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

Saggioro, E. ORCID: https://orcid.org/0000-0002-9543-6338, Shepherd, T. G. ORCID: https://orcid.org/0000-0002-6631-9968 and Knight, J. (2024) Probabilistic causal network modelling of Southern Hemisphere jet sub-seasonal to seasonal predictability. Journal of Climate, 37 (10). pp. 3055-3071. ISSN 1520-0442 doi: 10.1175/JCLI-D-23-0425.1

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115586
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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