Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2735-2025
© Author(s) 2025. 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-17-2735-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 Jun 2025
Data description paper |
| 18 Jun 2025
| 18 Jun 2025
A continual-learning-based multilayer perceptron for improved reconstruction of three-dimensional nitrate concentrations
Xiang Yu
Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Huadong Guo
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Jiahua Zhang
CORRESPONDING AUTHOR
Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Yi Ma
First Institute of Oceanography Ministry of National Resource, Qingdao, 266061, China
Xiaopeng Wang
Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
Guangsheng Liu
Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
Mingming Xing
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Nuo Xu
Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Ayalkibet M. Seka
Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Arba Minch Water Technology Institute, Water Resources Research Center, Arba Minch University, Arba Minch, Ethiopia
Related authors
No articles found.
Lamei Shi, Jiahua Zhang, Da Zhang, Jingwen Wang, Xianglei Meng, Yuqin Liu, and Fengmei Yao
Atmos. Chem. Phys., 22, 11255–11274, https://doi.org/10.5194/acp-22-11255-2022, https://doi.org/10.5194/acp-22-11255-2022, 2022
Short summary
Short summary
Dust impacts climate and human life. Analyzing the interdecadal change in dust activity and its influence factors is crucial for disaster mitigation. Based on a linear regression method, this study revealed the interdecadal variability of relationships between ENSO and dust over northwestern South Asia from 1982 to 2014 and analyzed the effects of atmospheric factors on this interdecadal variability. The result sheds new light on numerical simulation involving the interdecadal variation of dust.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Yuqin Liu, Tao Lin, Juan Hong, Yonghong Wang, Lamei Shi, Yiyi Huang, Xian Wu, Hao Zhou, Jiahua Zhang, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 12331–12358, https://doi.org/10.5194/acp-21-12331-2021, https://doi.org/10.5194/acp-21-12331-2021, 2021
Short summary
Short summary
The four-dimensional variation of aerosol properties over the BTH, YRD and PRD (east China) were investigated using satellite observations from 2007 to 2020. Distinct differences between the aerosol optical depth and vertical distribution of the occurrence of aerosol types over these regions depend on season, aerosol loading and meteorological conditions. Day–night differences between the vertical distribution of aerosol types suggest effects of boundary layer dynamics and aerosol transport.
Yuqin Liu, Jiahua Zhang, Putian Zhou, Tao Lin, Juan Hong, Lamei Shi, Fengmei Yao, Jun Wu, Huadong Guo, and Gerrit de Leeuw
Atmos. Chem. Phys., 18, 18187–18202, https://doi.org/10.5194/acp-18-18187-2018, https://doi.org/10.5194/acp-18-18187-2018, 2018
Y. Hu, Y. Ma, and J. An
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 573–577, https://doi.org/10.5194/isprs-archives-XLII-3-573-2018, https://doi.org/10.5194/isprs-archives-XLII-3-573-2018, 2018
J. Yang, G. Ren, Y. Ma, L. Dong, and J. Wan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2083–2087, https://doi.org/10.5194/isprs-archives-XLII-3-2083-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2083-2018, 2018
Yuqin Liu, Gerrit de Leeuw, Veli-Matti Kerminen, Jiahua Zhang, Putian Zhou, Wei Nie, Ximeng Qi, Juan Hong, Yonghong Wang, Aijun Ding, Huadong Guo, Olaf Krüger, Markku Kulmala, and Tuukka Petäjä
Atmos. Chem. Phys., 17, 5623–5641, https://doi.org/10.5194/acp-17-5623-2017, https://doi.org/10.5194/acp-17-5623-2017, 2017
Short summary
Short summary
The aerosol effects on warm cloud parameters over the Yangtze River Delta are systematically examined using multi-sensor retrievals. This study shows that the COT–CDR and CWP–CDR relationships are not unique, but are affected by atmospheric aerosol loading. CDR and cloud fraction show different behaviours for low and high AOD. Aerosol–cloud interaction (ACI) is stronger for clouds mixed with smoke aerosol than for clouds mixed with dust. Meteorological conditions play an important role in ACI.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
Short summary
Short summary
After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Related subject area
Domain: ESSD – Ocean | Subject: Chemical oceanography
An updated synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2023: the SNAPO-CO2-v2 dataset
A global monthly 3D field of seawater pH over 3 decades: a machine learning approach
Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning
The high-resolution global shipping emission inventory by the Shipping Emission Inventory Model (SEIM)
A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021
Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE)
A consistent ocean oxygen profile dataset with new quality control and bias assessment
Global database of actual nitrogen loss rates in coastal and marine sediments
CO2 and hydrography acquired by autonomous surface vehicles from the Atlantic Ocean to the Mediterranean Sea: data correction and validation
Exploring the CO2 fugacity along the east coast of South America aboard the schooner Tara
A 20-year (1998–2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
Distributions of in situ parameters, dissolved (in)organic carbon, and nutrients in the water column and pore waters of Arctic fjords (western Spitsbergen) during a melting season
Climatological distribution of ocean acidification variables along the North American ocean margins
Updated climatological mean ΔfCO2 and net sea–air CO2 flux over the global open ocean regions
The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product
Synthesis Product for Ocean Time Series (SPOTS) – a ship-based biogeochemical pilot
French coastal network for carbonate system monitoring: the CocoriCO2 dataset
A global database of dissolved organic matter (DOM) concentration measurements in coastal waters (CoastDOM v1)
A decade-long cruise time series (2008–2018) of physical and biogeochemical conditions in the southern Salish Sea, North America
A regional pCO2 climatology of the Baltic Sea from in situ pCO2 observations and a model-based extrapolation approach
A 12-year-long (2010–2021) hydrological and biogeochemical dataset in the Sicily Channel (Mediterranean Sea)
A decade of marine inorganic carbon chemistry observations in the northern Gulf of Alaska – insights into an environment in transition
A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020
A high-resolution synthesis dataset for multistressor analyses along the US West Coast
CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system
A synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2022: the SNAPO-CO2-v1 dataset
A year of transient tracers (chlorofluorocarbon 12 and sulfur hexafluoride), noble gases (helium and neon), and tritium in the Arctic Ocean from the MOSAiC expedition (2019–2020)
Database of nitrification and nitrifiers in the global ocean
GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades
Spatiotemporal variability in pH and carbonate parameters on the Canadian Atlantic continental shelf between 2014 and 2022
Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning
Global oceanic diazotroph database version 2 and elevated estimate of global oceanic N2 fixation
High-frequency, year-round time series of the carbonate chemistry in a high-Arctic fjord (Svalbard)
OceanSODA-UNEXE: a multi-year gridded Amazon and Congo River outflow surface ocean carbonate system dataset
Evaluating the transport of surface seawater from 1956 to 2021 using 137Cs deposited in the global ocean as a chemical tracer
Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis
OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons
Hyperspectral reflectance dataset of pristine, weathered, and biofouled plastics
A database of marine macronutrient, temperature and salinity measurements made around the highly productive island of South Georgia, the Scotia Sea and the Antarctic Peninsula between 1980 and 2009
GLODAPv2.2022: the latest version of the global interior ocean biogeochemical data product
Oil slicks in the Gulf of Guinea – 10 years of Envisat Advanced Synthetic Aperture Radar observations
Nicolas Metzl, Jonathan Fin, Claire Lo Monaco, Claude Mignon, Samir Alliouane, Bruno Bombled, Jacqueline Boutin, Yann Bozec, Steeve Comeau, Pascal Conan, Laurent Coppola, Pascale Cuet, Eva Ferreira, Jean-Pierre Gattuso, Frédéric Gazeau, Catherine Goyet, Emilie Grossteffan, Bruno Lansard, Dominique Lefèvre, Nathalie Lefèvre, Coraline Leseurre, Sébastien Petton, Mireille Pujo-Pay, Christophe Rabouille, Gilles Reverdin, Céline Ridame, Peggy Rimmelin-Maury, Jean-François Ternon, Franck Touratier, Aline Tribollet, Thibaut Wagener, and Cathy Wimart-Rousseau
Earth Syst. Sci. Data, 17, 1075–1100, https://doi.org/10.5194/essd-17-1075-2025, https://doi.org/10.5194/essd-17-1075-2025, 2025
Short summary
Short summary
This work presents a new synthesis of 67 000 total alkalinity and total dissolved inorganic carbon observations obtained between 1993 and 2023 in the global ocean, coastal zones, and the Mediterranean Sea. We describe the data assemblage and associated quality control and discuss some potential uses of this dataset. The dataset is provided in a single format and includes the quality flag for each sample.
Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Lijing Cheng, Jun Ma, Huamao Yuan, Liqin Duan, Ning Li, Qidong Wang, Jianwei Xing, and Jiajia Dai
Earth Syst. Sci. Data, 17, 719–740, https://doi.org/10.5194/essd-17-719-2025, https://doi.org/10.5194/essd-17-719-2025, 2025
Short summary
Short summary
The continuous uptake of atmospheric CO2 by the ocean leads to decreasing seawater pH, which is an ongoing threat to the marine ecosystem. This pH change has been globally documented in the surface ocean, but information is limited below the surface. Here, we present a monthly 1° gridded product of global seawater pH based on a machine learning method and real pH observations. The pH product covers the years from 1992 to 2020 and depths from 0 to 2000 m.
Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-17, https://doi.org/10.5194/essd-2025-17, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
In this study, we used machine learning models to produce the first global maps of Pb concentrations and isotope compositions in the ocean. We found that (i) the surface Indian Ocean has the highest levels of pollution, (ii) pollution from previous decades is sinking in the North Atlantic and Pacific Ocean, and (iii) waters carrying Pb pollution are spreading from the Southern Ocean throughout the Southern Hemisphere at intermediate depths.
