1. SPARC - Stratospheric Network for the Assessment of Predictability (SPARC-SNAP) SPARC-SNAP Team Om P Tripathi, Andrew Charlton-Perez, Greg Roff, Mark Baldwin, Martin Charron, Stephen Eckermann, Edwin Gerber, David Jackson, Yuhji Kuroda, Andrea Lang, Ryo Mizuta, Michael Sigmond, Seok-Woo Son

2. Important Potential sources of sub- seasonal (15-60 days) predictability (S2S Implementation plan) 1. Madden Julian Oscillation (MJO) 2. Stratospheric conditions 3. Land/Ice/Snow initial condition 4. Sea-surface temperature

3. Important Potential sources of sub- seasonal (15-60 days) predictability (S2S Implementation plan) 1. Madden Julian Oscillation (MJO) 2. Stratospheric conditions 3. Land/Ice/Snow initial condition 4. Sea-surface temperature

4. Important Potential sources of sub- seasonal (15-60 days) predictability (S2S Implementation plan) 1. Madden Julian Oscillation (MJO) 2. Stratospheric initial condition 3. Land/Ice/Snow initial condition 4. Sea-surface temperature Processes that impact sub-seasonal skill are not well understood Predictable skill might be higher in some Window of Opportunity Recognising these window of opportunity is still unclear

5. Stratospheric Impact at sub-seasonal scale S2S recognise that the importance of the stratosphere has not been fully assessed Case studies has shown that the stratosphere influence on the extra-tropics Stratosphere impact NAO and SAM during extreme vortex events such as SSW UK Met Office already runs with well resolved stratosphere and uses Window of Opportunity to re-run the sub-seasonal forecast For example Met Office predicted SSW 2013 15 days in advance and corrected (re-run) their sub-seasonal forecast for Europe

6. S2S and Stratosphere WCRP-CLIVAR Working group recognise its important and working on to quantify the improvement in forecast skill via its Stratospheric resolving Historical Forecast Project (SHFP) by employing better resolved stratosphere S2S encourages active Collaboration between SHFP and sub- seasonal forecast groups SPARC-SNAP’s focus is on the direct contribution at sub- seasonal scale by understanding the stratospheric predictability itself and its contribution to sub-seasonal forecast e.g. exploiting Windows of Opportunity S2S plan to archive variables in the stratosphere but the highest level is only 10 hPa SPARC-SNAP archives full stratosphere till 1 hPa with models having high vertical resolution in the stratosphere

7. Stratospheric Network for the Assessment of Predictability (SNAP) SNAP Introduction

8. SPARC – SNAP A network of research and operational communities aims to answer following fundamental questions:  Are stratosphere-troposphere coupling effects important throughout the winter season or only when major stratospheric dynamical events occur?  How far in advance can major stratospheric dynamical events be predicted and usefully add skill to tropospheric forecasts?  Which stratospheric processes, both resolved and unresolved need to be captured by models to gain optimal stratospheric predictability?

9.  Mark Baldwin University of Exeter, UK  Martin Charron Environment Canada, Canada  Steve Eckermann NRL, USA  Edwin Gerber New York University, USA  Yuhji Kuroda Japan Met Agency, Japan  David Jackson Met Office, UK  Andrea Lang University at Albany, USA  Greg Roff Bureau of Meteorology, Australia  Seok-Woo Son Seoul National University, S Korea  Om TripathiUniversity of Reading (Co-ordinator)  Andrew Charlton-Perez University of Reading (PI) Steering Committee

10. SNAP Activities A new multi-model experiment to quantify stratospheric predictability Stimulate the growth of a community of researchers interested in stratospheric predictability (workshop, web, newsletters etc). A review paper on current understanding of stratospheric predictability (under review) A SPARC report and peer-reviewed articles on the findings of the experiment.

11. SNAP Protocol Case-15-10-50+5 Phase 1: SSW NH 2013 23/12/201228/12/201202/01/201307/01/201312/01/2013 Phase 1: Final Warming SH 2012 05/10/201210/10/201215/10/201220/10/201225/10/2012 Run Length15 days No. of Ensemble members As many as possible Phase 0Current operational forecast for ONE year Phase 2TBD (Same as phase I for past cases)

12. SPARC-SNAP Operational Models and Database BADC is hosting SNAP experimental data Data is accessible on request at http://badc.nerc.ac.uk/help/jasmin_workspaces.html http://badc.nerc.ac.uk/help/jasmin_workspaces.html For info about SPARC-SNAP activity and data access: http://www.met.reading.ac.uk/~pn904784/snap/ http://www.met.reading.ac.uk/~pn904784/snap/ Environmental Canada (EC), CANADA Met Office, UK Meteorological Research Institute (MRI), JAPAN Naval Research Laboratory, USA (NAVGEM) Bureau of Meteorology, Australia (CAWCR) Korea Meteorological Administration (KMA), Korea Korea Air Force operational model, Korea Polar Research Institute, Korea (KOPRI)

13. Stratospheric Sudden Warming NH SSW of 2012-2013 Time line of how it happened ?

14. 10 hPa Geopotential Height Zonal mean wind (U) at 10 hPa (60 N) How it happened 22 December vortex was slight off-pole over Northern Russia 31 December its size reduced drastically and moved towards pole 05 January elongated over Northern Canada to Northern Russia Wind Reversed at 1 hPa 07 January broke into two pieces, larger one over Canada and smaller over Russia

15. CAWCR Predictability 4.ZONAL MEAN ZONAL WIND How Basic State (vertical wind configuration) differ during the start of vortex weakening in 15 days and 10 days forecast and 15 days forecast failed ?

