南海海平面长期变化及其趋势研究

On the basis of the satellite maps of sea level anomaly(MSLA) data and in situ tidal gauge sea level data,correlation analysis and empirical mode decomposition(EMD) are employed to investigate the applicability of MSLA data,sea level correlation,long-term sea level variability(SLV) trend,sea level rise(SLR) rate and its geographic distribution in the South China Sea(SCS).The findings show that for Dongfang Station,Haikou Station,Shanwei Station and Zhapo Station,the minimum correlation coefficient between the closest MSLA grid point and tidal station is 0.61.This suggests that the satellite altimeter MSLA data are effective to observe the coastal SLV in the SCS.On the monthly scale,coastal SLV in the western and northern part of SCS are highly associated with coastal currents.On the seasonal scale,SLV of the coastal area in the western part of the SCS is still strongly influenced by the coastal current system in summer and winter.The Pacific change can affect the SCS mainly in winter rather than summer and the affected area mostly concentrated in the northeastern and eastern parts of the SCS.Overall,the average SLR in the SCS is 90.8 mm with a rising rate of(5.0±0.4) mm/a during1993–2010.The SLR rate from the southern Luzon Strait through the Huangyan Seamount area to the Xisha Islands area is higher than that of other areas of the SCS.     

东海沿海季节性海平面异常成因

Based on the analysis of sea level, air temperature, sea surface temperature(SST), air pressure and wind data during 1980–2013, the causes of seasonal sea level anomalies in the coastal region of the East China Sea(ECS) are investigated. The research results show:(1) sea level along the coastal region of the ECS takes on strong seasonal variation. The annual range is 30–45 cm, larger in the north than in the south. From north to south, the phase of sea level changes from 140° to 231°, with a difference of nearly 3 months.(2) Monthly mean sea level(MSL)anomalies often occur from August to next February along the coast region of the ECS. The number of sea level anomalies is at most from January to February and from August to October, showing a growing trend in recent years.(3) Anomalous wind field is an important factor to affect the sea level variation in the coastal region of the ECS. Monthly MSL anomaly is closely related to wind field anomaly and air pressure field anomaly. Wind-driven current is essentially consistent with sea surface height. In August 2012, the sea surface heights at the coastal stations driven by wind field have contributed 50%–80% of MSL anomalies.(4) The annual variations for sea level,SST and air temperature along the coastal region of the ECS are mainly caused by solar radiation with a period of12 months. But the correlation coefficients of sea level anomalies with SST anomalies and air temperature anomalies are all less than 0.1.(5) Seasonal sea level variations contain the long-term trends and all kinds of periodic changes. Sea level oscillations vary in different seasons in the coastal region of the ECS. In winter and spring, the oscillation of 4–7 a related to El Ni?o is stronger and its amplitude exceeds 2 cm. In summer and autumn, the oscillations of 2–3 a and quasi 9 a are most significant, and their amplitudes also exceed 2 cm. The height of sea level is lifted up when the different oscillations superposed. On the other hand, the height of sea level is fallen down.     

Adaptation strategy for sea level rise in vulnerable areas along China's coast

I~IOXThe sea level rise threatens China's coastal plains and river deltas and makes them the vulnerable areas due to their loW elevation.Since the 1980s, the Chinese scientists have paid great attention to the problem of the sealevel rise caused by the global warming. They have analyZed and calculated the trend of the relative sea level change along the China's coast in the past 50 a. The result of study shows that therising rate of the sea level along China's coast is (1. 7 i 0. 3) rum/a.…     

Sea-level variation/change and thermal contribution in the Bering Sea

The long-term sea-level trend in the Bering Sea is obtained by the analysis of TOPEX/Poseidon altimeter data, including the data of two tide gauges. The averaged sea-level in the Bering Sea rises at a rate of 2.47 mm/a from 1992 to 2002. The mean sea-level is falling in the most part of the Bering Sea, especially in its central basin, and it is rising in the northeastern part of the Bering Sea. During the 1998/99 change, the sea-level anomaly differences exhibit a significant sea-level anomaly fall in the deep basin of the Bering Sea,which is roughly in the same position where a prominent SST fall exists. The maximal fall of sea-level is about 10 cm in the southwestern part of the Bering Sea, and the maximal fall of about 2℃ in the SST also appeared in the same region as the sea level did.The steric sea-level change due to temperature variations is discussed. The results are compared with the TOPEX/Poseidon altimeter data at the different spatial scales. It is indicated that the seasonal amplitude of the steric height is about 35% of the observed TOPEX/Poseidon amplitude, which is much smaller than the 83% in the mid-latitudes area. The systematic difference between the TOPEX/Poseidon data with the range of about 7.5 cm and the thermal contribution with the range of about 2.5 cm is about 5 cm. This indicates that the thermal effect on the sea level is not as important as the case in the mid-latitudes area. In the Bering Sea, the phase of the steric height leads the observed sea level by about three months.     

