Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1711-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1711-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Aug 2020
Data description paper |
| 03 Aug 2020
| 03 Aug 2020
A multi-year time series of observation-based 3D horizontal and vertical quasi-geostrophic global ocean currents
Bruno Buongiorno Nardelli
CORRESPONDING AUTHOR
Consiglio Nazionale delle Ricerche – Istituto di Scienze Marine,
Naples, 80133, Italy
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Lorenzo Della Cioppa and Bruno Buongiorno Nardelli
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Forecasting of particles trajectories transported by ocean currents is of great importance for research and operational tasks. Even with satellite observations data or numerical simulations, the problem challenging. In this paper a neural network approach is proposed which is capable of learning from observed trajectories and corresponding data observed from satellites to generate predictions. The network is trained and validated on synthetic data, but it is easily applicable in the real-world.
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Ocean surface currents are routinely derived from satellite observations of the sea level, allowing regional- to global-scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multi-sensor satellite observations. We rely on deep learning, physics-informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in situ measurements.
Claudia Fanelli, Daniele Ciani, Andrea Pisano, and Bruno Buongiorno Nardelli
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Sea surface temperature (SST) is an essential variable to understanding the Earth's climate system, and its accurate monitoring from space is essential. Since satellite measurements are hindered by cloudy/rainy conditions, data gaps are present even in merged multi-sensor products. Since optimal interpolation techniques tend to smooth out small-scale features, we developed a deep learning model to enhance the effective resolution of gap-free SST images over the Mediterranean Sea to address this.
Sarah Asdar, Daniele Ciani, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 16, 1029–1046, https://doi.org/10.5194/essd-16-1029-2024, https://doi.org/10.5194/essd-16-1029-2024, 2024
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Estimating 3D currents is crucial for the understanding of ocean dynamics, and a precise knowledge of ocean circulation is essential to ensure a sustainable ocean. In this context, a new high-resolution (1 / 10°) data-driven dataset of 3D ocean currents has been developed within the European Space Agency World Ocean Circulation project, providing 10 years (2010–2019) of horizontal and vertical quasi-geostrophic currents at daily resolution over the North Atlantic Ocean, down to 1500 m depth.
Andrea Pisano, Daniele Ciani, Salvatore Marullo, Rosalia Santoleri, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 14, 4111–4128, https://doi.org/10.5194/essd-14-4111-2022, https://doi.org/10.5194/essd-14-4111-2022, 2022
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A new operational diurnal sea surface temperature (SST) product has been developed within the Copernicus Marine Service, providing gap-free hourly mean SST fields from January 2019 to the present. This product is able to accurately reproduce the diurnal cycle, the typical day–night SST oscillation mainly driven by solar heating, including extreme diurnal warming events. This product can thus represent a valuable dataset to improve the study of those processes that require a subdaily frequency.
Lorenzo Della Cioppa and Bruno Buongiorno Nardelli
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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Forecasting of particles trajectories transported by ocean currents is of great importance for research and operational tasks. Even with satellite observations data or numerical simulations, the problem challenging. In this paper a neural network approach is proposed which is capable of learning from observed trajectories and corresponding data observed from satellites to generate predictions. The network is trained and validated on synthetic data, but it is easily applicable in the real-world.
Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli
Ocean Sci., 21, 199–216, https://doi.org/10.5194/os-21-199-2025, https://doi.org/10.5194/os-21-199-2025, 2025
Short summary
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Ocean surface currents are routinely derived from satellite observations of the sea level, allowing regional- to global-scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multi-sensor satellite observations. We rely on deep learning, physics-informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in situ measurements.
Claudia Fanelli, Daniele Ciani, Andrea Pisano, and Bruno Buongiorno Nardelli
Ocean Sci., 20, 1035–1050, https://doi.org/10.5194/os-20-1035-2024, https://doi.org/10.5194/os-20-1035-2024, 2024
Short summary
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Sea surface temperature (SST) is an essential variable to understanding the Earth's climate system, and its accurate monitoring from space is essential. Since satellite measurements are hindered by cloudy/rainy conditions, data gaps are present even in merged multi-sensor products. Since optimal interpolation techniques tend to smooth out small-scale features, we developed a deep learning model to enhance the effective resolution of gap-free SST images over the Mediterranean Sea to address this.
