Earth System Science Data最新文献

{"title":"EGO: a global 0.05° hourly GPP dataset for monitoring diurnal photosynthesis dynamics","authors":"Xi Liu,Xing Li,Dalei Hao,Jingfeng Xiao,Yanan Zhou,Cenliang Zhao,Zikang Diao,Fuqiang Qu,Shangrong Lin,Xiangzhuo Liu,Zhaoying Zhang,Xinjie Liu,Helin Zhang","doi":"10.5194/essd-18-6613-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6613-2026","url":null,"abstract":"Vegetation photosynthesis, quantified as gross primary productivity (GPP), regulates the terrestrial carbon sink and land–atmosphere exchanges. At sub-daily scales, diurnal GPP dynamics reveal rapid adjustments to changing light, temperature and water conditions that are largely obscured in daily-to-annual aggregates, underscoring the need for developing global hourly GPP products. However, existing hourly products mostly rely on traditional machine-learning schemes that lack explicit biophysical constraints and an adequate representation of water limitation, leading to large uncertainties, especially in dryland regions. Besides, the added value of hourly products for resolving diurnal behavior and responses to environmental stress remains poorly quantified. Here, we develop a causal knowledge-driven upscaling framework that couples the Peter and Clark Momentary Conditional Independence guided causal weights with ensemble learning strategies. Based on eddy-covariance measurements and multi-source meteorological variables, vegetation properties, and land-cover fields, we generated a global 0.05° monthly-averaged hourly GPP product covering 06:00–18:00 LST from 2000 to 2022, named EGO (Eddy covariance site-based Global hOurly) GPP, and then evaluated how well EGO reproduces observed diurnal cycles and their responses to extreme events. EGO GPP achieves an R 2 of 0.76 and an RMSE of 4.17 µmol CO 2  m −2  s −1 on independent test sites, and outperforms two hourly upscaling products (FLUXCOM and X-BASE; R 2  ≈ 0.60 and RMSE ≈ 5.5 µmol CO 2  m −2  s −1 ), with large improvement in drylands. EGO GPP clearly illustrates the diurnal progression of photosynthesis and captures observed diurnal metrics across diverse biomes, revealing strong midday depression and morning-skewed curves in drylands but near-symmetric cycles in high-latitude and humid tropical regions. Analyses of the June 2021 central North America drought and the August 2003 European heatwave further show that EGO reliably tracks diurnal photosynthetic responses to extremes, including GPP reductions, earlier centroid/peak times and intensified midday depression, consistent with tower-based results. Looking ahead, EGO GPP provides a reliable foundation for investigating diurnal photosynthetic behavior, exploring vegetation–climate interactions and benchmarking Earth system models at a sub-daily scale. EGO GPP is available at https://doi.org/10.5281/zenodo.18253238 (Liu and Li, 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"55 1","pages":"6613-6636"},"PeriodicalIF":11.4,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global satellite gravity data products for prompt detection of short-term Mass Change (MC)","authors":"Miao Tang,Shin-Chan Han,In-Young Yeo,Mehdi Khaki,Thomas Loudis Papanikolaou,Xinghai Yang","doi":"10.5194/essd-18-6589-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6589-2026","url":null,"abstract":"We present the globally available dataset of Line-of-sight Gravity Differences (LGD) as a new data product to fill the long-standing gap of investigating sub-monthly surface mass change from the satellite gravimetry along-track perspective. The dataset is generated directly from Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-1B intersatellite ranging observations, positioned conceptually between Level-1B measurements and Level-2 monthly gravity field solutions. Provided as along-track time series, LGD delivers instantaneous gravity change at satellite altitude, thus ensuring the fidelity of measurements by avoiding the complex preprocessing, regularization, and filtering applied to monthly products. The instantaneous LGD measurements enable monitoring of sub-monthly terrestrial water storage variability and provide accurate information on timing and magnitude of water mass changes. GRACE-FO's ground track allows LGD retrievals every 5–6 d in most low and mid latitude regions, while the revisit interval becomes shorter toward higher latitudes. Our LGD data products also contain climatology in the form of along-track gravity variations. We demonstrate its potential through case studies, including along-track diagnosis of flash drought evolution in the southeastern United States and the characterization of sub-monthly hydrological extremes in global hotspots. Results show that the LGD dataset has the potential to resolve the critical timing (e.g., onset and peak) of hydrological extremes, which is unattainable from monthly products. The LGD dataset is available at https://doi.org/10.5281/zenodo.17766177 (Tang et al., 2025a), which is expected to pave the way for advances in sub-monthly hydrological monitoring and can serve as a unique constraint for hydrological and geophysical models, opening new opportunities for Earth system sciences.