Arctic Sea Ice Reemergence Mechanisms in a Model Hierarchy

Abstract:

Lagged correlation analysis of Arctic sea ice area anomalies reveals that melt season sea ice anomalies tend to recur the following growth season, and growth season anomalies tend to recur the following melt season. In this work, the regional and temporal characteristics of this phenomenon, termed sea-ice reemergence, are investigated in a hierarchy of climate models. Coupled nonlinear Laplacian spectral analysis (NLSA), a multivariate data analysis technique, is used to study the covariability of Arctic sea-ice concentration (SIC), sea-surface temperature (SST), sea-level pressure (SLP), and sea-ice thickness (SIT). Two mechanisms related to melt season to growth season reemergence are identified: (1) An SST-SIC mechanism, related to local imprinting and persistence of SST anomalies in the seasonal ice zones, and (2) an SLP-SIC mechanism, related to winter-to-winter regime persistence of large-scale SLP teleconnection patterns. An SIT-SIC growth season to melt season reemergence mechanism is also identified, related to winter persistence of SIT anomalies in the central Arctic. The representation of these mechanisms is investigated using the model hierarchy to determine the relative roles of the ocean, atmosphere, and sea ice itself in producing reemergence. It is found that the SST-based and SIT-based mechanisms can exist as stand-alone processes, whereas the SLP mechanism cannot. Dynamical feedback from the ocean to the atmosphere is found to be essential in creating large-scale organized patterns of SIC-SLP covariability. A set of reemergence metrics is introduced, by which one can judge the amplitude and phase of reemergence events and associated mechanisms.