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Typical spatiotemporal patterns of the Kuroshio south of Japan and the Kuroshio extension using self-organizing maps and their causal relationship
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Youting Wu1, 2, Yang Yang3, *, Xiangsan Liang4, 5
Haiyang Xuebao | 2022, 44(9) : 38 - 54
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Haiyang Xuebao | 2022, 44(9): 38-54
Article
Typical spatiotemporal patterns of the Kuroshio south of Japan and the Kuroshio extension using self-organizing maps and their causal relationship
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Youting Wu1, 2, Yang Yang3, *, Xiangsan Liang4, 5
Affiliations
  • 1. Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  • 2. School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 3. College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
  • 4. Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
  • 5. IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
  • 6. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
Published: 2022-09-01 doi: 10.12284/hyxb2022069
Outline
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Previous studies have shown that the decadal modulation of the Kuroshio extension (KE) system is controlled by the Pacific decadal oscillation-associated forcing from downstream. However, recent observation reveals that this mechanism ceases to function after August 2017. Meanwhile, a large meander is under development in the KE’s upstream, i.e., south of Japan. Using the self-organizing map (SOM), we investigate the characteristic spatial and temporal patterns of the Kuroshio south of Japan and the KE and their causal relations, based on the 26-year (1993−2018) satellite altimetry data of sea level anomaly (SLA). The typical spatial patterns are well extracted, and their temporal trajectories indicate that the KE tends to be stable (unstable) when the upstream Kuroshio takes a large meander (an offshore nonlarge meander) path. To further unravel the underlying cause-and-effect relation between the two systems, we apply the information flow-based causality analysis to the typical regions of SLA and its associated temporal modes identified with the SOM. It is found that during the large meander event, the Kuroshio south of Japan and the KE are mutually causal, but have different hotspots. The information flows from the former to the latter mainly occur in the southeastern area off the Kii Peninsula and the time-mean ridge and trough of the KE jet, while those from the latter to the former are mainly concentrated in the time-mean ridge and trough of the KE jet, and the recirculation gyre of the Kuroshio. These results indicate that the Kuroshio large meander is an important factor influencing the KE’s stability, while the KE affects its upstream Kuroshio via modulating the associated recirculation gyres. In contrast, when the offshore nonlarge meander path is taken, a one-way causality is identified from the Kuroshio to the KE, mainly occurring over the Izu-Ogasawara Ridge and in the recirculation gyres. This may be attributed to the constantly downstream transport of negative SLAs into the KE’s recirculation gyre, which leads to an unstable KE.

Kuroshio large meandering  /  Kuroshio extension  /  self-organizing map (SOM)  /  causality analysis  /  information flow
Youting Wu, Yang Yang, Xiangsan Liang. Typical spatiotemporal patterns of the Kuroshio south of Japan and the Kuroshio extension using self-organizing maps and their causal relationship[J]. Haiyang Xuebao, 2022 , 44 (9) : 38 -54 . DOI: 10.12284/hyxb2022069
Year 2022 volume 44 Issue 9
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Article Info
doi: 10.12284/hyxb2022069
  • Receive Date:2021-10-12
  • Online Date:2026-02-02
  • Published:2022-09-01
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History
  • Received:2021-10-12
  • Revised:2022-01-25
Funding
Affiliations
    1. Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
    2. School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
    3. College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
    4. Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
    5. IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
    6. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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