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Temporal-spatial oceanic variation in relation with the three typical Kuroshio paths south of Japan
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Xiaobo Wu1, Yanling Zhao2, Guijun Han1, *, Wei Li1, 3, *, Qi Shao1, 3, Lige Cao1, Chaoliang Li1
Acta Oceanologica Sinica | 2022, 41(2) : 15 - 25
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Acta Oceanologica Sinica | 2022, 41(2): 15-25
• Ocean Data Assimilation •
Temporal-spatial oceanic variation in relation with the three typical Kuroshio paths south of Japan
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Xiaobo Wu1, Yanling Zhao2, Guijun Han1, *, Wei Li1, 3, *, Qi Shao1, 3, Lige Cao1, Chaoliang Li1
Affiliations
  • 1 School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
  • 2 The PLA 31010 Units, Beijing 100081, China
  • 3 Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China
Published: 2022-02-25 doi: 10.1007/s13131-021-1941-9
Outline
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Empirical orthogonal function (EOF) analysis was applied to a 50-year long time series of monthly mean positions of the Kuroshio path south of Japan from a regional reanalysis. Three leading EOF modes characterize the contributions from three typical paths of the Kuroshio meander: the typical large meander path, the offshore non-large meander path, and the nearshore non-large meander path, respectively. Accordingly, the spatial variation characteristics of oceanic anomaly fields can be depicted by their regression fields upon the associated three leading principal components (PCs), which are well-matched with the results of composite analysis corresponding to each period of the three typical Kuroshio paths. A new index for the typical large meander is defined by using the second leading PC, which is highly correlated with the Kushimoto-Uragami index. Spectral analysis of this new index series shows variability of the Kuroshio path south of Japan at time scales of about 7–8 years and 20 years.

Kuroshio path  /  south of Japan  /  empirical orthogonal function analysis  /  temporal-spatial oceanic variation  /  large meander index
Xiaobo Wu, Yanling Zhao, Guijun Han, Wei Li, Qi Shao, Lige Cao, Chaoliang Li. Temporal-spatial oceanic variation in relation with the three typical Kuroshio paths south of Japan[J]. Acta Oceanologica Sinica, 2022 , 41 (2) : 15 -25 . DOI: 10.1007/s13131-021-1941-9
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1 Introduction
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The Kuroshio is the western boundary current of the wind-driven subtropical circulation system in the North Pacific. It brings heat and nutrients northward, impacting local weathers and climate and shaping marine ecosystems (Nakata et al., 2000; Kelly et al., 2010; Xu et al., 2010; Nakamura et al., 2012; Hayasaki et al., 2013). It is well known that when the Kuroshio heads eastward along the southern coast of Japan, it takes one of the three typical paths: typical large meander (tLM) path, offshore non-large meander (oNLM) path, and nearshore non-large meander (nNLM) path (Kawabe, 1985; Fig. 1). The variation of the Kuroshio path south of Japan has always been an important topic due to such distinct features.
Early research mainly focused on the bimodality of the Kuroshio path south of Japan using ocean observation data (Taft, 1972; Nitani, 1975; Kawabe, 1985, 1987, 1995) and modeling (Chao and McCreary, 1982; Chao, 1984; Yamagata and Umatani, 1987, 1989; Awaji et al., 1991). The availability of high-quality satellite dataset since the 1990s marked the beginning of an era in studying the roles of mesoscale eddies in the variation of the Kuroshio path (Mitsudera et al., 2001; Ebuchi and Hanawa, 2003; Sugimoto and Hanawa, 2012; Ma, 2019). Moreover, the development of the ocean models and assimilation techniques, has laid the foundation for exploring actual Kuroshio path variations (Miyazawa et al., 2008; Usui et al., 2008, 2011, 2013) and long-term variations (Douglass et al., 2012; Usui et al., 2013; Tsujino et al., 2013). Miyazawa et al. (2008) used an ocean forecast system called the Japan Coastal Ocean Predictability Experiment, to simulate the formation of Kuroshio large meander in 2004; their results showed that this large meander is caused by baroclinic instability. This mechanism is confirmed by Usui et al. (2008) based on a data assimilation and prediction system of the Meteorological Research Institute Community Ocean Model. Usui et al. (2011) examined the decay mechanism of the 2004/05 Kuroshio large meander based on ocean general circulation model (OGCM), and proposed three mechanisms responsible for the event. Douglass et al. (2012) conducted a long-term global OGCM simulation, and their results indicated that the meander is from intrinsic oceanic variability. Usui et al. (2013) used an ocean data assimilation system to investigate the long-term variability of the Kuroshio path south of Japan, and pointed out that the Kuroshio large meander is closely related to Kuroshio transport. Tsujino et al. (2013) explored the effects of large-scale wind forcing on the Kuroshio path south of Japan using a 60-year historical OGCM simulation, and demonstrated the relationship between the path and Kuroshio transport.
