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Ocean fronts variations in strength are key to the terrestrial material transport and global material cycle. Ocean temperature fronts are formed between the branches of West Pacific Boundary Current and the coastal current during the winter and spring seasons in the eastern shelf of China. In order to investigate the multi-time scale variation and main controlling factors of temperature front over the Yellow Sea under the dual influence of winter storms and shelf circulation, we investigate the coupling of low-latitude driven circulation systems and high-latitude driven winter storms on frontal variability with the methods of signal decomposition and explainable deep learning on the decadal and weather scale. On the decadal scale, empirical orthogonal decomposition and ensemble empirical modal decomposition are used to relate temperature changes in the Yellow Sea to the strength of the Yellow Sea Warm Current. The results indicate that the spatial distribution of first sea surface temperature (SST) EOF mode has obvious characteristics of the Yellow Sea Warm Current-coastal current system; the time series of the first SST EOF mode correlates well with the Yellow Sea Warm Current intensity index and is modulated by the low frequency ENSO signal. On the weather scale, this paper trains CNN-LSTM models and uses interpretability metrics to conduct the research. The results show that, in windless or weak wind conditions, the strength of ocean front is maintained by the combination of pressure gradient forces resulted from sea surface height and Coriolis forces caused by flow field. However, in the storm conditions, influenced by Kelvin Wave propagation and shear front fragmentation, the flow field is responsible for the ocean front variation. The results of this study show that big data and machine learning methods are important means to establish connections between many ocean parameters and discover some unique physical ocean processes, which have broad application prospects.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 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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