Reply on RC1

Abstract

In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Arctic Pacific sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally-varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 6 month lead times in the Bering Sea and the Sea of Okhotsk. We find that surface radiative fluxes contribute to predictability in the cold season and geopotential height and winds play an indispensable role in the warm-season forecast, contrasting to the thermodynamic processes dominating the pan-Arctic predictability. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.