulations using parameters values from the averages of single site optimization (SSO_AVG) and the multi-site optimization (MSO), the biases averaged across all the studied sites were -7.39 mg m-2 d-1 and -8.36 mg m-2 d-1, respectively. The MSO simulations demonstrated more stable root mean square error (RMSE) between the simulated and observed methane emissions than the SSO_AVG simulations over the studied sites, when the RMSEs of SSO simulations were used as reference points. To further reduce the uncertainties in the simulated CH4 emissions by the JULES-HIMMELI model, model processes related to the environment conditions (e.g. water table, soil carbon and vegetation) of wetland and northern wetland CH4 emission processes (e.g. snow and ice covering effect) are suggested to be improved in JULES and HIMMELI, respectively. This study presents a comprehensive analysis of the impact of different parameters on the CH4 emission in the JULES-HIMMELI model and obtains optimal parameter values for modelling CH4 emissions at the studied northern wetlands. These findings pave the way for accurate regional estimates of northern wetland CH4 emission.

Forestation (afforestation and reforestation) could mitigate climate change by sequestering carbon within biomass and soils. However, global mitigation from forestation remains uncertain owing to varying estimates of carbon sequestration rates (notably in soil) and land availability. In this study, we developed global maps of soil carbon change that reveal carbon gains and losses with forestation, primarily in the topsoil. Constraining land availability to avoid unintended albedo-induced warming and safeguard water and biodiversity (389 million hectares available for forestation globally) would sequester 39.9 petagrams of carbon by 2050, substantially below previous estimates. This estimate drops to 12.5 petagrams of carbon with land further limited to existing policy commitments (120 million hectares). Achieving greater mitigation requires expanding dedicated forestation areas and strengthening commitments from nations with considerable but untapped potential.

National greenhouse gas (GHG) budget, including CO2, CH4 and N2O has increasingly become a topic of concern in international climate governance. China is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate change. Accurate estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in China. This study used comprehensive methods, including emission factor methods, process-based models, atmospheric inversions, and data-driven models, to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000-2023, and produced an up-to-date China GHG Budget dataset (CNGHG). The total gross emissions of the three GHGs show a 3-fold increase from 5.0 (95% CI: 4.9-5.1) Gt CO2-eq yr-1 (in 2000) to 14.3 (95% CI: 13.8-14.8) Gt CO2-eq yr-1 (in 2023). CO2 emissions represented 81.8% of the GHG emissions in 2023, while 12.7% and 5.5% were for CH4 and N2O, respectively. As the largest CO2 source, the energy sector contributed 87.4% CO2 emissions. In contrast, the agriculture, forestry and other land use sector was the largest sector of CH4 and N2O, representing 50.1% and 66.3% emissions, respectively. Moreover, China's terrestrial ecosystems serve as a net CO2 sink (1.0 Gt CO2 yr-1, 95% CI: 0.2-1.9 Gt CO2 yr-1) during 2012 to 2021, equivalent to an average of 14.3% of fossil CO2 emissions. Our GHG emission estimates showed a general consistency with national GHG inventories, with gridded and sector-specific estimates of GHG fluxes over China, providing the basis for curtailing GHG emissions for each region and sector.