Project. QUality Information Document. MyOcean V2 System WP 04 GLO MERCATOR

Project QUality Information MyOcean V2 System WP 04 GLO MERCATOR Reference: MYO-WP04-QUID-V2-MERCATOR-v1.0.doc Project N : FP7-SPACE-2007-1 Start Date of project : Work programme topic: SPA.2007.1.1.01 - development of upgraded capabilities for existing GMES fast-track services and related (pre)operational services Duration: 36 Months WP leader: Eric Dombrowsky Issue: V1.0 Contributors : Marie Drévillon, Coralie Perruche, Charly Régnier, Charles Desportes, Jean-Michel Lellouche, Olivier Legalloudec, Gilles Garric, Eric Greiner, Benoît Tranchant, Abdelali El Moussaoui MyOcean version scope : all project versions Approval Date : Approver: Dissemination level: CO

CHANGE RECORD Issue Date Description of Change Author Checked By 1.0 all First version of document My Ocean QUID Page 2/ 101

TABLE OF CONTENTS I External Operational Products... 5 II Production Subsystem description... 8 II.1 The high resolution global system... 8 II.2 The high resolution zoom system... 8 II.3 The intermediate resolution global system... 9 II.4 BIOMER system... 11 III Validation framework... 13 III.1 Methodology... 13 III.2 Disclaimer note on accuracy numbers determination by variable... 14 III.3 List of tested metrics used to assess the system s performance... 15 IV Validation results... 17 IV.1 High resolution hindcast and forecast (HR zoom products)... 17 IV.1.1 North Atlantic: NAT... 17 IV.1.1.1 Temperature... 17 IV.1.1.2 Salinity... 19 IV.1.1.3 SST... 22 IV.1.1.4 SLA... 23 IV.1.2 Tropical Atlantic: TAT... 25 IV.1.2.1 Temperature... 25 IV.1.2.2 Salinity... 26 IV.1.2.3 SST... 28 IV.1.2.4 SLA... 30 IV.1.3 Mediterranean Sea: MED... 31 IV.1.3.1 Temperature... 31 IV.1.3.2 Salinity... 32 IV.1.3.3 SST... 34 IV.1.3.4 SLA... 36 IV.2 High resolution hindcast (HR global) and intermediate resolution forecast (IR global products). 37 IV.2.1 South Atlantic: SAT... 37 IV.2.1.1 Temperature... 37 IV.2.1.2 Salinity... 38 IV.2.1.3 SST... 41 IV.2.1.4 SLA... 42 IV.2.2 Indian Ocean: IND... 43 IV.2.2.1 Temperature... 43 IV.2.2.2 Salinity... 45 IV.2.2.3 SST... 47 IV.2.2.4 SLA... 48 IV.2.3 Arctic Ocean: ARC... 49 IV.2.3.1 Temperature... 49 IV.2.3.2 Salinity... 51 IV.2.3.3 SST... 52 IV.2.3.4 SLA... 52 IV.2.3.5 Sea ice variables... 53 IV.2.4 Southern Ocean: ACC... 54 IV.2.4.1 Temperature... 54 IV.2.4.2 Salinity... 56 IV.2.4.3 SST... 58 My Ocean QUID Page 3/ 101

IV.2.4.4 SLA... 59 IV.2.4.5 Sea ice variables... 60 IV.2.5 South Pacific Ocean: SPA... 61 IV.2.5.1 Temperature... 61 IV.2.5.2 Salinity... 62 IV.2.5.3 SST... 64 IV.2.5.4 SLA... 66 IV.2.6 Tropical Pacific Ocean: TPA... 67 IV.2.6.1 Temperature... 67 IV.2.6.2 Salinity... 68 IV.2.6.3 SST... 70 IV.2.6.4 SLA... 72 IV.2.7 North Pacific Ocean NPA... 73 IV.2.7.1 Temperature... 73 IV.2.7.2 Salinity... 75 IV.2.7.3 SST... 77 IV.2.7.4 SLA... 78 IV.2.7.5 Surface currents: application in the Japan region... 79 IV.3 Intermediate resolution hindcast and forecast (IR global products): GLO... 80 IV.3.1 Temperature... 80 IV.3.2 Salinity... 83 IV.3.3 SST... 85 IV.3.4 SLA... 86 IV.3.5 Sea Ice... 87 IV.3.6 Biogeochemical variables... 87 V Validation synthesis... 93 V.1 Validation methodology... 93 V.2 Validation summary... 93 V.2.1 Temperature and salinity... 93 V.2.2 SST... 93 V.2.3 SLA... 93 V.2.4 Ocean currents... 94 V.2.5 Sea Ice... 94 V.2.6 Biogeochemical variables... 94 VI annex... 95 VI.1 References... 95 VI.2 Maps of regions for data assimilation statistics... 97 VI.2.1 North and tropical atlantic... 97 VI.2.2 Mediterranean Sea... 98 VI.2.3 Global ocean... 99 My Ocean QUID Page 4/ 101

I EXTERNAL OPERATIONAL PRODUCTS The products assessed in this document are referenced in the EPST (DA6) as: GLOBAL_ANALYSIS_FORECAST_PHYS_001_001_c for the physical 14-day hindcast (analyses, updated weekly) GLOBAL_ANALYSIS_FORECAST_PHYS_001_001_d for the physical 7-day forecast (updated daily) These products contain global nominal daily mean fields, on regional standard grids, 43 levels 0-5000m, in Med and Black Sea 40 levels 0-4000m for the following variables: * sea_ice_area_fraction * sea_ice_thickness * sea_ice_x_velocity * sea_ice_y_velocity * sea_surface_height_above_geoid * sea_water_potential_temperature * sea_water_salinity * sea_water_x_velocity * sea_water_y_velocity GLOBAL_ANALYSIS_FORECAST_BIO_001_008_a for the biogeochemical weekly average 14-day hindcast: these products contain weekly fields of Chl-a, dissolved oxygen, nitrate, phosphate, primary production and phytoplankton biomass at global scale on a global standard grid (43 levels 0-5000m, ½ ). The regional standard grids are defined in DR1 as the horizontal grids for the GODAE CLASS1 metrics and are referred to in the following as: GLO for Global ocean (1/2 ), ACC for Southern Ocean and Antarctic (1/4 ), ARC for Arctic ocean(12,5km), IND for Indian Ocean(1/6 ), NPA for North Pacific Ocean(1/6 ), SPA for South Pacific Ocean(1/6 ), My Ocean QUID Page 5/ 101

TPA for Tropical Pacific Ocean(1/4 ), SAT for South Atlantic Ocean (1/6 ), NAT for North Atlantic Ocean(1/6 ), TAT for Tropical Atlantic Ocean(1/4 ), MED for Mediterranean Sea(1/8 ). Products (on standard grids) Weekly 14-day physical hindcast (analysis) _PHYS_001_001_c Daily 7-day physical forecast _PHYS_001_001_d Biogeochemistry _BIO_001_008_a GLO From IR global From IR global From BIOMER NAT From HR zoom From HR zoom NA TAT From HR zoom From HR zoom NA MED From HR zoom From HR zoom NA ARC From HR global From IR global NA SAT From HR global From IR global NA ACC From HR global From IR global NA SPA From HR global From IR global NA TPA From HR global From IR global NA NPA From HR global From IR global NA IND From HR global From IR global NA Table 1: In color: monitoring and forecasting system used to produce each type of WP04 V2 product (region,physical hindcast or forecast, biogeochemical hindcast). The monitoring and forecasting system used for each product is detailed in Table 1, these four systems are calibrated in this document. The various systems will be referred to in this document as: IR Global products: global analyses and forecast performed with a ¼ co nfiguration and interpolated on standard grids of various resolutions. HR Zoom products: 1/12 regional analyses and forecast of the North A tlantic and Mediterranean interpolated on standard grids My Ocean QUID Page 6/ 101

NB: products from these systems were available at V1(weekly hindcast and 14-day forecast) as GLOBAL_ANALYSIS_FORECAST_PHYS_001_001_a HR Global product:1/12 global analyses interpolated on standard grid s BIOMER products:the external products GLOBAL_ANALYSIS_FORECAST_BIO_001_008_adelivered contain weekly fields of Chl-a, dissolved oxygen, nitrate, phosphate, primary production and phytoplankton biomass at global scale on a global standard grid (43 levels 0-5000m, ½ ). My Ocean QUID Page 7/ 101