Sarah Asdar, Daniele Ciani, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 16, 1029–1046, https://doi.org/10.5194/essd-16-1029-2024, https://doi.org/10.5194/essd-16-1029-2024, 2024
Short summary
Short summary
Estimating 3D currents is crucial for the understanding of ocean dynamics, and a precise knowledge of ocean circulation is essential to ensure a sustainable ocean. In this context, a new high-resolution (1 / 10°) data-driven dataset of 3D ocean currents has been developed within the European Space Agency World Ocean Circulation project, providing 10 years (2010–2019) of horizontal and vertical quasi-geostrophic currents at daily resolution over the North Atlantic Ocean, down to 1500 m depth.
Andrea Pisano, Daniele Ciani, Salvatore Marullo, Rosalia Santoleri, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 14, 4111–4128, https://doi.org/10.5194/essd-14-4111-2022, https://doi.org/10.5194/essd-14-4111-2022, 2022
Short summary
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A new operational diurnal sea surface temperature (SST) product has been developed within the Copernicus Marine Service, providing gap-free hourly mean SST fields from January 2019 to the present. This product is able to accurately reproduce the diurnal cycle, the typical day–night SST oscillation mainly driven by solar heating, including extreme diurnal warming events. This product can thus represent a valuable dataset to improve the study of those processes that require a subdaily frequency.
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This article presents the data of a 4-month observation of the Iberian Margin Cape St. Vincent ocean observatory, in Portugal (2022), a European Multidisciplinary Seafloor and water column Observatory node. Three instruments at depths between 150 and 200 m collected physical/biogeochemical parameters at different spatial and temporal scales. EMSO-ERIC aims at developing strategies to enable sustainable ocean observation with regards to costs, time, and resolution.
Owein Thuillier, Nicolas Le Josse, Alexandru-Liviu Olteanu, Marc Sevaux, and Hervé Tanguy
Earth Syst. Sci. Data, 16, 4529–4556, https://doi.org/10.5194/essd-16-4529-2024, https://doi.org/10.5194/essd-16-4529-2024, 2024
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Our study unveils a comprehensive catalogue of 17 700 unique coastal digital elevation models (DEMs) derived from the General Bathymetric Chart of the Oceans (GEBCO) as of 2022. These DEMs are designed to support a variety of scientific and educational purposes. Organised into three libraries, they cover a wide range of coastal geometries and different sizes. Data and custom colour palettes for visualisation are made freely available online, promoting open science and collaboration.
Meri Korhonen, Mateusz Moskalik, Oskar Głowacki, and Vineet Jain
Earth Syst. Sci. Data, 16, 4511–4527, https://doi.org/10.5194/essd-16-4511-2024, https://doi.org/10.5194/essd-16-4511-2024, 2024
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Since 2015, temperature and salinity have been monitored in Hornsund fjord (Svalbard), where retreating glaciers add meltwater and terrestrial matter to coastal waters. Therefore, turbidity and water sampling for suspended sediment concentration and sediment deposition are measured. The monitoring spans from May to October, enabling studies on seasonality and its variability over the years, and the dataset covers the whole fjord, including the inner basins in close proximity to the glaciers.
Kyla Drushka, Elizabeth Westbrook, Frederick M. Bingham, Peter Gaube, Suzanne Dickinson, Severine Fournier, Viviane Menezes, Sidharth Misra, Jaynice Pérez Valentín, Edwin J. Rainville, Julian J. Schanze, Carlyn Schmidgall, Andrey Shcherbina, Michael Steele, Jim Thomson, and Seth Zippel
Earth Syst. Sci. Data, 16, 4209–4242, https://doi.org/10.5194/essd-16-4209-2024, https://doi.org/10.5194/essd-16-4209-2024, 2024
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The NASA SASSIE mission aims to understand the role of salinity in modifying sea ice formation in early autumn. The 2022 SASSIE campaign collected measurements of upper-ocean properties, including stratification (layering of the ocean) and air–sea fluxes in the Beaufort Sea. These data are presented here and made publicly available on the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC), along with code to manipulate the data and generate the figures presented herein.
Le Gao, Yuan Guo, and Xiaofeng Li
Earth Syst. Sci. Data, 16, 4189–4207, https://doi.org/10.5194/essd-16-4189-2024, https://doi.org/10.5194/essd-16-4189-2024, 2024
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Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has become one of the world's largest marine disasters. Satellite remote sensing plays a pivotal role in detecting this phenomenon. This study uses AI-based models to extract the daily green tide from MODIS and SAR images and integrates these daily data to introduce a continuous weekly dataset, which aids research in disaster simulation, forecasting, and prevention.
