Mass and heat exchanges at the air-sea interface fundamentally drive global weather and climate systems. However, acquiring long-term, high-frequency, synchronous in-situ observations of both atmospheric and oceanic variables remain highly challenging, especially during extreme weather. This paper presents a high-resolution dataset from five air-sea drifting buoys deployed in the Bay of Bengal (BoB) in 2020 and 2022. These buoys captured precise, synchronous measurements of key meteorological parameters (air temperature, sea-level pressure, wind speed and direction, and relative humidity) alongside sea surface temperature. The dataset is typically sampled hourly; however, the sampling was increased to 5 min intervals during tropical cyclones Nivar, Burevi, Four and Asani. This high-frequency dataset offers invaluable in-situ records for studying diurnal variations and fine-scale processes in the BoB. Furthermore, it provides critical observational data to advance our understanding of air-sea coupling, validate high-frequency satellite products, and improve parameterizations in regional numerical weather prediction models under extreme conditions. The dataset is freely available at https://doi.org/10.5281/zenodo.21294873 (Huang et al., 2026).
High-resolution bathymetry is a critical resource for marine research, underpinning seafloor characterisation, benthic habitat mapping, geomorphological analyses, and marine spatial planning. In the Red Sea, however, publicly accessible high-resolution bathymetric data remain extremely limited along the eastern margin. Existing regional products such as the General Bathymetric Chart of the Oceans (GEBCO) provide only coarse resolution coverage, and detailed surveys have historically been concentrated along the central axis rather than the Saudi Arabian shelf and slope. Here, we present a high-resolution bathymetric dataset acquired during the 2022 Red Sea Decade Expedition (RSDE) along the Saudi Arabian margin of the Red Sea (https://doi.org/10.5281/zenodo.19065211, Marchese et al., 2026). Multibeam echosounder surveys span approximately 860 km of coastline and extend from shallow coastal waters to deep-sea environments, covering depths from ∼ 2 m > 2460 m. Data were acquired using multiple platforms and processed through a standardised workflow to produce a unified digital bathymetric model. Grid resolutions are 5 m for the shallow-water data and 40 m for the deep-water data, covering a total of 49 418.7 km2 of seafloor. This dataset provides unprecedented spatial coverage of the eastern Red Sea seafloor and represents a key resource for marine sciences and marine spatial planning in a region where available detailed bathymetry has historically been limited.
Natural and planted forests differ substantially in ecological functions and economic values, with forest age serving as a key indicator of their developmental and carbon dynamics. However, existing global forest age datasets remain limited by coarse spatial resolution and insufficient representation of forest origin differences. In this study, we developed a 30 m global disturbance-recovery age dataset (1985–2024) by integrating Landsat time series with the Continuous Change Detection and Classification (CCDC) algorithm on Google Earth Engine. To account for differences in successional behavior between natural forests (NF) and planted forests (PF), we introduced a type-stratified processing framework. Specifically, a Global Natural and Planted Forest (GNPF) mask was used to guide forest-type-dependent refinement during the post-segmentation stage, where dual-threshold segment fusion rules were applied (> 0.03 for NF and > 0.04 for PF, respectively) to better align spectral recovery trajectories with expected recovery patterns. In addition, we generated a pixel-level Spatial Uncertainty Index (SUI) derived from model residuals to quantify mapping reliability. Validation using extensive reference samples demonstrates good agreement between estimated and observed forest age (R2 = 0.74, RMSE = 7.17 years). Globally, NF displayed contrasting age structures, with older stands prevalent in Europe (84.38 %), South America (82.61 %), and North America (80.62 %), while Australia showed a bimodal distribution reflecting both mature and regenerating forests. PF were generally younger, with young stands concentrated in Australia (Age 1–5: 65.77 %) and relatively older plantations in Europe (Age 36–40: 53.99 %). The dataset provides a spatially explicit characterization of forest age patterns and can support studies of forest carbon dynamics, ecosystem modeling, and global assessments of forest structural changes. The dataset is publicly available at Zenodo (https://doi.org/10.5281/zenodo.22030377, Wang et al., 2026).
