Understanding the dynamics of climate variability across timescales requires going beyond the limited span of the instrumental record. This session highlights synergies between paleoclimate data and models to improve the quantification, interpretation, and simulation of variability from decadal to millennial and longer timescales. By integrating data- and model-based perspectives, we aim to advance how climate variability is represented, constrained, and understood across scales.

We particularly invite contributions along four complementary axes:
(1) Model evaluation and development: using paleoclimate constraints for tuning, retuning, and benchmarking variability across scales.
(2) Data assimilation and reconstruction: exploiting theoretical and numerical models to enhance reconstructions through spatial and cross-variable covariance structures.
(3) Empirical analyses across scales: including scaling, fractal, and multifractal approaches.
(4) Theoretical frameworks: conceptual and mathematical models that reproduce scaling behaviour in space and time, generate different forms of variability, and describe the scale- and state-dependence of climate dynamics.

We encourage contributions from the PAGES Climate Variability Across Scales (CVAS) working group and related communities, while welcoming all research that leverages paleoclimate information, models, or theory to deepen our understanding of climate variability across scales.