Seasonal and Interannual Variability in the Sea Surface Temperature Front in the Eastern Pacific Ocean

Wang, Yuntao; Jin, Liu; Liu, Hailong; Lin, Pengfei; Yuan, Yeping; Chai, Fei

doi:10.1029/2020jc016356

Cited by 23 publications

(49 citation statements)

References 86 publications

(142 reference statements)

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“…Therefore, shelf width is unlikely to be a major factor causing the different widths of the prominent SST/CI FGM zones. Offshore propagation of mesoscale eddies is an important factor affecting frontal activities in eastern boundary current systems (Chaigneau et al., 2009; Yuan & Castelao, 2017), South China Sea (Wang et al., 2020), and Eastern Pacific Ocean (Y. Wang et al., 2021). Some slope eddies can entrain freshwater from the Mississippi River and impact the distributions of frontal features near the Mississippi River delta (Schiller et al., 2011; Walker et al., 2005).…”

Section: Discussionmentioning

confidence: 99%

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

Zhang

2021

JGR Oceans

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Section: Discussionmentioning

confidence: 99%

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

Zhang

2021

JGR Oceans

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“…These methods have been applied to a variety of satellite datasets including GOCI, Advanced Very High‐Resolution Radiometer (AVHRR), Medium‐Resolution Imaging Spectrometer (MERIS), and Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS). In our study, the gradient‐based edge detection method (Canny, 1986) was used, which has been successfully applied to identify and extract ocean SST fronts (Castelao et al., 2006; Castelao & Wang, 2014; Y. Wang et al., 2015, 2021) and eddy boundaries (Zhang et al., 2019). This method can extract fronts automatically and precisely, and is very effective for processing long‐time‐series satellite data.…”

Section: Methodsmentioning

confidence: 99%

“…Following Castelao et al (2006), Castelao and Wang (2014), Wang et al (2015), and Wang et al (2021), the EOF decomposition method was used to analyze the spatial distribution and temporal variation of bi-monthly FP. Before EOF decomposition, the overall average FP of each pixel was removed.…”

Section: Empirical Orthogonal Function (Eof) Decomposition Methodsmentioning

confidence: 99%

Spatio‐Temporal Variability of Suspended Sediment Fronts (SSFs) on the Inner Shelf of the East China Sea: The Contribution of Multiple Factors

Zhang

Wei

et al. 2022

JGR Oceans

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Modulated by a host of complex processes, suspended sediment fronts (SSFs) on the inner shelf of the East China Sea persist strongly and vary notably. Using hourly suspended sediment concentration data collected by the Geostationary Ocean Color Imager over the period 2011–2021, a gradient‐based edge detection algorithm was implemented to extract SSFs; the frontal probability (FP) and seasonal and interannual variability were identified and interpreted. Pronounced frontal activity is principally confined to the nearshore waters within the 60‐m isobaths and decreases with increasing offshore distance. Frontogenesis is mainly determined by the bottom topography and tide‐induced mixing. Empirical orthogonal function decompositions reveal that the seasonal cycle dominates the variability of SSFs, which responds to the cycles of winds and related changes in coastal currents and upwelling. The highest FPs are identified in winter when wind‐induced suspended sediment transport and resuspension reach their annual maxima. In summer, stratification and the intrusion of Kuroshio subsurface water are not conducive to frontogenesis. Furthermore, the discharge of the Yangtze River has a certain influence on the frontal variability in the waters around the Zhoushan Archipelago. Notably, the interannual variability of SSFs is modulated by the El Niño‐Southern Oscillation. These findings, based on a comprehensive dataset of SSFs over 10 years, can usefully inform the studies of marine pollutant transport, sedimentary dynamics, fisheries, and wider ecological processes in the study area.

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“…It should be noted that the input variables used in this study might not be optimal combination for model. For example, previous studies have shown that wind stress, instead of wind speed, has a more significant impact on ocean temperature structure [62][63][64]. It would be interesting in future work to examine the different effects of wind stress and wind speed on OSTS estimation using a machine learning model.…”

Section: Discussionmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Liu

Chi

et al. 2022

Remote Sensing

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Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

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Seasonal and Interannual Variability in the Sea Surface Temperature Front in the Eastern Pacific Ocean

Cited by 23 publications

References 86 publications

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

Spatio‐Temporal Variability of Suspended Sediment Fronts (SSFs) on the Inner Shelf of the East China Sea: The Contribution of Multiple Factors

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

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