# Presentation: 393468

## 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**, *
*Kathy Pegion**3**, Arun Kumar**4**, Bhaskar Jha**4**, and Duane Waliser**4** *1 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

| The 30th Annual Climate Diagnostics & Prediction Workshop  |
| --- |
| The Pennsylvania State University  |
| October 24-28, 2005  |

## 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

## 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

## 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)

- Principal patterns of monthly 200 mb height (Rotated EOFs)

##  meters per STDV

-  meters per STDV

- ENSO

- AO

- AAO

- PNA

## ENSO

- AO

- AAO

- PNA

## NAO

## Variance of Leading Patterns

- (Unit: meter2)

## ENSO

- GFS AMIP

- NSIPP AMIP

- (var.=340 m2)

- (var.=364±9 m2)

## AO

## AAO

## PNA

## NAO

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

## ENSO response from NSIPP 9 AMIPs

## AO from NSIPP 9 AMIPs

- AAO from NSIPP 9 AMIPs

## PNA from NSIPP 9 AMIPs

- NAO from NSIPP 9 AMIPs

- ENSO simulations in the CGCMs

## Time-Longitude SSTA (5S-5N)

## Reanalysis

- CFS

- NSIPP

- Nino3 SSTA
- (5S-5N, 150-90W)

- Warm SST Composite
- (> 1σ)

- Cold SST Composite
- (< -1σ)

## 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)

-     - 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)

## Slide 23

## Subseasonal Variance

- Changes (La Nina-El Nino)

- contour: 200mb u-wind difference

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

## Variance of PCs (daily 200 mb height)

- Variance of PCs (daily 200 mb height)

- Reanalysis

- GFS AMIP

- NSIPP AMIP

- NSIPP (coupled)

- CFS (coupled)

- AAO

- AO

- NAO

- PNA

- AAO

- AO

- NAO

- PNA

- AAO

- AO

- NAO

- PNA

- AAO

- AO

- NAO

- PNA

- AAO

- AO

- NAO

- PNA

- NH pattern

- SH pattern

- Total variance(*0.1)

## Pattern 1

- Reanalysis

- Pattern 2

- Pattern 3

- Pattern 4

## Pattern 5

- Pattern 6

- Pattern 7

- Pattern 8

- Reanalysis

## Pattern 9

- r=0.92

- r=0.87

- r=0.96

- r=0.80

- r=0.92

- r=0.96

- r=0.94

- r=0.95

- r=0.95

- r=0.94

- r=0.82

- r=0.89

- r=0.84

- r=0.92

- r=0.96

- r=0.94

- Reanalysis

- GFS AMIP

- NSIPP AMIP

- CFS (coupled)

- NSIPP (coupled)

- #7

- #10

- #8

- #14

- #10

- #22

- #11

- #14

- #4

- #13

- #9

- #13

- #6

- #6

- #7

- #8

- Pattern 10

- Pattern 11

- Pattern 12

## Variance difference (cold-warm) – reconstructed from EOFs

- Variance difference (cold-warm) – reconstructed from EOFs

- (*1000 m2)

## Variance difference (cold-warm) - Reanalysis

- p1

- p2

- p3

- p4

- p5

- p6

- p7

- p8

- p9

- p10

- p11

- p12

- PNA

- NAO

- AO

## Variance difference (cold-warm) – GFS AMIP

- p1

- p2

- p3

- p4

- p5

- p6

- p7

- p8

- p9

- p10

- p11

- p12

- PNA

- NAO

- AO

## Variance difference (cold-warm) – NSIPP AMIP

- p1

- p2

- p3

- p4

- p5

- p6

- p7

- p8

- p9

- p10

- p11

- p12

- PNA

- NAO

- AO

## Variance difference (cold-warm) – CFS coupled

- p1

- p2

- p3

- p4

- p5

- p6

- p7

- p8

- p9

- p10

- p11

- p12

- PNA

- NAO

- AO

## Variance difference (cold-warm) – NSIPP coupled

- p1

- p2

- p3

- p4

- p5

- p6

- p7

- p8

- p9

- p10

- p11

- p12

- PNA

- NAO

- AO

## 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

## Summary (continued)

- 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

- Summary (continued)

## Thank You !

## Reanalysis

- pna

- nao

- ao

- pna

- nao

- ao

## Slide 40

## Slide 41