Genetic diversity and large-scale connectivity of the scleractinian coral Porites lutea in the South China Sea

Drought is one of the important issues in climate studies. A drought index, Taiwan Meteorological Drought index (TMD index), was previously proposed and is applied here to identify historical severe droughts in Taiwan in order to clarify the corresponding large-scale backgrounds as a potential alert to the society in future. Through the TMD index, several historical severe drought cases in Taiwan are detected and characterized by significant seasonal variability in the annual cycle. Composites for large-scale atmospheric and oceanic environments over different periods within the dry season are conducted. From October to December, the colder sea surface temperature (SST) pattern of Pacific Meridional Mode (PMM) and the PMM-induced local anomalous anticyclones over the South China Sea are both in charge of the extremely dry conditions in Taiwan. From January to February, cold SST in the South China Sea and its adjacent oceans dominates local atmospheric conditions above these regions and creates an unfavorable environment for convection systems. From March to May, a massive anomalous anticyclonic circulation centering beside Alaska and extending its properties to East Asia and Taiwan generates a descending environment and in turn suppresses convection systems to develop. Therefore, the extremely dry conditions under this system are expected.

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Geographic Variation for the Composition of Parrotfish in the South China Sea

10.21203/rs.3.rs-1100748/v1 ◽

2021 ◽

Author(s):

Qiumei Quan ◽

Yong Liu ◽

Teng Wang ◽

Chunhou Li

Keyword(s):

Species Richness ◽

South China Sea ◽

South China ◽

Scleractinian Coral ◽

Coral Species ◽

The South China Sea ◽

The South ◽

Nansha Islands ◽

Matrix Rank ◽

China Sea

Abstract Based on the key ecological processes of parrotfish in coral reefs, we compiled species presence-absence data across 51 sites in the South China Sea to identify the distribution and composition of parrotfish and explore the relationship between species distribution and environmental factors, and 50 species (the Pacific: 57 species) of parrotfish were record. Nansha islands had the highest abundance with 41 parrotfish species. Nestedness analysis indicated parrotfish community had statistically significant nested patterns in the South China Sea and Nansha islands was the topmost site of nested matrix rank. Scleractinian coral species richness and Log(reef area) both had a significant effect on sites nested matrix rank (P < 0.05), which supports habitat nestedness hypothesis in the South China Sea. Scrapers were the most important functional group composition while the browser had a greater contribution on species nested matrix rank. Linear regression model showed parrotfish species richness increased with increasing longitude, scleractinian coral species richness and reef area. Variations in the parrotfish species richness in longitude was related to distance from the biodiversity hotspot in the Indo-Australian Archipelago. Parrotfish was mainly distributed in the range of 26-29℃, which was almost the same as the optimum temperature for coral growth. Nansha islands should be as biodiversity conservation priority areas, which could provide important reference significance for conservation efforts of parrotfish in degraded coral reefs habitats, especially in the context of increasing natural variability and anthropogenic disturbance.

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Variabilities of sea level components over the South China Sea based on multi-sensor satellite observations

10.5194/egusphere-egu2020-6198 ◽

2020 ◽

Author(s):

Jin Sha ◽

Xiaoming Li

Keyword(s):

South China Sea ◽

Sea Level ◽

South China ◽

Large Scale ◽

Seawater Temperature ◽

Warm Pool ◽

The South China Sea ◽

The South ◽

China Sea ◽

Relative Contribution

Seawater temperature and salinity are the two key parameters related to the regional sea level variability. In this study, the spatial-temporal variabilities of the thermal and halo steric height over the South China Sea (SCS) are investigated using multi-senor satellite remote sensing products, in-situ measurements and reanalysis. The sea surface temperature and salinity products are used to reconstruct the upper layer sea level components, and the relative contribution of these two components are quantified. It is revealed that the thermal and halo components vary in an out-of-phase pattern, and dominant different regions within SCS. Variabilities of the sea level components on different timescale are further analyzed, and the linkage with large scale processes, such as the indo-pacific warm pool, will be presented.

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Large-scale seismic seafloor stability analysis in the South China Sea

Ocean Engineering ◽

10.1016/j.oceaneng.2021.109334 ◽

2021 ◽

Vol 235 ◽

pp. 109334

Author(s):

Yuxi Wang ◽

Rui Wang ◽

Jian-Min Zhang

Keyword(s):

Stability Analysis ◽

South China Sea ◽

South China ◽

Large Scale ◽

The South China Sea ◽

The South ◽

China Sea

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Coral δ18O records as an indicator of winter monsoon intensity in the South China Sea

Quaternary Research ◽

10.1016/s0033-5894(03)00042-5 ◽

2003 ◽

Vol 59 (3) ◽

pp. 285-292 ◽

Cited By ~ 34

Author(s):

Zicheng Peng ◽

Tegu Chen ◽

Baofu Nie ◽

M. John Head ◽

Xuexian He ◽

...

