Robustness Assessment of the RSDt‐Test for Detecting Trend Turning in a Time Series

Abstract

Trend turning (or trend change) is a type of structural change that is common in climate data, and methods for detecting it in time series with multiple turning-points need to be developed. A recently developed method for this, the running slope difference (RSD) t-test, examines trend differences in sub-series of the sample time series to identify the trend turning-points. In this paper, we use Monte Carlo simulation to evaluate this method's detection ability. Evaluation results show the method to be an effective tool for detecting trend turning time series and identify three major advantages of the RSD t-test: ability to detect multiple turning-points, capacity to detect all three types of trend turning, and great performance of reducing false alarm rate. Key Points: • The RSD t-test is a recently developed method for detecting trend turning in climate time series • Monte Carlo simulation is designed to evaluate the detection ability of the RSD t-test comprehensively • The RSD t-test has capacity to detect multiple turning-points of three turning types and great performance of reducing false alarm rate . Robustness assessment of the RSD t-test for detecting trend turning in a time series.

Modulation of the link between the Hadley circulation and the meridional structure of tropical SSTs by the Atlantic multidecadal oscillation

“…In addition to the two-sided Student's t test, we also used the running slope difference (RSD) t test, which is used to judge whether the numerator and denominator of a response ratio are independent (Zuo et al 2019(Zuo et al , 2020Hou et al 2020). Compared with traditional trend turning methods, the RSD t test has the major advantage that it can capture the trend turning of time series with the same sign but a significantly different trend value.…”