The 2023/24 El Niño strongly reduced land carbon uptake, but the persistence of this anomaly after surface cooling remains uncertain. Here we quantify the July 2024-June 2025 global CO2 budget using low-latency fossil emission estimates, three DGVMs, machine learning ocean flux emulators and OCO-2-constrained atmospheric inversions. The atmospheric CO2 growth rate was 2.62 ± 0.08 ppm yr-1, 6.5% above the 2013-2022 July-to-June mean. DGVMs estimate that the net land sink was 1.32 ± 0.19 GtC yr-1 weaker than the 2015-2022 July-to-June mean, whereas combining bottom-up and top-down constraints gives a smaller deficit of 0.49 GtC yr-1. The annual anomaly is dominated by late-2024 land carbon losses. Early-2025 recovery, however, is method-dependent: DGVMs retain a weak annual land-sink deficit, while all inversions indicate fluxes close to the reference mean and a stronger-than-normal northern sink in late spring 2025. Ocean uptake shows no global weakening. A statistical decomposition links global land flux variability mainly to temperature, with terrestrial water storage contributing more strongly at regional scales. These results identify Northern Hemisphere land-sink recovery as a central uncertainty in low-latency carbon-budget assessments.

Abstract Variations in the atmospheric CO 2 seasonal cycle across the Northern Hemisphere have historically been dominated by terrestrial ecosystems, making ground-based observations a reliable proxy for terrestrial carbon dynamics. However, whether this dominance will persist in the future remains uncertain. Here we combine atmospheric transport modeling with factorial simulations to assess and attribute future changes in the CO 2 seasonal cycle through 2100. We show that the dominant drivers of these changes shift fundamentally across scenarios. Under the high-emission scenario (SSP5-8.5), strengthening land sinks dominate and amplify CO 2 seasonal variability, preserving ground-based observations as a reliable terrestrial proxy. In contrast, under the low-emission scenario (SSP1-2.6), CO 2 seasonal amplitude declines widely, driven primarily by reduced fossil fuel emissions and their dampened seasonality. Consequently, established ground-based CO 2 observations may no longer reliably track terrestrial carbon dynamics under mitigation pathways, underscoring the need for new approaches for monitoring and climate policy verification.

Abstract Lateral transport of terrestrial carbon—via harvested biomass, bioenergy supply chains, and riverine export—redistributes atmospheric CO 2 uptake and subsequent CO 2 emissions across space and time, complicating regional carbon budgeting and atmospheric inversion estimates. We present Lateral Accounting of Transport in Terrestrial Ecosystems (LATTE), a high‐resolution (5 arc‐min) global gridded data set of annual land‐atmosphere CO 2 fluxes attributable to lateral carbon transfers for 1961–2022. LATTE quantifies paired CO 2 sinks at production locations and compensatory CO 2 sources at receptor locations, for four major lateral transport mechanisms: (a) crop harvest, trade, and consumption; (b) industrial roundwood harvest, trade, and storage in harvested wood product pools with country‐specific decay and delayed emissions, (c) biofuel production, and use, including crop biofuels and fuelwood; and (d) the inland water carbon transport loop, including soil‐to‐river leaching, river and lake CO 2 evasion, aquatic burial, and export to the ocean. National production and trade statistics and biofuel energy balances are converted to carbon units and downscaled using satellite net primary productivity, forest carbon removal maps, population density, livestock distributions, and gridded biofuel combustion proxies. Inland water fluxes are based on published climatology with basin‐scale mass balancing across major river basins. Example maps illustrate distinct spatial patterns of sinks and sources associated with crop products, wood products, biofuels, and inland waters, with global totals consistent with previous assessments. LATTE enables improved representation of lateral carbon transport in atmospheric inversions and supports spatially explicit regional and national carbon budget analyses.

In 2024,the atmospheric CO2 growth rate based on the globally averaged ma-rine boundary layer(MBL)observa-tions from the National Oceanic and Atmospheric Administration(NOAA)network reached 3.73±0.08 ppm yr-1,marking a record high since contin-uous measurements began in 1959(Fig.1a)[1].The whole-atmosphere growth rate derived from independent OCO-2 satellite observations for 2024 was 3.20±0.1 ppm yr-1 using the Growth Rates from Satellite Observa-tions data-driven approach(GRESO)from Ref.[2],the highest value of the OCO-2 record since 2015.The whole-atmosphere growth rate derived from our flux inversion models assimilating OCO-2 observations mainly over land for 2024 was 3.23±0.12 ppm yr-1,thus unsurprisingly being almost equal to GRESO and less than the MBL sta-tions but still a record high in the OCO-2 inversions record since 2015.

In summer 2021, the northern high-latitude plains (NHP) of the Eurasian continent endured their most severe drought in nearly two decades, with important implications for regional methane emissions. Using the Global ObservatioN-based system for monitoring Greenhouse GAses for methane (GONGGA-CH4) inversion system along with a merged Greenhouse gases Observing SATellite (GOSAT) + TROPOspheric Monitoring Instrument (TROPOMI) dataset, we quantified drought impacts on methane emissions. Independent validation confirmed the system's high accuracy, revealing a 20% summer emission reduction in NHP during 2021 compared to baseline years. This reduction is primarily attributed to a decrease in liquid water content, which strongly affected wetland emissions. The underlying causes were heightened evaporation and the presence of a blocking high-pressure system within the atmospheric circulation. These findings highlight the profound impact of summer droughts on methane emissions in high-latitude regions, and emphasize the critical importance of integrating diverse data sources to refine methane emission estimates.

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.

El Niño droughts can weaken tropical land carbon uptake and accelerate atmospheric CO2 growth. We updated the low-latency global CO2 budget through June 2024 using near-real-time fossil emissions, four dynamic global vegetation models (DGVMs), ocean carbon emulators, and three OCO-2 atmospheric inversions. From July 2023 to June 2024, atmospheric CO2 grew by 3.66 ± 0.09 ppm yr-1, the highest July-to-July rate since 1979; after detrending, the anomaly was 1.1 ppm yr-1, comparable to previous major El Niño events. The anomaly was mainly driven by a 2.24 GtC yr-1 weakening of the net land sink, partly offset by 0.38 GtC yr-1 stronger ocean uptake. The tropics accounted for 97.5% of the land anomaly, led by the Amazon, central Africa, and Southeast Asia. This update shows how recent El Niño droughts drove the high CO2 growth rate through mid-2024 and how low-latency budgets can track carbon cycle extremes.

The greenhouse gas budget on the Tibetan Plateau remains unknown and the potential for methane (CH4) and nitrous oxide (N2O) emissions from an intensifying livestock system and expanding surface water in offsetting terrestrial carbon dioxide (CO2) sinks are both of great concerns and uncertainties, which compromise an accurate assessment of Tibetan Plateau contribution to China's ambitious climate goals by 2060s. Here we integrated greenhouse gas flux measurements at ∼500 sites in empirical modeling approaches, emissions from the livestock sector with process-based biogeochemistry modeling to estimate CH4 and N2O fluxes across terrestrial ecosystems and inland waters in 2000s and 2010s. We found that emissions from livestock and inland waters, predominantly contributed by CH4, compensated ∼21% and ∼13% of carbon sinks provided by forests and grasslands after adjusting carbon burial in sediments and riverine carbon export, respectively. The Tibetan Plateau then acted as an appreciable greenhouse gas sink that almost compensated for its contemporary anthropogenic emissions, making it nearly climate-neutral. The enhancement of terrestrial CO2 sinks in the 2060s under medium warming scenario would be counterbalanced by livestock CH4 emissions when the current overgrazing status continues. By transitioning to a livestock-forage balance and implementing mitigation initiatives to reduce livestock emission intensity, the greenhouse gas sink is projected to increase by more than 1.5 times. We suggested that a transition towards sustainable pastoralism illuminates the path to minimizing ecosystem greenhouse gas emissions and amplifying the role of the Tibetan Plateau in fulfilling China's climate ambition.

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ15N) in plants (δP) and soil (δS). Together with pairwise δP and δS measurements from 18 forest sites covering diverse climates and thousands of δP and δS observations worldwide, we show that the spatial distribution of the fraction of symbiotic BNF relative to the total external N acquisition by plants (f BNFs) is primarily controlled by temperature (29%) and mycorrhizal fungi (14%), with colder climate and higher ectomycorrhizal fungi abundance leading to a lower f BNFs. We find a large discrepancy between the spatial distributions of isotope-based BNF and those simulated by using Earth System Models (ESMs) in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Moreover, we constrain the global total BNF from natural terrestrial ecosystems as 78.2-89.8 Tg N yr-1, suggesting a ≥18% underestimation of the global BNF in CMIP6 models. In addition to the temperature dependence found in previous laboratory studies, our isotope-based study suggests a competitive relationship between BNF and mycorrhizal N uptake as another important control mechanism. This complex interplay remains unresolved in ESMs and has the potential to improve BNF simulations in the next phase of CMIP.

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.

