A Journey to “ENSO” Simulation at COLA

Vasu Misra, Larry Marx, Zhichang Guo, Jim Kinter, Ben Kirtman, Dughong Min, David Straus, Paul Dirmeyer, Mike Fennessey

Acknowledgements: Ramesh Kallumal, Ben Cash, Byron Boville, M. Kanamitsu, Song Hong, S. Moorthi, J. Bacmeister, Kathy Pegion

Why is this exercise important?

R & D ofCenter xyz

Scientists outside xyz

As end users of model we could complain:

MJO/ISO is bad, ENSO is bad, split ITCZ is a

problem, no monsoon, mid-latitude response is

bad, fluxes are bad, clouds are bad, MODEL

IS BAD-Symptomatic analysis

Sometimes Generous!

Suggestfrom incremental (documented) changes what

reduced/increased the bias, variability-attribution of model errors.

But change in one model does not translate to similar response in another?

Yes, but does provide a motivation to pursue a testable hypothesis.

Stake holders

+

ENSO Metrics to evaluate a simulation

1. Mean state errors2. Spectrum of SST in the Nino3

region (power, width of the peak, frequency)

3. Evolution of ENSO (asymmetry in cold and warm phase; sub surface ocean anomalies)

4. Duration of ENSO event5. ENSO forcing (correlations) in

other ocean basins6. Seasonal phase locking of

ENSO Variability

7. Relationship of :• wind stress with SST• Precipitation with SST8. Mid-latitude response

1 2* 3 4 5 6 7a 7b 8Obs 2.5-7 Asymm ~14 months Subtle Yes

COLA V2.2

1. Nino3 root mean square SST errors.2. Spectrum of Nino3 SST3. Asymmetry of ENSO warm/cold events4. Duration of ENSO event5. Nino3 SST correlations with other ocean

basins

6. Seasonal phase locking of ENSO7. a) windstress-SST relationship b) precip-sst relationship8. Mid-latitude response

Starting from….

3.92 2 Symm ~10 months Erroneous No Un-verifiable Un-verifiable

Feature COLA AGCM V2.2.6 COLA AGCM V3.0

1 PBL Mellor and Yamada (1982)

Hong and Pan (1996)

2 Shortwave Briegleb, 1992 Briegleb, 1992

3 Longwave Harshvardhan et al. (1987)

Collins et al. 2002

4 Land Surface SSiB (Xue et al 1992) SSiB (3 layers); new soil hydrology

5 Convection (RAS; Moorthi and Suarez, 1992)

(RAS; Bacmeister et al. 2002)

5 Horizontal Diffusion Scale dependent and only del4 diffusion.

Reduced by 2 orders of magnitude. Introduced del2 diffusion with pressure correction at lower pressure levels.

6 Vertical resolution 18 sigma levels with skewed distribution (high in PBL and relatively coarse and uniform near TOA)

28 sigma levels, with parabolic distribution of resolution (high in PBL and near TOA) and coarse in the middle)

7 Horizontal resolution T42/T63 T62

What is new

Philosophy for improving simulation‘moving towards more physically based schemes’

• PBL: Local K-theory which parameterizes turbulent mixing with an eddy diffusivity based on local gradients of wind and temperature may fail in unstable boundary layers because influence of large eddy transports is not accounted for.

• Long wave: Developed from water vapor line and continuum treatments-uses line-by–line radiative transfer model GENLEN2-an improvement over broad-band absorptance method.

• Convection: Determination of fraction of detrained cloud liquid water was through an empirical profile. Now a budget for cloud liquid water is included in the convection scheme.

• SSiB: Going from 1 layer in root zone to 4 layers.• Horizontal Diffusion: Way too strong.• Consistency: Saturation vapor pressure and variation of Lv with T• Vertical Resolution: Skewed.

Profile of vertical resolution of the AGCM

Anecdote

“…..implementing the CAM long wave scheme produced excessive cold bias in the upper troposphere. I seek your advice to tune the long wave scheme……”

“…….I would not suggest adjusting the scheme itself. The new scheme is based upon much more recent water vapor line and continuum treatments……Problems in other parts of the model may be getting reflected.”-William Collins, NCAR

V2.2 V3.0 V3.1 V3.1.0.1 (101)

V3.1.0.2

(102)Control ‘First’

attempt to couple “new” AGCM

Tuning of clouds, 6 layer SSiB, tuning of large scale condensation

5 point smoothing of inversion clouds

3 point smoothing of inversion clouds

Experiment Design

Observational verification: 1955-2000; ODA: 1980-98IC of coupled integrations: Length of model experiments are not the same.

