The myriad physical processes that occur over a wide range of spatial and temporal scales in geophysical systems give rise to complex behavior and low frequency variability. Neither the importance of understanding the low frequency variability nor the difficulty of being able to appropriately model it can be over-emphasized. Indeed, several deficiencies in predicting such systems (e.g., climate) can be traced back to a misrepresentation of scale interactions. The latter, in turn, leads to systematic biases in resolved-scale processes.

From the modeling point of view, given the nonuniqueness of small scales with respect to large scales and possible separation of scales between those explicitly modeled and those studied, stochastic approaches can provide more realistic representations of the multiscale variability and allow transitions between attractors. On the other hand, such approaches also allow for a quantification of uncertainties that are inherent in the modeling of these multiscale systems. It is important to note that such quantification of uncertainty is directly linked to the ability to compare models and observations, and combine them as in data assimilation techniques.

We invite contributions spanning fundamental studies of stochastic approaches, stochastic modelling of geophysical systems, stochastic parameterisations, data assimilation using stochastic methods and extreme value studies using a stochastic framework