1. Breeding with the NSIPP global coupled model: applications to ENSO prediction and data assimilation Shu-Chih Yang Advisors: Profs. Eugenia Kalnay and Ming Cai Advisors: Profs. Eugenia Kalnay and Ming Cai

2. Outline – – Introduction – – Objectives – – NASA/NSIPP CGCM – – Breeding method – – Results from a 10-year perfect model experiment – – Comparison with breeding in NCEP CGCM – – Summary – – NSIPP operational system: preliminary results

3. Introduction ENSO simulation Because the coupled nature of ENSO phenomenon, the key factor to simulate and predict ENSO lies in the correct depiction of SST. ENSO prediction skill The prediction skill of a coupled model can be significantly improved through more refined initialization procedures (ex: Chen et al.,1995 and Rosati et al, 1997) Initialization of operational ensemble forecast for CGCMs Two-tier (Bengtsson et al., 1993) An ensemble of atmospheric forecast generated by a forecasted SST One-tier (Stockdale et al., 1998, adopted in ECMWF) Generate all the ensemble members via CGCM Initial perturbations are introduced in atmosphere components only

4. How to construct effective ensemble members? 2 methods have been considered to construct initial perturbations: Singular vectors have been used for ENSO prediction with the Cane and Zebiak model Limitations Strong dependence on the choice of norm and optimization time High computational cost makes it impractical for CGCMs

5. Breeding method Breeding method Toth and Kalnay (1996) Toth and Kalnay (1996) Cai et al. (2002) with CZ model Cai et al. (2002) with CZ model Bred vectors are sensitive to the background ENSO, showing that the growth rate is weakest at the peak time of the ENSO states and strongest between the events. Bred vectors can be applied to improve the forecast skill and reduce the impact of the “spring-barrier”. The results show the potential impact for ensemble forecast and data assimilation

6. “Spring Barrier”: The “dip” in the error growth chart indicates a large error growth for forecasts that begin in the spring and pass through the summer. Removing the BV from the initial errors reduces the spring barrier Monthly Amplification Factor of Bred Vector Background ENSO El Ni ño La Ni ña

7. Improvement on ensemble forecasts FCT error with  BVFCT error with  RDM

8. Objectives of this research Implement the breeding method with the NASA/NSIPP CGCM Construct effective perturbations for initial conditions of ENSO ensemble forecasts Test methods first with a “perfect model” simulation to develop understanding Apply methodology to NSIPP operational system, which is more complex (e.g. model errors) The ultimate goals is to improve seasonal and interannual forecasts through ensemble forecasting and data assimilation using coupled breeding

9. NASA Seasonal-to Interannual Prediction (NSIPP) coupled GCM AGCM (Suarez, 1996) AGCM (Suarez, 1996) Model features  Primitive equations  Empirical cloud diagnostic model  4th-order version of the enstrophy conserving scheme  4th-order horizontal advection schemes for potential temperature, moisture  Penetrative convection parameterized with Relaxed Arakawa-Schubert scheme Coordinates  Finite-difference C grid in horizontal  Generalized sigma coordinate Resolution 2  2.5  34 levels

10. OGCM OGCM Poseidon V4, (Schopf and Loughe,1995) Poseidon V4, (Schopf and Loughe,1995) Model features  Quasi-isopycnal model  Reduced-gravity formulation  Turbulent well-mixed layer with entrainment parameterized according to a Kraus-Turner bulk mixed layer model  Vertical mixing and diffusion are parameterized using a Richardson number dependent scheme  Horizontal mixing is implemented with high order Shapiro filtering Coordinates generalized horizontal and vertical coordinates Resolution 1  3  5  8  27 layers

11. Current prediction skill from NSIPP CGCM hindcasts Observations Ensemble member Ensemble mean Niño-3 Forecast SST anomalies up to 9-month lead April 1 starts September 1 starts

12. Breeding method Bred vectors : The differences between the control forecast and perturbed runs Tuning parameters Size of perturbation Rescaling period (important for coupled system) Advantages Low computational cost Easy to apply to CGCM 

13. 10 years breeding “perfect model” experiment Breeding Size of perturbation: 10% of the RMS of the SSTA (0.085  C) Rescaling period: one month CGCM AGCM NSIPP-1: 3° X 3.75° X 34 (global) OGCM Poseidon V4: 1/2° X 1.25° X 27 (90S - 72N) NINO3 INDEX(ºC) SOI INDEX

14. Snapshot of background SST (color) and bred vector SST (contour) Instabilities associated with the equatorial waves in the NSIPP coupled model are naturally captured by the breeding method model year JUN2024

15. BV grows before the background event Peak of the background event Lead/lag correlation between BV growth rate and absolute value of background NINO3 index 

16. Lead/lag correlation between BV growth rate and absolute value of BV NINO3 index

17. EOF analysis of SST Background SST anomaly EOF1 (46%) BV SST EOF1 (11%)

18. EOF analysis of thermocline (Z20) Background Z20 EOF1 (22%) Background Z20 EOF2 (16%) BV Z20 EOF1 (10%) BV Z20 EOF2 (7%) Z20 EOF2, SST EOF1 represent the mature phase of ENSO

19. Oceanic maps regressed with PCs Background: regressed with SST PC1 BV: regressed with Z20 PC1 SST Thermocline (Z20) Surface zonal current

20. Atmospheric maps regressed with PCs Tropical Pacific domain Wind at 850mb Surface pressure Geopotential at 500mb OLR Background BV

21. Atmospheric maps regressed with PCs Northern Hemisphere BVBackground Sea-level pressure Geopotential at 200mb Even though the breeding rescaling is in the Nino3 region, the atmospheric response is global

22. Southern Hemisphere BV Background Sea-level pressure Geopotential at 200mb Atmospheric maps regressed with PCs

23. Lead/lag regression maps BV SST vs. | CNT NINO3 | BV zonal wind stress vs. | CNT NINO3 | BV surface height vs. | CNT NINO3 | Bred vector leads ENSO episode in the Eastern Pacific Bred vector lags ENSO episode in the Central Pacific 

24. NASA/NSIPP BV vs. NCEP/CFS BV NSIPP NCEP SST Z20 xx Tropical Pacific domain

25. NASA/NSIPP BV vs. NCEP/CFS BV Z20 EOF2 Z20 EOF1 SST EOF1 NCEPNSIPP Results obtained with a 4-year NCEP run are extremely similar to ours

26. NASA/NSIPP BV vs. NCEP/CFS BV Northern Hemisphere NSIPP geopotential height at 500mb NCEP geopotential height at 500mb

27. Summary of “perfect model” results Larger BV growth rate leads the warm/cold events by about 3 months. The amplitude of BV in the eastern tropical Pacific increases before the development of the warm/cold events. The amplitude of BV in the eastern tropical Pacific increases before the development of the warm/cold events. The ENSO related coupled instability exhibits large amplitude in the eastern tropical Pacific. The ENSO related coupled instability exhibits large amplitude in the eastern tropical Pacific. In N.H, BV teleconnection pattern reflect their sensitivity associated with background ENSO. Rossby wave-train atmospheric anomalies over both Hemispheres. In N.H, BV teleconnection pattern reflect their sensitivity associated with background ENSO. Rossby wave-train atmospheric anomalies over both Hemispheres. Breeding method is able to isolate the slowly growing coupled ENSO instability from weather noise Bred vectors can capture the tropical instability waves Results of a “perfect model” experiment with the NCEP CGCM are very similar

28. Develop breeding strategy for the NASA/NSIPP coupled operational forecasting system Develop breeding strategy for the NASA/NSIPP coupled operational forecasting system   Perform breeding runs with different rescaling norms Perform experiments with a modified breeding cycle to reduce spin-up: Perform experiments with a modified breeding cycle to reduce spin-up:  Replace the restart file from an AMIP run to NCEP atmospheric re-analysis data Current work t=1t=2t=3t=4t=5     A F 1month B 2month  B’   

29. Relationship between bred vectors and background errors This case was chosen because the BV growth rate was large. The excellent agreement suggests that the operational OI could be improved by augmenting the background error covariance with the BV as in Corazza et al, 2002 BV Temp (contour) vs. analysis increment (color) at OCT1996

30. SST: Analysis - Control forecast Analysis – BV ensemble ave fcst For this case, we performed the first ensemble forecast: [(+BV fcst)+(-BV fcst)]/2 OCT1996

31. Summary of plans for application to the operational NSIPP system Develop a strategy to include the coupled growing modes extracted from coupled bred vectors in the initial condition of the ensemble system: For example, use perturbations +BV and –BV with an appropriate amplitude in the ensemble forecast system Develop a methodology for using advantage of the ENSO BVs within the operational NSIPP ocean ensemble data assimilation: For example, augment the OI background error covariance with BVs.

33. BV Geopotential at 500mb NCEP NSIPP

34. From 10 year perfect model simulation

35. Joint EOF map of BV SST

36. BV1 Z20PC1 vs. BV1 growth rate BV2 Z20PC1 vs. BV2 growth rate Growth rate Z20 PC1 Growth rate Z20 PC1

37. CNTCNT Background Z20 EOF1 Background Z20 PC1 Background Z20 EOF2 Background Z20 PC2

38. Background ENSO vs. ENSO embryo CNT EOF1BV1 EOF1BV2 EOF1 CNT EOF2BV1 EOF2BV2 EOF2

39. BV growth rate BV SST vs. (SST fcst -SST a ) MAR1996

40. BV regression maps constructed with Z20 PC1

41. Color: T fcst -T a Contour: BV (SST norm) Vertical cross-section along the Equator Color: T fcst -T a Contour: BV (Z20 norm) JAN2000

42. Color: T fcst -T a Contour: BV (SST norm) Color: T fcst -T a Contour: BV (Z20 norm) MAR1996 Vertical cross-section along the Equator