The Observatoire Haute Provence (OHP) station is one of the few long-term measuring stations for vertical ozone profiles in southern Europe. Since 1991, vertical ozone distribution has been monitored by the OHP weekly electrochemical concentration cell (ECC) ozonesonde. In this study, we have corrected the ECC datasets for the period 2002–2007. The correction of the ECC has been carried out using comparisons with other ozone-measuring instruments at the same station (stratospheric lidar and Système d'Analyse par Observation Zénithale (SAOZ) photometer) and with collocated satellite observations of the ozone vertical profile by Aura-Microwave Limb Sounder (MLS). Median ozone concentration of the ECC for the period 2002–2007 was −3.4 % lower than that of the stratospheric lidar and MLS. The ECC internal pump temperature showed a sudden drop of 16 K at 25 km for the period 2002–2007 compared to the period 1991–2001. Considering the long-term trends of the ECC current and stratospheric lidar ozone concentration at 25 km as well as the ECC pump flow rate trend, we show that the recorded ECC pump temperature between 2002 and 2007 is too low by 10 K at 25 km. The ECC pump temperature for the period 2002–2007 has been corrected using a linear altitude-dependent increasing rate of 0.33 K km−1. As a result, the corresponding ECC ozone bias at 25 km with the stratospheric lidar and MLS have been reduced to −0.5 %. The corrected OHP ECC data are available at https://doi.org/10.25326/855 (Ancellet and Godin-Beekmann, 2025).
Forest fires are a major ecological disturbance affecting carbon cycling, ecosystem structure, and landscape dynamics worldwide. Understanding long-term changes in forest fire patterns requires consistent information on the distribution, extent, and structure of fire patches across broad spatial and temporal scales. The Landsat archive provides nearly four decades of global observations at 30 m resolution, yet generating a consistent global record of forest fire patches remains challenging because of cloud contamination, heterogeneous observation availability, and the computational demands associated with processing multi-decadal imagery. Here we present an initial release of a global 30 m dataset characterizing forest fire patches (GlobMap FFP) from 1984–2022 based on the full Landsat archive (https://doi.org/10.5281/zenodo.17638167, Liu, 2025). To achieve a consistent representation of fire-related spectral signals under heterogeneous observation conditions, we generated multi-temporal Landsat composites using a minimum Brown Vegetation Index compositing approach implemented on the Google Earth Engine platform. Burned pixels were subsequently identified using artificial neural network modeling, and spatially connected pixels with the same burned years were grouped into fire patches through spatiotemporal clustering. The final raster-based product preserves fine-scale spatial heterogeneity while providing patch-level attributes. Across global forests, the dataset delineated 11.97 million fire patches and mapped an average annual burned area of 7.3 Mha yr−1 over 1984–2022. Agreement metrics calculated against a separately generated Landsat-derived reference dataset showed omission errors ranging from 12.2 %–36.8 % and commission errors ranging from 6.4 %–23.2 % across forest types. Performance varied among forest biomes, with lower agreement observed in tropical evergreen broadleaf forests. Intercomparison with existing burned area products revealed generally consistent large-scale spatial patterns, while discrepancies in burned area estimates and patch delineation reflect variations in observation systems, mapping methodologies, and temporal aggregation strategies. Rather than representing a complete global burned area inventory, GlobMap FFP provides a long-term, spatially explicit characterization of forest fire patch structure from Landsat. This dataset facilitates ecological research, particularly at regional scales, by characterizing fire patch structure and spatial organization over time.
Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data. To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614, Gagliano et al., 2026) for the 10-year period spanning 2015–2024, with 80 m spatial resolution and 9.5 d average temporal resolution. We created this dataset by identifying backscatter minima empirically associated with runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. We evaluated our dataset using in situ snow pillow estimates of runoff onset from 1116 automated weather stations across the Western United States, British Columbia, Norway, and Nepal, finding a median timing difference of −2.0 d and a median absolute deviation of 10.0 d. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products. The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.