II PRODUCTION SUBSYSTEM DESCRIPTION Production centre: WP04 Global Production unit: Mercator Ocean II.1 The high resolution global system This system produces ACC, ARC, SAT, IND, SPA, TPA and NPA hindcast products (see Table 1) The high resolution global analysis and forecasting V2 system is based on: A global configuration at 1/12 (configuration: OR CA12_LIM, MERCATOROCEAN system name:psy4v1r3) NEMO version 1.09, ORCA grid, 50 levels 1 to 450m spacing, non tidal free surface, ECMWF daily forcing (CLIO bulk formulation), LIM2 Sea ice Multivariate data assimilation (Kalman Filter kernel) of MyOcean in situ T and S, and along track SLA (with Mean Dynamic Topography MDTfrom RIOv5), together with intermediate resolution SST (1/2 SS T product RTG-SST from NOAA), Incremental analysis update (IAU) of T, S, U, V and SSH centred on the 4 th day of the 7- day assimilation window. The assimilation cycle consists of a first 7-day simulation called guess or forecast, at the end of which the analysis takes place. The IAU correction is then computed and the model is re-run on the same week, progressively adding the correction. The increment is distributed in time with a Gaussian shape which is centered on the 4 th day. This second run is called analyzed or analysis run. This system produces weekly a 14-day hindcast and a 7-day forecast The systemwas started in April 2009 from a 3D climatology of temperature and salinity (Levitus 2005). Several corrections were applied until July 2010. It is operational since February 2011.The 2009 analysis and forecast (up to 7-day forecast) are compared to satellite and in situ observations in DR7. II.2 The high resolution zoom system This system produces NAT, TAT, and MED hindcast and forecast variables (see Table 1) This system s configuration is smaller than the HR global s but it benefits from many scientific updates: My Ocean QUID Page 8/ 101

North Atlantic (north of 20 S) and Mediterranean s ea configuration at 1/12 (NATL12 configuration, MERCATOROCEAN system name: PSY2V4R2) NEMO version 3.1, ORCA grid, 50 levels 1 to 450m spacing, non tidal free surface, ECMWF 3-hourly forcing (CORE bulk formulation), LIM2-EVP Sea ice Multivariate data assimilation (Kalman Filter kernel) of: MyOcean in situ T and S, and along track SLA (with MDT CNES/CLS09 updated with GOCE observations and bias correction) And intermediate resolution SST Reynolds ¼ incl uding AVHRR and AMSRE observations Incremental analysis update (IAU) of T, S, U, V and SSH centred on the 4 th day of the 7- day assimilation window. The main improvements in comparison with the previous system concern the IAU, the adaptive scheme (tuning of the ratio between the variances of the background and the errors of the observations), the extension of the state vector and the introduction of pseudo-observations (innovations equal to zero).their main objective is to overcome the deficiencies of the backgrounderrors, in particular for extrapolated variables. We apply this kind of parameterization on the barotropic height, the variables under the ice, on coastal salinity (runoffs), at the equator on the velocities and on open boundaries (for the zoom at 1/12 ). In addition to the assimilation scheme, a method of bias correction has been developed. This method is based on a variational approach which takes into account cumulative innovations on recent period (typically 3 months) in order to estimate a large scale bias. The observation error for the assimilation of SLA increases near the coast and on the shelves, and near the coastfor the assimilation of SST. The correlation/influence radii for the analysis are updated specifically near the European coast. The boundary conditions of the HR zoom are forced by the intermediate resolution global products hereafter described. This system produces weekly a 14-day hindcast and daily updated (update of the atmospheric forcings) 7-day forecast II.3 The intermediate resolution global system This system produces GLO hindcast and forecastand daily 7-day forecast ACC, ARC, SAT, IND, SPA, TPA, NPA variables (see Table 1) The intermediate resolution global configuration is built on a configuration that is extensively used in the ocean modeling community. Its operational products feed the open boundaries of the HR zoom and give the physical forcings for the BIOMER system. It also benefits from many scientific updates with respect to the HR global, but does not use the following scientific improvements: The assimilation of Reynolds ¼ SST product My Ocean QUID Page 9/ 101

The update of the CNES-CLS09 MDT with GOCE and bias correction The observation error for the assimilation of SLA increases near the coast and on the shelves, and near the coast for the assimilation of SST. The correlation/influence radii for the analysis are updated specifically near the European coast. In other words, the components of the IR global system are: A global ¼ horizontal grid (configuration: ORCA02 5_LIM, MERCATOROCEAN system name:psy3v3r1) NEMO version 3.1, ORCA grid, 50 levels 1 to 450m spacing, non tidal free surface, ECMWF 3-hourly forcing (CORE bulk formulation), LIM2-EVP Sea ice Multivariate data assimilation (Kalman Filter kernel) of: MyOceanin situ T and S, and along track SLA (with MDT CNES/CLS09) together with intermediate resolution SST (1/2 SS T product RTG-SST from NOAA), Incremental analysis update (IAU) of T, S, U, V and SSH centred on the 4 th day of the 7- day assimilation window. The main improvements in comparison with the previous system concern the IAU, the adaptive scheme(tuning of the ratio between the variances of the background and the errors of the observations), the extension of the state vector and the introduction of pseudo-observations (innovations equal to zero).their main objective is to overcome the deficiencies of the backgrounderrors, in particular for extrapolated variables. We apply this kind of parameterization on the barotropic height, the variables under the ice, on coastal salinity (runoffs), at the equator on the velocities and on open boundaries (for the zoom at 1/12 ). In addition to the assimilation scheme, a method of bias correction has been developed. This method is based on a variational approach which takes into account cumulative innovations on recent period (typically 3 months) in order to estimate a large scale bias.. This system produces weekly a 14-day hindcast and daily updated (update of the atmospheric forcings) 7-day forecast System name domain resolution Model version Assimilation software version Assimilated observations Inter dependencies Status of production IR global global ¼ on the horizontal, 50 levels on the vertical ORCA025 LIM2 EVP NEMO 3.1 3-hourly atmospheric forcing from ECMWF, SAM2 (SEEK Kernel) + IAU and bias correction RTG-SST, SLA from Jason 1, Jason 2 and Envisat, in situ profile from CORIOLIS Weekly Daily update of atmospheric forcings for 7-day forecast My Ocean QUID Page 10/ 101

bulk CORE HR global global 1/12 on the horizontal, 50 levels on the vertical ORCA12 LIM2 NEMO 1.09 Daily atmospheric forcing from ECMWF, bulk CLIO SAM2 (SEEK Kernel) + IAU RTG-SST, SLA from Jason 1, Jason 2 and Envisat, in situ profile from CORIOLIS Weekly HR zoom Tropical, North Atlantic and Mediterranean Sea region 1/12 on the horizontal, 50 levels on the vertical NATL12 LIM2 EVP NEMO 3.1 3-hourly atmospheric forcing from ECMWF, bulk CORE SAM2 (SEEK Kernel) + IAU and bias correction + error covariance higher near coast Reynolds SST AVHRR_AMSRE, SLA from Jason 1, Jason 2 and Envisat, in situ profile from CORIOLIS Open boundary conditions from IR global Weekly Daily update of atmospheric forcings for 7-day forecast BIOMER global 1 on the horizontal, 50 levels on the vertical PISCES, NEMO 2.3, offline none none Two weeks hindcast with IR global forcing degraded at 1 1-week average two weeks back in time. Table 2: synthetic description of WP04 V2 production systems II.4 BIOMER system This system is used to produce GLOBAL_ANALYSIS_FORECAST_BIO_001_008_a biogeochemical hindcast in the GLO region (see Table 1) The BIOMER system (El Moussaoui et al., 2011) that is being calibrated here is a global hindcast biogeochemical simulation forced by a physical reanalysis. The biogeochemical model used is PISCES. It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It simulates the biogeochemical cycle of oxygen, carbon and the main nutrients controlling marine biological productivity: nitrate, ammonium, phosphate, silicic acid and iron. The model distinguishes four plankton functional types based on size: two phytoplankton groups (small = nanophytoplankton and large = diatoms) and two zooplankton groups (small = microzooplankton and large = mesozooplankton). For phytoplankton, the prognostic variables are total biomass, iron, chlorophyll and silicon (diatoms) contents. For zooplankton, total biomass is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES traces three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon, as well as biogenic silica and calcite. The model simulates dissolved inorganic carbon and total alkalinity. My Ocean QUID Page 11/ 101

