Skillful statistical prediction of sub-seasonal temperature by training on dynamical model data
Abstract
In this study, we derive statistical models for predicting wintertime sub-seasonal temperature over the western United States. The statistical models are trained on two separate datasets, namely observations and dynamical model simulations, and are based on Least Absolute Shrinkage and Selection Operator (lasso). Surprisingly, statistical models trained on dynamical model simulations can predict observations better than observation-trained models. One reason for this is that simulations involve orders of magnitude more data than observational datasets. Nevertheless, the skill of sub-seasonal prediction is very low when measured by spatial average squared error. This result does not automatically mean there is no significant skill. For instance, certain large-scale patterns may be predictable, but this predictability may be obscured by local weather variability when local mean square error is used to measure skill. To identify large-scale predictable patterns, an optimization technique, called Skill Component Analysis (SCA), is applied. SCA finds the linear combination of variables that minimizes the normalized mean square error. Applying SCA to the lasso predictions reveals at least two patterns of large-scale temperature variations that are skillfully predicted. The predictability of these patterns is consistent between climate model simulations and observations. Not surprisingly, the predictability is determined largely by sea surface temperature variations in the Pacific, particularly the region associated with the El Nino-Southern Oscillation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.A34E..03T