The earth's climate is highly variable on all spatial and temporal scales, and this has direct consequences for society. For example, changes in variability (spatial or temporal) can impact the recurrence frequency of extreme events. Yet, it is unclear if a warmer future is one with more, or with less, climate variability, and at which scales, as a multitude of feedbacks is involved, and the instrumental record is short.

We welcome contributions that improve quantification, understanding and prediction of climate variability in the Earth System across space and time scales through case studies, idealized or realistic modeling, synthesis, and model-data comparison studies that provide insights into past, present and future climate variability on local to global, and synoptic to orbital timescales. Members of the PAGES working group on Climate Variability Across Scales (CVAS) are welcome.

This session aims to provide a forum to present work on
• the characterization of climate dynamics using variety of techniques (e.g. scaling and multifractal techniques and models, recurrence plots or variance analyses) to study its variability including periodicities, noise levels, or intermittency.
• the relationship between changes in the mean state (e.g. glacial to interglacial, preindustrial to present to future), and higher-order moments of relevant climate variables, to changes in extreme event occurrence and the predictability of climate.
• the role of ocean, atmosphere, cryosphere and land surface processes in fostering long-term climate variability through linear – or nonlinear – feedbacks and mechanisms
• the attribution of climate variability to internal dynamics, or the response to natural (volcanic or solar) and anthropogenic forcing
• the interaction of external forcing (e.g. orbital forcing) and internal variability such as mechanisms for synchronization and pacing of glacial cycles.
• the characterization of probabilities of extremes, including rare “black swan” events and the linkage between slow (interannual to millennial) climate variability and extreme event recurrence
• the development and characterization of statistical tools and stochastic models to quantify the distribution, or scaling, of climate variability over a wide range of timescales from short, noisy and irregular (paleo-)climate time series, such as robust estimators for power spectral analyses, fluctuation analyses (detrended, Haar or other) and wavelets.