Chunhua Qiu, Zhenyang Du, Jiancheng Yu, Huabin Mao, Haibo Tang, Zhenhui Yi, Jiawei Qiao, Dongxiao Wang, Xiaoming Zhai, and Yeqiang Shu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-338, https://doi.org/10.5194/essd-2024-338, 2024
Revised manuscript accepted for ESSD
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The high dense AUVs’ dataset in SCS provides 24498 temperature and salinity profiles and covers 463 days’ experiments, including 83 AUGs’ and 2 AUVs’ experiments. To our knowledge, the resolution and length of this dataset is enough in detecting the asymmetry, vertical tilt, temporal evolution of MEs, and the submesoscale processes. The dataset is expected to improve the accuracy of current and biogeochemistry numerical model. More projects gathering AUVs network will be promoted in future.
Lianjun Yang, Taoyong Jin, and Weiping Jiang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-350, https://doi.org/10.5194/essd-2024-350, 2024
Revised manuscript accepted for ESSD
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Storm surges (SSs) cause massive loss of life and property in coastal areas each year. High spatial resolution and long-term SS records are the basis for assessing such events. However, tide gauges can provide limited SS information due to sparse and uneven distributions. Based on artificial intelligence technology and tide gauges, a high spatial coverage SS dataset was generated for period from 1940 to 2020, which can provide possible alternative support for deepening our understanding of SSs.
Lijing Cheng, Yuying Pan, Zhetao Tan, Huayi Zheng, Yujing Zhu, Wangxu Wei, Juan Du, Huifeng Yuan, Guancheng Li, Hanlin Ye, Viktor Gouretski, Yuanlong Li, Kevin E. Trenberth, John Abraham, Yuchun Jin, Franco Reseghetti, Xiaopei Lin, Bin Zhang, Gengxin Chen, Michael E. Mann, and Jiang Zhu
Earth Syst. Sci. Data, 16, 3517–3546, https://doi.org/10.5194/essd-16-3517-2024, https://doi.org/10.5194/essd-16-3517-2024, 2024
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Observational gridded products are essential for understanding the ocean, the atmosphere, and climate change; they support policy decisions and socioeconomic developments. This study provides an update of an ocean subsurface temperature and ocean heat content gridded product, named the IAPv4 data product, which is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1955) for monthly and 1° × 1° temporal and spatial resolutions.
Sönke Dangendorf, Qiang Sun, Thomas Wahl, Philip Thompson, Jerry X. Mitrovica, and Ben Hamlington
Earth Syst. Sci. Data, 16, 3471–3494, https://doi.org/10.5194/essd-16-3471-2024, https://doi.org/10.5194/essd-16-3471-2024, 2024
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Sea-level information from the global ocean is sparse in time and space, with comprehensive data being limited to the period since 2005. Here we provide a novel reconstruction of sea level and its contributing causes, as determined by a Kalman smoother approach applied to tide gauge records over the period 1900–2021. The new reconstruction shows a continuing acceleration in global mean sea-level rise since 1970 that is dominated by melting land ice. Contributors vary significantly by region.
Panagiotis Athanasiou, Ap van Dongeren, Maarten Pronk, Alessio Giardino, Michalis Vousdoukas, and Roshanka Ranasinghe
Earth Syst. Sci. Data, 16, 3433–3452, https://doi.org/10.5194/essd-16-3433-2024, https://doi.org/10.5194/essd-16-3433-2024, 2024
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The shape of the coast, the intensity of waves, the height of the water levels, the presence of people or critical infrastructure, and the land use are important information to assess the vulnerability of the coast to coastal hazards. Here, we provide 80 indicators of this kind at consistent locations along the global ice-free coastline using open-access global datasets. These can be valuable for quick assessments of the vulnerability of the coast and at data-poor locations.
Léo Seyfried, Laurie Biscara, Héloïse Michaud, Fabien Leckler, Audrey Pasquet, Marc Pezerat, and Clément Gicquel
Earth Syst. Sci. Data, 16, 3345–3367, https://doi.org/10.5194/essd-16-3345-2024, https://doi.org/10.5194/essd-16-3345-2024, 2024
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In Saint-Malo, France, an initiative to enhance marine submersion prevention began in 2018. Shom conducted an extensive sea campaign, mapping the bay's topography and exploring coastal processes. High-resolution data improve knowledge of the interactions between waves, tide and surge and determine processes responsible for submersion. Beyond science, these findings contribute crucially to a local warning system, providing a tangible solution to protect the community from coastal threats.
