Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-5037-2022
© Author(s) 2022. 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-14-5037-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 Nov 2022
Data description paper |
| 18 Nov 2022
| 18 Nov 2022
Reconstructing ocean subsurface salinity at high resolution using a machine learning approach
Tian Tian
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao,
266071, China
Gongjie Wang
National Climate Center, Chinese Meteorological Administration,
Beijing, 100081, China
John Abraham
School of Engineering, University of St. Thomas, St. Paul,
MN 55105, USA
Wangxu Wei
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Shihe Ren
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, 100081, China
Jiang Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Junqiang Song
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Hongze Leng
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
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Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Joshua S. Fu, Xuemei Wang, Syuichi Itahashi, Kazuyo Yamaji, Tatsuya Nagashima, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Lei Chen, Meigen Zhang, Zhining Tao, Jie Li, Mizuo Kajino, Hong Liao, Zhe Wang, Kengo Sudo, Yuesi Wang, Yuepeng Pan, Guiqian Tang, Meng Li, Qizhong Wu, Baozhu Ge, and Gregory R. Carmichael
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A biogeochemical process model-based approach for screening the best management practices (BMPs) of a three-crop system was proposed. The BMPs are the management alternatives with the lowest negative impact potentials that still satisfy all given constraints. Three BMP alternatives with overlapping uncertainties of simulated NIPs were screened from 6000 scenarios using the modified DNDC95 model, which could sustain crop yields, enlarge SOC stock, mitigate GHG, and reduce other nitrogen losses.
Lijing Cheng, Kevin E. Trenberth, Matthew D. Palmer, Jiang Zhu, and John P. Abraham
Ocean Sci., 12, 925–935, https://doi.org/10.5194/os-12-925-2016, https://doi.org/10.5194/os-12-925-2016, 2016
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A new method of observing ocean heat content throughout the entire ocean depth is provided. The new method is compared with simulated ocean heat content changes from climate models. The comparisons are carried out in various depth layers of the ocean waters. It is found that there is excellent agreement between the models and the observations. Furthermore, we propose that changes to ocean heat content be used as a fundamental metric to evaluate climate models.
L. Cheng, J. Zhu, and R. L. Sriver
Ocean Sci., 11, 719–741, https://doi.org/10.5194/os-11-719-2015, https://doi.org/10.5194/os-11-719-2015, 2015
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1. Argo floats were used to examine tropical cyclone (TC) induced ocean thermal changes on the global scale by comparing temperature profiles before and after TC passage.
2. Global average of the vertical structure of the average ocean thermal response for two different categories: tropical storms/depressions (TS/TD) and hurricanes were presented.
3. Significant differences between weak storm (TS/TD) and strong storm (hurricane) were found.
L. Cheng, J. Zhu, and R. L. Sriver
Ocean Sci. Discuss., https://doi.org/10.5194/osd-11-2907-2014, https://doi.org/10.5194/osd-11-2907-2014, 2014
Preprint withdrawn
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1. TCs are responsible for 1.87 PW (11.05 W/m2) of heat transfer annually from the global ocean to the atmosphere during storm passage (0-3 days) on a global scale. Of this total, 1.05±0.20 PW (4.80±0.85 W/m2) is caused by TS/TD and 0.82±0.21 PW (6.25±1.5 W/m2) is caused by hurricanes.
2.The net ocean heat uptake caused by all storms is 0.34 PW (4-20 days mean). Hurricanes induce 0.75±0.25 PW (5.98±2.1 W/m2) net heat gain, and TS/TD leads to 0.41±0.21 PW (1.90±0.96 W/m2) net heat loss.
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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.
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.
Han Zhang, Dake Chen, Tongya Liu, Di Tian, Min He, Qi Li, and Jian Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-224, https://doi.org/10.5194/essd-2024-224, 2024
Revised manuscript accepted for ESSD
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The manuscript provides a moored array dataset (MASCS 1.0) of the observation that consists of five buoys and four moorings in the northern South China Sea during 2014 to 2015. The moored array is influenced by atmospheric forcing such as tropical cyclones and monsoon, as well as oceanic tides and flows. The data reveals variations of air-sea interface and ocean itself, which are valuable for studies on air-sea interactions and ocean dynamics in the northern South China Sea.
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.
Giuseppe Zibordi and Jean-François Berthon
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-240, https://doi.org/10.5194/essd-2024-240, 2024
Revised manuscript accepted for ESSD
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The Coastal Atmosphere & Sea Time Series (CoASTS) and the Bio-Optical mapping of Marine optical Properties (BiOMaP) programs produced bio-optical data supporting satellite ocean color applications across European seas for almost two decades. CoASTS and BiOMaP applied equal standardized instruments, measurement methods, quality control schemes and processing codes to ensure temporal and spatial consistency to data products.
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.
Federica Foglini, Marzia Rovere, Renato Tonielli, Giorgio Castellan, Mariacristina Prampolini, Francesca Budillon, Marco Cuffaro, Gabriella Di Martino, Valentina Grande, Sara Innangi, Maria Filomena Loreto, Leonardo Langone, Fantina Madricardo, Alessandra Mercorella, Paolo Montagna, Camilla Palmiotto, Claudio Pellegrini, Antonio Petrizzo, Lorenzo Petracchini, Alessandro Remia, Marco Sacchi, Daphnie Sanchez Galvez, Anna Nora Tassetti, and Fabio Trincardi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-135, https://doi.org/10.5194/essd-2024-135, 2024
Revised manuscript accepted for ESSD
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In 2022, the new CNR Research Vessel GAIA BLU explored the seafloor of the Naples and Pozzuoli Gulfs, and the Amalfi coastal area (Tyrrhenian Sea, Italy) from 50 to 2000 m water depth, covering 5000 m2 of seafloor. This paper describes data acquisition and processing and provides maps in unprecedented detail of this area abrupt to geological changes and human impacts. These findings support future geological and geomorphological investigations and mapping and monitoring seafloor and habitats.
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.