Sea level change along the coast of China relative to the isostatic datum

On the basis of the analyses of significant periods for the sea level observation data taken from recent several decades at 12 tide stations, the monthly mean sea level observations are fitted by a model of linear trend of sea level change superimposed with several variations of different fixed periods. The trends of sea level relative changes and their errors are estimated by the LS method. The results are reduced to the isostatic datum proposed and established in the paper (Huang et al. , 1991, Seismology and Geology , 1, 1-15). The trends of sea level changes in the near future along the coast of China are studied. It is pointed out that the general trend of the sea level change along the coast of China is going up slowly and the rate of the change is not the same in different segments of the coasts. In a few segments, the sea level is even relatively going down. The numerical results given in this paper provide a basis for the predictions of the future sea level changes and their effects.     

基于CMIP5模拟预估21世纪珠江三角洲地区由于海平面上升而导致的淹没区面积

Projections of potential submerged area due to sea level rise are helpful for improving understanding of the influence of ongoing global warming on coastal areas. The Ensemble Empirical Mode Decomposition method is used to adaptively decompose the sea level time series in order to extract the secular trend component. Then the linear relationship between the global mean sea level(GMSL) change and the Zhujiang(Pearl) River Delta(PRD)sea level change is calculated: an increase of 1.0 m in the GMSL corresponds to a 1.3 m(uncertainty interval from1.25 to 1.46 m) increase in the PRD. Based on this relationship and the GMSL rise projected by the Coupled Model Intercomparison Project Phase 5 under three greenhouse gas emission scenarios(representative concentration pathways, or RCPs, from low to high emission scenarios RCP2.6, RCP4.5, and RCP8.5), the PRD sea level is calculated and projected for the period 2006–2100. By around the year 2050, the PRD sea level will rise 0.29(0.21 to 0.40) m under RCP2.6, 0.31(0.22 to 0.42) m under RCP4.5, and 0.34(0.25 to 0.46) m under RCP8.5, respectively.By 2100, it will rise 0.59(0.36 to 0.88) m, 0.71(0.47 to 1.02) m, and 1.0(0.68 to 1.41) m, respectively. In addition,considering the extreme value of relative sea level due to land subsidence(i.e., 0.20 m) and that obtained from intermonthly variability(i.e., 0.33 m), the PRD sea level will rise 1.94 m by the year 2100 under the RCP8.5scenario with the upper uncertainty level(i.e., 1.41 m). Accordingly, the potential submerged area is 8.57×103 km2 for the PRD, about 1.3 times its present area.     

Detection of the Kuroshio frontal instable processes (KFIP) in the East China Sea using the MODIS images

The Kuroshio frontal instable processes (KFIP) in the East China Sea (ECS) not only have a great impact on the hydrologic characteristics,the pollutants drift,the distribution of seafloor sediment and the ships navigation of the ECS,but also are closely related to the climate changes of the coastal areas of the ECS.However the frequency and area of occurrence of the KFIP have not been studied fully and detailedly.Because of its high spatial and temporal resolution,MODIS data is a kind of very good data source for surveying and researching the KFIP in the ECS.The aim of this study is to detect the KFIP in the ECS by using MODIS data,and to study the frequency and region of occurrence of the KFIP in the ECS.The selection has coverage of level 2 data of MODIS SST and Kd490 ranging from July 1,2002 to June 30,2009 of the ECS when there was no cloud impact or little.By using of the data,the minimum standard of the Kuroshio temperature fronts and the diffuse attenuation coefficient (Kd490) fronts of the ECS are given.Based on these standards and the curvature distinguish methods,the standard of curvature distinguish for the KFIP in the ECS are put forward.By making use of this standard,we study a total of 2073 satellite-derived images,and discover that as long as there is no cloud impact from January to May and October to December,the KFIP in the ECS are surely found in MODIS satellite images.From June to September,the frequency of occurrence can also reach to 82.9% at least.Moreover,it is obtained that there are three source regions of these instability processes,namely,(26°N,121.5°E) nearby,(27°N,125°E) nearby and (30°N,128°E) nearby.The differences of the characteristics of these instability processes which are generated in different regions are analyzed in the present study.     