Jisun Shin, Dae-Won Kim, So-Hyun Kim, Gi Seop Lee, Boo-Keun Khim, and Young-Heon Jo
Earth Syst. Sci. Data, 16, 3193–3211, https://doi.org/10.5194/essd-16-3193-2024, https://doi.org/10.5194/essd-16-3193-2024, 2024
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We overcame the limitations of satellite and reanalysis sea surface salinity (SSS) datasets and produced a gap-free gridded SSS product with reasonable accuracy and a spatial resolution of 1 km using a machine learning model. Our data enabled the recognition of SSS distribution and movement patterns of the Changjiang diluted water (CDW) front in the East China Sea (ECS) during summer. These results will further advance our understanding and monitoring of long-term SSS variations in the ECS.
Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen
Earth Syst. Sci. Data, 16, 3125–3147, https://doi.org/10.5194/essd-16-3125-2024, https://doi.org/10.5194/essd-16-3125-2024, 2024
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A deep-learning model for gap-filling based on expected variance was developed. OI-SwinUnet achieves good performance reconstructing chlorophyll-a concentration data on the South China Sea. The reconstructed dataset depicts both the spatiotemporal patterns at the seasonal scale and a fast-change process at the weather scale. Reconstructed data show chlorophyll perturbations of individual eddies at different life stages, giving academics a unique and complete perspective on eddy studies.
Robert W. Schlegel, Rakesh Kumar Singh, Bernard Gentili, Simon Bélanger, Laura Castro de la Guardia, Dorte Krause-Jensen, Cale A. Miller, Mikael Sejr, and Jean-Pierre Gattuso
Earth Syst. Sci. Data, 16, 2773–2788, https://doi.org/10.5194/essd-16-2773-2024, https://doi.org/10.5194/essd-16-2773-2024, 2024
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Fjords play a vital role in the Arctic ecosystems and human communities. It is therefore important to have as clear of an understanding of the processes within these systems as possible. While temperature and salinity tend to be well measured, light is usually not. The dataset described in this paper uses remotely sensed data from 2003 to 2022 to address this problem by providing high-spatial-resolution surface, water column, and seafloor light data for several well-studied Arctic fjords.
Nir Haim, Vika Grigorieva, Rotem Soffer, Boaz Mayzel, Timor Katz, Ronen Alkalay, Eli Biton, Ayah Lazar, Hezi Gildor, Ilana Berman-Frank, Yishai Weinstein, Barak Herut, and Yaron Toledo
Earth Syst. Sci. Data, 16, 2659–2668, https://doi.org/10.5194/essd-16-2659-2024, https://doi.org/10.5194/essd-16-2659-2024, 2024
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This paper outlines the process of creating an open-access surface wave dataset, drawing from deep-sea research station observations located 50 km off the coast of Israel. The discussion covers the wave monitoring procedure, from instrument configuration to wave field retrieval, and aspects of quality assurance. The dataset presented spans over 5 years, offering uncommon in situ wave measurements in the deep sea, and addresses the existing gap in wave information within the region.
Fengshun Zhu, Jinyun Guo, Huiying Zhang, Lingyong Huang, Heping Sun, and Xin Liu
Earth Syst. Sci. Data, 16, 2281–2296, https://doi.org/10.5194/essd-16-2281-2024, https://doi.org/10.5194/essd-16-2281-2024, 2024
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We used multi-satellite altimeter data to construct a high-resolution marine gravity change rate (MGCR) model on 5′×5′ grids, named SDUST2020MGCR. The spatial distribution of SDUST2020MGCR and GRACE MGCR are similar, such as in the eastern seas of Japan (dipole), western seas of the Nicobar Islands (rising), and southern seas of Greenland (falling). The SDUST2020MGCR can provide a detailed view of long-term marine gravity change, which will help to study the seawater mass migration.
Lisa Deyle, Thomas H. Badewien, Oliver Wurl, and Jens Meyerjürgens
Earth Syst. Sci. Data, 16, 2099–2112, https://doi.org/10.5194/essd-16-2099-2024, https://doi.org/10.5194/essd-16-2099-2024, 2024
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A dataset from the North Sea of 85 surface drifters from 2017–2021 is presented. Surface drifters enable the analysis of ocean currents by determining the velocities of surface currents and tidal effects. The entire North Sea has not been studied using drifters before, but the analysis of ocean currents is essential, e.g., to understand the pathways of plastic. The results show that there are strong tidal effects in the shallow North Sea area and strong surface currents in the deep areas.