This paper reviews the application of atmospheric inversions for estimating national CO 2 and CH 4 fluxes with a focus on China.After describing the fundamental principles and methodologies of the technique,we synthesize recent progress in estimating China's CO 2 and CH 4 budgets through atmospheric inversion,and compare these estimates with national greenhouse gas（GHG） inventory（NGHGI） reports.The inverted estimates for China's total CO 2 and CH 4 emissions amount to 8.35 ± 1.39 Pg CO 2 a -1 and 60.8 ± 5.9 Tg CH 4 a -1 ,respectively,in the last decade,which are in general consistent with NGHGIs.However,large uncertainties in spatial and temporal disaggregation of national budgets hinder the effectiveness of the method in verifying China's GHG budgets and improving NGHGI estimates.These uncertainties are largely driven by differences in inversion models,observational coverage and methodological assumptions.We recommend improving observational networks,conducting model intercomparison exercises and refining inversion methods to better support China's GHG reporting and future climate goals.

Terrestrial ecosystems play an important role in the global car-bon cycle,offsetting nearly one-third of annual anthropogenic car-bon emissions[1].This terrestrial carbon sink doubled in the past five decades and is projected to persist,primarily due to the fertil-ization of increasing CO2 on photosynthesis,particularly in tropical forest ecosystems and warming-induced productivity enhance-ment in arctic and boreal ecosystems[2].

The high growth rate of atmospheric CO2 in 2023 was found to be caused by a severe reduction of the global net land carbon sink. Here we update the global CO2 budget from January 1st to July 1st 2024, during which El Niño drought conditions continued to prevail in the Tropics but ceased by March 2024. We used three dynamic global vegetation models (DGVMs), machine learning emulators of ocean models, three atmospheric inversions driven by observations from the second Orbiting Carbon Observatory (OCO-2) satellite, and near-real-time fossil CO2 emissions estimates. In a one-year period from July 2023 to July 2024 covering the El Niño 2023/24 event, we found a record-high CO2 growth rate of 3.66 ± 0.09 ppm yr^-1 (± 1 standard deviation) since 1979. Yet, the CO2 growth rate anomaly obtained after removing the long term trend is 1.1 ppm yr^-1, which is marginally smaller than the July–July growth rate anomalies of the two major previous El Niño events in 1997/98 and 2015/16. The atmospheric CO2 growth rate anomaly was primarily driven by a 2.24 GtC yr^-1 reduction in the net land sink including 0.3 GtC yr^-1 of fire emissions, partly offset by a 0.38 GtC yr^-1 increase in the ocean sink relative to the 2015–2022 July–July mean. The tropics accounted for 97.5% of the land CO2 flux anomaly, led by the Amazon (50.6%), central Africa (34%), and Southeast Asia (8.2%), with extra-tropical sources in South Africa and southern Brazil during April–July 2024. Our three DGVMs suggest greater tropical CO2 losses in 2023/2024 than during the two previous large El Niño in 1997/98 and 2015/16, whereas inversions indicate losses more comparable to 2015/16. Overall, this update of the low latency budget highlights the impact of recent El Niño droughts in explaining the high CO2 growth rate until July 2024.

We estimated the emissions of different forms of gaseous nitrogen (N) from natural terrestrial ecosystems using newly upscaled soil delta 15N maps, data-constrained gas partitioning models, and incorporating the previously missing N input flux from rock weathering. The emissions for nitrous oxide (N2O), nitric oxide (NO) and dinitrogen (N2) are estimated at 12 +/- 3, 19 +/- 4, and 12 +/- 3 Tg N yr-1, respectively. The Sixth Phase of Coupled Intercomparison Model Project (CMIP6) models tend to overestimate total gaseous N emissions and thus N2O emissions. Correcting these total gaseous N emissions to match soil delta 15N maps and applying gas partitioning models, the CMIP6 models' N2O emission estimates drop to 7 +/- 2 Tg N yr-1, consistent with this study and N2O Model Intercomparison Project 2. Differences in gas partitioning models also contribute significantly to uncertainties in N2O emission estimates. This study underscores the need for improved modeling of gaseous N emissions and partitioning in CMIP6 models to better understand the responses and feedbacks of terrestrial ecosystems to climate change.

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.

>Current ground-based observation networks for atmospheric greenhouse gases (GHG) volume mixing ratios are sparse,with only two stations—namely Waliguan and Shangri-La—located on the eastern border of the Tibetan Plateau (TP)providing publicly available data (Masarie et al., 2014).

