Oscar Alves CAWCR (Centre for Australian Weather and Climate Research)

1. Towards Coupled data assimilation in an intra/seasonal forecast system Oscar Alves CAWCR (Centre for Australian Weather and Climate Research) Australian Bureau of Meteorology Contributors and Collaborators: Patricia Okely, Yonghong Yin, Debbie Hudson, Peter Oke, Terry O’Kane The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

2. Outline • Current data assimilation and ensemble generation strategies • What coupled covariances may look like • New coupled ensemble generation for multi-week prediction • Path towards fully coupled assimilation The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

3. Perturb forcing + noise Ocean Model Ocean Observations EnsembleOI (Oke et al 2005) Covariances from ensemble spread (3D multivariate-time evolving) ASSIM ASSIM POAMA-2 Ocean Data Assimilation PEODAS: POAMA Ensemble Ocean Data Assimilation System (Yin et al 2010) • Poor-persons EnKF: only assimilate into central member • Provides an ensemble of initial ocean states (11 ensembles, but 100 member lagged used for covariance calculation) • Assimilates in situ ocean temperature and salinity. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

4. Example of Ensemble Spread (Estimate of analysis error) Temperature Salinity From Yin et al 2010 The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

5. Coupled Model Ocean Observations + atmos anals EnsembleOI (Oke et al 2005) Covariances from ensemble spread (3D multivariate-time evolving) ASSIM ASSIM Coverting PEODAS to Fully Coupled Assimilation • Assimilate ocean obs and atmos re-analyses • Cross-covariances between ocean and atmos (&ice & land) • This will be done with the next version of our model based on UKMO UM coupled to MOM4 • What are going to be the issues ? The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

6. What might coupled co-variances look like Case study: 90 member ensemble forecast from Dec 1996 Estimate covariances from ensemble (e.g. after two months) Patricia Okely and Li Shi

7. Coupled Covariances Ref.: Temp. 100m Colour: Temp. Cont.: Zon. Current • Covariances consistent with intra-seasonal activity • Non-local covariances (real or not, desirable or not) • Large vertical extent (not shown) • Time/space covariance aliasing – should we represent this (past event that triggered independent event) Ref.: Temp. 100m Colour: SST Vect.: Surf. Wind Ref.: Temp. 100m Colour: SST Cont.: OLR Patricia Okely The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

8. POAMA-2 Seasonal and Multi-week systems • PEODAS is the bases of ocean data assimilation and ensemble perturbations in our POAMA-2 seasonal prediction system • Not suitable for multi-week predictions as no atmospheric perturbations. • Atmospheric initial conditions are taken from a atmospheric integration nudged to ERA-40 The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

9. Bred vectors are rescaled and centred to the central analyses Coupled Model integrations Central unperturbed analyses: PEODAS and ALI 1 day Generates coupled bred perturbations of both the atmosphere and ocean based on the breeding method Rescaling – zonal surface wind spread similar to NCEP-ERA Coupled Assimilation Step 1: Coupled ensemble generation scheme (Yonghong Yin) The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

10. EQ, 150E MJO Variability Case study: CEI Coupled Analysis 30 days to 1st Mar 1997

11. Variability Error Ensemble

12. Coupled Covariances Ref.: Surf. Temp. Colour: Surf. Zonal Wind Ref.: Surf. Temp. Colour: OLR Ref.: Surf. Zonal Wind Colour: Surf. Temp.

13. Conceptual example of real non-local covariances • Suppose you have an MJO error (eg. Speed error or structure error) • Some time later (e.g. 10 days)– there will be non local covariances due to different processes but triggered by the same earlier event MJO error over Brazil Rossby KW The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

14. Summary • Development of coupled breeding scheme for intraseasonal forecasts is first step towards coupled data assimilation • Co-variance structures capture ~large scale intra-seasonal dynamics • Practical issues: • non local covarariances – real or not • Localisation, especially in the vertical • Ocean and atmosphere on different grids (different time scales) • Future • Step 2: Semi coupled (PEODAS ocean, nudge atmos in coupled model) • Step 3: Fully coupled The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology