SNAP – Stratospheric Network for the Assessment of Predictability


Activity Leaders

Amy Butler, NOAA Chemical Sciences Division, USA;
Chaim Garfinkel, Hebrew University, Israel;

Steering Committee

Mark Baldwin, University of Exeter, UK
Daniela Domeisen, ETH Zürich, Switzerland
Peter Hitchcock, Cornell University, USA
Jeff Knight, Met Office, UK
Andrea Lopez Lang, University at Albany, USA
Isla Simpson, NCAR, USA
Aditi Sheshadri, Stanford University, USA
Seok-Woo Son, Seoul National University, Korea
Masakazu Taguchi, Aichi University of Education, Japan
Zachary Lawrence, NOAA/PSL, USA
Timothy Stockdale, ECMWF, UK
Laura Ciasto, NOAA/NCEP, USA
Jian Rao, Nanjing University of Information Science and Technology, China
Andrew Charlton-Perez, University of Reading, UK
Eun-Pa Lim, Bureau of Meteorology, Australia

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.

Special Issue:

Bridging Weather and Climate: Subseasonal-to-Seasonal (S2S) Prediction, JGR Special Issue – submission period May 2018 to December 2019, contains 56 articles which are available here.

Journal publications:

Hitchcock, P., Butler, A., Charlton-Perez, A., Garfinkel, C.I., Stockdale, T., Anstey, J., et al., 2022: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts. Geoscientific Model Development, 15(13), 5073–5092. doi: 10.5194/gmd-15-5073-2022.

Lawrence, Z. D., et al., 2022: Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems. Weather Clim. Dynam., doi:10.5194/wcd-2022-12, accepted.

Lim, E.-P. et al., (2021): The 2019 Southern Hemisphere Stratospheric Polar Vortex Weakening and Its Impacts. Bulletin of the American Meteorological Society, 102, E1150-E1171, DOI: 10.1175/BAMS-D-20-0112.1.

Domeisen, D. et al. (2019): The role of the stratosphere in subseasonal to seasonal prediction Part I: Predictability of the stratosphere. Journal of Geophysical Research: Atmospheres, 124. DOI: 10.1029/2019JD030920.

Domeisen, D. I. V., Butler, A. H., Charlton‐Perez, A. J., Ayarzagüena, B., Baldwin, M. P., Dunn‐Sigouin, E., et al., 2019: The role of the stratosphere in subseasonal to seasonal prediction Part II: Predictability arising from stratosphere ‐ troposphere coupling. Journal of Geophysical Research: Atmospheres, 124. DOI: 0.1029/2019JD030923.

Tripathi, O. P., Baldwin, M., Charlton-Perez, A., Charron, M., Cheung, J. C. H., Eckermann, S. D., Gerber, E., Jackson, D. R., Kuroda, Y., Lang, A., Mclay, J., Mizuta, R., Reynolds, C., Roff, G., Sigmond, M., Son, S.-W. and Stockdale, T., 2016: Examining the predictability of the Stratospheric Sudden Warming of January 2013 using multiple NWP systems. Monthly Weather Review, 144 (5). pp. 1935-1960. doi: 10.1175/MWR-D-15-0010.1

Tripathi, O. P., Baldwin, M., Charlton-Perez, A., Charron, M., Eckermann, S. D., Gerber, E., Harrison, R. G., Jackson, D. R., Kim, B.-M., Kuroda, Y., Lang, A., Mahmood, S., Mizuta, R., Roff, G., Sigmond, M. and Son, S.-W., 2015: Review: he predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts. Quarterly Journal of the Royal Meteorological Society, 141 (689). pp. 987-1003. doi: 10.1002/qj.2432

Tripathi, O. P., Charlton-Perez, A., Sigmond, M. and Vitart, F., 2015: Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions. Environmental Research Letters, 10 (10). 104007.doi: 10.1088/1748-9326/10/10/104007

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. 57, 2021, p. 21: Stratospheric Nudging and Predictable Surface Impacts (SNAPSI), by Hitchcock, P., A. Butler, C. Garfinkel, and A. Charlton-Perez

SPARC Newsletter No. 54, 2020, p. 33-39: Joint DynVarMIP/CMIP6 and SPARC DynVar & SNAP Workshop: Atmospheric circulation in a changing climate, by Karpechko, A., A.H. Butler, N. Calvo, A. Charlton-Perez, D. Domeisen, E. Gerber, E. Manzini, and A. Ming

SPARC Newsletter No. 54, 2020, p. 14-18: The role of the stratosphere in sub-seasonal to seasonal prediction, by Domeisen, D.I.V., A.H. Butler, A.J. Charlton-Perez

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


More information can be found on the S2S Project/SNAP website