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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: P. Zemunik; J. Šepić; H. Pellikka; L. Ćatipović; +2 Authors

    Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research on sea-level extremes is restricted to hourly datasets, which prevent the quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter-period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low tidal basins. Therefore, a new global 1 min sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T<2 h) obtained from 331 tide-gauge sites (https://doi.org/10.14284/456, Zemunik et al., 2021b). This paper describes data quality control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites, and lengths of time series. The dataset is appropriate for global, regional or local research of atmospherically induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.

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    Earth System Science Data (ESSD)
    Other literature type . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: R. Khaldi; R. Khaldi; R. Khaldi; D. Alcaraz-Segura; +8 Authors

    Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart opensource global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging TerraCAqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping. This work was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018), DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovacion/FEDER), SMART-DASCI (grant no. TIN2017-89517-P, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BigDDL-CET (grant no. P18-FR-4961, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER), Ecopotential (grant no. 641762, European Commission), PID2020-119478GB-I00, the Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana, the European Social Fund (grant no. APOSTD/2021/188), the European Research Council (ERC grant no. 647038/BIODESERT), and the Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project). Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project) PID2020-119478GB-I00 RESISTE (Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER) P18-RT-1927 LifeWatch SmartEcoMountains (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) SMART-DASCI (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) TIN2017-89517-P BigDDL-CET (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) P18-FR-4961 BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018) Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana DeepL-ISCO (Ministerio de Ciencia e Innovacion/FEDER) A-TIC-458-UGR18 DETECTOR (Universidad de Granada/FEDER) A-RNM-256-UGR18 European Social Fund (ESF) APOSTD/2021/188 European Commission 647038/BIODESERT European Research Council (ERC) European Commission 641762 LifeWatch-2019-10-UGR-01

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    Authors: Khaldi, Rohaifa; Alcaraz-Segura, Domingo; Guirado, Emilio; Benhammou, Yassir; +3 Authors

    Abstract. Land Use and Land Cover (LULCs) mapping and change detection are of paramount importance to understand the distribution and effectively monitor the dynamics of the Earth’s system. An unexplored way to create global LULC maps is by building good quality LULC-models based on state-of-the-art deep learning networks. Building such models requires large global good quality time series LULC datasets, which are not available yet. This paper presents TimeSpec4LULC (Khaldi et al., 2021), a smart open-source global dataset of multi-Spectral Time series for 29 LULC classes. TimeSpec4LULC was built based on the 7 spectral bands of MODIS sensor at 500 m resolution from 2002 to 2021, and was annotated using a spatial agreement across the 15 global LULC products available in Google Earth Engine. The 19-year monthly time series of the seven bands were created globally by: (1) applying different spatio-temporal quality assessment filters on MODIS Terra and Aqua satellites, (2) aggregating their original 8-day temporal granularity into monthly composites, (3) merging their data into a Terra+Aqua combined time series, and (4) extracting, at the pixel level, 11.85 million time series for the 7 bands along with a set of metadata about geographic coordinates, country and departmental divisions, spatio-temporal consistency across LULC products, temporal data availability, and the global human modification index. To assess the annotation quality of the dataset, a sample of 100 pixels, evenly distributed around the world, from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing and evaluating various machine learning models, including deep learning networks, to perform global LULC mapping and change detection.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://essd.coperni...arrow_drop_down
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    https://essd.copernicus.org/pr...
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    https://doi.org/10.5194/essd-2...
    Preprint . 2021 . Peer-reviewed
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    Earth System Science Data (ESSD)
    Other literature type . 2021
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      https://doi.org/10.5194/essd-2...
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      Earth System Science Data (ESSD)
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    Authors: Pierluigi Penna; Filippo Domenichetti; Andrea Belardinelli; Michela Martinelli;

    In the last decade, an enormous amount of georeferenced oceanographic data, co-located with catch information, have been collected through the use of commercial fishing vessels operating in the Adriatic Sea and belonging to the fleet monitored by the AdriFOOS (Adriatic Fishery and Oceanography Observing System) infrastructure. This document describes the dataset of depth (pressure) and temperature profiles collected by means of AdriFOOS in the period 2012–2020 (available at https://doi.org/10.17882/73008, Penna et al., 2020) and briefly illustrates the structure of this facility, the oceanographic dataset validation procedures, and some of the results achieved, and it also lists some possible operational applications. This information is of the utmost importance to provide data to feed oceanographic models and advance knowledge on climate change, as well as to improve the ecosystem approach to fishery management.