Yan Wang, Jie Yang, and Ge Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-188, https://doi.org/10.5194/essd-2024-188, 2024
Revised manuscript accepted for ESSD
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Mesoscale eddies are ubiquitous in the ocean and account for 90 % of its kinetic energy, but their generation and dissipation struggle to observe with current remote sensing technology. Our submesoscale eddy dataset, formed by suppressing large-scale circulation signals and enhancing small-scale chlorophyll structures, has important implications for understanding marine environments and ecosystems, as well as improving climate model predictions.
Gang Yang, Ke Huang, Lin Zhu, Weiwei Sun, Chao Chen, Xiangchao Meng, Lihua Wang, and Yong Ge
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-123, https://doi.org/10.5194/essd-2024-123, 2024
Revised manuscript accepted for ESSD
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Continuous monitoring of shoreline dynamics is critical to understanding the drivers of shoreline change and evolution. This study uses long-term sequences of Landsat TM/ETM+/OLI images to analyze the spatiotemporal evolution characteristics of the coastlines of China's Hainan, Taiwan and other countries from 1990 to 2019.
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.
Nicolas Kolodziejczyk, Esther Portela, Virginie Thierry, and Annaig Prigent
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-106, https://doi.org/10.5194/essd-2024-106, 2024
Revised manuscript accepted for ESSD
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Oceanic Dissolved Oxygen (DO) is fundamental for ocean biogeochemical cycles and the marine life. To ease the of computation of Ocean oxygen budget from in situ DO data, the mapping of data on regular 3D grid is useful. Here, we present a new DO gridded product from Argo database. We compare it with existing DO mapping from historical data set. We suggest that the Ocean is generally losing oxygen since the 1980's, but large interannual and regional variabilities are to be considered.
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.
Simona Simoncelli, Franco Reseghetti, Claudia Fratianni, Lijing Cheng, and Giancarlo Raiteri
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-525, https://doi.org/10.5194/essd-2023-525, 2024
Revised manuscript accepted for ESSD
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This data review is about the reprocessing of historical XBT profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019. A thorough data analysis has been performed starting from the original raw data and operational log sheets. The data have been first formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never used before. A new Quality Control procedure has been applied.
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.
Robin V. F. de Vries, Shungudzemwoyo P. Garaba, and Sarah-Jeanne Royer
Earth Syst. Sci. Data, 15, 5575–5596, https://doi.org/10.5194/essd-15-5575-2023, https://doi.org/10.5194/essd-15-5575-2023, 2023
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We present a high-quality dataset of hyperspectral point and multipixel reflectance observations of virgin, ocean-harvested, and biofouled multipurpose plastics. Biofouling and a submerged scenario of the dataset further extend the variability in open-access spectral reference libraries that are important in algorithm development with relevance to remote sensing use cases.
Oriane Bruyère, Romain Le Gendre, Mathilde Chauveau, Bertrand Bourgeois, David Varillon, John Butscher, Thomas Trophime, Yann Follin, Jérôme Aucan, Vetea Liao, and Serge Andréfouët
Earth Syst. Sci. Data, 15, 5553–5573, https://doi.org/10.5194/essd-15-5553-2023, https://doi.org/10.5194/essd-15-5553-2023, 2023
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During 2018–2022, four pearl farming Tuamotu atolls (French Polynesia) were studied with oceanographic instruments to measure lagoon hydrodynamics and ocean-lagoon water exchanges. The goal was to gain knowledge on the processes influencing the spat collection of the pearl oyster Pinctada margaritifera, the species used to produce black pearls. A worldwide unique oceanographic atoll data set is provided to address local pearl farming questions and other fundamental and applied investigations.
Edwin Rainville, Jim Thomson, Melissa Moulton, and Morteza Derakhti
Earth Syst. Sci. Data, 15, 5135–5151, https://doi.org/10.5194/essd-15-5135-2023, https://doi.org/10.5194/essd-15-5135-2023, 2023
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Measuring ocean waves nearshore is essential for understanding how the waves impact our coastlines. We designed and deployed many small wave buoys in the nearshore ocean over 27 d in Duck, North Carolina, USA, in 2021. The wave buoys measure their motion as they drift. In this paper, we describe multiple levels of data processing. We explain how this dataset can be used in future studies to investigate nearshore wave kinematics, transport of buoyant particles, and wave-breaking processes.
Alberto Ribotti, Antonio Bussani, Milena Menna, Andrea Satta, Roberto Sorgente, Andrea Cucco, and Riccardo Gerin
Earth Syst. Sci. Data, 15, 4651–4659, https://doi.org/10.5194/essd-15-4651-2023, https://doi.org/10.5194/essd-15-4651-2023, 2023
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Over 100 experiments were realized between 1998 and 2022 in the Mediterranean Sea using surface coastal and offshore Lagrangian drifters. Raw data were initially unified and pre-processed. Then, the integrity of the received data packages was checked and incomplete ones were discarded. Deployment information was retrieved and integrated into the PostgreSQL database. Data were interpolated at defined time intervals, providing a dataset of 158 trajectories, available in different formats.
Ole Baltazar Andersen, Stine Kildegaard Rose, Adili Abulaitijiang, Shengjun Zhang, and Sara Fleury
Earth Syst. Sci. Data, 15, 4065–4075, https://doi.org/10.5194/essd-15-4065-2023, https://doi.org/10.5194/essd-15-4065-2023, 2023
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The mean sea surface (MSS) is an important reference for mapping sea-level changes across the global oceans. It is widely used by space agencies in the definition of sea-level anomalies as mapped by satellite altimetry from space. Here a new fully global high-resolution mean sea surface called DTU21MSS is presented, and a suite of evaluations are performed to demonstrate its performance.
Robert W. Schlegel and Jean-Pierre Gattuso
Earth Syst. Sci. Data, 15, 3733–3746, https://doi.org/10.5194/essd-15-3733-2023, https://doi.org/10.5194/essd-15-3733-2023, 2023
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A single dataset was created for investigations of changes in the socio-ecological systems within seven Arctic fjords by amalgamating roughly 1400 datasets from a number of sources. The many variables in these data were organised into five distinct categories and classified into 14 key drivers. Data for seawater temperature and salinity are available from the late 19th century, with some other drivers having data available from the 1950s and 1960s and the others starting from the 1990s onward.
