W4. Climate and Environment

Workshop organized by:
- D. Hristopulos 
- S. Blesic

Section I

Statistical Physics and Machine Learning for Environmental Modeling

 D. Hristopulos 

The aim of this inter-disciplinary workshop is to bring together theoretical, experimental and computational contributions that focus on the understanding and modeling of environmental processes and datasets.  Statistical, stochastic, and machine learning methods, which are routinely used to analyze and extract information from complex patterns in environmental datasets, have strong links with statistical physics. This workshop aims to highlight such connections and the applications of stochastic, machine learning, and statistical physics approaches in climate and the environment.  

A non-exclusive list of topics of interest includes computational and theoretical methods for the analysis of large spatiotemporal data sets, causality detection in environmental and climate series, methods that address multi-scale interactions, applications of Gaussian process regression and other machine learning methods to spatiotemporal environmental datasets, simulation of non-Gaussian heterogeneous media, applications of stochastic differential equations to environmental and ecological processes, change-of-scale methods for spatiotemporal systems, applications of complex network theory to hydrological processes, and estimation of long-range correlations. Physical phenomena of interest include environmental flow and pollutant transport, natural hazards (earthquakes, fires, avalanches, floods, and landslides), extreme meteorological and hydrological events, atmospheric precipitation, and ocean waves. 

Section II

Understanding climate, contributing to overall adaptation efforts

 S. Blesic

The most pressing issues facing current interdisciplinary efforts that deal with the complexity of climate change are in advancements in understanding and explaining the physical basis of climate dynamics realized, in parallel, with utilization of that knowledge to effectively contribute to new lines of research that will develop innovative applications that drive particularly adaption efforts. This workshop will be organized to showcase current research and research potential of both paths. Statistical physics community has already done a lot of important work in understanding climate variability. Therefore, the workshop will present statistical physics approaches to understanding of physical aspects of climate phenomena. The workshop will be extended with work that investigates various physical and non-physical phenomena in climatic context, thus contributing to the overall adaptation efforts. These kinds of investigations have an additional appeal, particularly to statistical physics community that standardly works in interdisciplinary areas, for they usually bring together researchers, practitioners and communities with discrete expertise, in order to better understand climate change and its impacts. Focuses of these researches are different but areas of interest are vast and can include any critical issue that climate change threatens to seriously exacerbate. What statistical physics can offer in this context is data- or model-led understandings that are of wider value to the scientific community and applicable local-scale insights.