Non-Linear Post-Processing of Numerical Seasonal Climate Forecasts

Although numerical models are increasingly being used to generate operational seasonal forecasts, the reliability of these products remains relatively low. Regression-based post-processing methods have proven useful in increasing forecast skill, but efforts have focused on linear regression. Given the non-linear nature of the climate system and sources of model error, non-linear analogues of these post-processing methods may offer considerable improvements. The current study tests this hypothesis, applying both linear and non-linear regression to the correction of climate hindcasts produced with general circulation models. Results indicate that non-linear support vector regression is better able to extract indices of the Pacific/North American teleconnection pattern and the North Atlantic Oscillation from coupled model output, while linear approaches are better suited to atmosphere-only model output. Statistically significant predictions are produced at lead times of up to nine months and can be obtained from model output with no forecast skill prior to processing.