Mercator Ocean activity Yann Drillet and Mercator Ocean team

Outline Operational production and services R&D activities Conclusions Model Assimilation OSE/OSSE Intercomparison Conclusions

Mercator Ocean operational production and services Monitoring of the quality: http://www.mercator-ocean.fr/eng/science/Qualification-validation2 Monitoring of the production : Production is delivery in time in more than 98% Monitoring the of users

Carateristitics of the systems Global IBI RT RAN Physical model NEMO ¼° and 1/12° 50L NEMO ¼° 75L NEMO 1/36° 50L tide and pressure NEMO 1/12° 75L Tide and pressure Biogeochemistry model PISCES ¼° forced by RT ¼° PISCES ¼° forced by free simulation ¼° N/A PISCES 1/12° online Assimilation SEEK and 3Dvar bias correction (SLA, SST, T/S) SEEK and 3Dvar bias correction (SLA, SST, T/S, ICE) N/A weekly initialised with 1/12° solution. In development Atmospsheric Forcing ECMWF ERA interim Period 2007 (2013)-RT 1993-2013 2010-present 2002-2011 Products available on MyOcean (http://www.myocean.eu/) and Mercator (products@mercator-ocean.fr) Part are distributed on ftp server GOV multi model approach Lellouche et al., 2013, Ocean Science.

Useful tool for mapping errors velocities for drift applications Quality of the analyses and forecast, Lagrangian drift - Drifters give observed velocities and positions. - Model velocities give virtual positions. Distance between observed and virtual positions after 1 day And after a 3-day Lagrangian drift Useful tool for mapping errors velocities for drift applications Scott et al., 2012; Drévillon et al., 2013, Ocean Dynamics + QuO Va Dis?

Validation of chlorophyl interannual variability Winter 2002 NAO+ 1st EOF Winter 2005 NAO- Model Observations

Ocean Model NEMO consortium at european level. Partnership between Global and regional ocean physic and biogeochemistry configurations Reference simulation : global 1/12° 1979-2012 Sensitivity experiments: Numerical scheme in NEMO model, advection, diffusion, mixing Surface forcing Coupling physical ocean with atmosphere, Sea Ice and biogeochemistry

Impact on advection and diffusion schemes on global 1/12° configuration UBS EEN 1 EEN 2 EEN 3.

Assimilation Bias correction Observation error Ensemble approach Assimilation of new observations (sea ice, surface velocity)

Adaptive tuning of observations errors The prescription of observation errors in the assimilation systems is often too approximate... Ideally, ratio=1 ratio < 1 => obs. error overestimated ratio > 1 => obs. error underestimated [ residual (innovation)T ] R E Ratio Desroziers = a Jason1 Envisat SST The objective of this diagnostic is to improve the error specification by tuning an adaptive weight coefficient a acting on the error of each assimilated observation.

Adaptive tuning of observations errors The prescription of observation errors in the assimilation systems is often too approximate... Ideally, ratio=1 ratio < 1 => obs. error overestimated ratio > 1 => obs. error underestimated [ residual (innovation)T ] R E Ratio Desroziers = Jason1 Envisat SST

Adaptive tuning of observations errors - SLA - cm 0 5 10 Envisat error on 20061227 without tuning cm 0 5 10 Envisat error on 20061227 with tuning Fit Slope= 0.78 Fit Slope= 0.71

OSEs and OSSEs experiments Sensitivity of the forecasting system to current observations network Number of altimeter satellite Argo vs other in situ observations New satellites in the system (Saral, HY2) Design/impact of new observation network Deep argo SWOT

(5day-Assimilation window) SWOT OSSE Simulated Observations from IBI36 (Free Model, 1/36°~3km, 2009) : SSH : (25 hours mean ; Inverse Barometer and tide removed) Altimeters : J2, J1n, En Swot ( 7Km) Insitu : Temperature and salinity profiles (CORA Data positions) SST : Daily Mean with 25 Km for horizontal resolution SSH From NR(IBI36) : 09-14/03/2009 (5day-Assimilation window) J2; J1n; En Swot SSH IBI36 : 12/03/2009

Ssh Correlation (2009) : NR(Data) vs FreeSim vs OSSE1 vs OSSE2 NR/FreeSim 0.0 0.5 1.0 Mean : 59% 0.0 0.5 1.0 NR/OSSE1 Mean : 72% 0.0 0.5 1.0 NR/OSSE2 Mean : 80% NR (IBI36, ‘True Ocean’) FreeSim; OSSE1; OSSE2

Model intercomparison Ryan et al, GODAE OceanView Class 4 forecast verication framework: Global ocean inter-comparison

Conclusions Operational service with daily forecast Update annually ocean reanalysis In development new version of the global 1/12° analysis and forecasting system. R&D work to improve the system and to improve interaction and coupling with atmosphere, sea ice, biogeochemistry. Development of assimilation scheme (SAM2) and NEMO model Involvement in GOV TT