Sack Lunch Seminar (SLS)

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. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea-ice concentration, sea-surface temperature (SST), and sea-level pressure (SLP) are studied in the North Pacific and Arctic sectors of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the reemergence signal of the raw sea-ice data and also provide SST and SLP-based mechanisms for reemergence. These mechanisms are related to the phase and amplitude of large-scale SST and SLP patterns, namely the North Pacific Gyre Oscillation, Arctic Oscillation, and Arctic Dipole Anomaly. Distinct phase relationships are identified between sea-ice anomalies in the Barents-Kara Seas, the Labrador Sea, and the Bering Sea, three regions that comprise the majority of Arctic sea-ice variability. It is found that the SLP patterns of each family, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between these geographically disconnected regions.