A Nonlinear Cause for the Seasonal Predictability Barrier of SST Anomaly in the Tropical Pacific. Issue 10 (22nd October 2022)

• Record Type: Journal Article
• Authors: Yu, Dakuan; Zhou, Meng; Hang, Chaoxun; Sun, De‐Zheng
• Is Part Of: Journal of geophysical research. Volume 127:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-22
• Subjects: predictability barrier -- Sample Entropy -- chaos; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2022JC018723
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: The seasonal Predictability Barrier (PB) of Sea Surface Temperature Anomaly (SSTA) is characterized by a rapid loss of prediction skills at a particular season in dynamic models. Here, the connection between seasonal PB and the inherent nonlinearity of SSTA was investigated using a statistical method known as Sample Entropy (SamEn). When the SamEn value is large, the chaotic degree of SSTA is high. In the Niño 3 and Niño 3.4 regions, the chaotic degree of SSTA was high in the spring; in the Niño 4 region, it was high in the summer. This result was consistent with the known PB occurrence season in these regions. The month when the chaotic degree of SSTA peaked moved westward longitudinally from March to June. This spatial‐temporal variation of the chaotic degree was consistent with that of the low SSTA variance and PB occurrence timing. Specifically, when the variance of SSTA was low, the low‐amplitude background SSTA signal was more dominant than the El Niño/Southern Oscillation (ENSO)‐related signal, the chaotic degree of SSTA was high, and the PB phenomenon occurred. Hence, the results indicated that the background SSTA signal was more chaotic than the ENSO‐related signal and the seasonal PB may result from the inherent low predictability of the chaotic background SSTA signal. Furthermore, the correlation coefficient between SSTA and shortwave flux anomaly showed similar variation along the longitude compared with the chaotic degree, which suggested that the seasonal connection between SSTA and atmospheric forcing may be responsible for the spatial‐temporal variation of the chaotic degree. Plain Language Summary: Accurate predictions of Sea Surface Temperature Anomaly (SSTA) in the tropical Pacific are in high demand. However, dynamic models tend to lose their prediction skills of SSTA during a certain season, which is known as the seasonal Predictability Barrier (PB). Based on a nonlinear statistic method—SamEn, the chaotic degree of SSTA was investigated in the tropical Pacific to explore the nonlinear cause for the seasonal PB. The spatial‐temporal distribution of the chaotic degree was consistent with that of the PB occurrence timing and low‐amplitude background SSTA signal. Specifically, when the low‐amplitude background SSTA signal was dominant in SSTA data, the chaotic degree of SSTA was high, and the PB phenomenon occurred. Hence, this result suggests that the low‐amplitude background SSTA signal is chaotic and the seasonal PB results from the inherent low predictability of this chaotic background SSTA signal. Furthermore, the seasonal connection between SSTA and atmospheric forcing may be responsible for the spatial‐temporal variation of the chaotic degree. Key Points: Sample Entropy method is utilized to study the nonlinearity of the seasonal Predictability Barrier (PB) of Sea Surface Temperature Anomaly (SSTA) in the tropical Pacific The low‐amplitude background SSTA signal, which is more chaotic than the El Niño/Southern Oscillation‐related signal, potentially results in PB The seasonal connection between SSTA and atmospheric forcing could lead to the spatial‐temporal variation of the chaotic signal