Global sea level variations from altimetry,GRACE and Argo data over 2005-2014

Total sea level variations（SLVs） are caused by two major components：steric variations due to thermal expansion of seawater,and mass-induced variations due to mass exchange between ocean and land.In this study,the global SLV and its steric and mass components were estimated by satellite altimetry,Argo float data and the Gravity Recovery and Climate Experiment（GRACE） data over 2005-2014.Space gravimetry observations from GRACE suggested that two-thirds of the global mean sea level rise rate observed by altimetry（i.e.,3.1 ± 0.3 mm/a from 2005 to 2014） could be explained by an increase in ocean mass.Furthermore,the global mean sea level was observed to drop significantly during the2010/2011 La Nina event,which may be attributed to the decline of ocean mass and steric SLV.Since early 2011,the global mean sea level began to rise rapidly,which was attributed to an increase in ocean mass.The findings in this study suggested that the global mean sea-level budget was closed from 2005 to 2014 based on altimetry,GRACE,and Argo data.

Reassessing the contributions of terrestrial waters to sea level variations in the South China Sea and its response to alternating ENSO events

Regional sea level variability is linked to regional terrestrial water and the El Ni?o-Southern Oscillation(ENSO).This study assessed the relationships between the sea level variations in the South China Sea(SCS)and ENSO,the impact of terrestrial water storage(TWS)on non-steric sea level(NSSL),and the contributions of steric sea level(SSL)and NSSL to sea level anomaly(SLA),respectively.From 2003 to 2015,the SLAs exhibited a long-term trend of 6.65±0.78 mm/yr,which was primarily attributed to the SSLs.Additionally,during 2003-2015,ENSO events alternating with varying intensities might also be responsible for the unusually high SLA trend.Compared to the SSLs,the NSSLs contributed the seasonal signals to the SLAs,while the NSSLs changes were largely explained by the TWS in the Mekong River Basin at the seasonal scale and in the Pearl River Basin and Red River Basin at other time scales.In contrast to the TWS,the contributions of precipitation and evapotranspiration were relatively minor.A negative correlation between the sea level variations and ENSO was also found,with cross-correlation coefficients between the oceanic Ni?o index and SLAs/SSLs/NSSLs of -0.36/-0.37/-0.62 with lags of 2/3/2 months,respectively.These findings systematically reassessed the contributions of different components to the sea level variations.This study provided a benchmark for in-depth analysis of the impacts of terrestrial water and other potential causes on sea level rise in the SCS.

Sea level variations in the South China Sea inferred from satellite gravity,altimetry,and oceanographic data

Sea level variations(SLVs) can be divided into two major components:the steric SLV and the mass-induced SLV.These two components of SLV in the South China Sea(SCS) are studied by using satellite altimetry,GRACE(Gravity Recovery and Climate Experiment) satellite gravity,and oceanographic data on annual and inter-annual timescales.On the annual timescale,the geographic distribution of mass-induced SLV's amplitude jointly estimated from altimetry and the ECCO(Estimation of the Circulation and Climate of the Ocean) model agrees very well with that from GRACE.GRACE observes obvious seasonal mass-induced SLV in the SCS with annual amplitude of 2.7±0.4 cm,which is consistent with the annual amplitude of 2.7±0.3 cm estimated from the steric-corrected altimetry.On the inter-annual timescales,the mean SLV in the SCS shows a large oscillation,which is mainly caused by the steric effect.The trend of mean SLV inferred from altimetry in the SCS is 5.5±0.7 mm/yr for the period of 1993-2009,which is significantly higher than the global sea level rise rate of 3.3±0.4 mm/yr in the same period.There is no obvious trend signal in the mass-induced SLV detected from GRACE that indicates the water exchange between the SCS and its adjacent seas and land is in balance within the study period.

Sea level variability in East China Sea and its response to ENSO

Sea level variability in the East China Sea （ECS） was examined based primarily on the analysis of TOPEX/Poseidon altimetry data and tide gauge data as well as numerical simulation with the Princeton ocean model （POM）. It is concluded that the inter-annual sea level variation in the ECS is negatively correlated with the ENSO index, and that the impact is more apparent in the southern area than in the northern area. Both data analysis and numerical model results also show that the sea level was lower during the typical E1 Niflo period of 1997 to 1998. E1 Nifio also causes the decrease of the annual sea level variation range in the ECS. This phenomenon is especially evident in the southern ECS. The impacts of wind stress and ocean circulation on the sea level variation in the ECS are also discussed in this paper. It is found that the wind stress most strongly affecting the sea level was in the directions of 70° and 20° south of east,, respectively, over the northern and southern areas of the ECS. The northwest wind is particularly strong when E1 Nifio occurs, and sea water is transported southeastward, which lowers the sea level in the southern ECS. The sea level variation in the southern ECS is also significantly affected by the strengthening of the Kuroshio. During the strengthening period of the Kuroshio, the sea level in the ECS usually drops, while the sea level rises when the Kuroshio weakens.

The global mean sea surface model WHU2013

The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84°N, is established in this paper by combining nearly 20 years of multi-satellite altimetric data that include Topex/Poseidon （T/P）, Jason-1, Jason-2, ERS-2, ENVISAT and GFO Exact Repeat Mission （ERM） data, ERS-1/168, Jason-1/C geodetic mission data and Cryosat-2 low resolution mode （LRM） data. All the ERM data are adjusted by the collinear method to achieve the mean along-track sea surface height （SSH）, and the combined dataset of T/P, Jason-1 and Jason-2 from 1993 to 2012 after collinear adjustment is used as the reference data. The sea level variations in the non-ERM data （geodetic mission data and LRM data） are mainly investigated, and a combined method is proposed to correct the sea level variations between 66°S and 66°N by along-track sea level variation time series and beyond 66°S or 66°N by seasonal sea level variations. In the crossover adjustment between multi-altimetric data, a stepwise method is used to solve the problem of inconsistency in the reference data between the high and low latitude regions. The proposed model is compared with the CNES-CLS2011 and DTU13 MSS models, and the standard derivation （STD） of the differences between the models is about S cm between 80°S and 84°N, less than 3 cm between 66°S and 66°N, and less than 4 cm in the China Sea and its adjacent sea. Furthermore, the three models exhibit a good agreement in the SSH differences and the along-track gradient of SSH following comparisons with satellite altimetry data.

Observation on Height Changes of Chinese Tide Gauges by GPS

The height changes of tide gauges directly influence sea level observation. For research of sea level variation in large region or globe, the land vertical displacement must be considered. Two sessions of GPS and absolute gravity observations in 2001 and 2003 are used to determine the horizontal and vertical motion of China coast in ITRF2000 and Eurasia frame. The difference between results of continuous observation and periodic observation is discussed.