A minimal model to diagnose the contribution of the stratosphere to
tropospheric forecast skill
- Andrew James Charlton-Perez,
- Jochen Bröcker,
- Alexey Yurievich Karpechko,
- Simon Haydn Lee,
- Michael Sigmond,
- Isla Ruth Simpson
Alexey Yurievich Karpechko
Finnish Meteorological Institute
Author ProfileIsla Ruth Simpson
National Center for Atmospheric Research (UCAR)
Author ProfileAbstract
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