1 / 44

SPARC - Stratospheric Network for the Assessment of Predictability (SPARC-SNAP)

A core project of. SPARC - Stratospheric Network for the Assessment of Predictability (SPARC-SNAP). SPARC-SNAP Team

lopezm
Download Presentation

SPARC - Stratospheric Network for the Assessment of Predictability (SPARC-SNAP)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A core project of 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) • Madden Julian Oscillation (MJO) • Stratospheric conditions • Land/Ice/Snow initial condition • Sea-surface temperature

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

  4. Important Potential sources of sub-seasonal (15-60 days) predictability (S2S Implementation plan) • Madden Julian Oscillation (MJO) • Stratospheric initial condition • Land/Ice/Snow initial condition • 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. Steering Committee • Mark Baldwin University of Exeter, UK • Martin CharronEnvironment Canada, Canada • Steve EckermannNRL, 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 RoffBureau of Meteorology, Australia • Seok-Woo SonSeoul National University, S Korea • Om Tripathi University of Reading (Co-ordinator) • Andrew Charlton-Perez University of Reading (PI)

  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

  12. SPARC-SNAP Operational Models and Database • 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) • BADC is hosting SNAP experimental data • Data is accessible on request at 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/

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

  14. How it happened 10 hPa Geopotential Height Zonal mean wind (U) at 10 hPa (60 N) 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. INITIAL PHASE - 1 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

  26. INITIAL PHASE - 1 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

  27. INITIAL PHASE - 2 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

  28. INITIAL PHASE - 2 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

  29. SSW PHASE -1 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

  30. SSW PHASE -1 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

  31. SSW PHASE -2 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

  32. SSW PHASE -2 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

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

  34. RECOVERY PHASE 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

  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. 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

  37. 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

  38. THANK YOU QUESTIONS ?

More Related