Why Seasonal Prediction of California Winter Precipitation Is Challenging

Why Seasonal Prediction of California Winter Precipitation Is Challenging

Due to the recurrent and severe drought conditions, there is an urgent demand for reliable seasonal prediction of precipitation in California. Being the nation’s most populous state and one that plays a crucial role in the U.S. economy, including agriculture and food supplies, accurate prediction of CA precipitation several months ahead becomes exceptionally important for disaster preparation and mitigation as well as for drought and water restriction policy-making purposes. However, predictive skill for CA winter precipitation remains limited, and relies heavily on tropical sea surface temperature (SST) anomalies, such as the El Niño–Southern Oscillation (ENSO).

In a new Bulletin of the American Meteorological Society, authors Xianan Jiang, Duane Waliser, Peter Gibson, Gang Chen, and Weina Guan analyzed long-term hindcasts from multiple atmosphere–ocean coupled models to examine the underlying processes that limit seasonal prediction skill for CA winter precipitation. From this research it was found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by ENSO. Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation.

This study indicates that significantly improved prediction of the circulation anomalies over the west coast United States that are independent from the ENSO will be critical for any major breakthrough in predicting CA precipitation.The authors also note that chances may exist for improvement of CA precipitation prediction with possible missing predictability sources in the climate forecast models.

Funding for this project was provided in part by CPO’s CVP program.

Read the full paper »

For more information, contact Courtney Byrd.




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