Pierluigi Penna, Filippo Domenichetti, Andrea Belardinelli, and Michela Martinelli
Earth Syst. Sci. Data, 15, 3513–3527, https://doi.org/10.5194/essd-15-3513-2023, https://doi.org/10.5194/essd-15-3513-2023, 2023
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This work presents the pressure (depth) and temperature profile dataset provided by the AdriFOOS infrastructure in the Adriatic Sea (Mediterranean basin) from 2012 to 2020. Data were subject to quality assurance (QA) and quality control (QC). This infrastructure, based on the ships of opportunity principle and involving the use of commercial fishing vessels, is able to produce huge amounts of useful data both for operational oceanography and fishery biology purposes.
Carmen Zarzuelo, Alejandro López-Ruiz, María Bermúdez, and Miguel Ortega-Sánchez
Earth Syst. Sci. Data, 15, 3095–3110, https://doi.org/10.5194/essd-15-3095-2023, https://doi.org/10.5194/essd-15-3095-2023, 2023
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This paper presents a hydrodynamic dataset for the Bay of Cádiz in southern Spain, a paradigmatic example of a tidal bay of complex geometry under high anthropogenic pressure. The dataset brings together measured and modeled data on water levels, currents, density, and waves for the period 2012–2015. It allows the characterization of the bay dynamics from intratidal to seasonal scales. Potential applications include the study of ocean–bay interactions, wave propagation, or energy assessments.
Zuzanna M. Swirad, Mateusz Moskalik, and Agnieszka Herman
Earth Syst. Sci. Data, 15, 2623–2633, https://doi.org/10.5194/essd-15-2623-2023, https://doi.org/10.5194/essd-15-2623-2023, 2023
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Monitoring ocean waves is important for understanding wave climate and seasonal to longer-term (years to decades) changes. In the Arctic, there is limited freely available observational wave information. We placed sensors at the sea bottom of six bays in Hornsund fjord, Svalbard, and calculated wave energy, wave height and wave period for full hours between July 2013 and February 2021. In this paper, we present the procedure of deriving wave properties from raw pressure measurements.
Tiziana Ciuffardi, Zoi Kokkini, Maristella Berta, Marina Locritani, Andrea Bordone, Ivana Delbono, Mireno Borghini, Maurizio Demarte, Roberta Ivaldi, Federica Pannacciulli, Anna Vetrano, Davide Marini, and Giovanni Caprino
Earth Syst. Sci. Data, 15, 1933–1946, https://doi.org/10.5194/essd-15-1933-2023, https://doi.org/10.5194/essd-15-1933-2023, 2023
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This paper presents the results of the first 2 years of the Levante Canyon Mooring, a mooring line placed since 2020 in the eastern Ligurian Sea, to study a canyon area at about 600 m depth characterized by the presence of cold-water living corals. It provides hydrodynamic and thermohaline measurements along the water column, describing a water-mass distribution coherent with previous evidence in the Ligurian Sea. The data also show a Northern Current episodic and local reversal during summer.
Pierre L'Hégaret, Florian Schütte, Sabrina Speich, Gilles Reverdin, Dariusz B. Baranowski, Rena Czeschel, Tim Fischer, Gregory R. Foltz, Karen J. Heywood, Gerd Krahmann, Rémi Laxenaire, Caroline Le Bihan, Philippe Le Bot, Stéphane Leizour, Callum Rollo, Michael Schlundt, Elizabeth Siddle, Corentin Subirade, Dongxiao Zhang, and Johannes Karstensen
Earth Syst. Sci. Data, 15, 1801–1830, https://doi.org/10.5194/essd-15-1801-2023, https://doi.org/10.5194/essd-15-1801-2023, 2023
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In early 2020, the EUREC4A-OA/ATOMIC experiment took place in the northwestern Tropical Atlantic Ocean, a dynamical region where different water masses interact. Four oceanographic vessels and a fleet of autonomous devices were deployed to study the processes at play and sample the upper ocean, each with its own observing capability. The article first describes the data calibration and validation and second their cross-validation, using a hierarchy of instruments and estimating the uncertainty.
Tongya Liu and Ryan Abernathey
Earth Syst. Sci. Data, 15, 1765–1778, https://doi.org/10.5194/essd-15-1765-2023, https://doi.org/10.5194/essd-15-1765-2023, 2023
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Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its operational simplicity. Using satellite observations and a Lagrangian method, we present a global Lagrangian eddy dataset (GLED v1.0). We conduct the statistical comparison between two types of eddies and the dataset validation. Our dataset offers relief from dilemma that the Eulerian eddy dataset is nearly the only option for studying mesoscale eddies.
Fabio Raicich
Earth Syst. Sci. Data, 15, 1749–1763, https://doi.org/10.5194/essd-15-1749-2023, https://doi.org/10.5194/essd-15-1749-2023, 2023
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In the changing climate, long sea level time series are essential for studying the variability of the mean sea level and the occurrence of extreme events on different timescales. This work summarizes the rescue and quality control of the ultra-centennial sea level data set of Trieste, Italy. The whole time series is characterized by a linear trend of about 1.4 mm yr−1, the period corresponding to the altimetry coverage by a trend of about 3.0 mm yr−1, similarly to the global ocean.
Giulia Bonino, Simona Masina, Giuliano Galimberti, and Matteo Moretti
Earth Syst. Sci. Data, 15, 1269–1285, https://doi.org/10.5194/essd-15-1269-2023, https://doi.org/10.5194/essd-15-1269-2023, 2023
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We present a unique observational dataset of marine heat wave (MHW) macroevents and their characteristics over southern Europe and western Asian (SEWA) basins in the SEWA-MHW dataset. This dataset is the first effort in the literature to archive extremely hot sea surface temperature macroevents. The advantages of the availability of SEWA-MHWs are avoiding the waste of computational resources to detect MHWs and building a consistent framework which would increase comparability among MHW studies.
