Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability

The North Pacific exhibits patterns of low-frequency variability on the intra-annual to decadal time scales, which manifest themselves in both model data and the observational record, and prediction of such low-frequency modes of variability is of great interest to the community. While parametric models, such as stationary and non-stationary autoregressive models, possibly including external factors, may perform well in a data-fitting setting, they may perform poorly in a prediction setting. Ensemble analog forecasting, which relies on the historical record to provide estimates of the future based on past trajectories of those states similar to the initial state of interest, provides a promising, nonparametric approach to forecasting that makes no assumptions on the underlying dynamics or its statistics. We apply such forecasting to low-frequency modes of variability for the North Pacific sea surface temperature and sea ice concentration fields extracted through Nonlinear Laplacian Spectral Analysis, an algorithm which produces clean time-scale separation from data without pre-filtering. We find such methods may outperform parametric methods and simple persistence with increased predictive skill, and are more skillful when initialized in an active phase, rather than a quiescent phase. We also apply these methods to the predict integrated sea ice extent anomalies in the North Pacific from both models and observations, and find an increase of predictive skill over the persistence forecast by about 2 months.