Abstract. We present a new version of the PROMICE | GC-NET automatic weather station (AWS) data product, combining observations from two Greenland AWS networks; PROMICE and GC-NET. As of late 2025, the dataset integrates records from 52 active and historical AWS sites across the Greenland Ice Sheet, peripheral glaciers and land areas. This new version includes improvements in station design, sensor configuration, and data processing. Two primary station types are used: dual-boom masts in the accumulation area, and free-standing tripods with a single instrument boom in the ablation area. Data are processed with pypromice, an open-source Python package designed for standardized, transparent, and reproducible workflows, including calibration, filtering, variable derivation, and correction. The resulting products are distributed in CF-compliant NetCDF and CSV formats and include both measured and derived variables for applications in polar meteorology, climatology, and glaciology. Access is open under license CC-BY 4.0. A GitHub-based issue tracker (https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues, last access: 12 November 2025) supports community-driven quality control within a living data framework. The datasets are openly available at https://doi.org/10.22008/FK2/IW73UU (How et al., 2022a).

Abstract. River ice plays a critical role in Arctic freshwater routing, navigation safety, and biogeochemical exchange. However, consistent, daily-resolved observations across the pan-Arctic remain scarce. Here we present a harmonized, multi-decadal dataset of daily river ice concentration (RIC) and annual phenology (freeze-up, breakup, and ice duration) for the six largest Arctic rivers – Yukon, Mackenzie, Ob, Yenisey, Lena, and Kolyma – covering hydrological years 2001–2024. Built from >590 000 MODIS Terra/Aqua scenes, our workflow integrates a scalable threshold-based classifier on Google Earth Engine with dual-satellite daily synthesis, temporal-window cloud reclassification, and a high-latitude dark-period correction. Technical validation against higher-resolution optical imagery shows a mean RIC accuracy of 0.83 across basins. Phenological metrics derived from MODIS agree with in situ records with mean absolute errors (MAE) of 10.8 d for freeze-up and 11.4 d for breakup (improving to 8.4 d relative to the onset of ice drift), and with Landsat-based river-section phenology with MAE of 10.5 d (freeze-up) and 16.0 d (breakup). RIC correlates strongly with surface air temperature (mean Pearson r=-0.91) and increases systematically with latitude. Trend analysis from 2001 through 2024 shows delayed freeze-up in over 66 % of river segments, earlier breakup in more than 65 %, and shorter ice seasons in over 65 %. On average, freeze-up is delayed by 9.0 d, breakup occurs 7.8 d earlier, and ice duration shortens by 14.1 d over the study period. These basin-consistent, temporally resolved records provide an open benchmark for diagnosing cryospheric change in Arctic river corridors and for constraining model–data intercomparisons. The river-ice dataset is available via Zenodo (https://doi.org/10.5281/zenodo.17054619, Qiu et al., 2025a).

Abstract. The stable isotope values of seawater (δ18O and δ2H) provide valuable information on the exchange of water between the ocean, atmosphere, and cryosphere and on ocean mixing processes. As such, observational seawater δ18O and δ2H data place powerful constraints on hydrologic changes in the modern ocean. Seawater δ18O data are also essential for calibrating paleoclimate proxies based on the δ18O of marine carbonates and are an increasingly critical diagnostic tool for assessing model performance and skill in isotope-enabled climate models. Despite their broad value, no centralized and actively-curated database for this type of data exists, even though a growing number of new seawater δ18O datasets have been generated over the last decade. As such, many seawater δ18O datasets remain “hidden”. To improve the accessibility of seawater δ18O data for the Earth Science research community, the Past Global Changes (PAGES) CoralHydro2k project has created a new, machine-readable, and metadata-rich database of observational seawater δ18O data, paired with seawater δ2H and salinity data, that is compliant with findability, accessibility, interoperability, and reusability (FAIR) standards for digital assets. The data has been collected from public databases and repositories, direct researcher data submissions, scientific papers, and student theses. In total, the PAGES CoralHydro2k Seawater δ18O Database contains over 18 600 data points with extensive metadata that makes the database suitable for a myriad of research applications. For hidden data, we searched for and included all datasets within the global ocean. For public data, our data collation efforts were focused on the upper 50 m from 35° N to 35° S (to aid in CoralHydro2k's seawater δ18O reconstruction studies using δ18O and Sr/Ca in tropical-subtropical coral skeletons). We also provide a set of best practices to the community for reporting seawater isotope data in the future. The database is available on the NOAA NCEI World Data Service for Paleoclimatology landing page: https://www.ncei.noaa.gov/access/paleo-search/study/34575 (last access: 11 February 2026; https://doi.org/10.25921/ap7d-2k16, Atwood et al., 2026). A Seawater Oxygen Isotopes Community was also developed within the EarthChem Library (https://www.earthchem.org/communities/seawater-oxygen-isotopes/, last access: 20 February 2026) to help researchers submit new datasets and obtain a dataset DOI. This template is aligned with the CoralHydro2k Seawater δ18O Database to facilitate future updates to the database.

