Estimating trends of the Mediterranean Sea level changes from tide gauge and satellite altimetry data (1993-2015)

The impact of climate change on sea level has received a great deal of attention by scientists worldwide. In this context, the problem of sea levels on global and regional scales have been analyzed in a number of studies based on tide gauges observations and satellite altimetry measurements. This study focuses on trend estimates from 18 high-quality tide gauge stations along the Mediterranean Sea coast. The seasonal Mann-Kendall test was run at a 5% significance level for each of the 18 stations for the period of 1993-2015 (satellite altimetry era). The results of this test indicate that the trends for 17 stations were statistically significant and showed an increase (no significant trend was observed only at one station). The rates of sea level change for the 17 stations that exhibit significant trends, estimated using seasonal Sen's approach, range after correction for Vertical Land Motion (VLM) from 1.48 to 8.72 mm/a for the period 1993-2015. Furthermore, the magnitude of change at the location of each tide gauge station was estimated using the satellite altimetry measurements. Thus, the results obtained agree with those from the tide-gauge data analysis.     

Inferring the global mean sea level from a global tide gauge network

An attempt is made to infer the global mean sea level(GMSL) from a global tide gauge network and frame the problem in terms of the limitations of the network. The network,owing to its limited number of gauges and poor geographical distribution complicated further by unknown vertical land movements,is ill suited for measuring the GMSL. Yet it remains the only available source for deciphering the sea level rise over the last 100 a. The poor sampling characteristics of the tide gauge network have necessitated the usage of statistical inference. A linear optimal estimator based on the Gauss-Markov theorem seems well suited for the job. This still leaves a great deal of freedom in choosing the estimator. GMSL is poorly correlated with tide gauge measurements because the small uniform rise and fall of sea level are masked by the far larger regional signals. On the other hand,a regional mean sea level(RMSL) is much better correlated with the corresponding regional tide gauge measurements. Since the GMSL is simply the sum of RMSLs,the problem is transformed to one of estimating the RMSLs from regional tide gauge measurements. Specifically for the annual heating and cooling cycle,we separate the global ocean into 10-latitude bands and compute for each 10-latitude band the estimator that predicts its RMSL from tide gauges within. In the future,the statistical correlations are to be computed using satellite altimetry. However,as a first attempt,we have used numerical model outputs instead to isolate the problem so as not to get distracted by altimetry or tide gauge errors. That is,model outputs for sea level at tide gauge locations of the GLOSS network are taken as tide gauge measurements,and the RMSLs are computed from the model outputs. The results show an estimation error of approximately 2 mm versus an error of 2.7 cm if we simply average the tide gauge measurements to estimate the GMSL,caused by the much larger regional seasonal cycle and mesoscale variation plaguing the individual tide gauges. The numerical model,Los Alamos POP model Run 11 lasting 3 1/4 a,is one of the best eddy-resolving models and does a good job simulating the annual heating and cooling cycle,but it has no global or regional trend. Thus it has basically succeeded in estimating the seasonal cycle of the GMSL. This is still going to be the case even if we use the altimetry data because the RMSLs are dominated by the seasonal cycle in relatively short periods. For estimating the GMSL trend,longer records and low-pass filtering to isolate the statistical relations that are of interest. Here we have managed to avoid the much larger regional seasonal cycle plaguing individual tide gauges to get a fairly accurate estimate of the much smaller seasonal cycle in the GMSL so as to enhance the prospect of an accurate estimate of GMSL trend in short periods. One should reasonably expect to be able to do the same for longer periods during which tide gauges are plagued by much larger regional interannual(e. g.,ENSO events) and decadal sea level variations. In the future,with the availability of the satellite altimeter data,we could use the same approach adopted here to estimate the seasonal variations of GMSL and RMSL accurately and remove these seasonal variations accordingly so as to get a more accurate statistical inference between the tide gauge data and the RMSLs(therefore the GMSL) at periods longer than 1 a,i. e.,the long-term trend.     

Trend analysis of relative sea level rise or fall of the tide gauge stations in the Pacific

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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 a...     

南海北部海面高度的季节内变异及其传播特征 *

利用25年(1993—2017)的卫星高度计资料, 采用复经验正交函数(complex empirical orthogonal function, CEOF)方法, 分析南海北部海区海面高度季节内变异的时空分布及传播特征。标准差分析表明, 南海北部海面高度的季节内变异(intra-seasonal variability of sea level anomalies, SLA-ISV)在沿陆坡外侧区较强, 且SLA-ISV表现出明显的季节性变化, 冬半年强于夏半年。CEOF前两个主要模态能较好地揭示研究海区SLA-ISV的时空分布及其传播特征, 并表明SLA-ISV的强度受到季节性变化和年际变化的调制。全年CEOF的第一模态揭示SLA-ISV从台湾岛西南至西沙群岛以东区域的冬半年西南向传播特征; 而全年CEOF的第二模态则表现了SLA-ISV分别在台湾岛西南和东沙群岛西南的西南向传播特征。南海北部中尺度涡季节变化统计分析表明, CEOF的分解结果与南海北部的涡旋活动一致。