Robustness of the long-term nonlinear evolution of global sea surface temperature trend

• Published in: Geoscience Letters
• Main Authors: Zhenhao Xu, Gang Huang, Fei Ji, Bo Liu, Fei Chang, Xichen Li
• Format: Article in Journal/Newspaper
• Language: English
• Published: SpringerOpen 2022
• Subjects: Global sea surface temperature; Robustness analysis; EEMD; Nonlinear trends; Science; Q; Geology; QE1-996.5; Southern Ocean; Pacific; Start Point
• Online Access: https://doi.org/10.1186/s40562-022-00234-x; https://doaj.org/article/453b28ff43744b979e3fe53d865dee74

Abstract The multi-scale variability of global sea surface temperature (GSST), which is often dominated by secular trends, significantly impacts global and regional climate change. Previous studies were mainly carried out under linear assumptions. Even if the nonlinear evolution patterns have been discussed based on annual-mean data, the conclusions are still insufficient due to several factors. Here, based on the Ensemble Empirical Mode Decomposition (EEMD) method, the robustness of GSST trends tied to the sampling frequency and time interval selection is further explored. The main features derived from the annual-mean data are maintained. However, monthly and seasonal-mean data both mute the cooling in the equatorial central Pacific and the Southern Ocean in the Pacific sector, meanwhile intensify and expand the warming over the North Pacific. The results also highlight that early data cause a minimal effect on secular trends except for the portion near the start point of the interval due to the local temporal nature of EEMD. Overall, the long-term GSST trends extracted by EEMD have good robustness. Our research also clarifies that quadratic fitting cannot reveal all the meaningful evolution patterns, even as a nonlinear solution.