This study presents a global multi compartment Persistent Organic Pollutant Emissions model and inventory: POPE. The model computes temporally and spatially resolved model ready emissions for 23 Per- and Polyfluoroalkyl Substances (PFAS). It focuses on some of the most widely used substances and distinguishes between emissions to air and emissions to water. POPE covers the time span from the first industrial scale production in 1950 up until 2020 on an annual basis on a grid with 0.5° resolution. The POPE model distributes estimated total PFAS emissions in space and time based on several data sets such as the E-PRTR, NACE and US-EPA FRS in combination with socio-economic data as population and GDP complemented by estimates for individual point sources, such as industrial sites and airports, whereby the source activity is dependent on regional changes in production volumes, usage quotas, and recapturing efficiency over time. It includes emissions by industrial production, diffuse emissions through usage and disposal of consumer products, secondary emissions from the reaction of precursors, and emissions by firefighting exercises on airports using Aqueous Film Forming Foams (AFFF). It is demonstrated that the POPE emission inventory is compatible with current global emission estimates, and temporal and spatial variability of the emissions is explored. A comparison of independent measurements with modelled river concentrations based on the POPE emission inventory is provided. The POPE emission inventory is meant to be used as input for atmospheric and marine chemistry transport models, eventually allowing to assess the environmental fate of PFAS. POPE can be used to create hypothetical future emission scenarios, enabling model based predictions which can inform policy decisions. This is important given that even with a theoretical global fade-out of PFAS production, significant legacy pollution is still to be expected. The POPE emission inventory for PFAS is publicly available in GEIA's (Global Emission InitiAtive) ECCAD data portal: (https://permalink.aeris-data.fr/POPE, last access: 3 June 2025) or including the source code at zenodo (https://doi.org/10.5281/zenodo.12783504, Simon, 2024).
Large amounts of sub-daily temperature data are shared globally through the Global Telecommunication System (GTS) in near-real time and through international data exchanges. However, converting these data into a global daily temperature dataset with a uniform definition – especially for daily maximum (Tmax) and minimum (Tmin) temperatures – has proven challenging due to the independent observation schedules across the world. To address this issue, we developed a new method that decomposes sub-daily Tmax and Tmin records from the Integrated Surface Database (ISD) into finer intervals, subsequently reaggregating them into daily Tmax and Tmin based on a prospective 00:00–24:00 UTC dateline. This new method increased the global daily Tmax and Tmin data counts by 64 % and 45 %, respectively, compared to the original method, which relied on either two consecutive Tmax/Tmin records over 12 h or a single Tmax/Tmin record over 24 h. The Global Land Base Dataset-First Estimate Daily Data (GLBD-FED) was established for the period from 1981 to 2024, following corrections for misrecorded Tmax and Tmin and quality control. GLBD-FED includes daily maximum (Tmax), average (Tave), and minimum temperature (Tmin) from approximately 17 000 global sites, with daily data amounts reaching about 10 000 entries per day in the current decade. When compared to the Global Summary of the Day (GSOD) dataset, GLBD-FED exhibits less extreme daily values over the last 40 years, showing a slightly lower daily Tmax (around −0.3 °C) and a higher daily Tmin (around +0.3 °C), with a nearly identical daily Tave (approximately +0.1 °C). These systematic differences arise from multiple sources, including: (1) UTC-boundary double-counting artifacts in GSOD (which alone introduced an average bias of +0.28 °C in Tmax and −0.85 °C in Tmin across ∼18 million and ∼11 million duplicated records, respectively); (2) differing sub-daily data source preferences (Synoptic vs. METAR reports); and (3) divergent date boundary treatment methods. The database and associated data can be found at https://doi.org/10.5281/zenodo.17895292 (Yang et al., 2025).
High-resolution temperature and salinity (T & S) gridded datasets are essential for exploring large and mesoscale ocean phenomena. In this study, a global, weekly T & S gridded dataset with a horizontal resolution of 1/4° and a depth range of 0–1500 m from 2005 to 2023 is reconstructed using a four-dimensional multigrid analysis (4D-MGA) framework, based on the T & S profiles in WOD23. With minimal prior statistical assumptions, the 4D-MGA efficiently extracts information from in-situ T & S profiles and satellite-observed sea level anomalies by integrating multiscale spatiotemporal correlation and physical constraints. The results show that the 4D-MGA product successfully delivers a credible, high-resolution analysis that combines robustness with reliable mesoscale information. Specifically, our product exhibits a lower root mean square error and excellent unbiased performance on a global scale when compared with the ARMOR3D product and the GLORYS reanalysis. Furthermore, the clear recirculation in the western boundary currents (WBCs) can be well captured by the 4D-MGA product. The product is then applied to investigate the linear trends of geostrophic transport within five key sections in WBCs. The Kuroshio and Agulhas Current transports exhibit significant decreasing trends, with rates of 0.34 ± 0.07/0.26 ± 0.06, 0.23 ± 0.05/0.10 ± 0.06, and 0.30 ± 0.06/0.12 ± 0.05 Sv yr−1 in 4D-MGA, ARMOR3D, and GLORYS, respectively. The weekly reconstructed dataset (4D-MGA) is freely available at https://doi.org/10.5281/zenodo.19378150 (Wu et al., 2026a).