Keyword(s):

South China Sea ◽

South China ◽

Southern Oscillation ◽

Winter Monsoon ◽

The South China Sea ◽

The South ◽

Porites Lutea ◽

China Sea ◽

Meteorological Observatory ◽

Monsoon Intensity

AbstractWe have used correlative analysis between mean December–January–February winter wind velocities, measured at the Xisha Meteorological Observatory (16°50′N, 112°20′E) in the middle of the South China Sea, and mean δ18O data for the corresponding month from Porites lutea coral, collected in Longwan waters (19°20′N, 110°39′E), to obtain a linear equation relating the two datasets. This winter wind velocity for the South China Sea (WMIIscs) can then be correlated to the coral δ18O by the equation WMIIscs = −1.213–1.351 δ18O (‰ PDB), r = −0.60, n = 40, P = 0.01. From this, the calculated WMIIscs-δ18O series from 1944 to 1997 tends to decrease during the 1940s to the 1960s; it increases slightly during the 1970s and then decreases again in the 1980s and 1990s. The calculated decadal mean WMIIscs-δ18O series had a obvious decrease from 5.92 to 4.63 m/s during the period of 1944–1997. The calculated yearly mean WMIIscs-δ18O value is 5.58 m/s from 1944 to 1976 and this decreases to 4.85 m/s from 1977 to 1998. That is the opposite trend to the observed yearly mean SST variation. The yearly mean SST anomaly is −0.27° from 1943 to 1976 and this increases to +0.16° from 1977 to 1998. Spectral analysis used on a 54-year-long calculated WMIIscs-δ18O series produces spectral peaks at 2.4–7 yr, which can be closely correlated with the quasibiennial oscillation band (QBO band, 2–2.4 yr) and the El Ñino southern oscillation band (ENSO band, 3–8 yr). Hence most of the variability of the winter monsoon intensity in the middle of the South China Sea is mainly constrained by changes in the thermal difference between the land and the adjoining sea area, perhaps due to global warming.

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Accurate Quantification of Seamount Volcanism in Abyssal Sediments Using Gaussian Process Regression

10.5194/egusphere-egu21-4073 ◽

2021 ◽

Author(s):

Yanghui Zhao ◽

Bryan Riel

Keyword(s):

Pacific Ocean ◽

South China Sea ◽

Gaussian Process ◽

South China ◽

Large Scale ◽

Semimajor Axis ◽

Gaussian Process Regression ◽

The South China Sea ◽

The South ◽

China Sea

Seamounts are isolated, underwater volcanoes with more than 100 m in relief. This kind of volcanism arises from the lithosphere or asthenosphere through fractional melt and is a direct manifestation of the tectonic-magmatic activity of the interior of the earth. While previous studies have quantified the global distribution of seamounts by their physical properties (e.g., height, semimajor axis, angle, etc.), these studies usually (1) assume an elliptical cone to model seamount shape, and (2) neglect the sediment coverage on the seamount, which results in significant uncertainties when comparing properties of seamounts near the continents covered with thick sediments to those in the open ocean covered with thin sediments.We apply a large-scale Gaussian Process regression to recover the seamount topography covered by sediments for an accurate distribution of volcanism in the South China Sea basin (with an average thickness of 1.5 km sediments) and the entire Pacific Ocean (with < 300 m thick sediments). Specifically, we first use Tophat filtering to isolate short-spatial-wavelength seamount topography above long-wavelength seafloor. Subsequently, we apply Gaussian Process regression to learn the seamount structure above the seafloor in order to extrapolate the structure beneath the sediment. Lastly, we compute the seamount volume above the sedimentary basement (i.e., top boundary of the oceanic crust) and compare it to the volume above the seafloor. Our results show that for the South China Sea, there is a significant increase in estimated seamount volume above the basement as compared to above the seafloor. For the Pacific Ocean, due to the thin sediment coverage, we observe negligible differences between the two volume estimates. Thus, analysis of seamount properties in marginal basins in the West Pacific with thick sediment coverage can lead to significant underestimation of volcanism intensity if sub-seafloor topography is not accounted for. For these marginal basins, without massive hotspots or apparent evidence of mantle plumes, normal plate tectonic processes are likely responsible for the intensive oceanic volcanism.

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