南海沿海季节性海平面异常变化特征及成因分析

Based on sea level, air temperature, sea surface temperature(SST), air pressure and wind data during 1980–2014,this paper uses Morlet wavelet transform, Estuarine Coastal Ocean Model(ECOM) and so on to investigate the characteristics and possible causes of seasonal sea level anomalies along the South China Sea(SCS) coast. The research results show that:(1) Seasonal sea level anomalies often occur from January to February and from June to October. The frequency of sea level anomalies is the most in August, showing a growing trend in recent years. In addition, the occurring frequency of negative sea level anomaly accounts for 50% of the total abnormal number.(2) The seasonal sea level anomalies are closely related to ENSO events. The negative anomalies always occurred during the El Ni?o events, while the positive anomalies occurred during the La Ni?a(late El Ni?o) events. In addition, the seasonal sea level oscillation periods of 4–7 a associated with ENSO are the strongest in winter, with the amplitude over 2 cm.(3) Abnormal wind is an important factor to affect the seasonal sea level anomalies in the coastal region of the SCS. Wind-driven sea level height(SSH) is basically consistent with the seasonal sea level anomalies. Moreover, the influence of the tropical cyclone in the coastal region of the SCS is concentrated in summer and autumn, contributing to the seasonal sea level anomalies.(4) Seasonal variations of sea level, SST and air temperature are basically consistent along the coast of the SCS, but the seasonal sea level anomalies have no much correlation with the SST and air temperature.     

Evaluation of vertical crustal movements and sea level changes around Greenland from GPS and tide gauge observations

To better monitor the vertical crustal movements and sea level changes around Greenland, multiple data sources were used in this paper, including global positioning system(GPS), tide gauge, satellite gravimetry, satellite altimetry, glacial isostatic adjustment(GIA). First, the observations of more than 50 GPS stations from the international GNSS service(IGS) and Greenland network(GNET) in 2007–2018 were processed and the common mode error(CME) was eliminated with using the principal component analysis(PCA). The results show that all GPS stations show an uplift trend and the stations in southern Greenland have a higher vertical speed. Second, by deducting the influence of GIA, the impact of current Gr IS mass changes on GPS stations was analysed, and the GIA-corrected vertical velocity of the GPS is in good agreement with the vertical velocity obtained by gravity recovery and climate experiment(GRACE). Third, the absolute sea level change around Greenland at 4 gauge stations was obtained by combining relative sea level derived from tide gauge observations and crustal uplift rates derived from GPS observations, and was validated by sea level products of satellite altimetry. The results show that although the mass loss of Gr IS can cause considerable global sea level rise, eustatic movements along the coasts of Greenland are quite complex under different mechanisms of sea level changes.     

Argo时代东南印度洋上层盐度变化对海平面年代际演变的影响

In the past nearly two decades, the Argo Program has created an unprecedented global observing array with continuous in situ salinity observations, providing opportunities to extend our knowledge on the variability and effects of ocean salinity. In this study, we utilize the Argo data during 2004–2017, together with the satellite observations and a newly released version of ECCO ocean reanalysis, to explore the decadal salinity variability in the Southeast Indian Ocean(SEIO) and its impacts on the regional sea level changes. Both the observations and ECCO reanalysis show that during the Argo era, sea level in the SEIO and the tropical western Pacific experienced a rapid rise in 2005–2013 and a subsequent decline in 2013–2017. Such a decadal phase reversal in sea level could be explained, to a large extent, by the steric sea level variability in the upper 300 m. Argo data further show that, in the SEIO, both the temperature and salinity changes have significant positive contributions to the decadal sea level variations. This is different from much of the Indo-Pacific region, where the halosteric component often has minor or negative contributions to the regional sea level pattern on decadal timescale. The salinity budget analyses based on the ECCO reanalysis indicate that the decadal salinity change in the upper 300 m of SEIO is mainly caused by the horizontal ocean advection. More detailed decomposition reveals that in the SEIO, there exists a strong meridional salinity front between the tropical low-salinity and subtropical high salinity waters. The meridional component of decadal circulation changes will induce strong cross-front salinity exchange and thus the significant regional salinity variations.     

Absolute Sea Level Surface Modeling for the Mediterranean from Satellite Altimeter and Tide Gauge Measurements

Changes in the height of the ocean can be described through the relative and absolute sea level changes depending on the geodetic reference the sea level records are related to. Satellite altimetry provides absolute sea level (ASL) measurements related to the global geodetic reference, whereas tide gauges provide relative sea level (RSL) measurements related to the adjacent land. This study aims at computing the ASL surfaces for different time epochs from combined satellite altimeter and tide gauge records. A method of sea level data fusion is proposed to enable modeling of the impact of present and future sea level changes on the coast. Sea surface modeling was investigated for ten different gridding methods commonly used for the interpolation of altimeter data over the open ocean and extrapolation over the coastal zones. The performance of gridding methods was assessed based on the comparison of the gridded altimeter data and corrected tide gauge measurements. Finally, the sea level surfaces related to the GRS80 global reference ellipsoid were computed for the Mediterranean Sea over the altimeter period. In addition, the current sea level trends were estimated from both sea level measurements.     