Johannes J. Rick, Mirco Scharfe, Tatyana Romanova, Justus E. E. van Beusekom, Ragnhild Asmus, Harald Asmus, Finn Mielck, Anja Kamp, Rainer Sieger, and Karen H. Wiltshire
Earth Syst. Sci. Data, 15, 1037–1057, https://doi.org/10.5194/essd-15-1037-2023, https://doi.org/10.5194/essd-15-1037-2023, 2023
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The Sylt Roads (Wadden Sea) time series is illustrated. Since 1984, the water temperature has risen by 1.1 °C, while pH and salinity decreased by 0.2 and 0.3 units. Nutrients (P, N) displayed a period of high eutrophication until 1998 and have decreased since 1999, while Si showed a parallel increase. Chlorophyll did not mirror these changes, probably due to a switch in nutrient limitation. Until 1998, algae were primarily limited by Si, and since 1999, P limitation has become more important.
Cited articles
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L.,
Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R.,
Makarenkov, V., and Nahavandi, S.: A review of uncertainty quantification in
deep learning: Techniques, applications and challenges, Inf. Fusion, 76, 243–297,
https://doi.org/10.1016/j.inffus.2021.05.008, 2021.
Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M., Crate,
S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E.,
Kudela, R. M., Rice, J., Steffen, K., and von Schuckmann, K.: Framing and
Context of the Report, in: IPCC Special Report on the Ocean and Cryosphere
in a Changing Climate, IPCC, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 73–129,
https://doi.org/10.1017/9781009157964.003, 2019.
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J. J., Gu, G.,
Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P.,
Ferraro, R., and Shin, D. B.: The Global Precipitation Climatology Project
(GPCP) monthly analysis (New Version 2.3) and a review of 2017 global
precipitation, Atmosphere (Basel), 9, 138, https://doi.org/10.3390/atmos9040138,
2018.
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C.,
Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean
surface wind velocity product for meteorological and oceanographic
applications, B. Am. Meteorol. Soc., 92, 157–174,
https://doi.org/10.1175/2010BAMS2946.1, 2011.
Auger, M., Morrow, R., Kestenare, E., Sallée, J. B., and Cowley, R.:
Southern Ocean in-situ temperature trends over 25 years emerge from
interannual variability, Nat. Commun., 12, 514,
https://doi.org/10.1038/s41467-020-20781-1, 2021.
Bagnell, A. and DeVries, T.: 20th century cooling of the deep ocean
contributed to delayed acceleration of Earth's energy imbalance, Nat.
Commun., 12, 4604, https://doi.org/10.1038/s41467-021-24472-3, 2021.
Balmaseda, M. A., Trenberth, K. E., and Källén, E.: Distinctive
climate signals in reanalysis of global ocean heat content, Geophys. Res.
Lett., 40, 1754–1759, https://doi.org/10.1002/grl.50382, 2013.
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi,
L., Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D.,
Boyer, T., Chang, Y. S., Chepurin, G. A., Ferry, N., Forget, G., Fujii, Y.,
Good, S., Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A.,
Lee, T., Martin, M. J., Masina, S., Masuda, S., Meyssignac, B., Mogensen,
K., Parent, L., Peterson, K. A., Tang, Y. M., Yin, Y., Vernieres, G., Wang,
X., Waters, J., Wedd, R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J. F.,
Dupont, F., Kuragano, T., Kamachi, M., Awaji, T., Caltabiano, A.,
Wilmer-Becker, K., and Gaillard, F.: The ocean reanalyses intercomparison
project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97,
https://doi.org/10.1080/1755876X.2015.1022329, 2015.
Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W.: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies, Earth Syst. Sci. Data, 8, 165–176, https://doi.org/10.5194/essd-8-165-2016, 2016.
Berrar, D.: Cross-validation, in: Encyclopedia of Bioinformatics and
Computational Biology: ABC of Bioinformatics, 1–3, 542–545,
https://doi.org/10.1016/B978-0-12-809633-8.20349-X, 2018.
Boyer, T. P., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A.,
Locarnini, R. A., Mishonov, A. V., Paver, C. R., Reagan, J. R., Seidov, D.,
Smolyar, I. V., Weathers, K. W., and Zweng, M. M.: World Ocean Database
2018, edited by: Mishonov, A. V., Tech. Ed. NOAA Atlas NESDIS 87, 1–207, 2018.
Bushaev, V.: Understanding RMSprop – faster neural network learning,
Towar. Data Sci., 36, 1–7, 2018.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A new ocean climate
reanalysis, J. Climate, 31, 6967–6983,
https://doi.org/10.1175/jcli-d-18-0149.1, 2018.
Chassignet, E. P., Fox-Kemper, B., Yeager, S. G., and Bozec, A.: Sources and
Sinks of Ocean Mesoscale Eddy Energy, CLIVAR Exch. CLIVAR Var., 18, 3–8,
2020.
Chau, S. L., Hu, R., Gonzalez, J., and Sejdinovic, D.: RKHS-SHAP: Shapley Values for Kernel Methods, arXiv [preprint], arXiv:2110.09167, 18 October 2021.
Chen, G., Peng, L., and Ma, C.: Climatology and seasonality of upper ocean
salinity: a three-dimensional view from argo floats, Clim. Dynam., 50,
2169–2182, https://doi.org/10.1007/s00382-017-3742-6, 2018.
Cheng, L. and Zhu, J.: Benefits of CMIP5 multimodel ensemble in
reconstructing historical ocean subsurface temperature variations, J. Climate,
29, 5393–5416, https://doi.org/10.1175/JCLI-D-15-0730.1, 2016.
Cheng, L., Trenberth, K. E., Fasullo, J., Boyer, T., Abraham, J., and Zhu,
J.: Improved estimates of ocean heat content from 1960 to 2015, Sci. Adv.,
3, e1601545, https://doi.org/10.1126/sciadv.1601545, 2017.
