The aim of this workshop is to bring together contributions on theoretical,  experimental, and computational approaches to climate and environmental modeling which are inspired by statistical physics. 
The workshop will focus on applications of statistical physics in the modeling of environmental systems and climate as well the analysis of environmental and climate data. Statistical physics has traditionally centered on the behavior of the microscopic systems. Environmental and climate processes, on the other hand, typically involve macroscopic systems. In spite of the difference in physical scales, statistical physics and environmental/climate modeling both  investigate partially determined systems and require a stochastic approach, thus creating the potential for interdisciplinary transfer of  knowledge. 
The climate is governed by the interchange of energy and mass between atmosphere, oceans, icecaps, land masses and biosphere. From a dynamical systems perspective the climate can be seen as the long term mean of the state of the system, while from a statistical point, the climate can be seen as the equilibrium state as response to the external forcing and boundary conditions. In recent years the problem of understanding and determining the state of the climate has been attacked with different approaches, such as maximum entropy principles, scaling theories, networks, system reduction theories, bifurcations and critical transitions, just to mention some. These different approaches are rooted in statistical physics.  Statistical physics also influenced subsurface hydrology which adapted and incorporated methods and ideas from the theory turbulence (structure functions, perturbation expansions, closure schemes), statistical field theory (Feynman diagrams, Renormalization Group  theory, replica variational approach), and classical statistical mechanics (Liouville’s theorem, fractional Brownian motion). To date, statistical physics concepts are also used in seismology and other environmental processes. In addition, statistical and machine learning methods originating in statistical physics are used to analyze and process complex patterns in environmental data. This workshop aims to highlight such contributions and to present novel ideas and methods motivated by statistical physics that can lead to new environmental applications and insights into the Earth's climate.  
A non-exclusive list of topics of interest includes novel computational and theoretical tools for the analysis of large spatiotemporal data sets, innovative approaches to complex  environmental processes and climate that combine nonlinear and stochastic components, methods that address the interaction of multiple scales, approaches for the reconstruction and simulation of non-Gaussian natural or artificial media, applications of stochastic differential equations to environmental processes, higher-order upscaling methods, applications  of complex network theory, statistical and stochastic models of extreme  events, and 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, the ocean and the subsurface, natural hazards (earthquakes, fires, avalanches, and landslides), precipitation, global circulation and climate.