Assessment of sea level variability derived by EOF reconstruction
Assessment of sea level variability derived by EOF reconstruction
复制标题
通过 EOF 重建得出的海平面变化评估
DOI：
10.1093/gji/ggy126
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发表时间：
2018
影响因子：
2.8
通讯作者：
Feng Wei
中科院分区：
文献类型：
--
作者：
Mu Dapeng;Yan Haoming;Feng Wei
经验正交函数重建（Empirical Orthogonal Function Reconstruction，EOFR）是将覆盖时间较短的卫星测高数据或模式输出与覆盖时间较长的稀疏验潮记录（TGR）相结合来反演过去的海平面变化（SLV）。考虑到TGRs的数量随着时间的推移显著向后减少，有必要评估稀疏TGRs如何影响重建的SLV。同时，EOFR涉及两种技术，即使用误差矩阵或不使用，这尚未得到充分的评估。我们发现误差矩阵在EOFR中起着重要的作用。使用误差矩阵产生比没有误差矩阵的更好的重构SLV，特别是当TGRs稀疏时（例如<100）。如果不包括误差矩阵，稀疏的TGRs重建的SLV往往会被严重高估。研究还发现，即使使用误差矩阵，用稀疏TGRs重建的全球平均海平面变率也不可靠。然而，第一个模式的恢复被证明是强大的，通过整个世纪（相关性>0.7）。由于第一次厄尔尼诺现象与厄尔尼诺-南方涛动（ENSO）的相关性很高，这表明EOFR对揭示ENSO变率的演变是有用的。
The empirical orthogonal function (EOF) reconstruction (EOFR) combines the dense observations, for example, satellite altimetry data or model output covering a short period, and sparse tide gauge records (TGRs) spanning a long period to infer the past sea level variability (SLV). Given that the number of the TGRs reduces significantly backward over time, it is necessary to assess how the sparse TGRs affect the reconstructed SLV. Meanwhile, EOFR involves two techniques, that is, using error matrix or not, which has not yet been fully assessed. We find that error matrix plays an important role in the EOFR. Using error matrix produces better reconstructed SLV than those without error matrix, especially when the TGRs are sparse (e.g. <100). If the error matrix is not included, the SLV reconstructed with sparse TGRs tends to be seriously overestimated. It is also found that global mean sea level variability reconstructed with sparse TGRs is not reliable even the error matrix is used. However, the recovery of first EOF pattern is shown to be robust through the entire 20th century (correlation >0.7). Since the first EOF is highly related to El Niño–Southern Oscillation (ENSO), this suggests that EOFR is useful for revealing the evolution of ENSO variability.