Showing the last 45 years. At a minimum the first 20 years have been removed in the analysis.

Ocean model: MOM3 -1.50 (zonal resolution), 0.50 from 10 S to 10 N and 1.50 in the extra-tropics. 25 vertical levels with 17 in the upper 450 m.

Will be looking at annual mean quantities

Downwelling Shortwave flux at surface

Annual Mean SST Errors

Annual Cycle of Equatorial Pacific SST

Jan

Jun

Dec

Jan

Jun

Dec ERSST-V2

Small changes can lead to significant change in model variability

Seasonal phase locking of ENSO to the annual cycle

ERSST-V2

Nino3 SST regression on observed and simulated SSTERSST-V2

Lead/Lag regression of the Nino3 SST with equatorial Pacific SST

28

0

-28

-28

28

0

ERSST-V2

-28

28

0

ERSST-V2

Joseph and Nigam, 2005

Nino3 SST regression on sub-surface ocean anomalies over equatorial Pacific

e-1=0.368 ERSST-V2

Contemporaneous correlation of annual mean Nino3 SST with global tropical SST

ERSST-V2

Wind Stress-Nino3 Regression (dynes/cm2)

Precipitation-Nino3 SST regression (mm/day)

1 2* 3 4 5 6 7a 7b 8

Obs 2.5-7 Asymm ~14 months Subtle Yes

COLA V2.2 3.92 2 Symm ~10 months Erroneous No Unreasonable Unreasonable

COLA V3.1.0.2

1. Nino3 Mean SST errors.2. Spectrum of Nino3 SST3. Asymmetry of ENSO warm/cold events4. Duration of ENSO event5. Nino3 SST correlation with other ocean basins

6. Seasonal phase locking of ENSO7. a) windstress-SST relationship b) precip-sst relationship8. Mid-latitude response

*Spectrum has the largest peak between 2.5-7 years and falls within the 95% confidence interval of the observed spectrum

Summary

0.933 ~12months Improvement Improvement ImprovementImprovementAssym

1 2* 3 4 5 6 7a 7b 8

Obs 2.5-7 Asymm ~14 months Subtle Yes

GFDL CM2.1 0.97 Yes? Less Asymm ~16 months No

GISS(E-H) 1.55 No No cold event ~10 months No

CCSM3 0.96 No Symm ~10 months Yes

PCM 1.91 Yes Asymm ~10 months No

HADCM3 1.27 Yes Asymm ~14 months Yes

MIROC3.2(hires) 0.90 No No cold event ~18 months No

CFS 0.99 Yes? Less Asymm Strong No

COLA V3.1.0.2 0.93 Yes Asymm ~12 months Weak Yes

1. Nino3 Root Mean square SST errors.2. Spectrum of Nino3 SST3. Asymmetry of ENSO warm/cold events4. Duration of ENSO event5. Nino3 SST correlations in other ocean basins

6. Seasonal phase locking of ENSO7. a) windstress-SST relationship b) precip-sst relationship8. Mid-latitude response

*Spectrum has the largest peak between 2.5-7 years and falls within the 95% confidence interval of the observed spectrum

Where we stand…(Thanks to PCMDI)

Concluding Remarks

“It is easy to abandon models that don’t simulate ENSO. But it will be a great learning experience if we make an attempt to change these models.”

From our exercise in COLA we are learning:

• Development of climate models are best achieved in a coupled framework.

• All eight metrics by themselves are necessary but not sufficient conditions for verifiable seasonal-interannual simulation. To get every metric of ENSO right even in ball park is important for at least seasonal to inter-annual prediction.

• Wind stress simulation is important in the eastern Pacific to get the bulk of the annual cycle right besides the stratus clouds. We got that to a large part by having a bottom heavy convective heating profile. We are investigating the asymmetry of ENSO causality from 3.1 to 101 to 102.

• Small changes can lead to significant change in the model variability. The coupled model has to be integrated for long periods to determine the efficacy of a change.

• Not flux correction but improved models is the way to move forward. Flux correction, in the short term may help and could be given as a testable magic wand for operational R&D teams.