The Qinghai-Tibetan Plateau (QTP) is a key system that impacts the global carbon balance, but greenhouse gas (GHG) mole fraction measurements in this region are limited due to the tough environment. Supported by the Second Tibetan Plateau Scientific Expedition Program, we carried out an integrated GHG measurement campaign in May 2022 as part of the Earth Summit Mission-2022 at the Qomolangma station for atmospheric and environmental observation and research (QOMS; 28.362 degrees N, 86.949 degrees E; 4276 m a.s.l.). In this study, the first GHG column-averaged mole fraction measurements (Xgas) at QOMS are presented, including XCO2, XCH4, XCO, and XN2O, derived from a ground-based Fourier-transform infrared spectrometer (FTIR; Bruker EM27/SUN). We then compare them to surface in situ and satellite (the TROPOspheric Monitoring Instrument, TROPOMI, and the Orbiting Carbon Observatory-2, OCO-2) measurements. The mean FTIR XCO2 and XCH4 are 7.8 ppm and 97 ppb less than those near the surface, respectively. The difference between OCO-2 land nadir and EM27/SUN XCO2 measurements is 0.21 +/- 0.98 ppm, which is consistent with OCO-2 retrieval uncertainty. However, a relatively large bias (1.21 +/- 1.29 ppm) is found for OCO-2 glint XCO2 measurements, which is related to the surface albedos and surface altitudes. The EM27/SUN measurements indicate that the uncertainty in OCO-2 satellite XCO2 measurements is relatively large in the QTP mountain region, and its quality needs to be further assessed. The difference between FTIR and TROPOMI XCO measurements is -5.06 +/- 5.36 (1 sigma) ppb (-4.7 +/- 5.1 %) within the satellite retrieval uncertainty. The XCO measurements at QOMS show that the local air mass is largely influenced by atmospheric transport from southern Asia, and it is important to carry out long-term measurements to quantify the contribution of the cross-regional transport in this region.

Understanding the vertical profile of greenhouse gases (GHGs) is crucial for elucidating their sources and sinks, transport pathways, and influence on Earth's radiative balance, as well as for enhancing predictive capabilities for climate change. Remote sensing methods for measuring vertical GHG profiles often involve substantial uncertainties, while in situ measurements are limited by high equipment costs and operational expenses, rendering them impractical for long-term continuous observation efforts. In this study, we have developed an automatic low-cost and user-friendly multi-altitude atmospheric sampling device designed for small- and medium-sized unoccupied aerial vehicles (UAVs), balloons, and other flight platforms. A field campaign was carried out in the Mount Qomolangma (also known as Mount Everest) region, at an average surface altitude of 4300 m above sea level (a.s.l.). During the campaign, we conducted 15 flights and collected 139 samples from the ground surface up to a height of 1215 m using a hexacopter UAV platform equipped with the sampling device. The samples were analyzed using the Agilent gas chromatography (GC) 7890A instrument, enabling the derivation of the vertical profiles for four GHG species (CO2, CH4, N2O, and SF6) within the boundary layer of the Mount Qomolangma region. To enable long-term monitoring using small UAVs, future efforts should prioritize reducing the weight of the equipment and improving the sampling efficiency.

Affected by numerous uncertainties, climate change is a critical issue linked to carbon emissions that warm the planet. Although scholars have conducted detailed research on carbon emissions and established predictive models for them, there are few models specifically designed for predicting carbon emissions during public health emergencies. With the concentrated outbreak of various uncertain factors, organizations and institutions urgently need a model capable of predicting carbon emissions during public health emergencies. This study introduces a novel self-attention multi-neuron time series (SAMNTS) model to evaluate the previously unexplored impact of public health emergencies on carbon emissions. Specifically, we have designed a more comprehensive deep learning prediction framework that can effectively utilize a large amount of relevant data to conduct detailed reasoning and analysis on the issue of carbon emissions, enabling more accurate predictions of daily carbon emissions. To better test its effectiveness, we used COVID-19 as an example to test the model. The results proved that the model can effectively make predictions and analyze various factors that affect carbon emissions.

Atmospheric moisture transport is pivotal in regulating water resources over the Tibetan Plateau (TP). With the growing concerns about climate change, understanding the evolution of atmospheric moisture transport over the TP has become increasingly critical. however, the spatiotemporal distinctions of this transport remain poorly understood in the CMIP6 models. Here, we conducted a comprehensive evaluation of simulated historical atmospheric moisture transport from 33 CMIP6 models, utilizing a novel methodology that assesses the accuracy of model simulations in replicating regional atmospheric moisture transport over the TP. Our results indicate that the CMIP6 models generally succeed in reproducing the broad spatial patterns of atmospheric moisture transport. Nonetheless, substantial errors occur during the monsoon period, primarily attributable to inaccuracies in the location, movement, and intensity of the simulated Indian summer monsoon. The coarser resolution and poor representation of physical processes are potential reasons for errors in atmospheric moisture transport simulation over the TP. The Failure to simulate the terrain blocking on atmospheric moisture transport exacerbates these deficiencies, leading to significant discrepancies. Of the 33 CMIP6 models we investigated, over one-third displayed serious deficiencies in this regard. While coarser resolution and orographic gravity waves are plausible factors, they do not fully account for all the results obtained in this study. Insufficiently detailed or inaccurate topographic data used in the models may also contribute to this deficiency. This study highlights the necessity of using rigorously evaluated models to develop effective regional adaptation strategies over the Tibetan Plateau.