Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3517-2024
© Author(s) 2024. 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-16-3517-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Aug 2024
Data description paper |
| 02 Aug 2024
| 02 Aug 2024
IAPv4 ocean temperature and ocean heat content gridded dataset
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Yuying Pan
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Zhetao Tan
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Huayi Zheng
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Yujing Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Wangxu Wei
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Juan Du
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Huifeng Yuan
Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Guancheng Li
Eco-Environmental Monitoring and Research Center, Pearl River Valley and South China Sea Ecology and Environment Administration, Ministry of Ecology and Environment, PRC, Guangzhou, 510611, China
Hanlin Ye
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Viktor Gouretski
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Yuanlong Li
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Kevin E. Trenberth
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307, USA
Department of Physics, University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
John Abraham
University of St. Thomas, School of Engineering, 2115 Summit Ave, St Paul, MN 55105, USA
Yuchun Jin
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Franco Reseghetti
Istituto Nazionale di Geofisica e Vulcanologia, 40127, Bologna, Italy
Xiaopei Lin
Frontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China
Bin Zhang
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
Gengxin Chen
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Michael E. Mann
Dept. of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA, USA
Jiang Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
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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.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Jingzhe Sun, Yingjing Jiang, Shaoqing Zhang, Weimin Zhang, Lv Lu, Guangliang Liu, Yuhu Chen, Xiang Xing, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 15, 4805–4830, https://doi.org/10.5194/gmd-15-4805-2022, https://doi.org/10.5194/gmd-15-4805-2022, 2022
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An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.
Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Xiaoxia Sun, Wuchang Zhang, Zhenyan Wang, Jun Ma, Huamao Yuan, and Liqin Duan
Biogeosciences, 19, 845–859, https://doi.org/10.5194/bg-19-845-2022, https://doi.org/10.5194/bg-19-845-2022, 2022
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A predictor selection algorithm was constructed to decrease the predicting error in the surface ocean partial pressure of CO2 (pCO2) mapping by finding better combinations of pCO2 predictors in different regions. Compared with previous research using the same combination of predictors in all regions, using different predictors selected by the algorithm in different regions can effectively decrease pCO2 predicting errors.
Zhaohui Chen, Parvadha Suntharalingam, Andrew J. Watson, Ute Schuster, Jiang Zhu, and Ning Zeng
Biogeosciences, 18, 4549–4570, https://doi.org/10.5194/bg-18-4549-2021, https://doi.org/10.5194/bg-18-4549-2021, 2021
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As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this global warming. The increased CO2 is mainly caused by emissions from fossil fuel use and land use. At the same time, the ocean is a significant sink in the carbon cycle. The North Atlantic is a critical ocean region in reducing CO2 concentration. We estimate the CO2 uptake in this region based on a carbon inverse system and atmospheric CO2 observations.
Weiqi Xu, Chun Chen, Yanmei Qiu, Ying Li, Zhiqiang Zhang, Eleni Karnezi, Spyros N. Pandis, Conghui Xie, Zhijie Li, Jiaxing Sun, Nan Ma, Wanyun Xu, Pingqing Fu, Zifa Wang, Jiang Zhu, Douglas R. Worsnop, Nga Lee Ng, and Yele Sun
Atmos. Chem. Phys., 21, 5463–5476, https://doi.org/10.5194/acp-21-5463-2021, https://doi.org/10.5194/acp-21-5463-2021, 2021
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Here aerosol volatility and viscosity at a rural site (Gucheng) and an urban site (Beijing) in the North China Plain (NCP) were investigated in summer and winter. Our results showed that organic aerosol (OA) in winter in the NCP is more volatile than that in summer due to enhanced primary emissions from coal combustion and biomass burning. We also found that OA existed mainly as a solid in winter in Beijing but as semisolids in Beijing in summer and Gucheng in winter.
Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Tao Song, Fei Li, Haitao Zheng, Guanglin Jia, Miaomiao Lu, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 13, 529–570, https://doi.org/10.5194/essd-13-529-2021, https://doi.org/10.5194/essd-13-529-2021, 2021
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China's air pollution has changed substantially since 2013. Here we have developed a 6-year-long high-resolution air quality reanalysis dataset over China from 2013 to 2018 to illustrate such changes and to provide a basic dataset for relevant studies. Surface fields of PM2.5, PM10, SO2, NO2, CO, and O3 concentrations are provided, and the evaluation results indicate that the reanalysis dataset has excellent performance in reproducing the magnitude and variation of air pollution in China.
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Short summary
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.
Observational gridded products are essential for understanding the ocean, the atmosphere, and...
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