The distinction of two phytoplankton size classes, along with the description of multiple nutrient colimitations allows the model to represent ocean productivity and biogeochemical cycles across major biogeographic ocean provinces (Longhurst, 1998). PISCES has been successfully used in a variety of biogeochemical studies (e.g. Bopp et al. 2005; Gehlen et al. 2006; 2007; Schneider et al. 2008; Steinacher et al. 2010; Tagliabue et al. 2010). Biogeochemical simulations were initialized with corresponding climatologies for nutrients (WOA 2001, Conkright et al. 2002), carbon cycle (GLODAP, Key et al. 2004) and, in the absence of corresponding data products, with model fields for dissolved iron and dissolved organic carbon The high demand in computing time of online global biogeochemical simulations at increased spatial resolution prompted the choice of off-line coupling between ocean physics and biogeochemistry. With focus on the long term goal of implementing biogeochemistry to the Mercator real-time physical system at 1/12 (analysis), we opted for the spatia l degradation of the physical fields with the use of the tool DEGINT (Aumont et al. 1998). For the time being, BIOMER physical forcings come from IR global outputs. The degraded physical model is built from the original (or parent ) model by averaging fields of advection, turbulent diffusion, and tracers onto squares of four boxes along longitude by four boxes along latitude. The vertical resolution is not degraded. The horizontal resolution (number of grid cells) of the degraded model is only one sixteenth of that of the parent model. The degradation procedure is designed to conserve both water fluxes at the boundaries of each degraded grid cell. The optimal forcing frequency for the biogeochemical model was tested by comparing 1, 3 and 7-day forcing frequencies. Modelled chlorophyll-a fields were not significantly different and a 7-day forcing was adopted as input of PISCES. This time period is in accordance with the time scale of physical processes considered in a simulation at ¼ ( eddy-p ermitting ). My Ocean QUID Page 12/ 101

III VALIDATION FRAMEWORK III.1 Methodology The calibration task mainly consists in comparisons of model outputs with available observations (collocation in space and time). The second most important stage is to check the stability of the system and the realism of its physics over a time period of at least one year. Data assimilation statistics and CLASS4 are used to assess the accuracy of the products whenever observations are available. Climatologies and literature values are used to check the consistency of poorly observed variables. Intercomparisons (CLASS1 visual inspection) and accuracy comparisons are used to ensure the non-regression of V2 with respect to V1. The calibration metrics that were applied at V2 are: the statistics (bias and rms) in the observations space of: Sea Surface Height (SSH): data assimilation statistics (on track SLA minus model equivalent SSH-Mean Dynamic Topography MDT), obs-forecast, obsanalysis Sea Surface Temperature (SST): assimilation statistics (RTG-SST, or L2P minus model equivalent) Temperature and Salinity profiles T&S: class4 statistics (CORIOLIS or GTS T&S profiles minus hindcast daily average outputs) Sea ice: class4 statistics (CERSAT sea ice concentrations minus hindcast daily average outputs) Comparisons with independent data (drifters, tide gauges). NB: comparisons with drifters are still performed but the observations dataset may be biased (Grodsky et al, 2011) the physics in the model space (not shown in this report, see DR5 and DR7): Seasonal signals (class1 comparisons with climatology) High frequencies (class 2 high frequency moorings) Equatorial waves with hovmueller diagrams Volume transports with respect to litterature (class 3) The systematic biases, the modes and tendencies are also quantified. My Ocean QUID Page 13/ 101

A difficulty for the calibration of this V2 is that the WP04 products are a patchwork of products of different origins (from different monitoring and forecasting systems operated by Mercator Océan). The user will now benefit from high resolution V2 hindcast products in all individual ocean basins regions. The intermediate resolution global system products that were available in V1 are still available with the GLO V2 products. It is important that the user still can have access to these products as they are used as boundary conditions for the HR products in the North Atlantic and Mediterranean, and as physical forcing for the biogeochemistry products. Moreover, the HR global products are of good quality in terms of accuracy with respect to in situ observations but there is still room for improvement (especially in terms of bias correction as it will be shown in this document). The user will thus be able to select either intermediate resolution hindcast products (1/2, coming from a ¼ configuration) wit h very good accuracy or high resolution hindcast products with higher energy levels but relatively degraded accuracy. Considering the large number of zones and variables, only the most pertinent metrics are displayed in this document. The accuracy metrics are the most pertinent in the context of using the scientific quality information to produce this scientific quality information (QUID). For a few zones we display examples of CLASS3 and CLASS1 validation that is performed on a regular basis (DR8) on the products. III.2 Disclaimer note on accuracy numbers determination by variable 3D T and S accuracy numbers are computed with CLASS4, whereas SLA and SST accuracy numbers are an estimate based on data assimilation statistics. In all cases these numbers are an estimate of the likely error in most sub-regions of one given zone (NAT, TAT, MED, etc ) and not a maximum error on the zone. In the average estimate, sub-zones or depths were there are a large number of in situ information weight the same as zones with only ARGO profiles. There is no quantitative accuracy estimate for 3D ocean currents in this document: Further studies are necessary on the potential bias of near surface drifter observations Current meter comparisons are planned in the future Their consistency is ensured by regular comparisons to observed products. A general evaluation of surface currents is given in the summary There is no quantitative accuracy estimate for Sea Ice and biogeochemistry variablesbut orders of magnitude of departures from observations are given. My Ocean QUID Page 14/ 101

III.3 List of tested metrics used to assess the system s performance Most of the scientific assessment of HR global, HR zoom and IR global is summarized by the table of Metrics (Table 3). Moreover, the products issued from the three physical systems are regularly intercompared in the Mercator Océan scientific validation bulletins QuO Va Dis? (available on request from qualif@mercator-ocean.fr). In this document we will emphasize accuracy metrics (CLASS4 and data assimilation scores) when available. Results are presented following each type of product (region, variable). variable Region Type of metric MERSEA/GODAE classification Reference observational dataset 3D temperature Global, and regional basins Error=model-obs Time evolution of RMS error on 0-500m CLASS4 MyOcean: CORIOLIS T (z) profiles Vertical profile of mean error. 3D salinity Global, and regional basins Error=model-obs Time evolution of RMS error on 0-500m CLASS4 MyOcean: CORIOLIS S(z) profiles Vertical profile of mean error. Sea level anomaly (SLA) Global, regional basins and Error=obs-model Time evolution of RMS and mean error Data statistics assimilation MyOcean: On track AVISO sla observations from Jason1 Jason2 and Envisat Sea height surface At tide gauges (Global but near coastal regions) Error=model-obs Time series correlation and RMS error CLASS4 GLOSS, BODC, Imedea, WOCE, OPPE and SONEL Sea Surface Temperature SST Global, regional basins and Error=obs-model Time evolution of RMS and mean error Data statistics assimilation RTG-SST (NOAA), Reynolds AVHRR ¼ for HR zoom Surface layer zonal current U Global, regional basins and Error=model-obs Mean error and vector correlation CLASS4 SVP drifting buoys from CORIOLIS Surface meridional current V layer Global, regional basins and Error=model-obs Mean error and vector correlation CLASS4 SVP drifting buoys from CORIOLIS Sea ice concentration Antarctic and Arctic regions Visual inspection of seasonal and interannual signal CLASS1 CERSAT Sea ice concentration My Ocean QUID Page 15/ 101

Sea ice concentration Antarctic and Arctic regions Error=obs-model Time evolution of RMS and mean error CLASS4 CERSAT Sea ice concentration Sea ice concentration Antarctic and Arctic regions Time evolution of sea ice extent CLASS3 NSIDC sea ice extent from SSM/I observations Monthly 3D T and S Global Visual inspection of seasonal and interannual signal CLASS1 Levitus 2005 monthly climatology of temperature and salinity T, S, U, V, SSH, atmospheric forcings Global, CLASS2 locations at Visual inspection of high frequencies, comparisons with observed time series CLASS2 MyOcean: CORIOLIS SLA and SST Tropical basins Visual inspection of hovmueller diagrams and comparisons with satellite observations CLASS1 AVISO and RTG-SST 3D U and V Global Visual inspection of volume transports through sections CLASS3 litterature Chlorophyll a Global Visual comparison CLASS1 MyOcean: Globcolour (ACRI) OCEANCOLOUR_GLO_L3_L4_OBSERVATIONS_009_001 Nitrates Global Visual comparison CLASS1 WOA 2005 Phosphates Global Visual comparison CLASS1 WOA 2005 Oxygen Global Visual comparison CLASS1 WOA 2005 Primary Productivity Global Visual comparison CLASS1 Globcolour ACRI (Antoine and Morel, 1996) VGPM algorithm (Behrenfeld and Falkowski, 1997a) Chlorophyll a Global Visual comparison, temporal correlation, standard deviation Nitrates Global Visual comparison, temporal correlation, standard deviation Phosphates Global Visual comparison, temporal correlation, standard deviation Oxygen Global Visual comparison, temporal correlation, standard deviation CLASS2 BATS, HOT (Steinberg et al., 2001; Karl and Lukas, 1996) CLASS2 BATS, HOT (Steinberg et al., 2001; Karl and Lukas, 1996) CLASS2 BATS, HOT (Steinberg et al., 2001; Karl and Lukas, 1996) CLASS2 BATS, HOT (Steinberg et al., 2001; Karl and Lukas, 1996) Table 3: list of metrics that were computed to assess the systems that produce WP04 V2 products: HR global, HR zoom, IR global, BIOMER My Ocean QUID Page 16/ 101