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    Earth System Science Data (ESSD); CNR ExploRA
    Article . 2023 . Peer-reviewed
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      Earth System Science Data (ESSD); CNR ExploRA
      Article . 2023 . Peer-reviewed
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    Authors: L. M. Weydt; Á. A. Ramírez-Guzmán; A. Pola; B. Lepillier; +11 Authors

    Earth system science data : ESSD 13(2), 571-598 (2021). doi:10.5194/essd-13-571-2021 Published by Copernicus Publications, Katlenburg-Lindau

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    Earth System Science Data (ESSD)
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    https://doi.org/10.5194/essd-2...
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      Earth System Science Data (ESSD)
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      https://doi.org/10.5194/essd-2...
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    Authors: Francesco Paladini de Mendoza; Katrin Schroeder; Leonardo Langone; Jacopo Chiggiato; +4 Authors

    This work presents an 8-years-long dataset of monitoring activities conducted on the western margin of the southern Adriatic Sea where two moorings have been placed since 2012 in sites that are representative of different morpho-dynamic conditions of the continental slope (open slope vs. submarine canyon). The dataset includes measurements conducted with both current meters and conductivity, temperature and depth (CTD) probes, and it provides information about the hydrodynamics and thermohaline properties of the last 100 m of the water column. The hydrodynamics in both sites are dominated by weak currents (< 0.1 m s−1) which undergo yearly to episodic pulsation able to exceed intensity greater than 0.5 m s−1, which are linked to the passage of dense waters. The 8-years records presented here represent a starting point for the continuous observation activity set up on occasion of the “Operation Dense Water” in 2012 focussed on the southern Adriatic deep-water dynamics. Since then, the observatory has been ongoing since 2012 and the database is regularly updated. All the data described here are made publicly available from https://doi.org/10.5281/zenodo.6770201 (Paladini de Mendoza et al., 2022) and are compliant with the FAIR principles (findable, accessible, interoperable and reusable).

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    Earth System Science Data (ESSD)
    Article . 2022 . Peer-reviewed
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    Earth System Science Data (ESSD)
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    Earth System Science Data (ESSD)
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    https://doi.org/10.5194/essd-2...
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      Earth System Science Data (ESSD)
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      https://doi.org/10.5194/essd-2...
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    Authors: Paprotny, Dominik; Morales-Nápoles, Oswaldo; Jonkman, Sebastiaan N.;

    The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population, or assets available to study this topic. Here, we present the Historical Analysis of Natural Hazards in Europe (HANZE) database, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations, and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively simple disaggregation techniques and rule-based land use reallocation schemes. Data encompassed in HANZE allow one to "normalize" information on losses due to natural hazards by taking into account inflation as well as changes in population, production, and wealth. This database of past events currently contains 1564 records (1870–2016) of flash, river, coastal, and compound floods. The HANZE database is freely available at https://data.4tu.nl/repository/collection:HANZE.

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    Earth System Science Data (ESSD); Earth System Science Data
    Article . 2018 . Peer-reviewed
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      Earth System Science Data (ESSD); Earth System Science Data
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    Authors: Enza Di Tomaso; Jeronimo Escribano; Sara Basart; Paul Ginoux; +20 Authors

