SNAP – Stratospheric Network for the Assessment of Predictability


Activity Leaders

Andrew Charlton-Perez
University of Reading, UK

Amy Butler
NOAA Chemical Sciences Division, USA

Steering Committee

Mark Baldwin, University of Exeter, UK
Daniela Domeisen, ETH Zürich, Switzerland
Chaim Garfinkel, Hebrew University, Israel
Peter Hitchcock, Cornell University, USA
Erik Kolstad, Bjerknes Centre, Norway
Jeff Knight, Met Office, UK
Craig Long, NOAA CPC, USA
Andrea Lopez Lang, University at Albany, USA
Andrew Marshall, BOM, Australia
Isla Simpson, NCAR, USA
Aditi Sheshadri, Stanford university, USA
Seok-Woo Son, Seoul National University, Korea
Lantao Sun, NOAA ESRL, USA
Masakazu Taguchi, Aichi University of Education, Japan
Om Tripathi, USA

Activity description

During winter and spring, the stratosphere is a dynamically exciting place, with intense and dramatic stratospheric major warming events occurring typically in two out of every three years in the Northern hemisphere and minor warming events occurring more frequently still. It is not surprising, therefore, that there has long been interest in understanding what role the stratosphere might play in influencing tropospheric weather and climate.

The SPARC Network on Assessment of Predictability (SNAP) project will seek to answer several outstanding questions about stratospheric predictability and its tropospheric impact, namely: (i) Are stratosphere-troposphere coupling effects important throughout the winter season or only when major stratospheric dynamical events occur? (ii) How far in advance can major stratospheric dynamical events be predicted and usefully add skill to tropospheric forecasts? (iii) Which stratospheric processes, both resolved and unresolved need to be captured by models to gain optimal stratospheric predictability?

SNAP’s scientific goals include: (i) assessing current skill in forecasting the extra-tropical stratosphere; (ii) investigating the extent to which accurate forecasts of the stratosphere contribute to improved tropospheric predictability; and (iii) understanding the partitioning of any gains in predictability with a well resolved stratosphere between improvements in the estimation of initial conditions and improvements in forecast skills. The central aim of SNAP will be to design and organise a new intercomparison of stratospheric forecasts. This will also leave a legacy of datasets to be used by a broad community of researchers.

Published results

Book chapters:

Butler, A.H., A. Charlton-Perez, D.I.V. Domeisen, C. Garfinkel, E.P. Gerber, P. Hitchcock, A.-Y. Karpechko, A.C. Maycock, M. Sigmond, I. Simpson, S.-W. Son, Sub-seasonal Predictability and the Stratosphere- Chapter 11, The Gap Between Weather and Climate Forecasting, p. 223-241, Elsevier,, 2019.

Journal publications:

Tripathi, O. P., M. Baldwin, A. Charlton-Perez, M. Charron, S. D. Eckermann, E. Gerber, R. G. Harrison, D. R. Jackson, B.-M. Kim, Y. Kuroda, A. Lang, S. Mahmood, R. Mizuta, G. Roff, M. Sigmond and S.-W. Son, 2014: The predictability of the extratropical stratosphere on monthly time-scales and its impact on the skill of tropospheric forecasts. Q.J.R. Meteorol. Soc.. doi: 10.1002/qj.2432

SPARC activity reports:

SPARC Newsletter No. 46, 2016, p. 11: The next phase of SNAP: Analysis of the WWRP/WCRP initiative S2S data by the SPARC commuity, by O.P. Tripathi, A. Charlton-Perez, G. Roff, and F. Vitart

SPARC Newsletter No. 41, 2013, p. 44-51: Report on the 1st SPARC Stratospheric Network for the Assessment of Predictability (SNAP), by O. P. Tripathi, A. Charlton-Perez, E. Gerber, E. Manzini, M. Baldwin, M. Charron, D. Jackson, Y. Kuroda, and G. Roff

SPARC Newsletter No. 41, 2013, p. 40-43: Report on the 3rd SPARC DynVar Workshop on Modelling the Dynamics and Variability of the Stratosphere-Troposphere System, by E. Manzini, A. Charlton-Perez, E. Gerber, T. Birner, A. Butler, S. Hardiman, A. Karpechko, F. Lott, A. Maycock, S. Osprey, O. P. Tripathi, T. Shaw, and M. Sigmond

SPARC Newsletter No. 39, 2012, p. 40: SNAP: The Stratospheric Network for the Assessment of Predictability, by A. Charlton-Perez, and D. Jackson


Website for further information