Abstract
The understanding of the long-term trend in climatic variables is necessary for the climate change impacts studies and for modeling several processes in environmental engineering. However, for climatic variables, long-term trend is usually unknown whether there is a trend component and, if so, the functional form of this trend is also unknown. In this context, a conventional strategy consists to assume randomly the shape of the local trends in the time series. For example, the polynomial forms with random order are arbitrarily chosen as the shape of the trend without any previous justification. This study aims to 1) estimate the real long-term nonlinear trend and the changing rate of the yearly high temperature among the daily minimum (YHTaDMinT) and maximum temperatures (YHTaDMaxT) observed at Cotonou city, 2) find out for these real trend and trend increment, the best polynomial trend model among four trend models (linear, quadratic, third-order and fourth-order polynomial function). For both time series, the results show that YHTaDMinT and YHTaDMaxT time series are characterized by nonlinear and monotonically increasing trend. The trend increments present different phases in their nonmonotone variations. Among the four trend estimations models, the trend obtained by third-order and fourth-order polynomial functions exhibits a close pattern with the real long-term nonlinear trend given by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). But, the fourth-order polynomial function is optimal, therefore, it can be used as the functional form of trend. In the trend increment case, for the YHTaDMaxT time series, the fourth-order fit is systematically the best among the four proposed trend models. Whereas for the YHTaDMinT time series, the third-order and fourth-order polynomial functions present the same performance. They can both be used as the functional form of trend increments. Overall, the fourth-order polynomial function presents a good performance in terms of trend and trend increments estimation.
Highlights
To modelize several processes in environmental engineering, agriculture, climatology and hydrology, the understanding of the real nonlinear trend and trend model’s estimation of climate variables as extrema temperatures are necessary [1]
In Benin Republic, a sub-Saharan country affected by climate change effects, few studies are focused on the real nonlinear trend investigation and on the estimation of the functional form of this trend in the extrema temperatures during historical period
Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise, namely ICEEMDAN, developed by Colominas et al [9] is used for estimating nonlinear trends and trend increments in YHTaDMinT and YHTaDMaxT
Summary
To modelize several processes in environmental engineering, agriculture, climatology and hydrology, the understanding of the real nonlinear trend and trend model’s estimation of climate variables as extrema temperatures are necessary [1]. In Benin Republic, a sub-Saharan country affected by climate change effects, few studies are focused on the real nonlinear trend investigation and on the estimation of the functional form of this trend in the extrema temperatures during historical period. In this country, one of the major challenges for hydrologists and meteorologists is the estimation of the real functional form of historical climate variables trend. Linear regression technique is used to analyze the trends in the climatic variables time series This situation can lead to erroneous conclusions
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