Yasser O. Abualnaja, Alexandra Pavlidou, James H. Churchill, Ioannis Hatzianestis, Dimitris Velaoras, Harilaos Kontoyiannis, Vassilis P. Papadopoulos, Aristomenis P. Karageorgis, Georgia Assimakopoulou, Helen Kaberi, Theodoros Kannelopoulos, Constantine Parinos, Christina Zeri, Dionysios Ballas, Elli Pitta, Vassiliki Paraskevopoulou, Afroditi Androni, Styliani Chourdaki, Vassileia Fioraki, Stylianos Iliakis, Georgia Kabouri, Angeliki Konstantinopoulou, Georgios Krokos, Dimitra Papageorgiou, Alkiviadis Papageorgiou, Georgios Pappas, Elvira Plakidi, Eleni Rousselaki, Ioanna Stavrakaki, Eleni Tzempelikou, Panagiota Zachioti, Anthi Yfanti, Theodore Zoulias, Abdulah Al Amoudi, Yasser Alshehri, Ahmad Alharbi, Hammad Al Sulami, Taha Boksmati, Rayan Mutwalli, and Ibrahim Hoteit
Earth Syst. Sci. Data, 16, 1703–1731, https://doi.org/10.5194/essd-16-1703-2024, https://doi.org/10.5194/essd-16-1703-2024, 2024
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We present oceanographic measurements obtained during two surveillance cruises conducted in June and September 2021 in the Red Sea and the Arabian Gulf. It is the first multidisciplinary survey within the Saudi Arabian coastal zone, extending from near the Saudi–Jordanian border in the north of the Red Sea to the south close to the Saudi--Yemen border and in the Arabian Gulf. The objective was to record the pollution status along the coastal zone of the kingdom related to specific pressures.
Alexandra E. Jones-Kellett and Michael J. Follows
Earth Syst. Sci. Data, 16, 1475–1501, https://doi.org/10.5194/essd-16-1475-2024, https://doi.org/10.5194/essd-16-1475-2024, 2024
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Ocean eddies can limit horizontal mixing, potentially isolating phytoplankton populations and affecting their concentration. We used two decades of satellite data and computer simulations to identify and track eddy-trapping boundaries in the Pacific Ocean for application in phytoplankton research. Although some eddies trap water masses for months, many continuously mix with surrounding waters. A case study shows how eddy trapping can enhance the signature of a phytoplankton bloom.
Richard Fiifi Annan, Xiaoyun Wan, Ruijie Hao, and Fei Wang
Earth Syst. Sci. Data, 16, 1167–1176, https://doi.org/10.5194/essd-16-1167-2024, https://doi.org/10.5194/essd-16-1167-2024, 2024
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Gravity gradient tensor, a set of six unique gravity signals, is suitable for detecting undersea features. However, due to poor spatial resolution in past years, it has received less research interest and investment. However, current datasets have better accuracy and resolutions, thereby necessitating a revisit. Our analysis shows comparable results with reference models. We conclude that current-generation altimetry datasets can precisely resolve all six gravity gradients.
Simon Treu, Sanne Muis, Sönke Dangendorf, Thomas Wahl, Julius Oelsmann, Stefanie Heinicke, Katja Frieler, and Matthias Mengel
Earth Syst. Sci. Data, 16, 1121–1136, https://doi.org/10.5194/essd-16-1121-2024, https://doi.org/10.5194/essd-16-1121-2024, 2024
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This article describes a reconstruction of monthly coastal water levels from 1900–2015 and hourly data from 1979–2015, both with and without long-term sea level rise. The dataset is based on a combination of three datasets that are focused on different aspects of coastal water levels. Comparison with tide gauge records shows that this combination brings reconstructions closer to the observations compared to the individual datasets.
Sarah Asdar, Daniele Ciani, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 16, 1029–1046, https://doi.org/10.5194/essd-16-1029-2024, https://doi.org/10.5194/essd-16-1029-2024, 2024
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Estimating 3D currents is crucial for the understanding of ocean dynamics, and a precise knowledge of ocean circulation is essential to ensure a sustainable ocean. In this context, a new high-resolution (1 / 10°) data-driven dataset of 3D ocean currents has been developed within the European Space Agency World Ocean Circulation project, providing 10 years (2010–2019) of horizontal and vertical quasi-geostrophic currents at daily resolution over the North Atlantic Ocean, down to 1500 m depth.
