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
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.
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.
- NSF-PAR ID:
- 10494159
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 37
- Issue:
- 7
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 2217-2230
- Size(s):
- p. 2217-2230
- Sponsoring Org:
- National Science Foundation
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