Analysis of sea surface height variabilities in the Kuroshio Current region by using Geosat altimeter data

INTRODUCTIONBeing a current of high temperature and high salinity, the Kuroshio carries a large amount ofheat from low latitude tropical ocean to high latitude ocean, and plays an imPOrtant role in theheat balance in East Asia. The variability of the Kurosl,io can affect the climate of East Asia, aswell as the ocean environment and the fishery resources. A lot of studies showed that the variabilitiies of the Kuroshio were related to the global changes especially to the onset of ENSO.…     

Influence of the Kuroshio Fluctuations on Sea Level Variations along the South Coast of Japan

The correlation between the Kuroshio and coastal sea level south of Japan has been examined using the altimetry and tide gauge data during the period 1992–2000. The sea level varies uniformly in a region bounded by the coast and the mean Kuroshio axis, which stretches for several hundred kilometers along the coast. These variations are related with the Kuroshio velocity, as coastal sea level decreases (or increases) when the Kuroshio is faster (or slower). To the east of the Kii Peninsula, where sea level variations are different from these to the west, movement of the Kuroshio axis additionally affects coastal sea level variations.     

春季东海黑潮海表温度与风场的年代际变化特征

利用高分辨率遥感海表温度和海表面风场数据,通过经验正交分解(EOF)和合成分析等方法对春季(3—5月)东海黑潮海温暖舌和海表面风场的年代际变化特征进行分析。结果表明:春季黑潮海温暖舌存在明显的年代际变化特征,在1996/1997年发生由弱到强的位相转换,该年代际变化主要受到北太平洋涡旋振荡(NPGO)的调制。进一步研究表明,与气候态相反,春季黑潮海表温度和风场散度在年代际尺度上表现出显著的负相关关系,合成分析表明,该现象主要是由黑潮西侧东海陆架海域海温的异常增暖所造成。     

东海海平面变化的综合分析

利用1993年1月至2011年12月的卫星高度计数据,研究了东海海平面变化的季节信号、线性趋势和低频信号,并结合风应力资料、Ishii温盐数据和海表面温度数据分析了季节信号和低频信号的驱动机制。东海季节性海平面变化主要由年信号组成,其占海平面变化的大部分;年信号振幅和相位的分布具有明显的区域差异;东海季节性海平面变化主要受海面风和海水热膨胀驱动,而且在不同季节、不同区域,两种驱动机制的作用存在明显差异,主导地位也不断变化;季节信号还受到黑潮的一定影响。1993-2011年间东海海平面线性上升速率为3.28mm/a,各海域海平面上升速率不同。东海海平面变化低频信号与比容海平面变化低频信号具有显著相关性,最大相关系数为0.55;东海比容海平面变化低频信号与SOI低频信号同样具有一定的相关性,最大相关系数为0.3。ENSO通过大气环流和黑潮洋流等对东海海域的比容海平面变化产生影响,比容海平面变化进而对东海年际间海平面变化产生调制作用,因此ENSO可以通过东海年际间比容海平面变化对东海年际间海平面变化产生影响。     

南海潮汐主要分潮振幅变化趋势研究

潮汐变化研究对于海洋工程、沿海地区洪涝灾害预防、海上交通等各个方面都有着重要的意义。由于验潮站都集中在近海,所以之前潮汐变化研究主要集中在近海海域。相比之下,深海地区由于长期高频水位观测的缺乏导致相关的潮汐变化研究非常少。基于近海验潮站数据和深海卫星高度计数据,本文首次用非平稳潮汐调和分析工具包S_TIDE提取了南海4大主要分潮(M₂、S₂、K₁、O₁)振幅的长期趋势。研究发现在南海大部分地区,4大主要分潮的振幅都是比较稳定的,不存在显著的上升趋势或下降趋势。在南海少部分地区4大主要分潮的振幅存在显著的趋势,最大的上升趋势可达2.91 mm/a,最大的下降趋势可达3.50 mm/a。该海域潮汐的长期趋势可能与内潮海表面信号的变化有关。卫星观测到的潮汐既包含正压潮,也包含内潮海表面信号。南海作为全球内潮活动最活跃的海域之一,其内潮海表面信号是非常显著的。而内潮对海洋层化的变化是非常敏感的,海洋层化的变化会影响内潮的生成、传播和耗散以及内潮在海表的显示,最终引起该海域潮汐振幅的长期趋势。     

