Earthquake sequencing is an intriguing research topic and the dynamics involved are complex. For directed graphs that represent earthquake sequencing, the Kuramoto model yields synchronization. Inclusion of non-local effects evokes the occurrence of chimera states or the co-existence of synchronous and asynchronous behavior among earthquake zones. It is the chaotic dynamics of them resulting in certain patterns that we begin to see in the sequence of seismic events with a very simple model.

This paper describes chaos and dynamical complexity to reveal the state of the underlying dynamics of the ionosphere on a daily basis. This is to show the daily/transient variations of chaoticity and dynamical complexity so as to reveal the degree of changes that occur in the ionospheric process and dynamics from one day to another. This paper will point the space science community in the direction of the use of chaoticity and dynamical complexity as indices to describe the process and dynamics.

The paper reviews the available visualisation techniques and tools for the visual analysis of geo-physical climate networks. The results from a questionnaire with experts from non-linear physics are presented, and the paper surveys recent developments from information visualisation and cartography with respect to their applicability for visual climate network analytics. Several case studies based on own solutions illustrate the potentials of state-of-the-art network visualisation technology.

The present study aims to improve the calculus of finite-time Lyapunov exponents (FTLEs) applied to describe the transport of inertial particles in a fluid flow. To this aim, the deformation tensor is modified to take into account that the stretching rate between particles separated by a certain distance is influenced by the initial velocity of the particles. Results are presented for two different flows and compared with the classical method by Shadden (2005).

We introduced a new modified Markov chain model to generate a time series of the earthquake sequences from a global catalogue with an optimum time sampling of 9 days. Here, we subject the time series to a known analysis method namely an ensemble empirical mode decomposition to study the state-to-state fluctuations in each of the intrinsic mode functions. Also, we establish the power-law behaviour of the time series with the Fano factor and Allan factor used in time-correlative behaviour studies

Superparameterization is a multiscale computational method that significantly improves the representation of cloud processes in global atmosphere and climate models. We present a framework for assimilating observational data into superparameterized models to initialize them for forecasts. The framework is demonstrated in the context of a new system of ordinary differential equations that constitutes perhaps the simplest model of superparameterization.

Intermittent dynamics of particle size distribution in coastal waters is studied. Particle sizes are separated into four size classes: silt, fine, coarse and macro particles. The time series of each size class is derived, and their multiscaling properties studied. Similar analysis has been done for suspended particulate matter and total volume concentration. All quantities display a nonlinear moment function and a negative Hurst scaling exponent.