A comparison of the strength and position variability of the Kuroshio Extension SST front

Yu, Peilong; Zhang, Lifeng; Li, Mingyang; Zhong, Quanjia; Zhang, Yongchui; Li, Xin

doi:10.1007/s13131-020-1567-3

Cited by 24 publications

(4 citation statements)

References 38 publications

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“…The SSH data find extensive application in estimating the kinetic energy (KE) and the geostrophic currents field within ocean circulation [30,31]. Figure 3 illustrates an instance of KE fields derived from high-resolution SSH data and reconstructed super-resolution SSH data.…”

Section: Resultsmentioning

confidence: 99%

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

Zhang,

Sun,

Guo

et al. 2024

Remote Sensing

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In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

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Section: Resultsmentioning

confidence: 99%

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

Zhang,

Sun,

Guo

et al. 2024

Remote Sensing

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“…The Kuroshio Extension Front varies significantly at interannual to decadal frequencies with respect to strength, latitude, and elongated versus convoluted pathway (Wang et al 2016;Yu et al 2020), and its annual mean position off Japan has shifted between 33°and 37°N over the period 1993 to 2013 (Wang et al 2016). This variability strongly correlates with the North Pacific Oscillation, a north-south seesaw between the Aleutian Low and the Pacific High below it (Fig.…”

Section: Modern Oceanography and Climatementioning

confidence: 90%

“…16). Although the latitudinal position and flow speed of the Kuroshio Extension change little through the year, the magnitude of the Kuroshio Extension Front strength, as measured by the horizontal temperature gradient, is greatest during the cold season and least during the warm season (Chen 2008, Kida et al 2015, Yu et al 2020. Mesoscale perturbations along the Kuroshio Extension Front are also greater in winter (Wei et al 2017).…”

Section: Modern Oceanography and Climatementioning

confidence: 99%

“…4). The Aleutian Low intensifies during its positive phase and shifts northwards (Sugimoto and Hanawa 2009;Yu et al 2020), favouring a strengthened and northward-moving Kuroshio Extension Front. Changes in the latitude of the Aleutian Low, a cold-season phenomenon that dissipates almost entirely in summer, may therefore be linked to the position of the Kuroshio Extension Front during MIS 19.…”

Section: Modern Oceanography and Climatementioning

confidence: 99%

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Review of the Early–Middle Pleistocene boundary and Marine Isotope Stage 19

Head

2021

Prog Earth Planet Sci

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The Global Boundary Stratotype Section and Point (GSSP) defining the base of the Chibanian Stage and Middle Pleistocene Subseries at the Chiba section, Japan, was ratified on January 17, 2020. Although this completed a process initiated by the International Union for Quaternary Research in 1973, the term Middle Pleistocene had been in use since the 1860s. The Chiba GSSP occurs immediately below the top of Marine Isotope Substage (MIS) 19c and has an astronomical age of 774.1 ka. The Matuyama–Brunhes paleomagnetic reversal has a directional midpoint just 1.1 m above the GSSP and serves as the primary guide to the boundary. This reversal lies within the Early–Middle Pleistocene transition and has long been favoured to mark the base of the Middle Pleistocene. MIS 19 occurs within an interval of low-amplitude orbital eccentricity and was triggered by an obliquity cycle. It spans two insolation peaks resulting from precession minima and has a duration of ~ 28 to 33 kyr. MIS 19c begins ~ 791–787.5 ka, includes full interglacial conditions which lasted for ~ 8–12.5 kyr, and ends with glacial inception at ~ 774–777 ka. This inception has left an array of climatostratigraphic signals close to the Early–Middle Pleistocene boundary. MIS 19b–a contains a series of three or four interstadials often with rectangular-shaped waveforms and marked by abrupt (< 200 year) transitions. Intervening stadials including the inception of glaciation are linked to the calving of ice sheets into the northern North Atlantic and consequent disruption of the Atlantic meridional overturning circulation (AMOC), which by means of the thermal bipolar seesaw caused phase-lagged warming events in the Antarctic. The coherence of stadial–interstadial oscillations during MIS 19b–a across the Asian–Pacific and North Atlantic–Mediterranean realms suggests AMOC-originated shifts in the Intertropical Convergence Zone and pacing by equatorial insolation forcing. Low-latitude monsoon dynamics appear to have amplified responses regionally although high-latitude teleconnections may also have played a role.

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Dissolved trace metal (Cu, Cd, Co, Ni, and Ag) distribution and Cu speciation in the southern Yellow Sea and Bohai Sea, China

Wang

Liu

et al. 2017

J. Geophys. Res. Oceans

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Trace metals play an important role in biogeochemical cycling in ocean systems. However, because the use of trace metal clean sampling and analytical techniques has been limited in coastal China, there are few accurate trace metal data for that region. This work studied spatial distribution of selected dissolved trace metals (Ag, Cu, Co, Cd, and Ni) and Cu speciation in the southern Yellow Sea (SYS) and Bohai Sea (BS). In general, the average metal (Cu, Co, Cd, and Ni) concentrations found in the SYS were lower by a factor of two than those in BS, and they are comparable to dissolved trace metal concentrations in coastal seawater of the United States and Europe. Possible sources and sinks and physical and biological processes that influenced the distribution of these trace metals in the study region were further examined. Close relationships were found between the trace metal spatial distribution with local freshwater discharge and processes such as sediment resuspension and biological uptake. Ag, owing to its extremely low concentrations, exhibited a unique distribution pattern that magnified the influences from the physical and biological processes. Cu speciation in the water column showed that, in the study region, Cu was strongly complexed with organic ligands and concentrations of free cupric ion were in the range of 10−12.6–10−13.2 mol L−1. The distribution of Cu‐complexing ligand, indicated by values of the side reaction coefficient α′, was similar to the Chl a distribution, suggesting that in situ biota production may be one main source of Cu‐complexing organic ligand.

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A comparison of the strength and position variability of the Kuroshio Extension SST front

Cited by 24 publications

References 38 publications

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

Review of the Early–Middle Pleistocene boundary and Marine Isotope Stage 19

Dissolved trace metal (Cu, Cd, Co, Ni, and Ag) distribution and Cu speciation in the southern Yellow Sea and Bohai Sea, China

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