Wen Yi, Xiaotong Wang, Tingkun He, Huan Liu, Zhenyu Luo, Zhaofeng Lv, and Kebin He
Earth Syst. Sci. Data, 17, 277–292, https://doi.org/10.5194/essd-17-277-2025, https://doi.org/10.5194/essd-17-277-2025, 2025
Short summary
Short summary
This study presents a detailed global dataset on ship emissions, covering the years 2013 and 2016–2021, using advanced modeling techniques. The dataset includes emissions data for four types of greenhouse gases and five types of air pollutants. The data, available for research, offer valuable insights into ship emission spatiotemporal patterns by vessel type and age, providing a solid data foundation for fine-scale scientific research and shipping emission mitigation.
Zelun Wu, Wenfang Lu, Alizée Roobaert, Luping Song, Xiao-Hai Yan, and Wei-Jun Cai
Earth Syst. Sci. Data, 17, 43–63, https://doi.org/10.5194/essd-17-43-2025, https://doi.org/10.5194/essd-17-43-2025, 2025
Short summary
Short summary
This study addresses the lack of comprehensive sea surface partial pressure of CO2 (pCO2) data in the North American Atlantic Coastal Ocean Margin (NAACOM) by developing the Reconstructed Coastal Acidification Database (ReCAD-NAACOM-pCO2). The product reconstructed sea surface pCO2 from 1993 to 2021 using machine-learning and environmental data, capturing seasonal cycles, regional variations, and long-term trends of pCO2 for coastal carbon research.
Brendan R. Carter, Jörg Schwinger, Rolf Sonnerup, Andrea J. Fassbender, Jonathan D. Sharp, and Larissa M. Dias
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-560, https://doi.org/10.5194/essd-2024-560, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We infer ocean gas exchange and circulation from ocean tracer measurements and use this to create code to estimate the amount of carbon dioxide dissolved in the ocean that is there due to human emissions of CO2 into the atmosphere. The code works across the ocean depths for the past, present, or future from information about the location, temperature, and saltiness of the seawater. We produce a data product with estimates throughout the ocean throughout the last ~300 and next ~500 years.
Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, Fei Chai, and Zhetao Tan
Earth Syst. Sci. Data, 16, 5503–5530, https://doi.org/10.5194/essd-16-5503-2024, https://doi.org/10.5194/essd-16-5503-2024, 2024
Short summary
Short summary
High-quality observations are crucial to understanding ocean oxygen changes and their impact on marine biota. We developed a quality control procedure to ensure the high quality of the heterogeneous ocean oxygen data archive and to prove data consistency. Oxygen data obtained by means of oxygen sensors on autonomous Argo floats were compared with reference data based on the chemical analysis, and estimates of the residual offsets were obtained.
Yongkai Chang, Ehui Tan, Dengzhou Gao, Cheng Liu, Zongxiao Zhang, Zhixiong Huang, Jianan Liu, Yu Han, Zifu Xu, Bin Chen, and Shuh-Ji Kao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-539, https://doi.org/10.5194/essd-2024-539, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Denitrification and anaerobic ammonium oxidation (anammox) are two important nitrogen removal pathways by converting reactive nitrogen into dinitrogen gas. Here we construct a global database on actual nitrogen loss rates, covering over 30 years of observations, measured in coastal and marine sediments. This work provides a global overview of the biogeography and potential controlling factors of denitrification and anammox, and highlights the potential applications of this database.
Riccardo Martellucci, Michele Giani, Elena Mauri, Laurent Coppola, Melf Paulsen, Marine Fourrier, Sara Pensieri, Vanessa Cardin, Carlotta Dentico, Roberto Bozzano, Carolina Cantoni, Anna Lucchetta, Alfredo Izquierdo, Miguel Bruno, and Ingunn Skjelvan
Earth Syst. Sci. Data, 16, 5333–5356, https://doi.org/10.5194/essd-16-5333-2024, https://doi.org/10.5194/essd-16-5333-2024, 2024
Short summary
Short summary
As part of the ATL2MED demonstration experiment, two autonomous surface vehicles undertook a 9-month mission from the northeastern Atlantic to the Adriatic Sea. Biofouling affected the measurement of variables such as conductivity and dissolved oxygen. COVID-19 limited the availability of discrete samples for validation. We present correction methods for salinity and dissolved oxygen. We use model products to correct salinity and apply the Argo floats in-air correction method for oxygen
Léa Olivier, Jacqueline Boutin, Gilles Reverdin, Christopher Hunt, Thomas Linkowski, Alison Chase, Nils Haentjens, Pedro C. Junger, Stephane Pesant, and Douglas Vandemark
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-452, https://doi.org/10.5194/essd-2024-452, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The air-sea CO2 flux in coastal waters plays a key role in the global carbon budget, yet remains poorly understood. In 2021, the Tara schooner collected 14,000 km of CO2 fugacity (fCO2) data along the South American coast. This dataset improves our understanding of fCO2 in the under-sampled Brazilian coastal region, and provides a unique insight into the complex biogeochemistry of the Amazon River-Ocean continuum.
Shengqian Zhou, Ying Chen, Shan Huang, Xianda Gong, Guipeng Yang, Honghai Zhang, Hartmut Herrmann, Alfred Wiedensohler, Laurent Poulain, Yan Zhang, Fanghui Wang, Zongjun Xu, and Ke Yan
Earth Syst. Sci. Data, 16, 4267–4290, https://doi.org/10.5194/essd-16-4267-2024, https://doi.org/10.5194/essd-16-4267-2024, 2024
Short summary
Short summary
Dimethyl sulfide (DMS) is a crucial natural reactive gas in the global climate system due to its great contribution to aerosols and subsequent impact on clouds over remote oceans. Leveraging machine learning techniques, we constructed a long-term global sea surface DMS gridded dataset with daily resolution. Compared to previous datasets, our new dataset holds promise for improving atmospheric chemistry modeling and advancing our comprehension of the climate effects associated with oceanic DMS.
Seyed Reza Saghravani, Michael Ernst Böttcher, Wei-Li Hong, Karol Kuliński, Aivo Lepland, Arunima Sen, and Beata Szymczycha
Earth Syst. Sci. Data, 16, 3419–3431, https://doi.org/10.5194/essd-16-3419-2024, https://doi.org/10.5194/essd-16-3419-2024, 2024
Short summary
Short summary
A comprehensive study conducted in 2021 examined the distributions of dissolved nutrients and carbon in the western Spitsbergen fjords during the high-melting season. Significant spatial variability was observed in the water column and pore water concentrations of constituents, highlighting the unique biogeochemical characteristics of each fjord and their potential impact on ecosystem functioning and oceanographic processes.
Li-Qing Jiang, Tim P. Boyer, Christopher R. Paver, Hyelim Yoo, James R. Reagan, Simone R. Alin, Leticia Barbero, Brendan R. Carter, Richard A. Feely, and Rik Wanninkhof
Earth Syst. Sci. Data, 16, 3383–3390, https://doi.org/10.5194/essd-16-3383-2024, https://doi.org/10.5194/essd-16-3383-2024, 2024
Short summary
Short summary
In this paper, we unveil a data product featuring ten coastal ocean acidification variables. These indicators are provided on 1°×1° spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 m, along the North American ocean margins.
Amanda R. Fay, David R. Munro, Galen A. McKinley, Denis Pierrot, Stewart C. Sutherland, Colm Sweeney, and Rik Wanninkhof
Earth Syst. Sci. Data, 16, 2123–2139, https://doi.org/10.5194/essd-16-2123-2024, https://doi.org/10.5194/essd-16-2123-2024, 2024
Short summary
Short summary
Presented here is a near-global monthly climatological estimate of the difference between atmosphere and ocean carbon dioxide concentrations. The ocean's ability to take up carbon, both now and in the future, is defined by this difference in concentrations. With over 30 million measurements of surface ocean carbon over the last 40 years and utilization of an extrapolation technique, a mean estimate of surface ocean ΔfCO2 is presented.
Siv K. Lauvset, Nico Lange, Toste Tanhua, Henry C. Bittig, Are Olsen, Alex Kozyr, Marta Álvarez, Kumiko Azetsu-Scott, Peter J. Brown, Brendan R. Carter, Leticia Cotrim da Cunha, Mario Hoppema, Matthew P. Humphreys, Masao Ishii, Emil Jeansson, Akihiko Murata, Jens Daniel Müller, Fiz F. Pérez, Carsten Schirnick, Reiner Steinfeldt, Toru Suzuki, Adam Ulfsbo, Anton Velo, Ryan J. Woosley, and Robert M. Key
Earth Syst. Sci. Data, 16, 2047–2072, https://doi.org/10.5194/essd-16-2047-2024, https://doi.org/10.5194/essd-16-2047-2024, 2024
Short summary
Short summary
GLODAP is a data product for ocean inorganic carbon and related biogeochemical variables measured by the chemical analysis of water bottle samples from scientific cruises. GLODAPv2.2023 is the fifth update of GLODAPv2 from 2016. The data that are included have been subjected to extensive quality controlling, including systematic evaluation of measurement biases. This version contains data from 1108 hydrographic cruises covering the world's oceans from 1972 to 2021.
Nico Lange, Björn Fiedler, Marta Álvarez, Alice Benoit-Cattin, Heather Benway, Pier Luigi Buttigieg, Laurent Coppola, Kim Currie, Susana Flecha, Dana S. Gerlach, Makio Honda, I. Emma Huertas, Siv K. Lauvset, Frank Muller-Karger, Arne Körtzinger, Kevin M. O'Brien, Sólveig R. Ólafsdóttir, Fernando C. Pacheco, Digna Rueda-Roa, Ingunn Skjelvan, Masahide Wakita, Angelicque White, and Toste Tanhua
Earth Syst. Sci. Data, 16, 1901–1931, https://doi.org/10.5194/essd-16-1901-2024, https://doi.org/10.5194/essd-16-1901-2024, 2024
Short summary
Short summary
The Synthesis Product for Ocean Time Series (SPOTS) is a novel achievement expanding and complementing the biogeochemical data landscape by providing consistent and high-quality biogeochemical time-series data from 12 ship-based fixed time-series programs. SPOTS covers multiple unique marine environments and time-series ranges, including data from 1983 to 2021. All in all, it facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations.
