Conventional techniques for understanding the time evolution of global climate and ice sheets often employs process-based models that solve long chains of differential equations to calculate how these systems evolve through a series of incremental changes. Yet such approaches have certain drawbacks, such as being computationally expensive, and prone to smoothing errors. In some cases it is preferable to approach the matter differently, and look at the statistical properties of a system and how these properties change through time. To that end, we are currently exploring a suite of related projects that investigate scale-invariant ice sheet flow, multi-fractal fluctuations in climate records, and network theory-based assessments of how environmental changes cascade through the global climate system.
People: Nick Golledge, Markus Luczak-Roesch, Béatrice Désy