Abstract. Clouds play a crucial role in the Earth's energy budget and the hydrological cycle. However, differences in the spatiotemporal resolution of satellite sensors and in retrieval algorithms lead to substantial heterogeneity among retrieved cloud products. Based on global geostationary satellite thermal infrared brightness temperature data from the Gridded Satellite (GridSat-B1) project, this study applied the single-layer Cloud retrieval model – Small Attention-UNet (Cloud-SmaAtUNet) algorithm in the DaYu CLoud Analysis System (DaYu-CLAS) to retrieve a global cloud product with a 3 h temporal resolution, 0.07° spatial resolution, and 23 year temporal span (2000–2022). This product is referred to as the DaYu Global Cloud physical properties Products (DaYu-GCP). The DaYu-GCP includes CLoud Phase (CLP), Cloud Top Height (CTH), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER), covering all regions between 70° S–70° N and 180° W–180° E. Evaluation based on the Moderate-resolution Imaging Spectroradiometer (MODIS) official cloud products shows that the annual CLP identification accuracy of DaYu-GCP remains stable at 85 % ± 0.7 %, while the annual RMSE for CTH, COT, and CER stabilize at 1.50 ± 0.03 km, 10.71 ± 0.15, and 6.75 ± 0.10 µm, respectively. The multi-year variations in accuracy are within 2 %, with no evident interannual differences, and the spatiotemporal distributions are continuous. In addition, evaluation based on observations from the Cloud Profiling Radar and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) indicates that the DaYu-GCP products show reasonable day–night consistency for optically thin cloud. Furthermore, the DaYu-GCP products are compared with other global cloud products. Taking the Northern Hemisphere as an example, the interannual variations of Cloud Cover Frequency (CCF), CTH, COT, and CER retrieved from DaYu-GCP show correlation coefficients of 0.760, 0.486, 0.764, and 0.514 with the ISCCP product, respectively, and 0.444, 0.778, 0.171, and 0.412 with the CLARA-A3 product. The DaYu-GCP dataset, which is stored in the Network Common Data Format (NetCDF), is freely available on the Science Data Bank at https://doi.org/10.57760/sciencedb.26292 (Zhao et al., 2026). The corresponding code can be found at https://github.com/lingxiao-zhao/DaYu-GCP (last access: 25 June 2025).

Abstract. Remote sensing-based observations are used to map tree cover extent, estimate canopy height, detect disturbances, and classify land cover and land use. However, comprehensive global information on forest cover, capturing both physical characteristics and land use components as defined by the United Nations Food and Agriculture Organization (FAO), remains limited. Here, we present a harmonized and globally consistent map of forest presence or absence at 10 m spatial resolution for the year 2020, hereafter referred to as GFC2020. Our approach combines multiple spatial datasets, primarily derived from Earth observation (EO), to harness their complementary strengths within a transparent, flexible, and open science framework. GFC2020 maps 4562 million hectares (Mha) of forests globally, which is 9.5 % more than the estimate from latest FAO Global Forest Resources Assessment (FRA) for 2020. GFC2020 forest area does not include 578 Mha of tree cover (11 % of the global tree cover area) because these areas do not meet the height threshold or occur on agricultural or urban land. Conversely, around 0.6 % (∼ 25 Mha) of the area classified as forest in GFC2020 is unstocked, due to forest management practices or natural disturbances such as fire. Based on the reinterpretation of a previously collected reference set of 21 752 sample units, GFC2020 achieves an overall accuracy of 91 %, with a commission error of 18 % and an omission error of 8 % for forest. Improvements in EO products, such as better detection of trees in dry and open landscapes, distinguishing natural from human drivers of forest disturbance, mapping tree crops at high spatial resolution or identifying agroforestry systems, will contribute to enhancing future versions of GFC2020. The shift from tree cover to forest cover mapping is not only essential for ecological and climate-related applications but also provides new opportunities to support policy needs. GFC2020 is one of many tools to inform the deforestation risk assessments under supply chain oriented regulations such as the European Union's Deforestation Regulation (EUDR). Even though this map follows the EUDR's definition of forest, it is a non-exclusive, non-mandatory, and not legally binding source. The data availability is as follows: GFC2020 (Bourgoin et al., 2024a, http://data.europa.eu/89h/e554d6fb-6340-45d5-9309-332337e5bc26) and the validation dataset (Colditz et al., 2025a, http://data.europa.eu/89h/8fbace34-a2fe-47b9-ad82-3e9226b7a9a6).

