Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

TIDAL FEATURES IN THE CHINA SEAS AND THEIR ADJACENT SEA AREAS AS DERIVED FROM TOPEX/POSEIDON ALTIMETER DATA

Some important tidal features of 8 major tidal constituents ( M 2, S 2, K 1, O 1, P 1, Sa, N 2 and K 2 ) in the China Seas and their adjacent sea areas were obtained using six years’ TOPEX/POSEIDON altimeter data. The results showed that the obtained co tidal and co range charts for these major tidal constituents agreed well with those of previous researches using observational data from coastal tidal gauge stations and numerical models.

Analysis on Long-Term Change of Sea Surface Temperature in the China Seas

Long-term change of sea surface temperature (SST) in the China Seas from 1900 to 2006 is examined based on two different observation datasets (HadISST1 and HadSST3). Similar to the Atlantic, SST in the China Seas has been well observed dur-ing the past 107 years. A comparison between the reconstructed (HadISST1) and un-interpolated (HadSST3) datasets shows that the SST warming trends from both datasets are consistent with each other in most of the China Seas. The warming trends are stronger in winter than in summer, with a maximum rate of SST increase exceeding 2.7℃ (100 year)-1 in the East China Sea and the Taiwan Strait during winter based on HadISST1. However, the SST from both datasets experienced a sudden decrease after 1999 in the China Seas. The estimated trend from HadISST1 is stronger than that from HadSST3 in the East China Sea and the east of Taiwan Island, where the difference in the linear SST warming trends are as large as about 1℃ (100 year)-1 when using respectively HadISST1 and HadSST3 datasets. When compared to the linear winter warming trend of the land surface air temperature (1.6℃ (100 year)-1), HadSST3 shows a more reasonable trend of less than 2.1℃ (100 year)-1 than HadISST1's trend of larger than 2.7℃ (100 year)-1 at the mouth of the Yangtze River. The results also indicate large uncertainties in the estimate of SST warming patterns.

Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

Empirical Orthogonal Function (EOF) analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas (here referring to Chinese marine territories) and adjacent waters from 1930 to 2002 (510 143 profiles). A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that: 1) For the study area, the former four principal components can explain 95% of the overall variance, and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2) The model verifications based on the observed CTD data from the East China Sea (ECS), South China Sea (SCS) and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level >95%, especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69°C, 0.52°C and 1.18°C respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3) Comparing the thermocline characteristics between the reconstructed and observed profiles, the results in the ECS show that the average absolute errors are 1.5m, 1.4 m and 0.17°C/m, and the average relative errors are 24.7%, 8.9% and 22.6% for the upper, lower thermocline boundaries and the gradient, respectively. Although the relative errors are obvious, the absolute error is small. In the SCS, the average absolute errors are 4.1 m, 27.7 m and 0.007°C/m, and the average relative errors are 16.1%, 16.8% and 9.5% for the upper, lower thermocline boundaries and the gradient, respectively. The average relative errors are all <20%. Although the average absolute error of the lower thermocline boundary is considerable, but contrast to the spatial scale of average depth of the lower thermocline boundary (165 m), the average relative error is small (16.8%). Therefore the model can be used to well estimate the thermocline.

Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East China Sea based on reconstructions of satellite data of 2001-2020

Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.

Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea

Data assimilation is a powerful tool to improve ocean forecasting by reducing uncertainties in forecast initial conditions.Recently,an ocean data assimilation system based on the ensemble optimal interpolation(EnOI) scheme and HYbrid Coordinate Ocean Model(HYCOM) for marginal seas around China was developed.This system can assimilate both satellite observations of sea surface temperature(SST) and along-track sea level anomaly(SLA) data.The purpose of this study was to evaluate the performance of the system.Two experiments were performed,which spanned a 3-year period from January 1,2004 to December 30,2006,with and without data assimilation.The data assimilation results were promising,with a positive impact on the modeled fields.The SST and SLA were clearly improved in terms of bias and root mean square error over the whole domain.In addition,the assimilations provided improvements in some regions to the surface field where mesoscale processes are not well simulated by the model.Comparisons with surface drifter trajectories showed that assimilated SST and SLA also better represent surface currents,with drifter trajectories fitting better to the contours of SLA field than that without assimilation.The forecasting capacity of this assimilation system was also evaluated through a case study of a birth-and-death process of an anticyclone eddy in the Northern South China Sea(NSCS),in which the anticyclone eddy was successfully hindcasted by the assimilation system.This study suggests the data assimilation system gives reasonable descriptions of the near-surface ocean state and can be applied to forecast mesoscale ocean processes in the marginal seas around China.

Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas

A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and 99°E to135°E in longitude including the Bohai Sea, the Yellow Sea, the East China Sea, the South China Sea and the Indonesian seas. To get precise initial conditions for the coupled forecasting model, the forecasting system conducts a 24-h hindcast simulation with data assimilation before forecasting. The Ensemble Adjustment Kalman Filter(EAKF) data assimilation method was adopted for the wave model MASNUM with assimilating Jason-2 significant wave height(SWH) data. The EAKF data assimilation method was also introduced to the ROMS model with assimilating sea surface temperature(SST), mean absolute dynamic topography(MADT) and Argo profiles data. To improve simulation of the structure of temperature and salinity, the vertical mixing scheme of the ocean model was improved by considering the surface wave induced vertical mixing and internal wave induced vertical mixing. The wave and current models were integrated from January 2014 to October 2015 driven by the ECMWF reanalysis 6 hourly mean dataset with data assimilation. Then the coupled atmosphere-wave-ocean forecasting system was carried out 14 months operational running since November 2015. The forecasting outputs include atmospheric forecast products, wave forecast products and ocean forecast products. A series of observation data are used to evaluate the coupled forecasting results, including the wind, SHW, ocean temperature and velocity.The forecasting results are in good agreement with observation data. The prediction practice for more than one year indicates that the coupled forecasting system performs stably and predict relatively accurate, which can support the shipping safety, the fisheries and the oil exploitation.

Advances in Studying Oceanic Circulation from Hydrographic Data with Applications in the South China Sea

Methods for studying oceanic circulation from hydrographic data are reviewed in the context of their applications in the South China Sea. These methods can be classified into three types according to their different dynamics as follows: (1) descriptive methods, (2) diagnostic methods without surface and bottom forcing, and (3) diagnostic methods with the above boundary forcing. The paper discusses the progress made in the above methods together with the advancement of study in the South China Sea circulation.

Relationship between the Extent of Igneous Rocks and Deep Structures as Determined by Gravitational and Magnetic Data in the South China Sea

The distribution of oil and gas resources in the South China Sea and adjacent areas is closely related to the structural pattern that helped to define the controlling effect of deep processes on oil-bearing basins.Igneous rocks can record important information from deep processes.Deep structures such as faults,basin uplift and depression,Cenozoic basement and magnetic basement are all the results of energy exchange within the earth.The study of the relationship between igneous rocks and deep structures is of great significance for the study of the South China Sea.By using the minimum curvature potential field separation technique and the correlation analysis technique of gravitational and magnetic anomalies,the fusion of gravitational and magnetic data reflecting igneous rocks can be obtained,through which the igneous rocks with high susceptibility/high density or high susceptibility/low density can be identified.In this study area,igneous rocks do not develop in the Yinggehai basin,Qiongdongnan basin,Zengmu basin and Brunei-Sabah basin whilst igneous rocks with high susceptibility/high density or high susceptibility/low density are widely-developed in other basins.In undeveloped igneous areas,faults are also undeveloped the Cenozoic thickness is greater,the magnetic basement depth is greater and the Cenozoic thickness is highly positively correlated with the magnetic basement depth.In igneously developed regions,the distribution pattern of the Qiongtai block is mainly controlled by primary faults,while the distribution of the Zhongxisha block,Xunta block and Yongshu-Taiping block is mainly controlled by secondary faults,the Cenozoic thickness having a low correlation with the depth of the magnetic basement.

