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A minimal model to diagnose the contribution of the stratosphere to tropospheric forecast skill
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  • Andrew James Charlton-Perez,
  • Jochen Bröcker,
  • Alexey Yurievich Karpechko,
  • Simon Haydn Lee,
  • Michael Sigmond,
  • Isla Ruth Simpson
Andrew James Charlton-Perez
University of Reading

Corresponding Author:a.j.charlton-perez@reading.ac.uk

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Jochen Bröcker
University of Reading
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Alexey Yurievich Karpechko
Finnish Meteorological Institute
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Simon Haydn Lee
University of Reading
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Michael Sigmond
Environment and Climate Change Canada
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Isla Ruth Simpson
National Center for Atmospheric Research (UCAR)
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Many recent studies have confirmed that variability in the stratosphere is a significant source of surface sub-seasonal prediction skill during northern hemisphere winter. It may be beneficial, therefore, to think about times in which there might be windows-of-opportunity for skilful sub-seasonal predictions based on the initial or predicted state of the stratosphere. In this study, we propose a simple, minimal model that can be used to understand the impact of the stratosphere on tropospheric predictability. Our model purposefully excludes state dependent predictability in either the stratosphere or troposphere or in the coupling between the two. Model parameters are set up to broadly represent current sub-seasonal prediction systems by comparison with four dynamical models from the sub-seasonal to seasonal prediction project database. The model can reproduce the increases in correlation skill in sub-sets of forecasts for weak and strong stratospheric states over neutral states despite the lack of dependence of coupling or predictability on the stratospheric state. We demonstrate why different forecast skill diagnostics can give a very different impression of the relative skill in the three sub-sets. Forecasts with large stratospheric signals and low amounts of noise are demonstrated to also be windows-of-opportunity for skilful tropospheric forecasts, but we show that these windows can be obscured by the presence of unrelated tropospheric signals.
27 Dec 2021Published in Journal of Geophysical Research: Atmospheres volume 126 issue 24. 10.1029/2021JD035504