While the trimodality in the Kuroshio path south of Japan has been a hot topic since its recognition in observations (Kawabe, 1985, 1995), observational and modeling studies to date have targeted different states of the path separately, with the large meander receiving more attention. There have been limited studies on the three distinct Kuroshio paths, which address the associated temporal-spatial variations of oceanic anomaly fields simultaneously based on long-term reanalysis datasets. In this study, an ocean reanalysis dataset for the Northwest Pacific with a 50-year time span from 1958 to 2007 is used as the baseline to retrieve the Kuroshio path south of Japan, and to investigate the temporal-spatial variations of ocean state variables in the coastal waters off southern Japan. The rest of the paper is arranged as follows. Data and methods are introduced in Section 2. Analysis results are presented in Section 3, followed by summary and discussion in Section 4.
2 Data and methods
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The oceanic reanalysis dataset employed in this study is produced by a regional ocean reanalysis system for the Northwest Pacific, called the China Ocean Reanalysis (CORA; http://www.cmoc-china.cn; Han et al., 2011, 2013). The CORA system assimilated satellite remote sensing sea surface temperature (SST), altimetry sea surface height anomaly (SSHA), and in situ temperature/salinity profiles into the parallelized Princeton Ocean Model with a generalized coordinate system (POMgcs; Mellor et al., 2002; Ezer and Mellor, 2004) by using a sequential three-dimensional variational (3D-Var) scheme implemented within a multigrid framework (Li et al., 2008). The daily reanalysis spans 50 years from January 1958 to December 2007. The atmospheric forcing fields used are 0.25° daily wind from the cross-calibrated multi-platform (Atlas et al., 2011), combining 6-hourly wind at 1.875° and heat and water fluxes from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR; Kalnay et al., 1996) reanalysis. Considering the tidal mixing effect on the formation of tidal front in the coastal region, a tidal open boundary condition was introduced into the CORA system. The horizontal resolution of the CORA reanalysis data for the Kuroshio south of Japan is 0.25°. A total of 35 hybrid vertical coordinate surfaces is employed by combining sigma and z-level. Throughout the rest of this paper, this regional ocean reanalysis dataset is called the CORA data. Based on the CORA data, the three typical Kuroshio paths south of Japan are obtained (see Section 3.1 for the classification of the typical Kuroshio paths) and shown in Fig. 1. The daily time series of the latitude of Kuroshio path is then obtained from the CORA data by defining a specific sea surface height (SSH) isoline of 70 cm as the axis (Qiu and Chen, 2005; Sugimoto and Hanawa, 2012).
To examine the skill of the CORA data in the study area (29°–36°N, 131°–141°E), the daily absolute dynamic topography (ADT) data of the Ssalto/Duacs altimeter products during January 1993 to December 2007 from the Copernicus Marine and Environment Monitoring Service (CMEMS) (https://marine.copernicus.eu) is used as a reference. The monthly time series of the southernmost latitude of the Kuroshio path between 136°E and 140°E is also used, which is produced by the Japan Meteorological Agency (JMA) based on the temperature at the depth of 200 m and satellite-derived SST data (Sugimoto et al., 2020), hereinafter the JMA data. The JMA data can be obtained at http://www.data.jma.go.jp/kaiyou/data/shindan/b_2/kuroshio_stream/kuro_slat.txt. It is worth noting that 110 cm ADT isoline is used to define the axis as the reference mean sea surface employed in the altimetry data is different from that of the CORA data.
Figure 2 provides the validation of the CORA data (blue solid line) against both the altimetry observations (red solid line) and JMA data (black dashed line). It can be seen that the CORA data well capture the observed monthly averaged axis positions between 136°E and 140°E. The correlation coefficients of the CORA data with the altimetry and JMA data exceed 0.90, with a 99% confidence level; hereafter all correlations mentioned in this study are significant with the 99% confidence level.
