Earth System Science Data最新文献

{"title":"A high-resolution air-sea synoptic observation dataset from drifting buoys in the Bay of Bengal","authors":"Wei Huang,Guihua Wang,Changlin Chen,Gengxin Chen,Weiqiang Wang","doi":"10.5194/essd-18-6411-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6411-2026","url":null,"abstract":"Abstract. 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).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"138 1","pages":"6411-6419"},"PeriodicalIF":11.4,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895413","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":"ABCFlux v2: Arctic–boreal CO\u0000 2\u0000 and CH\u0000 4\u0000 monthly flux observations and ancillary information across terrestrial and freshwater ecosystems","authors":"Anna-Maria Virkkala,Isabel Wargowsky,Judith Vogt,Simran Madaan,Tiffany Windholz,Kyle A. Arndt,Gerard Rocher-Ros,Mathias Göckede,David Olefeldt,Edward A. G. Schuur,David Bastviken,Richard O'Keefe,McKenzie A. Kuhn,Brendan M. Rogers,Jennifer D. Watts,Kelcy Kent,Kristoffer Aalstad,Kelly Aho,Joonatan Ala-Könni,Haley Alcock,Inge Althuizen,Christopher D. Arp,Jun Asanuma,Mika Aurela,Sivakiruthika Balathandayuthabani,Katrin Attermeyer,Alan Barr,Maialen Barret,Ochirbat Batkhishig,Christina Biasi,Mats P. Björkman,Andrew Black,Elena Blanc-Betes,Pascal Bodmer,Julia Boike,Abdullah Bolek,Frédéric Bouchard,Ingeborg Bussmann,Lea Cabrol,Eleonora Canfora,Sean Carey,Karel Castro-Morales,Namyi Chae,Andreas Christen,Torben R. Christensen,Casper T. Christiansen,Housen Chu,Graham Clark,Francois Clayer,Patrick Crill,Christopher Cunada,Scott J. Davidson,Joshua F. Dean,Sigrid Dengel,Matteo Detto,Catherine Dieleman,Florent Domine,Egor Dyukarev,Colin Edgar,Bo Elberling,Craig A. Emmerton,Eugenie Euskirchen,Grant Falvo,Thomas Friborg,Michelle Garneau,Mariasilvia Giamberini,Mikhail V. Glagolev,Miquel A. Gonzalez-Meler,Gustaf Granath,Jón Guðmundsson,Konsta Happonen,Yoshinobu Harazono,Lorna Harris,Josh Hashemi,Nicholas Hasson,Janna Heerah,Liam Heffernan,Manuel Helbig,Warren Helgason,Michal Heliasz,Greg Henry,Geert Hensgens,Tetsuya Hiyama,Beth Holmes,Macall Hock,David Holl,Jutta Holst,Thomas Holst,Gabriel Hould-Gosselin,Elyn Humphreys,Jacqueline Hung,Jussi Huotari,Hiroki Ikawa,Danil V. Ilyasov,Mamoru Ishikawa,Go Iwahana,Hiroki Iwata,Marcin Antoni Jackowicz-Korczynski,Joachim Jansen,Järvi Järveoja,Vincent E. J. Jassey,Rasmus Jensen,Katharina Jentzsch,Robert G. Jespersen,Carl-Fredrik Johannesson,Cheristy P. Jones,Anders Jonsson,Ji Young Jung,Sari Juutinen,Evan Kane,Jan Karlsson,Sergey Karsanaev,Kuno Kasak,Julia Kelly,Kasha Kempton,Marcus Klaus,George W. Kling,Natascha Kljun,Jacqueline Knutson,Hideki Kobayashi,John Kochendorfer,Kukka-Maaria Kohonen,Pasi Kolari,Mika Korkiakoski,Aino Korrensalo,Pirkko Kortelainen,Egle Koster,Kajar Koster,Ayumi Kotani,Praveena Krishnan,Juliya Kurbatova,Lars Kutzbach,Min Jung Kwon,Ethan D. Kyzivat,Jessica Lagroix,Theodore Langhorst,Elena Lapshina,Tuula Larmola,Klaus S. Larsen,Isabelle Laurion,Justin Ledman,Hanna Lee,A. Joshua Leffler,Lance Lesack,Anders Lindroth,David Lipson,Annalea Lohila,Efrén López-Blanco,Vincent L. St. Louis,Erik Lundin,Miska Luoto,Takashi Machimura,Marta Magnani,Avni Malhotra,Marja Maljanen,Ivan Mammarella,Elisa Männistö,Luca Belelli Marchesini,Phil Marsh,Pertti J. Martkainen,Maija E. Marushchak,Mikhail Mastepanov,Alex Mavrovic,Trofim Maximov,Christina Minions,Daniel F. Nadeau,Marco Montemayor,Tomoaki Morishita,Patrick Murphy,Erin Nicholls,Mats B. Nilsson,Anastasia Niyazova,Jenni Nordén,Koffi Dodji Noumonvi,Hannu Nykanen,Walter Oechel,Anne Ojala,Tomohiro Okadera,Sujan Pal,Alexey V. Panov,Tim Papakyriakou,Dario Papale,Sang-Jong Park,Frans-Jan W. Parmentier,Gilberto Pastorello,Mike Peacock,Matthias Peichl,Roman Petrov,Kyra St. Pierre,Norbert Pirk,Jessica Plein,Vilmantas Preskienis,Anatoly Prokushkin,Jukka Pumpanen,Hilary A. Rains,Niklas Rakos,Aleksi Räsänen,Helena Rautakoski,Riikka Rinnan,Janne Rinne,Adrian Rocha,Nigel Roulet,Alexandre Roy,Anna Rutgersson,Aleksandr F. Sabrekov,Torsten Sachs,Erik Sahlée,Alejandro Salazar,Henrique Oliveira Sawakuchi,Christopher Schulze,Roger Seco,Armando Sepulveda-Jauregui,Svetlana Serikova,Abbey Serrone,Hanna M. Silvennoinen,Sofie Sjogersten,June Skeeter,Jo Snöälv,Sebastian Sobek,Oliver Sonnentag,Emily H. Stanley,Maria Strack,Lena Strom,Patrick Sullivan,Ryan Sullivan,Anna Sytiuk,Torbern Tagesson,Pierre Taillardat,Julie Talbot,Suzanne E. Tank,Mario Tenuta,Irina Terenteva,Frederic Thalasso,Antoine Thiboult,Halldor Thorgeirsson,Fenix Garcia Tigreros,Margaret Torn,Amy Townsend-Small,Claire Treat,Alain Tremblay,Carlo Trotta,Eeva-Stiina