The natural land carbon sink (SLAND) absorbs roughly 25–30% of anthropogenic CO2 emissions, thus playing a critical role in offsetting climate warming. In the Global Carbon Budget (GCB), SLAND is estimated using model simulations that isolate the carbon response of land to environmental changes (i.e. rising atmospheric CO2, nitrogen deposition, and changes in climate). However, these simulations assume fixed pre-industrial land cover, failing to represent today’s human-altered landscapes. This leads to a systematic overestimation of forest area, and thus CO2 sink strength, in regions heavily altered by human activity. We present a new process-based approach to estimate SLAND using Dynamic Global Vegetation Models. Our corrected estimate reduces SLAND by ~20% (0.6 PgC yr-1) over 2015–2024, from 3.00 ± 0.94 to 2.42 ± 0.77 PgC yr-1. We incorporate this new SLAND estimate with emissions from land-use change from bookkeeping models, to estimate a net land sink of 1.19 ± 1.04 PgC yr-1, which aligns closely with atmospheric inversion constraints. This downward revision of SLAND reduces the magnitude of the budget imbalance for 2015–2024, indicating a more consistent partitioning of the global carbon budget.

Forest cover has expanded across tropical and subtropical Asia in recent decades, but area alone is an inadequate metric because it does not capture forest structure, which is critical for supporting key ecosystem functions and services, including biodiversity. Here, we apply sub-meter resolution satellite imagery to characterize the complexity of forests from a horizontal perspective by quantifying the diversity of tree crown sizes and their spatial arrangement across India, Southeast Asia, and southern China. We reveal large mismatches between reported forest area and the complexity of the forests. While overall, the majority of the forests still have a relatively high complexity, a recent change towards low complexity forests is observed. India, despite its large forest extent, shows a particularly low forest complexity, with more than half of its forests classified as low-complexity. Analysing forests affected by gain or loss in area between 2000 and 2024, we find that approximately three quarters of newly established forests are of low structural complexity, while high-complexity forests, concentrated in Myanmar, Laos, Cambodia, and Indonesia, continue to disappear. Our results highlight the need to move beyond forest cover statistics toward quality-based indicators for monitoring in the support of conservation policy.

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 Afforestation connects isolated forests into larger contiguous forests, reducing forest fragmentation. This process decreases edge areas by transforming edge forests into new interior forests (termed transformed forests). However, the extra climate benefits from edge reductions in transformed forests, beyond those provided by the planted forests themselves, remain unclear. Here, CO 2 sequestration from increased biomass (biogeochemical effect) and emissions from decreased albedo (biophysical effect) of transformed forests in China are estimated, using multiple high‐resolution remote‐sensing datasets. The planted forest area (89.6 M ha) accounted for 35.5% of China's forest area in 2015, transforming 51.8 M ha of edge forests into interior forests. A cumulative increase of 1.4±0.2 Pg CO 2 e in the transformed forests is found, compared with a biomass increase of 10.3±0.4 Pg CO 2 e in the planted forests over ~1980–2015. These transformed forests also induce a biophysical warming effect of −0.9 Pg CO 2 e, partially offsetting the cooling effect from increased biomass. Combining both effects, transformed forests provide a net CO 2 e gain of 0.5±0.2 Pg CO 2 e, representing an extra 6.6±2.7% of the direct climate benefits from planted forests. This study reveals previously ignored extra climate benefits from reduced forest fragmentation alongside forest expansion, offering new perspectives on mitigating climate warming through afforestation.

China, the world’s largest CO2 emitter, leverages forest area expansion as a strategy to achieve carbon neutrality by the 2060s. However, the respective contribution of reforestation on previously forested land and afforestation on non-forested land, remains uncertain due to limited observational data and the absence of spatially and temporally explicit analyses. Here we used locally derived aboveground biomass accumulation curves for each 1° grid cell and a spatially explicit bookkeeping model to track annual carbon uptake from reforestation and afforestation alongside emissions from forest disturbances. We then estimated the forest carbon balance of China for different forest types at 30 m resolution from 1986 to 2019. Forest biomass carbon sinks averaged 0.139 ± 0.052 PgC yr−1, increasing from 0.1 ± 0.015 PgC yr−1 in the 1990s to 0.2 ± 0.012 PgC yr−1 in the 2010s. Despite afforested tree cover expanding faster (1.31 Mha yr⁻1) than reforestation after disturbance (1.06 Mha yr⁻1), post-disturbance regrowth exhibited a higher growth rate (1.47 ± 0.42 MgC ha−1 yr−1) in carbon sequestration than that of afforestation (0.96 ± 0.28 MgC ha−1 yr−1), making reforestation the dominant contributor to China’s forest carbon sink over the past three decades. The results inform forest carbon accounting and prioritize protecting regrowing forests alongside targeted afforestation to achieve carbon neutrality. Reforestation on previously forested areas after disturbances exhibits higher post-disturbance carbon sequestration than afforestation on previously unforested land, and has dominated China’s forest carbon sink over the past three decades, according to above- and below-ground biomass estimates.

