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Abstract The Arctic marginal ice zone (MIZ) is the transitional region between dense pack ice and open ocean. As an increasingly important component of the polar marine environment, recent investigations have focused on changes in MIZ size and location as the climate has warmed. Fractal geometry offers a universal measure of complexity, shape, and self-similarity across scales, and a powerful tool for characterizing MIZ evolution. Here we analyze the fractal dimension of the Arctic MIZ boundary and find a pronounced seasonal cycle that is repeated almost exactly each year, with a sharp maximum in late summer. The long-term trend is slight, with a decrease of less than 2% over the satellite era, while MIZ width has increased over the same period by almost 40%. Our results have important implications for climatic and ecological processes which depend critically on MIZ geometry. We demonstrate thermodynamic feedbacks through statistical analysis and provide context for future applications. 

Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: Λ, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate Λ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized Λ(t), the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics.