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Abstract Although useful at short and medium ranges, current dynamical models provide little additional skill for precipitation forecasts beyond week 2 (14 days). However, recent studies have demonstrated that downstream forcing by the Madden–Julian oscillation (MJO) and quasi-biennial oscillation (QBO) influences subseasonal variability, and predictability, of sensible weather across North America. Building on prior studies evaluating the influence of the MJO and QBO on the subseasonal prediction of North American weather, we apply an empirical model that uses the MJO and QBO as predictors to forecast anomalous (i.e., categorical above- or below-normal) pentadal precipitation at weeks 3–6 (15–42 days). A novel aspect of our study is the application and evaluation of the model for subseasonal prediction of precipitation across the entire contiguous United States and Alaska during all seasons. In almost all regions and seasons, the model provides “skillful forecasts of opportunity” for 20%–50% of all forecasts valid weeks 3–6. We also find that this model skill is correlated with historical responses of precipitation, and related synoptic quantities, to the MJO and QBO. Finally, we show that the inclusion of the QBO as a predictor increases the frequency of skillful forecasts of opportunity over most of the contiguous United States and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks. 

Correlations between springtime stratospheric ozone extremes and subsequent surface temperatures have been previously reported for both models and observations at particular locations in the Northern Hemisphere. Here we quantify for the first time the potential use of ozone information for Northern Hemisphere seasonal forecasts, using observations and a nine‐member chemistry climate model ensemble. The ensemble composite correlations between March total column ozone (TCO) and April surface temperatures display a similar structure to observations, but with slightly lower correlation magnitudes. This is likely due to the larger number of cases smoothing out sampling error in the pattern, which is visible in the difference between correlations calculated from individual ensemble members. Using a linear regression model with March TCO as the predictor, predictions of the following April surface temperatures in regions that show large correlations are possible up to 4 years following the regression model end date in individual ensemble members, and up to 6 years in observations. We create an empirical forecast model to predict the sign of the observed as well as the modeled surface temperature anomalies using March TCO. Through a leave‐three‐years‐out cross‐validation method, we show that March TCO can forecast the sign of the April surface temperature anomalies well in parts of Eurasia that show the lowest model internal variability.

 

Intrusions of warm, moist air into the Arctic during winter have emerged as important contributors to Arctic surface warming. Previous studies indicate that temperature, moisture, and hydrometeor enhancements during intrusions all make contributions to surface warming via emission of radiation down to the surface. Here, datasets from instrumentation at the Atmospheric Radiation Measurement User Facility in Utqiaġvik (formerly Barrow) for the six months from November through April for the six winter seasons of 2013/14–2018/19 were used to quantify the atmospheric state. These datasets subsequently served as inputs to compute surface downwelling longwave irradiances via radiative transfer computations at 1-min intervals with different combinations of constituents over the six winter seasons. The computed six winter average irradiance with all constituents included was 205.0 W m⁻², close to the average measured irradiance of 206.7 W m⁻², a difference of −0.8%. During this period, water vapor was the most important contributor to the irradiance. The computed average irradiance with dry gas was 71.9 W m⁻². Separately adding water vapor, liquid, or ice to the dry atmosphere led to average increases of 2.4, 1.8, and 1.6 times the dry atmosphere irradiance, respectively. During the analysis period, 15 episodes of warm, moist air intrusions were identified. During the intrusions, individual contributions from elevated temperature, water vapor, liquid water, and ice water were found to be comparable to each other. These findings indicate that all properties of the atmospheric state must be known in order to quantify the radiation coming down to the Arctic surface during winter.