Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability

Abstract:

Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. Pan-Arctic sea ice reemergence is present in both observations and global climate models (GCMs), yet the amplitude and regional details of the reemergence signals vary substantially. In this work, a novel data analysis technique, coupled Nonlinear Laplacian Spectral Analysis (NLSA), is employed to study the spatiotemporal co-variability of sea ice concentration, sea surface temperature (SST), and sea level pressure (SLP) in the Arctic. NLSA modes are obtained for observational data and GCM output, and are used to examine the statistical characteristics and physical mechanisms of sea ice reemergence. It is found that lagged correlation features of the raw sea ice data can be efficiently reproduced using low-dimensional families of modes. These families provide an SST-sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). Moreover, the ice anomalies of each family exhibit clear phase relationships between the Barents-Kara, Bering, and Labrador seas. These regional phase relationships have a natural explanation via the SLP patterns and associated geostrophic winds of each family, which closely resemble the Arctic Oscillation and Arctic Dipole Anomaly. Additionally, the winter-to-winter persistence of these SLP patterns suggests another plausible mechanism for sea ice reemergence.