Cheng, L., Trenberth, K. E., Gruber, N., Abraham, J. P., Fasullo, J. T., Li,
G., Mann, M. E., Zhao, X., and Zhu, J.: Improved estimates of changes in
upper ocean salinity and the hydrological cycle, J. Climate, 33, 10357–10381,
https://doi.org/10.1175/JCLI-D-20-0366.1, 2020.
Cheng, Y. H., Ho, C. R., Zheng, Q., and Kuo, N. J.: Statistical
characteristics of mesoscale eddies in the north pacific derived from
satellite altimetry, Remote Sens., 6, 5164–5183, https://doi.org/10.3390/rs6065164, 2014.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C.,
Quéré, C. Le, Myneni, R. B., Piao, S., and Thornton, P.: The
physical science basis. Contribution of working group I to the fifth
assessment report of the intergovernmental panel on climate change, Chang.
IPCC Clim., https://doi.org/10.1017/CBO9781107415324.015, 2013.
Contractor, S. and Roughan, M.: Efficacy of Feedforward and LSTM Neural
Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen,
Nutrients, and Temperature, Front. Mar. Sci., 8, 637759,
https://doi.org/10.3389/fmars.2021.637759, 2021.
Curry, R., Dickson, B., and Yashayaev, I.: A change in the freshwater
balance of the Atlantic Ocean over the past four decades, Nature, 426,
826–829, https://doi.org/10.1038/nature02206, 2003.
Dan Foresee, F. and Hagan, M. T.: Gauss-Newton approximation to bayesian
learning, in: IEEE International Conference on Neural Networks – Conference
Proceedings, 1930–1935, https://doi.org/10.1109/ICNN.1997.614194, 1997.
Denvil-Sommer, A., Gehlen, M., Vrac, M., and Mejia, C.: LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean, Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, 2019.
Domingues, C. M., Church, J. A., White, N. J., Gleckler, P. J., Wijffels, S.
E., Barker, P. M., and Dunn, J. R.: Improved estimates of upper-ocean
warming and multi-decadal sea-level rise, Nature, 453, 1090–1093,
https://doi.org/10.1038/nature07080, 2008.
Durack, P. J.: Ocean salinity and the global water cycle, Oceanography, 28,
20–31, https://doi.org/10.5670/oceanog.2015.03, 2015.
Durack, P. J., Gleckler, P. J., Landerer, F. W., and Taylor, K. E.:
Quantifying underestimates of long-term upper-ocean warming, Nat. Clim.
Chang., 4, 999–1005, https://doi.org/10.1038/nclimate2389, 2014.
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015.
Frederikse, T., Jevrejeva, S., Riva, R. E. M., and Dangendorf, S.: A
consistent sea-level reconstruction and its budget on basin and global
scales over 1958–2014, J. Climate, 31, 1267–1280,
https://doi.org/10.1175/JCLI-D-17-0502.1, 2018.
Frenger, I., Münnich, M., Gruber, N., and Knutti, R.: Southern Ocean
eddy phenomenology, J. Geophys. Res.-Ocean., 120, 7413–7449,
https://doi.org/10.1002/2015JC011047, 2015.
Gabella, M.: Topology of Learning in Feedforward Neural Networks, IEEE
Trans. Neural Networks Learn. Syst., 32, 3588–3592,
https://doi.org/10.1109/TNNLS.2020.3015790, 2021.
Gaillard, F., Reynaud, T., Thierry, V., Kolodziejczyk, N., and Von
Schuckmann, K.: In situ-based reanalysis of the global ocean temperature and
salinity with ISAS: Variability of the heat content and steric height, J.
Climate, 29, 1305–1323, https://doi.org/10.1175/JCLI-D-15-0028.1, 2016.
Gal, Y. and Ghahramani, Z.: Dropout as a Bayesian approximation:
Representing model uncertainty in deep learning, in: 33rd International
Conference on Machine Learning, ICML 2016, 1651–1660, 2016.
Gan, M., Pan, S., Chen, Y. ping, Cheng, C., Pan, H., and Zhu, X.:
Application of the Machine Learning LightGBM Model to the Prediction of the
Water Levels of the Lower Columbia River, J. Mar. Sci. Eng., 9, 496,
https://doi.org/10.3390/jmse9050496, 2021.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Ocean., 118, 6704–6716,
https://doi.org/10.1002/2013JC009067, 2013.
Gould, J., Sloyan, B., and Visbeck, M.: In situ ocean observations. A brief
history, present status, and future directions., in: International
Geophysics, vol. 103, edited by: Siedler, G., Griffies, S. M., Gould, J.,
and Church, J. A. B. T.-I. G., Academic Press, 59–81,
https://doi.org/10.1016/B978-0-12-391851-2.00003-9, 2013.
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in
bathythermograph data: Development of a new correction scheme based on
analysis of a global ocean database, Deep-Sea Res. Pt. I,
57, 812–833, https://doi.org/10.1016/j.dsr.2010.03.011, 2010.
Gui, K., Che, H., Zeng, Z., Wang, Y., Zhai, S., Wang, Z., Luo, M., Zhang,
L., Liao, T., Zhao, H., Li, L., Zheng, Y., and Zhang, X.: Construction of a
virtual PM2.5 observation network in China based on high-density surface
meteorological observations using the Extreme Gradient Boosting model,
Environ. Int., 141, 105801, https://doi.org/10.1016/j.envint.2020.105801, 2020.
Guinehut, S., Dhomps, A.-L., Larnicol, G., and Le Traon, P.-Y.: High resolution 3-D temperature and salinity fields derived from in situ and satellite observations, Ocean Sci., 8, 845–857, https://doi.org/10.5194/os-8-845-2012, 2012.
Hannachi, A., Jolliffe, I. T., and Stephenson, D. B.: Empirical orthogonal
functions and related techniques in atmospheric science: A review, Int. J.
Climatol., 27, 1119–1152, https://doi.org/10.1002/joc.1499, 2007.