Sébastien Petton, Fabrice Pernet, Valérian Le Roy, Matthias Huber, Sophie Martin, Éric Macé, Yann Bozec, Stéphane Loisel, Peggy Rimmelin-Maury, Émilie Grossteffan, Michel Repecaud, Loïc Quemener, Michael Retho, Soazig Manac'h, Mathias Papin, Philippe Pineau, Thomas Lacoue-Labarthe, Jonathan Deborde, Louis Costes, Pierre Polsenaere, Loïc Rigouin, Jérémy Benhamou, Laure Gouriou, Joséphine Lequeux, Nathalie Labourdette, Nicolas Savoye, Grégory Messiaen, Elodie Foucault, Vincent Ouisse, Marion Richard, Franck Lagarde, Florian Voron, Valentin Kempf, Sébastien Mas, Léa Giannecchini, Francesca Vidussi, Behzad Mostajir, Yann Leredde, Samir Alliouane, Jean-Pierre Gattuso, and Frédéric Gazeau
Earth Syst. Sci. Data, 16, 1667–1688, https://doi.org/10.5194/essd-16-1667-2024, https://doi.org/10.5194/essd-16-1667-2024, 2024
Short summary
Short summary
Our research highlights the concerning impact of rising carbon dioxide levels on coastal areas. To better understand these changes, we've established an observation network in France. By deploying pH sensors and other monitoring equipment at key coastal sites, we're gaining valuable insights into how various factors, such as freshwater inputs, tides, temperature, and biological processes, influence ocean pH.
Christian Lønborg, Cátia Carreira, Gwenaël Abril, Susana Agustí, Valentina Amaral, Agneta Andersson, Javier Arístegui, Punyasloke Bhadury, Mariana B. Bif, Alberto V. Borges, Steven Bouillon, Maria Ll. Calleja, Luiz C. Cotovicz Jr., Stefano Cozzi, Maryló Doval, Carlos M. Duarte, Bradley Eyre, Cédric G. Fichot, E. Elena García-Martín, Alexandra Garzon-Garcia, Michele Giani, Rafael Gonçalves-Araujo, Renee Gruber, Dennis A. Hansell, Fuminori Hashihama, Ding He, Johnna M. Holding, William R. Hunter, J. Severino P. Ibánhez, Valeria Ibello, Shan Jiang, Guebuem Kim, Katja Klun, Piotr Kowalczuk, Atsushi Kubo, Choon-Weng Lee, Cláudia B. Lopes, Federica Maggioni, Paolo Magni, Celia Marrase, Patrick Martin, S. Leigh McCallister, Roisin McCallum, Patricia M. Medeiros, Xosé Anxelu G. Morán, Frank E. Muller-Karger, Allison Myers-Pigg, Marit Norli, Joanne M. Oakes, Helena Osterholz, Hyekyung Park, Maria Lund Paulsen, Judith A. Rosentreter, Jeff D. Ross, Digna Rueda-Roa, Chiara Santinelli, Yuan Shen, Eva Teira, Tinkara Tinta, Guenther Uher, Masahide Wakita, Nicholas Ward, Kenta Watanabe, Yu Xin, Youhei Yamashita, Liyang Yang, Jacob Yeo, Huamao Yuan, Qiang Zheng, and Xosé Antón Álvarez-Salgado
Earth Syst. Sci. Data, 16, 1107–1119, https://doi.org/10.5194/essd-16-1107-2024, https://doi.org/10.5194/essd-16-1107-2024, 2024
Short summary
Short summary
In this paper, we present the first edition of a global database compiling previously published and unpublished measurements of dissolved organic matter (DOM) collected in coastal waters (CoastDOM v1). Overall, the CoastDOM v1 dataset will be useful to identify global spatial and temporal patterns and to facilitate reuse in studies aimed at better characterizing local biogeochemical processes and identifying a baseline for modelling future changes in coastal waters.
Simone R. Alin, Jan A. Newton, Richard A. Feely, Dana Greeley, Beth Curry, Julian Herndon, and Mark Warner
Earth Syst. Sci. Data, 16, 837–865, https://doi.org/10.5194/essd-16-837-2024, https://doi.org/10.5194/essd-16-837-2024, 2024
Short summary
Short summary
The Salish cruise data product provides 2008–2018 oceanographic data from the southern Salish Sea and nearby coastal sampling stations. Temperature, salinity, oxygen, nutrient, and dissolved inorganic carbon measurements from 715 oceanographic profiles will facilitate further study of ocean acidification, hypoxia, and marine heatwave impacts in this region. Three subsets of the compiled datasets from 35 cruises are available with consistent formatting and multiple commonly used units.
Henry C. Bittig, Erik Jacobs, Thomas Neumann, and Gregor Rehder
Earth Syst. Sci. Data, 16, 753–773, https://doi.org/10.5194/essd-16-753-2024, https://doi.org/10.5194/essd-16-753-2024, 2024
Short summary
Short summary
We present a pCO2 climatology of the Baltic Sea using a new approach to extrapolate from individual observations to the entire Baltic Sea. The extrapolation approach uses (a) a model to inform on how data at one location are connected to data at other locations, together with (b) very accurate pCO2 observations from 2003 to 2021 as the base data. The climatology can be used e.g. to assess uptake and release of CO2 or to identify extreme events.
Francesco Placenti, Marco Torri, Katrin Schroeder, Mireno Borghini, Gabriella Cerrati, Angela Cuttitta, Vincenzo Tancredi, Carmelo Buscaino, and Bernardo Patti
Earth Syst. Sci. Data, 16, 743–752, https://doi.org/10.5194/essd-16-743-2024, https://doi.org/10.5194/essd-16-743-2024, 2024
Short summary
Short summary
Oceanographic surveys were conducted in the Strait of Sicily between 2010 and 2021. This paper provides a description of the time series of nutrients and hydrological data collected in this zone. The dataset fills an important gap in field observations of a crucial area where exchanges with the Mediterranean sub-basin take place, providing support for studies aimed at describing ongoing processes and at realizing reliable projections of the effects of these processes in the near future.
Natalie M. Monacci, Jessica N. Cross, Wiley Evans, Jeremy T. Mathis, and Hongjie Wang
Earth Syst. Sci. Data, 16, 647–665, https://doi.org/10.5194/essd-16-647-2024, https://doi.org/10.5194/essd-16-647-2024, 2024
Short summary
Short summary
As carbon dioxide is released into the air through human-generated activity, about one third dissolves into the surface water of oceans, lowering pH and increasing acidity. This is known as ocean acidification. We merged 10 years of ocean carbon data and made them publicly available for adaptation planning during a time of change. The data confirmed that Alaska is already experiencing the effects of ocean acidification due to naturally cold water, high productivity, and circulation patterns.
Alizée Roobaert, Pierre Regnier, Peter Landschützer, and Goulven G. Laruelle
Earth Syst. Sci. Data, 16, 421–441, https://doi.org/10.5194/essd-16-421-2024, https://doi.org/10.5194/essd-16-421-2024, 2024
Short summary
Short summary
The quantification of the coastal air–sea CO2 exchange (FCO2) has improved in recent years, but its multiannual variability remains unclear. This study, based on interpolated observations, reconstructs the longest global time series of coastal FCO2 (1982–2020). Results show the coastal ocean acts as a CO2 sink, with increasing intensity over time. This new coastal FCO2-product allows establishing regional carbon budgets and provides new constraints for closing the global carbon cycle.
Esther G. Kennedy, Meghan Zulian, Sara L. Hamilton, Tessa M. Hill, Manuel Delgado, Carina R. Fish, Brian Gaylord, Kristy J. Kroeker, Hannah M. Palmer, Aurora M. Ricart, Eric Sanford, Ana K. Spalding, Melissa Ward, Guadalupe Carrasco, Meredith Elliott, Genece V. Grisby, Evan Harris, Jaime Jahncke, Catherine N. Rocheleau, Sebastian Westerink, and Maddie I. Wilmot
Earth Syst. Sci. Data, 16, 219–243, https://doi.org/10.5194/essd-16-219-2024, https://doi.org/10.5194/essd-16-219-2024, 2024
Short summary
Short summary
We present a new synthesis of oceanographic observations along the US West Coast that has been optimized for multiparameter investigations of coastal warming, deoxygenation, and acidification risk. This synthesis includes both previously published and new observations, all of which have been consistently formatted and quality-controlled to facilitate high-resolution investigations of climate risks and consequences across a wide range of spatial and temporal scales.
Thi-Tuyet-Trang Chau, Marion Gehlen, Nicolas Metzl, and Frédéric Chevallier
Earth Syst. Sci. Data, 16, 121–160, https://doi.org/10.5194/essd-16-121-2024, https://doi.org/10.5194/essd-16-121-2024, 2024
Short summary
Short summary
CMEMS-LSCE leads as the first global observation-based reconstructions of six carbonate system variables for the years 1985–2021 at monthly and 0.25° resolutions. The high-resolution reconstructions outperform their 1° counterpart in reproducing horizontal and temporal gradients of observations over various oceanic regions to nearshore time series stations. New datasets can be exploited in numerous studies, including monitoring changes in ocean carbon uptake and ocean acidification.
Nicolas Metzl, Jonathan Fin, Claire Lo Monaco, Claude Mignon, Samir Alliouane, David Antoine, Guillaume Bourdin, Jacqueline Boutin, Yann Bozec, Pascal Conan, Laurent Coppola, Frédéric Diaz, Eric Douville, Xavier Durrieu de Madron, Jean-Pierre Gattuso, Frédéric Gazeau, Melek Golbol, Bruno Lansard, Dominique Lefèvre, Nathalie Lefèvre, Fabien Lombard, Férial Louanchi, Liliane Merlivat, Léa Olivier, Anne Petrenko, Sébastien Petton, Mireille Pujo-Pay, Christophe Rabouille, Gilles Reverdin, Céline Ridame, Aline Tribollet, Vincenzo Vellucci, Thibaut Wagener, and Cathy Wimart-Rousseau
Earth Syst. Sci. Data, 16, 89–120, https://doi.org/10.5194/essd-16-89-2024, https://doi.org/10.5194/essd-16-89-2024, 2024
Short summary
Short summary
This work presents a synthesis of 44 000 total alkalinity and dissolved inorganic carbon observations obtained between 1993 and 2022 in the Global Ocean and the Mediterranean Sea at the surface and in the water column. Seawater samples were measured using the same method and calibrated with international Certified Reference Material. We describe the data assemblage, quality control and some potential uses of this dataset.
