Team

Activity Leaders

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

Steering Committee

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 seeks to answer several outstanding questions about stratospheric predictability and its tropospheric impact, namely: (i) How far in advance can major stratospheric dynamical events be predicted and usefully add skill to tropospheric forecasts? (ii) 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; and (ii) investigating the extent to which accurate forecasts of the stratosphere contribute to improved tropospheric predictability. 

Ongoing Activities (1 of 2):

Our initial analysis of stratospheric predictability and processes in the S2S prediction systems revealed issues with biases in these models that vary as a function of lead-time and initialization time. A community project is ongoing to evaluate stratosphere-troposphere coupling biases in S2S prediction systems, and how these biases may influence predictive skill.     We currently have researchers from 11 countries contributing to this analysis (Switzerland, Israel, Spain, United States, Finland, South Korea, United Kingdom, Japan, Australia, Norway, Argentina).  Core questions we seek to answer include:

a. What are the lead time dependent mean biases in the stratosphere and in stratosphere-troposphere coupling processes, and how do they compare among models? What about biases in variability?

b. Which biases have the greatest impact on predictive skill? How large are the impacts?

c. What are the sources of the biases? Can they be linked to biases in the troposphere, and/or can biases in the troposphere be linked to those in the stratosphere?

d. Are biases linked to stratospheric processes/variability? E.g., are biases larger before or after SSWs, vortex intensifications, phases of the QBO, etc?

 These efforts are being led by Zachary Lawrence. The first paper was published in 2022.

A second community paper will be submitted in Q2 of 2024. A third community paper is planned. If you would like to join these efforts, please contact the SNAP co-chairs (Amy Butler, NOAA and Chaim Garfinkel, Hebrew University).

Ongoing Activities (2 of 2):
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) – experiments for operational centres to perform that isolate the role of the stratosphere on predictive skill

Recent studies including our review papers have highlighted that stratosphere-troposphere coupling in the extra-tropics contributes to S2S predictability. However, not all SSWs are followed by impactful weather events; furthermore, there is substantial spread in the ability of models to represent this coupling. Similarly, in the tropics, a focus on the links between the QBO and MJO predictability is very important for surface predictability. The Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project aims to isolate the role of the stratosphere for surface climate and predictability, and also explore the role of stratospheric biases for inter-model spread in the representation of stratosphere-troposphere coupling. Specifically SNAPSI targets three SSW case study events, in which the stratospheric state can be either freely-evolving or nudged towards a climatological or observational state.  The test cases will be NH SSWs during two recent winters with very different tropospheric impact (2018 and 2019) and the 2019 SH warming, which has likely contributed to the extreme wildfires over Australia in 2019/20. The goals of this project are:

a. To assess the contribution of the stratospheric evolution to forecast skill in a controlled fashion.

b. To assess the representation of coupling processes across different operational models.

c. Attribute particular meteorological events to stratospheric conditions

d. To assess the representation of stratospheric wave driving.

The basic experimental protocol consists of a set of forecast ensembles: (1) a standard, free running forecast ensemble, (2) a ‘perfect stratosphere’ forecast in which the stratosphere is relaxed towards the observed evolution, and (3) a ‘control’ forecast in which the stratosphere is relaxed towards climatology. The paper documenting the Experiment Protocol for these experiments was published in 2022. To date, twelve modeling groups at eleven centers have completed integrations following this protocol, and nearly all of the data has been added to the SNAPSI archive at CEDA. This allows for an unprecedented, multi-model comparison of the dynamics underlying the surface responses to sudden stratospheric warmings. Moreover, by including ‘counterfactual’ forecasts in which the stratospheric circulation remains in a climatological state, the experimental protocol allows for formal attribution statements to be made regarding the surface extremes that followed the stratospheric anomalies.

In addition to the four aforementioned goals, two working groups are analyzing stratosphere-troposphere coupling in the tropics in these experiments in collaboration with with QBOi and SATIO-TCS. Six working groups are working on papers documenting the results – these publications will be submitted in Q3 and Q4 of 2024.

Anyone interested in participating in the analysis for the 6 community papers is encouraged to contact Peter Hitchcock (). The data embargo for the broader community will tentatively be lifted in 2024. 

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, https://doi.org/10.1016/B978-0-12-811714-9.00011-5, 2019.

Special Issues:

Stratospheric Impacts on Climate Variability and Predictability in Nudging Experiments (WCD/GMD inter-journal SI).

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., Abalos, M., Ayarzagüena, B., Barriopedro, D., Butler, A.H., Calvo, N., de la Cámara, A., Charlton-Perez, A., Domeisen, D.I., Dunn-Sigouin, E. and García-Serrano, J., 2022. Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems. Weather and Climate Dynamics Discussions, 2022, pp.1-37.  

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:

APARC newsletter, No. 62, 2024, pp. 12-16: APARC Dynvar & SNAP Workshop: The Role of Atmospheric Dynamics for Climate and Extremes, by Hitchcock, P., A. Butler, C. Garfinkel, A. Karpechko, T. Birner, and H. Garny

SPARC newsletter, No. 57, 2021, p. 21: Stratospheric Nudging and Predictable Surface Impacts (SNAPSI), by Domeisen, D., 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

Website

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