Long-time correlations of sea-level and local atmospheric pressure fluctuations at Trieste

Power-Type Functions of Prediction Error of Sea Level Time Series

“…Sea level time series has the property of long-range dependence (LRD), as can be seen from Ercan et al [90], Barbosa et al [91], Beretta et al [92], and Li et al [93] …”

“…As previously mentioned in the Introduction, there are two categories of predictors, namely, linear predictors and nonlinear ones [73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89], which may be considered for a specific type of time series, such as sea level. Since sea level is of LRD [90][91][92][93], which is nonlinear, and since, ANN is a nonlinear predictor, it was consequently used in this research.…”

Significance of trends in long-term correlated records

“…,L, depends on its (internal) correlation properties and can be influenced by external mechanisms. Prominent examples are temperature records , river flows [29][30][31][32][33][34][35][36], and sea level heights [37][38][39], which all show a strong natural long-term persistence and, in addition, are effected by anthropogenic influences that may lead to an additional trend.…”

Do climate models reproduce complexity of observed sea level changes?

“…Sea level fluctuations result from complex interactions between diverse physical processes and, as many other geophysical signals, exhibit long‐term correlations (LTC), also called long‐term memory or long‐term persistence [ Agnew , ], that can be effectively modeled as outcomes of stochastic power law process with a Hurst exponent H > 0.5 [ Beretta et al , ; Barbosa et al , ; Bos et al , ]. The Hurst exponent 0.5 < H < 1 indicates the presence of LTC that manifest themselves as persistent low‐frequency oscillations [ Feder , ; Beran , ; Rybski and Bunde , ].…”

“…Thus, adequate modeling of the observed sea level power law behavior is crucial for distinguishing externally driven trends from natural climate variability [ Lennartz and Bunde , , ; Bunde and Lennartz , ]. Inspection of the longest TG records worldwide demonstrated that the power law scaling exponent is a useful metric to characterize the sea level regional variability [ Beretta et al , ; Barbosa et al , , ; Bos et al , ; Becker et al , ; Dangendorf et al , , ]. Moreover, several studies have previously demonstrated through other parameters (temperature, precipitation, water discharge…) the ability of this metric to characterize the stochastic variability of climate and to provide an important test of the validity of AOGCMs [ Bunde et al , ; Govindan et al , , ; Vjushin et al , ; Blender and Fraedrich , ; Blender et al , ; Koutsoyiannis et al , ; Rybski et al , ; Kumar et al , ; Bordbar et al , ].…”