Xiaoxia Zhang and Heidi Nepf
Earth Syst. Sci. Data, 16, 1047–1062, https://doi.org/10.5194/essd-16-1047-2024, https://doi.org/10.5194/essd-16-1047-2024, 2024
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This study measured the wave-induced plant drag, flow structure, turbulent intensity, and wave energy attenuation in the presence of a salt marsh. We showed that leaves contribute to most of the total plant drag and wave dissipation. Plant resistance significantly reshapes the velocity profile and enhances turbulence intensity. Adding current obviously impact the plants' wave decay capacity. The dataset can be reused to develop and calibrate marsh-flow theoretical and numerical models.
Michael Hemming, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data, 16, 887–901, https://doi.org/10.5194/essd-16-887-2024, https://doi.org/10.5194/essd-16-887-2024, 2024
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We present new datasets that are useful for exploring extreme ocean temperature events in Australian coastal waters. These datasets span multiple decades, starting from the 1940s and 1950s, and include observations from the surface to the bottom at four coastal sites. The datasets provide valuable insights into the intensity, frequency and timing of extreme warm and cold temperature events and include event characteristics such as duration, onset and decline rates and their categorisation.
Marlies A. van der Lugt, Jorn W. Bosma, Matthieu A. de Schipper, Timothy D. Price, Marcel C. G. van Maarseveen, Pieter van der Gaag, Gerben Ruessink, Ad J. H. M. Reniers, and Stefan G. J. Aarninkhof
Earth Syst. Sci. Data, 16, 903–918, https://doi.org/10.5194/essd-16-903-2024, https://doi.org/10.5194/essd-16-903-2024, 2024
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A 6-week field campaign was carried out at a sheltered sandy beach on Texel along the Dutch Wadden Sea with the aim of gaining new insights into the driving processes behind sheltered beach morphodynamics. Detailed measurements of the local hydrodynamics, bed-level changes and sediment composition were collected. The morphological evolution on this sheltered site is the result of the subtle interplay between waves, currents and bed composition.
Oriane Bruyère, Romain Le Gendre, Vetea Liao, and Serge Andréfouët
Earth Syst. Sci. Data, 16, 667–679, https://doi.org/10.5194/essd-16-667-2024, https://doi.org/10.5194/essd-16-667-2024, 2024
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During 2019–2020, the lagoon and forereefs of Gambier Island (French Polynesia) were monitored with oceanographic instruments to measure lagoon hydrodynamics and ocean–lagoon water exchanges. Gambier Island is a key black pearl producer and the study goal was to understand the processes influencing spat collection of pearl oyster Pinctada margaritifera, the species used to produce black pearls. The data set is provided to address local pearl farming questions and other investigations as well.
Tahiana Ratsimbazafy, Thibaud Dezutter, Amélie Desmarais, Daniel Amirault, Pascal Guillot, and Simon Morisset
Earth Syst. Sci. Data, 16, 471–499, https://doi.org/10.5194/essd-16-471-2024, https://doi.org/10.5194/essd-16-471-2024, 2024
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The Canadian Coast Guard Ship has collected oceanographic data across the Canadian Arctic annually since 2003. Such activity aims to support Canadian and international researchers. The ship has several instruments with cutting-edge technology available for research each year during the summer. The data presented here include measurements of physical, chemical and biological variables during the year 2021. Datasets collected from each expedition are available free of charge for the public.
Md Jamal Uddin Khan, Inge Van Den Beld, Guy Wöppelmann, Laurent Testut, Alexa Latapy, and Nicolas Pouvreau
Earth Syst. Sci. Data, 15, 5739–5753, https://doi.org/10.5194/essd-15-5739-2023, https://doi.org/10.5194/essd-15-5739-2023, 2023
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Established in the southwest of France in 1875, the Socoa tide gauge is part of the national sea level monitoring network in France. Through a data archaeology exercise, a large part of the records of this gauge in paper format have been rescued and digitized. The digitized data were processed and quality controlled to produce a uniform hourly sea level time series covering 1875 to the present day. This new dataset is important for climate research on sea level rise, tides, and storm surges.
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Short summary
To better understand ocean dynamics and assess their responses and feedbacks to natural and anthropogenic pressures, 3D ocean circulation estimates are needed. Here we present the OMEGA3D product, an observation-based time series (1993–2018) of global 3D ocean currents developed within the European Copernicus Marine Environment Monitoring Service. OMEGA3D provides vertical velocities – an observational barrier due to their small intensity – and full horizontal velocities down to 1500 m depth.
To better understand ocean dynamics and assess their responses and feedbacks to natural and...
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