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The role of the stratosphere in subseasonal to seasonal prediction part II: predictability arising from stratosphere ‐ troposphere coupling

Authors :
Domeisen, Daniela I. V.
Butler, Amy H.
Charlton-Perez, Andrew J.
Ayarzagüena, Blanca
Baldwin, Mark P.
Dunn‐Sigouin, Etienne
Furtado, Jason C.
Garfinkel, Chaim I.
Hitchcock, Peter
Karpechko, Alexey Yu.
Kim, Hera
Knight, Jeff
Lang, Andrea L.
Lim, Eun‐Pa
Marshall, Andrew
Roff, Greg
Schwartz, Chen
Simpson, Isla R.
Son, Seok‐Woo
Taguchi, Masakazu
Publication Year :
2020
Publisher :
American Geophysical Union, 2020.

Abstract

The stratosphere can have a signi_cant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and tropo sphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Prob abilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Paci_c and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Nin~o Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the USA, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

Details

Language :
English
ISSN :
21698996
Database :
OpenAIRE
Accession number :
edsair.core.ac.uk....146db6acaa3759c436817990f448642c