IV VALIDATION RESULTS IV.1 High resolution hindcast and forecast (HR zoom products) IV.1.1 IV.1.1.1 North Atlantic: NAT Temperature 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of NAT 0-5m NAT 5-100m NAT 100-300m NAT 300-800m NAT 800-2000m NAT 2000-5000m NAT 0-5000m 0.85 0.01 0.95 0.00 0.95-0.01 0.97-0.03 1.03-0.15 1.27-0.20 1.29-0.22 1.27-0.22 0.74-0.07 0.94-0.08 0.98-0.08 0.97-0.08 0.62 0.10 0.78 0.11 0.80 0.12 0.81 0.11 0.33 0.15 0.37 0.15 0.38 0.14 0.37 0.14 0.09-0.09 0.12-0.12 0.13-0.13 0.14-0.13 0.53 0.10 0.64 0.10 0.66 0.10 0.66 0.09 Table 4:NAT RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In NAT zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics. My Ocean QUID Page 17/ 101

3D temperature accuracy results are synthesized in Table 4. One can note that the biases computed from average departures from the observations are generally small (< 0.2 K). The RMS errors are large near the thermocline where the variability is higher. The surface is slightly too cold on average, while the subsurface (until 300m) is generally warmer than the observations. This bias is especially important during summer and is linked with a lack of stratification in the model (summer heat penetrates too deep into the ocean). Figure 1:HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: NAT RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. Intercomparisons between Mercator Océan systems in the NAT zone (Figure 1) show the consistency of the temperature RMS errors. The HR zoom performs better than the HR global in that zone. In the HR global, a cold bias is diagnosed on the whole water column. In the IR global the cold bias near the My Ocean QUID Page 18/ 101

surface is far more important (around 0.3 K). It is important to bear in mind that only few observations are available deeper than 2000m, thus the estimate for the layer 2000-5000m is less reliable. Moreover, statistics for the three systems are computed on three different time periods, and the HR zoom period is the shortest and does not favour this system (period of larger biases). IV.1.1.2 Salinity The salinity biases (Table 5) generally stay below 0.02 psu, except at the surface where a fresh bias takes place, and between 800m and 2000m where a salty bias can be diagnosed in HR zoom products. The RMS error increases with the forecast range, especially between 0 and 300 m. The comparison with HR global (Figure 2) shows that the RMS error is lower in HR zoom with respect to all other systems at all depths except at the surface and between 800 and 2000m. 3DS (psu) NAT 0-5m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.291 0.032 0.344 0.061 0.345 0.058 0.351 0.060 NAT5-100m NAT 100-300m NAT 300-800m NAT 800-2000m NAT 2000-5000m NAT 0-5000m 0.176-0.011 0.202-0.003 0.207-0.006 0.203-0.004 0.117-0.006 0.139-0.001 0.145-0.002 0.145-0.001 0.089 0.003 0.105 0.006 0.107 0.006 0.109 0.006 0.043 0.003 0.048 0.003 0.049 0.002 0.047 0.002 0.053-0.053 0.058-0.057 0.057-0.057 0.058-0.058 0.079 0.002 0.091 0.003 0.094 0.003 0.093 0.003 Table 5: NAT RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In NAT zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics. My Ocean QUID Page 19/ 101

Figure 2:HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: NAT RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. Unrealistic salinities were diagnosed in V1 products on the continental shelves, and in particular in the Celtic Seas, the North Sea and the Bay of Biscay. The HR zoom was upgraded in the beginning of July 2011 in order to correct these biases. CLASS1 diagnostics such as in Figure 3 allowed us to control the improvement of the HR zoom upgraded system. The V0 HR zoom system had been extensively used and validated in these regions and was used as a reference. The V1 products and the V0 products were not consistent, while the upgraded V1 products were. Independent in situ observations from MyOcean in situ TAC were used to validate the upgrade thanks to CLASS4 and My Ocean QUID Page 20/ 101

CLASS2 (Figure 4) comparisons. The HR zoom system calibrated in this document corresponds to the upgraded V1 version. Figure 3: salinity differences in the North East Atlantic (psu) between HR zoom V1 (upper panel) V0and HR zoom V1_upgraded (lower panel) V0. On the left average difference on the period 200910-2010-06, on the right RMS differences for the same period. My Ocean QUID Page 21/ 101

Figure 4: Comparison of various experiments (lower panel) with a salinity time series (upper panel) from ferry box observations (red dot on the map). The black curve stands for HR zoom V1, the red curve for HR zoom V1_updated (and V2), green is HR global V2, blue is HR zoom V0. IV.1.1.3 SST Intercomparison of SST data assimilation statistics over the year 2011 shows a very good consistency between HR zoom, HR global and IR global, as shown on Figure 5. The HR zoom display the best performance except in the Dakar, Gulf Stream 1 and Florida Straits boxes (see annex). These regions are small and not very representative of the whole NAT zone. The Newfoundland-Iceland zone RMS error is also a little large in HR zoom. HR zoom assimilates AVHRR-AMSRE ¼ analysis from Reynolds, while IR and HR global assimilate RTG-SST at ½. The latter happens to be biased in the Northern part of the Atlantic, thus this gives us even more confidence in HR zoom SST statistics and performance in general. No bias (slightly negative but not significant) is detected on average over the region (Table 6). The data assimilation statistics (innovations) are representative of a 3-day forecast. The hindcast accuracy could have been computed from residuals but we kept innovations as they are more representative of unconstrained regions. My Ocean QUID Page 22/ 101

Figure 5: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the North Atlantic NAT region. For each region from bottom to top, the bars refer respectively to IR global (blue), HR zoom (cyan), and HR global (green). The geographical location of regions is displayed in the annex. SST (K) Hindcast 3-day forecast RMS of RMS of NAT 0.7 K -0.03 K 0.7 K -0.03 K Table 6: NAT RMS and average SST s in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. Statistics from Jan uary 2011to August 2011 are used to build these regional estimates. IV.1.1.4 SLA The same consistency is observed on Figure 6 in terms of SLA data assimilation. On average between the three available satellites, HR zoom biases do not exceed 1.5 cm in all regions. RMS errors of all systems are generally lower than 10 cm, depending on the energetic level of the region (higher errors in regions of high spatiotemporal variability). Here again HR zoom is slightly different from the other systems as it uses a different MDT, which can explain local differences and smaller biases My Ocean QUID Page 23/ 101

On average over the NAT zone (Table 7) we note a small positive bias of around 1 cm (especially in summer and consistent with a cold bias at the surface). Figure 6: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the Tropical and North Atlantic. The scores are averaged for all available satellite along track data (Jason 1, Jason 2 and Envisat). For each region from bottom to top, the bars refer respectively to IR global (blue), HR zoom (cyan), and HR global (green). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of NAT 10 cm +1 cm 10 cm +1cm Table 7:NAT RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 24/ 101

IV.1.2 IV.1.2.1 Tropical Atlantic: TAT Temperature The behaviour of HR zoom in the Tropical Atlantic (Table 8) is very similar to the North Atlantic in terms of 3D temperature except a cold bias in the 2000-5000m layer. Again, statistics in that particular layer are not really trusted. As in the NAT zone, a cold bias appears at the surface and a warm bias in subsurface until 300m. The surface bias of the TAT zone is an order of magnitude stronger than in the NAT region. As can be seen in Figure 7, the biases and RMS errors are reduced in HR zoom with respect to HR global and IR global (except in the dubious 2000-5000m layer). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of TAT 0-5m TAT 5-100m TAT 100-300m TAT 300-800m TAT 800-2000m TAT 2000-5000m TAT 0-5000m 0.60 0.10 0.69 0.13 0.72 0.14 0.74 0.13 1.01-0.12 1.17-0.14 1.19-0.15 1.20-0.15 0.74-0.05 0.88-0.06 0.91-0.06 0.92-0.07 0.51 0.09 0.60 0.09 0.62 0.09 0.62 0.09 0.27 0.09 0.30 0.09 0.30 0.09 0.30 0.09 0.26 0.23 0.26 0.22 0.27 0.23 0.27 0.23 0.49 0.09 0.56 0.09 0.58 0.09 0.58 0.13 Table 8:TAT RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In TAT zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics My Ocean QUID Page 25/ 101

