资源环境科技发展态势分析平台

There is a significant gap between the potential predictability of Arctic sea ice area and the current forecast skill of operational prediction systems. One route to closing this gap is improving understanding of the physical mechanisms, such as sea ice reemergence, which underlie this inherent predictability. Sea ice reemergence refers to the tendency of melt-season sea ice area anomalies to recur the following growth season and growth-season anomalies to recur the following melt season. This study builds on earlier work, providing a mode-based analysis of the seasonality and interannual variability of three distinct reemergence mechanisms. These mechanisms are studied using a common set of coupled modes of variability obtained via coupled nonlinear Laplacian spectral analysis, a data analysis technique for high-dimensional multivariate datasets. The coupled modes capture the covariability of sea ice concentration (SIC), sea surface temperature (SST), sea level pressure (SLP), and sea ice thickness (SIT) in a control integration of a global climate model. Using a parsimonious reemergence mode family, the spatial characteristics of growth-to-melt reemergence are studied, and an SIT-SIC reemergence mechanism is examined. A set of reemergence metrics to quantify the amplitude and phase of growth-to-melt reemergence are introduced. Metrics quantifying SST-SIC and SLP-SIC mechanisms for melt-to-growth reemergence are also computed. A simultaneous comparison of the three reemergence mechanisms, with focus on their seasonality and interannual variability, is performed. Finally, the conclusions are tested in a model hierarchy, consisting of models that share the same sea ice component but differ in their atmospheric and oceanic formulation.