Hosoda, S., Ohira, T., and Nakamura, T.: A monthly mean dataset of global
oceanic temperature and salinity derived from Argo float observations,
JAMSTEC Rep. Res. Dev., 8, 47–59, https://doi.org/10.5918/jamstecr.8.47, 2008.
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith,
T., and Zhang, H.-M.: Improvements of the Daily Optimum Interpolation Sea
Surface Temperature (DOISST) Version 2.1, J. Climate, 34, 2923–2939,
https://doi.org/10.1175/JCLI-D-20-0166.1, 2021.
Ishii, M., Kimoto, M., and Kachi, M.: Historical ocean subsurfaces
temperature analysis with error estimates, Mon. Weather Rev., 131,
https://doi.org/10.1175/1520-0493(2003)131<0051:HOSTAW>2.0.CO;2, 2003.
Ishii, M., Fukuda, Y., Hirahara, S., Yasui, S., Suzuki, T., and Sato, K.:
Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present
Observational Data Sets, SOLA, 13, 163–167,
https://doi.org/10.2151/sola.2017-030, 2017.
Jean-Michel, L., Eric, G., Romain, B. B., Gilles, G., Angélique, M.,
Marie, D., Clément, B., Mathieu, H., Olivier, L. G., Charly, R., Tony,
C., Charles-Emmanuel, T., Florent, G., Giovanni, R., Mounir, B., Yann, D.,
and Pierre-Yves, L. T.: The Copernicus Global 1/12∘ Oceanic and
Sea Ice GLORYS12 Reanalysis, Front. Earth Sci., 9, 698876,
https://doi.org/10.3389/feart.2021.698876, 2021.
LeCun, Y. A., Bottou, L., Orr, G. B., and Müller, K. R.: Efficient
backprop, in: Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.
7700 LECTU, edited by: Montavon, G., Orr, G. B., and Müller, K.-R.,
Springer Berlin Heidelberg, Berlin, Heidelberg, 9–48,
https://doi.org/10.1007/978-3-642-35289-8_3, 2012.
Lei, J.: Cross-Validation With Confidence, J. Am. Stat. Assoc., 115,
1978–1997, https://doi.org/10.1080/01621459.2019.1672556, 2020.
Levitus, S., Antonov, J. I., Boyer, T. P., Locarnini, R. A., Garcia, H. E.,
and Mishonov, A. V.: Global ocean heat content 1955–2008 in light of
recently revealed instrumentation problems, Geophys. Res. Lett., 36, L07608,
https://doi.org/10.1029/2008GL037155, 2009.
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E.,
Locarnini, R. A., Mishonov, A. V., Reagan, J. R., Seidov, D., Yarosh, E. S.,
and Zweng, M. M.: World ocean heat content and thermosteric sea level change
(0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603,
https://doi.org/10.1029/2012GL051106, 2012.
Li, G., Zhang, Y., Xiao, J., Song, X., Abraham, J., Cheng, L., and Zhu, J.:
Examining the salinity change in the upper Pacific Ocean during the Argo
period, Clim. Dynam., 53, 6055–6074,
https://doi.org/10.1007/s00382-019-04912-z, 2019.
Liang, X., Liu, C., Ponte, R. M., and Chambers, D. P.: A comparison of the
variability and changes in global ocean heat content from multiple objective
analysis products during the Argo period, J. Climate, 34, 7875–7895,
https://doi.org/10.1175/JCLI-D-20-0794.1, 2021.
Liashchynskyi, P. and Liashchynskyi, P.: Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS, arXiv [preprint], arXiv:1912.06059, 12 December 2019.
Liu, H., Zhou, H., Yang, W., Liu, X., Li, Y., Yang, Y., Chen, X., and Li,
X.: A three-dimensional gravest empirical mode determined from hydrographic
observations in the western equatorial Pacific Ocean, J. Mar. Syst., 214,
103487, https://doi.org/10.1016/j.jmarsys.2020.103487, 2021.
Liu, Y., Cheng, L., Pan, Y., Abraham, J., Zhang, B., Zhu, J., and Song, J.:
Climatological seasonal variation of the upper ocean salinity, Int. J.
Climatol., 42, 3477–3498, https://doi.org/10.1002/joc.7428, 2022.
Llovel, W. and Lee, T.: Importance and origin of halosteric contribution to
sea level change in the southeast Indian Ocean during 2005–2013, Geophys.
Res. Lett., 42, 1148–1157, https://doi.org/10.1002/2014GL062611, 2015.
Lu, S., Liu, Z., Li, H., Li, Z., and Xu, J.: Manual of Global Ocean Argo
gridded data set (BOA_Argo) (Version 2020), SOED & DESS, Hangzhou, China, 14 pp., 2020.
Lu, W., Su, H., Yang, X., and Yan, X. H.: Subsurface temperature estimation
from remote sensing data using a clustering-neural network method, Remote
Sens. Environ., 229, 213–222, https://doi.org/10.1016/j.rse.2019.04.009,
2019.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, December 2017, 4768–4777, 2017.
Lyman, J. M. and Johnson, G. C.: Estimating global ocean heat content
changes in the upper 1800 m since 1950 and the influence of climatology
choice, J. Climate, 27, 1945–1957, https://doi.org/10.1175/JCLI-D-12-00752.1, 2014.
Marvel, K., Biasutti, M., Bonfils, C., Taylor, K. E., Kushnir, Y., and Cook,
B. I.: Observed and projected changes to the precipitation annual cycle, J.
Clim., 30, 4983–4995, https://doi.org/10.1175/JCLI-D-16-0572.1, 2017.
McWilliams, J. C.: Submesoscale currents in the ocean, Proc. R. Soc. A Math.
Phys. Eng. Sci., 472, 20160117, https://doi.org/10.1098/rspa.2016.0117,
2016.
Mertz, F., Rosmorduc, V., Maheu, C., and Faugère, Y.: CMEMS Product User
manual For Sea Level SLA products, Copernicus Mar. Environ. Monit. Serv.,
0–41, 2016.
