The session is concerned with the development and application of new empirical, as well as first-principle approaches to modeling and prediction of climate. In spite of the progress in the first-principle modeling of climatic phenomena, predictive capabilities of these dynamical models are fundamentally restricted by the Earth system complexity: inherent sensitivity to initial conditions, sensitivity to model parameters, and complex nonlinearities and feedbacks involved hinder the development of robust prediction methods solely based on first principle model usage. At the same time, shortness of climatic time series restricts capabilities of purely empirical modeling. The main intention of this session is to bring together specialists who use empirical models as well as who use first-principle knowledge — from mathematical statistics and statistical physics to complex systems science — to present and combine different modern approaches for analyzing climatic processes and their predictability.

Specific topics include but are not limited to the following:

• Data-driven (empirical) approaches to modeling climate variability: stochastic empirical models, qualitative and quantitative forecast, optimal model selection, etc.;
• Combination of empirical and first-principle modeling; improving empirical models by analysis of time series generated by first principle models, etc.;
• Use of dynamical systems and statistical methods for studying climate dynamics and constructing reduced-order models;
• Characterizing forced and internal fluctuations by using of methods based on fluctuation dissipation theorem and response theory;
• Efficient expansions of spatially distributed data: from EOFs and its extensions to spatiotemporal and/or nonlinear decompositions;
• Complex network approaches to investigation of teleconnection patterns and their stability;
• Empirical modeling and predictability of extreme events;
• Model intercomparison in terms of their predictive capabilities on different time scales.