Christopher Wyburn-Powell, Alexandra Jahn

Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple timescales that introduces large uncertainties into projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at lead times of 4-20 years, with up to 58% of the low-frequency variability explained by our linear model at a 5-year lead time. The dominant driver of low-frequency SIC variability is the Interdecadal Pacific Oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years, but with large uncertainty between GCMs and internal variability realization. The Niño 3.4 Index has good agreement between GCMs of being positively correlated with low-frequency SIC anomalies for up to approximately 12 years. The Atlantic Multidecadal Oscillation is simulated as being negatively correlated for up to approximately 10 years. No other climate modes investigated were found to be of high importance in driving low-frequency Arctic SIC anomalies. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade.