Evolution of the Mercator Océan system, main components for reanalysis and forecast

Transcription

1 Evolution of the Mercator Océan system, main components for reanalysis and forecast Y. Drillet, J.M. Lellouche, O. Le Galloudec, M. Drévillon, G. Garric, R. Bourdallé- Badie, C. Bricaud, J. Chanut, G. Reffray, C.E. Testut, L. Parent, E. Remy, J. Beuvier, C. Regnier, E. Dombrowsky, B. Tranchant, M. Benkiran, E. Greiner, B. Barnier

2 Plan Brief overview of the Mercator systems Available real time and reanalysis systems Performance of the systems Ocean modelling Atmospheric forcing Vertical mixing Vertical coordinates Data assimilation Sea Ice assimilation Incremental Analysis Update Bias correction Ongoing and futur works Conclusions 2

3 MERCATOR OCÉAN SYSTEMS 3

4 Operational systems running today and reanalysis Global: Regional Global 1, Operational since mid 2011 real time for seasonal forecasts application. Replacing a previous 2 (started in 2003). Global ¼, Operational since 2005 real time, daily service. Provides the physics to the BGC system GLORYS2V3 Global ¼ reanalysis. From 1993 to Global 1/12, Operational since 2010 real time, daily service Natl + Med 1/12, Operational since 2003 real-time, daily service, embedded in the Global ¼, provides IC and BC to the IBI system IBI 1/36, operational since Dec 2011 real-time, daily service, include tides, aimed at delivering service to coastal systems IBIRYS1V1 IBI 1/12 reanalysis over North East Atlantic area including tide, pressure forcing, and online biogeochemistry. From 2002 to

5 Time in hours Elapsed time for operational forecast (weekly scenario including 2 analysis and 1 forecast) Systems update 500 Gflops 1 Tflops 1 Pflops 50 Tflops 10 Tflops Computational power available at Météo France 5

6 Evolution of performance scores Present version Temperature Salinity SLA «Quo Va Dis?» Bulletin available online Lellouche et al, Ocean Science. 6

7 OCEAN MODELLING 7

8 brief history : The community model is called NEMO, it is choosen by several groups in Europe. This version is based on OPA9 including free surface and partial step, ice model (LIM) and tracer module (TOP). It is freely distributed : NEMO include mesh refinement (AGRIF), several vertical mixing (KPP) and advection (UBS) schemes, IO module : non linear free surface with variable volume, AGRIF for passive tracers, new open boundary module (bdy, obc), multi category ice model (LIM3), surface module, interpolation on the fly 2008 : Consortium agreement signed between CNRS, Mercator Océan, MetOffice, NERC : tidal mixing, light penetration, variable volume with time splitting, GLS vertical mixing scheme, runoff, diurnal cycle 2011 : Consortium agreement includes INGV and CMCC : new pressure gradient for s-coordinate, semi implicit bottom friction, new bulk formulation, tidal potential forcing, XIOS parallel output server 8

9 physics and parameterisations in standard configurations: Atmospheric Fields: 3h ERAinterim or operational ECMWF Precipitations corrected towards GPCP Long & short wave corrected towards = GEWEX code : NEMO 3.4 (OPA9+LIM) Grid: ORCA at 1/4, 1/12, 1/36 50 levels (1 to 450 m) or 75 levels (1 to 200m) z coordinates with partial step Parameterizations: Filtered or explicit free surface; TVD; Lap. Iso. on tracers ; biharmonic on momentum; TKE or K-ε turbulent closure scheme FORCING: Bulk: CORE + analytic or explicit diurnal c. Initialization: Levitus 2005 Or global reanalysis Or real time analysis 9

10 Atmospheric forcing impact on salinity Corrections implemented Rainfalls fluxes The correction is : Local Large scales Based on GPCP No correction are applied : Northward 65 N For small value of heat flux or precipitation Method induces : No change of interannual signal. No change of synoptic patterns (cyclones). «error» SSS without correction «error» SSS with correction -1 psu +1 psu 0 m Mean 2009 after 20 yrs simulation, comparison with ARMOR salinity. Fresh bias largely corrected Still fresh in Northern Atlantic Too salty in lat band Impact of correction at depth Still too fresh in Atlantic Ocean. Too salty in Pacific Ocean ARMOR No correction Correction Atlantic Ocean Indian Ocean Pacific Ocean 1500 m 10

11 Vertical mixing scheme in global configuration, impact on stratification and SST, 3D error (model-levitus05) 3 global ¼ experiments TKE + Solar penetration rgb bands TKE + Solar penetration 2 bands K-ε + Solar penetration 2 bands 11