Céline Heuzé, Oliver Huhn, Maren Walter, Natalia Sukhikh, Salar Karam, Wiebke Körtke, Myriel Vredenborg, Klaus Bulsiewicz, Jürgen Sültenfuß, Ying-Chih Fang, Christian Mertens, Benjamin Rabe, Sandra Tippenhauer, Jacob Allerholt, Hailun He, David Kuhlmey, Ivan Kuznetsov, and Maria Mallet
Earth Syst. Sci. Data, 15, 5517–5534, https://doi.org/10.5194/essd-15-5517-2023, https://doi.org/10.5194/essd-15-5517-2023, 2023
Short summary
Short summary
Gases dissolved in the ocean water not used by the ecosystem (or "passive tracers") are invaluable to track water over long distances and investigate the processes that modify its properties. Unfortunately, especially so in the ice-covered Arctic Ocean, such gas measurements are sparse. We here present a data set of several passive tracers (anthropogenic gases, noble gases and their isotopes) collected over the full ocean depth, weekly, during the 1-year drift in the Arctic during MOSAiC.
Weiyi Tang, Bess B. Ward, Michael Beman, Laura Bristow, Darren Clark, Sarah Fawcett, Claudia Frey, François Fripiat, Gerhard J. Herndl, Mhlangabezi Mdutyana, Fabien Paulot, Xuefeng Peng, Alyson E. Santoro, Takuhei Shiozaki, Eva Sintes, Charles Stock, Xin Sun, Xianhui S. Wan, Min N. Xu, and Yao Zhang
Earth Syst. Sci. Data, 15, 5039–5077, https://doi.org/10.5194/essd-15-5039-2023, https://doi.org/10.5194/essd-15-5039-2023, 2023
Short summary
Short summary
Nitrification and nitrifiers play an important role in marine nitrogen and carbon cycles by converting ammonium to nitrite and nitrate. Nitrification could affect microbial community structure, marine productivity, and the production of nitrous oxide – a powerful greenhouse gas. We introduce the newly constructed database of nitrification and nitrifiers in the marine water column and guide future research efforts in field observations and model development of nitrification.
Jonathan D. Sharp, Andrea J. Fassbender, Brendan R. Carter, Gregory C. Johnson, Cristina Schultz, and John P. Dunne
Earth Syst. Sci. Data, 15, 4481–4518, https://doi.org/10.5194/essd-15-4481-2023, https://doi.org/10.5194/essd-15-4481-2023, 2023
Short summary
Short summary
Dissolved oxygen content is a critical metric of ocean health. Recently, expanding fleets of autonomous platforms that measure oxygen in the ocean have produced a wealth of new data. We leverage machine learning to take advantage of this growing global dataset, producing a gridded data product of ocean interior dissolved oxygen at monthly resolution over nearly 2 decades. This work provides novel information for investigations of spatial, seasonal, and interannual variability in ocean oxygen.
Olivia Gibb, Frédéric Cyr, Kumiko Azetsu-Scott, Joël Chassé, Darlene Childs, Carrie-Ellen Gabriel, Peter S. Galbraith, Gary Maillet, Pierre Pepin, Stephen Punshon, and Michel Starr
Earth Syst. Sci. Data, 15, 4127–4162, https://doi.org/10.5194/essd-15-4127-2023, https://doi.org/10.5194/essd-15-4127-2023, 2023
Short summary
Short summary
The ocean absorbs large quantities of carbon dioxide (CO2) released into the atmosphere as a result of the burning of fossil fuels. This, in turn, causes ocean acidification, which poses a major threat to global ocean ecosystems. In this study, we compiled 9 years (2014–2022) of ocean carbonate data (i.e., ocean acidification parameters) collected in Atlantic Canada as part of the Atlantic Zone Monitoring Program.
Öykü Z. Mete, Adam V. Subhas, Heather H. Kim, Ann G. Dunlea, Laura M. Whitmore, Alan M. Shiller, Melissa Gilbert, William D. Leavitt, and Tristan J. Horner
Earth Syst. Sci. Data, 15, 4023–4045, https://doi.org/10.5194/essd-15-4023-2023, https://doi.org/10.5194/essd-15-4023-2023, 2023
Short summary
Short summary
We present results from a machine learning model that accurately predicts dissolved barium concentrations for the global ocean. Our results reveal that the whole-ocean barium inventory is significantly lower than previously thought and that the deep ocean below 1000 m is at equilibrium with respect to barite. The model output can be used for a number of applications, including intercomparison, interpolation, and identification of regions warranting additional investigation.
Zhibo Shao, Yangchun Xu, Hua Wang, Weicheng Luo, Lice Wang, Yuhong Huang, Nona Sheila R. Agawin, Ayaz Ahmed, Mar Benavides, Mikkel Bentzon-Tilia, Ilana Berman-Frank, Hugo Berthelot, Isabelle C. Biegala, Mariana B. Bif, Antonio Bode, Sophie Bonnet, Deborah A. Bronk, Mark V. Brown, Lisa Campbell, Douglas G. Capone, Edward J. Carpenter, Nicolas Cassar, Bonnie X. Chang, Dreux Chappell, Yuh-ling Lee Chen, Matthew J. Church, Francisco M. Cornejo-Castillo, Amália Maria Sacilotto Detoni, Scott C. Doney, Cecile Dupouy, Marta Estrada, Camila Fernandez, Bieito Fernández-Castro, Debany Fonseca-Batista, Rachel A. Foster, Ken Furuya, Nicole Garcia, Kanji Goto, Jesús Gago, Mary R. Gradoville, M. Robert Hamersley, Britt A. Henke, Cora Hörstmann, Amal Jayakumar, Zhibing Jiang, Shuh-Ji Kao, David M. Karl, Leila R. Kittu, Angela N. Knapp, Sanjeev Kumar, Julie LaRoche, Hongbin Liu, Jiaxing Liu, Caroline Lory, Carolin R. Löscher, Emilio Marañón, Lauren F. Messer, Matthew M. Mills, Wiebke Mohr, Pia H. Moisander, Claire Mahaffey, Robert Moore, Beatriz Mouriño-Carballido, Margaret R. Mulholland, Shin-ichiro Nakaoka, Joseph A. Needoba, Eric J. Raes, Eyal Rahav, Teodoro Ramírez-Cárdenas, Christian Furbo Reeder, Lasse Riemann, Virginie Riou, Julie C. Robidart, Vedula V. S. S. Sarma, Takuya Sato, Himanshu Saxena, Corday Selden, Justin R. Seymour, Dalin Shi, Takuhei Shiozaki, Arvind Singh, Rachel E. Sipler, Jun Sun, Koji Suzuki, Kazutaka Takahashi, Yehui Tan, Weiyi Tang, Jean-Éric Tremblay, Kendra Turk-Kubo, Zuozhu Wen, Angelicque E. White, Samuel T. Wilson, Takashi Yoshida, Jonathan P. Zehr, Run Zhang, Yao Zhang, and Ya-Wei Luo
Earth Syst. Sci. Data, 15, 3673–3709, https://doi.org/10.5194/essd-15-3673-2023, https://doi.org/10.5194/essd-15-3673-2023, 2023
Short summary
Short summary
N2 fixation by marine diazotrophs is an important bioavailable N source to the global ocean. This updated global oceanic diazotroph database increases the number of in situ measurements of N2 fixation rates, diazotrophic cell abundances, and nifH gene copy abundances by 184 %, 86 %, and 809 %, respectively. Using the updated database, the global marine N2 fixation rate is estimated at 223 ± 30 Tg N yr−1, which triplicates that using the original database.
Jean-Pierre Gattuso, Samir Alliouane, and Philipp Fischer
Earth Syst. Sci. Data, 15, 2809–2825, https://doi.org/10.5194/essd-15-2809-2023, https://doi.org/10.5194/essd-15-2809-2023, 2023
Short summary
Short summary
The Arctic Ocean is subject to high rates of ocean warming and acidification, with critical implications for marine organisms, ecosystems and the services they provide. We report here on the first high-frequency (1 h), multi-year (5 years) dataset of the carbonate system at a coastal site in a high-Arctic fjord (Kongsfjorden, Svalbard). This site is a significant sink for CO2 every month of the year (9 to 17 mol m-2 yr-1). The saturation state of aragonite can be as low as 1.3.
Richard P. Sims, Thomas M. Holding, Peter E. Land, Jean-Francois Piolle, Hannah L. Green, and Jamie D. Shutler
Earth Syst. Sci. Data, 15, 2499–2516, https://doi.org/10.5194/essd-15-2499-2023, https://doi.org/10.5194/essd-15-2499-2023, 2023
Short summary
Short summary
The flow of carbon between the land and ocean is poorly quantified with existing measurements. It is not clear how seasonality and long-term variability impact this flow of carbon. Here, we demonstrate how satellite observations can be used to create decadal time series of the inorganic carbonate system in the Amazon and Congo River outflows.
Yayoi Inomata and Michio Aoyama
Earth Syst. Sci. Data, 15, 1969–2007, https://doi.org/10.5194/essd-15-1969-2023, https://doi.org/10.5194/essd-15-1969-2023, 2023
Short summary
Short summary
The behavior of 137Cs in surface seawater in the global ocean was analyzed by using the HAMGlobal2021 database. Approximately 32 % of 137Cs existed in the surface seawater in 1970. The 137Cs released into the North Pacific Ocean by large-scale nuclear weapons tests was transported to the Indian Ocean and then the Atlantic Ocean on a 4–5 decadal timescale, whereas 137Cs released from nuclear reprocessing plants was transported northward to the Arctic Ocean on a decadal scale.