Tuittila,Merritt Turetsky,Masahito Ueyama,Muhammad Umair,Aki Vähä,Lona van Delden,Maarten van Hardenbroek,Andrej Varlagin,Ruth K. Varner,Tarmo Virtanen,Elena Veretennikova,Timo Vesala,Carolina Voigt,Jorien E. Vonk,Robert Wagner,Katey Walter Anthony,Qinxue Wang,Masataka Watanabe,Hailey Webb,Jeffrey M. Welker,Andreas Westergaard-Nielsen,Sebastian Westermann,Jeffrey R. White,Christian Wille,Scott N. Williamson,Scott Zolkos,Donatella Zona,Susan M. Natali","doi":"10.5194/essd-18-6357-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6357-2026","url":null,"abstract":"Abstract. Measurements of surface-atmosphere carbon dioxide (CO2) and methane (CH4) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO2 flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO2 fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH4 fluxes. For aquatic ecosystems, we split CH4 fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO2 and CH4 concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH4 fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO2 dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH4 flux site-months (3098) were more frequent than CO2 flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO2 flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH4 data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our und","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"15 1","pages":"6357-6409"},"PeriodicalIF":11.4,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895412","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":"Hydrographic CTD profiles capturing the onset of near-surface stratification in Southwest Greenland fjords during two consecutive spring 2025 field campaigns","authors":"Marta Umbert,Nina Hoareau,Júlia Crespin,Maria Sánchez-Urrea,Ferran Hernández-Macià,Carolina Gabarró,Eva De-Andrés","doi":"10.5194/essd-18-6321-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6321-2026","url":null,"abstract":"Abstract. In situ hydrographic observations in Greenland fjords remain sparse, particularly during late winter and early spring, despite their importance for Arctic freshwater export, glacier–ocean interactions, and the seasonal development of stratification. A major limitation of many existing hydrographic datasets is the poor representation of the near-surface layer, where meltwater lenses, sharp salinity gradients, and strong density gradients are often concentrated within the upper few metres. Here we present a quality-controlled conductivity, temperature, and depth dataset comprising 73 vertical hydrographic profiles collected across Southwest Greenland fjords (60.2–69.5° N, 43.6–53.9° W) during two expeditions aboard the icebreaker Le Commandant Charcot: the Sea Ice Measurements for Satellite thickness retrieval Validation campaign (32 profiles, 24 March–4 April 2025) and the Freshwater fluxes and Atlantic-Nordic Seas hydrography campaign (41 profiles, 18–28 April 2025). The dataset includes pressure, temperature, conductivity, practical salinity, and derived thermodynamic variables. Two RBR Concerto3 conductivity, temperature, and depth instruments sampling at 16 Hz and 2 Hz were processed through a reproducible eight-step workflow including atmospheric-pressure correction, automatic soak detection, signal conditioning, loop removal, thermodynamic calculations, and automated quality control following international ocean-data recommendations. Unlike many standard conductivity, temperature, and depth products, the processing strategy retains the shallowest valid observations and preserves the native vertical sampling. This enables users to examine thin freshwater lenses and sharp near-surface gradients without systematically excluding the upper few metres of the water column. It also allows alternative vertical averaging or filtering strategies to be applied according to the objectives of future analyses. Thermohaline diagnostics identify four main hydrographic regimes: coastal and glacier meltwater-influenced waters, Polar Water, Winter Water, and modified Atlantic or Irminger Water. Comparison of the late-winter and early-spring campaigns documents the onset of surface restratification across Southwest Greenland fjords. The strongest freshening and shallow stratification occur in fjords directly influenced by tidewater glaciers, whereas deeper water masses remain comparatively stable over the one-month period. The dataset is distributed in NetCDF-4 format compliant with Climate and Forecast Metadata Conventions version 1.8 and Attribute Convention for Data Discovery version 1.3, and is publicly available through