1. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 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 Towards Coupled data assimilation in an intra/seasonal forecast system

2. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 1.Current data assimilation and ensemble generation strategies 2.What coupled covariances may look like 3.New coupled ensemble generation for multi- week prediction 4.Path towards fully coupled assimilation Outline

3. 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. ASSIM Ocean Model Ocean Observations Ensemble OI (Oke et al 2005) Covariances from ensemble spread (3D multivariate-time evolving) Perturb forcing + noise PEODAS: POAMA Ensemble Ocean Data Assimilation System (Yin et al 2010) POAMA-2 Ocean Data Assimilation 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) From Yin et al 2010 Temperature Salinity

5. 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 ? ASSIM Coupled Model Ocean Observations + atmos anals Ensemble OI (Oke et al 2005) Covariances from ensemble spread (3D multivariate-time evolving) Coverting PEODAS to Fully Coupled Assimilation 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 Estimate covariances from ensemble (e.g. after two months) Case study: 90 member ensemble forecast from Dec 1996 Patricia Okely and Li Shi

7. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Coupled Covariances Ref.: Temp. 100m Colour: Temp. Cont.: Zon. Current Ref.: Temp. 100m Colour: SST Vect.: Surf. Wind Ref.: Temp. 100m Colour: SST Cont.: OLR Patricia Okely 1.Covariances consistent with intra-seasonal activity 2.Non-local covariances (real or not, desirable or not) 3.Large vertical extent (not shown) 4.Time/space covariance aliasing – should we represent this (past event that triggered independent event)

8. POAMA-2 Seasonal and Multi-week systems The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 1.PEODAS is the bases of ocean data assimilation and ensemble perturbations in our POAMA-2 seasonal prediction system 2.Not suitable for multi-week predictions as no atmospheric perturbations. 3.Atmospheric initial conditions are taken from a atmospheric integration nudged to ERA-40

9. 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 Model integrations 1 day Bred vectors are rescaled and centred to the central analyses Central unperturbed analyses: PEODAS and ALI (Yonghong Yin) Coupled Assimilation Step 1: Coupled ensemble generation scheme The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

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

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. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 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 KW MJO error over Brazil Rossby

14. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 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