The climate is highly variable over wide ranges of scale in both space and time so that the amplitude of fluctuations systematically depends on the scale of observations. This has direct consequences for understanding and modelling the climate and for socially important applications including climate projections. This variability across scales is often scaling and the climate variations recorded in time series or spatial distributions, which are produced through modelling or empirical analyses are inextricably linked to their space-time scales and is a significant part of the uncertainties in the “proxy” approaches.

This session aims at bringing together climatologists and paleoclimatologists from the modelling and proxy-data acquisition communities in addition to scientists from the nonlinear geoscience community with the aim to develop tools for understanding, comparing and modelling time series and spatial distributions over wide scale ranges so as to better understand and quantify the climate variability in time and space while taking into account intrinsic uncertainties. Participants in the PAGES working group on Climate Variability Across Scales (CVAS) are welcome.

Contributions that improve the quantification, understanding and prediction of climate variability in the Earth System across space and time scales are encouraged. This includes 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.

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.