An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

Anomalous behaviors of Wyrtki Jets in the equatorial Indian Ocean during 2013

In-situ measurement of the upper ocean velocity discloses significant abnormal behaviors of two Wyrtki Jets (WJs) respectively in boreal spring and fall, over the tropical Indian Ocean in 2013. The two WJs both occurred within upper 130 m depth and persisted more than one month. The exceptional spring jet in May was unusually stronger than its counterpart in fall, which is clearly against the previous understanding. Furthermore, the fall WJ in 2013 unexpectedly peaked in December, one month later than its climatology. Data analysis and numerical experiments illustrate that the anomalous changes in the equatorial zonal wind, associated with the strong intra-seasonal oscillation events, are most likely the primary reason for such anomalous WJs activities.