Climate change has dramatically altered the Arctic seas with significant decrease in sea ice extent and thickness and warming water temperature. The ecological impacts of such change have been described for many parts of the Arctic Ocean, but long-term records of biological indicators are still missing. Among those, photosynthetic and accessory pigments are one of the key tools that aid quantification of phytoplankton and sea–ice algae biomass and characterisation of community composition. To address this gap, we present the first pan-Arctic compilation of in situ algal pigment data obtained exclusively by High-Performance Liquid Chromatography (HPLC), containing 10 798 samples collected across 77 Arctic research cruises between 2000 and 2024. As a result of large-scale collaborative effort, this database covers both open water and sea–ice environments across coastal, shelf and open domains. The database (https://doi.org/10.11583/DTU.29445104, Heidemann et al., 2026) includes measures of up to 26 pigments, with 8 major marker/accessory pigments being considered in this study, namely Alloxanthin (Allo), 19'-Butanoyloxyfucoxanthin (But-fuco), Chlorophyll a (Chl a), Chlorophyll b (Chl b), Fucoxanthin (Fuco), 19'-Hexanoyloxyfucoxanthin (Hex-fuco), Peridinin (Peri), and Zeaxanthin (Zea). This publicly available database provides crucial data that can be used to assess phytoplankton dynamics, validating remote sensing observations and can serve as a resource for future Arctic ecological- and modelling studies.
Abstract. Field-based measurements are foundational to the study of short- and long-term peatland carbon dynamics. For decades, the scientific community has amassed hundreds of valuable empirical datasets in the form of peat core records from around the world. Those records typically include peat depth, basal age, peat organic matter content, peat dry bulk density, peat organic density, and/or carbon and nitrogen content. Once combined with chronological constraints and models, peat core time series can be used to estimate changes in peat-carbon accumulation rates through time. Consolidating these peat records can help improve global peat-carbon stock estimates and quantifications of past, present, and future greenhouse gas exchanges between peatlands and the atmosphere. Large-scale synthesis can also shed light on the sensitivity of peat-carbon accumulation processes to climate change and provide context for current and future global environmental change. We can also use spatial and temporal peat data to inform, validate, and benchmark existing models that include peatland representations. This paper presents the first formal version of PAGES’ C-PEAT Global Peatland Carbon Database (GD), which is available for download in the PANGAEA and International Soil Carbon Network (ISCN) data repositories. The C-PEAT GD contains 267 independently catalogued peat cores and a large number of observations from those cores, including: peat depths, organic matter content values, dry bulk density values, organic density values, as well as carbon and nitrogen content values. Raw and calibrated chronological data are included for each individual dataset when available. The metadata fields are easily searchable and interoperable, as per PANGAEA’s standards. The main objective of this article is to describe the structure and content of the database, itself aimed at increasing the use, assimilation, and interoperability of peat-core data across disciplines. The C-PEAT GD can be accessed at the PANGAEA data repository (https://doi.org/10.1594/PANGAEA.986891; Loisel et al., 2025).
NOAA's Pacific Marine Environmental Laboratory (PMEL) has made measurements of aerosol chemical, microphysical, optical, and cloud nucleating properties onboard research cruises since 1991. The twenty-five cruises have covered all of the world's oceans - the Pacific, Atlantic, Indian, Arctic, and Southern. The result is the most comprehensive, publicly available database of aerosol properties in the marine atmosphere to date. The database also contains gas-phase species (O3, SO2), Radon, and dimethylsulfide (DMS), seawater species (DMS, NH4+, NO3-, and chlorophyll a), and meteorological parameters. Details of the cruises (locations, dates, and objectives), parameters measured, instrumentation used, and data availability are provided here. Also included are PMEL's high-level major findings and past usage of the data by others. The goal of this paper is to promote broader awareness of the database to the atmospheric aerosol in situ measurement, satellite, and modelling communities. Data are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/, NOAA, 2025) (see also the list of all data sets in Table 7). Links to the Digital Object Identifiers (DOIs) for each cruise are provided herein.
