The myriad physical processes that occur over a wide range of spatial and temporal scales in geophysical systems give rise to complex multi-scale behavior like atmospheric and oceanic low-frequency variability. It is of paramount importance to understand this multi-scale behavior but difficult to take into account both the chaotic nonlinearity and the scale interactions in modeling it. Several deficiencies in predicting the climate system, for instance, can be traced back to their misrepresentation in numerical weather and climate prediction models. These deficiencies, in turn, lead to systematic biases in the resolved-scale processes.

Given the unpredictability of the unresolved, small-scale components for observed values of the large-scale ones, stochastic approaches can provide more realistic representations of the multi-scale variability and allow transitions between chaotic attractors, as well as interactions among the scales (e.g., between climate and weather). Furthermore, such approaches also allow for a quantification of the uncertainties that are inherent in the modeling of nonlinear, chaotic and multi-scale systems. Quantification of uncertainty is directly linked to the ability to compare models and observations, and to combine them, as in data assimilation techniques.

We invite contributions spanning fundamental studies of chaotic and stochastic approaches, nonlinear and stochastic modeling of geophysical systems, uncertainty quantification, stochastic parameterizations, data assimilation using nonlinear and stochastic methods, and extreme-value studies using a nonlinear and stochastic framework.