Zenodo. The processing code is archived on GitHub and Zenodo (https://doi.org/10.5281/zenodo.20489479, Hoareau et al., 2026). Together, these observations provide a valuable resource for studies of Arctic freshwater dynamics, glacier–ocean interactions, and fjord stratification, as well as for ocean model evaluation and satellite p","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"15 1","pages":"6321-6343"},"PeriodicalIF":11.4,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895465","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 30 m disturbance-recovery age dataset for young natural and planted forests (1985–2024)","authors":"Yan Wang,Hengbin Wang,Chaoran Liang,Xiyi Li,Zhe Liu,Xiaodong Zhang,Shaoming Li,Jinming Yang,Yuanyuan Zhao","doi":"10.5194/essd-18-6225-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6225-2026","url":null,"abstract":"Abstract. 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).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"74 1","pages":"6225-6249"},"PeriodicalIF":11.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895414","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 surface mining and land reclamation of time series from 1985–2022","authors":"Sucheng Xu,Jiatong Zhou,Kechao Wang,Stefan Giljum,Victor Maus,Tim T. Werner,Liang Tang,Jiwang Guo,Wu Xiao","doi":"10.5194/essd-18-6293-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6293-2026","url":null,"abstract":"Abstract. Surface mining has profound impacts on ecosystems, contributing to land degradation, vegetation loss, pollution, and threats to biodiversity. Given the rapidly rising demand for raw materials, understanding the dynamics of mining and reclamation processes is essential to support sustainable development. Here, we integrate and analyze a large set of mines distributed worldwide based on their known land extent circa year 2020. We integrated time-series data of the Normalized Difference Vegetation Index (NDVI), nighttime light (NTL) intensity, and land use to detect and identify changes within mine sites from 1985 to 2022 and assess spatiotemporal trajectories of mining and reclamation processes. The dataset comprises 74 726 polygons, covering a total area of 82 552 km2. Our dataset obtained the maximum potential mining disturbance boundary – the cumulative outer envelope of mining-induced land disturbance over the study period. China leads in both the number and the areal extent of mining sites, followed by the United States and Australia. Within the analyzed set of polygons, mining land footprint expanded steadily between 1985 and 2022, with the annual disturbed area peaking at 1943 km2 in 2015, with a slowing expansion after 2015. From 1985 to 2022, the cumulative area of land converted to mine reached 40 596 km2, accounting for 49 % of the total surface mining area in our set, while the reclaimed area was 29 285 km2. Active mining areas dominated the global mining landscape, comprising 31.6 % of all polygons, with approximately 50.6 % concentrated in Asia. The spatiotemporal processes and patterns revealed in this study provide crucial insights into the development of mine sites and provide new data to support ecological impact assessments and sustainable development research in global mining regions. The dataset is available at Zenodo at https://doi.org/10.5281/zenodo.17085099 (Xu et al., 2025).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"51 1","pages":"6293-6320"},"PeriodicalIF":11.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895415","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":"Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors","authors":"Ankita Saxena,Calum Brown,Karina Winkler,Almut Arneth,Mark Rounsevell","doi":"10.5194/essd-18-6251-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6251-2026","url":null,"abstract":"Abstract. Land systems are increasingly influenced not only by land-use change but also by land management intensity. However, persistent data limitations prevent systematic understanding of management intensity and its dependencies on behavioural, socioeconomic, and biophysical factors. We develop a global dataset of land management intensity for 2020 at 0.01° spatial resolution, distinguishing unmanaged, very extensive, extensive, and intensive management across cropland, pasture, and forest systems. Intensive management occupies about 22 % of global managed land, while extensive and very extensive management dominate (78 %). Intensive cropland management accounts for around 44 % of cropland area, but intensive pasture is limited to 10 % of pasture area, and