Variations in Summer Marine Heatwaves in the South China Sea

Yao, Yulong; Wang, Chunzai

doi:10.1029/2021jc017792

Cited by 93 publications

(84 citation statements)

References 82 publications

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“…The close correlation between the local mean SST and MHW days and frequency suggest that the background monthly mean atmospheric and oceanic conditions caused by climate change are important for the formation of MHWs. The heat budget analyses conducted by, for example, Li and Wang [22] and Saranya et al [24] are required to fully unravel the physical processes of the BOB MHWs occurring at different locations and during different seasons. The linear trend caused by global warming has not fully emerged for the warm season MHWs in the BOB in recent decades, but once the background temperatures significantly changes, it would fuel more powerful and devastating MHWs.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…Climate modes play an important role in modulating the variability of MHWs, but the dominant modes substantially vary across regions. Generally, MHWs are closely associated with the El Niño and Southern Oscillation (ENSO), the Indian Ocean basin mode (IOB) and dipole mode (IOD) and the Pacific Decadal Oscillation (PDO) in Indo-Pacific regions [19][20][21][22][23][24][25][26]. The North Atlantic Oscillation (NAO) is also shown to be connected with the formation of Indian Ocean MHWs [13,24].…”

Section: Introductionmentioning

confidence: 99%

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The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

Gao

Liu

et al. 2022

Atmosphere

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Marine heatwaves (MHWs) are long-lasting extreme oceanic warming events that can cause devastating effects on warm-water corals and associated ecosystems. The linear trend and interannual variability of MHWs over the Bay of Bengal (BOB) during 1982–2020 are investigated by a high-resolution daily sea surface temperature (SST) dataset. In regions where warm-water coral reefs are concentrated, annual MHW days and frequency significantly increase during 1982–2020, at rates exceeding that of the global mean. The coldest boreal winter season witnesses significant and steady increase trends in MHW days and frequency. In contrast, the trend is insignificant in the climatological warmest season (March to June) south of 15° N in the BOB, mainly due to large interannual variability. El Niño and Southern Oscillation (ENSO) dominates the interannual variability of BOB MHWs, which are highly consistent with the evolution of the mean SST. The negative phase of North Atlantic Oscillation (NAO) also modulates the occurrences of MHWs, especially over the northeastern BOB. The two climate modes synergistically explain about 50~70% of the interannual variances in the BOB’s MHWs. Correlation analysis reveals that south of 15° N in the BOB, the effect of El Niño on MHWs is evident from the boreal autumn of its developing phase to the boreal summer of its decaying phase, along with limited influence from NAO. However, in the northeast of the BOB, the effect of El Niño merely emerges from April to August of its decaying stage. In comparison, boreal winter-to-spring NAO exerts a strong control over March-to-June MHWs in the northeastern BOB. The results suggest that various climate modes may jointly or separately influence MHWs at certain seasons and locations, which is important for the seasonal prediction of MHWs. Indeed, when combining the Niño3.4 mature winter index and boreal winter-to-spring NAO index to build a regression model, it is more effective in reproducing the BOB’s MHW frequency compared to the Niño3.4 index alone.

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Section: Discussion and Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

Gao

Liu

et al. 2022

Atmosphere

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“…Recently, summer MHWs in the SCS have been reported (Yao and Wang, 2021). Under the influence of intensification of the western North Pacific subtropical high, upwelling in the midwestern SCS weakens and then causes basin-wide severe summer MHWs.…”

Section: Introductionmentioning

confidence: 99%

Classifications and Characteristics of Marine Heatwaves in the Northern South China Sea

et al. 2022

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The classifications and associated characteristics of marine heatwaves (MHWs) in the northern South China Sea (SCS) have been investigated. Based on the ratio of the area with MHWs to the total area of the northern SCS, the MHWs are divided into three categories, i.e., narrow coverage, moderate coverage, and wide coverage. Different kinds of MHWs (i.e., narrow coverage, moderate coverage, and wide coverage) contribute around 64, 28, and 8% to the total days with MHWs, respectively. Narrow coverage MHW scatters over the northern SCS continental shelf, and moderate (wide) coverage MHWs are concentrated east of Hainan Island (mid of northern SCS continental shelf). Wide coverage MHWs contribute more than 50% to the total MHW cumulative sea surface temperature (SST) anomalies. The cumulative days and temperature anomalies of all kinds of MHWs exhibit significant long-term trends and decadal variability.

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“…Previous studies confirmed that ocean temperature plays a vital role in the ecosystems, dynamics and thermodynamics of the SCS. For example, ocean temperature variability can significantly influence the occurrence of marine heatwaves in the SCS [18]. In addition, ocean temperature can also play a role in the processes of air-sea interactions in the SCS, which can exert noticeable impacts on the East Asian climate [19].…”

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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Variations in Summer Marine Heatwaves in the South China Sea

Cited by 93 publications

References 82 publications

The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

Classifications and Characteristics of Marine Heatwaves in the Northern South China Sea

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

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