1 / 38

An Analysis of Subseasonal Variability in the NCEP CFS and NASA NSIPP Coupled GCMs

An Analysis of Subseasonal Variability in the NCEP CFS and NASA NSIPP Coupled GCMs Myong-In Lee 1,2 , Siegfried Schubert 2 , Max Suarez 2 , Phil Pegion 2 , Ben Kirtman 3 ,

lei
Download Presentation

An Analysis of Subseasonal Variability in the NCEP CFS and NASA NSIPP Coupled GCMs

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. An Analysis of Subseasonal Variability in the NCEP CFS and NASA NSIPP Coupled GCMs Myong-In Lee1,2, Siegfried Schubert2, Max Suarez2, Phil Pegion2, Ben Kirtman3, Kathy Pegion3, Arun Kumar4, Bhaskar Jha4, and Duane Waliser41 Goddard Earth Sciences and Technology Center/ UMBC 2 NASA/GSFC Global Modeling and Assimilation Office 3 COLA/George Mason University 4 NCEP/ Climate Prediction Center 5 NASA/Jet Propulsion Laboratory/CalTech

  2. Questions ?? • How well do the current coupled models reproduce the leading patterns of extratropical subseasonal variability? • CGCM intercomparisons ( CFS and NSIPP CGCM) • Coupling versus prescribed (comparison with AMIP) • How well do the current coupled models reproduce the changes in subseasonal variability associated with ENSO ? • ENSO simulation in CGCMs • Subseasonal variance changes

  3. Model Descriptions • NCEP CFS T62L64 • NCEP Global Forecast System (GFS) for the atmospheric component • GFDL Modular Ocean Model version 3 (MOM3) for the ocean component • NASA NSIPP CGCM v.1 • NSIPP1 AGCM (1x1.25) • Poseidon v4 (1/3x5/8xL27) OGCM

  4. Datasets • 50 years of NCEP/NCAR Reanalysis-1 • 1951-2000 • Daily and monthly GPH200 • Monthly U200 • 50 years of monthly HadISST • 50 years coupled runs • NCEP CFS (T62L64) • NSIPP CGCM (1x1.25) • 50 years of AMIP (1951-2000) Runs • NCEP GFS T62L64 (T62L64) • 9-member NSIPP AGCM ensemble runs (2x2.5)

  5. Principal patterns of monthly 200 mb height (Rotated EOFs)

  6. AO ENSO AAO PNA meters per STDV

  7. ENSO AO AAO PNA

  8. NAO

  9. Variance of Leading Patterns (Unit: meter2)

  10. ENSO CFS (coupled) (var.=252 m2) Reanalysis (var.=341 m2) NSIPP (coupled) (var.=134 m2) GFS AMIP (var.=340 m2) NSIPP AMIP (var.=364±9 m2)

  11. AO CFS(coupled) GFS AMIP (var.=217 m2) (var.=371 m2) Reanalysis (var.=314 m2) NSIPP(coupled) NSIPP AMIP (var.=339 m2) (var.=308±33 m2)

  12. AAO GFS AMIP (var.=283 m2) CFS (var.=242 m2) Reanalysis (var.=224 m2) NSIPP AMIP (var.=231±25 m2) NSIPP (var.=282 m2)

  13. PNA CFS GFS AMIP (var.=193 m2) (var.=180 m2) Reanalysis (var.=215 m2) NSIPP AMIP (var.=176±14 m2) NSIPP (var.=197 m2)

  14. NAO GFS AMIP (var.=164 m2) CFS (var.=109 m2) Reanalysis (var.=172 m2) NSIPP AMIP (var.=128±26 m2) (var.=109 m2) NSIPP

  15. Ensemble spreads in NSIPP AMIP runs (spaghetti diagram of EOFs) - contours from each ensemble members - compared with the reanalysis (shading)

  16. ENSO response from NSIPP 9 AMIPs

  17. AO from NSIPP 9 AMIPs AAO from NSIPP 9 AMIPs

  18. PNA from NSIPP 9 AMIPs NAO from NSIPP 9 AMIPs

  19. ENSO simulations in the CGCMs

  20. Time-Longitude SSTA (5S-5N)

  21. Nino3 SSTA (5S-5N, 150-90W) Warm SST Composite (> 1σ) Cold SST Composite (< -1σ) Reanalysis CFS NSIPP

  22. Subseasonal Variance Analysis - GPH200 daily - remove seasonal cycle (0-3 harmonics of 50 year-averaged daily climatology) - 10-60 day band-pass filtered - variance in NH winter (DJF)