The degree of fidelity of the CORA data can be further demonstrated by comparing to the altimetry data during 2004/05 tLM event, which is the first tLM event since the altimetry data became available in 1993, and to the sea-level difference between Kushimoto and Uragami obtained by using 43 years (1965–2007) monthly mean sea-level observational data from the Permanent Service for Mean Sea Level (PSMSL) (https://www.psmsl.org; Holgate et al., 2013). The comparison confirms that the CORA data used in this study can be considered as a competent candidate for exploring the variation of the Kuroshio in this region (see Section 3.3 for detail).
For analysis, the empirical orthogonal function (EOF) analysis is used, which is a method of decomposition of a signal in terms of orthogonal basis functions, same as performing a principal components analysis (PCA) on the data (Hannachi et al., 2007). It has been adopted to investigate the tempo-spatial variation of ocean state variables related to the Kuroshio (Yasuda and Sakurai, 2006; Wei et al., 2013; Wang and Oey, 2014, 2016).
In this study, the main focus is the variability of the Kuroshio path south of Japan at the interannual time scale and beyond. Based on the 50-year daily time series of the Kuroshio path extracted from the CORA data with the axis position represented by latitude with 0.25° longitude resolution between 131°E and 141°E, the corresponding monthly mean time series was calculated and then a 13-month lowpass filter was applied. The EOF analysis was performed to the anomalies of the monthly mean axis position by subtracting the climatological monthly means calculated using all 50 years of the data.
3 Results
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3.1 Classification of the three Kuroshio paths
Based on the definition of Sugimoto and Hanawa (2012), the typical Kuroshio paths are defined as follows:
(1) If the average position of the Kuroshio path between 135°E and 136°E is located north of 32°N, the average position of the Kuroshio path between 137°E and 138°E is located north of 32.8°N, and the average position of the Kuroshio path around 140°E is located north of 33.2°N, it is the nNLM path.
(2) If the average position of the Kuroshio path between 135°E and 136°E is located north of 32°N, and the average position of the Kuroshio path around 140°E is located south of 33.0°N, it is the oNLM path.
(3) If the average position of the Kuroshio path between 136°E and 138°E is located south of 32.3°N, it is the tLM path.
According to the above classification, the numbers of monthly mean paths for nNLM, oNLM, and tLM are 110 (18.3%), 244 (40.7%), and 150 (25%), respectively. The remaining 96 paths (10%), called the “others” when the Kuroshio takes an undulated path that is not regarded as any of the three typical paths, represent the transitioning paths of the Kuroshio, which is not considered in this study.
3.2 EOF analysis
By performing EOF analysis on the 50-year monthly mean time series of the Kuroshio path, the EOF modes (i.e., spatial patterns) and their corresponding principal components (PCs) (i.e., temporal coefficients) were obtained. All the modes pass the significant test (North et al., 1982). The cumulative fraction of the total variance accounted for by the first three leading EOF modes is listed in Table 1, which contribute more than 91% of the total variance.
The spatial distributions of the first three EOF modes are shown in Fig. 3. The first leading EOF mode (EOF1) accounts for 46.26% of the total variance, with a negative (southward) curve between 138°E and 141°E (solid line). Such spatial distribution of EOF1 resembles the oNLM path south of Japan. EOF2, with a positive curve between 136°E and 138°E (dashed line), accounts for 28.23% of the total variance. The spatial distribution of EOF2 with the curve being regarded as the corresponding southward meander is similar to the tLM path. Finally, 16.71% of the total variance is accounted for by EOF3. There is a similarity between EOF3 with all spatial values being positive (northward) and the nNLM path (dash-dotted line).
Figure 4 displays the temporal coefficients corresponding to the first three leading EOF modes. As shown in Fig. 4a, the oNLM events mostly correspond to positive values of PC1. The typical Kuroshio large meander periods, occurred in 1959–1963, 1981–1984, 1986–1988, 1989–1990, and 2004–2005, respectively, well matched with all negative peaks of PC2 in Fig. 4b. It seems that the amplitude of PC2 needed to be larger than 0.35 (as a threshold) when the Kuroshio large meander occurred. For instance, the amplitude of PC2 was only about 0.2 around 2000, during which no large meander occurred. Similarly, most positive peaks of PC3 are in the nNLM periods.
In summary, it seems that the first three leading modes of the EOF and their corresponding temporal coefficients reflect the characteristics of the three typical Kuroshio paths. Next, the physical meaning of each leading mode in terms of the three typical Kuroshio paths is searched by exploring these spatial patterns and time series signals.