Milanés-Hermosilla, D., Codorniú, R. T., López-Baracaldo, R.,
Sagaró-Zamora, R., Delisle-Rodriguez, D., Villarejo-Mayor, J. J., and
Núñez-Álvarez, J. R.: Monte carlo dropout for uncertainty
estimation and motor imagery classification, Sensors, 21, 7241,
https://doi.org/10.3390/s21217241, 2021.
Mulet, S., Rio, M. H., Mignot, A., Guinehut, S., and Morrow, R.: A new
estimate of the global 3D geostrophic ocean circulation based on satellite
data and in-situ measurements, Deep-Sea Res. Pt. II,
77–80, 70–81, https://doi.org/10.1016/j.dsr2.2012.04.012, 2012.
Palmer, M. D., Roberts, C. D., Balmaseda, M., Chang, Y. S., Chepurin, G.,
Ferry, N., Fujii, Y., Good, S. A., Guinehut, S., Haines, K., Hernandez, F.,
Köhl, A., Lee, T., Martin, M. J., Masina, S., Masuda, S., Peterson, K.
A., Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Wang, O., and
Xue, Y.: Ocean heat content variability and change in an ensemble of ocean
reanalyses, Clim. Dynam., 49, 909–930,
https://doi.org/10.1007/s00382-015-2801-0, 2017.
Pauthenet, E., Bachelot, L., Balem, K., Maze, G., Tréeguier, A.-M., Roquet, F., Fablet, R., and Tandeo, P.: Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks, Ocean Sci., 18, 1221–1244, https://doi.org/10.5194/os-18-1221-2022, 2022.
Ponte, R. M., Sun, Q., Liu, C., and Liang, X.: How Salty Is the Global
Ocean: Weighing It All or Tasting It a Sip at a Time?, Geophys. Res. Lett.,
48, e2021GL092935, https://doi.org/10.1029/2021GL092935, 2021.
Reagan, J., Seidov, D., and Boyer, T.: Water Vapor Transfer and Near-Surface
Salinity Contrasts in the North Atlantic Ocean, Sci. Rep.-UK, 8, 8830,
https://doi.org/10.1038/s41598-018-27052-6, 2018.
Reed, E. V., Thompson, D. M., and Anchukaitis, K. J.: Coral-Based Sea Surface Salinity Reconstructions and the Role of Observational Uncertainties in Inferred Variability and Trends, Paleoceanogr. Paleoclimatol., 37, e2021PA004371, https://doi.org/10.1029/2021PA004371, 2022.
Reverdin, G., Friedman, A. R., Chafik, L., Holliday, N. P., Szekely, T.,
Valdimarsson, H., and Yashayaev, I.: North Atlantic extratropical and
subpolar gyre variability during the last 120 years: a gridded dataset of
surface temperature, salinity, and density. Part 1: dataset validation and
RMS variability, Ocean Dynam., 69, 385–403,
https://doi.org/10.1007/s10236-018-1240-y, 2019.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily high-resolution-blended analyses for sea surface
temperature, J. Climate, 20, 5473–5496,
https://doi.org/10.1175/2007JCLI1824.1, 2007.
Rhines, P. B.: Mesoscale eddies, in: Encyclopedia of Ocean Sciences, edited
by: Cochran, J. K., Bokuniewicz, H. J., and Yager, P. L. B. T.-E., 3rd edn., Academic Press, Oxford, 115–127,
https://doi.org/10.1016/B978-0-12-409548-9.11642-2, 2019.
Roemmich, D. and Gilson, J.: The 2004–2008 mean and annual cycle of
temperature, salinity, and steric height in the global ocean from the Argo
Program, Prog. Oceanogr., 82, 81–100, https://doi.org/10.1016/j.pocean.2009.03.004,
2009.
Roemmich, D., Johnson, G. C., Riser, S., Davis, R., Gilson, J., Owens, W.
B., Garzoli, S. L., Schmid, C., and Ignaszewski, M.: The Argo Program:
Observing the global ocean with profiling floats, Oceanography, 22, 34–43,
https://doi.org/10.5670/oceanog.2009.36, 2009.
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J.,
Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T.,
Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig,
H. C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D.
Ó., d'Ortenzio, F., Dall'Olmo, G., Desbruyeres, D., Fennel, K., Fer, I.,
Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B.,
Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G. C.,
Kang, K., Kolodziejczyk, N., Körtzinger, A., Traon, P.-Y. Le, Lenn,
Y.-D., Maze, G., Mork, K. A., Morris, T., Nagai, T., Nash, J., Garabato, A.
N., Olsen, A., Pattabhi, R. R., Prakash, S., Riser, S., Schmechtig, C.,
Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T.,
Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T. W., Turton, J.,
Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse,
A. F., Waterman, S., Watson, A., Wilson, C., Wong, A. P. S., Xu, J., and
Yasuda, I.: On the Future of Argo: A Global, Full-Depth, Multi-Disciplinary
Array, Front. Mar. Sci., 6, 439, https://doi.org/10.3389/fmars.2019.00439, 2019.
Siegelman, L., Roquet, F., Mensah, V., Rivière, P., Pauthenet, E.,
Picard, B., and Guinet, C.: Correction and accuracy of high- and
low-resolution CTD data from animal-borne instruments, J. Atmos. Ocean.
Technol., 36, 745–760, https://doi.org/10.1175/JTECH-D-18-0170.1, 2019.
Skliris, N., Marsh, R., Josey, S. A., Good, S. A., Liu, C., and Allan, R.
P.: Salinity changes in the World Ocean since 1950 in relation to changing
surface freshwater fluxes, Clim. Dynam., 43, 709–736,
https://doi.org/10.1007/s00382-014-2131-7, 2014.
Skliris, N., Marsh, R., Mecking, J. V., and Zika, J. D.: Changing water
cycle and freshwater transports in the Atlantic Ocean in observations and
CMIP5 models, Clim. Dynam., 54, 4971–4989,
https://doi.org/10.1007/s00382-020-05261-y, 2020.
Sohail, T., Zika, J. D., Irving, D. B., and Church, J. A.: Observed poleward freshwater transport since 1970, Nature, 602, https://doi.org/10.1038/s41586-021-04370-w, 2022.