Abstract. Accurate, high-resolution soil moisture data are critical for hydrological modeling, climate studies, and ecosystem management. Unfortunately, current existing global products suffer from inconsistencies, coverage gaps, and biases. In this study, we evaluated the surface layers of three widely used soil moisture products, including ERA5-Land, ESA-CCI (v09.1 Combined), and SMAP L4 with resolutions ranging from 0.1 to 0.25°, against in situ measurements across five networks, including ISMN, CMA, Cemaden, COSMOS-Europe, and SONTE-China. The in situ dataset, to our knowledge, represents the most extensive global soil moisture compilation to date, comprising approximately 3.8 million records, organized into a primary dataset for modern validation (2015–2020) and an independent historical dataset (1960–2015). It is found that during the primary validation period (2015–2020), ERA5-Land exhibits high correlation (with correlation coefficient of 0.69) between measured and predicted soil moisture but the data also shows significant bias. SMAP L4 provides the highest accuracy (with root mean square error (RMSE) value of 0.088 m3 m−3) and low bias, but is limited by its temporal coverage from 2015 to the present. To address these gaps, we developed an adjusted ERA5-Land dataset spanning 1950 to 2025 by fusing ERA5-Land and SMAP L4 using a mean-variance rescaling method optimized for long time-series alignment, which enhanced the spatiotemporal coverage and reduced bias. Validation against the primary validation period demonstrates a reduction in RMSE of 24.6 % and an improvement in normalized Nash-Sutcliffe Efficiency (NNSE) of 30.6 % compared to the original ERA5-Land products. Crucially, the reliability of the backward extension was verified against independent historical observations spanning 1960 to 2015, demonstrating sustained improvements over ERA5-Land with 19.7 % RMSE reduction and 26.6 % NNSE increase. This confirms the robustness of the adjustment parameters in the mean-variance rescaling method. The adjusted ERA5-Land dataset, which is publicly available, can be used as benchmark for future research and support drought monitoring, weather prediction, and water resource management, contributing to global climate resilience across diverse ecosystems. The dataset is provided for the surface layer with global coverage at a 0.1° spatial and daily temporal resolution, spanning from 1950 to 2025, at https://doi.org/10.57760/sciencedb.30546 (Wang et al., 2026).

Abstract. On-road transportation is a major contributor to CO2 emissions in cities, and high-resolution CO2 traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly on-road CO2 emission maps with a 100 × 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023. We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle-type-dependent emission factors. These models were calibrated using independent traffic observations available for Paris and Berlin, and subsequently applied to the remaining 18 cities in an extrapolated manner due to data availability constraints. Hourly emissions, initially estimated at the street level, were aggregated to 100 × 100 m grid cells. Annual on-road CO2 emissions across the 20 European cities in 2023 ranged from 0.4 to 7.9 Mt CO2, with emissions strongly correlated with urban area (R2= 0.98) and, to a lesser extent, population size (R2= 0.74). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO2 emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO2 emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy-making. The datasets generated by this study are available on Zenodo https://doi.org/10.5281/zenodo.16600210 (Shi et al., 2025).

