Zhetao Tan

Zhetao Tan

he/him

Ph.D. in Meteorology

Posts by Collection

portfolio

publications

A new ocean data quality control system reveals a stronger ocean warming rate

Published in Deep Sea Research Part I: Oceanographic Research Papers, 2023

This paper is about the development of the ocean data quality control algorithm (CODC-QC)

Recommended citation: Tan Z, Cheng L*, Gouretski V, Zhang B, Wang Y, Li F, Liu Z, and Zhu J., 2023: A new automatic quality control system for ocean profile observations and impact on ocean warming estimate. Deep Sea Research Part I: Oceanographic Research Papers, 194, 103961
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IAPv4 ocean temperature and ocean heat content gridded dataset

Published in Earth System Science Data, 2024

This paper is about the release of the IAP temperature grid data (version 4)

Recommended citation: Cheng, L., Pan, Y., Tan, Z., Zheng, H., Zhu, Y., Wei, W., Du, J., Yuan, H., Li, G., Ye, H., Gouretski, V., Li, Y., Trenberth, K., Abraham, J., Jin, Y., Reseghetti, F., Lin, X., Zhang, B., Chen, G., Mann, M., and Zhu, J., 2024: IAPv4 ocean temperature and ocean heat content gridded dataset, Earth Syst. Sci. Data.
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Record High Temperatures in the Ocean in 2024

Published in Advances in Atmospheric Sciences, 2025

This paper is about the sea surface temperatures and deeper water temperatures reached a new record high in 2024

Recommended citation: Cheng L. J. Abraham, K. E. Trenberth, J. Reagan, H.-M. Zhang, A. Storto, K. von Schuckmann, Y. Pan, Y. Zhu, M. E. Mann, J. Zhu, F. Wang, F. Yu, R. Locarnini, J. Fasullo, B. Huang, G. Graham, X. Yin, V. Gouretski, F. Zheng, Y. Li, B. Zhang, L. Wan, X. Chen, D. Wang, L. Feng, X. Song, Y. Liu, F. Reseghetti, S. Simoncelli, G. Chen, R. Zhang, A. Mishonov, Z. Tan, W. Wei, H. Yuan, G. Li, Q. Ren, L. Cao, Y. Lu, J. Du, K. Lyu, A. Sulaiman, M. Mayer, H. Wang, Z. Ma, S. Bao, H. Yan, Z. Liu, C. Yang, X. Liu, Z. Hausfather, T. Szekely, F. Gues: 2025: Record High Temperatures in the Ocean in 2024. Advances in Atmospheric Sciences
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CODC-S: a quality-controlled global ocean salinity profiles dataset

Published in Scientific Data, 2025

This paper is about a quality-controlled global ocean salinity profiles dataset (CODC-S) used to support diverse oceanographic and climatic research

Recommended citation: Tan Z, Zhu Y, Cheng L, Gouretski V, Pan Y, Yuan H, Wang Z, Li G, Song X, Zhang B, Bao S, Li Y, Zhu J. 2025. CODC-S: A quality-controlled global ocean salinity profiles dataset. Scientific Data, 12: 917. https://doi.org/10.1038/s41597-025-05172-9
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talks

Toward a practical quantification of uncertainty in ocean heat content (OHC) due to data quality control (QC) procedures

Published:

Ocean heat content (OHC) is a fundamental indicator for climate change. Understanding and quantifying the uncertainty in OHC record and the underlyinggridded product is an essential work. One of the uncertainty (error) sources is the Quality-Control process (QC). However, the quantitative estimate on this uncertainty source is missing to our knowledge.

Duplicate checking task team: progress and future work

Published:

As a task team leader of ‘duplicate checking’ group to develop an automatic duplicate checking algorithm for ocean profiles with robustness examination. The duplicate checking results have been incorporated into the World Ocean Database (WOD).

A new ocean data quality control system reveals a stronger ocean warming rate

Published:

Over the past century, more than 20 million in-situ profiles with heterogeneous quality had been collected, leading to a high demand of a quality control (QC) procedure to identify outliers efficiently, accurately, and automatically. In this study, we present a new automatic QC system (namely CODC-QC) for ocean temperature and salinity observations. Unlike many existing QC procedures, no assumption is made of a Gaussian distribution law in CODC-QC as the statistical distribution of the variables are typically skewed. Instead, the 99% quantiles are used as thresholds to define the local climatological ranges. In addition, these thresholds are time-varying, which aims at reducing erroneously excluding good real-time data. The above strategies are used in the local climatological range check for temperature, salinity and vertical temperature gradient, in which the anisotropic feature of water properties and the topography barriers adjustment is accounted for. Additionally, the CODC-QC under the benchmark evaluation was applied to all temperature and salinity profiles in the World Ocean Database from 1940 to 2021. Result shows a significant dependency of the quality on instrumentation types. Finally, as ocean heat content (OHC) is a fundamental indicator of climate change, the impact of different QC systems on OHC estimation is examined. We found that applying CODC-QC will lead to a 15% stronger warming rate within 1991–2021 compared to the application of WOD-QC (NOAA/NCEI), implying a non-negligible source of error in OHC estimate. The new AutoQC system could support further improvement of the ocean sciences and other applications.

气候灾害风险评估助力气候变化适应进程

Published:

受邀作为青年嘉宾参与论坛，分享了气候灾害风险评估对推动气候变化适应进程的意义。分享强调了气候灾害的广泛影响，包括海洋热浪对海洋生态的威胁及复合极端气候事件（如威尼斯洪水）对经济造成的损失。依据IPCC评估框架，报告讨论了气候风险的多维度评估重要性，并以海水养殖业为例分析其气候致灾风险。此外，还探讨了气候减灾策略与国际合作机制，如《联合国仙台减灾框架》等。最后，报告强调了青年在气候减灾和适应方面赋能气候行动的担当和责任，并鼓励青年持续思考气候减灾与适应行动的相互作用及在各行业的具体实施措施。