The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Coupled Breeding for Ensemble Multiweek Prediction Harry Hendon, Patricia Okely, Debra Hudson, Yonghong Yin, Oscar Alves, Griff Young, Andrew Marshall plus others

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) 2

Developing an intraseasonal forecasting capability from seasonal system POAMA 1.5b (ABOM1) AGCM T47-L17 OGCM 2  -0.5  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; days) realtime: once per day POAMA M24 (ABOM2) 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) 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 POAMA: Predictive Ocean Atmosphere Model for Australia 3

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

Coupled Model forecasts 1 day Central unperturbed analyses: PEODAS (ocean) and ALI (atmos) Coupled Breeding Initialisation System Member perturbations rescaled separate norms for ocean and atmos then centred to the central analyses First Step Towards Coupled Assimilation... Based on the PEODAS and ALI infrastructures: 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 Output an ensemble of atmos/ocean/land perturbations 5

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

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

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

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) 9

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

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 1 st Dec, 1 st Jan, and 1 st Feb hour lagged mean abs(r)=0.55 Bred 0.18 Jumbled

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

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

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 14

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

Bred Lagged day 4 day 11 16

Bredlagged day 1 Perturbation Growth rate day 5 Wavenumber

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) 18

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 19