Figure 7:HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: TAT RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.1.2.2 Salinity A fresh bias can be diagnosed in Table 9 and Figure 8in the Tropical Atlantic from the surface to 100 m, in link with unrealistically large convective precipitations in the ECMWF atmospheric forcings. Between 100m and 2000m the average bias is small, of the order of 10-3 psu. The 2000-5000m layer exhibits again relatively large errors towards a salty bias. This (probably artificially) reduces the impact of the surface fresh bias in the water column average. My Ocean QUID Page 26/ 101

3DS (psu) TAT 0-5m TAT 5-100m TAT 100-300m TAT 300-800m TAT 800-2000m TAT 2000-5000m TAT 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.297 0.013 0.350 0.018 0.358 0.020 0.360 0.021 0.187 0.023 0.217 0.036 0.220 0.035 0.217 0.037 0.126-0.006 0.143-0.005 0.145-0.005 0.147-0.005 0.084 0.000 0.094 0.000 0.095 0.000 0.097 0.000 0.039 0.003 0.043 0.002 0.043 0.002 0.042 0.002 0.027-0.020 0.029-0.021 0.028-0.021 0.028-0.021 0.081 0.002 0.091 0.002 0.093 0.002 0.093 0.002 Table 9:TAT RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In NAT zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics My Ocean QUID Page 27/ 101

Figure 8:HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: TAT RMS (upper panel) and average (lower panel) salinity s in psu(observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m IV.1.2.3 SST In the TAT region the bias is generally positive and less than 0.25 K as can be seen on Figure 9. RMS errors vary around 0.5 K, and the average over the zone is 0.6 K as indicated in Table 10. On average over the TAT zone the cold bias is around 0.1 K. My Ocean QUID Page 28/ 101

Figure 9: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the TAT region. For each region from bottom to top, the bars refer respectively to IR global (blue), HR zoom (cyan), and HR global (green). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of TAT 0.6 K +0.1 K 0.6 K +0.1 K Table 10: TAT RMS and average SST s in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. Statistics from Jan uary 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 29/ 101

IV.1.2.4 SLA Figure 10: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the TAT region. The scores are averaged for all available satellite along track data (Jason 1, Jason 2 and Envisat). For each region from bottom to top, the bars refer respectively to IR global (blue), HR zoom (cyan), and HR global (green). The geographical location of regions is displayed in the annex. HR zoom SLA innovations in the same subregions of the Tropical Atlantic exhibit a 1 cm bias on average (Table 11, Figure 10). As in the NAT region this bias is consistent with the cold surface temperature bias. RMS errors are very similar among the three systems. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of TAT 4 cm +1 cm 4 cm +1cm Table 11: TAT RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 30/ 101

IV.1.3 IV.1.3.1 Mediterranean Sea: MED Temperature 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of MED 0-5m MED 5-100m MED 100-300m MED 300-800m MED 800-2000m MED 2000-5000m MED 0-5000m 0.75 0.25 1.00 0.19 1.05 0.25 1.11 0.26 0.85-0.21 1.19-0.47 1.26-0.51 1.33-0.60 0.24 0.02 0.37 0.00 0.40 0.00 0.41-0.02 0.17 0.12 0.23 0.12 0.24 0.12 0.24 0.13 0.19 0.14 0.19 0.12 0.19 0.12 0.19 0.13 0.19 0.14 0.19 0.12 0.19 0.12 0.19 0.13 0.32 0.08 0.44 0.06 0.46 0.05 0.48 0.04 Table 12: MED RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In MED zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics The lack of stratification can also be diagnosed in the Mediterranean Sea, as can be seen in Table 12 and in Figure 11. The cold bias of around 0.25 K at the surface is stronger than in the Atlantic. In the MED region, the HR zoom temperatures are significantly more accurate than HR global and IR global temperatures. My Ocean QUID Page 31/ 101

Figure 11: HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: MED RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.1.3.2 Salinity A fresh bias of 0.1 psu can be diagnosed at the surface (Table 13), while there is no significant bias in subsurface. The salinity accuracy is significantly higher in HR zoom products than in global products, as can be seen in Figure 12. My Ocean QUID Page 32/ 101

3DS (psu) MED 0-5m MED 5-100m MED 100-300m MED 300-800m MED 800-2000m MED 2000-5000m MED 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.253 0.096 0.311 0.132 0.322 0.135 0.310 0.138 0.173 0.009 0.213 0.014 0.222 0.011 0.220 0.016 0.073 0.001 0.091 0.004 0.094 0.003 0.091 0.006 0.028 0.005 0.033 0.007 0.036 0.008 0.038 0.008 0.020 0.000 0.018-0.001 0.019 0.000 0.019 0.000 0.020 0.000 0.018-0.001 0.019 0.000 0.019 0.000 0.066 0.004 0.081 0.005 0.084 0.005 0.083 0.006 Table 13: MED RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In MED zone hindcast and forecast are produced with HR zoom, and July and August 2011 were used to compute the statistics My Ocean QUID Page 33/ 101

Figure 12: HR global (blue), HR zoom (brown) and IR global (green) hindcast accuracy intercomparison: MED RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.1.3.3 SST Consistently with 3D temperature accuracy estimates, a cold bias (Table 14) of 0.3 K on average (Figure 13) is diagnosed at the surface with Reynolds ¼ SST assimilation statistics in the MED region. The Adriatic Sea, the Aegean Seas, and the Rhodes regions display more bias and higher RMS errors. My Ocean QUID Page 34/ 101

Figure 13: SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 for HR zoom in the MED region. The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of MED 0.75 K +0.3 K 0.75 K +0.3 K Table 14: MED RMS and average SST s in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. Statistics from Jan uary 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 35/ 101

IV.1.3.4 SLA Figure 14: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 for HR zoom in the MED region. The scores are averaged for all available satellite along track data (Jason 1, Jason 2 and Envisat). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of MED 7 cm +4 cm 7 cm +4 cm Table 15: MED RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. The SLA bias is more important in the MED region than in the Atlantic, and reaches 4 cm on average. This high value is due to strong biases of more than 5 cm in the Adriatic Sea, Aegean Sea and Rhodes regions. These regions are shallow and close to the coast, in consequence the SLA observation error is high these zones in the HR zoom. Thus these regions are poorly constrained and biases are observed. My Ocean QUID Page 36/ 101

IV.2 High resolution hindcast (HR global) and intermediate resolution forecast (IR global products) IV.2.1 IV.2.1.1 South Atlantic: SAT Temperature In the south Atlantic SAT zone, a cold bias (less than 0.2 K) is observed over the whole water column in the HR global hindcast product (Table 16). The forecast from the IR global are less biased than the hindcast from HR global (Figure 15), especially between 5 and 300 m, where most in situ observations lie and where the bias correction is effective. 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of SAT 0-5m SAT 5-100m SAT 100-300m SAT 300-800m SAT 800-2000m SAT 2000-5000m SAT 0-5000m 0.60 0.19 0.61 0.11 0.63 0.10 0.67 0.10 1.10 0.14 0.97-0.02 1.00-0.03 1.04-0.03 0.99 0.22 0.86-0.02 0.88-0.03 0.93-0.03 0.60 0.19 0.52 0.08 0.54 0.07 0.57 0.08 0.20 0.12 0.19 0.12 0.20 0.12 0.20 0.12 0.26 0.04 0.25 0.16 0.25 0.16 0.28 0.20 0.54 0.15 0.48 0.09 0.49 0.08 0.52 0.08 Table 16: SAT RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In SAT zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 37/ 101

Figure 15: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: SAT RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.1.2 Salinity A fresh bias (less than 0.05 psu) is diagnosed from the surface to 2000 m in the HR global hindcast. Biases are corrected in the IR global forecast, as can be seen in both Table 17 and Figure 16. My Ocean QUID Page 38/ 101