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"31 1","pages":"6589-6611"},"PeriodicalIF":11.4,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a dataset of offshore oil and gas extraction platforms from satellite data (2017–2023)","authors":"Lulu Si,Shanyu Zhou,Itziar Irakulis-Loitxate,Javier Roger,Luis Guanter","doi":"10.5194/essd-18-6545-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6545-2026","url":null,"abstract":"Accurate information on the location and operational status of offshore oil and gas platforms (OOGPs) is important to inform decision-making by various stakeholders and to evaluate the environmental impacts of OOGPs. However, existing OOGP databases are often incomplete or contain outdated data. In this work, we used satellite data and the Google Earth Engine (GEE) platform to construct a new database of OOGPs for six major offshore oil and gas basins in the world between 2017 and 2023. We used synthetic aperture radar (SAR) data from the Sentinel-1 satellite mission to detect OOGP candidates due to its high sensitivity to OOGPs, dense spatio-temporal sampling, and global coverage. Our main processing steps comprise the detection of OOGP candidates using monthly averages of SAR recordings and the removal of noise and false positive objects from annual image composites. With the resulting dataset of OOGPs, we mapped the spatiotemporal distribution of OOGPs in the study regions and analyzed the platform status after the post-processing of the platform targets. Using these methods, we identified a total of 5358 OOGPs distributed in six offshore basins: the Gulf of Mexico (GoM) (1593), Persian Gulf (PG) (1437), North Sea (NS) (440), Caspian Sea (CS) (794), Gulf of Guinea (GoG) (460), and Gulf of Thailand (GoT) (634). An independent validation dataset was used to evaluate the performance of the detection algorithm, which achieved precision of 0.95, recall of 0.89, and F 1 score of 0.92. This OOGPs dataset substantially enhances and complements the existing offshore platform database in terms of spatial and temporal coverage. From our analysis of this OOGP dataset, we observed that offshore platform activity has declined in regions like the GoM, consistent with documented trends of aging infrastructure and shifting energy investment, while it has expanded in the PG and CS, consistent with ongoing offshore development. These different regional trends highlight the need for targeted environmental oversight and region-specific mitigation strategies. The dataset of this paper can be accessed through https://doi.org/10.5281/zenodo.18350974 (Si et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"77 1","pages":"6545-6564"},"PeriodicalIF":11.4,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term temperature, oxygen and water clarity trends in Swiss lakes","authors":"Fabian Bärenbold,Camilla Capelli,Lukas De Ventura,Stefanie Eyring,Elise Folly,Nicole Gallina,Barbara Känel,Peter Keller,Oliver Köster,Fabio Lepori,Robert Lovas,Michela Rogora,David J. Janssen,Damien Bouffard,Martin Schmid","doi":"10.5194/essd-18-6527-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6527-2026","url":null,"abstract":"Abstract. Water temperature, electrical conductivity, dissolved oxygen concentration, and water clarity are key variables routinely measured in Swiss lakes by lake monitoring programs run by cantonal environmental offices and international lake commissions. In most lakes, data is collected at bi-weekly to monthly intervals; however, the data processing pipelines remain largely manual, non-automated, and decentralized, making data access and use difficult. The Swiss Federal Office for the Environment (FOEN) consolidates and stores this information at the national level, but this process is not always done on a regular basis, and the data are not directly accessible to the public. This limited accessibility restricts their use in scientific research, particularly for comparative studies across different lakes. Here, we present a harmonised dataset of temperature, electrical conductivity, dissolved oxygen and Secchi depth (a widely used proxy of water clarity) collected by cantonal offices, and research institutes in 21 large Swiss lakes and lake basins from the beginning of consistent records (between 1938 and 2001 depending on the lake) to the end of 2023. In addition, we provide consistently calculated variables including lake heat content, Schmidt stability, thermocline depth and hypolimnetic oxygen. We used the measured and calculated variables to identify long term trends in large Swiss lakes. Specifically, we investigated whether the effects of climate change and re-oligotrophication led to a coherent pattern across Switzerland or whether the responses varied from lake to lake. We found a clear warming trend in all lakes with consistently increasing mean annual surface (since ∼ 1980) and bottom temperature (since ∼ 2010), heat content, and stronger autumn stratification, which is consistent with expected effects of climate change. Similarly, there is a general trend towards greater water clarity, consistent with re-oligotrophication. Thermocline depth and lake oxygen concentrations showed less clear patterns. For the former, observations indicate modest shifts toward shallower thermoclines in deeper lakes and towards deeper thermoclines in shallower lakes. In contrast, deepwater oxygen concentrations show no consistent trends over recent decades, potentially reflecting opposing influences of climate change and re-oligotrophication. By publishing this data, we aim to advocate for open data policies at national and international levels, facilitating its reuse in further scientific research, and contributing to evidence-based lake management and decision-making. The data presented in this work are available at https://doi.org/10.25678/000GJC (Bärenbold et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"11 1","pages":"6527-6544"},"PeriodicalIF":11.4,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C-PEAT's Global Peatland Carbon Database (v.2025)","authors":"Julie Loisel,Angela Gallego-Sala,Daniela Ransby,Emily Rabel,Cornelia Behrens,Stefanie Schumacher,Kathe Todd-Brown,Vaasuki Marupaka,C-PEAT Working Group Members","doi":"10.5194/essd-18-6485-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6485-2026","url":null,"abstract":"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).