Predicting the climate more than a few weeks in advance requires forecasters to bet on more than one computer model, similar to the method of diversifying an investment portfolio. Many earlier studies have shown that the combination of predictions generated by different computer models, known as a multi‐model ensemble, almost always achieves better forecast skill than using a single model alone. The North American Multi‐Model Ensemble (NMME), a sub-seasonal and seasonal prediction system combining individual North American state‐of‐the‐art climate prediction models, has become an integral part of sub-seasonal and seasonal research and applications.

The NMME has continually evolved, as newer models replace older ones; it is assumed that this evolution will produce more skillful predictions over time. But, until now, this assumption has not been tested. We examine the skill of NMME predictions in four different model combinations, including the oldest configuration, two transitional suites, and the operational configuration as of early 2020. Temperature prediction over both land and ocean has improved noticeably through the exchange of older models for newer ones, but precipitation prediction has not substantially improved. Overcoming the difficulty in precipitation prediction may require higher‐resolution climate models in the NMME.

Read more . . .

Becker, E., Kirtman, B. P., & Pegion, K. (2020). Evolution of the North American Multi‐Model Ensemble. Geophysical Research Letters, 47, e2020GL087408. https://doi.org/10.1029/2020GL087408