3DS (psu) SAT 0-5m SAT 5-100m SAT 100-300m SAT 300-800m SAT 800-2000m SAT 2000-5000m SAT 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.213 0.050 0.220 0.019 0.226 0.019 0.230 0.028 0.205 0.007 0.170-0.004 0.174-0.007 0.179-0.007 0.165 0.025 0.139 0.006 0.143 0.005 0.146 0.005 0.093 0.027 0.076 0.007 0.078 0.007 0.081 0.007 0.046 0.011 0.033 0.000 0.033 0.000 0.034 0.000 0.015-0.012 0.009-0.008 0.010-0.008 0.010-0.007 0.093 0.017 0.076 0.003 0.078 0.002 0.080 0.003 Table 17: SAT RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In SAT zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 39/ 101

Figure 16: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: SAT RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 40/ 101

IV.2.1.3 SST As can be seen in Figure 17, most subregions of the SAT zone display a cold bias at the surface of around 0.2 K in the HR global hindcast while no significant bias appears in the IR global. The RMS errors are consistent between the two global systems and reach 0.6 K. Figure 17: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the SAT region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of SAT 0.6 K +0.2 K 0.65 K +0.04 K Table 18: SAT RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 41/ 101

On average over the SAT region, the RMS error will thus be 0.6K for the hindcast and 0.65K for the forecast, while a bias of 0.2 K is noted in the hindcast (Table 18). IV.2.1.4 SLA As can be seen in Table 19 and Figure 18 the hindcast and forecast are close to the SLA observations. A very small positive bias of 0.3 cm (consistent with a cold bias) can be diagnosed, while the RMS error is less than 5 cm in most of the South Atlantic, and higher (near 15 cm) in regions of mesoscale activity such as the Falkland and Agulhas currents. Figure 18: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the SAT region. The scores are averaged for all available satellite along track data (Jason 1, Jason 2 and Envisat). For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of SAT 8 cm +0.3 cm 7.2 cm +0.4 cm Table 19: SAT RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 42/ 101

IV.2.2 IV.2.2.1 Indian Ocean: IND Temperature In the Indian ocean region, the cold bias is close to 0.1 K in the IR global forecast and 0.15 K in the HR global (Table 20 and Table 20). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of IND 0-5m 0.40 0.12 0.40 0.08 0.41 0.09 0.43 0.10 IND 5-100m IND 100-300m IND 300-800m IND 800-2000m 1.06 0.25 1.03 0.11 1.06 0.11 1.10 0.13 0.87 0.20 0.89 0.12 0.92 0.12 0.95 0.12 0.39 0.14 0.37 0.10 0.38 0.10 0.39 0.10 0.32 0.14 0.26 0.10 0.26 0.10 0.26 0.10 IND20 00-5000m 0.09-0.02 0.26 0.10 0.26 0.10 0.26 0.10 IND0-5000m 0.49 0.12 0.47 0.10 0.48 0.10 0.49 0.10 Table 20: IND RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In IND zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 43/ 101

Figure 19: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: IND RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 44/ 101

IV.2.2.2 Salinity As diagnosed in Table 21, the 3D salinity hindcast exhibit a fresh bias at the surface (0.02 psu) and a salty bias (-0.03 psu) in subsurface.the IR global forecast bias are very small except the fresh surface bias (Figure 20). 3DS (psu) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of IND 0-5m 0.238 0.019 0.247 0.038 0.255 0.040 0.262 0.040 IND 5-100m IND 100-300m IND 300-800m IND 800-2000m 0.217-0.038 0.209-0.003 0.213-0.002 0.216-0.003 0.132-0.025 0.135-0.012 0.136-0.012 0.139-0.013 0.060-0.008 0.055 0.002 0.056 0.002 0.057 0.002 0.029 0.006 0.026 0.000 0.026 0.000 0.026 0.000 IND20 00-5000m 0.029 0.006 0.026 0.000 0.026 0.000 0.026 0.000 IND0-5000m 0.075-0.004 0.073-0.001 0.074-0.001 0.075-0.001 Table 21: IND RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In IND zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 45/ 101

Figure 20: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: IND RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 46/ 101

IV.2.2.3 SST As seen in Table 22 and Figure 21, in the Indian Ocean the cold bias is 0.1K for both hindcast and forecast, consistently with 3D T bias estimation. The RMS error is close to 0.5 K. Figure 21: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the IND region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of IND 0.5 K +0.1 K 0.55 K +0.1 K Table 22: IND RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 47/ 101

IV.2.2.4 SLA The SLA bias is close to zero or negative in the Indian ocean, as seen in Figure 22 and Table 23.The RMS error is around 5 cm. Figure 22: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the IND region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of IND 5.1 cm -0.3 cm 5.4 cm -0.35cm Table 23: IND RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2, Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 48/ 101

IV.2.3 IV.2.3.1 Arctic Ocean: ARC Temperature In the Arctic Ocean a cold bias is diagnosed on the whole water column (Table 24). However one must keep in mind that very few in situ observations are available to constrain the systems in this region, the bias correction does not reduce the bias in the IR global forecast as in the other regions (Figure 23). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of ARC 0-5m ARC 5-100m ARC 100-300m ARC 300-800m ARC 800-2000m ARC 2000-5000m ARC 0-5000m 0.59 0.15 0.59-0.03 0.61 0.16 0.65 0.00 1.02 0.19 1.01 0.05 1.04 0.06 1.07 0.06 0.95 0.19 0.96 0.15 0.98 0.10 1.02 0.06 0.52 0.11 0.53 0.15 0.55 0.10 0.57 0.06 0.26 0.11 0.24 0.15 0.25 0.10 0.25 0.06 0.23 0.17 0.29 0.25 0.29 0.25 0.29-0.55 0.52 0.17 0.52 0.25 0.53 0.25 0.55-0.55 Table 24: ARC RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In ARC zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 49/ 101

Figure 23: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: ARC RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 50/ 101

IV.2.3.2 Salinity Salty biases appear in HR global (up to -0.03 psu) except at the surface. On the contrary, salinity biases are reduced in the IR global forecast with respect to the HR global hindcast, as can be seen in Figure 24. 3DS (psu) ARC 0-5m ARC 5-100m ARC 100-300m ARC 300-800m ARC 800-2000m ARC 2000-5000m ARC 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.261 0.008 0.293-0.005 0.296-0.008 0.304-0.011 0.211-0.019 0.199-0.012 0.201-0.012 0.205-0.013 0.145-0.028 0.130 0.002 0.131 0.001 0.133 0.002 0.075-0.005 0.064 0.003 0.065 0.003 0.067 0.004 0.034-0.001 0.026-0.001 0.027-0.001 0.027-0.001 0.055-0.030 0.055-0.030 0.055-0.030 0.056-0.030 0.082-0.006 0.073 0.000 0.074 0.000 0.076 0.000 Table 25:ARC RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In ARC zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 51/ 101

Figure 24: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: ARC RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.3.3 SST No observations IV.2.3.4 SLA No observations My Ocean QUID Page 52/ 101

IV.2.3.5 Sea ice variables In the Arctic Ocean Sea ice products from the HR global are generally less realistic than IR global products (IR global Sea Ice hindcast is available in the GLO region). The HR global Sea ice cover is overestimated throughout the year, as can be seen in Figure 25. Both HR global hindcast and IR global forecast overestimate the sea ice cover in boreal summer, the signal in the Marginal Seas being well reproduced (especially in IR global) but the Sea Ice front induces large discrepancies in the Sea ice concentration. The accumulation of multiannual Sea Ice in the Central arctic is overestimated by the models and especially by HR global all year long as synthesized in Table 26. Figure 25: Sea ice area (upper panel, 10 3 km2) and extent (lower panel, 10 3 km2) in the Arctic Ocean in IR global products (blue line), HR global products (black line) and SSM/I observations (red line) for a one year period ending in June 2011 Sea ice concentration (%) ARC Hindcast Description of +50 to locally 100% in marginal seas, +20% all year long in central Arctic. 3-day forecast Description of +50 to locally 100% marginal seas, + 10% in Central Arctic in boreal summer Table 26: qualitative estimate of the Sea Ice concentration accuracy in the ARC region My Ocean QUID Page 53/ 101

