Abstract
Automatic ocean eddy identification algorithms are crucial for global eddy research. In this study, a scale-selective eddy identification algorithm (SEIA) that features improvements in the detection and tracking processes is presented for the global ocean based on sea level anomalies. First, the previous strategy of using thresholds to define eddy boundaries is replaced with a scale-selective scheme, which restricts the numbers of upper and lower grid points based on the data resolution and eddy spatial scale. Under such conditions, overestimated eddy boundaries will be flexibly removed. Furthermore, an effective overlap scheme is used to track eddies by calculating the intersection ratio of time-step-successive eddies. The SEIA generates approximately 1.6 million anticyclonic eddies and 1.5 million cyclonic eddies by the satellite altimetry product from the French Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) over a 29-year period (1993–2021; https://doi.org/10.11922/sciencedb.o00035.00004). Assessments of the global distribution of eddies, eddy propagation speed, eddy path and evolution characteristics, and observation-based eddy hydrological conditions verify the validity of the SEIA. This study provides solid support for ocean eddy-related research in a warming climate.
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Data availability
This study benefited from numerous datasets made freely available: AVISO + dataset disseminated by Copernicus Marine Environment Monitoring Service (https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047), SMAP salinity product (https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4#), and Argo profiles (http://www.argodatamgt.org/Access-to-data/Argo-GDAC-ftp-and-https-servers). The SEIA eddy dataset generated during the current study is available in the Science Data Bank, https://doi.org/10.11922/sciencedb.o00035.00004.
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Acknowledgements
Heartfelt thanks to the reviewers for their valuable feedback and comments, which greatly improved this research paper. This research was supported by the National Natural Science Foundation of China (41890805, 42076209, 42006035), the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0306), the Rising Star Foundation of the South China Sea Institute of Oceanology (NHXX2019WL0101) and the Science and Technology Program of Guangzhou (202102080363). The construction of MATLAB-based SEIA was supported by the High Performance Computing Division at the South China Sea Institute of Oceanology. We are grateful for the assistance provided by Xisha Marine Environment National Observation and Research Station, Hainan, China in the conduct of this work.
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YY established the overarching research goals and aims, developed the methodology, programmed the codes, generated the SEIA datasets and wrote the initial draft. LZ and QW verified the research protocol and supervised the planning and execution of research activities. QW assisted with the analysis of the results, and LZ revised the draft.
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Yang, Y., Zeng, L. & Wang, Q. Assessment of global eddies from satellite data by a scale-selective eddy identification algorithm (SEIA). Clim Dyn 62, 881–894 (2024). https://doi.org/10.1007/s00382-023-06946-w
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DOI: https://doi.org/10.1007/s00382-023-06946-w