3.3 Spatial variation characteristics of anomaly fields
In this section, regression analysis is employed to explore the spatial variations of anomaly fields of SSH, SST, sea surface salinity (SSS), 200-m depth temperature, and 200-m depth salinity in the study region, namely, SSHA, SSTA, SSSA, 200TA, and 200SA. The regression coefficient fields of these variables are obtained by regressing their anomaly fields onto PC1, PC2, and PC3, respectively. Composite analysis is used to examine the regression results. Comparison between the regression maps and composite anomalies can confirm the previous interpretation of the contributions of the three typical Kuroshio paths to the first three leading EOF modes.
3.3.1 Regression and composite analyses associated with PC1
The regression fields for all the five ocean variables with respect to PC1 are depicted in the left panels of Fig. 5. All the regression fields mainly present a dipole pattern with its center being symmetrical along the northeast–southwest (NE–SW) direction. The positive anomaly center is located south of the Kii Peninsula, and the negative anomaly center appears downstream of the positive one. The positive and negative anomaly areas are located on offshore and inshore sides of the Kuroshio path, respectively. During the oNLM period, each composite field of SSHA, SSSA, 200TA, and 200SA (right panels in Fig. 5) shows the same dipole pattern. For the composite SSTA field (Fig. 5d) with the dipole absent, there is only a negative anomaly center, but not obvious in strength or range. The dipoles produced by the regression being the same in strength and spatial structure with the composite can be the evidence that the first leading EOF mode is contributed by the oNLM path.
According to previous studies, such dipole distribution in the regression field is due to an anticyclonic circulation, i.e., the Shikoku Recirculation Gyre (SRG), located to the south of the Kuroshio in the Shikoku Basin (Sugimoto et al., 1986; Mitsudera et al., 2006). Following Qiu and Chen (2005) and Li et al. (2014), the formula below is employed to calculate the strength of SRG using the CORA data.
$ S\left( t \right) \equiv \iint_A {h\left( {x,y,t} \right){\rm{d}}x{\rm{d}}y} , $
where A denotes the area with SSH>0.9 m in the region of 30°–33.5°N, 132°–140°E.
As shown in Fig. 6a, when the Kuroshio transitions to the oNLM path, the SRG strength increases, which leads to positive oceanic anomalies south of the Kii Peninsula (Qiu and Miao, 2000; Li et al., 2014). At the same time, a cyclonic circulation, associated with the large meander, propagates eastward to the south of the Izu Peninsula, resulting in negative oceanic anomalies. Taking the 2004/05 tLM event as an example, Figs 7c and d describe the time evolution of SSH and its anomaly obtained from the first 10-day mean in each month using the CORA and altimetry data during the decay period (from April to September 2005; see Usui et al., 2011). The eastward propagation process of the negative oceanic anomalies can be clearly observed in both CORA and altimetry data, which match each other well.
3.3.2 Regression and composite analyses associated with PC2
As shown in the left panels of Fig. 8, the manifestation for each of the regression fields of SSHA, SSSA, 200TA, and 200SA (Figs 8a, e, g, and i) upon PC2 is a spatially asymmetric dipole pattern but exhibiting the same orientation of NE–SW with that for PC1. The negative anomaly area has a greater amplitude and range than the positive one for each of these four variables. The range of the negative anomaly area stretches across the mean path of the Kuroshio large meander, with a notable center located between the inshore side of the Kuroshio path and the southern coast of Honshu. Meanwhile, the relatively weak positive anomaly center exists on the inshore side of the Kuroshio path. Although the regression field of SSTA (Fig. 8c) also presents the same NE–SW oriented dipole pattern, it is weak in strength and small in range in contrast to the other four variables. This situation is just like regression field upon PC1, the negative regression center at 200-m depth (Fig. 8e), comparing with that of SSTA (Fig. 8c), is both strengthened and enlarged in range with the negative center moving southward from the inshore side of the Kuroshio path to coincide with the mean state of the path, while the positive center is clearly presented downstream of the negative one. Comparatively speaking, the positive regression field of 200SA (Fig. 8i) exhibits the same spatial pattern with that of SSSA (Fig. 8e) as those upon PC1. This similarity in spatial pattern is demonstrated between the regression fields upon PC2 and the composite anomaly fields in the tLM periods (see the right panels of Fig. 8), except the positive and negative centers are exactly the opposite. Thus, it indicates that EOF2 is contributed by the Kuroshio large meander path.