IV.2.4 IV.2.4.1 Southern Ocean: ACC Temperature As in the Arctic, the High latitudes of the Southern Hemisphere diaplay a cold bias of around 0.15 K on the whole water column. RMS erros reach 0.5 K on average for both HR global hindcast and IR global forecast (Table 27). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of ACC 0-5m ACC 5-100m ACC 100-300m ACC 300-800m ACC 800-2000m ACC 2000-5000m ACC 0-5000m 0.59 0.15 0.60 0.13 0.63 0.13 0.66 0.13 1.02 0.19 1.07 0.05 1.09 0.05 1.12 0.06 0.95 0.19 1.04 0.15 1.07 0.15 1.11 0.16 0.52 0.11 0.51-0.02 0.54 0.06 0.57 0.16 0.26 0.11 0.19-0.02 0.19 0.06 0.20 0.16 0.23 0.17 0.28-0.02 0.28 0.06 0.28 0.16 0.52 0.17 0.53-0.02 0.54 0.06 0.57 0.16 Table 27: ACC RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In ACC zone hindcast are produced by HR global and forecast are produced with IR global. The bias disappears between 300 and 5000 m in IR global forecast (Table 27) but not in IR global hindcast, as indicated by Figure 26 This suggests a inconsistency of the system in this region. My Ocean QUID Page 54/ 101

Figure 26: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: ACC RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 55/ 101

IV.2.4.2 Salinity Salty biases of about 0.04 psu exist between 5 and 300 m in the HR global hindcast, that are well reduced in the IR global forecast (Figure 27 and Table 28). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of ACC 0-5m ACC 5-100m ACC 100-300m ACC 300-800m ACC 800-2000m ACC 2000-5000m ACC 0-5000m 0.242 0.009 0.316-0.008 0.315-0.015 0.326-0.020 0.210-0.028 0.189-0.020 0.191-0.021 0.196-0.021 0.146-0.046 0.124 0.000 0.125 0.000 0.128 0.001 0.063-0.007 0.052 0.000 0.054 0.000 0.055 0.000 0.031-0.002 0.022-0.002 0.022-0.003 0.023-0.003 0.093-0.059 0.104-0.078 0.104-0.079 0.105-0.081 0.078-0.009 0.067-0.002 0.068-0.003 0.070-0.002 Table 28:ACC RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In ACC zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 56/ 101

Figure 27: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: ACC RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 57/ 101

IV.2.4.3 SST The absence of SST bias in HR global and the warm bias of 0.1 K observed in IR global ACC products (Table 29 and Figure 28) is not consistent with the 3D temperature analysis. This is probably in link with biases of the RTG-SST product in this region. The RMS error is around 0.5 to 0.6 K. Figure 28: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the ACC region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of ACC 0.53 K +0 K 0.57 K -0.13 K Table 29: ACC RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 58/ 101

IV.2.4.4 SLA The SLA bias in this region is close to zero or negative (consistent with a cold bias) while the RMS error is around 8 cm on average (Figure 29 and Table 30). Figure 29: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the ACC region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of ACC 8.4 cm -0.3 cm 8 cm +0.05cm Table 30: ACC RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 59/ 101

IV.2.4.5 Sea ice variables The Sea Ice cover is overestimated by the HR global while is underestimated by the IR global (especially in austral summer), as can be seen in Figure 30 and Table 31. Figure 30: Sea ice area (upper panel, 10 3 km2) and extent (lower panel, 10 3 km2) in the Antarctic Ocean in IR global products (blue line), HR global products (black line) and SSM/I observations (red line) for a one year period ending in June 2011. Sea ice concentration (m) Hindcast Description of 3-day forecast Description of ACC +10% all year long, +50 to locally 100% in austral summer -50 to locally -100% in austral summer Table 31: qualitative estimate of the Sea Ice concentration accuracy in the ACC region My Ocean QUID Page 60/ 101

IV.2.5 IV.2.5.1 South Pacific Ocean: SPA Temperature 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of SPA 0-5m SPA 5-100m SPA 100-300m SPA 300-800m SPA 800-2000m SPA 2000-5000m SPA 0-5000m 0.48 0.19 0.46 0.12 0.47 0.12 0.49 0.13 0.82 0.17 0.78 0.04 0.80 0.04 0.84 0.03 0.84 0.26 0.84 0.14 0.86 0.14 0.89 0.15 0.45 0.00 0.42 0.10 0.42 0.10 0.44 0.10 0.20 0.04 0.20 0.10 0.20 0.10 0.20 0.10 0.12 0.11 0.18 0.10 0.18 0.10 0.18 0.10 0.43 0.07 0.42 0.10 0.43 0.10 0.45 0.10 Table 32:SPA RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In SPA zone hindcast are produced by HR global and forecast are produced with IR global. Cold biases of around 0.1 K (to 0.2 K in HR global) are observed in the SPA region as displayed in Figure 31 and Table 32. RMS errors are large near the thermocline like in all other regions (here 0.8 K) and less than 0.4K deeper than 300 m and at the surface. My Ocean QUID Page 61/ 101

Figure 31: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: SPA RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.5.2 Salinity A slightly fresh bias at the surface and salty (0.02 psu) in subsurface is noted in the HR global hindcast (Table 33 and Figure 32). A smaller bias of opposite sign is observed in the IR global products (of the order of 0.01 psu) with salty anomalies between the surface and 100m and fresh anomalies between 100 and 800m. My Ocean QUID Page 62/ 101

3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of SPA 0-5m SPA 5-100m SPA 100-300m SPA 300-800m SPA 800-2000m SPA 2000-5000m SPA 0-5000m 0.194 0.008 0.198-0.018 0.196-0.023 0.200-0.028 0.157-0.018 0.129-0.009 0.130-0.009 0.133-0.010 0.120-0.025 0.101 0.003 0.101 0.003 0.104 0.004 0.053-0.001 0.044 0.004 0.044 0.005 0.046 0.005 0.023-0.003 0.017-0.002 0.017-0.002 0.018-0.002 0.010-0.002 0.011 0.001 0.011 0.001 0.011 0.000 0.061-0.006 0.051 0.000 0.051 0.000 0.052 0.000 Table 33: SPA RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In SPA zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 63/ 101

Figure 32: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: SPA RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.5.3 SST Again Figure 33 and Table 34, a consistent 0.2 K cold bias is found in SST in the SPA zone. The RMS error is around 0.5 K, also consistent with the other regions. My Ocean QUID Page 64/ 101

Figure 33: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the SPA region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of SPA 0.54 K +0.2 K 0.5 K +0.15 K Table 34: SPA RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 65/ 101

IV.2.5.4 SLA No significant SLA bias can be diagnosed in the SPA region Figure 34 and Table 35, while the RMS error is 5 cm on average. Figure 34: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the ACC region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of SPA 5 cm -0.05 cm 4.7 cm +0.05cm Table 35: SPA RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 66/ 101

IV.2.6 IV.2.6.1 Tropical Pacific Ocean: TPA Temperature A large 0.3 K bias is diagnosed Table 37 and Figure 36 in the HR global thermocline, while the IR global is better adjusted to the strong interannual ENSO variability in the region. Again the bias is cold through all the water column (around 0.1 K and smaller in IR global forecast). 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of TPA 0-5m TPA 5-100m TPA 100-300m TPA 300-800m TPA 800-2000m TPA 2000-5000m TPA 0-5000m 0.53 0.16 0.54 0.13 0.56 0.13 0.59 0.13 0.99 0.18 1.00 0.04 1.03 0.04 1.06 0.04 0.99 0.33 1.00 0.16 1.02 0.17 1.06 0.12 0.47 0.06 0.47-0.01 0.49 0.07 0.52 0.12 0.20 0.06 0.19-0.01 0.19 0.07 0.19 0.12 0.23 0.15 0.27-0.01 0.27 0.07 0.27 0.12 0.50 0.08 0.50-0.01 0.52 0.07 0.54 0.12 Table 36:TPA RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In TPA zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 67/ 101

Figure 35: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: TPA RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.6.2 Salinity As in the SPA zone, HR global salinity bias is fresh at the surface and salty between 5 and 300 m. The bias is strongly reduced in IR global (Table 37, Figure 36). A strong signal appears in the 2000 5000 m layer as in other regions but this signal is due to the lack of observations in this layer and to several biased observations that alter the statistic (to be corrected in a further version of QUID). My Ocean QUID Page 68/ 101

3DS (psu) TPA 0-5m TPA 5-100m TPA 100-300m TPA 300-800m TPA 800-2000m TPA 2000-5000m TPA 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.234 0.011 0.290-0.011 0.289-0.017 0.298-0.023 0.196-0.021 0.177-0.014 0.178-0.015 0.183-0.016 0.136-0.034 0.117 0.000 0.118 0.000 0.120 0.001 0.057-0.006 0.048 0.001 0.049 0.001 0.050 0.001 0.026-0.003 0.018-0.002 0.018-0.002 0.019-0.002 0.086-0.051 0.096-0.069 0.096-0.069 0.097-0.071 0.072-0.008 0.062-0.002 0.063-0.002 0.064-0.002 Table 37:TPA RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In TPA zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 69/ 101