Zhixuan Wang, Guizhi Wang, Xianghui Guo, Yan Bai, Yi Xu, and Minhan Dai
Earth Syst. Sci. Data, 15, 1711–1731, https://doi.org/10.5194/essd-15-1711-2023, https://doi.org/10.5194/essd-15-1711-2023, 2023
Short summary
Short summary
We reconstructed monthly sea surface pCO2 data with a high spatial resolution in the South China Sea (SCS) from 2003 to 2020. We validate our reconstruction with three independent testing datasets and present a new method to assess the uncertainty of the data. The results strongly suggest that our reconstruction effectively captures the main features of the spatiotemporal patterns of pCO2 in the SCS. Using this dataset, we found that the SCS is overall a weak source of atmospheric CO2.
Peter Edward Land, Helen S. Findlay, Jamie D. Shutler, Jean-Francois Piolle, Richard Sims, Hannah Green, Vassilis Kitidis, Alexander Polukhin, and Irina I. Pipko
Earth Syst. Sci. Data, 15, 921–947, https://doi.org/10.5194/essd-15-921-2023, https://doi.org/10.5194/essd-15-921-2023, 2023
Short summary
Short summary
Measurements of the ocean’s carbonate system (e.g. CO2 and pH) have increased greatly in recent years, resulting in a need to combine these data with satellite measurements and model results, so they can be used to test predictions of how the ocean reacts to changes such as absorption of the CO2 emitted by humans. We show a method of combining data into regions of interest (100 km circles over a 10 d period) and apply it globally to produce a harmonised and easy-to-use data archive.
Giulia Leone, Ana I. Catarino, Liesbeth De Keukelaere, Mattias Bossaer, Els Knaeps, and Gert Everaert
Earth Syst. Sci. Data, 15, 745–752, https://doi.org/10.5194/essd-15-745-2023, https://doi.org/10.5194/essd-15-745-2023, 2023
Short summary
Short summary
This paper illustrates a dataset of hyperspectral reflectance measurements of macroplastics. Plastic samples consisted of pristine, artificially weathered, and biofouled plastic items and field plastic debris. Samples were measured in dry conditions and a subset of plastics in wet and submerged conditions. This dataset can be used to better understand plastic optical features when exposed to natural agents and to support the development of algorithms for monitoring environmental plastics.
Michael J. Whitehouse, Katharine R. Hendry, Geraint A. Tarling, Sally E. Thorpe, and Petra ten Hoopen
Earth Syst. Sci. Data, 15, 211–224, https://doi.org/10.5194/essd-15-211-2023, https://doi.org/10.5194/essd-15-211-2023, 2023
Short summary
Short summary
We present a database of Southern Ocean macronutrient, temperature and salinity measurements collected on 20 oceanographic cruises between 1980 and 2009. Vertical profiles and underway surface measurements were collected year-round as part of an integrated ecosystem study. Our data provide a novel view of biogeochemical cycling in biologically productive regions across a critical period in recent climate history and will contribute to a better understanding of the drivers of primary production.
Siv K. Lauvset, Nico Lange, Toste Tanhua, Henry C. Bittig, Are Olsen, Alex Kozyr, Simone Alin, Marta Álvarez, Kumiko Azetsu-Scott, Leticia Barbero, Susan Becker, Peter J. Brown, Brendan R. Carter, Leticia Cotrim da Cunha, Richard A. Feely, Mario Hoppema, Matthew P. Humphreys, Masao Ishii, Emil Jeansson, Li-Qing Jiang, Steve D. Jones, Claire Lo Monaco, Akihiko Murata, Jens Daniel Müller, Fiz F. Pérez, Benjamin Pfeil, Carsten Schirnick, Reiner Steinfeldt, Toru Suzuki, Bronte Tilbrook, Adam Ulfsbo, Anton Velo, Ryan J. Woosley, and Robert M. Key
Earth Syst. Sci. Data, 14, 5543–5572, https://doi.org/10.5194/essd-14-5543-2022, https://doi.org/10.5194/essd-14-5543-2022, 2022
Short summary
Short summary
GLODAP is a data product for ocean inorganic carbon and related biogeochemical variables measured by the chemical analysis of water bottle samples from scientific cruises. GLODAPv2.2022 is the fourth update of GLODAPv2 from 2016. The data that are included have been subjected to extensive quality controlling, including systematic evaluation of measurement biases. This version contains data from 1085 hydrographic cruises covering the world's oceans from 1972 to 2021.
Zhour Najoui, Nellya Amoussou, Serge Riazanoff, Guillaume Aurel, and Frédéric Frappart
Earth Syst. Sci. Data, 14, 4569–4588, https://doi.org/10.5194/essd-14-4569-2022, https://doi.org/10.5194/essd-14-4569-2022, 2022
Short summary
Short summary
Oil spills could have serious repercussions for both the marine environment and ecosystem. The Gulf of Guinea is a very active area with respect to maritime traffic as well as oil and gas exploitation (platforms). As a result, the region is subject to a large number of oil pollution events. This study aims to detect oil slicks in the Gulf of Guinea and analyse their spatial and temporal distribution using satellite data.
Cited articles
Akbari, E., Alavipanah, S., Jeihouni, M., Hajeb, M., Haase, D., and Alavipanah, S.: A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods, Water, 9, 936, https://doi.org/10.3390/w9120936, 2017. a
Ali, M. M., Swain, D., and Weller, R. A.: Estimation of Ocean Subsurface Thermal Structure from Surface Parameters: A Neural Network Approach, Geophys. Res. Lett., 31, 2004GL021192, https://doi.org/10.1029/2004GL021192, 2004. a
Altieri, K. E., Fawcett, S. E., Peters, A. J., Sigman, D. M., and Hastings, M. G.: Marine Biogenic Source of Atmospheric Organic Nitrogen in the Subtropical North Atlantic, P. Natl. Acad. Sci. USA, 113, 925–930, https://doi.org/10.1073/pnas.1516847113, 2016. a
Altieri, K. E., Fawcett, S. E., and Hastings, M. G.: Reactive Nitrogen Cycling in the Atmosphere and Ocean, Annu. Rev. Earth Planet. Sci., 49, 523–550, https://doi.org/10.1146/annurev-earth-083120-052147, 2021. a
Ansper, A. and Alikas, K.: Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes, Remote Sens., 11, 64, https://doi.org/10.3390/rs11010064, 2019. a
Ascani, F., Richards, K. J., Firing, E., Grant, S., Johnson, K. S., Jia, Y., Lukas, R., and Karl, D. M.: Physical and Biological Controls of Nitrate Concentrations in the Upper Subtropical North Pacific Ocean, Deep-Sea Res. Pt. II, 93, 119–134, https://doi.org/10.1016/j.dsr2.2013.01.034, 2013. a
Asdar, S., Ciani, D., and Buongiorno Nardelli, B.: 3D Reconstruction of Horizontal and Vertical Quasi-Geostrophic Currents in the North Atlantic Ocean, Earth Syst. Sci. Data, 16, 1029–1046, https://doi.org/10.5194/essd-16-1029-2024, 2024. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Baretta, J. W., Ebenhöh, W., and Ruardij, P.: The European Regional Seas Ecosystem Model, a Complex Marine Ecosystem Model, Neth. J. Sea Res., 33, 233–246, https://doi.org/10.1016/0077-7579(95)90047-0, 1995. a
Ben-Ezra, T., Blachinsky, A., Gozali, S., Tsemel, A., Fadida, Y., Tchernov, D., Lehahn, Y., Tsagaraki, T. M., Berman-Frank, I., and Krom, M.: Interannual Changes in Nutrient and Phytoplankton Dynamics in the Eastern Mediterranean Sea (EMS) Predict the Consequences of Climate Change; Results from the Sdot-Yam Time-Series Station 2018–2022, bioRxiv [preprint], https://doi.org/10.1101/2024.06.24.600321, 2024. a
Berglund, S., Döös, K., Groeskamp, S., and McDougall, T.: North Atlantic Ocean Circulation and Related Exchange of Heat and Salt Between Water Masses, Geophys. Res. Lett., 50, e2022GL100989, https://doi.org/10.1029/2022GL100989, 2023. a, b, c
Bishop, C. M.: Neural Networks for Pattern Recognition., Oxford University Press, ISBN 0-19-853864-2, 1995. a
Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., Körtzinger, A., and Gattuso, J.-P.: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations from T, S, and O2 Data Using Bayesian Neural Networks, Front. Mar. Sci., 5, 328, https://doi.org/10.3389/fmars.2018.00328, 2018. a
Bristow, L. A., Mohr, W., Ahmerkamp, S., and Kuypers, M. M.: Nutrients That Limit Growth in the Ocean, Curr. Biol., 27, R474–R478, https://doi.org/10.1016/j.cub.2017.03.030, 2017. a
Bruggeman, J. and Bolding, K.: A General Framework for Aquatic Biogeochemical Models, Environ. Model. Softw., 61, 249–265, https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a
Buongiorno Nardelli, B.: A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements, Remote Sens., 12, 3151, https://doi.org/10.3390/rs12193151, 2020. a, b
Cao, Z., Ma, R., Duan, H., Pahlevan, N., Melack, J., Shen, M., and Xue, K.: A Machine Learning Approach to Estimate Chlorophyll-a from Landsat-8 Measurements in Inland Lakes, Remote Sens. Environ., 248, 111974, https://doi.org/10.1016/j.rse.2020.111974, 2020. a