Abstract. Argo profiles of temperature/salinity (T/S) at specific times and locations from January 2003 through December 2023 are mapped into monthly maps of steric sea level (SSL), thermosteric sea level (TSL), and Ocean Heat Content (OHC) anomalies at long wavelengths (>1500 km) down to depths of 2000 m. The mapping uses a monthly satellite reference computed from the difference between satellite altimetry and gravimetry, so that in periods where there is not sufficient global Argo coverage (generally before 2007), the satellite estimate is used instead of a mean monthly climatology or zero, as other analysis centers use. Longwave mapping is done to reduce large errors introduced by poor sampling of mesoscale eddies by the Argo floats. We demonstrate that on global- and basin-scales, the longwave mapping does not substantially affect calculations of mean SSL, TSL, or OHC changes. Monthly standard error maps from the mapping are also provided. These maps are intended for users interested in understanding global- and basin-scale sea level budgets, as well as combining with deep ocean measurements to study ocean heat uptake. The complete dataset is available from https://doi.org/10.17632/dsjkkhvywr.1 (Chambers and Reinelt, 2025).

Abstract. Volatile organic compounds (VOCs) and ozone (O3) are key constituents of tropospheric chemistry, affecting both air quality and climate. Forests are major emitters of biogenic VOCs (BVOCs), yet large uncertainties remain regarding the diversity of exchanged compounds, the drivers of their bidirectional fluxes, and their in-canopy chemistry. Long-term and comprehensive in situ datasets remain scarce, limiting our understanding of these complex processes. We conducted a 3-year field campaign (2022–2024) at the Integrated Carbon Observation System mixed temperate forest station of Vielsalm (BE-Vie), combining vertical concentration profile and eddy covariance flux measurements above and below the canopy (concentration dataset: https://doi.org/10.18758/NVFBA74V, Verreyken et al., 2025c; flux dataset: https://doi.org/10.18758/KHV8ZXU2, Dumont et al., 2025a; concentration-turbulence profile dataset: https://doi.org/10.18758/BED4Q2VY, Dumont et al., 2025b). Using a PTR-ToF-MS and an open-source data-processing workflow, we identified 48 significantly exchanged VOCs. The vertical and diurnal gradients of the mixing ratios reflected the interplay between emission, deposition, chemistry, and transport. Combined with a profile of turbulence statistics, these observations offer an opportunity to investigate their behaviour within the canopy. The forest acted as a net VOC source in summer (∼ 1.25 µg m−2 s−1), while deposition dominated in autumn. Many oxygenated VOCs displayed bidirectional exchange. Monoterpenes, isoprene, and methanol were the most abundant flux contributors, but 15–30 (30–43) compounds were needed to account for 90 % of total emissions (depositions), depending on the season. Below-canopy BVOC and O3 fluxes reached ∼ 10 % of above-canopy ones, with proportionally enhanced below-canopy ozone uptake at night. This study provides one of the most detailed long-term datasets of VOC and O3 exchange in a temperate forest and serves as a key reference for improving process-based models of biogenic, physical, and chemical exchange in forest ecosystems.

Abstract. The soil organic carbon pool is a crucial component of carbon storage in terrestrial ecosystems, playing a key role in regulating the carbon cycle and mitigating atmospheric CO2 concentration increases. To combat soil degradation and enhance soil organic carbon on the Loess Plateau, the Grain-for-Green Program (GFGP) has been implemented. Accurately quantifying change in soil organic carbon stock (ΔSOC) resulting from farmland retirement is essential for informing land use management. In this study, the spatial and temporal distribution of retired farmlands on the Loess Plateau was analyzed using Landsat imagery from 1999–2021. To assess the effects of the years since retirement, climate, soil properties, elevation, and other factors on ΔSOC, climate-zone-specific multivariable linear regression models were developed based on field-sampled soil data. These models were then used to map ΔSOC across the retired farmlands. Results indicated that a total of 39 065 km2 of farmland was retired over the past two decades, with 45.61 % converted to grasslands, 29.75 % to shrublands, and 24.64 % to forestlands. The years since retirement showed a significant positive correlation with ΔSOC, and distinct models were developed for different climatic zones to achieve high-resolution (30 m) ΔSOC mapping. The total ΔSOC from retired farmland on the Loess Plateau was estimated at 21.77 Tg in carbon equivalent, with grasslands contributing 81.10 %, followed by forestlands (11.16 %) and shrublands (7.74 %). The associated datasets are freely available at 30 m resolution from 2000 to 2021 (Yang, 2025, https://doi.org/10.6084/m9.figshare.28785971).