Stamell, J., Rustagi, R. R., Gloege, L., and McKinley, G. A.: Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-311, 2020.
Stoean, C., Stoean, R., Atencia, M., Abdar, M., Velázquez-Pérez, L.,
Khosravi, A., Nahavandi, S., Rajendra Acharya, U., and Joya, G.: Automated
detection of presymptomatic conditions in spinocerebellar ataxia type 2
using monte carlo dropout and deep neural network techniques with
electrooculogram signals, Sensors (Switzerland), 20, 3032,
https://doi.org/10.3390/s20113032, 2020.
Storto, A., Alvera-Azcárate, A., Balmaseda, M. A., Barth, A.,
Chevallier, M., Counillon, F., Domingues, C. M., Drévillon, M., Drillet,
Y., Forget, G., Garric, G., Haines, K., Hernandez, F., Iovino, D., Jackson,
L. C., Lellouche, J. M., Masina, S., Mayer, M., Oke, P. R., Penny, S. G.,
Peterson, A. K., Yang, C., and Zuo, H.: Ocean reanalyses: Recent advances
and unsolved challenges, Front. Mar. Sci., 6, 418, https://doi.org/10.3389/fmars.2019.00418, 2019.
Su, H., Zhang, H., Geng, X., Qin, T., Lu, W., and Yan, X. H.: OPEN: A new
estimation of global ocean heat content for upper 2000 meters from remote
sensing data, Remote Sens., 12, 2294, https://doi.org/10.3390/rs12142294, 2020.
Tian, T., Cheng, L., Wang, G., Abraham, J., Ren, S., Zhu, J., and Song, J.:
Reconstructing ocean subsurface salinity at high resolution using a machine
learning approach, Science Data Bank [data set],
https://doi.org/10.57760/sciencedb.o00122.00001, 2022.
Vinogradova, N., Lee, T., Boutin, J., Drushka, K., Fournier, S., Sabia, R.,
Stammer, D., Bayler, E., Reul, N., Gordon, A., Melnichenko, O., Li, L.,
Hackert, E., Martin, M., Kolodziejczyk, N., Hasson, A., Brown, S., Misra,
S., and Lindstrom, E.: Satellite salinity observing system: Recent
discoveries and the way forward, Front. Mar. Sci., 6, 243, https://doi.org/10.3389/fmars.2019.00243,
2019.
von Schuckmann, K., Salléee, J.-B., Chambers, D., Le Traon, P.-Y., Cabanes, C., Gaillard, F., Speich, S., and Hamon, M.: Consistency of the current global ocean observing systems from an Argo perspective, Ocean Sci., 10, 547–557, https://doi.org/10.5194/os-10-547-2014, 2014.
Wang, G., Cheng, L., Boyer, T., and Li, C.: Halosteric sea level changes
during the Argo era, Water (Switzerland), 9, 484,
https://doi.org/10.3390/w9070484, 2017.
Wang, H., Song, T., Zhu, S., Yang, S., and Feng, L.: Subsurface temperature
estimation from sea surface data using neural network models in the western
pacific ocean, Mathematics, 9, 852, https://doi.org/10.3390/math9080852, 2021.
Wang, J., Flierl, G. R., Lacasce, J. H., Mcclean, J. L., and Mahadevan, A.:
Reconstructing the ocean's interior from surface data, J. Phys. Oceanogr.,
43, 1611–1626, https://doi.org/10.1175/JPO-D-12-0204.1, 2013.
Warin, T.: Multiple Linear Regression, Figshare,
https://doi.org/10.6084/M9.FIGSHARE.8178812.V1, 2019.
Wentz, F. J., Scott, J., Hoffman, R., Leidner, M., Atlas, R., and Ardizzone,
J.: Cross-Calibrated Multi-Platform Ocean Surface Wind Vector Analysis
Product V2, 1987–ongoing, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/4TSY-K140, 2016.
Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S.,
Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J.,
Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval,
T., Maze, G., Cabanes, C., André, X., Poffa, N., Yashayaev, I., Barker,
P. M., Guinehut, S., Belbéoch, M., Ignaszewski, M., Baringer, M. O. N.,
Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N.,
Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins,
P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouët,
R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk,
N., Bernard, V., Bourlès, B., Claustre, H., D'Ortenzio, F., Le Reste,
S., Le Traon, P. Y., Rannou, J. P., Saout-Grit, C., Speich, S., Thierry, V.,
Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain,
P. M., Vélez-Belchí, P., Suga, T., Ando, K., Iwasaska, N.,
Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K.,
Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk,
E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch,
S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland,
H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T.,
Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K. R., Jo, H. J., Kim, S. D., and Park, H. M.: Argo
Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface
Velocity Observations From a Global Array of Profiling Floats, Front. Mar. Sci., 7, 700,
https://doi.org/10.3389/fmars.2020.00700, 2020.
Xu, C., Chen, G., Shang, X.-D., and Huang, R. X.: The spatial distribution
of sources and sinks of ocean mesoscale eddies, J. Trop. Oceanogr., 32,
37–46, 2013.
Yan, H., Wang, H., Zhang, R., Bao, S., Chen, J., and Wang, G.: The
Inconsistent Pairs Between In Situ Observations of Near Surface Salinity and
Multiple Remotely Sensed Salinity Data, Earth Space Sci., 8, e2020EA001355,
https://doi.org/10.1029/2020EA001355, 2021.
Zhou, G., Fu, W., Zhu, J., and Wang, H.: The impact of location-dependent
correlation scales in ocean data assimilation, Geophys. Res. Lett., 31, L21306,
https://doi.org/10.1029/2004GL020579, 2004.
Zweng, M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A., and
Garcia, H. E.: World Ocean Atlas 2018, Volume 2: Salinity, edited by: Mishonov A., NOAA Atlas NESDIS 82, 50
pp., http://www.nodc.noaa.gov/OC5/indprod.html (last access: 12 September 2022), 2019.
Short summary
A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
A high-resolution gridded dataset is crucial for understanding ocean processes at various...
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