Comparison of remote sensing data with in-situ wind observation during the development of the South China Sea monsoon

Wind measurements derived from QuikSCAT data were compared with those measured by anemometer on Yongxing Island in the South China Sea (SCS) for the period from April 2008 to November 2009. The comparison confirms that QuikSCAT estimates of wind speed and direction are generally accurate, except for the extremes of high wind speeds (>13.8m/s) and very low wind speeds (<1.5m/s) where direction is poorly predicted. In-situ observations show that the summer monsoon in the northern SCS starts between May 6 and June 1. From March 13, 2010 to August 31, 2010, comparisons of sea surface temperature (SST) and rainfall from AMSR-E with data from a buoy located at Xisha Islands, as well as wind measurements derived from ASCAT and observations from an automatic weather station show that QuikSCAT, ASCAT and AMSR-E data are good enough for research. It is feasible to optimize the usage of remote-sensing data if validated with in-situ measurements. Remarkable changes were observed in wind, barometric pressure, humidity, outgoing longwave radiation (OLR), air temperature, rainfall and SST during the monsoon onset. The eastward shift of western Pacific subtropical high and the southward movement of continental cold front preceded the monsoon onset in SCS. The starting dates of SCS summer monsoon indicated that the southwest monsoon starts in the Indochinese Peninsula and forms an eastward zonal belt, and then the belt bifurcates in the SCS, with one part moving northeastward into the tropical western North Pacific, and another southward into western Kalimantan. This largely determined the pattern of the SCS summer monsoon. Wavelet analysis of zonal wind and OLR at Xisha showed that intra-seasonal variability played an important role in the summer. This work improves the accuracy of the amplitude of intra-seasonal and synoptic variation obtained from remote-sensed data.

Stratal Carbonate Content Inversion Using Seismic Data and Its Applications to the Northern South China Sea

On the basis of the relationship between the carbonate content and the stratal velocity and density, an exercise has been attempted using an artificial neural network on high-resolution seismic data for inversion of carbonate content with limited well measarements as a control. The method was applied to the slope area of the northern South China Sea near ODP Sites 1146 and 1148, and the results are satisfaetory. Before inversion calculation, a stepwise regression method was applied to obtain six properties related most closely to the carbonate content variations among the various properties on the seismic profiles across or near the wells. These include the average frequency, the integrated absolute amplitude, the dominant frequency, the reflection time, the derivative instantaneous amplitude, and the instantaneous frequency. The results, with carbonate content errors of mostly ±5 % relative to those measured from sediment samples, show a relatively accurate picture of carbonate distribution along the slope profile. This method pioneers a new quantitative model to acquire carbonate content variations directly from high-resolution seismic data. It will provide a new approach toward obtaining substitutive high-resolution sediment data for earth system studies related to basin evolution, especially in discussing the coupling between regional sedimentation and climate change.

Variability of the deep South China Sea circulation derived from HYCOM reanalysis data

This study aims to investigate variability of the deep South China Sea(SCS)circulation using the Hybrid Coordinate Ocean Model(HYCOM)global reanalysis product.The results reveal that annual cycle is a dominant component in the deep SCS circulation.Meanwhile,the boundary circulation strength is the weakest in January and peaks between June and September.The eastern and southern boundary currents strengthen/weaken one to three months earlier than that in the western and northern boundaries.Vector Empirical Orthogonal Functions(VEOF)analysis results reveal that semiannual and intraseasonal fluctuations are significant components,of which the spatial patterns are mainly confined in the northern and western boundary areas as well as the southwestern sub-basin.Wavelet analysis results show the strength of significant fluctuation varies year to year.Trend analysis results indicate a decadal weakening in the deep SCS circulation.An anomalous anticyclonic circulation,50–70 km apart from the slope break,tends to weaken the cyclonic boundary circulation in the western and northern boundaries as well as the southwestern sub-basin.This trend is similar to the observed decadal weakening in the North Atlantic deep circulation.Thus,the findings of this study reveal that the variation of the deep SCS circulation has a remarkable response to the climate change.The mechanisms responsible for the variation are worth pursuing if more observations are available.

