Climate Dynamics and Modelling

Background

In recent decades, the increasing scientific and public interest in climate dynamics and modeling has triggered enormous efforts to obtain, analyze and interpret the behavioral properties and data containing information which, in a broad sense, are to be framed within the areas of complex systems science. The availability of new sources of data, as for example the ones obtained by ground and satellite monitoring, has led to develop physics, mathematics, numerical and analogic interpretative models, new techniques for statistical evaluation, innovative methods for data analysis, and data-based models which have led to significant progress in our understanding of the mechanisms responsible for the climate dynamics as well as to a better understanding of the dynamics of the different components of the Earth climate. In this context, the understanding of the complex dynamics of the climate system and of its modeling highlights the necessity to explore the climate system through the lens of complexity science, to introduce innovative modeling strategies and approaches in order to understand past behavior, to fingerprint the causes of current climate changes, and to plan measures for piloting future climate changes. Furthermore, on that score, it is important to adopt interdisciplinary and transdisciplinary approaches since the assessments of climate change impacts require high integration among disciplines and since interactions between exact human sciences and society become essential when collective behavioral measurements are to be adopted.

Aim

The aim of Climate Dynamics and Modeling is to offer a platform to discuss, in the widest sense and by emphasizing the interdisciplinary approach, all the aspects of the dynamics and modeling governing the global climate system. The achievement of these goals requires an integrated view of the physical, mathematical, analogic and numerical modeling, complex systems theories, machine learning approaches and image analyses. In addition to theoretical, modeling, as well computational and observational approaches to climate dynamics, Climate Dynamics and Modeling coverage includes transdisciplinary aspects as well as the education impacts of these studies.

Scope

• Climate dynamics;
• Climate modeling;
• Physical and mathematical approaches;
• Analogic modeling;
• Climate and complex systems dynamics;
• Case studies;
• Big data;
• Machine learning approach;
• Climate and meteorology correlations;
• Numerical climate and weather predictions;
• Dynamics and modeling of volcanic ash emissions;
• Climate dynamics and modeling by satellite images investigations ;
• Data assimilation techniques;
• Transdisciplinary approach to climate dynamics and modeling;
• Climate dynamics and modeling in education.