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 article
| 
18 Nov 2022
Data description article |
| 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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Qingyou He, Yunchen Liu, Weikang Zhan, Yinghui He, Zhenting Mo, Peiya Zhang, Shuqun Cai, Lijing Cheng, and Haigang Zhan
Earth Syst. Sci. Data, 18, 7817–7838, https://doi.org/10.5194/essd-18-7817-2026, https://doi.org/10.5194/essd-18-7817-2026, 2026
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We integrated nearly 30 years of global ocean temperature and salinity profile data with satellite mesoscale eddy observations, matching 5.46 million in-situ profiles to adjacent eddies. This dataset enables researchers to characterize the regional three-dimensional eddy structure and investigate how eddies modify ocean thermohaline distributions, alter stratification and mixing, trigger extreme events, drive heat and tracer transport, and respond to climate change.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
Geosci. Model Dev., 19, 9807–9829, https://doi.org/10.5194/gmd-19-9807-2026, https://doi.org/10.5194/gmd-19-9807-2026, 2026
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This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on Graphics Processing Unit. Through implementing thread-block indexing, minimizing Central Processing Unit and Graphics Processing Unit communication, and an parallel framework, we demonstrate speedups: 556.5× faster offline performance for the Heterogeneous Interface advection solver and 20.5× acceleration in coupled simulations.
Baoxu Li, Hongze Leng, Junqiang Song, Wuxin Wang, Taikang Yuan, Xiang Wang, Jinhui Yang, and Hang Cao
EGUsphere, https://doi.org/10.5194/egusphere-2026-3488, https://doi.org/10.5194/egusphere-2026-3488, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Ocean forecasting relies on costly operational systems that use small ensembles, producing unreliable uncertainty estimates. We developed a deep learning method generating about 5,000 members to map into an error covariance matrix, reducing computation over 200-fold. Our sea surface temperature forecasts achieve 60-day errors about 0.6 degrees Celsius, remove false error connections, and remain stable over a year, overcoming key operational barriers.
Zhaokun Wang, Bin Lu, Yi Xin, Takamitsu Ito, Lei Zhou, Lijing Cheng, Yuanlong Li, Xinbing Wang, and Meng Jin
Earth Syst. Sci. Data, 18, 5069–5092, https://doi.org/10.5194/essd-18-5069-2026, https://doi.org/10.5194/essd-18-5069-2026, 2026
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We present Jingwei-Nutrients, a global monthly data product of ocean nitrate, phosphate, and silicate from 1965 to 2023 down to 2000 meters. Built using a multi-task deep learning framework, it combines sparse historical observations with hydrographic information. This continuous record helps scientists understand marine ecosystems and climate change responses. We also provide the Jingwei web platform (https://jingwei.acemap.info) for dynamic data exploration and visualization without coding.
Viktor Gouretski, Lijing Cheng, and Juan Du
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-416, https://doi.org/10.5194/essd-2026-416, 2026
Preprint under review for ESSD
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This study depicts a three-step quality control procedure applied to the Nansen cast temperature profiles (1873–2025, especially before 1970), including automatic/manual quality control and temperature anomaly quality control. Corrections for depth bias are also suggested with improved vertical interpolation procedure. Based on this dataset we access for the first time the change of the ocean heat content of the upper 2000 m layer over a 100 year time period between 1870s and 2020s.
Piers M. Forster, Tristram Walsh, Chris Smith, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Robbie M. Andrew, Chris Atkinson, Richard A. Betts, Antonio Bombelli, Samantha N. Burgess, Lijing Cheng, Helen E. Claxton, Pierre Friedlingstein, Thomas L. Frölicher, Catia M. Domingues, Thomas Gasser, Catherine H. Gregory, Rachel M. Hoesly, Daniel Huppmann, Masayoshi Ishii, Christopher Kadow, Alexia Karwat, John Kennedy, Rachel E. Killick, Mahesh V. M. Kovilakam, Paul B. Krummel, Xin Lan, Jean-François Lamarque, Aurélien Liné, Belén Martín-Míguez, Didier P. Monselesan, Colin Morice, Jens Mühle, Pino Mussak, Glen P. Peters, Anna Pirani, Julia Pongratz, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Steven J. Smith, Ghassan Taha, Caterina Tassone, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Marco Zecchetto, Junting Zhong, Xiao-Ye Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 18, 3889–3933, https://doi.org/10.5194/essd-18-3889-2026, https://doi.org/10.5194/essd-18-3889-2026, 2026
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We give our annual update of key climate indicators. Our work quantifies the human contribution to global warming and the pace of climate change. This represents a large effort by the international community akin to an Intergovernmental Panel on Climate Change (IPCC) report.