    One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1◦ latitude × 0.1◦ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022). Article signat per 24 autors/es: Enza Di Tomaso (1) , Jerónimo Escribano (1) , Sara Basart (1) , Paul Ginoux (2) , Francesca Macchia (1) , Francesca Barnaba (3) , Francesco Benincasa (1), Pierre-Antoine Bretonnière (1), Arnau Buñuel (1), Miguel Castrillo (1), Emilio Cuevas (4) , Paola Formenti (5) , María Gonçalves (1,6), Oriol Jorba (1), Martina Klose (1,7), Lucia Mona (8), Gilbert Montané Pinto (1) , Michail Mytilinaios (8), Vincenzo Obiso (1,a), Miriam Olid (1), Nick Schutgens (9) , Athanasios Votsis (10,11), Ernest Werner (12), and Carlos Pérez García-Pando (1,13) // (1) Barcelona Supercomputing Center (BSC), Barcelona, Spain; (2) NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA; (3) Consiglio Nazionale delle Ricerche–Istituto di Scienze dell’Atmosfera e del Clima (CNR–ISAC), Rome, Italy; (4) Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain; (5) Université Paris Cité and Univ Paris-Est Créteil, CNRS, LISA, 75013 Paris, France; (6) Department of Project and Construction Engineering, Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Terrassa, Spain; (7) Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; (8) Consiglio Nazionale delle Ricerche–Istituto di Metodologie per l’Analisi Ambientale (CNR–IMAA), Tito Scalo (PZ), Italy; (9) Department of Earth Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands; (10) Section of Governance and Technology for Sustainability (BMS-CSTM), University of Twente, Enschede, the Netherlands; (11) Weather and Climate Change Impact Research, Finnish Meteorological Institute (FMI), Helsinki, Finland; (12) Agencia Estatal de Meteorología (AEMET), Barcelona, Spain; (13) ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain anow at: NASA Goddard Institute for Space Studies (GISS), New York, New York, USA This research has been supported by the DustClim project, which is part of ERA4CS, an ERA-NET programme co-funded by the European Union’s Horizon 2020 research and innovation programme (grant no. 690462); the European Research Council (FRAGMENT (grant no. 773051)); grant no. RYC-2015- 18690 funded by MCIN/AEI/10.13039/501100011033 and ESF Investing in your future; grant no. CGL2017-88911-R funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe; the AXA Research Fund (AXA Chair on Sand and Dust Storms); the European Commission, Horizon 2020 Framework Programme (grant no. 792103 (SOLWARIS)); and ATMO-ACCESS (Access to Atmospheric Research Facilities) funded in the frame of the programme H2020-EU.1.4.1.2 (grant no. 101008004, 1 April 2021–31 March 2025). Jerónimo Escribano and Martina Klose have received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements H2020-MSCACOFUND-2016-754433 and H2020-MSCA-IF-2017-789630, respectively. Martina Klose received further support through the Helmholtz Association’s Initiative and Networking Fund (grant no. VH-NG-1533). This work has been partially funded by the contribution agreement between AEMET and BSC to carry out development and improvement activities of the products and services supplied by the World Meteorological Organization (WMO) Barcelona Dust Regional Center (i.e. the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center for Northern Africa, the Middle East and Europe). Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l’educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l’adaptació a aquest, la reducció dels efectes i l’alerta primerenca Objectius de Desenvolupament Sostenible::13 - Acció per al Clima Peer Reviewed

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    Earth System Science Data (ESSD)
    Other literature type . 2022
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    UPCommons. Portal del coneixement obert de la UPC
    Other literature type . Article . 2022 . Peer-reviewed
    License: CC BY
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    Earth System Science Data
    Article . 2022
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    https://doi.org/10.5194/essd-2...
    Preprint . 2021 . Peer-reviewed
    License: CC BY
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    Earth System Science Data (ESSD)
    Other literature type . 2021
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    KITopen
    Article . 2022
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    https://essd.copernicus.org/pr...
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    Earth System Science Data
    Article . 2022
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    https://doi.org/10.5445/ir/100...
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    Authors: Paprotny, Dominik; Morales-Nápoles, Oswaldo; Jonkman, Sebastiaan N.;

    The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population, or assets available to study this topic. Here, we present the Historical Analysis of Natural Hazards in Europe (HANZE) database, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations, and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively simple disaggregation techniques and rule-based land use reallocation schemes. Data encompassed in HANZE allow one to "normalize" information on losses due to natural hazards by taking into account inflation as well as changes in population, production, and wealth. This database of past events currently contains 1564 records (1870–2016) of flash, river, coastal, and compound floods. The HANZE database is freely available at https://data.4tu.nl/repository/collection:HANZE.

