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Author: Fan, Jingfang Ɨ End date: 2023 Ɨ Start date: 2020 Ɨ Publisher: [London] : IOP Ɨ

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    Evolution mechanism of principal modes in climate dynamics
    ([London] : IOP, 2020) Zhang, Yongwen; Fan, Jingfang; Li, Xiaoteng; Liu, Wenqi; Chen, Xiaosong
    Eigen analysis has been a powerful tool to distinguish multiple processes into different simple principal modes in complex systems. For a non-equilibrium system, the principal modes corresponding to the non-equilibrium processes are usually evolving with time. Here, we apply the eigen analysis into the complex climate systems. In particular, based on the daily surface air temperature in the tropics (30? Sā€“30? N, 0? Eā€“360? E) between 1979-01-01 and 2016-12-31, we uncover that the strength of two dominated intra-annual principal modes represented by the eigenvalues significantly changes with the El NiƱo/southern oscillation from year to year. Specifically, according to the ā€˜regional correlationā€™ introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El NiƱo region and the northern (southern) hemisphere usually signals the beginning (end) of the El NiƱo. We discuss the underlying physical mechanism and suppose that the evolution of the first intra-annual principal mode is related to the meridional circulations; the evolution of the second intra-annual principal mode responds positively to the Walker circulation. Our framework presented here not only facilitates the understanding of climate systems but also can potentially be used to study the dynamical evolution of other natural or engineering complex systems. Ā© 2020 The Author(s).
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    Improved earthquake aftershocks forecasting model based on long-term memory
    ([London] : IOP, 2021) Zhang, Yongwen; Zhou, Dong; Fan, Jingfang; Marzocchi, Warner; Ashkenazy, Yosef; Havlin, Shlomo
    A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.