Coupled Breeding for Ensemble Multiweek Prediction

1. Coupled Breeding for Ensemble Multiweek Prediction www.cawcr.gov.au Harry Hendon, Patricia Okely, Debra Hudson, Yonghong Yin, Oscar Alves, Griff Young, Andrew Marshall plus others

2. Outline • Motivation for coupled breeding approach • A step toward extending seasonal system to multiweek • lagged ensemble is underdispersive • need a consistent set of atmos/ocean/land perturbations • On the path to coupled ensemble-based assimilation • Examples of forecast benefits for multiweek leads • Some (limited) analysis of statistics of bred perturbations • focus on first 10 days comparing bred/lagged • primarily focused on atmos perturbations (not discounting importance of coupling)

3. Developing an intraseasonal forecasting capability from seasonal system POAMA: Predictive Ocean Atmosphere Model for Australia POAMA 1.5b (ABOM1) 2007-2011 AGCM T47-L17 OGCM 2-0.5 POAMA M24 (ABOM2) 2013- Ensemble-based ocean data assimilation; T and S (PEODAS) ALI atmos/land initialization Coupled breeding to generate atmos-ocean perturbations Multi-model (3 versions) 33 member burst ensemble every 5 days (twice per week operationally) Basic ocean data assimilation (T only) in offline OGCM atmos/land initialization (ALI); strongly nudge AGCM to ERA in AMIP run hindcasts 10 member lagged ensemble (successive 6 hour earlier; 0.25-2.5 days) realtime: once per day Some useful intraseasonal forecasts Lagged ensemble cumbersome Inconsistent realtime/hindcasts Reliability issues (under-dispersive) Improved Intraseasonal forecasting capability Improved reliability and products Consistency hindcasts/realtime

4. Coupled Breeding builds on Ensemble Ocean Assimilation POAMA Ensemble Ocean Data Assimilation SystemYin et al. 2011 runs in OGCM forced by ERA surface fluxes and SST relaxed strongly to Reynolds OI provides ensemble of ocean states, but not for atmos Ensemble of OGCM integrations Compress Ensemble Nudge to central analysis 1 day ASSIM ASSIM Observations T&S Synthetic Perturbed wind forcing (Alves and Robert 2005) + ocean perturbation EnsembleOI (Oke et al. 2005) Cross-Covariances from ensemble spread (3D multi-variate-time evolving) Assimilation only into central member

5. Coupled Breeding Initialisation System First Step Towards Coupled Assimilation... Based on the PEODAS and ALI infrastructures: Output an ensemble of atmos/ocean/land perturbations Member perturbations rescaled Coupled Model forecasts separate norms for ocean and atmos then centred to the central analyses Central unperturbed analyses: PEODAS (ocean) and ALI (atmos) 1 day Atmos: zonal mean rmsd surface zonal wind=analysis uncertainty (ERA-NCEP) Ocean: 3-d T/S rmsd = analysis uncertainty (PEODAS) rescale threshold met ~everyday in midlat atmos; every 4-5 days in tropics and oceans

6. NRMSE of ensemble mean Ensemble spread SHEM 500 hPa Geopotential hghts Impact of ensemble generation (and multimodel) POAMA-1.5 Time-lagged ensemble Ocean IC 6 hour lagged Atmos IC 0 10 20 30d Burst ensemble POAMA-2 intraseasonal Coupled breeding 0 10 20 30d

7. Upper tercile rainfall Improved forecast reliability Probability of rainfall in upper tercile All grid points over Australia Weeks 1 and 2 Weeks 3 and 4 POAMA-1.5 POAMA-2 (seas) POAMA-2 (intra) (all forecast start months 1980-2006)

8. MJO Forecast skill 1982-2011 (1st each mnth)RMM1 and RMM2 Bivariate RMSE/Spread Improvement of ensemble mean over individual members Lagged ABOM1 Bred RMSE ABOM2 Bred spread Courtesy D. Waliser Coupled breeding significant improvement over lagged, but still under-dispersed in Tropics

9. Some analyses of the statistics of the perturbations • Compare lagged to bred, plus a sensitivity exp using jumbled • Jumbled: make a new set of perturbations by randomly sampling the bred perturbations from all other years • should elucidate day-to-day “flow dependence” • How flow dependent are the bred perturbations? (highly) • Are “flow of the day” any better than jumbled? (not really) • How coupled are they, or does coupling matter? (can’t fully answer yet but apparently not important for longer leads) • How optimal are the bred perturbations? (certainly better than lagged but still under-dispersed in Tropics)

10. Spread and RMSE (9 member ensemble) DJF Southern Hemisphere Z500 Slight benefit of “flow of the day” perturbations rmse Black = control bred Red = lagged Yellow = old lagged: p15b Blue = jumbled bred spread

11. Spatial correlation of Initial Perturbations (90S-90N) Perturbations defined wrt to central member Bred, Jumbled, Lagged (6hourly) Mean of absolute correlation of perturbations from member 1 with other 9 members 1st Dec, 1st Jan, and 1st Feb 1982-2011 0.7 6 hour lagged mean abs(r)=0.55 Bred 0.18 Jumbled 0.14 0.0

12. Examining flow-of-day sensitivity: U850 spread composites for ENSO U850 spread/anomaly along equator Initial time warm cold After 10 days warm-cold

13. Control vs Jumbled results Jumbled at initialisation Control at initialisation Factor 10 smaller scale After 10 days Implication: coupled ocean-atmos perturbations not critical for reliable long lead prediction of ENSO; might not be true for short lead prediction of MJO

14. Association of spread with westerly anomalies is general throughout tropics Correlation U850 spread (from breeding) with obs U850 anomaly at initialisation high spread goes with westerly anomalies in tropics> convective regimes

15. Comparing Bred vs Lagged (6 hourly: 0.25-2.5 days) Mean variance of perturbations MSLP initial time DJF1982-2011 Bred Lagged Zonal mean amplitude Variance normalized by zonal mean at each lat. variance

16. Bred Lagged day 4 day 11

17. Perturbation Growth rate Bred lagged day 1 0 20 day 5 Wavenumber

18. Summary • Coupled-breeding ensemble generation has led to increased multiweek skill and reliability in POAMA-2 (ABOM2) • But breeding is still underdispersive in Tropics • Despite simplicity, lagged ICs are far from optimal for multiweek, esp due to slow growth in Tropics • Lagged initial conditions 6hr apart are too similar and not good sample of analysis uncertainty • Infer> need to be 2-3 days apart but pay accuracy penalty for multiweek • Next steps • Further analysis of perturbations (coupling, MJO-dependence, etc) • Refine breeding cycle to target increased tropical spread • Implement weakly coupled assimilation (show tomorrow) • Fully coupled assimilation (cross covariance ocean-atmos)

19. Define mean perturbation amplitude for initial conditions and forecasts over the M ensemble members at each time as Assuming exponential growth , define growth rate: Decompose ICamp and Famp as functions of zonal wavenumber perturbation growth rates as function of zonal scale