A Journey to “ENSO” Simulation at COLA

1. 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

2. Why is this exercise important? 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! Suggest from 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. + R & D of Center xyz Scientists outside xyz Stake holders

3. Mean state errors Spectrum of SST in the Nino3 region (power, width of the peak, frequency) Evolution of ENSO (asymmetry in cold and warm phase; sub surface ocean anomalies) Duration of ENSO event ENSO forcing (correlations) in other ocean basins Seasonal phase locking of ENSO Variability 7. Relationship of : wind stress with SST Precipitation with SST 8. Mid-latitude response ENSO Metrics to evaluate a simulation

4. Starting from…. 3.92 2 Symm ~10 months Erroneous No Un-verifiable Un-verifiable • Nino3 root mean square SST errors. • Spectrum of Nino3 SST • Asymmetry of ENSO warm/cold events • Duration of ENSO event • Nino3 SST correlations with other ocean basins 6. Seasonal phase locking of ENSO 7. a) windstress-SST relationship b) precip-sst relationship 8. Mid-latitude response

5. What is new

6. 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.

7. Profile of vertical resolution of the AGCM

8. 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

9. Experiment Design Observational verification: 1955-2000; ODA: 1980-98 IC 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

10. Downwelling Shortwave flux at surface

11. Annual Mean SST Errors

12. Dec ERSST-V2 Jun Jan Dec Jun Jan Annual Cycle of Equatorial Pacific SST

13. Small changes can lead to significant change in model variability

14. ERSST-V2 Seasonal phase locking of ENSO to the annual cycle

15. Nino3 SST regression on observed and simulated SST ERSST-V2

16. ERSST-V2 28 0 -28 28 0 -28 Lead/Lag regression of the Nino3 SST with equatorial Pacific SST

17. ERSST-V2 28 0 -28 Joseph and Nigam, 2005

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

19. e-1=0.368 ERSST-V2

20. ERSST-V2 Contemporaneous correlation of annual mean Nino3 SST with global tropical SST

21. Wind Stress-Nino3 Regression (dynes/cm2)

22. Precipitation-Nino3 SST regression (mm/day)

23. Summary 0.93 3 Assym ~12months Improvement Improvement Improvement Improvement • Nino3 Mean SST errors. • Spectrum of Nino3 SST • Asymmetry of ENSO warm/cold events • Duration of ENSO event • Nino3 SST correlation with other ocean basins 6. Seasonal phase locking of ENSO 7. a) windstress-SST relationship b) precip-sst relationship 8. Mid-latitude response *Spectrum has the largest peak between 2.5-7 years and falls within the 95% confidence interval of the observed spectrum

24. Where we stand… (Thanks to PCMDI) • Nino3 Root Mean square SST errors. • Spectrum of Nino3 SST • Asymmetry of ENSO warm/cold events • Duration of ENSO event • Nino3 SST correlations in other ocean basins 6. Seasonal phase locking of ENSO 7. a) windstress-SST relationship b) precip-sst relationship 8. Mid-latitude response *Spectrum has the largest peak between 2.5-7 years and falls within the 95% confidence interval of the observed spectrum

25. 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.