Impacts of El Niño on the South China Sea surface salinity as seen from satellites

Qi, Jia; Du, Yan; Chi, Jinling; Yi, Daling Li; Li, Delei; Yin, Baoshu

doi:10.1088/1748-9326/ac6a6a

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…Bersinggungan dengan LCS memberikan Laut Natuna Utara memiliki massa air yang kompleks, dengan musim yang mempengaruhi kondisi interaksi laut dan atmoster (Hariyadi et al, 2021), yang memiliki karakteristik iklim mengikuti Pasifik tengah katulistiwa (Liu et al, 2002). El Nino Southern Oscillation (ENSO) berkontribusi dalam meningkatnya salinitas di LCS (Qi et al, 2022), begitu pula dengan probabilitas pusaran pilin tertutup pada fase dewasa (Zu et al, 2019). Nampaknya, Indian Ocean Dipole (IOD) juga memiliki peranan dalam kompleksitas suhu permukaan laut dan presipitasi di sekitar Laut Natuna Utara dan LCS (Khaldun et al, 2018).…”

Section: Pendahuluanunclassified

Karakteristik Natuna Off-Shelf Current (NOC) Berdasarkan Komputasi Data Laut Skala Kecil Berbasis Awan

Kurniawan¹,

Pranowo²,

Khakim³

et al. 2023

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Morfologi perairan Laut Natuna Utara yang unik menghasilkan arus yang memiliki kecepatan yang signifikan sebagai perpanjangan dari Vietnam Coastal Jest (VCJ) yang disebut sebagai Natuna Off-Shelf Current (NOC). Identifikasi NOC dilakukan untuk melihat pola arus pada setiap musim serta pengaruh fenomena ENSO terhadap pola arus yang dihasilkan. Visualisasi dilakukan menggunakan pendekatan komputasi awan berkecepatan tinggi diproses melalui platform google earth engine (GEE) dengan menggunakan input data dari HYCOM. Artikel ini bertujuan untuk melakukan identifikasi dan visualisasi NOC menggunakan pendekatan data berbasis raster yang diolah menggunakan komputasi awan di GEE. Tahun kajian sebagai periode observasi diantaranya adalah periode La Niña tahun 2022, periode El Niño kuat pada tahun 1997, dan tahun netral pada tahun 1996. Secara visual NOC terbentuk akibat perpanjangan VCJ pada bulan musim barat, dan terlihat di semua tahun kajian observasi. Arus paling signifikan dijumpai pada musim barat tahun 1996 (tahun netral) dengan kecepatan 1,7 m/detik di lapisan permukaan, 1,4 m/detik di kedalaman 50m untuk bulan November 1996 (tahun netral), dan 1,4 m/detik di kedalaman 100m pada November 1997 (periode ENSO) dan Februari 1996 (tahun netral).

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Section: Pendahuluanunclassified

Karakteristik Natuna Off-Shelf Current (NOC) Berdasarkan Komputasi Data Laut Skala Kecil Berbasis Awan

Kurniawan¹,

Pranowo²,

Khakim³

et al. 2023

chartdatum

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“…A low salinity tongue extends from the Southern part of the SCS, reaching as far as 10°N, which is closely related to the freshwater discharge from the Mekong and Rajang Rivers. Due to its special geographical location, the spatial distribution of the salinity in the SCS has significant features which are closely related to El Niño-Southern Oscillation (ENSO) [16][17][18][19], Asian monsoons, and the Pacific Western boundary current system [15,20]. Previous studies have suggested that the variability of the salinity in the SCS has a significant influence on the regional circulation and climate changes [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method

Lin

Yin

et al. 2022

Remote Sensing

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Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.

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“…Extended continental shelves exist along the north boundary and southwest boundary, while the deeper water is oriented in a southwest-northeast direction across the central part of the SCS [12]. Due to its unique geographical location and complicated dynamical features, the temperature structures in the SCS have remarkable characteristics associated with El Niño-Southern Oscillation (ENSO) [13], Asian monsoon and Pacific western boundary current system [14,15], which have a far-reaching influence on the regional and global climate [16,17]. In the climatological mean, sea surface temperature (SST) in the SCS shows a chiefly southwest-northeast oriented pattern, which is gradually increasing southward (Figure 1b).…”

Section: Introductionmentioning

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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Impacts of El Niño on the South China Sea surface salinity as seen from satellites

Cited by 7 publications

References 48 publications

Karakteristik Natuna Off-Shelf Current (NOC) Berdasarkan Komputasi Data Laut Skala Kecil Berbasis Awan

Karakteristik Natuna Off-Shelf Current (NOC) Berdasarkan Komputasi Data Laut Skala Kecil Berbasis Awan

Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method

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

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