The widespread increase in vegetation productivity plays an important role in enhancing ecosystem carbon uptake. While it is well established that biodiversity increases ecosystem productivity, its influence on long-term changes in photosynthesis remains unclear. Here we integrate a high-resolution map of tree species richness with satellite-derived photosynthesis proxies during 2001–2020 to show that high richness not only enhances current levels of photosynthesis but also correlates with a greater increase in photosynthesis over time. This pattern is largely driven by an amplified CO2 fertilization effect (CFE) in species-rich forests. The ability of diverse forests to mitigate water and nutrient limitations probably contributes to the CFE enhancement and photosynthesis rise. Projections suggest that biodiversity losses by 2050 could reduce photosynthesis trends by 3–17%, representing a cumulative forest photosynthesis loss of 4.4–35.7 PgC. These findings underscore the critical need to integrate biodiversity conservation into climate mitigation strategies to safeguard the terrestrial carbon sink. The authors integrate tree species richness with satellite-derived photosynthesis proxies to show that richness correlates with greater current levels of photosynthesis and greater increases over time. Projected biodiversity loss by 2050 could lead to cumulative forest photosynthesis loss of 4.4–35.7 PgC.

The stability of permafrost is regulated by the thermal insulating properties of soil organic carbon (SOC). However, intensifying wildfires across the Arctic and boreal regions are removing the protective soil organic layer, which may trigger positive feedback that accelerates thaw, yet the pan-Arctic scale of this threat remains unknown. Here we present a data-driven framework, aiming to address two questions: i) what is the net SOC loss due to fire across the northern permafrost zones, accounting for immediate SOC combustion and post-fire recovery, and ii) to what extent does this fire-induced SOC reduction accelerate permafrost degradation. To achieve this, we developed a bookkeeping model parameterized by SOC data from over 1,000 paired burned and unburned sites across diverse ecosystems to simulate fire-induced SOC dynamics, and a permafrost probability model based on air temperature and SOC content, advancing earlier temperature-only approaches. Driven by CMIP6 climate and burned area projections, we find that under SSP1-2.6, fire-induced SOC loss, considering both combustion and post-fire recovery, reaches 15.0±3.6 Pg C by 2100. This SOC reduction diminishes the soil’s insulative capacity, leading to an additional permafrost loss of 2.7±0.7 million hectares. This impact is most pronounced under low-emission scenarios, where permafrost exists in a climatically marginal state; here, every 1 km² of increased burned area causes 0.19 km² of additional permafrost loss. Under SSP5-8.5, fire-driven permafrost loss is less pronounced as rapid atmospheric warming is the predominant driver. Our findings reveal that wildfire is an efficient agent of permafrost thaw, highlighting the urgent need to incorporate dynamic fire-SOC-thermal interactions into ESMs to avoid underestimation of future permafrost degradation.

Global warming accelerates the breakdown of carbon stored in permafrost regions, releasing it into the atmosphere and amplifying climate change, particularly during winter when photosynthesis ceases. The Northern Hemisphere's permafrost is primarily concentrated in two key regions — the Arctic and the Tibetan Plateau — each with distinct environmental characteristics. However, previous studies often treat these regions separately, missing the opportunity to compare their winter CO2 emissions within a unified framework. Here, we synthesized 2,487 monthly CO2 flux measurements from 166 in-situ sites to quantify the spatial and temporal variations and key drivers of winter CO2 emissions in these two regions. Our analysis reveals that combined winter emissions from the Arctic and Tibetan Plateau are estimated to be 1,289 ± 25 Tg C yr-1. From 1982 to 2022, winter CO2 emissions increased by 2.10 ± 0.23 Tg C yr-1. Notably, since 2001, winter CO2 emissions have surged in the Arctic while declining in the Tibetan Plateau. The driving factors also differ: soil temperature dominates in the Arctic (51%), whereas soil moisture plays the most significant role on the Tibetan Plateau (33%). These findings highlight the contrasting mechanisms governing winter carbon emissions in these regions and underscore the importance of incorporating region-specific factors when predicting permafrost-carbon feedbacks in a warming world.

