An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models

Abstract

The annual temperatures recorded for the last two centuries in fifteen european stations around the Alps are analyzed. They show a global warming whose growth rate is not however constant in time. An analysis based on linear Arima models does not provide accurate results. Thus, we propose threshold nonlinear nonstationary models based on several regimes both in time and in levels. Such models fit all series satisfactorily, allow a closer description of the temperature changes evolution, and help to discover the essential differences in the behavior of the different stations.

An Empirical Mode-Spatial Model for Environmental Data Imputation

“…While researchers have reported many advanced approaches in the literature, including combining EMD decomposition with Bayesian compressive sensing approaches [3]. Earth sciences often have data gaps and there are many different reported methods ranging from simple replacement [14,15], an offset method with only target site data which replaced a single missing value with averaged observed values before and after and for longer gaps used an average of the values from the previous and following year [16] (we used a simple version of this called the temporal method in this manuscript) to complex statistical models [17,18]; to spatial models [19,20]. Other methods include spatiotemporal models [21], pattern matching [10], and data modeling [22].…”