An Adaptive Method for Nonlinear Sea Level Trend Estimation by Combining EMD and SSA

Abstract

Sea level records exhibit complex nonlinear variability as a result of competing physical processes, some of which are largely unknown, and the available length of the record will also affect the trend estimation, therefore robust methods for determining consistent long-period, nonlinear trends are needed to effectively analyze different sea level records holistically, which is important for timely research and applications to address current and future coastal vulnerability and adaptations due to sea level rise (Ghil et al., 2002; Plattner & GianKasper, 2014; Zhang & Church, 2012). At least 30 methods classified into five categories, have been used to estimate or search for sea level trends (see Visser et al., 2015 for a detailed review), and some methods have been compared in terms of different aspects, including accuracy, standard deviation, computational cost, consistency, capacity to provide temporal information, resolution, complexity, and predictive performance (Watson, 2016a). Although there are differences between the results in terms of which methods are better, one could draw some common conclusions. A simple parabola (Houston & Dean, 2011; Woodworth, 1990) or straight line is commonly used to fit the trend in sea level change time series (Douglas, 1991). However, the trend estimate provides limited, if any, information about transient variations of the trend, and the trend estimate is likely to be influenced by the particular data span (Parker et al., 2013). Some other predefined functions or models, such as piecewise functions (Fenoglio-Marc & Tel, 2010), exponential functions (Parker et al., 2013), and different autoregressive moving average models (Beenstock et al., 2015; Thompson, 1980), used to search for sea level trends are also not sufficiently universal to apply to all sea level change time series. Although the moving average is a simple method, data are lost from the two ends of the averaged record, leading to the absence of re-Abstract Adaptive and accurate trend estimation of the sea level record is critically important for characterizing its nonlinear variations and its study as a consequence of anthropogenic climate change. Sea level change is a nonstationary or nonlinear process. The present modeling methods, such as least squares fitting, are unable to accommodate nonlinear changes, including the choice of a priori information to help constrain the modeling. All these problems affect the accuracy and adaptability of nonlinear trend estimation. Here, we propose a method called EMD-SSA, that effectively combines adaptive empirical mode decomposition (EMD) and singular spectrum analysis (SSA). First, the sea level change time series is decomposed by EMD to estimate the intrinsic mode functions. Second, the periodic or quasiperiodic signals in the intrinsic mode functions can be determined using Lomb-Scargle spectral analysis. Third, the numbers of the identified periodicities/quasiperiodicities are used as embedding dimensions of SSA to identify possible nonlinear trends. Then, the optima...

Analysis of sea level variability and its contributions in the Bohai, Yellow Sea, and East China Sea

“…Sea level change is a non-stationary and non-linear process (Jin et al, 2021), characterized by significant inter-decadal variability influenced by natural physical phenomena. Since the 20th century, the rate of GMSL rise has been on an accelerating trend (Llovel et al, 2019).…”