The oceanic anomalies revealed by both regression and composite are confirmed by modeling results for individual large meander events (Douglass et al., 2012; Tsujino et al., 2013). Figures 7a and b depict the time evolution of SSH and its anomaly obtained from the first 10-day mean in each month using the CORA and altimetry data during the 2004/05 Kuroshio large meander formation period (from March to August 2004; see Miyazawa et al., 2008; Usui et al., 2008). It shows that an accompanying anticyclonic eddy with the trigger meander (a small-scale meander first appears off the southeast of Kyushu (Solomon, 1978; not shown here) and then grows during its eastward propagation, resulting in the tLM; see Usui (2019) was developed on the opposite side of the Kuroshio path and the positive anomaly was advected downstream until the large meander blocked it and hence the anomaly accumulated there. By comparison, during the formation process of the large meander (Figs 7a and b) the CORA data do not match the altimetry data as well as during the decay process (Figs 7c and d).
3.3.3 Regression and composite analyses associated with PC3
The regression fields of SSHA, SSTA, SSSA, 200TA, and 200SA upon PC3 (left panels in Fig. 9) are mainly characterized by a large positive area to the south of Honshu, with the center appearing at the offshore side of the Kuroshio path. Meanwhile, all these regression fields display weak negative anomalies in the southern coastal areas of Honshu. The composite anomaly fields of these five variables related to the nNLM path (right panel in Fig. 9) are well matched in spatial patterns with the regression fields upon PC3, which hints that the nNLM path of the Kuroshio contributes to EOF3.
The following mechanism is attributed to such characteristics of the anomaly fields: when the Kuroshio is in the period of the non-large meander, the current continuously transports low-potential vorticity (PV) water from the upstream of the Kuroshio south of Japan and accumulates it in the region south of Japan, resulting in the strengthening of the SRG (Qiu and Miao, 2000; Li et al., 2014; Usui, 2019) and a huge positive anomaly region on the offshore side of the Kuroshio south of Japan. Following Qiu and Miao (2000), the averaged relative vorticity ( $ \zeta = \partial v/ \partial x - \partial u/\partial y $) over the upper 500 m in the region of (30.0°–33.5°N, 132°–140°E) is calculated by using the CORA data. As shown in Fig. 6b, a decreasing trend of the relative vorticity is captured in the nNLM path (e.g., 1985–1986, 1988–1989), which denotes the accumulation of low-PV anomalies south of Japan. At the same time, the strength of the SRG is larger compared to that in the tLM period (see Fig. 6a).
In summary, the spatial patterns of SSTA illustrated by regression in this study are essentially in agreement with those illustrated in Sugimoto et al. (2020) using the multiscale ultrahigh-resolution SST product from 2003 to 2018 with a horizontal resolution of 1 km developed by the NASA Jet Propulsion Laboratory. Especially the regression map of SSTA upon PC2 is matched well with that in Sugimoto et al. (2020), which was obtained from the ERA5 SST data for the six events of large meander since 1975. At the same time, in their paper the coastal warming off the Tokai district at 200-m depth during the tLM period was confirmed in the temperature profiles archived in the World Ocean Database 2018, which is in good agreement with the regression map of 200TA. Since the regression fields upon the first three PCs revealing the spatial anomaly distribution characteristics of the ocean variables are consistent with the composite maps of SSH and thermohaline anomalies in the Kuroshio region south of Japan, it can thus be concluded that contributions of the three typical paths can be separated into the first three leading EOF modes, respectively.
3.4 A new index to detect the Kuroshio large meander
It has been found that the sea-level difference between Kushimoto and Uragami depicts the Kuroshio variation around the Kii Peninsula by exhibiting relatively small (large) in large meander (non-large meander) periods (Kawabe, 1995; Sekine and Fujita, 1999) and gives an excellent index of the large meander (abbreviated as the KUI) (Nakamura et al., 2012; Miyama and Miyazawa, 2013). The temporal variation of PC2 (solid line) against the monthly mean time series of the KUI (dashed line) is illustrated in Fig. 10. The correlation coefficient of the two time series is as high as 0.78. As can be seen in Fig. 10, the normalized PC2 matches well with the KUI during the large meander years except for the meander of 1975–1980. The reason may lie in the fact that there was a short period of northward shift of the Kuroshio path in 1978; and a significant northward overshoot (about half degree) was found in the reconstruction of the Kuroshio path from the CORA system (see the blue solid line in Fig. 2). Consequently, the southernmost positions of the Kuroshio path cannot be reproduced correctly either by the CORA system right after that short period of northward shift. However, the similarity between PC2 and KUI implies that PC2 can be defined as a new Kuroshio large meander index.
