  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. We find such methods may outperform parametric
methods and simple persistence with increased predictive skill.
