Breeding with the NSIPP global coupled model: applications to ENSO prediction and data assimilation

1. Breeding with the NSIPP global coupled model: applications to ENSO predictionanddata assimilation Shu-Chih Yang 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 • Toth and Kalnay (1996) • Cai et al. (2002) with CZ model • Bred vectors are sensitive to the background ENSO, showing that the growth rate isweakest at the peak time of the ENSO statesandstrongest 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. Monthly Amplification Factor of Bred Vector El Niño Background ENSO La Niña La Niña “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

7. Improvement on ensemble forecasts FCT error with BV FCT 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)

10. OGCM • Poseidon V4, (Schopf and Loughe,1995)

11. Observations Ensemble member Ensemble mean Current prediction skillfrom NSIPP CGCMhindcasts 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. NINO3 INDEX(ºC) SOI INDEX

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

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

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

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

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

20. Atmospheric maps regressed with PCs Northern Hemisphere Background BV Sea-levelpressure Geopotential at 200mb Even though the breeding rescaling is in the Nino3 region, the atmospheric response is global

21. Atmospheric maps regressed with PCs Southern Hemisphere BV Background Sea-levelpressure Geopotential at 200mb

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

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

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

25. 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 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. • 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

26.   Current work • 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:  • Replace the restart file from an AMIP run to NCEP atmospheric re-analysis data B2month F1month  B’ B’  A     t=1 t=2 t=3 t=4 t=5

27. Relationship between bred vectors and background errors BV Temp (contour) vs. analysis increment (color) at OCT1996 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

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

29. 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.

31. BV Geopotential at 500mb NSIPP NCEP

32. From 10 year perfect model simulation

33. Joint EOF map of BV SST

34. BV1 Z20PC1vs. BV1 growth rate Growth rate Z20 PC1 BV2 Z20PC1 vs. BV2 growth rate Growth rate Z20 PC1

35. CNT Background Z20 EOF1 Background Z20 PC1 Background Z20 EOF2 Background Z20 PC2

36. Background ENSO vs. ENSO embryo CNT EOF1 BV1 EOF1 BV2 EOF1 CNT EOF2 BV1 EOF2 BV2 EOF2

37. BV growth rate BV SST vs. (SSTfcst-SSTa) MAR1996

38. BV regression maps constructed with Z20 PC1

39. Vertical cross-section along the Equator Color: Tfcst-Ta Contour: BV (SST norm) Color: Tfcst-Ta Contour: BV (Z20 norm) JAN2000

40. Vertical cross-section along the Equator Color: Tfcst-Ta Contour: BV (SST norm) Color: Tfcst-Ta Contour: BV (Z20 norm) MAR1996