  23. Subseasonal Variance Changes (La Nina-El Nino) contour: 200mb u-wind difference

  24. Rotated EOFs from daily band-pass (10-60d) filtered 200 mb height

  25. CFS (coupled) GFS AMIP PNA Reanalysis Variance of PCs (daily 200 mb height) AO NAO NAO PNA AO AAO AAO PNA NAO AO AAO NSIPP (coupled) NSIPP AMIP NH pattern AO PNA SH pattern NAO AO NAO PNA Total variance(*0.1) AAO AAO

  26. #2 #1 #6 #20 GFS AMIP r=0.97 r=0.88 r=0.80 r=0.73 #5 #7 #4 #1 NSIPP AMIP r=0.97 r=0.97 r=0.94 r=0.83 #1 #2 #3 #8 CFS (coupled) r=0.96 r=0.85 r=0.83 r=0.95 #4 #1 #13 #8 NSIPP (coupled) r=0.96 r=0.94 r=0.79 r=0.72 PNA NAO Pattern 4 Pattern 3 Pattern 2 Pattern 1 Reanalysis

  27. #13 #4 #9 #5 GFS AMIP r=0.94 r=0.95 r=0.68 r=0.81 #3 #6 #12 #2 NSIPP AMIP r=0.92 r=0.93 r=0.94 r=0.78 #11 #7 #9 #5 CFS (coupled) r=0.91 r=0.80 r=0.71 r=0.95 #5 #9 #2 #3 NSIPP (coupled) r=0.89 r=0.96 r=0.90 r=0.47 AO Pattern 8 Pattern 7 Pattern 6 Pattern 5 Reanalysis

  28. Pattern 12 Pattern 11 Pattern 10 Pattern 9 Reanalysis #10 #7 #8 #14 GFS AMIP r=0.92 r=0.80 r=0.87 r=0.96 #8 #13 #9 #10 NSIPP AMIP r=0.94 r=0.92 r=0.95 r=0.96 #6 #13 #4 #22 CFS (coupled) r=0.95 r=0.94 r=0.82 r=0.89 #14 #7 #11 #6 NSIPP (coupled) r=0.96 r=0.92 r=0.84 r=0.94

  29. CFS (coupled) GFS AMIP Reanalysis NSIPP AMIP NSIPP (coupled) Variance difference (cold-warm) – reconstructed from EOFs (*1000 m2)

  30. NAO PNA p3 p4 p2 p1 Variance difference (cold-warm) - Reanalysis AO p7 p8 p6 p5 p11 p12 p10 p9

  31. NAO PNA p3 p4 p2 p1 Variance difference (cold-warm) – GFS AMIP AO p7 p8 p6 p5 p11 p12 p10 p9

  32. NAO PNA p3 p4 p2 p1 Variance difference (cold-warm) – NSIPP AMIP AO p7 p8 p6 p5 p11 p12 p10 p9

  33. NAO PNA p3 p4 p2 p1 Variance difference (cold-warm) – CFS coupled AO p7 p8 p6 p5 p11 p12 p10 p9

  34. NAO PNA p3 p4 p2 p1 Variance difference (cold-warm) – NSIPP coupled AO p7 p8 p6 p5 p11 p12 p10 p9

  35. Summary 1. Current models reproduce the leading wintertime extra-tropical patterns of monthly variability reasonably well • REOFs identify the patterns of ENSO, AO, AAO, PNA and NAO • an assessment of the spread of the NSIPP AMIP ensemble shows these patterns to be robust in samples of 50 years • there are, however, large differences in the variance of individual patterns • total monthly variance is in general weaker in the simulations

  36. Summary (continued) 2. An assessment of the full subseasonal (10-60 day) variance shows the following: • the variance is weak in the coupled runs, whereas it is comparable to the reanalysis in the AMIP runs • but, all models have a sign of variance increase over the northern reach of Pacific and Alaska region and decrease over the arctic and northern Atlantic region • REOFs from daily band-pass 200 mb height show a much richer spectrum of patterns compared with the monthly results, apparently a variation of several leading principal patterns • interannual changes in the subseasonal variance associated with ENSO have realistic patterns (but are weak, especially in the coupled runs) • changes in the PNA and NAO are robust, though not so for other leading patterns 3. Future work will focus on furthering our understanding of the nature of the various subseasonal patterns and their underlying dynamics

  37. Thank You !

More Related