Climate change reshapes forest biophysical effects, yet the impact direction and strength remain uncertain. Here we quantify the growing-season land surface temperature between forests and adjacent open land (triangle LSTgs) and show contrasting temporal trends in triangle LSTgs across the globe during 2001-2023. Rising vapour pressure deficit (VPD) has emerged as the primary driver of these contrasting trends, surpassing other common climatic factors. By contrast, plant anisohydricity-an indicator of stomatal regulation behaviour-is the most important forest trait that negatively modulates the strength of the triangle LSTgs response to VPD variability. At low latitudes, forests are more isohydric, and rising VPD has exceeded the hydraulic safety margin, resulting in weakened cooling. Conversely, high-latitude forests are more anisohydric; VPD remains below the safety margin, and rising VPD thus leads to enhanced cooling. These results highlight that the overall climate benefits of global forests may be undermined if global VPD continues to intensify in future.

Accelerating permafrost thaw may release vast deep (>3 meters) frozen soil carbon as carbon dioxide (CO 2 ), but this magnitude remains uncertain because current Earth system models (ESMs) lack deep carbon processes. Using an updated ORCHIDEE-MICT model simulating Pleistocene Yedoma formation and Holocene peatland development, we project northern (>30°N) carbon responses under climate change. Compared to the original model, including these deep carbon pools improves agreement with observations and reduces net CO 2 uptake by 47 to 74 petagrams of carbon from 1900 to 2100 across three future scenarios because of deep carbon decomposition with accelerated active-layer deepening. Under high-emission pathways, the northern soil carbon balance shifts from a sink to a source of 32 petagrams of carbon, advancing the reversal reported in earlier studies into the 21st century. Consistent with field data, our model shows that colder soils retain more labile carbon—contrary to assumptions in many Coupled Model Intercomparison Project (CMIP) models—helping explain their persistent sink bias. Our results highlight the need to represent both the quantity and quality of permafrost carbon in ESMs.

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.

Wastewater treatment is an increasingly important yet poorly quantified source of anthropogenic methane (CH4). Here we report facility-level emissions based on atmospheric measurements from 105 wastewater treatment plants (WWTPs) across five climatic-economic regions in China-the largest dataset to date. We found that emission factors are primarily driven by organic load and concentration. Using updated facility-level emission factors, our analysis shows that emissions from Chinese WWTP, driven by rising organic loads, grew by 12% per year since 2003, reaching 254 ± 26 Gg CH4 year-1 in 2023. However, the rapid expansion of WWTPs lowered the average emission factor for the urban domestic wastewater sector, limiting total emissions growth to 32% over the same period. Scenario modeling suggests that, under current technology, emissions will peak around 2040. Deploying low-emission configurations and CH4 recovery technologies could advance the peak by 15 years and reduce 2040 emissions by 23%. Incorporating such measures into China's decarbonization strategy will be essential for achieving climate mitigation goals.

Peatlands cover just 3% of Earth's land surface, yet store an estimated 600-700 Pg carbon (PgC), approximately one-third of Earth's soil carbon, making them critical regulators of the global carbon cycle. However, peatland spatial extent remains highly uncertain, particularly at fine spatial scales and in data-sparse regions. Existing global peatland datasets rely on heterogeneous inventories and regional products, leading to large inconsistencies in both total peat area and spatial distribution. These limitations hinder accurate assessments of peatland-climate feedbacks, carbon budgets, national policy development, and restoration efforts. We propose a machine learning framework that combines a priori information from existing peat databases (PEATMAP, Global Peatland Database, and CORINE Land Cover) with satellite observations in the visible, together with topographic and hydrological information. Our methodology employs a neural network trained with 17 input variables including Landsat-8 surface reflectance, topographic attributes from the MERIT database (elevation, slope, distance to drainage, height above drainage), and water table depth data. The model first generates a continuous Peatland Index (PI) at 3 arc-second (~90m) resolution, that can be thresholded to obtain a binary peat classification. In regions with reliable coarse resolution peat information, the PI can be used to downscale it and obtain a coherent high resolution peat classification. The obtained pan-boreal/Northern Hemisphere peatland map at 90m was evaluated through both quantitative and qualitative approaches. Fully independent validation using the Peat-DBase field dataset (over 180,000 peat and non-peat observations) demonstrates an overall accuracy of 68.4% and an F1-score of 0.80. Regional assessments show 69.2% overall accuracy (F1=0.81) in Eurasia and 63.8% (F1=0.74) in North America. Qualitative spatial evaluation across multiple case-study regions reveals that the proposed map successfully captures fine-scale spatial details absent in existing inventories, including explicit delineation of open water bodies, river networks, and topographic constraints on peatland distribution. The product exhibits improved spatial coherency with high-resolution imagery while remaining consistent with large-scale patterns from current peat databases. This work provides a spatially coherent, high-resolution peatland dataset spanning the Northern Hemisphere, offering improved capabilities for carbon stock estimation, hydrological modeling, and monitoring peatland degradation. Future improvements will incorporate SAR data, additional environmental drivers, and deep learning-based feature extraction to further enhance classification accuracy, spatial details, time-evolution, and peat information.