12 NEMO (v 3.4) vertical coordinates z s-z z+part. cells s NEMO 60 s levels (Song and Haidvogel steching) s-z + partial cells Uncoded yet Hybrid systems: Coordinates defined thanks to a smoothed bathymetry enveloppe to reduce pressure gradient errors. NEMO 75 levels hybrid system Max slope =5% 12

13 2400m, z-vcoord. 2400m, hybrid-vcoord. Impact of vertical coordinate on the overflow Bottom salinity Depth of max salinity 13

14 DATA ASSIMILATION 14

15 SAM2 Data Assimilation System - Development steps of SAM2 : - [ ] : First developments of the SAM2 kernel - [2004-present] : Introduction of new parametrisations for the operational applications (new background error, adaptive scheme, IAU, ) - [2007-present] : Deployment through all Mercator operational configurations - Baseline of ocean observation datasets : - along-track sea level anomalies; - vertical T/S profiles; -Global SST maps - Sea ice concentration - Assimilation scheme Based on a multivariate SEEK filter Innovation are calculated using 3DFGAT - Forecast error covariances Pf computed with a pseudo ensemble - IAU (Incremental Analysis Update) scheme implemented - Bias correction scheme implemented in all systems 15

16 Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation Context - French Reanalysis project produced at Mercator - Based on Mercator operational systems + tuning and developments - Produce reanalysis spanning the altimetric + ARGO" era Model - Nemo 3.1, LIM2-EVP - Global ¼, 75 levels Assimilation - 2 separate SAM2 analyses, 7 days cycle - Ocean Analysis (SLA, InSitu Data from CORA3.2, SST), IAU on (h,t,s,u,v) - Ice Analysis (SIC), IAU on (SIC) - SIC Error: 1% open ocean, linear from 25% to 5% for SIC values between 0.01 and 1 - Temperature and salinity bias correction using Argo (3DVar method) Sea Ice Concentration from CERSAT (IFREMER) 16

17 Assimilation of Sea Ice Concentration : Impact in Antarctica with GLORYS2V3 System Sea Ice Concentration on 15 th September 1992 (assimilation start in December 1991) CERSAT Sea Ice Concentration RMS misfits G2V3-NOASSIM/ICE GLORYS2V3-ASSIM/ICE Global ¼, 75 levels GLORYS2V3-NOASSIM/ICE Global ¼, 75 levels Jan 1992 Sep 1992 May 1993 G2V3-ASSIM/ICE Jan 1992 Sep 1992 May 1993 Sea Ice Concentration Misfits to Observation (CERSAT) on 15 th September

18 SAM2 Data Assimilation System : Incremental Analysis Update method Objectives: Better control timely the analysed trajectory and improve the backward (and the forward) propagation of the information from the observation SAM2 scheme : All operational systems since days cycle - 1 Analysis using 3D modes at time t=4.0-3d model update at time t=4.0 - IAU during 8 days IAU weight function : increasing during 1 day, constant during 6 days and decreasing during 1 day 18

19 SAM2 Data Assimilation System : Comparison Forecast run vs Analysed run (Best) Global ¼ (ORCA025) Forecast Run 1st Run SST RMS T RMS S Analysed Run 2 nd Run Altimetry 19

20 Mercator Data Assimilation System : Temperature and salinity bias correction using Argo Due to Argo network good spatial coverage, it is possible to perform bias correction for Temperature and Salinity. Bias method correction : Step 1. Collection of innovations (T&S) over the past 3 months Step 2. Analysis of the bias (3DVAR method) Step 3. Model correction using a Incremental Analysis Update (IAU) method Salinity innovation at 130m Exemple for Sep.-Nov

21 Mercator Data Assimilation System : Temperature and salinity bias correction using Argo No bias correction Mean misfits on Global ¼ (PSY3) With bias correction T S 21

22 Ongoing and futur works for next systems version Modelisation New NEMO options or modules in global configuration (Agrif, tide, numerical scheme ) Improvment of ocean atmosphere interaction Multi category sea ice model (LIM3) biogeochemistry Assimilation Adaptative observation errors based on Desroziers criteria Assimilation of new observations (SSS, high resolution SST, velocity, ocean color, sea ice ) Atmospheric forcing correction Evolution of the assimilation system including 4D error covariances, anamorphosys transformation, ensemble approach 22

23 Conclusions Computational power increases but systems are more and more complex and costly. Resolution, ensemble methods, biogeochemistry, atmosphere coupling are costly. Highlight system improvement is not straightforward. Error of systems with assimilation are close to the observation errors specified in the system. Independent observations are rare. Intercomparison of systems, new diagnostics, statistics, metrics and link with applications are useful. 23