Philippe Ciais

Species- and region-specific growth curves, forest age (2020), and disturbance trends for French forests at 30 m resolution

Spatially explicit biomass growth curves data that jointly resolve tree species, stand age and disturbance regime are scarce, particularly at fine spatial resolutions. We present five consistent data products for metropolitan French forests around 2020, derived from a 30 m dominant-species map, the 30 m FORMS-B satellite above-ground biomass (AGB) map and the Landsat-based European Forest Disturbance Atlas: (1) 290 AGB growth curves for 28 species categories across 11 large ecological regions; (2) a 30 m forest age map for 2020 covering 17.01 Mha of forests, obtained by inverting these curves against the AGB map; (3) 3,752 harvest and natural disturbance rates and patch sizes, with their 1999–2024 trends; (4) 3,752 event-based disturbance severities (AGB loss per disturbed hectare); and (5) 1,874 rotation periods from National Forest Inventory (NFI) plot samples remeasurements. Products 3–5 cover 28 species categories across 86 small ecological regions. These cover the species×region combinations present in French forests, not the full combinatorial grid. Not all 28 species categories occur in every ecological region; the totals reflect strata with forest area only. All products include uncertainties and gap-filling flags. Growth curves match National Forest Inventory-derived curves for early growth (+12±15 %, 95 % CI) but give a higher asymptotic biomass (+22±18 %) and a later half-saturation year (+33±23 %), partly reflecting differences in the representation of age extremes, as NFI measurements exclude trees <7.5 cm in diameter and we aggregate stands >60 years into a single age class. The age map matches the NFI nationally aggregated age distribution (median offset −2 [−20 to +42] yr). The 2020 harvested area (44±6 kha) and the resulting AGB loss (2.5±0.3 MtC.yr−1) are of the same order as independent clear-cut estimates (62 kha from the NFI; ~3 MtC.yr−1 from satellite-mapped clear-cuts over 2018–2024). The Landsat disturbance product captures only stand-replacing and high severity events and underestimate selective logging and diffuse mortality. These products can be combined directly to model forest carbon dynamics at species and regional resolution. All data are openly available at https://doi.org/10.5281/zenodo.23499335 (Pellissier-Tanon et al., 2026).

HaNi-Crop: A Harmonized and Gridded Global Dataset of Crop-Specific Nitrogen Inputs from Nitrogen Fertilizer and Manure

Crop-specific dataset on nitrogen (N) inputs from synthetic fertilizer and manure are critical for assessing crop productivity, nutrient use efficiency, and environmental impacts, as crops differ substantially in their N demands and utilization. However, existing datasets remain limited in their simultaneous coverage of crop-group specificity, spatial resolution, long-term temporal coverage, and fertilizer-form differentiation. These limitations have constrained comprehensive global and regional assessments of crop-specific greenhouse gas emissions, N use efficiency, N budgets, and agrifood-system resilience. Based upon the History of Anthropogenic Nitrogen Inputs (HaNi) data infrastructure (Tian et al., 2022), we present HaNi-Crop, a harmonized global dataset of crop-specific N inputs from synthetic fertilizer and manure for 21 major crop groups at a spatial resolution of 0.1° × 0.1°, spanning the period of 1961–2023. This dataset integrates national and sub-national fertilizer and manure statistics, crop harvested-area maps, fertilizer product information, and spatial allocation procedures to generate annual gridded estimates of crop-specific N application rates differentiated by NH4+-N and NO3--N forms. Globally, synthetic fertilizer N inputs to cropland increased sharply from 11 Tg N yr-1 in the early 1960s to more than 100 Tg N yr-1 by 2023, while manure N inputs increased from 14 to 23 Tg N yr-1. Throughout 1961–2023, three major cereal crops–wheat, maize, and rice, consistently accounted for approximately half of global synthetic fertilizer N inputs. Among N fertilizer forms, NH4+-N inputs increased from 8 to nearly 90 Tg N yr-1, while the inputs for NO3--N fertilizers only increased from 3 to about 12 Tg N yr-1. Overall, the HaNi-Crop dataset provides a critical foundation for crop-specific greenhouse gas accounting, Earth system and agroecosystem modeling, and N budget analyses to enhance food security, environmental sustainability, and climate mitigation. The dataset is publicly available at https://doi.org/10.5281/zenodo.21364978 (Hao et al., 2026).

