Regional characteristics of the effects of the El Niño-Southern Oscillation on the sea level in the China Sea

Wang, Hui; Liu, Kexiu; Feng, Jianlong; Fan, Wenjie; Liu, Qiulin; Xu, Yao; Zhang, Zengjian

doi:10.1007/s10236-018-1144-x

Cited by 26 publications

(15 citation statements)

References 62 publications

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“…In the western North Pacific, ENSO influence on coastal SLAs weakens with latitude, as shown by the analysis of 27 tide gauges along the China Coastline from 1968 to 2016 and satellite altimetry (Wang et al 2018). From 1993 to 2015, ENSO-SLA correlations are the largest in the South China Sea (> 0.6 in most areas; also see Cheng et al 2015), weaker in the East China Sea (> 0.4 in most areas) and weakest in the Bohai and Yellow Seas (< 0.4), with all correlation coefficients being statistically significant at the 95% confidence level.…”

Section: The Pacific Oceanmentioning

confidence: 99%

Impacts of Basin-Scale Climate Modes on Coastal Sea Level: a Review

et al. 2019

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Global sea level rise (SLR) associated with a warming climate exerts significant stress on coastal societies and low-lying island regions. The rates of coastal SLR observed in the past few decades, however, have large spatial and temporal differences from the global mean, which to a large part have been attributed to basin-scale climate modes. In this paper, we review our current state of knowledge about climate modes’ impacts on coastal sea level variability from interannual-to-multidecadal timescales. Relevant climate modes, their impacts and associated driving mechanisms through both remote and local processes are elaborated separately for the Pacific, Indian and Atlantic Oceans. This paper also identifies major issues and challenges for future research on climate modes’ impacts on coastal sea level. Understanding the effects of climate modes is essential for skillful near-term predictions and reliable uncertainty quantifications for future projections of coastal SLR.

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Section: The Pacific Oceanmentioning

confidence: 99%

Impacts of Basin-Scale Climate Modes on Coastal Sea Level: a Review

et al. 2019

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“…El Nino Southern Oscillation (ENSO) is a complex climate phenomenon that occurs in the tropical Pacific Ocean and has a significant impact on global weather patterns. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina (Wang et al, 2018). ENSO is characterized by periodic variations in sea surface temperatures, atmospheric pressure, and wind patterns, which can lead to changes in global climate patterns.…”

Section: Introductionmentioning

confidence: 99%

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

Lin¹,

Tang²,

Wijaya³

2022

Idn. J. Earth Sci.

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El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.

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“…Sea level rise is one of the most important indicators of global warming, and much effort has been devoted to studying the variation of mean sea level (MSL, Douglas, ; Bromirski et al, ; Church et al, ; Wang et al, ). Various factors, such as changes in oceanic and atmospheric circulation (as dynamics changes), large‐scale deformation of ocean basins, variation in Earth's gravity, and local land movement can cause sea level to change on local scales (Church et al, ; Stammer et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Compared with MSL (Ding et al, ; Rong et al, ; Wang et al, ; Yu et al, ), fewer studies have been conducted on the changes in the ESLs along the Chinese coast. Chen and Wang () analyzed tide gauge data at Wusong and Huangpu and indicated that the ESLs at these locations increased from 1915 to 1985.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of the Extreme Sea Level Rise Along the Chinese Coast

Feng

Wang

et al. 2019

Earth and Space Science

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Using high-frequency tide gauge observations from 1980 to 2017, we analyzed changes in extreme sea levels (ESLs) along the Chinese coast to examine whether extreme sea-level rise acceleration could be observed. The results indicated that the ESL has risen with fluctuations along the Chinese coast. The ESL also featured significant decadal variabilities. The ESL rise was accelerated at most tide gauges throughout the whole study period, especially after the 1980s, while the acceleration patterns varied with station location. The mean sea level (MSL) change was confirmed as the major driver of the ESL change according to the analysis. However, the skew surge may also have affected the extremes at many of the tide gauges, which was observed after detrending. The extreme/MSL had a significant positive correlation with the Atlantic Multidecadal Oscillation, which is supposed to affect the MSL through a combination of variation in sea surface temperature, pressure system, and wind. That sea level oscillation is dominated by Atlantic Multidecadal Oscillation should be taken into consideration when studying accelerations in the rate of ESLs and of MSLs along the Chinese coast.Other studies revealed that the long-term trend of MSL is nonlinear and that the MSL rise has accelerated in recent years

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Regional characteristics of the effects of the El Niño-Southern Oscillation on the sea level in the China Sea

Cited by 26 publications

References 62 publications

Impacts of Basin-Scale Climate Modes on Coastal Sea Level: a Review

Impacts of Basin-Scale Climate Modes on Coastal Sea Level: a Review

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

Acceleration of the Extreme Sea Level Rise Along the Chinese Coast

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