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

TL;DR

This paper explores nonparametric ensemble analog forecasting for low-frequency North Pacific climate variability, demonstrating it can outperform traditional parametric models in predicting sea surface temperature and sea ice concentration patterns.

Contribution

It introduces the application of ensemble analog forecasting to low-frequency climate modes in the North Pacific, showing improved predictive skill over parametric methods.

Findings

Ensemble analog forecasting outperforms parametric models.

Forecasting accuracy improves with this nonparametric approach.

Method shows promise for predicting climate variability patterns.

Abstract

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.