Improved estimation of net ecosystem CO 2 exchange over North America using LSTM-based flux upscaling (2001–2021)

Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) network method shows potential for improving regional carbon budget upscaling estimations. Here, using LSTM, we upscaled regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1 ° ×0.1 ° over 2001–2021 (labelled as MemoryFlux). Unlike existing upscaling estimates, our dataset properly identified the Midwest Corn Belt as a region of large seasonal carbon uptake during the peak growing season, a feature revealed by previous top-down studies and recognized as a model benchmark. Moreover, the seasonal variations in NEE estimated by MemoryFlux were strongly correlated with those from independent atmospheric inversions, including the ensemble mean of the Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r =0.96, p <0.001) and CarbonTracker2022 (CT2022) ( r =0.97, p <0.001). The mean annual NEE was estimated at −1.27 ± 0.12 Pg C yr −1 , which was closer in magnitude to inversions (−0.83 to −0.70 Pg C yr −1 ) than existing upscaling estimates (−3.30 to −1.68 Pg C yr −1 ). In addition, MemoryFlux captured spatial NEE anomaly patterns associated with six selected severe drought and flood events. We further found that explicitly incorporating historical predictor information improved the representation of NEE interannual variability and spatial anomalies associated with climate extremes. MemoryFlux provides an improved bottom-up estimation of North American NEE and shows greater consistency in magnitude with independent atmospheric inversion estimates than several existing EC-based upscaling products. The MemoryFlux dataset is available at https://doi.org/10.5281/zenodo.20482274 (Huang and He, 2026).

Net carbon losses in Central African forests revealed by high-resolution biomass change maps

Dense humid forests in Central Africa hold vast biomass carbon stocks but are increasingly affected by small-scale disturbances that are difficult to detect with current satellite observations. Here, we generate 10 m canopy height maps for 2019–2022 using deep learning with spaceborne lidar and Sentinel-1/−2 imagery, and derive aboveground biomass changes at 30 m resolution. The maps reveal widespread small disturbance patches, 87% smaller than 1 ha. These account for 48% of biomass carbon gains in regrowing forests and 37% of carbon losses from deforestation. We estimate a net forest carbon loss of −58 ± 14 Tg C yr−1, with gross losses of −126 ± 21 Tg C yr−1 partially offset by gains of 68 ± 10 Tg C yr−1. The Democratic Republic of the Congo emerges as a net carbon source (−46 ± 11 Tg C yr−1), representing 79% of the regional loss, primarily due to degradation, despite having the largest carbon gains in young secondary forests. Our remote-sensing estimates of national carbon budgets are broadly consistent with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring. High-resolution biomass changes maps reveal net carbon losses across Central African forests, show that small disturbances drive carbon changes, and provide spatially explicit national carbon budgets broadly consistent with independent estimates.

Biome classification across global vegetation models reveals consistent biome shifts under future climate change

A Global Database of Soil Methane Uptake (SMUD)

Soil methane uptake (SMU), an important biological sink for atmospheric methane (CH 4 ) in terrestrial ecosystems, represents an important component of the global CH 4 budget. Although field SMU observations have been conducted since the 1980s, they have not yet been systematically compiled into a unified and openly available database. Here, we present the global Soil Methane Uptake Database (SMUD), which includes 2427 site-level records from 920 peer-reviewed publications spanning 1986 to 2025. SMUD contains SMU measurements at multiple temporal scales, including daily ( n =1425), monthly ( n =2001), seasonal ( n =1720), and annual ( n =1098), together with associated soil moisture and soil temperature data at daily and monthly scales where available. The collected datasets cover all major climatic zones and ecosystem types and reveal pronounced spatial and temporal variability in SMU. Our analyses show that seasonal- and biome-specific patterns are important for understanding intra-annual variability in SMU. In particular, quantitative classification showed that the relationships of SMU with soil temperature and moisture differed among biomes, resulting in four response types: temperature-dominated, moisture-dominated, jointly regulated by temperature and moisture, and no clear dominant control. Overall, SMUD provides a comprehensive basis for characterizing global SMU patterns, investigating underlying mechanisms, and improving estimates of the global SMU budget. The database, associated data, and code for reproducing the results are available at https://doi.org/10.5281/zenodo.20735332 (Jiang et al., 2026). We aim to maintain SMUD as a collaborative, community-supported, and regularly updated resource for the research community.

Increased maximum carbon release offsets half of the growth trend in annual land carbon sink

Low Latency Global Carbon Budget Reveals Late 2024 Carbon Losses and Contrasting Early 2025 Land Sink Recovery Signals

CiaisGlobal Biogeochemical Cycles

Lateral Accounting of Transport in Terrestrial Ecosystems (LATTE): A High‐Resolution Global Land‐Atmosphere CO 2 Flux Data Set From 1961 to 2022 for Atmospheric Inversions and Carbon Budgeting Studies

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.

