Modified Signal-to-Noise Ratio Method for Early Detection of Climate Change

Abstract

Efficient climate change detection is vital to mitigate the ill effects of climate change. Several methods of change point detection have been proposed earlier that do have some limitations. Nonparametric Mann–Whitney–Pettit (MWP) and signal-to-noise ratio (SNR) methods for climate change point detection have been widely accepted and followed by researchers. SNR method is considered better than other parametric and nonparametric change point detection methods as it considers the natural internal variability of the variable to detect significant change. Here, a simple yet powerful climate change detection method called noise-based change point (NBCP) is proposed which is an advancement of the SNR method. The performance of the NBCP method was compared with the SNR and MWP methods for a synthetically generated series as well as real-world data. Different scenarios for synthetic series were considered by introducing varying trend magnitude, variance, and location of change point. The scenarios signifying limitations of SNR as well as MWP methods were also identified. The performance of MWP was not found satisfactory for climate change point detection as it highly depends on the location of the actual change point and performs well only when the change point occurs in the middle of the series. The NBCP method detected the change point earlier than SNR for all generated scenarios as well as real-world data. This study indicates that the NBCP method could be a better option to early detect climate change points and is more consistent than both SNR and MWP methods.