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    Authors: Y. Cotroneo; G. Aulicino; G. Aulicino; S. Ruiz; +10 Authors

    We present data collected in the framework of the Algerian BAsin Circulation Unmanned Survey (ABACUS) project. The main objective of ABACUS is the monitoring of the basin circulation and of the surface and intermediate water masses' physical and biological properties in a key region of the Mediterranean Sea circulation. Data presented here have been collected through deep glider cruises in the Western Mediterranean Sea during the autumns of 2014, 2015 and 2016; activities at sea are expected to be repeated during the coming years, so that the dataset will be extended. Glider missions were conducted in the Algerian Basin, between the island of Mallorca and the Algerian coast. Across the three glider missions, eight repeated transects were completed which enabled us to investigate the basin-scale circulation and the presence of mesoscale structures, utilising both the adaptive sampling capabilities of the gliders and the higher resolution of the data. After collection, all data passed a quality control procedure and were then made available through an unrestricted repository host by the SOCIB Data Centre at https://doi.org/10.25704/b200-3vf5. The actual dataset spans three autumn seasons, providing an important contribution to the data collection in the chronically undersampled Algerian Basin. Temperature and salinity data collected in the first 975¿m of the water column allowed us to identify the main water masses and describe their characteristics, improving the understanding of the dynamics of the region. On the timescale of the project, data show a large variability in the surface layer and reduced variability in the intermediate and deep layers. Our measurements have been successfully compared to data previously collected in the area from 1909 to 2011. Results showed similar overall distribution, ranges and variability to the historical data, with no outliers in the surface or deep layers. The ABACUS 1 missions (2014) were supported by the Joint European Research Infrastructure network for Coastal Observatories (JERICO) TransNational Access (TNA) third call (grant agreement no. 262584). The research leading to ABACUS 3 (2016) was supported by the European Union's H2020 Framework Programme (h2020-INFRAIA-2014-2015) (JERICO-NEXT grant agreement no. 654410). Additional EU funding (PERSEUS grant agreement no. 287600) is acknowledged. The activities described in this paper were developed in the framework of the Italian Flagship Project RITMARE. Ananda Pascual wishes to acknowledge support from the PRE-SWOT project (CTM2016-78607-P) funded by the Spanish National Research Program.

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    Earth System Science Data (ESSD)
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    Article . 2019
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    Authors: P. Zemunik; J. Šepić; H. Pellikka; L. Ćatipović; +2 Authors

    Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research on sea-level extremes is restricted to hourly datasets, which prevent the quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter-period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low tidal basins. Therefore, a new global 1 min sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T<2 h) obtained from 331 tide-gauge sites (https://doi.org/10.14284/456, Zemunik et al., 2021b). This paper describes data quality control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites, and lengths of time series. The dataset is appropriate for global, regional or local research of atmospherically induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.

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    Earth System Science Data (ESSD)
    Other literature type . 2021
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    Authors: R. Khaldi; R. Khaldi; R. Khaldi; D. Alcaraz-Segura; +8 Authors

    Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart opensource global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging TerraCAqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping. This work was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018), DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovacion/FEDER), SMART-DASCI (grant no. TIN2017-89517-P, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BigDDL-CET (grant no. P18-FR-4961, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER), Ecopotential (grant no. 641762, European Commission), PID2020-119478GB-I00, the Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana, the European Social Fund (grant no. APOSTD/2021/188), the European Research Council (ERC grant no. 647038/BIODESERT), and the Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project). Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project) PID2020-119478GB-I00 RESISTE (Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER) P18-RT-1927 LifeWatch SmartEcoMountains (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) SMART-DASCI (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) TIN2017-89517-P BigDDL-CET (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) P18-FR-4961 BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018) Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana DeepL-ISCO (Ministerio de Ciencia e Innovacion/FEDER) A-TIC-458-UGR18 DETECTOR (Universidad de Granada/FEDER) A-RNM-256-UGR18 European Social Fund (ESF) APOSTD/2021/188 European Commission 647038/BIODESERT European Research Council (ERC) European Commission 641762 LifeWatch-2019-10-UGR-01