Nitrous oxide (N2O) and methane (CH4) are potent greenhouse gases for which oceanic contributions remain uncertain, particularly in undersampled regions like the Southwest and Southeast margins of the Mediterranean Sea, where there is a major observational gap. This data paper presents a comprehensive dataset of monthly N2O and CH4 concentrations and air-sea fluxes collected over a full seasonal cycle (April 2023–June 2024 at most sites, with one station extended to September 2024) from eight coastal stations across three distinct Mediterranean ecoregions (Alboran, Balearic, and Levantine Seas) as part of the ROADSTER collaborative project. Sampling, preservation, and analytical procedures were standardized across sites, and dissolved-gas analyses were performed in a single laboratory to ensure comparability. We detail standardized sampling and analytical methodologies, including ancillary variables (temperature, salinity, dissolved oxygen, chlorophyll a and inorganic nutrients). The complied dataset reveals distinct seasonal and spatial variability: N2O concentrations exhibit a strong negative correlation with temperature, with all stations acting as moderate N2O sources. Conversely, CH4 concentrations show greater variability and a positive correlation with temperature, with the Levantine sub-basin stations displaying episodic high-flux events (up to 35.20 µmol m−2 d−1) indicative of localized seafloor sources. This dataset bridges significant data gaps in the Mediterranean, providing a crucial baseline for regional climate modeling, understanding biogeochemical processes, and future climate change impact assessments. The dataset is publicly available at Zenodo (https://doi.org/10.5281/zenodo.19351642; de la Paz et al., 2026).
Soil water erosion is the dominant soil degradation driver worldwide. Rainfall erosivity (also known as the R factor) quantifies the potential of rainfall to cause soil water erosion and is regarded as a dominant factor determining it. This contribution presents the first annual R factor database across the continental Mexico for three climate normals (1968–1997, 1978–2007, and 1988–2017). The workflow comprised three main steps. Firstly, a harmonaized daily rainfall time series dataset was obtained by compiling and harmonizing (through quality control, homogeneity analysis and gap-filling) 5410 raw rainfall time series distributed across Mexico. Secondly, three combinations of the α and β coefficients in a power model were tested to estimate daily R factors using three validation databases at global, national, and local scales. Thirdly, a validated and continuously distributed annual R factor for all three climate normal was obtained. Thus, the database presented herein encompasses 1369 (climate normal 1968–1997), 1678 (climate normal 1978–2007), and 1676 (climate normal 1988–2017) rainfall time series and the corresponding R factor. Our results indicate that the median values of the R factor for the three climate normals were 3245; 3070; and 3327 MJ mm ha−1 h−1 yr−1, respectively. The statistical distribution of the R factor is right-skewed for the three climate normals, with high erosivity values reaching >12 000 MJ mm ha−1 h−1 yr−1 in all cases. The R factor across Mexico showed a strong ecoregional differentiation, with the highest values concentrated in the Tropical Rain Forest and Tropical Dry Forest ecoregions, whereas the North American Deserts and Mediterranean California exhibited the lowest values. The annual behavior of the R factor was similar for the three climate normals. However, September had the highest contribution to the annual R factor. By identifying areas with higher susceptibility to soil loss due to rainfall action and providing spatially distributed and well-documented estimates, we believe that the present R factor database has the potential to support the study of soil water erosion in the continental Mexico at country scale. Said database is available from a scholarly-accepted repository at https://doi.org/10.6073/pasta/dd2b30e28ee25ff2d60d8a9f436951d2 (Varón-Ramírez et al., 2026) for public consultation.
Detailed and comprehensive data sets on microphysical cloud properties in icing conditions are rare. In April 2023, fifteen research flights were performed with the SAFIRE ATR 42 research aircraft during the SENS4ICE-EU airborne measurement campaign over France and adjacent marine regions to measure clouds containing supercooled large droplets (SLD) at altitudes between 2 and 6 km and temperatures of 0 to −18 °C. Ten cloud probes were deployed on the aircraft, comprising four imaging probes, two light-scattering probes, and three hotwire probes, in order to characterise natural SLD conditions and serve as reference instruments for novel aircraft icing detection sensors. This work presents a comprehensive cloud dataset derived from the in-situ instruments used during the campaign, which is accessible on the HALO (High Altitude and Long Range Research Aircraft) database (https://doi.org/10.17616/R39Q0T, Menekay et al., 2026). The dataset includes measurements of liquid and ice water content, combined particle size distributions, cloud microphysical properties, and meteorological parameters relevant to icing environments. In addition to documenting the dataset structure and processing methods, the paper provides an overview of flight strategies, instrument configurations, and statistical characteristics of the observed cloud properties, including their dependence on temperature and altitude. The dataset is suitable for studies of atmospheric icing conditions, mid-level clouds, sensor development, and model evaluation. It represents a rare collection of in-situ observations of SLD characteristics in icing environments and supports the evaluation of numerical weather prediction models under icing conditions to improve weather forecasts in hazardous conditions.
