ALBERT

Description: 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° S–30° N, 0° E–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 Ni$\tilde {\mathrm{n}}$o/southern oscillation from year to year. Specifically, according to the 'regional correlation' introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El Ni$\tilde {\mathrm{n}}$o region and the northern (southern) hemisphere usually signals the beginning (end) of the El Ni$\tilde {\mathrm{n}}$o. 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.

Description: Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

Description: Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.

Description: The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11±0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

Description: There is an increasing interest to study the interactions between atmospheric electrical parameters and living organisms at multiple scales. So far, relatively few studies have been published that focus on possible biological effects of atmospheric electric and magnetic fields. To foster future work in this area of multidisciplinary research, here we present a glossary of relevant terms. Its main purpose is to facilitate the process of learning and communication among the different scientific disciplines working on this topic. While some definitions come from existing sources, other concepts have been re-defined to better reflect the existing and emerging scientific needs of this multidisciplinary and transdisciplinary area of research.

Description: The atmosphere is a thermo-hydrodynamical complex system and provides oxygen to most animal life at the Earth's surface. However, the detection of complexity for the atmosphere remains elusive and debated. Here we develop a percolation-based framework to explore its structure by using the global air temperature field. We find that the percolation threshold is much delayed compared with the prototypical percolation model and the giant cluster eventually emerges explosively. A finite-size-scaling analysis reveals that the observed transition in each atmosphere layer is genuine discontinuous. Furthermore, at the percolation threshold, we uncover that the boundary of the giant cluster is self-affine, with fractal dimension df, and can be utilized to quantify the atmospheric complexity. Specifically, our results indicate that the complexity of the atmosphere decreases superlinearly with height, i.e., the complexity is higher at the surface than at the top layer and vice versa, due to the atmospheric boundary forcings. The proposed methodology may evaluate and improve our understanding regarding the critical phenomena of the complex Earth system and can be used as a benchmark tool to test the performance of Earth system models.

Description: Lagrangian particle-based smoothed particle hydrodynamics (SPH) is increasingly widely used in landslide modelling. This paper investigates four important issues not addressed by previous studies on SPH modelling of large-scale landslides, i.e., convergence property, influence of constitutive parameters, scale effect and friction reduction, and influence of different treatments of the viscous effect. The GPU-acceleration technique is employed to achieve high-resolution three-dimensional (3D) modelling. The Baige landslide is investigated by comparing numerical results with field data, and detailed analyses on the four issues are provided. Suggestions on particle resolution, constitutive parameter, and formulations of viscous discretization are also presented for future SPH modelling of large-scale landslides.