Stratospheric Network for the Assessment of Predictability (SNAP) Department of Meteorology Stratospheric Network for the Assessment of Predictability (SNAP) Andrew Charlton-Perez and Amy Butler on behalf of SNAP SSG

SNAP AIMS Understanding how we can make use of coupling between the stratosphere and troposphere to improve prediction capability on sub-seasonal to seasonal timescales. How well do monthly forecasting systems predict long-lived stratospheric anomalies and their tropospheric impact? Why do some prediction systems fail to capture the amplitude of stratosphere-troposphere coupling? Can we develop a test set of experiments and diagnostics to assess the role of different processes in stratosphere-troposphere coupling in models?

PAST WOrk Our previous set of projects focused on coordinated forecasts of SSW events and their analysis Tripathi et al. (2016) Examining the predictability of the Stratospheric Sudden Warming of January 2013 using multiple NWP systems. Mon. Wea. Rev. Tripathi et al. (2015) Review: the predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts. QJRMS Tripathi et al. (2015) Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions. Env. Res. Lett. Our current focus is towards the sub-seasonal timescale, working with S2S model forecasts, and on coupling between the stratosphere and troposphere

Steering Group Name Institution Mark Baldwin Univ. Exeter, UK Daniela Domeisen ETH, Switzerland Chaim Garfinkel Herbrew Univ., Israel Peter Hitchcock NCAR, USA Erik Kolstad Bjerknes Centre, Norway Jeff Knight Met Office, UK Craig Long NOAA CPC, USA Andrea Lopez Lang Univ. at Albany, USA Andrew Marshall BoM, Australia Isla Simpson Aditi Sheshadri Columbia Univ., USA Seok-Woo Son Seoul National Univ., Korea Lantao Sun NOAA ESRL, USA Masakazu Taguchi Aichi Univ. of Education, Japan Om Tripathi LA Valley College, USA Amy Butler has been working as co-chair of SNAP since December 2016 We also refreshed the membership of the steering group to reflect the shift in emphasis towards the sub-seasonal timescale Ad-hoc committee meeting at AMS Middle Atmosphere in Portland

Book Chapter We were approached to contribute a chapter to a forthcoming book on sub-seasonal predictability led by the S2S community (Book Editors: Frederic Vitart and Andrew Robertson). We used this opportunity as a way to discuss and review current knowledge of stratosphere-troposphere coupling and predictability. Chapter authors: Amy Butler, Andrew Charlton-Perez, Daniela Domeisen, Chaim Garfinkel, Edwin Gerber, Peter Hitchcock, Alexey Karpechko, Amanda Maycock, Michael Sigmond, Isla Simpson and Seok-Woo Son

Material Covered Stratosphere-Troposphere coupling in the Extra-Tropics Predictability related to stratosphere-troposphere coupling Stratosphere-troposphere coupling in the Tropics Future Outlook

Topics from Outlook What determines how well a model represents stratosphere-troposphere coupling? Model lid height and vertical resolution Stratospheric and Tropospheric state and biases Different drivers and stratosphere-troposphere coupling efficiency How can we use sub-seasonal prediction data in new ways to study stratospheric dynamics and stratosphere-troposphere coupling? Growth of model errors in the troposphere and stratosphere Separating competing drivers of stratospheric variability and coupling Probabilistic understanding of stratospheric variability

SNAP paper analysing S2S Lead: Daniela Domeisen (discussed at AMS meeting in Portland) Currently soliciting sub-projects and constructing working teams (deadline Oct 15th) Three broad areas of interest covering upward and downward coupling Next slides show teams and areas of interest (and afterwards) some preliminary results Lots of interesting ideas – need to work hard to turn this into coordinated understanding and analysis

Team Topic Seok-Woo Son & Hera Kim MJO/QBO coupling Jeff Knight Signal to noise problem as forecasts progress (e.g. Eade, 2014) Etienne Dunn-Sigouin Predictability of wave reflection events Chaim Garfinkel & Chen Schwartz MJO tropospheric and stratospheric pathway / SSW impact on NH  Masakazu Taguchi & Jason Furtado Prediction of SSW events Andrea Lang Early winter tendencies for weak/strong vortex events Alexey Karpechko & Andrew Charlton-Perez Basic analysis of coupling Peter Hitchcock & Isla Simpson Surface impacts of PJO vs. SSW events Mark Baldwin & Blanca Ayarzagüena Mechanistic understanding of coupling Amy Butler Surface impacts of final warming events Eun-Pa Lim & Andrew Marshall Surface impacts on SAM Erik Kolstad Surface impacts on polar lows

WEAK Vortex composites All ten models in the database which have been studied are able to capture coupling Composite of ensemble mean forecasts from ECMWF model for weak vortex events and corresponding ERA-Interim composite and difference ACP, Alexexy Karpechko, Isla Simpson & Peter Hitchcock

Forecast Skill Can assess the skill of all model hindcasts for sub-sets where the vortex was weak, strong or in a neutral state (c.f. Tripathi et al., 2015) Example shown for CMA model Skill is Anomaly Correlation Coefficient of NAM Index

Comparing Models All models show enhancement of skill for weak and strong stratospheric states Differences both in lower stratosphere and troposphere in absolute skill and gain relative to neutral cases

Forecasting SSWs DEC 1998 DEC 2001 JAN 2001 JAN 2013 SEP 2002 LT= -15 d LT= -10 d (including spread) LT= -5 d BoM CMA ECMWF NCEP MEAN DEC 1998 DEC 2001 JAN 2001 JAN 2013 SEP 2002 Masakazu Taguchi

Forecasting SSWs II How does model configuration effect early season weak vortex events (example in red) Large ensemble spread and inter-model differences Andrea Lopez Lang

Summary Two major achievements in last year (refresh of steering group and mission and review chapter) Focus for next year on analysis of S2S data with new energy from Daniela – leading to at least one paper Already discussed this week: Firming up links with S2S Thinking beyond analysis of S2S data – are there experiments we can propose to target improving coupling in models Model initialisation in the stratosphere – links to S-RIP/DAWGß?

REAL-Time Diagnostics At previous meetings I promised to produce diagnostic tools which could be used to provide information from S2S model real-time forecasts Example figure shown here Technical challenge is too large (particularly due to infrastructure issues) Will work with colleagues at ECMWF to implement on ECMWF servers during my time on sabbatical