Sea‐ice reemergence in a model hierarchy

Lagged correlation analysis of Arctic sea‐ice area 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, a climate model hierarchy is used to investigate the relative role of the atmosphere and the ocean in driving this phenomenon, termed sea‐ice reemergence. The covariability of sea‐ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) is studied via coupled nonlinear Laplacian spectral analysis, and families of modes that capture reemergence are constructed. In model configurations with ocean‐to‐atmosphere coupling, these “reemergence families” display a pan‐Arctic scale organization of SIC anomalies, related to SLP teleconnection patterns. The ocean is found to provide the key source of memory for reemergence, as an SST‐based reemergence mechanism can operate as a stand‐alone process, while an SLP‐based 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.