1. Development of an advanced ensemble-based ocean data assimilation approach for ocean and coupled reanalyses
Eric de Boisséson, Hao Zuo, Magdalena Balmaseda and Patricia de Rosnay
CMEMS Service Evolution 2 Lot 4 kick-off meeting, 6 April 2018

2. Context CMEMS Service Evolution 2 Lot 4
Section 4.7 of the CMEMS service evolution strategy: coupled ocean-atmosphere models with assimilative capability
Advanced assimilation methods targeted to provide improved estimations of upper ocean properties consistent with sea-surface observations and air-sea fluxes

3. Context What we propose:
A way to improve estimates of upper ocean properties is to improve the uncertainty estimate of the ocean state
Develop a generic ensemble generation scheme for ocean analysis applicable in both ocean-only and coupled models.
Optimize the use of the information from the ocean observing system
Account for uncertainties of the surface variables and air-sea fluxes

4. Context
Optimize the use of the information from the ocean observing system
Major source of uncertainty: observation representativeness errors
Mismatch between obs. and model resolutions
Standard method: super-observation and thinning techniques
But loss of information from the observing system
Alternative: random perturbation + ensemble approach
Better exploit the information from the obs.
Uncertainty estimate

5. Context
Optimize the use of the information from the ocean observing system: 2 examples
1. Assimilation of sea-ice observations from CMEMS OSTIA:
Alternative: random selection within the thinning grid
Ensemble spread uncertainty estimate
Standard: 0.5x0.5 thinning
Ensemble of analysis
The random selection + ensemble approach provide estimate of the uncertainty and allow to further exploit the observing system

6. Context
Optimize the use of the information from the ocean observing system: 2 examples
2. Assimilation of vertical T/S profiles:
Vertical resolution of T/S profiles much higher than model grid
Standard method: thinning
Alternative: random selection within the thinning grid
Ensemble approach
The random selection + ensemble approach provide estimate of the uncertainty and allow to further exploit the information on the vertical axis

7. Context
Account for uncertainties of the surface variables and air-sea fluxes
Partially taken into account in current ocean analysis systems
Example: current ECMWF ocean analysis uses monthly random perturbations on wind stress and SST based on differences between analyses
How can we extend it?
Other sources of uncertainty: bulk formulation parametrisations, sea-ice constraints and heat and freshwater fluxes
A wider range of temporal scales (submonthly to monthly)
Multivariate relationships
In practice
Use a wider variety of analysis datasets to provide structural and analysis uncertainties

8. Working plan System and input data: ORAS5 (CMEMS MFC GLO-RAN)
Ocean only forced by atmospheric reanalysis
NEMO at 1/4˚ degree and 75 vertical levels
LIM2 sea-ice model
NEMOVAR DA
CERA-SAT
Coupled reanalysis system
Ocean component similar to ORA-S5
Atmosphere ECMWF IFS 60km resolution and 137 levels
4D-Var DA and 10-member EDA system
Input datasets
In-situ T/S profiles
SLA from CMEMS SL TAC
SIC from CMEMS OSTIA
SST (HadISST2, OSTIA)

9. Working plan ORAS5 CERA-SAT NEMO Coupled NEMO-IFS NEMO
Observations
Surface forcing
Observations
Obs operator
Bulk formula
Obs operator
NEMO
Coupled NEMO-IFS
Model departures
Model departures
NEMOVAR
3DVAR FGAT
IFS 4DVAR
NEMOVAR
3DVAR FGAT
Increment
Increment
NEMO
Coupled NEMO-IFS
Next cycle
Next cycle
ORAS5 and CERA-SAT: perturbations + ensemble approach = uncertainty estimate
CERA-SAT: Uncertainty on surface forcing coming from atmos. EDA, flow dependent

10. Working plan WP1: development of the new perturbation scheme
Task 1: Perturbations of ocean observations
Task 2: Evaluation of the perturbation scheme for assimilated observations
Task 3: Building a perturbation repository for surface forcing and surface variables
Task 4: Evaluation of the perturbation scheme for surface forcing and surface variables
Evaluation of the scheme in ORAS5:
Ensemble spread diagnostics
ECVs
Against independent observations
Against CMEMS GREP

11. Working plan
WP 2: Application and evaluation in both ORAS5 and CERA-SAT systems
Task 5: Application in both ocean-only and coupled assimilation systems
Task 6: Evaluation in both ocean-only and coupled assimilation systems
Evaluation of the scheme in ORAS5 and CERA-SAT:
Coupled vs uncoupled, EDA vs static
Ensemble spread diagnostics
ECVs
Against independent observations
Against CMEMS GREP

12. Outcomes Section 4.7 of the CMEMS service evolution strategy:
Advanced assimilation methods targeted to provide improved estimations of upper ocean properties consistent with sea-surface observations and air-sea fluxes.
Main outcome:
Generic ensemble generation scheme for ocean analysis applicable in both ocean-only and coupled models
Optimize the use of the ocean observing system
Account for the uncertainties from air-sea fluxes
Potential impact of coupling on the upper ocean properties and their uncertainty
Deliverables:
Progress and final reports (+ code, possibly)

13. Outcomes Potential benefits on the CMEMS services:
A better estimate of the ocean state consistent with air-sea fluxes and sea-surface observations
An ensemble of ocean-only or ocean-atmosphere coupled IC for the CMEMS forecasting system with uncertainty information
Feedback and potential improvement for different CMEMS TAC products (e.g. in-situ TAC and Sea-Level TAC)
Improved monitoring of the ocean physical state, variability and change using an ensemble of reanalyses with uncertainty in climate signals
Results and lessons learnt from this work will be transferred to CMEMS to help future CMEMS service update.

14. The team
Eric de Boisséson (PI), ECMWF, coupled data assimilation (CDA) team
Hao Zuo, ECMWF, CDA team
Magdalena Balmaseda, ECMWF, Head of Predictability section
Patricia de Rosnay, ECMWF, CDA team leader

15. Thank you for your attention
Any question?