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North American Stormtrack Climatology: GFS vs. CFS Models

This study compares the climatology and interannual variability of North American stormtracks in the GFS and CFS global climate models. Analysis includes storm track frequency climatology, differences in storm frequency between models and observations, storm tracks by El Nino phase, and composite storm track frequency anomalies. Results reveal that the GFS produces reasonably spatial storm tracks but at a lower frequency compared to CFS. Both models exhibit a seasonal cycle for stormtracks and respond to ENSO events. Future work involves exploring model physics for storm variability, investigating storm structure, and utilizing storm tracks software for prognostic purposes.

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North American Stormtrack Climatology: GFS vs. CFS Models

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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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