Chang, N.-B., Xuan, Z., and Yang, Y. J.: Exploring Spatiotemporal Patterns of Phosphorus Concentrations in a Coastal Bay with MODIS Images and Machine Learning Models, Remote Sens. Environ., 134, 100–110, https://doi.org/10.1016/j.rse.2013.03.002, 2013. a
Chau, S. L., Hu, R., Gonzalez, J., and Sejdinovic, D.: RKHS-SHAP: Shapley Values for Kernel Methods, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.09167, 2022. a
Chen, S., Hu, C., Barnes, B. B., Xie, Y., Lin, G., and Qiu, Z.: Improving Ocean Color Data Coverage through Machine Learning, Remote Sens. Environ., 222, 286–302, https://doi.org/10.1016/j.rse.2018.12.023, 2019. a
Chen, S., Meng, Y., Lin, S., Yu, Y., and Xi, J.: Estimation of Sea Surface Nitrate from Space: Current Status and Future Potential, Sci. Total Environ., 899, 165690, https://doi.org/10.1016/j.scitotenv.2023.165690, 2023. a, b
Claustre, H., Johnson, K. S., and Takeshita, Y.: Observing the Global Ocean with Biogeochemical-Argo, Annu. Rev. Mar. Sci., 12, 23–48, https://doi.org/10.1146/annurev-marine-010419-010956, 2020. a, b
Copernicus: Global Ocean Biogeochemistry Hindcast, Copernicus [data set], https://doi.org/10.48670/moi-00019, 2024. a
D'Ortenzio, F. and Ribera d'Alcalà, M.: On the Trophic Regimes of the Mediterranean Sea: A Satellite Analysis, Biogeosciences, 6, 139–148, https://doi.org/10.5194/bg-6-139-2009, 2009. a
Du, C., Li, Y., Lyu, H., Liu, N., Zheng, Z., and Li, Y.: Remote Estimation of Total Phosphorus Concentration in the Taihu Lake Using a Semi-Analytical Model, Int. J. Remote Sens., 41, 7993–8013, https://doi.org/10.1080/01431161.2020.1767826, 2020. a
Eppley, R. W. and Peterson, B. J.: Particulate Organic Matter Flux and Planktonic New Production in the Deep Ocean, Nature, 282, 677–680, https://doi.org/10.1038/282677a0, 1979. a
Fisher, R. A. and Russell, E. J.: On the Mathematical Foundations of Theoretical Statistics, Philos. T. Roy. Soc. Lond. A, 222, 309–368, https://doi.org/10.1098/rsta.1922.0009, 1997. a
Fourrier, M., Coppola, L., Claustre, H., D'Ortenzio, F., and Gattuso, J. P.: A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED, Front. Mar. Sci., 7, 620, https://doi.org/10.3389/fmars.2020.00620, 2020. a
Fourrier, M., Coppola, L., D'Ortenzio, F., Migon, C., and Gattuso, J.-P.: Impact of Intermittent Convection in the Northwestern Mediterranean Sea on Oxygen Content, Nutrients, and the Carbonate System, J. Geophys. Res.-Oceans, 127, e2022JC018615, https://doi.org/10.1029/2022JC018615, 2022. a
Friedlingstein, P., O'sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., and Sitch, S.: Global Carbon Budget 2020, Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, 2020. a
Fripiat, F., Martínez-García, A., Marconi, D., Fawcett, S. E., Kopf, S. H., Luu, V. H., Rafter, P. A., Zhang, R., Sigman, D. M., and Haug, G. H.: Nitrogen Isotopic Constraints on Nutrient Transport to the Upper Ocean, Nat. Geosci., 14, 855–861, https://doi.org/10.1038/s41561-021-00836-8, 2021. a
Gao, H., Huang, B., Chen, G., Xia, L., and Radenkovic, M.: Deep Learning Solver Unites SDGSAT-1 Observations and Navier–Stokes Theory for Oceanic Vortex Streets, Remote Sens. Environ., 315, 114425, https://doi.org/10.1016/j.rse.2024.114425, 2024. a
Gattuso, J.-P., Frankignoulle, M., and Wollast, R.: Carbon and Carbonate Metabolism in Coastal Aquatic Ecosystems, Annu. Rev. Ecol. Syst., 29, 405–434, https://doi.org/10.1146/annurev.ecolsys.29.1.405, 1998. a
Goes, J. I., Saino, T., Oaku, H., and Jiang, D. L.: A Method for Estimating Sea Surface Nitrate Concentrations from Remotely Sensed SST and Chlorophyll Aa Case Study for the North Pacific Ocean Using OCTS/ADEOS Data, IEEE T. Geosci. Remote, 37, 1633–1644, https://doi.org/10.1109/36.763279, 1999. a, b, c, d, e
Goes, J. I., Saino, T., Oaku, H., Ishizaka, J., Wong, C. S., and Nojiri, Y.: Basin Scale Estimates of Sea Surface Nitrate and New Production from Remotely Sensed Sea Surface Temperature and Chlorophyll, Geophys. Res. Lett., 27, 1263–1266, https://doi.org/10.1029/1999GL002353, 2000. a, b, c, d
Gregg, W. W., Conkright, M. E., Ginoux, P., O'Reilly, J. E., and Casey, N. W.: Ocean Primary Production and Climate: Global Decadal Changes, Geophys. Res. Lett., 30, 1089, https://doi.org/10.1029/2003GL016889, 2003. a
Hagan, M. T., Demuth, H. B., and Beale, M.: Neural Network Design, PWS Publishing Co., ISBN 0-534-94332-2, 1997. a
Harkort, L. and Duan, Z.: Estimation of Dissolved Organic Carbon from Inland Waters at a Large Scale Using Satellite Data and Machine Learning Methods, Water Res., 229, 119478, https://doi.org/10.1016/j.watres.2022.119478, 2023. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Holt, J., Harle, J., Proctor, R., Michel, S., Ashworth, M., Batstone, C., Allen, I., Holmes, R., Smyth, T., and Haines, K.: Modelling the Global Coastal Ocean, Philos. T. Roy. Soc. A, 367, 939–951, https://doi.org/10.1098/rsta.2008.0210, 2009. a
Holt, J., Butenschön, M., Wakelin, S. L., Artioli, Y., and Allen, J. I.: Oceanic Controls on the Primary Production of the Northwest European Continental Shelf: Model Experiments under Recent Past Conditions and a Potential Future Scenario, Biogeosciences, 9, 97–117, https://doi.org/10.5194/bg-9-97-2012, 2012. a
Hu, Q., Chen, X., Bai, Y., He, X., Li, T., and Pan, D.: Reconstruction of 3-D Ocean Chlorophyll a Structure in the Northern Indian Ocean Using Satellite and BGC-Argo Data, IEEE T. Geosci. Remote, 61, 1–13, https://doi.org/10.1109/TGRS.2022.3233385, 2023. a, b
Huang, J., Wang, D., Gong, F., Bai, Y., and He, X.: Changes in Nutrient Concentrations in Shenzhen Bay Detected Using Landsat Imagery between 1988 and 2020, Remote Sens., 13, 3469, https://doi.org/10.3390/rs13173469, 2021. a
Hutchins, D. A. and Capone, D. G.: The Marine Nitrogen Cycle: New Developments and Global Change, Nat. Rev. Microbiol., 20, 401–414, https://doi.org/10.1038/s41579-022-00687-z, 2022. a
Johnson, K., Maurer, T., Plant, J., Bittig, H., Schallenberg, C., and Schmechtig, C.: BGC-Argo Quality Control Manual for Nitrate Concentration, ARCHIMER, https://doi.org/10.13155/84370, 2021. a
Johnson, K. S., Plant, J. N., Coletti, L. J., Jannasch, H. W., Sakamoto, C. M., Riser, S. C., Swift, D. D., Williams, N. L., Boss, E., Haëntjens, N., Talley, L. D., and Sarmiento, J. L.: Biogeochemical Sensor Performance in the SOCCOM Profiling Float Array, J. Geophys. Res.-Oceans, 122, 6416–6436, https://doi.org/10.1002/2017JC012838, 2017. a
Johnson, K. S., Plant, J. N., Sakamoto, C., Maurer, T. L., Pasqueron De Fommervault, O., Serra, R., D'Ortenzio, F., Schmechtig, C., Claustre, H., and Poteau, A.: Processing BGC-Argo Nitrate Concentration at the DAC Level, ARCHIMER, https://doi.org/10.13155/46121, 2024. a
Joo, H., Son, S., Park, J.-W., Kang, J. J., Jeong, J.-Y., Lee, C. I., Kang, C.-K., and Lee, S. H.: Long-Term Pattern of Primary Productivity in the East/Japan Sea Based on Ocean Color Data Derived from MODIS-aqua, Remote Sens., 8, 25, https://doi.org/10.3390/rs8010025, 2016. a
Joo, H., Lee, D., Kang, J. J., Lee, J. H., Jeong, J.-Y., Son, S.-H., Kwon, J.-I., and Lee, S. H.: Inter-Annual Variation of the Annual New Production of Phytoplankton in the Southwestern East/Japan Sea Estimated from Satellite-Derived Surface Nitrate Concentration, J. Coast. Res., 85, 336–340, https://doi.org/10.2112/SI85-068.1, 2018. a
Kamykowski, D., Zentara, S.-J., Morrison, J. M., and Switzer, A. C.: Dynamic Global Patterns of Nitrate, Phosphate, Silicate, and Iron Availability and Phytoplankton Community Composition from Remote Sensing Data, Global Biogeochem. Cy., 16, 25-1–25-29, https://doi.org/10.1029/2001GB001640, 2002. a
Kay, S. and Butenschön, M.: Projections of Change in Key Ecosystem Indicators for Planning and Management of Marine Protected Areas: An Example Study for European Seas, Estuar. Coast. Shelf Sci., 201, 172–184, https://doi.org/10.1016/j.ecss.2016.03.003, 2018. a
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R.: Overcoming Catastrophic Forgetting in Neural Networks, P. Natl. Acad. Sci. USA, 114, 3521–3526, https://doi.org/10.1073/pnas.1611835114, 2017. a, b, c, d, e
Kudela, R. M. and Dugdale, R. C.: Nutrient Regulation of Phytoplankton Productivity in Monterey Bay, California, Deep-Sea Res. Pt. II, 47, 1023–1053, https://doi.org/10.1016/S0967-0645(99)00135-6, 2000. a, b, c
Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Álvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim Da Cunha, L., Feely, R. A., Van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterström, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., Pérez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An Updated Version of the Global Interior Ocean Biogeochemical Data Product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565–5589, https://doi.org/10.5194/essd-13-5565-2021, 2021. a
Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Alin, S., Álvarez, M., Azetsu-Scott, K., Barbero, L., Becker, S., Brown, P. J., Carter, B. R., Da Cunha, L. C., Feely, R. A., Hoppema, M., Humphreys, M. P., Ishii, M., Jeansson, E., Jiang, L.-Q., Jones, S. D., Lo Monaco, C., Murata, A., Müller, J. D., Pérez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Ulfsbo, A., Velo, A., Woosley, R. J., and Key, R. M.: GLODAPv2.2022: The Latest Version of the Global Interior Ocean Biogeochemical Data Product, Earth Syst. Sci. Data, 14, 5543–5572, https://doi.org/10.5194/essd-14-5543-2022, 2022a. a
Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Alin, S. R., Álvarez, M., Azetsu-Scott, K., Barbero, L., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., Hoppema, M., Humphreys, M. P., Ishii, M., Jeansson, E., Jiang, L.-Q., Jones, S. D., Lo Monaco, C., Murata, A., Müller, J. D., Pérez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Ulfsbo, A., Velo, A., Woosley, R. J., and Key, R. M.: Global Ocean Data Analysis Project version 2.2022 (GLODAPv2.2022) (NCEI Accession 0257247), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/1f4w-0t92, 2022b. a
Lavender, S., Antoine, D., Maritorena, S., Morel, A., Barrot, G., Demaria, J., and Pinnock, S.: GlobColour – The European Service for Ocean Colour, in: Proceedings of the 2009 IEEE International Geoscience & Remote Sensing Symposium, 12–17 July 2009, Cape Town, South Africa, 12–17, 2009. a
Lavigne, H., D'ortenzio, F., Ribera D'Alcalà, M., Claustre, H., Sauzède, R., and Gacic, M.: On the Vertical Distribution of the Chlorophyll a Concentration in the Mediterranean Sea: a Basin-Scale and Seasonal Approach, Biogeosciences, 12, 5021–5039, https://doi.org/10.5194/bgd-12-4139-2015, 2015. a
Liu, Y., Tang, D., and Evgeny, M.: Chlorophyll Concentration Response to the Typhoon Wind-Pump Induced Upper Ocean Processes Considering Air–Sea Heat Exchange, Remote Sens., 11, 1825, https://doi.org/10.3390/rs11151825, 2019. a
Longhurst, A., Sathyendranath, S., Platt, T., and Caverhill, C.: An Estimate of Global Primary Production in the Ocean from Satellite Radiometer Data, J. Plankt. Res., 17, 1245–1271, https://doi.org/10.1093/plankt/17.6.1245, 1995. a
Lv, Q., Wang, D., Gong, F., Bai, Y., and He, X.: Nitrate Inversion Based on Remote Sensing in the Pearl River Estuary, China, in: IOP Conference Series: Earth and Environmental Science, IOP Publishing, 502, 012027, https://doi.org/10.1088/1755-1315/502/1/012027, 2020. a
Ma, Y., Chen, S., Ermon, S., and Lobell, D. B.: Transfer Learning in Environmental Remote Sensing, Remote Sens. Environ., 301, 113924, https://doi.org/10.1016/j.rse.2023.113924, 2024. a
MacKay, D. J. C.: Bayesian Interpolation, Neural Comput., 4, 415–447, https://doi.org/10.1162/neco.1992.4.3.415, 1992. a
Macovei, V. A., Torres-Valdés, S., Hartman, S. E., Schuster, U., Moore, C. M., Brown, P. J., Hydes, D. J., and Sanders, R. J.: Temporal Variability in the Nutrient Biogeochemistry of the Surface North Atlantic: 15 Years of Ship of Opportunity Data, Global Biogeochem. Cy., 33, 1674–1692, https://doi.org/10.1029/2018GB006132, 2019. a
Madec, G.: NEMO Reference Manual 3_6_STABLE: NEMO Ocean Engine, Note du Pôle modél, IPSL – Inst. Pierre-Simon Laplace, France, https://www.nemo-ocean.eu/doc/ (last access: 13 June 2025), 2016. a
Miao, Y., Zhang, X., Li, Y., Zhang, L., and Zhang, D.: Monthly Extended Ocean Predictions Based on a Convolutional Neural Network via the Transfer Learning Method, Front. Mar. Sci., 9, 1073377, https://doi.org/10.3389/fmars.2022.1073377, 2023. a
Mignot, A., D'Ortenzio, F., Taillandier, V., Cossarini, G., and Salon, S.: Quantifying Observational Errors in Biogeochemical-Argo Oxygen, Nitrate, and Chlorophyll a Concentrations, Geophys. Res. Lett., 46, 4330–4337, https://doi.org/10.1029/2018GL080541, 2019. a
Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., Galbraith, E. D., Geider, R. J., Guieu, C., and Jaccard, S. L.: Processes and Patterns of Oceanic Nutrient Limitation, Nat. Geosci., 6, 701–710, https://doi.org/10.1038/ngeo1765, 2013. a
Morel, A. and Berthon, J.-F.: Surface Pigments, Algal Biomass Profiles, and Potential Production of the Euphotic Layer: Relationships Reinvestigated in View of Remote-sensing Applications, Limnol. Oceanogr., 34, 1545–1562, https://doi.org/10.4319/lo.1989.34.8.1545, 1989. a
Mortula, M., Ali, T., Bachir, A., Elaksher, A., and Abouleish, M.: Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis, Water, 12, 1954, https://doi.org/10.3390/w12071954, 2020. a
Nittis, K., Tziavos, C., Bozzano, R., Cardin, V., Thanos, Y., Petihakis, G., Schiano, M. E., and Zanon, F.: The M3A Multi-Sensor Buoy Network of the Mediterranean Sea, Ocean Sci., 3, 229–243, https://doi.org/10.5194/os-3-229-2007, 2007. a
Olsen, A., Lange, N., Key, R. M., Tanhua, T., Bittig, H. C., Kozyr, A., Álvarez, M., Azetsu-Scott, K., Becker, S., Brown, P. J., Carter, B. R., Cotrim Da Cunha, L., Feely, R. A., Van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterström, S., Landa, C. S., Lauvset, S. K., Michaelis, P., Murata, A., Pérez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., and Woosley, R. J.: An Updated Version of the Global Interior Ocean Biogeochemical Data Product, GLODAPv2.2020, Earth Syst. Sci. Data, 12, 3653–3678, https://doi.org/10.5194/essd-12-3653-2020, 2020. a
Padin, X. A., Velo, A., and Pérez, F. F.: ARIOS: A Database for Ocean Acidification Assessment in the Iberian Upwelling System (1976–2018), Earth Syst. Sci. Data, 12, 2647–2663, https://doi.org/10.5194/essd-12-2647-2020, 2020. a
Pauthenet, E., Bachelot, L., Balem, K., Maze, G., Tréguier, 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. a
Pujo-Pay, M., Conan, P., Oriol, L., Cornet-Barthaux, V., Falco, C., Ghiglione, J.-F., Goyet, C., Moutin, T., and Prieur, L.: Integrated Survey of Elemental Stoichiometry (C, N, P) from the Western to Eastern Mediterranean Sea, Biogeosciences, 8, 883–899, https://doi.org/10.5194/bg-8-883-2011, 2011. a
Qi, J., Liu, C., Chi, J., Li, D., Gao, L., and Yin, B.: An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea, Remote Sens., 14, 3207, https://doi.org/10.3390/rs14133207, 2022. a
Qun'ou, J., Lidan, X., Siyang, S., Meilin, W., and Huijie, X.: Retrieval Model for Total Nitrogen Concentration Based on UAV Hyper Spectral Remote Sensing Data and Machine Learning Algorithms – A Case Study in the Miyun Reservoir, China, Ecol. Indic., 124, 107356, https://doi.org/10.1016/j.ecolind.2021.107356, 2021. a
Rafter, P. A., Sigman, D. M., and Mackey, K. R.: Recycled Iron Fuels New Production in the Eastern Equatorial Pacific Ocean, Nat. Commun., 8, 1–10, https://doi.org/10.1038/s41467-017-01219-7, 2017. a
Reale, M., Cossarini, G., Lazzari, P., Lovato, T., Bolzon, G., Masina, S., Solidoro, C., and Salon, S.: Acidification, Deoxygenation, and Nutrient and Biomass Declines in a Warming Mediterranean Sea, Biogeosciences, 19, 4035–4065, https://doi.org/10.5194/bg-19-4035-2022, 2022. a
Ribera d'Alcalà, M., Civitarese, G., Conversano, F., and Lavezza, R.: Nutrient Ratios and Fluxes Hint at Overlooked Processes in the Mediterranean Sea, J. Geophys. Res.-Oceans, 108, 8106, https://doi.org/10.1029/2002JC001650, 2003. a
Richardson, A. J., Pfaff, M. C., Field, J. G., Silulwane, N. F., and Shillington, F. A.: Identifying Characteristic Chlorophyll a Profiles in the Coastal Domain Using an Artificial Neural Network, J. Plankt. Res., 24, 1289–1303, https://doi.org/10.1093/plankt/24.12.1289, 2002. a
Rossi, P., Ponti, M., Righi, S., Castagnetti, C., Simonini, R., Mancini, F., Agrafiotis, P., Bassani, L., Bruno, F., Cerrano, C., Cignoni, P., Corsini, M., Drap, P., Dubbini, M., Garrabou, J., Gori, A., Gracias, N., Ledoux, J.-B., Linares, C., Mantas, T. P., Menna, F., Nocerino, E., Palma, M., Pavoni, G., Ridolfi, A., Rossi, S., Skarlatos, D., Treibitz, T., Turicchia, E., Yuval, M., and Capra, A.: Needs and Gaps in Optical Underwater Technologies and Methods for the Investigation of Marine Animal Forest 3D-Structural Complexity, Front. Mar. Sci., 8, 591292, https://doi.org/10.3389/fmars.2021.591292, 2021. a