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"40 1","pages":"6485-6502"},"PeriodicalIF":11.4,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-driven reconstruction of global surface temperature with emphasis on refining the Antarctic record","authors":"Chenxi Ouyang,Qingxiang Li,Zichen Li,Sihao Wei","doi":"10.5194/essd-18-6503-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6503-2026","url":null,"abstract":"Abstract. Accurate estimates of long-term surface temperature (ST) changes are fundamental not only for assessing observed warming, but also for improving the reliability of future climate projections. However, substantial missing information in global ST datasets, remains a major source of uncertainty in estimating global or regional temperature changes. Recent advances in artificial intelligence (AI) have promoted the effective application of deep learning approaches, such as image inpainting and transfer learning, in reconstructing incomplete geophysical datasets. This study develops a historical climate field reconstruction framework based on a Partial Convolutional (PConv) neural network, using climate model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) as training samples. Global surface temperature observations were constructed by merging the China Global Land Surface Air Temperature dataset (C-LSAT2.1) with the 200-member sea surface temperature ensemble from HadSST4.2.0.0, provided by the UK Met Office Hadley Centre, thereby forming an input ensemble. Model uncertainty was further quantified using the Deep Ensemble approach, generating multiple reconstruction members. The final monthly global surface temperature reconstruction was obtained by averaging all reconstruction ensemble members, while the corresponding uncertainty was estimated from the ensemble. Furthermore, validation against station observations indicates that the reconstructions perform well over Antarctica after 1961, where observational coverage is extremely sparse. Based on this framework, we developed the China global Artificial Intelligence Reconstructed Surface TemperatureCMIP6 (C-AIRSTM) dataset, providing spatially complete global monthly ST anomaly reconstructions since 1850 with a spatial resolution of 5°×2.5°, with anomalies referenced to the 1961–1990 climatology. This dataset provides improved support for extending long-term climate records and for applications in polar climate assessment, as well as in climate monitoring, detection, and attribution studies. The C-AIRSTM datasets can be downloaded at https://doi.org/10.6084/m9.figshare.33122444 (Ouyang et al., 2026). It is also available from http://www.gwpu.net/en/h-col-103.html (last access: 30 July 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"6 1","pages":"6503-6526"},"PeriodicalIF":11.4,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Global Surface Turbulence Heat Flux Dataset resolving tropical cyclones","authors":"Weixiang Peng,Zhanhong Ma,Deyuan Zhang,Hexin Ye,Jianfang Fei","doi":"10.5194/essd-18-6421-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6421-2026","url":null,"abstract":"Abstract. The Global Surface Turbulence Heat Flux Dataset (GHFD) presents a dataset of surface latent heat flux and sensible heat flux, at spatial resolutions of 0.25° × 0.25° and available daily from 1993 to 2023. The GHFD is generated using the IBTrACS, OISST, CCMP, Copernicus Marine, and ERA5 datasets, with the wind speed field adjusted to incorporate tropical cyclone (TC) information and using the COARE 3.6 algorithm to conduct the heat flux calculations. The GHFD includes seven meteorological elements at the air-sea interface, including surface latent and sensible heat flux, 2 m specific humidity, sea surface temperature, 2 m air temperature, sea surface salinity and 10 m wind speed. A comparison between GHFD and various flux products (J-OFURO 3, OAFlux, ifremerflux) in terms of the fundamental components is conducted with moored observation data, in-situ observation data, and high-resolution simulation data. Results show an improvement of GHFD compared to the other three flux products in resolving TCs. The GHFD dataset in NetCDF format is freely available for download at https://doi.org/10.57760/sciencedb.24400 (Peng et al., 2025).