GENOA: Generic Ensemble generation for Ocean Analysis Eric de Boisséson, Hao Zuo, Magdalena Balmaseda and Patricia de Rosnay CMEMS Service Evolution 2 mid-term review, 19 March 2019

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

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

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

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

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 WP 2: Application and evaluation in both ocean-only and coupled analysis systems Task 5: Application in both ocean-only and coupled assimilation systems Task 6: Evaluation in both ocean-only and coupled assimilation systems

Working plan System and input data: ORAS5 (CMEMS MFC GLO-RAN) Ocean only forced by atmospheric reanalysis NEMO 3.4.1 at 1/4˚ degree and 75 vertical levels LIM2 sea-ice model NEMOVAR DA CERA-SAT Coupled reanalysis system Ocean component similar to ORAS5 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)

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

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

Summary of achievements Specific Objectives Status Scientific results Impact on CMEMS WP1 task 1: Implementation of a new perturbation scheme for ocean observations Complete The perturbation scheme is working as intended exploiting better the variety of the observing system and accounting for uncertainties None at the moment but the perturbation code could be provided to MFCs for implementation in other systems WP1 task 2: Evaluation of the perturbation scheme for assimilated observations On-going The new perturbation scheme produces an ensemble of ocean analyses that better fits observations but remains underdispersive. Results suggests that the current specification for background errors is not always ideal Same as above WP1 task 3: Building a perturbation repository for surface forcing and surface variables The building of the repository is on-going   WP1 task 4: Evaluation of the perturbation scheme for surface forcing and surface variables To be done in the second half of GENOA WP2 task 1: Application in both ocean-only and coupled assimilation systems WP2 task 2: Evaluation in both ocean-only and coupled assimilation systems

WP1 task 1: implementation of a new perturbation scheme for ocean observations System and input data: ORAS5 (CMEMS MFC GLO-RAN) Ocean only forced by atmospheric reanalysis NEMO 3.4.1 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)

WP1 task 1: implementation of a new perturbation scheme for ocean observations Perturbation of in situ T/S profiles ORAS5: OE stdev for T/S specified by empirical analytical function that provides approximate fit to the vertical profiles of T/S stdev estimated from observations by Ingleby and Huddleston (2007) Uneven sampling on horizontal/vertical -> RE not fully taken into account To account for the RE of profiles: Perturbations of the horizontal locations of the in-situ observations Stratified random thinning on the vertical.

WP1.1: Perturbation of horizontal location of T/S profiles 5 possible strategies are implemented P1: perturbs a profile with random distance (0 to PD) and random angle (0-360 degree) P2: perturbs a profile with constant distance (as PD) and random angle (0-360 degree) P3: perturbs latitude of a profile with random distance (0 to PD) P4: perturbs longitude of a profile with random distance (0 to PD) P5: perturbs latitude and longitude of a profile with the same random distance (0 to PD) Example: P2 for XBTs and P5 for moorings The perturbations + ensemble approach provide estimate of uncertainties accounting for RE

WP1.1: Perturbation of vertical location of 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

WP1.1: Perturbation of sea-surface observations 1. Perturbation of sea-ice observations: ORAS5 assimilates SIC from CMEMS OSTIA (1/4 degree) Too many observations -> regular interval thinning (1/2 degree) Information lost -> new method with stratified random thinning: random selection within thinning grid The random selection + ensemble approach to better exploit the info from SIC data

WP1.1: Perturbation of sea-surface observations 1. Perturbation of sea-ice observations: Standard: 0.5x0.5 thinning ORAS5 assimilates SIC from CMEMS OSTIA (1/4 degree) Too many observations -> regular interval thinning (1/2 degree) Information lost -> new method with stratified random thinning: random selection within thinning grid Alternative: random selection within the thinning grid The random selection + ensemble approach to better exploit the info from SIC data

WP1.1: Perturbation of sea-surface observations Raw data 2. Perturbation of sea-level observations: ORAS5 assimilates along-track SLA from CMEMS SL TAC Too many observations ->super-obbing (average) according to mission, area and time. Take into account part of RE Information lost -> new method using random selection within the superob grid Superobbing Random thinning The random selection + ensemble approach to better exploit the info from SIC data

WP1 task 2: evaluation of a new perturbation scheme for ocean observations Evaluation of perturbation of in situ T/S profiles ORAS5 low-res (ORCA1_Z42) with 5 ensemble members over 2004-2011 DA: T/S profiles from EN4 and SIC from OSTIA. SST nudging and SLA assimilation are deactivated Experiment Horizontal Perturbation (P1) Vertical Perturbation Hpert50 PD=50km - Hpert100 PD=100km Hpert200 PD=200km HpertS15 PD=200km; 15 members Vpert PD=0km N=3 observations per model level HVpert100 Same as above HVpert200

WP1.2: ensemble diagnostics Ensemble spread diagnostics: method Ensemble spread produced from the background fields of the experiments. Compare ensemble spread to the specified BGE stdev in observation space Specified BGE cov: analytical formulation (Mogensen et al, 2012) T: based on dT/dz, max in the thermocline S: based on dS/dT, max in the mixed layer Desroziers’ diagnostic of a-posteriori BGE covariances based on innovations and analysis increments (Weaver et al, 2013) -> reference for the statistical consistency of specified BGE covariance.

