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Subseasonal Prediction Skill of Winter Quasi-Stationary Waves in the Northern Hemisphere

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Quasi-stationary Rossby waves modulate persistent (lasting days to weeks) atmospheric ridges and troughs, and can lead to extreme weather events, particularly in the midlatitudes. Due to their persistent nature, these quasi-stationary waves (QSWs) provide a unique opportunity to improve subseasonal forecasts of extreme events. Here, we evaluate the skill of the ECMWF dynamical subseasonal-to-seasonal (S2S) forecast model to forecast QSWs in Northern Hemisphere winter. The model shows spatially varying prediction skill, with the North Pacific region showing the highest skill across all lead times studied (7 to 35 days). We find very large inter-annual and intra-seasonal variability in the subseasonal skill of this North Pacific region. The inter-annual variability is statistically consistent across different S2S models, indicating that physical conditions varying from year to year influence the prediction skill. Further investigation shows improvements in the S2S skill under certain phases of the Madden-Julian Oscillation (MJO), under La Niña ocean conditions, during the westerly phase of the Quasi-Biennial Oscillation (QBO), and following the onset of Sudden Stratospheric Warmings (SSWs). These identified conditions may be windows of opportunity for better S2S QSW forecast skill. Our results indicate that although the S2S skill of QSWs is low, there is potential to utilize natural modes of variability to better capture uncertainty of model outputs, and identify times when skill is likely to be higher.
Title: Subseasonal Prediction Skill of Winter Quasi-Stationary Waves in the Northern Hemisphere
Description:
Quasi-stationary Rossby waves modulate persistent (lasting days to weeks) atmospheric ridges and troughs, and can lead to extreme weather events, particularly in the midlatitudes.
Due to their persistent nature, these quasi-stationary waves (QSWs) provide a unique opportunity to improve subseasonal forecasts of extreme events.
Here, we evaluate the skill of the ECMWF dynamical subseasonal-to-seasonal (S2S) forecast model to forecast QSWs in Northern Hemisphere winter.
The model shows spatially varying prediction skill, with the North Pacific region showing the highest skill across all lead times studied (7 to 35 days).
We find very large inter-annual and intra-seasonal variability in the subseasonal skill of this North Pacific region.
The inter-annual variability is statistically consistent across different S2S models, indicating that physical conditions varying from year to year influence the prediction skill.
Further investigation shows improvements in the S2S skill under certain phases of the Madden-Julian Oscillation (MJO), under La Niña ocean conditions, during the westerly phase of the Quasi-Biennial Oscillation (QBO), and following the onset of Sudden Stratospheric Warmings (SSWs).
These identified conditions may be windows of opportunity for better S2S QSW forecast skill.
Our results indicate that although the S2S skill of QSWs is low, there is potential to utilize natural modes of variability to better capture uncertainty of model outputs, and identify times when skill is likely to be higher.

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