intensive forest systems to only 5 % of forest area, revealing distinct sectoral contrasts. Management intensity is highly heterogeneous, with intensive cropland concentrated in North America, Europe, and South and East Asia, while extensive management dominates in Africa and Latin America. Five countries account for nearly half of global intensive cropland. Income, market access, population density, and aridity influence cropland management intensity, whereas pastures and forests show more complex relationships. Comparison with global land decision-making types shows spatial consistency (68 %), suggesting that land management intensity is influenced by land-user behaviour. This dataset may be useful for various earth system modelling applications in land system science and environmental studies. The dataset can be visualised through global land management intensity dataset explorer: https://ee-ankitasaxena03as.projects.earthengine.app/view/global-land-use-management (last access: 27 August 2026; Saxena et al., 2025), and accessed through https://doi.org/10.5281/zenodo.18249970 (Saxena et al., 2026) for research purposes. The input data for socioeconomic variables (GDP, GINI, market access and working age population) can be accessed through https://doi.org/10.5281/zenodo.17580780 (Perkins et al., 2025).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"46 1","pages":"6251-6292"},"PeriodicalIF":11.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895416","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":"Nitrous oxide and methane concentrations and air-sea fluxes in undersampled areas of the Mediterranean basin","authors":"Mercedes de la Paz,Susana Flecha,Fatima Zohra Bouthir,Milad Fahkri,Abed El Rahman Hassoun,Valeria Ibello,Korhan Özkan,Fiz F. Pérez,Adil Chair,Iris E. Hendriks","doi":"10.5194/essd-18-6207-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6207-2026","url":null,"abstract":"Abstract. 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).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"82 1","pages":"6207-6224"},"PeriodicalIF":11.4,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894918","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":"Integrated dataset of atmospheric bioaerosols over east Asia","authors":"Zhongwei Huang,Wenjin Zhang,Qing Dong,Teruya Maki,Yongkai Wang,Yuanzong Ji,Fanli Xue,Xuefei Huo,Da Lu,Dongdong Wang,Jinsen Shi,Jianrong Bi,Jianping Huang","doi":"10.5194/essd-18-6191-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6191-2026","url":null,"abstract":"Abstract. Bioaerosols are one of the main types of aerosols originating from the Earth's biosphere and are widely found in the atmosphere. They possess both biological attributes and aerosol characteristics, thereby exerting significant influences on climate, the environment, ecosystems, and public health. However, their regional-scale distribution, influencing factors, climatic and environmental impacts remain unclear due to the scarcity of observational data. This study firstly establishes an integrated bioaerosol dataset based on a large-scale dust–bioaerosol field campaign conducted across East Asia using unified sampling and analytical methods. The dataset systematically integrates atmospheric bioaerosol number concentrations and bacterial community structure at multiple taxonomic levels across 45 sites in China, Japan, South Korea, and Mongolia. In addition, meteorological variables (e.g., air temperature, relative humidity, wind speed and direction), air quality parameters (e.g., PM10 and PM2.5), and Normalized Difference Vegetation Index (NDVI) data during the sampling period were incorporated from multiple sources. Further analysis of this integrated dataset indicates that bioaerosol number concentrations are negatively correlated with local NDVI. Moreover, there is a clear relationship between bioaerosol number concentration and air temperature, with a peak observed at approximately 20 °C. A pronounced diurnal variation in bioaerosol concentrations is also found, which is strongly associated with Aerosol Optical Depth (AOD) and particulate matter concentrations. In addition, substantial differences in community structure were observed across different underlying surface types, and the α-diversity indices (Shannon and Chao1 indices) were negatively correlated with NDVI. This dataset provides a robust foundation for advancing research on atmospheric bioaerosol processes, as well as their implications for climate, the environment, public health, and interdisciplinary studies. The dataset generated in this study is openly available via Zenodo (https://doi.org/10.5281/zenodo.21337360, Huang et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"59 1","pages":"6191-6206"},"PeriodicalIF":11.