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    Authors: Khaldi, Rohaifa; Alcaraz-Segura, Domingo; Guirado, Emilio; Benhammou, Yassir; +3 Authors

    Abstract. Land Use and Land Cover (LULCs) mapping and change detection are of paramount importance to understand the distribution and effectively monitor the dynamics of the Earth’s system. An unexplored way to create global LULC maps is by building good quality LULC-models based on state-of-the-art deep learning networks. Building such models requires large global good quality time series LULC datasets, which are not available yet. This paper presents TimeSpec4LULC (Khaldi et al., 2021), a smart open-source global dataset of multi-Spectral Time series for 29 LULC classes. TimeSpec4LULC was built based on the 7 spectral bands of MODIS sensor at 500 m resolution from 2002 to 2021, and was annotated using a spatial agreement across the 15 global LULC products available in Google Earth Engine. The 19-year monthly time series of the seven bands were created globally by: (1) applying different spatio-temporal quality assessment filters on MODIS Terra and Aqua satellites, (2) aggregating their original 8-day temporal granularity into monthly composites, (3) merging their data into a Terra+Aqua combined time series, and (4) extracting, at the pixel level, 11.85 million time series for the 7 bands along with a set of metadata about geographic coordinates, country and departmental divisions, spatio-temporal consistency across LULC products, temporal data availability, and the global human modification index. To assess the annotation quality of the dataset, a sample of 100 pixels, evenly distributed around the world, from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing and evaluating various machine learning models, including deep learning networks, to perform global LULC mapping and change detection.

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    https://doi.org/10.5194/essd-2...
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    Earth System Science Data (ESSD)
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      https://doi.org/10.5194/essd-2...
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    Authors: Pierluigi Penna; Filippo Domenichetti; Andrea Belardinelli; Michela Martinelli;

    In the last decade, an enormous amount of georeferenced oceanographic data, co-located with catch information, have been collected through the use of commercial fishing vessels operating in the Adriatic Sea and belonging to the fleet monitored by the AdriFOOS (Adriatic Fishery and Oceanography Observing System) infrastructure. This document describes the dataset of depth (pressure) and temperature profiles collected by means of AdriFOOS in the period 2012–2020 (available at https://doi.org/10.17882/73008, Penna et al., 2020) and briefly illustrates the structure of this facility, the oceanographic dataset validation procedures, and some of the results achieved, and it also lists some possible operational applications. This information is of the utmost importance to provide data to feed oceanographic models and advance knowledge on climate change, as well as to improve the ecosystem approach to fishery management.

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    Earth System Science Data (ESSD); CNR ExploRA
    Article . 2023 . Peer-reviewed
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      Earth System Science Data (ESSD); CNR ExploRA
      Article . 2023 . Peer-reviewed
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    Authors: L. M. Weydt; Á. A. Ramírez-Guzmán; A. Pola; B. Lepillier; +11 Authors

    Earth system science data : ESSD 13(2), 571-598 (2021). doi:10.5194/essd-13-571-2021 Published by Copernicus Publications, Katlenburg-Lindau

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    Earth System Science Data (ESSD)
    Other literature type . 2020
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    https://doi.org/10.5194/essd-2...
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      Earth System Science Data (ESSD)
      Other literature type . 2020
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      https://doi.org/10.5194/essd-2...
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    Authors: Francesco Paladini de Mendoza; Katrin Schroeder; Leonardo Langone; Jacopo Chiggiato; +4 Authors

