W4. Climate and Environments
Workshop organized by:
- D. Hristopulos and D. Valenti
- S. Blesic
- P. Ditlevsen, M. Ghil, N. Boers and M. Rypdal
Section I
Natural Systems, Complexity and Environmental Modeling: The Triangle of Statistical Physics, Statistics, and Machine Learning.
D. Hristopulos and D. Valenti
The aim of this workshop is to bring together contributions on theoretical, experimental, and computational approaches for studying the complexity in natural systems by exploiting tools inspired by statistical physics, spatio-temporal statistics, and machine learning. Statistical approaches and machine learning methods ---which have strong links with statistical physics--- are used to analyze and extract information from complex patterns in environmental data. This workshop aims to highlight such connections and to present novel ideas and methods motivated by statistical physics that can lead to new environmental applications and insights. Both stochastic methods and approaches based on the theory of dynamical systems are welcome. A non-exclusive list of topics of interest includes novel computational and theoretical tools for the analysis of large spatio-temporal data sets, modeling of natural systems as intrinsically nonlinear open systems, methods that address multiple-scale interactions, approaches for the reconstruction and simulation of natural or engineered porous media with non-Gaussian statistics, applications of stochastic differential equations to environmental processes, higher-order upscaling methods, applications of complex network theory, estimation of long-range correlations in environmental systems. Physical phenomena of interest include (but are not limited to): the flow and transport of pollutants in the atmosphere, oceans and subsurface, natural hazards (earthquakes, fires, avalanches, and landslides), heat waves and precipitation.
Section II
Understanding climate, contributing to overall adaptation efforts
S. Blesic
Section III
Critical transitions and climate change
P. Ditlevsen, M. Ghil, N. Boers and M. Rypdal
Several components of the climate system have been identified as possessing a potential risk for undergoing abrupt transitions; such components have been called Tipping Elements (TEs). The interaction between different components in the complex Earth system could lead to a cascade of tipping events, with the probability of critical transitions within one TE depending on the evolving state of one or more other TEs. Understanding this kind of cascading behavior and the phenomena underpinning the tipping events involved requires use of statistical physics tools to understand critical transitions in complex systems. Such tools are provided by the theory of fast-slow systems, dynamical and stochastic systems theory, nonlinear time series analysis, and multiple time-scale dynamics. The applications include investigations of paleoclimatic records and present day’s observations, as well as the behavior of TEs in Earth System Models, where computer simulations must be carefully designed to explore the possible transitions.
In this symposium, we invite contributions that further develop and apply methods from statistical physics. Particular emphasis will be placed on the study of climate response to increased greenhouse gas concentrations, climate tipping points, time-dependent forcing and associated pullback attractors in climate evolution, as well as extreme and rare events in observations and models and the uses of statistical mechanics across the hierarchy of Earth System Models.