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894919","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":"Riverine phosphorus gain and loss across the conterminous United States.","authors":"Yiming Wang, Xuesong Zhang, Kaiguang Zhao, Robert D Sabo, Yuxin Miao, Christopher M Clark","doi":"10.5194/essd-18-3355-2026","DOIUrl":"10.5194/essd-18-3355-2026","url":null,"abstract":"<p><p>Excess riverine phosphorus represents a preeminent catalyst for water quality degradation. Spatial mapping and characterization of the net gain and loss of riverine phosphorus help discern the critical source areas. Here, we developed a dataset encompassing phosphate ( <math> <msubsup><mrow><mtext>PO</mtext></mrow> <mrow><mn>4</mn></mrow> <mrow><mn>3</mn> <mo>-</mo></mrow> </msubsup> </math> ) and total phosphorus (TP) gain and loss across catchments in the conterminous United States (CONUS). We compiled 51 394 <math> <msubsup><mrow><mtext>PO</mtext></mrow> <mrow><mn>4</mn></mrow> <mrow><mn>3</mn> <mo>-</mo></mrow> </msubsup> </math> and 285 675 TP concentration measurements and estimated <math> <msubsup><mrow><mtext>PO</mtext></mrow> <mrow><mn>4</mn></mrow> <mrow><mn>3</mn> <mo>-</mo></mrow> </msubsup> </math> and TP loads at 963 and 2317 stations, respectively. Next, we leveraged the upstream-downstream topology information from the National Hydrography Dataset Plus (NHDPlus) catchment map at the Hydrologic Unit Catalogue-12 (HUC12) level to derive the net gain and loss of riverine phosphorus across catchments in the CONUS. Such maps can be used to estimate potential contributions of point and non-point sources to riverine phosphorus pollution at refined spatial scales, identify different major factors controlling local riverine P gain and loss compared to P loads, and evaluate watershed model's fidelity for representing riverine P cycling. The resultant dataset is provided in Excel (.xlsx) format, accessible at Figshare (https://doi.org/10.6084/m9.figshare.28509317, Wang et al., 2025b). Leveraging the HUC12 information for spatialization, the new datasets aim to address the existing gap in regional characterization of riverine phosphorus and support effective management practices across the CONUS.</p>","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"18 5","pages":"3355-3365"},"PeriodicalIF":13.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gap-filled sub-surface mooring dataset off Western Australia during 2010–2023","authors":"Toan Bui, Ming Feng, Christopher Chapman","doi":"10.5194/essd-17-1693-2025","DOIUrl":"https://doi.org/10.5194/essd-17-1693-2025","url":null,"abstract":"Abstract. Coastal moorings allow scientists to collect long-term datasets that are valuable in understanding shelf dynamics, detecting climate variability and changes, and evaluating shelf dynamics and climate variability and change impacts on marine ecosystems. However, we often cannot obtain continuous time series data from moorings due to mooring losses or instrument failures. Here, we present an updated version of the 14-year sub-surface mooring dataset off the south-western coast of Western Australia (WA) during 2010–2023 (https://doi.org/10.25919/myac-yx60, Bui and Feng, 2024). This updated dataset offers continuous daily temperature and current data with a 5 m vertical resolution, collected from six coastal Integrated Marine Observing System (IMOS) moorings at depths between 48 and 500 m. The self-organizing map (SOM) machine learning technique is applied to fill in the data gaps in the previous version. The data capture the Leeuwin Current variability on the shelf from intraseasonal to interannual timescales. The data also capture the variability of the Capes Current, a wind-driven northward current on the middle shelf. The usage of the in-filled data product is demonstrated by detecting extreme temperature events on the Rottnest Shelf. The data products can be used to characterize sub-surface features of extreme events such as marine heat waves and marine cold spells, which are influenced by the Leeuwin Current and the wind-driven Capes Current, and to detect decadal change signals along the WA coast.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"17 4","pages":"1693-1705"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://essd.copernicus.org/articles/17/1693/2025/essd-17-1693-2025.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147920437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}