How Do Model Biases Affect Large-Scale Teleconnections That Control Southwest US Precipitation? Part II: Seasonal Models

We explore the skill in predicting southwest United States (SWUS) October-March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models' capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models' North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Ni & ntilde;o-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic-flow-inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests reexamining high-latitude drivers of SWUS precipitation.