21世纪海上丝绸之路海洋上层热含量及热比容海平面异常变化

气候变暖背景下,全球平均海洋变暖和海平面上升显著,为人类社会的可持续发展带来巨大挑战。上层海洋热力状况是海平面变化的主导因子之一。本文围绕"21世纪海上丝绸之路"途经海区（文中简称为丝路海区）上层海洋热含量异常的区域性时空特征,分析探讨了丝路海区热比容海平面异常的时空变化、演变特征及可能影响,以期为"21世纪海上丝绸之路"海洋环境安全保障提供服务支撑。结果表明,自20世纪70年代中后期,丝路海区上层（0~700 m）海洋已明显变暖,尤其20世纪90年代中后期增暖幅度显著加大。近60年来,在丝路海区热带海洋中,西太平洋的北赤道流区及以北海域、东海黑潮流域以及南海北部和南部海区、阿拉伯海西北部海域、马来西亚西北部海域及南印度洋部分海域具有长期增暖趋势。热带西太平洋暖池区整体增暖不明显,主要与印度洋中部海域呈反位相变化,且明显受到季节和年际变化的调制。长江口附近沿岸、南海北部沿岸、中南半岛南部沿岸以及阿拉伯海西北部沿岸的近岸海域长期增暖明显,自20世纪90年代中后期,中南半岛东部和西部沿海、澳大利亚西部沿海以及我国东南沿海热比容海平面上升明显。近岸热比容海平面的季节演变对沿海地区社会和经济发展会造成一定影响。此外,东亚夏季风与东海、黄海和渤海热比容海平面的上升显著相关,同时,ENSO、太平洋年代际振荡和印度洋偶极子的发生也均与我国东南沿海和印度洋西部沿海热比容海平面上升明显关联。特别是,气候变暖情形下,各种区域性致灾因子和气候变率的协同影响会对丝路海区海岸带和沿海地区的防灾减灾与社会经济发展带来较大挑战,开展海岸带和沿海地区全球变化综合风险研究成为当前首要任务。     

Absolute sea level variability of Arctic Ocean in 1993–2018 from satellite altimetry and tide gauge observations

Arctic absolute sea level variations were analyzed based on multi-mission satellite altimetry data and tide gauge observations for the period of 1993–2018. The range of linear absolute sea level trends were found ?2.00 mm/a to 6.88 mm/a excluding the central Arctic, positive trend rates were predominantly located in shallow water and coastal areas, and negative rates were located in high-latitude areas and Baffin Bay. Satellite-derived results show that the average secular absolute sea level trend was (2.53±0.42) mm/a in the Arctic region. Large differences were presented between satellite-derived and tide gauge results, which are mainly due to low satellite data coverage, uncertainties in tidal height processing and vertical land movement (VLM). The VLM rates at 11 global navigation satellite system stations around the Arctic Ocean were analyzed, among which 6 stations were tide gauge co-located, the results indicate that the absolute sea level trends after VLM corrected were of the same magnitude as satellite altimetry results. Accurately calculating VLM is the primary uncertainty in interpreting tide gauge measurements such that differences between tide gauge and satellite altimetry data are attributable generally to VLM.     

Interannual Sea Level Variations in the South China Sea Over 1950–2009

Spatial patterns of interannual sea level variations in the South China Sea (SCS) are investigated by analyzing an EOF-based 2-dimensional past sea level reconstruction from 1950 to 2009 and satellite altimetry data from 1993 to 2009. Long-term tide gauge records from 14 selected stations in this region are also used to assess the quality of reconstructed sea levels and determine the rate of sea level along the coastal area. We found that the rising rate of sea levels derived from merged satellite altimetry data during 1993–2009 and past sea level reconstruction over 1950–2009 is about 3.9 ± 0.6 mm/yr and 1.7 ± 0.1 mm/yr, respectively. For the longer period, this rate is not significantly different from the global mean rate (of 1.8 ± 0.3 mm/yr). The interannual mean sea level of the SCS region appears highly correlated with Niño 4 indices (a proxy of El Niño-Southern Oscillation/ENSO), suggesting that the interannual sea level variations over the SCS region is driven by ENSO events. Interpolation of the reconstructed sea level data for 1950–2009 at sites where tide gauge records are of poor quality (either short or gapped) show that sea level along the Chinese coastal area is rising faster than the global mean rate of 1.8 mm/yr. At some sites, the rate is up to 2.5 mm/yr.