    This work presents an 8-years-long dataset of monitoring activities conducted on the western margin of the southern Adriatic Sea where two moorings have been placed since 2012 in sites that are representative of different morpho-dynamic conditions of the continental slope (open slope vs. submarine canyon). The dataset includes measurements conducted with both current meters and conductivity, temperature and depth (CTD) probes, and it provides information about the hydrodynamics and thermohaline properties of the last 100 m of the water column. The hydrodynamics in both sites are dominated by weak currents (< 0.1 m s−1) which undergo yearly to episodic pulsation able to exceed intensity greater than 0.5 m s−1, which are linked to the passage of dense waters. The 8-years records presented here represent a starting point for the continuous observation activity set up on occasion of the “Operation Dense Water” in 2012 focussed on the southern Adriatic deep-water dynamics. Since then, the observatory has been ongoing since 2012 and the database is regularly updated. All the data described here are made publicly available from https://doi.org/10.5281/zenodo.6770201 (Paladini de Mendoza et al., 2022) and are compliant with the FAIR principles (findable, accessible, interoperable and reusable).

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    Earth System Science Data (ESSD)
    Article . 2022 . Peer-reviewed
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    https://doi.org/10.5194/essd-2...
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    Authors: Paprotny, Dominik; Morales-Nápoles, Oswaldo; Jonkman, Sebastiaan N.;

    The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population, or assets available to study this topic. Here, we present the Historical Analysis of Natural Hazards in Europe (HANZE) database, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations, and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively simple disaggregation techniques and rule-based land use reallocation schemes. Data encompassed in HANZE allow one to "normalize" information on losses due to natural hazards by taking into account inflation as well as changes in population, production, and wealth. This database of past events currently contains 1564 records (1870–2016) of flash, river, coastal, and compound floods. The HANZE database is freely available at https://data.4tu.nl/repository/collection:HANZE.

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    Earth System Science Data (ESSD); Earth System Science Data
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    Authors: Enza Di Tomaso; Jeronimo Escribano; Sara Basart; Paul Ginoux; +20 Authors

    One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1◦ latitude × 0.1◦ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022). Article signat per 24 autors/es: Enza Di Tomaso (1) , Jerónimo Escribano (1) , Sara Basart (1) , Paul Ginoux (2) , Francesca Macchia (1) , Francesca Barnaba (3) , Francesco Benincasa (1), Pierre-Antoine Bretonnière (1), Arnau Buñuel (1), Miguel Castrillo (1), Emilio Cuevas (4) , Paola Formenti (5) , María Gonçalves (1,6), Oriol Jorba (1), Martina Klose (1,7), Lucia Mona (8), Gilbert Montané Pinto (1) , Michail Mytilinaios (8), Vincenzo Obiso (1,a), Miriam Olid (1), Nick Schutgens (9) , Athanasios Votsis (10,11), Ernest Werner (12), and Carlos Pérez García-Pando (1,13) // (1) Barcelona Supercomputing Center (BSC), Barcelona, Spain; (2) NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA; (3) Consiglio Nazionale delle Ricerche–Istituto di Scienze dell’Atmosfera e del Clima (CNR–ISAC), Rome, Italy; (4) Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain; (5) Université Paris Cité and Univ Paris-Est Créteil, CNRS, LISA, 75013 Paris, France; (6) Department of Project and Construction Engineering, Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Terrassa, Spain; (7) Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; (8) Consiglio Nazionale delle Ricerche–Istituto di Metodologie per l’Analisi Ambientale (CNR–IMAA), Tito Scalo (PZ), Italy; (9) Department of Earth Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands; (10) Section of Governance and Technology for Sustainability (BMS-CSTM), University of Twente, Enschede, the Netherlands; (11) Weather and Climate Change Impact Research, Finnish Meteorological Institute (FMI), Helsinki, Finland; (12) Agencia Estatal de Meteorología (AEMET), Barcelona, Spain; (13) ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain anow at: NASA Goddard Institute for Space Studies (GISS), New York, New York, USA This research has been supported by the DustClim project, which is part of ERA4CS, an ERA-NET programme co-funded by the European Union’s Horizon 2020 research and innovation programme (grant no. 690462); the European Research Council (FRAGMENT (grant no. 773051)); grant no. RYC-2015- 18690 funded by MCIN/AEI/10.13039/501100011033 and ESF Investing in your future; grant no. CGL2017-88911-R funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe; the AXA Research Fund (AXA Chair on Sand and Dust Storms); the European Commission, Horizon 2020 Framework Programme (grant no. 792103 (SOLWARIS)); and ATMO-ACCESS (Access to Atmospheric Research Facilities) funded in the frame of the programme H2020-EU.1.4.1.2 (grant no. 101008004, 1 April 2021–31 March 2025). Jerónimo Escribano and Martina Klose have received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements H2020-MSCACOFUND-2016-754433 and H2020-MSCA-IF-2017-789630, respectively. Martina Klose received further support through the Helmholtz Association’s Initiative and Networking Fund (grant no. VH-NG-1533). This work has been partially funded by the contribution agreement between AEMET and BSC to carry out development and improvement activities of the products and services supplied by the World Meteorological Organization (WMO) Barcelona Dust Regional Center (i.e. the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center for Northern Africa, the Middle East and Europe). Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l’educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l’adaptació a aquest, la reducció dels efectes i l’alerta primerenca Objectius de Desenvolupament Sostenible::13 - Acció per al Clima Peer Reviewed

