Reduced-order modeling of Arctic Amplification feedbacks

High latitude regions are warming at an accelerated rate compared to other regions of the Earth. This Arctic Amplification (AA) has significant impacts within and outside the arctic. While it is known from observations and Earth system models that multi-component feedbacks contribute to AA, disentangling the full effects of these complex nonlinear interactions is a major challenge. Here we present initial work on data-driven reduced-order models to analyze the key feedback between ice albedo and surface temperature. Our preliminary analysis utilizes best-fit linear models based on the Dynamic Mode Decomposition with control (DMDc). We show that DMDc captures known linear feedbacks between ice albedo and surface temperature, and includes dominant spatial patterns of variability. We also show how to generalize to nonlinear reduced-order models and how reduced-order models may be used to capture causal relations from data.