Modal recovery of sea-level variability in the South China Sea using merged altimeter data

A bstract Using 20 years(1993–2012) of merged data recorded by contemporary multi-altimeter missions,a variety of sea-level variability modes are recovered in the South China Sea employing threedimensional harmonic extraction. In terms of the long-term variation,the South China Sea is estimated to have a rising sea-level linear trend of 5.39 mm/a over these 20 years. Among the modes extracted,the seven most statistically significant periodic or quasi-periodic modes are identified as principal modes. The geographical distributions of the magnitudes and phases of the modes are displayed. In terms of intraannual and annual regimes,two principal modes with strict semiannual and annual periods are found,with the annual variability having the largest amplitudes among the seven modes. For interannual and decadal regimes,five principal modes at approximately 18,21,23,28,and 112 months are found with the most modeactive region being to the east of Vietnam. For the phase distributions,a series of amphidromes are observed as twins,termed "amphidrome twins",comprising rotating dipole systems. The stability of periodic modes is investigated employing joint spatiotemporal analysis of latitude/longitude sections. Results show that all periodic modes are robust,revealing the richness and complexity of sea-level modes in the South China Sea.

Validation of the multi-satellite merged sea surface salinity in the South China Sea

Sea surface salinity(SSS)is an essential variable of ocean dynamics and climate research.The Soil Moisture and Ocean Salinity(SMOS),Aquarius,and Soil Moisture Active Passive(SMAP)satellite missions all provide SSS measurements.The European Space Agency(ESA)Climate Change Initiative Sea Surface Salinity(CCI-SSS)project merged these three satellite SSS data to produce CCI L4SSS products.We validated the accuracy of the four satellite products(CCI,SMOS,Aquarius,and SMAP)using in-situ gridded data and Argo floats in the South China Sea(SCS).Compared with in-situ gridded data,it shows that the CCI achieved the best performance(RMSD:0.365)on monthly time scales.The RMSD of SMOS,Aquarius,and SMAP(SMOS:0.389;Aquarius:0.409;SMAP:0.391)are close,and the SMOS takes a slight advantage in contrast with Aquarius and SMAP.Large discrepancies can be found near the coastline and in the shelf seas.Meanwhile,CCI with lower RMSD(0.295)perform better than single satellite data(SMOS:0.517;SMAP:0.297)on weekly time scales compared with Argo floats.Overall,the merged CCI have the smallest RMSD among the four satellite products in the SCS on both weekly time scales and monthly time scales,which illustrates the improved accuracy of merged CCI compared with the individual satellite data.

Declined trends of chlorophyll a in the South China Sea over 2005–2019 from remote sensing reconstruction

Chlorophyll a concentration(CHL)is an important proxy of the marine ecological environment and phytoplankton production.Long-term trends in CHL of the South China Sea(SCS)reflect the changes in the ecosystem’s productivity and functionality in the regional carbon cycle.In this study,we applied a previously reconstructed 15-a(2005–2019)CHL product,which has a complete coverage at 4 km and daily resolutions,to analyze the long-term trends of CHL in the SCS.Quantile regression was used to elaborate on the long-term trends of high,median,and low CHL values,as an extended method of conventional linear regression.The results showed downward trends of the SCS CHL for the 75th,50th,and 25th quantile in the past 15 a,which were−0.0040 mg/(m^(3)·a)(−1.62%per year),−0.0023 mg/(m^(3)·a)(−1.10%per year),and−0.0019 mg/(m^(3)·a)(−1.01%per year).The negative trends in winter(November to March)were more prominent than those in summer(May to September).In terms of spatial distribution,the downward trend was more significant in regions with higher CHL.These led to a reduced standard deviation of CHL over time and space.We further explored the influence of various dynamic factors on CHL trends for the entire SCS and two typical systems(winter Luzon Strait(LZ)and summer Vietnam Upwelling System(SV))with single-variate linear regression and multivariate Random Forest analysis.The multivariate analysis suggested the CHL trend pattern can be best explained by the trends of wind speed and mixed-layer depth.The divergent importance of controlling factors for LZ and SV can explain the different CHL trends for the two systems.This study expanded our understanding of the long-term changes of CHL in the SCS and provided a reference for investigating changes in the marine ecosystem.