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    Authors: Paprotny, Dominik; Morales-Nápoles, Oswaldo; Jonkman, Sebastiaan N.;

    The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population, or assets available to study this topic. Here, we present the Historical Analysis of Natural Hazards in Europe (HANZE) database, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations, and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively simple disaggregation techniques and rule-based land use reallocation schemes. Data encompassed in HANZE allow one to "normalize" information on losses due to natural hazards by taking into account inflation as well as changes in population, production, and wealth. This database of past events currently contains 1564 records (1870–2016) of flash, river, coastal, and compound floods. The HANZE database is freely available at https://data.4tu.nl/repository/collection:HANZE.

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    Authors: Y. Cotroneo; G. Aulicino; G. Aulicino; S. Ruiz; +10 Authors

    We present data collected in the framework of the Algerian BAsin Circulation Unmanned Survey (ABACUS) project. The main objective of ABACUS is the monitoring of the basin circulation and of the surface and intermediate water masses' physical and biological properties in a key region of the Mediterranean Sea circulation. Data presented here have been collected through deep glider cruises in the Western Mediterranean Sea during the autumns of 2014, 2015 and 2016; activities at sea are expected to be repeated during the coming years, so that the dataset will be extended. Glider missions were conducted in the Algerian Basin, between the island of Mallorca and the Algerian coast. Across the three glider missions, eight repeated transects were completed which enabled us to investigate the basin-scale circulation and the presence of mesoscale structures, utilising both the adaptive sampling capabilities of the gliders and the higher resolution of the data. After collection, all data passed a quality control procedure and were then made available through an unrestricted repository host by the SOCIB Data Centre at https://doi.org/10.25704/b200-3vf5. The actual dataset spans three autumn seasons, providing an important contribution to the data collection in the chronically undersampled Algerian Basin. Temperature and salinity data collected in the first 975¿m of the water column allowed us to identify the main water masses and describe their characteristics, improving the understanding of the dynamics of the region. On the timescale of the project, data show a large variability in the surface layer and reduced variability in the intermediate and deep layers. Our measurements have been successfully compared to data previously collected in the area from 1909 to 2011. Results showed similar overall distribution, ranges and variability to the historical data, with no outliers in the surface or deep layers. The ABACUS 1 missions (2014) were supported by the Joint European Research Infrastructure network for Coastal Observatories (JERICO) TransNational Access (TNA) third call (grant agreement no. 262584). The research leading to ABACUS 3 (2016) was supported by the European Union's H2020 Framework Programme (h2020-INFRAIA-2014-2015) (JERICO-NEXT grant agreement no. 654410). Additional EU funding (PERSEUS grant agreement no. 287600) is acknowledged. The activities described in this paper were developed in the framework of the Italian Flagship Project RITMARE. Ananda Pascual wishes to acknowledge support from the PRE-SWOT project (CTM2016-78607-P) funded by the Spanish National Research Program.

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