Soil radiocarbon (14C) measurements are crucial for understanding soil carbon cycling over timescales ranging from years to millennia. However, the global synthesis and comparison of radiocarbon data have been limited due to the variety of measurement methodologies and data formats. The International Soil Radiocarbon Database (ISRaD) is an open-access, community-driven archive designed to compile soil radiocarbon data and facilitate large-scale research on soil carbon dynamics. Here, we present ISRaD version 2 (v2), which has grown significantly since its initial release in 2020 (https://doi.org/10.5281/zenodo.17860507, Beem-Miller et al., 2025). It now contains data from 515 unique studies spanning 1669 sites globally, with over 20 000 radiocarbon observations across multiple hierarchical levels, including bulk soil layers, soil fractions, laboratory incubations, interstitial carbon in soil pores, and in situ fluxes of CO2 and CH4. Major updates include expanded metadata structures to capture emerging measurement techniques and an improved soil fractionation template to better capture diverse methods. There has also been a substantial increase in data from underrepresented ecosystems, including cultivated soils and wetlands. Despite this growth, significant geographic and data-type gaps persist. Tropical and arid regions, soils deeper than 100 cm, and certain types of measurements, including incubation, interstitial, and flux, are severely undersampled. We discuss the scientific advances enabled by ISRaD v1 and the major updates to the database and data representation. We also explore future opportunities for ISRaD and the soil radiocarbon community. ISRaD v2 continues to serve as a living archive and dynamic platform for the soil radiocarbon research community. It supports synthesis efforts that are critical for predicting how soil carbon will respond to environmental and climatic changes.
Atmospheric rivers (ARs) are long, narrow corridors of enhanced water vapor transport that play an important role in transporting moisture from lower latitudes toward the extratropical and polar regions. On one hand, ARs can cause precipitation, end drought, accumulate snowpacks, and support ecosystems and society. On the other hand, ARs also represent a type of hazard and are responsible for economic losses, such as by extreme precipitation and winds, rain on existing snowpacks, or causing debris flows and landslides. Given the importance of ARs, this paper proposes a multi-method fusion algorithm for more objectively identifying ARs on a global scale. The proposed algorithm, based on the vertically integrated water vapor transport (IVT), integrates advanced strategies from multiple existing algorithms and introduces a dual-axis test method to enhance the stability of AR identification. Using IVT data from ERA5, a global AR database is constructed at a 1° × 1° horizontal resolution and a six-hourly temporal resolution for 1940–2024, and is publicly available at https://doi.org/10.5281/zenodo.18051602 (Chen and Rao, 2025). Comparative evaluation against established AR databases reveals strong agreement in mid-latitude ocean basins where ARs are most active. The usefulness of the new AR database is also demonstrated by examining the role of ARs in two extreme events in the recent past: atypical AR activity during the East Asian Meiyu rainfalls in late June 2018, and rare AR activity during the Australian Black Summer in late January 2020. The results show that the new AR database helps to reduce the uncertainty in AR identification and to better understand extreme events and their variations in time and space.
The annual MOOSE-GE cruise series is the backbone of the MOOSE regional ocean observing system providing a unique dataset to observe and understand large-scale physical, biogeochemical and biological processes in the northwestern Mediterranean basin, a key region that is responding to climate change faster than many other parts of the world. These cruises address major scientific challenges, such as monitoring the variability and impact of deep and intermediate convection, which plays a crucial role in deep-water ventilation, coastal–open ocean exchanges, carbon sequestration and the evolution of phytoplankton production in this highly dynamic system. They also allow the assessment of climate change effects on ocean physics, marine biodiversity, biological resources, and seawater chemistry, including oxygen, nutrients, and dissolved carbon. Sustained observations are required to track rapid trends such as increasing temperature and salinity in intermediate and deep waters, declining oxygen concentrations, nutrients and carbonate system inter-annual variabilities, expected increased stratification of the water column and variations in heat and salt contents. These long-term datasets are indispensable both for climate model validation and process studies, as well as for assessing the environmental status of the Mediterranean Sea. The data can be accessed at https://doi.org/10.18142/235 (Coppola et al., 2010), https://doi.org/10.17882/99825 (Bosse et al., 2024a), https://doi.org/10.17882/44411 (Bosse et al., 2025), https://doi.org/10.17882/45980 (Durrieu de Madron et al., 2024), https://doi.org/10.17882/43749 (Coppola et al., 2025), https://doi.org/10.17882/99865 (Dimier et al., 2024).