Following the wavelet analysis method described in Torrence and Compo (1998), the Morlet wavelet transform was applied to the time series of PC2 and the JMA data. Then, the global wavelet spectrum, which is the time average of the wavelet power spectrum (Farge, 1992; Nakamura et al., 2006), can be achieved. As shown in Fig. 11, periods of about 7–8 years and 20 years present in the spectra of PC2 and the JMA data, with the significance exceeding the 95% confidence level for a red-noise process. The variability of the Kuroshio path south of Japan on such time scales was reported in previous studies. For instance, Nitani (1975) concluded that the long-term variation of the Kuroshio south of Japan exhibits a period of 7–9 years. Kawabe (1987) also pointed out that the Kuroshio large meander has periods of about 7–8.5 years and 20 years base on his power spectral analysis. Although a period of about 4 years is derived in the spectra of both PC2 and the JMA data, the latter is not statistically significant at the 95% confidence level.
4 Summary and discussion
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In this study, the temporal-spatial variations of the Kuroshio path south of Japan and oceanic anomaly fields in the coastal region related to the three typical paths are investigated based on a long time series reconstruction from 1958 to 2007 in the Northwest Pacific by the CORA. The EOF analysis was applied to the latitude deviation of monthly mean axis position of the Kuroshio path south of Japan in the CORA data. Results show that the first three leading EOF modes (EOF1, EOF2, and EOF3) can explain more than 91% of the total variance. A thorough examination through comparing research findings with observations is performed on the results of EOF analysis by using regression and composite analyses. The regression fields upon the first three leading PCs clearly demonstrate the spatial variation patterns of the ocean state variables, including SSH, SST and SSS together with 200-m depth temperature and salinity, which are contributed by the three typical Kuroshio paths; they are in good agreement with the composites. Therefore, it can be concluded that the first three leading EOFs and their corresponding time coefficients (PC1, PC2, and PC3) can be closely linked to the three typical paths taken by the Kuroshio, in which the oNLM path contributes to the EOF1 mode, the tLM path contributes to the EOF2 mode, and the nNLM path contributes to the EOF3 mode, respectively.
Through comparing with the KUI (the sea-level difference between Kushimoto and Uragami), PC2 can be defined as a new index to detect the Kuroshio large meander. A correlation coefficient of 0.78 indicates a strong correlation between PC2 and KUI. Spectral structures of PC2 and the JMA data exhibit variability of the Kuroshio path south of Japan at time scales of about 7–8 years and 20 years.
From this study, it is recognized that there is room for improving the capability of the CORA system in the Kuroshio region. It is definitely a topic worthy of further investigation to achieve the goal of continuously upgrading the CORA system, which has been developed for more than a decade (Han et al., 2011). Another interesting topic as a follow-up study is the prediction of the Kuroshio path south of Japan, which has always been a topic of concern. At present, numerical modeling is still the main method for the prediction of the Kuroshio path south of Japan (Miyazawa et al., 2005; Usui et al., 2006). With the increasing availability of oceanographic observations and ocean reanalysis products with reliable error estimates, extensive applications of various types of artificial neural networks based on deep learning framework have recently been directed to ocean prediction (Zhang et al., 2017; Song et al., 2020). Thus, it motivates us to explore the feasibility of such method in the prediction of the Kuroshio path south of Japan.
Acknowledgements
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We thank the Copernicus Marine and Environment Monitoring Service for providing the ADT data (https://resources.marine.copernicus.eu/?option=com_csw& task=results), the Permanent Service for Mean Sea Level for providing the monthly mean sea level data of Kushimoto and Uragami (https://www.psmsl.org/data/obtaining/), and the Japan Meteorological Agency for providing monthly time series of the southernmost latitude of the Kuroshio path between 136°E and 140°E (http://www.data.jma.go.jp/kaiyou/data/shindan/b_2/kuroshio_stream/kuro_slat.txt). We also thank the editor and two anonymous reviewers for their helpful comments to improve the quality of this paper.
Funding
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  • The National Natural Science Foundation of China under contract No. 41876014.
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doi: 10.1007/s13131-021-1941-9
  • Receive Date：2021-05-24
  • Online Date：2025-11-20
  • Published：2022-02-25
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  • Received：2021-05-24
  • Accepted：2021-09-03
Funding
The National Natural Science Foundation of China under contract No. 41876014.
Affiliations
    1 School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
    2 The PLA 31010 Units, Beijing 100081, China
    3 Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China

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表12种不同金属材料的力学参数
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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