Marina Lévy, Karina von Schuckmann, Mahé Butel, William W. L. Cheung, Joachim Claudet, Thomas L. Frölicher, Patrice Guillotreau, Peter Haugan, Janine Adams, Diva Amon, Tamatoa Bambridge, Cynthia Barzuna, Bruno Blanke, Lijing Cheng, Sanae Chiba, Jorge Cortés, Pierre Friedlingstein, Jean-Pierre Gattuso, Stefan Gelcich, Jessica Gephart, Deborah Greaves, Audrey Hasson, Claire Jolly, Daoji Li, Yunne-Jai Shin, Aimée Slangen, Mere Takoko, Olivier Thébaud, Adrien Vincent, and Patrick Vincent
State Planet, 7-osr10, 1, https://doi.org/10.5194/sp-7-osr10-1-2026, https://doi.org/10.5194/sp-7-osr10-1-2026, 2026
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The Starfish Barometer provides an annual, science-based synthesis of global Ocean-related developments, structured around five interconnected dimensions: the Ocean state, human pressures, societal harms, protection efforts, and opportunities for humanity. It is released each year on World Ocean Day. This article presents the second edition of the Barometer, which shows a growing gap between increasing human pressures on the Ocean and the efforts being made to protect it and drive change.
Juan Du, Lijing Cheng, Takamitsu Ito, Hernan E. Garcia, Zhankun Wang, Jonathan D. Sharp, Christopher J. Roach, Shoshiro Minobe, Yuntao Zhou, Bin Lu, Gian Giacomo Navarra, and Seth M. Bushinsky
Ocean Sci., 22, 1483–1499, https://doi.org/10.5194/os-22-1483-2026, https://doi.org/10.5194/os-22-1483-2026, 2026
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This study assesses the spread of mean state and annual cycle of ocean dissolved oxygen by multiple observational gridded data products. A good consistency is validated globally, although substantial local differences exist in areas of strong spatial gradient. Quantifying the discrepancies could give an insight into regions relatively more sensitive to data reconstruction processes and further advance the improvement of oxygen data products.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2918, https://doi.org/10.5194/egusphere-2025-2918, 2025
Preprint archived
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This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on GPU-like accelerators. Through implementing thread-block coordinated indexing, minimizing CPU-GPU communication, and an hybrid parallelization framework, we demonstrate prominent speedups: 556.5× faster offline performance for the Heterogeneous Interface PPM solver and 20.5× acceleration in coupled simulations.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
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In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
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
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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.
Simona Simoncelli, Franco Reseghetti, Claudia Fratianni, Lijing Cheng, and Giancarlo Raiteri
Earth Syst. Sci. Data, 16, 5531–5561, https://doi.org/10.5194/essd-16-5531-2024, https://doi.org/10.5194/essd-16-5531-2024, 2024
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This data review is about the reprocessing of historical eXpendable BathyThermograp (XBT) profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019. A new automated quality control analysis has been performed starting from the original raw data and operational log sheets. The data have been formatted and standardized according to the latest community best practices, and all available metadata have been inserted, including calibration information and uncertainty specification.
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
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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.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 16, 4351–4387, https://doi.org/10.5194/essd-16-4351-2024, https://doi.org/10.5194/essd-16-4351-2024, 2024
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A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study, which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10, and NMVOCs in China from 2013 to 2020 with a horizontal resolution of 15 km. Emissions of different air pollutants and their changes during 2013–2020 were investigated and compared with previous emission inventories, which sheds new light on the complex variations of air pollutant emissions in China.
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.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Xueming Zhu, Ziqing Zu, Shihe Ren, Miaoyin Zhang, Yunfei Zhang, Hui Wang, and Ang Li
Geosci. Model Dev., 15, 995–1015, https://doi.org/10.5194/gmd-15-995-2022, https://doi.org/10.5194/gmd-15-995-2022, 2022
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SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS's physical model and data assimilation scheme in order to improve its forecasting skill. The three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.
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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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