The impact of the quasi‐biennial oscillation (QBO) on the prediction of tropical intraseasonal convection, including the Madden Julian Oscillation (MJO) and Boreal Summer Intraseasonal Oscillation (BSISO), is assessed in the WMO Subseasonal to Seasonal (S2S) forecast database using the real‐time OLR based MJO (ROMI) index. It is shown that the ROMI prediction skill for the boreal winter MJO, measured by the maximum time at which the anomaly correlation coefficient exceeds 0.6, is higher by 5 to 10 days in the QBO easterly phase than its westerly phase. This difference occurs even in models with low tops and poorly resolved stratospheres. MJO predictability, as measured by signal to noise ratio in the S2S ensemble, also shows a similar difference between the two QBO phases, and results from a simple linear regression model show consistent behavior as well. Analysis of the ROMI index derived from observations indicates that the MJO is more coherent and stronger in the QBO easterly phase than in the westerly phase. These results suggest that the skill dependence on QBO phase results from the initial state of the MJO, the regularity of its propagation in the verifying observations, or most likely a combination of the two, but not on an actual stratospheric influence on the MJO within the model simulations. In contrast to the robust QBO‐MJO connection in boreal winter, the BSISO prediction skill exhibited by the S2S models in boreal summer is greater in the QBO
- Award ID(s):
- 2025057
- NSF-PAR ID:
- 10373940
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Bulletin of the American Meteorological Society
- Volume:
- 103
- Issue:
- 2
- ISSN:
- 0003-0007
- Page Range / eLocation ID:
- E463 to E484
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract westerly phase than in theeasterly phase during the 1999 to 2010 period. This is consistent with the observation that BSISO OLR anomalies are stronger in the QBO westerly phase during that period. However, this relationship between the QBO and BSISO in boreal summer changes in recent decades: BSISO is weaker in QBO westerly than easterly during 1979–2000. Correspondingly, the QBO impact on BSISO prediction in boreal summer also reverses in that period as well in a statistical model, whereas this statistical model shows a consistent QBO impact on MJO prediction in boreal winter over the past four decades. -
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null (Ed.)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.more » « less
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Abstract Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO.
Significance Statement The advance of subseasonal-to-seasonal (S2S) prediction largely depends on dynamical models’ ability to predict some major intrinsic modes in the climate system, including the boreal summer intraseasonal oscillation (BSISO). Using a newly developed S2S prediction system, we thoroughly evaluated its performance in predicting BSISO, and revealed the skill dependence on the BSISO propagation diversity. Here we provide physical explanations of what influences the BSISO predictions and identify different precursory signals for two types of BSISO, which have important implications for operational forecasts.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/BAMS-D-21-0124.1
