Probabilistic causal network modelling of Southern Hemisphere jet sub-seasonal to seasonal predictability

• Published in: Journal of Climate
• Main Authors: Saggioro, Elena, Shepherd, Theodore G., Knight, Jeff
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Meteorological Society 2024
• Subjects: polar night
• Online Access: https://centaur.reading.ac.uk/115586/; https://centaur.reading.ac.uk/115586/19/clim-JCLI-D-23-0425.1%20%281%29.pdf; https://centaur.reading.ac.uk/115586/1/SaggioroElena_30012024_mod.pdf; https://centaur.reading.ac.uk/115586/2/SaggioroElena_SI_07012024.pdf

Skilful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for representation of mid-to-high latitude SH climate variability. In the austral spring-to-summer months, the jet and the stratospheric polar vortex variabilities are strongly coupled. Since the vortex is more predictable and influenced by long-lead drivers one month or more ahead, the stratosphere is considered a promising pathway for improving forecasts in the region on subseasonal to seasonal (S2S) timescales. However, a quantification of this predictability has been lacking, as most modelling studies address only one of the several interacting drivers at a time, while statistical analyses quantify association but not skill. This methodological gap is addressed through a knowledge-driven probabilistic causal network approach, quantified with seasonal ensemble hindcast data. The approach enables to quantify the jet’s long-range predictability arising from known late-winter drivers, namely El Ni˜no Southern Oscillation, Indian Ocean Dipole, upward wave activity flux and polar night jet oscillation, mediated by the vortex variability in spring. Network-based predictions confirm the vortex as determinant for skilful jet predictions, both for the jet’s poleward shift in late spring and its equatorward shift in early summer. ENSO, IOD, late-winter wave activity flux and polar night jet oscillation only provide moderate prediction skill to the vortex. This points to early spring sub-monthly variability as important for determining the vortex state leading up to its breakdown, creating a predictability bottle-neck for the jet. The method developed here offers a new avenue to quantify the predictability provided by multiple, interacting drivers on S2S timescales.