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"374 1","pages":"6421-6433"},"PeriodicalIF":11.4,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A gridded dataset of European Forest Types to support forest monitoring, modelling and reporting","authors":"Francesca Giannetti,Ilaria Zorzi,Stefanie Linser,Mathias Neumann,Sorin Cheval,Alessio Collalti,Elia Vangi,Elisa Grieco,Mauro Morichetti,Giovanni D'Amico,Nicu Constantin Tudose,Alice Ludvig,Livia Passarino,Jessica Scriva,Yamuna Giambastiani,Irene Fattoretto,Giuliano Secchi,Davide Travaglini,Gherardo Chirici,Piermaria Corona,Marco Marchetti,Anna Barbati","doi":"10.5194/essd-18-6435-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6435-2026","url":null,"abstract":"Abstract. A standardized system of nomenclature for forest types is essential for effectively monitoring and understanding the impacts of climate change on diverse ecosystems in Europe and beyond. A comprehensive classification system, such as the European Forest Types (EFTs) scheme, is essential for assessing baseline conditions, tracking changes, and guiding conservation decisions. A unified forest type nomenclature supports international collaboration, enables researchers and policymakers to accurately compare data across regions and time periods, and enhances the development of targeted conservation strategies and adaptive management practices aimed at preserving biodiversity and ecosystem services. This classification breaks down forested areas in Europe into a handful of ecologically homogeneous units, thus facilitating the analysis of data related to forest conditions and management practices across a wide range of climatic and edaphic conditions. The current lack of an EFTs map for Europe prompted its processing, using a shared rule-based expert system algorithm. Utilizing a dataset featuring 39 “relative probability of presence (RPP) maps” of tree species and various forest masks, the algorithm identified 14 EFT categories. This initiative filled a critical gap in spatial monitoring, providing the first consistent pan-European EFT maps gridded dataset. The availability of standardized and comprehensive spatial data on forest types enhances our capacity to understand, manage, and conserve forest ecosystems effectively. Such data support biodiversity conservation and ensure the sustained provision of essential ecosystem services, highlighting the critical role of forest types in maintaining ecological balance and supporting human well-being. The European Forest Types map gridded dataset is freely available in Zenodo at https://doi.org/10.5281/zenodo.18496150 (Giannetti et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"266 1","pages":"6435-6463"},"PeriodicalIF":11.4,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GloPINE dataset: model-ready measurements of INP concentrations using PINE instruments","authors":"Ross J. Herbert,Larissa Lacher,Alexander Böhmländer,Mark D. Tarn,Antoine Canzi,Aidan Pantoya,Evelyn Freney,Kristina Höhler,Pia Bogert,Céline Planche,Ping Tian,Michael Adams,Sarah Barr,David Brus,Nicole Büttner,Martin Daily,Konstantinos Doulgeris,Konstantinos Eleftheriadis,Grant Forster,Romy Fösig,Dimitrios G. Georgakopoulos,Maria I. Gini,A. Gannet Hallar,Radovan Krejci,Elke Ludewig,Mauro Mazzola,Ian B. McCubbin,Athanasios Nenes,Tuukka Petäjä,Joseph Robinson,Franziska Vogel,Paul Zieger,Stephen Arnold,Kenneth S. Carslaw,Naruki Hiranuma,Ottmar Möhler,Benjamin J. Murray","doi":"10.5194/essd-18-6465-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6465-2026","url":null,"abstract":"Abstract. Ice-nucleating particles (INPs) are a subset of aerosol particles that facilitate the freezing of supercooled cloud droplets heterogeneously and influence the radiative properties of supercooled clouds. The role of INPs in the Earth system remains unquantified in part due to poorly constrained representations of their spatial distributions and properties in global and regional models. In this study, we present a quality controlled dataset (https://doi.org/10.5281/zenodo.16745514, Herbert et al., 2026), called GloPINE, comprising 70 000 h of INP concentrations measured using Portable Ice Nucleation Experiment (PINE) instruments that use an expansion chamber to make automated long-term (months to years) and high temporal resolution (<10 min). We collate measurements from 20 recent ground-based PINE field campaigns in the Northern Hemisphere conducted between January 2018 and December 2023, totaling more than 400 000 expansions, sampling over 800 m3 of ambient air, and performed under conditions relevant for mixed-phase clouds. In the GloPINE dataset, we subset and average the PINE measurements across synoptically relevant time intervals of 6 h and 2 K temperature bins, providing 36 000 INP measurements. Combining PINE expansions over these intervals enhances counting statistics at higher freezing temperatures, decreases the lower limit of measurable INP concentrations, and provides an INP dataset readily applicable to model simulation data and meteorological reanalysis products. Together with complementary INP datasets, GloPINE provides a valuable resource for advancing model evaluation, improving INP source attribution, and informing parameterizations across the full temperature range over which aerosols influence ice formation in clouds.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"59 1","pages":"6465-6483"},"PeriodicalIF":11.4,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new regional-scale multibeam echosounder bathymetric dataset of the Saudi Arabian Red Sea","authors":"Fabio Marchese,Marta Andrea Ezeta Watts,Megan Klein Breteler Nolan,Natalie Dunn,Katherine von Krusenstiern,Craig Foy,Mattie Rodrigue,Mohammed Qurban,Carlos Manuel Duarte,Vincent Pieribone,Francesca Benzoni","doi":"10.5194/essd-18-6345-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6345-2026","url":null,"abstract":"Abstract. 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.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"49 1","pages":"6345-6355"},"PeriodicalIF":11.4,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}