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Timothy Eichler and Wayne Higgins Climate Prediction Center/NCEP

The Climatology and Interannual Variability of North American Stormtracks in the GFS and CFS Global Climate Models. Timothy Eichler and Wayne Higgins Climate Prediction Center/NCEP. GFS Simulation. T62 GFS run with AMIP II SST Run from 1950-2002 SLP saved twice daily.

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Timothy Eichler and Wayne Higgins Climate Prediction Center/NCEP

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  1. The Climatology and Interannual Variability of North American Stormtracks in the GFS and CFS Global Climate Models Timothy Eichler and Wayne Higgins Climate Prediction Center/NCEP

  2. GFS Simulation • T62 GFS run with AMIP II SST • Run from 1950-2002 • SLP saved twice daily CFS Simulation • 2.5x2.5 fully coupled simulation • Run from 2002-2033 • Free Run (i.e. climate mode) • SLP saved twice daily

  3. Storm Track Frequency Climatology (5x5 grid) for I: Obs II: GFS III: CFS from a: Winter through d: Fall I II III

  4. Storm Track Frequency Difference a: GFS-OBS b: CFS-OBS a b

  5. Storm Track Frequency Difference CFS-GFS

  6. SLP (hPa) of Storms I: Obs II: GFS III: CFS a-d: (winter through fall) I II III

  7. SLP Diff I: GFS-Obs II: CFS-OBS (a-d: winter through fall) I II

  8. Standard Deviation Analysis for I: GFS and II: CFS a I II

  9. Storm Tracks by El Nino Phase (OBS ) a: Strong El Nino b: Weak El Nino c: Neutral d: weak La Nina e: Strong La Nina. a d b e c

  10. Storm Tracks by El Nino Phase (GFS Model) a: Strong El Nino b: Weak El Nino c: Neutral d: weak La Nina e: Strong La Nina. a d e b c

  11. Storm Tracks by El Nino Phase (CFS Model) a: Strong El Nino b: Weak El Nino c: Neutral d: weak La Nina e: Strong La Nina. a d b e c

  12. Composite Stormtrack Frequency Anomaly for a: Strong El Nino-neutral and b: strong La Nina-neutral a b

  13. Composite GFS Stormtrack Frequency Anomaly for a: Strong El Nino-Neutral and b: Strong La Nina-Neutral a b

  14. Composite CFS Stormtrack Frequency Anomaly for a: Strong El Nino-Neutral and b: Strong La Nina-Neutral a b

  15. Stormtrack difference (Strong El Nino – Strong La Nina) for a: Observations b: GFS Model c: CFS Model a b c

  16. Merdional Temp. Gradient ((degrees C/ km)*100) for a: obs b: GFS model c: GFS-obs a b c

  17. Merdional Temp. Gradient ((degrees C/ km)*100) for a: obs b: CFS model c: CFS-obs a b c

  18. H500 Gradient ((m/km)*100) for a: obs b: CFS model c: CFS-obs a b c

  19. H500 Gradient ((m/km)*100) for a: obs b: GFS model c: GFS-obs a b c

  20. Conclusions • GFS Produces Stormtrack Climatology spatially reasonable though approximately 50% less frequent… CFS slightly better. • Storm tracks less frequent and weaker especially in the North Pacific and North Atlantic in areas normally associated with strong baroclinicity. • GFS and CFS models exhibit a seasonal cycle for stormtracks though weaker than obs. • GFS and CFS stormtracks show a response to ENSO evident especially when comparing strong events • Reduced storm frequency relative to observed implies weak model variability. Evidence suggests that the GFS and CFS models have less baroclincity than observed.

  21. Future Work • Explore GFS and CFS model physics to ascertain why models’ storm variability is less than observed (e.g. look at surface heat budget) • Investigate Storm structure (e.g. fronts, precipitation, etc.) to see if they are realistic • Use stormtracks software on other data: rr data, ETA model… Program has potential as a prognostic tool…

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