WP1.2: ensemble diagnostics Horizontal distribution 100m depth in Hpert 200 Horizontal distribution of the spread Temperature spread shows right features but underdispersive, esp. in the Tropics Salinity spread very different from specified BGE stdev. More similar to Desroziers diagnostic. Specified BGE stdev for S overestimated? Need to be revised? Ensemble approach for BGE specification?

WP1.2: ensemble diagnostics Vertical profiles - area average Vertical distribution of the spread Spread comes mainly from horizontal perturbations. Vertical perturb have some impact in poorly-sampled deep ocean Spread lower than specified BGE stdev in the thermocline for T and mixed layer for S Underdispersive in Tropical thermocline -> increase ensemble, inflate in thermocline Spread closer to diagnosed BGE stdev in extratropics: energetic areas (sharp fronts, eddies …), perturbations more impactful Temperature Salinity N ExtraTropics S ExtraTropics Tropics

WP1.2: ensemble diagnostics Temporal evolution of the spread OHC in model space: ensemble mean and spread Global OHC – Ens mean Global OHC detrended Global OHC – Ens spread Ens mean: similar values and trend in pertub experiments. Larger PD = larger spread and interannual variability Most spread comes from horizontal perturb

WP1.2: ensemble diagnostics Comparisons to independent observations – WOA13 Ensemble mean temperature at 800m Largest biases in WBC Increasing the PD reduces bias in Gulf Stream Ens mean from Hpert exp shows lower bias wrt to WO13 compared to the control member (not shown)

WP1.2: ensemble diagnostics Comparisons to independent observations – OSTIA SST Verification against OSTIA SST Ensemble variance (Model departure)^2 – (Obs error)^2 Hpert50 Largest departures in WBC and ACC Larger PD, larger ensemble variance Ideally ens variance comparable to departures Under dispersive ensemble Hpert100 Hpert200

WP1 task 2: evaluation of a new perturbation scheme for ocean observations Evaluation of perturbation of sea-ice concentration Experiment: ORCA1_Z42 with 5 ensemble members over 2004-2011 with 100km thinning scale Large spread close at ice edge and next to coast (Canadian archipelago, Greenland …) Large seasonal variability in spread (forming/retreating ice) Impact on other ocean processes such as transport across Arctic straits RMSE wrt OSTIA SIC Ens spread

WP1 task 2: evaluation of a new perturbation scheme for ocean observations Evaluation of perturbation of sea-ice concentration Experiment: ORCA1_Z42 with 5 ensemble members over 2004-2011 with 100km thinning scale Large spread close at ice edge and next to coast (Canadian archipelago, Greenland …) Large seasonal variability in spread (forming/retreating ice) Impact on other ocean processes such as transport across Arctic straits RMSE wrt OSTIA SIC Ens spread July

WP1 task 2: evaluation of a new perturbation scheme for ocean observations Evaluation of perturbation of sea-level anomalies Ensemble variance Experiment: ORCA025_Z75 with 5 ensemble members over 2000-2004 with 1-degree thinning scale Large spread/departures in energetic areas such as WBC and ACC -> not fully resolved -> large REs Ensemble slightly underdispersive (Model departure)^2 – (Obs error)^2

Conclusions Next tasks and WP: WP1 tasks 3 and 4 on perturbations of sea surface constraints Build the perturbation repository Variables selected: wind stress, SST, SIC, P-E and solar radiation Structural errors: wind stress from different sources (ERA-Int, NWP, NCEP, ERA40) + different settings (wave effects, bulk) + SST and SIC from ESA-CCI and HadISST (v2.1 and v2.0) Analysis errors: wind stress, SST, P-E and solar radiation from ERA-20C (monthly) + SST/SIC from HadISSTv2 (pentad) -> multiple timescales Test and evaluate the new perturbation repository with the ORAS5 system

Conclusions Next tasks and WP: WP2 on application and evaluation of the perturbation scheme in ocean-only and coupled analysis systems run the full ORAS5 and CERA-SAT systems using the scheme from WP1 At the air-sea interface, ORAS5 will use uncertainty information from the perturbation repository while CERA-SAT will use the atmospheric EDA -> comparison EDA – perturbation rep

Conclusions Issues and risks: so far everything is going according to plan Deliverables: Quarterly reports: up to date Mid-term report: delivered Final report: to be delivered KO+23 Outcomes: status Generic ensemble generation scheme for ocean analysis applicable in both ocean- only and coupled models Optimize the use of the ocean observing system (WP1 tasks 1-2) Account for the uncertainties from air-sea fluxes (WP1 tasks 3-4, to do) Potential impact of coupling on the upper ocean properties and their uncertainty (WP2, to do)

Thank you for your attention Any question?