16. 31 DECEMBER ERAI 08 days Forecast: 23 Dec 04 days Forecast: 28 Dec

17. 01 JANUARY ERAI 09 days Forecast: 23 Dec 05 days Forecast: 28 Dec

18. 02 JANUARY ERAI 10 days Forecast: 23 Dec 06 days Forecast: 28 Dec

19. 03 JANUARY ERAI 11 days Forecast: 23 Dec 07 days Forecast: 28 Dec

20. 04 JANUARY ERAI 12 days Forecast: 23 Dec 08 days Forecast: 28 Dec

21. 05 JANUARY ERAI 13 days Forecast: 23 Dec 09 days Forecast: 28 Dec

22. 06 JANUARY ERAI 14 days Forecast: 23 Dec 10 days Forecast: 28 Dec

23. CAWCR Predictability Stratosphere and Troposphere predictability How tropospheric forecast of 500 hPa polar cap (60-90 N) Mean Geopotential height differ in 15 day forecast and other forecasts ?

24. U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa

25. 23 DECEMBER U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -15 days Forecast: Strong ensemble spread in one side for tropospheric forecast after about 4 days INITIAL PHASE - 1

26. 23 DECEMBER U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -15 days Forecast: Strong ensemble spread in one side for tropospheric forecast after about 4 days Ensemble mean tropospheric forecast lost track after 4 days INITIAL PHASE - 1

27. 28 DECEMBER U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -10 days Forecast: Ensemble spread for tropospheric forecast lies both side INITIAL PHASE - 2

28. 28 DECEMBER U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -10 days Forecast: Ensemble spread for tropospheric forecast lies both side AND Ensemble mean tropospheric predictability is more skilful than 15 days forecast INITIAL PHASE - 2

29. 02 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -5 days Forecast: Here also spread is both sided particularly after 5 days in comparison to 15 days forecast SSW PHASE -1

30. 02 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa -5 days Forecast: Here also spread is both sided particularly after 5 days in comparison to 15 days forecast AND ensemble mean better represents the tropospheric state SSW PHASE -1

31. 07 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa 0 days Forecast: Spread is similar to the 10 days and 5 days forecast SSW PHASE -2

32. 07 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa 0 days Forecast: Spread is similar to the 10 days and 5 days forecast AND tropospheric predictability has similar skill to the last two SSW PHASE -2

33. 12 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa +5 days Forecast: Similar spread here RECOVERY PHASE

34. 12 JANUARY U at 10hPa 60 N Polar cap Geopotential Height at 500 hPa +5 days Forecast: Similar spread here AND similar tropospheric skill for up to 12 days RECOVERY PHASE

35. Models Comparison 1. CAWCR - 24 member ensemble mean 2. JMA - 51 member ensemble mean 3. Korea Polar Research Institute (KOPRI) – GRIMs_V3.2 -3 member (single run initialized by NOAA GDAS1 with model top at 3 hPa on the day, one day before, and one day after)

36. CAWCRJMAKOPRI Initial and Boundary condition APS1 ACCESS-G system, assimilation using observational satellite data, NCEP 1/12 sea ice analysis, fixed SST and sea ice ERAI + perturbations using BGM cycle, SST anomaly (fix) NOAA gdas1 analysis Gravity wave OGWD scheme and spectral GWD scheme OGWD Scheme No Non-OGWD ResolutionN216L70 about 60km horizontal resolution T159L60 (top at 0.1 hPa), about 110 km T62L28 (model top at 3hPa)

42. Summary – S2S and SNAP  SNAP is a network to understand stratospheric predictability and its impact on tropospheric forecast may be able to contribute in the S2S RESEARCH ISSUES  SNAP Researchers can use S2S archived data to complement the SNAP experimental data to further the understanding of the key processes  Many of the S2S models are also part of SNAP making it easier for attribution studies.  Research communities are welcome to participate in the Stratospheric Predictability Study  SNAP group are keen in the Active and Engaged collaboration with S2S

43. Summary  SNAP aims to look for stratospheric predictability and its impact on tropospheric forecast  First results of SNAP activities are presented  10 day forecast from Australian Operational forecast model has shown a reasonably good predictive skill  15 day forecast, however, failed to predict the SSW  The reason appears to be the lack of wave amplification during pre-stage of SSW in 15 day forecast  Once the Stratospheric Sudden Warming happens the model has shown to have very good predictive skill up to 15 days during recovery phase  It appears that when model fails to predict the stratosphere in case of the forecast run of 23 December, the tropospheric predictive skill is poorest  Fore other forecasts, when the model was able to predict the stratosphere well the tropospheric forecast appeared more skilful  CAWCR appears to have slightly more skill in SSW prediction  You are welcome to participate in the Stratospheric Predictability Study  Active and Engaged collaboration with S2S communities

44. THANK YOU QUESTIONS ?