Time-variable gravity field solutions from GRACE and GRACE-FO have been successfully applied in hydrological and geophysical studies; however, inter- and intra-mission gaps and limited record length constrain their broader utility. Current approaches involve hydrometeorological-forced machine-learning reconstructions and satellite-tracking-observation combinations; however, the former is constrained by the accuracy and completeness of data inputs, while the latter requires additional filtering due to limited spectral sensitivity, resulting in filtering-dependent solutions. Both approaches neglect covariance information of observation noise and signal, precluding optimal solutions. To address these limitations, this study develops gapless monthly solutions up to degree/order 60 spanning January 1993 to December 2024 using Constrained Collocation Model (CCM) based on Tikhonov regularization, which integrates combination and denoising processes of gravity field solutions without explicit filtering. CCM-based Combined Solutions (CCM-CS) integrates trends, annual and semi-annual variations, and non-seasonal signals from multi-satellite observations (GRACE/-FO, Low Earth Orbit satellites, and Satellite Laser Ranging) without external hydrometeorological inputs, while incorporating covariance matrices of observation errors and combined signals to optimally balance error reduction and signal preservation. Evaluation results indicate that CCM-CS significantly eliminates striping noise and high-degree coefficient noise while effectively preserving low-degree gravity signals (e.g., C20 and C30) and achieving high signal-to-noise ratios. Comparison with three reconstructed products (IGG-SLR-DORIS, RESDCAE, BNML) shows that CCM-CS achieves the lowest sea level budget misclosures, with reductions of 40 %, 2.9 %, and 49 %, respectively. Across 52 major basins, CCM-CS achieves lower water balance errors in 98.1 %, 82.7 %, and 63.5 % of the basins, respectively. For Antarctic and Greenland ice sheet mass changes, CCM-CS closely match IMBIE (Ice Sheet Mass Balance Inter-comparison Exercise) estimates, with trend consistency improvements of 46.8 % and 32.7 % over IGG-SLR-DORIS and 48.6 % and 67.4 % over RESDCAE, respectively. The combined monthly gravity field solutions are available at https://doi.org/10.5281/zenodo.18589507 (Zhang et al., 2026).
Seasonal forecasts offer valuable information on upcoming conditions for the water, energy, and agricultural sectors. However, applications of raw data from global seasonal forecasts are limited, as they can show substantial biases and temporal drifts. In this study, we present a bias-corrected and downscaled global seasonal forecast reference dataset for precipitation and 2 m temperature for 1981 until 2024, provided at monthly resolution. We achieve this with the Bias Correction and Spatial Disaggregation (BCSD) method, combining ECMWF SEAS5 seasonal forecasts with ERA5 reanalysis data. The resulting post-processed product is a spatially refined and improved dataset for a wide range of seasonal applications in the water, energy and agricultural sectors. Unlike existing products, the dataset provides bias-corrected forecasts for all SEAS5 ensemble members over the full hindcast period (1981–2016) and even beyond (until 2024). The dataset spans all global land areas at 0.25° spatial resolution with a forecast lead time of up to seven months. It comprises 25 ensemble members for the period 1981–2016 and 51 ensemble members for 2017–2024. To assess probabilistic forecast quality, we conduct a comprehensive performance evaluation, using the Brier Skill Score (BSS) and the Continuous Ranked Probability Skill Score (CRPSS). The BCSD-corrected temperature forecasts outperform climatology across nearly all regions and lead times, with highest skill in flat and warm regions. Precipitation skill is highest in the tropics and humid regions. Semi-arid areas show solid skill during the rainy season but reduced performance in dry months. This skillful global seasonal forecast reference dataset can now be explored by the community for subsequent forecast evaluation, drought prediction studies, and water resource management applications. The BCSD-corrected seasonal forecast dataset is publicly available as NetCDF data under a Creative Commons Attribution 4.0 International License (CC BY 4.0) at the World Data Center for Climate (WDCC; https://doi.org/10.26050/WDCC/SEAS5-BCSD, Weber et al., 2026).