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning Representations by Back-Propagating Errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986. a
Salazar, J. J., Garland, L., Ochoa, J., and Pyrcz, M. J.: Fair Train-Test Split in Machine Learning: Mitigating Spatial Autocorrelation for Improved Prediction Accuracy, J. Petrol Sci. Eng., 209, 109885, https://doi.org/10.1016/j.petrol.2021.109885, 2022. a
Sammartino, M., Nardelli, B., Marullo, S., and Santoleri, R.: An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations, Remote Sens., 12, 4123, https://doi.org/10.3390/rs12244123, 2020. a, b, c
Sarangi, R. and Devi, K. N.: Space-Based Observation of Chlorophyll, Sea Surface Temperature, Nitrate, and Sea Surface Height Anomaly over the Bay of Bengal and Arabian Sea, Adv. Space Res., 59, 33–44, https://doi.org/10.1016/j.asr.2016.08.038, 2017. a, b, c
Sathyendranath, S., Platt, T., Horne, E. P., Harrison, W. G., Ulloa, O., Outerbridge, R., and Hoepffner, N.: Estimation of New Production in the Ocean by Compound Remote Sensing, Nature, 353, 129–133, https://doi.org/10.1038/353129a0, 1991. a
Sauzède, R., Bittig, H. C., Claustre, H., Pasqueron de Fommervault, O., Gattuso, J.-P., Legendre, L., and Johnson, K. S.: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks, Front. Mar. Sci., 4, 128, https://doi.org/10.3389/fmars.2017.00128, 2017. a, b
Severin, T., Kessouri, F., Rembauville, M., Sánchez-Pérez, E. D., Oriol, L., Caparros, J., Pujo-Pay, M., Ghiglione, J.-F., D'Ortenzio, F., Taillandier, V., Mayot, N., Durrieu De Madron, X., Ulses, C., Estournel, C., and Conan, P.: Open-ocean Convection Process: A Driver of the Winter Nutrient Supply and the Spring Phytoplankton Distribution in the Northwestern Mediterranean Sea, J. Geophys. Res.-Oceans, 122, 4587–4601, https://doi.org/10.1002/2016JC012664, 2017. a
Silió-Calzada, A., Bricaud, A., and Gentili, B.: Estimates of Sea Surface Nitrate Concentrations from Sea Surface Temperature and Chlorophyll Concentration in Upwelling Areas: A Case Study for the Benguela System, Remote Sens. Environ., 112, 3173–3180, https://doi.org/10.1016/j.rse.2008.03.014, 2008. a, b
Smith, P. A. H., Sørensen, K. A., Buongiorno Nardelli, B., Chauhan, A., Christensen, A., St. John, M., Rodrigues, F., and Mariani, P.: Reconstruction of Subsurface Ocean State Variables Using Convolutional Neural Networks with Combined Satellite and in Situ Data, Front. Mar. Sci., 10, 1218514, https://doi.org/10.3389/fmars.2023.1218514, 2023. a
Smith, S. V. and Hollibaugh, J. T.: Coastal Metabolism and the Oceanic Organic Carbon Balance, Rev. Geophys., 31, 75–89, https://doi.org/10.1029/92rg02584, 1993. a
Stéphane, M., Fanton d'Andon, O. H., Mangin, A., and Siegel, D. A.: Merged Satellite Ocean Color Data Products Using a Bio-Optical Model: Characteristics, Benefits and Issues, Remote Sens. Environ., 114, 1791–1804, https://doi.org/10.1016/j.rse.2010.04.002, 2010. a
Storto, A., Alvera-Azcárate, A., Balmaseda, M. A., Barth, A., Chevallier, M., Counillon, F., Domingues, C. M., Drevillon, 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, K. A., 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. a
Su, H., Zhang, T., Lin, M., Lu, W., and Yan, X.-H.: Predicting Subsurface Thermohaline Structure from Remote Sensing Data Based on Long Short-Term Memory Neural Networks, Remote Sens. Environ., 260, 112465, https://doi.org/10.1016/j.rse.2021.112465, 2021. a
Su, H., Qiu, J., Tang, Z., Huang, Z., and Yan, X.-H.: Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer, IEEE T. Geosci. Remote, 62, 1–13, https://doi.org/10.1109/TGRS.2024.3350346, 2024. a
Switzer, A. C., Kamykowski, D., and Zentara, S.-J.: Mapping Nitrate in the Global Ocean Using Remotely Sensed Sea Surface Temperature, J. Geophys. Res.-Oceans, 108, 2000JC000444, https://doi.org/10.1029/2000JC000444, 2003. a, b
Syariz, M. A., Lin, C.-H., Nguyen, M. V., Jaelani, L. M., and Blanco, A. C.: WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval, Remote Sens., 12, 1966, https://doi.org/10.3390/rs12121966, 2020. a
Tian, T., Cheng, L., Wang, G., Abraham, J., Wei, W., Ren, S., Zhu, J., Song, J., and Leng, H.: Reconstructing Ocean Subsurface Salinity at High Resolution Using a Machine Learning Approach, Earth Syst. Sci. Data, 14, 5037–5060, https://doi.org/10.5194/essd-14-5037-2022, 2022. a, b, c, d
Tuerena, R. E., Williams, R. G., Mahaffey, C., Vic, C., Green, J. M., Naveira-Garabato, A., Forryan, A., and Sharples, J.: Internal Tides Drive Nutrient Fluxes into the Deep Chlorophyll Maximum over Mid-ocean Ridges, Global Biogeochem. Cy., 33, 995–1009, https://doi.org/10.1029/2019gb006214, 2019. a
Uitz, J., Claustre, H., Morel, A., and Hooker, S. B.: Vertical Distribution of Phytoplankton Communities in Open Ocean: An Assessment Based on Surface Chlorophyll, J. Geophys. Res.-Oceans, 111, C08005, https://doi.org/10.1029/2005JC003207, 2006. a
Von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Djavidnia, S., Gattuso, J.-P., Grégoire, M., Nolan, G., Aaboe, S., and Aguiar, E.: Copernicus Marine Service Ocean State Report, J. Oper. Oceanogr., 12, S1–S123, https://doi.org/10.1080/1755876X.2019.1633075, 2019. a
Wang, L., Xu, Z., Gong, X., Zhang, P., Hao, Z., You, J., Zhao, X., and Guo, X.: Estimation of Nitrate Concentration and Its Distribution in the Northwestern Pacific Ocean by a Deep Neural Network Model, Deep-Sea Res. Pt. I, 195, 104005, https://doi.org/10.1016/j.dsr.2023.104005, 2023. a
Wang, T., Chai, F., Xing, X., Ning, J., Jiang, W., and Riser, S. C.: Influence of Multi-Scale Dynamics on the Vertical Nitrate Distribution around the Kuroshio Extension: An Investigation Based on BGC-Argo and Satellite Data, Prog. Oceanogr., 193, 102543, https://doi.org/10.1016/j.pocean.2021.102543, 2021. a
Webb, P.: Introduction to Oceanography, Roger Williams University, https://rwu.pressbooks.pub/webboceanography/ (last access: 13 June 2025), 2021. a
Yang, G. G., Wang, Q., Feng, J., He, L., Li, R., Lu, W., Liao, E., and Lai, Z.: Can Three-Dimensional Nitrate Structure Be Reconstructed from Surface Information with Artificial Intelligence? – a Proof-of-Concept Study, Sci. Total Environ., 924, 171365, https://doi.org/10.1016/j.scitotenv.2024.171365, 2024. a, b, c
Yu, X., Yi, H., Liu, X., Wang, Y., Liu, X., and Zhang, H.: Remote-Sensing Estimation of Dissolved Inorganic Nitrogen Concentration in the Bohai Sea Using Band Combinations Derived from MODIS Data, Int. J. Remote Sens., 37, 327–340, https://doi.org/10.1080/01431161.2015.1125555, 2016. a
Yu, X., Chen, S., and Chai, F.: Remote Estimation of Sea Surface Nitrate in the California Current System From Satellite Ocean Color Measurements, IEEE T. Geosci. Remote, 60, 1–17, https://doi.org/10.1109/TGRS.2021.3095099, 2022. a
Yu, X., Guo, H., Zhang, J., Ma, Y., Wang, X., Liu, G., Xing, M., Xu, N., and Seka, A. M.: The Reconstructed Three-Dimensional Nitrate Field Dataset for the Pan-European Ocean Using a Continual Learning-Based Multilayer Perceptron, Zenodo [data set], https://doi.org/10.5281/zenodo.14010813, 2024. a, b
Zakem, E. J., Al-Haj, A., Church, M. J., Van Dijken, G. L., Dutkiewicz, S., Foster, S. Q., Fulweiler, R. W., Mills, M. M., and Follows, M. J.: Ecological Control of Nitrite in the Upper Ocean, Nat. Commun., 9, 1206, https://doi.org/10.1038/s41467-018-03553-w, 2018. a
Zhou, L. and Zhang, R.-H.: A Self-Attention–Based Neural Network for Three-Dimensional Multivariate Modeling and Its Skillful ENSO Predictions, Sci. Adv., 9, eadf2827, https://doi.org/10.1126/sciadv.adf2827, 2023. a, b, c
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F.: Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, IEEE Geosci. Remote Sens. Mag., 5, 8–36, https://doi.org/10.1109/MGRS.2017.2762307, 2017. a
Short summary
Mapping the 3D distribution of oceanic nitrate is challenging. We developed a continual-learning-based multilayer perceptron, integrating prior knowledge from numerical models and BGC-Argo validation to reconstruct a pan-European 3D nitrate field from 2010 to 2023 (0–2000 m depth, monthly, 0.25° horizontal resolution) using sea surface environmental features. This dataset helps bridge observational gaps and enhances understanding of the ocean's interior environment.
Mapping the 3D distribution of oceanic nitrate is challenging. We developed a...
Altmetrics
Final-revised paper
Preprint