Resolving effects of leaf pigmentation changes and plant residue on the energy balance of winter wheat cultivation in the ORCHIDEE-CROP model

Accelerating biomass loss from forest disturbances across Europe

Forest disturbances are rising globally due to climate change and land-use pressures, weakening the terrestrial carbon sink. However, declines in aboveground biomass from tree cover loss remain poorly quantified, limiting our understanding of the role of disturbances in global carbon dynamics. Here we present a spatially explicit estimate of aboveground biomass losses from natural disturbances and harvest across Europe’s 216 million hectares of forests, using satellite remote sensing. From 1985 to 2023, gross aboveground biomass losses totalled 6.5 ± 0.8 Pg. Of these, 18% were caused by high-severity natural disturbances, whereas stand-replacing harvests accounted for 82%. From 2018 onward, aboveground biomass losses increased by 46% reaching annual values unprecedented in the preceding four decades. This acceleration coincided with high natural disturbance activity in biomass-rich temperate forests. After 2018, the sensitivity of aboveground biomass loss to disturbance area increased substantially, suggesting that even small increases in natural disturbances can result in large losses. As climate-driven natural disturbances intensify, we thus expect sustained aboveground biomass losses across Europe, despite policies to enhance the forest carbon sink until 2030.

Pervasive Building Height Thresholds for Urban Heat Response and Implications for Climate Mitigation

Urbanization is accelerating vertical urban development, yet the complex relationship between building height (BH) and urban heat island (UHI) intensity remains underexplored. Using 30‐m high‐resolution urban building data from 2005 to 2020, we quantify the impact of BH on UHI across 11,290 urban grid cells in 331 Chinese prefecture‐level cities and assess the potential for UHI mitigation through BH composition reorganization. Our analysis reveals a critical BH threshold (BH crit ) that marks a shift in the thermal effect of BH. Below BH crit , increasing BH intensifies UHI effects; beyond this threshold, further height increases result in cooling—a finding that challenges the conventional assumption that taller buildings consistently exacerbate urban heat. We further project that, in principle, China's urban grid cells could reduce UHI intensity through BH reorganization, with a national average mitigation potential of 0.31 ± 0.004°C. These findings underscore the importance of optimizing vertical urban composition as a proactive UHI mitigation strategy and provide valuable insights for sustainable urban densification from a three‐dimensional perspective.

Low latency global carbon budget reveals land sink decline during 2023 to 2024 El Niño

Vulnerability to high temperature shapes global warming impacts on rice yield

Bayesian Inversion of NO x Emissions Over Northern Egypt Using TROPOMI‐NO ₂ Satellite Observations

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems

A meta-analysis of carbon losses and gains from tropical moist forest degradation and regeneration

Aboveground carbon (AGC) fluxes from deforestation and subsequent regrowth in tropical moist forest (TMF) are increasingly well characterized, but carbon losses and gains following partial disturbance are uncertain. We synthesized 146 studies quantifying postdisturbance AGC changes relative to undisturbed forests across TMF. Immediate AGC losses (mean ± 1 SD; 2.5 ± 2.3 years after disturbance) following partial anthropogenic disturbances were greatest for forest fires (49 ± 26%), selective logging (34 ± 20%), and edge effects (31 ± 19%). Higher-frequency and -intensity disturbances significantly increased carbon loss. After 20 years of regeneration, AGC stock was higher in recovering degraded forests (41 to 117%) compared to secondary regrowth forests after complete deforestation (1 to 74%), indicating greater regeneration potential when forest structure is preserved. Our compiled database and associated meta-analysis improve accuracy and completeness for carbon inventory reporting and modeling. Substantial AGC losses and gains from distinct degradation and recovery processes are now better characterized, serving as an evidence base for policies to halt degradation and foster recovery for climate mitigation.

Assimilation of Satellite‐Based Forest Biomass, Productivity and Leaf Area Index in a Global Land Model Incorporating Parameter Spatial Variations and Age Effects

Observation-constrained sensitivities of Arctic methane emissions to warming

Hidden Hurdles in Agricultural Water Scarcity Assessments: A Case Study in China

Precise agricultural water scarcity assessment is the precondition for advancing agricultural water management under climate change. It reflects the deficit between agricultural water demand () and supply potential (), primarily in the irrigation sector. However, neither nor can be directly observed at large scales, and current assessments rely on model‐derived estimates whose accuracy cannot be validated. These unverified estimates remain the only means by which scientists assess water scarcity. Here we propose a novel observational approach to qualify the difference between and by monitoring how real irrigation water withdrawal responds to climate‐induced variability in water demand and supply. Using China as a case study, we demonstrate remarkable uncertainties in Global Hydrological Model estimates, including inverse prediction of inter‐annual irrigation withdrawal fluctuations in many provinces. Contrary to previous studies, our findings reveal that water stress seems more prevalent in southern China than in northern China during the study period. Earlier studies may underestimate in northern China by overlooking non‐renewable water sources such as groundwater and glacier melt. While in southern China, may be systematically overestimated by ignoring the strong limitations imposed by storage, conveyance, and irrigation infrastructure. This is the first time that the imbalance between and has been evaluated from an observation perspective, revealing overlooked scarcity in water‐rich regions and challenging long‐held perceptions of China's water stress patterns. We anticipate that our approach will serve as an new path for validating water scarcity assessments. It can also be an auxiliary tool to identify missing factors in Global Hydrological Models, improving the accuracy of future projections for agricultural water management.