Mangroves are characterized by high carbon sequestration rates, and future changes in mangrove biomass will be impacted by factors such as climate change, sea level rise, and management strategies. Here, we produce a map of present-day global mangrove aboveground biomass (AGB) based on extensive field observations and satellite data, giving a global mangrove AGB of 1.76 Pg dry matter (DM). After accounting for potential growth to maturity, future changes in climatic and hydrological conditions, possible restoration strategies, and sea-level rise, the global total mangrove AGB is projected to increase by 16.7% under the low-warming scenario (Shared Socioeconomic Pathway 1 and Representative Concentration Pathway 2.6, SSP126) and decrease by 19.3% under the high-warming scenario (SSP585) by 2100. Sea-level rise encroaching upon the growth space of mangroves will cause a biomass loss of 41.6% in the high-warming scenario, whereas restoration strategies would only increase AGB by 2.0%. Our study shows that sea-level rise limits the potential of mangrove restoration to maintain carbon stocks, and regions less constrained by sea-level rise offer greater potential for the long-term permanence.

The CO2-fertilisation effect (CFE) on vegetation productivity is the major driver of the enhanced land carbon sink in recent decades. CFE theoretically increases with elevation due to the higher sensitivity of carboxylation to an increase of CO2 under lower CO2 partial pressure, but the elevation-dependent CFE pattern has been largely overlooked. By conducting a 6-year CO2 enrichment experiment (+100 ppm) in an alpine grassland, we show that elevated CO2 increased gross primary production (GPP) by 25.5% ± 4.6%. Water availability and plant biomass allocation modulates CFE during different seasons. A global synthesis of 10 CO2 enrichment experiments reveals that CFE increased with elevation. The satellite-based EC-LUE model also demonstrates a positive global elevation-dependent CFE pattern, albeit substantially weaker than that from experimental observations. Current terrestrial biosphere models, however, could not represent the elevation-dependent pattern, highlighting the need to improve the representations of plants' elevational physiological adaptation to rising CO2 in models.

In 2023, the biogeographic Amazon experienced temperature anomalies of 1.5 degrees C above the 1991-2020 average from September to November. These conditions were driven by high sea surface temperature in the Atlantic and Pacific oceans, together with reduced moisture advection from the Atlantic, causing large vapor pressure and water deficits in the second semester of 2023. Here, we evaluate the response of the Amazon carbon cycle to this extreme event across different spatial scales. We combined atmospheric CO2 mole fractions and eddy covariance flux data from the Amazon Tall Tower Observatory (ATTO, -2.1441, -58.99), low-latency simulations by Dynamic Global Vegetation Models (DGVMs), an atmospheric inversion, and remote sensing data. We find that in 2023 the Amazon region was, including fires, a net carbon source of 0.01-0.17 PgC. Fire emissions (0.15 [0.13-0.17] PgC) were within typical variability of the 2003-2023 period, thus we attribute the weak carbon source to reduced vegetation uptake during the dry season (August-October). A stronger-than-normal vegetation uptake early in the year (January-April), consistent across data streams and spatial scales, mitigated the total carbon losses by the end of the year. We find a shift from carbon sink to source in May and a peak source in October. Our findings show a reduced vegetation carbon uptake over the Amazon region, leading to a weak carbon source that contributed up to 30% of the net carbon loss in the tropical land in 2023.