Figure 36: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: TPA RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV.2.6.3 SST As can be seen in Table 38 and Figure 37 a cold 0.1 K bias is diagnosed in HR global hindcast as in all other regions, while no bias is present on average in IR global forecast. The nino 5 region behaves differently as it displays a warm bias. The RMS error is 0.5K and is reduced to 0.45 K in the IR system with no bias. My Ocean QUID Page 70/ 101

Figure 37: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the TPA region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of TPA 0.5 K +0.15 K 0.45 K -0.02 K Table 38: TPA RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 71/ 101

IV.2.6.4 SLA A strong negative bias (-4 cm) appears in the nino 5 subregion of the Tropical Pacific in Figure 38 and Table 39, consistently with the warm bias already diagnosed. This signal is due to SLA assimilation problems in the Indonesian region where MDT is not well known. The RMS error is consistently higher in this region (around 8 cm) than in the other subregions (around 4 cm). The bias is thus slightly negative on average on the region while it is nearly zero in most subregions. Figure 38: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the TPA region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of TPA 4 cm -0.25 cm 4 cm -0.4cm Table 39: TPA RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 72/ 101

IV.2.7 IV.2.7.1 North Pacific Ocean NPA Temperature Table 40 and Figure 39 show that the 3D temperature hindcast and forecast behave similarly as in the TPA region. The 1-day forecast does not display the usual cold bias of 0.1 K under 300m as the hindcast and the other forecast ranges, suggesting rapid adjustments take place in the model on the whole water column. Again RMS errors are stronger near the thermocline and in the HR global with respect to the IR global. 3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of NPA 0-5m NPA 5-100m NPA 100-300m NPA 300-800m NPA 800-2000m NPA 2000-5000m NPA 0-5000m 0.56 0.15 0.59 0.13 0.61 0.13 0.65 0.13 1.13 0.19 1.16 0.04 1.19 0.04 1.22 0.04 1.11 0.25 1.13 0.18 1.15 0.18 1.20 0.20 0.48 0.12 0.52-0.02 0.55 0.08 0.58 0.20 0.20 0.12 0.18-0.02 0.18 0.08 0.19 0.20 0.35 0.09 0.31-0.02 0.31 0.08 0.31 0.20 0.55 0.10 0.57-0.02 0.59 0.08 0.61 0.20 Table 40:NPA RMS and average temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In NPA zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 73/ 101

Figure 39: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: NPA RMS (upper panel) and average (lower panel) temperature s in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 74/ 101

IV.2.7.2 Salinity In salinity as in temperature the NPA follows the behaviour of the TPA region and the same comments apply to Figure 40 and Table 41. 3DS (psu) NPA 0-5m NPA 5-100m NPA 100-300m NPA 300-800m NPA 800-2000m NPA 2000-5000m NPA 0-5000m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.258 0.013 0.339-0.005 0.338-0.014 0.350-0.020 0.219-0.025 0.210-0.019 0.212-0.020 0.217-0.020 0.145-0.045 0.128-0.002 0.130-0.002 0.132-0.002 0.056-0.013 0.048-0.002 0.050-0.002 0.051-0.002 0.028-0.003 0.019-0.003 0.019-0.003 0.020-0.003 0.138-0.129 0.127-0.121 0.127-0.121 0.127-0.121 0.078-0.012 0.071-0.003 0.072-0.003 0.073-0.003 Table 41:NPA RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In NPA zone hindcast are produced by HR global and forecast are produced with IR global. My Ocean QUID Page 75/ 101

Figure 40: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: NPA RMS (upper panel) and average (lower panel) salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean QUID Page 76/ 101

IV.2.7.3 SST The SST biases in Table 42 and Figure 41 are consistent between systems and with all other regions, indicating a cold bias of 0.1K and RMS errors of around 0.6 K Figure 41: Comparison of SST data assimilation scores (left: average in K, right: RMS in K) averaged since January 2011 and between all available systems in the NPA region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of NPA 0.6 K +0.13 K 0.65 K +0.15 K Table 42: NPA RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 77/ 101

IV.2.7.4 SLA In the North Pacific SLA biases are small (less than 0.5 cm) and the RMS error is 6 cm on average (Figure 42 and Table 43). Figure 42: Comparison of SLA data assimilation scores (left: average in cm, right: RMS in cm) averaged since January 2011 and between all available systems in the NPA region. For each region from bottom to top, the bars refer respectively to IR global (blue) and HR global (cyan). The geographical location of regions is displayed in the annex. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of NPA 6 cm -0.17 cm 6 cm -0.33 cm Table 43: NPA RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 78/ 101

IV.2.7.5 Surface currents: application in the Japan region The HR global surface currents have been validated in the Japan region for their use in drift computations in the context of the Fukushima nuclear pollution in March 2011. It is illustrated here with a CLASS1 comparison between HR surface currents and surface current products deduced from SLA and wind observations (Figure 43). The benefit of high resolution is clear here with the trace of eddies and fine structures in the surface currents at the monthly mean scale. Figure 43: SURCOUF (left) and HR global (right) sea surface current velocity (m/s), monthly mean, March 2011. The colorbar is the same for both maps. My Ocean QUID Page 79/ 101

IV.3 Intermediate resolution hindcast and forecast (IR global products): GLO IV.3.1 Temperature Figure 44: temperature in K (observation forecast) from data assimilation statistics in IR global over time and depth. Positive anomalies in Figure 44 indicate a cold bias of the IR system at the surface and deeper than 100m. We also note that these biases are stable in time and representative of the instantaneous behaviour of the system. This is confirmed by Table 44 computed with CLASS4. Again, the deeper biases are reduced in the forecast with respect to the hindcast, suggesting that the cold bias at depth does not come from the model. As seen in the previous sections, this behaviour on global average might come from the behaviour of the model in the Southern Ocean and in the Tropical and North Pacific. A 0.25 K cold bias is measured in the 2000-5000 m layer: again biased observations have interfered in our computations. My Ocean QUID Page 80/ 101

3DT (K) Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of GLO 0-5m GLO 5-100m GLO 100-300m GLO 300-800m GLO 800-2000m GLO 2000-5000m GLO 0-5000m 0.51 0.14 0.60 0.01 0.62 0.19 0.66 0.00 0.84 0.03 1.01 0.03 1.04 0.03 1.08 0.03 0.79 0.12 0.94 0.09 0.97 0.03 1.00 0.03 0.44 0.12 0.53 0.09 0.54 0.03 0.56 0.03 0.22 0.12 0.24 0.09 0.24 0.03 0.25 0.03 0.28 0.25 0.28 0.24 0.28 0.24 0.29 0.24 0.44 0.25 0.52 0.24 0.53 0.24 0.55 0.24 Table 44:GLO RMS and average temperature s in K(observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In GLO zone hindcast and forecast are produced with IR global. The quality differences between HR global and IR global systems are illustrated in Figure 45. These differences are most sensible between 100 and 300m where there are many in situ observations. The hindcast from either system is more accurate than the forecast. 1-day and 3 day forecast from IR global are more accurate than HR global 3-day forecast. Finally, all estimates are from 10% to 50% more accurate than the WOA09 reference climatology. My Ocean QUID Page 81/ 101

Figure 45: Intercomparison of RMS temperature in K, on average in the 100 300 m layer and in the GLO region. Orange: reference climatology Levitus (2009) WOA09, black: IR global hindcast, blue: HR global hindcast, cyan: IR global 1-day forecast, green: IR global 3-day forecast, red: HR global 3-day forecast. My Ocean QUID Page 82/ 101

IV.3.2 Salinity Figure 46: salinity in psu (observation forecast) from data assimilation statistics in IR global over time and depth. As shown in Figure 46 and Table 45, the GLO salinity products display a constant fresh surface bias (from 0 to 100m), and a salty bias (from 100m to 800m) both less than 0.01 psu. A fresh bias (0.03 psu) appears under 2000m and also has to be confirmed with further studies. 3DS (psu) GLO 0-5m GLO 5-100m GLO 100-300m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 0.265-0.003 0.288-0.001 0.291-0.004 0.299-0.005 0.173-0.007 0.200-0.009 0.202-0.010 0.206-0.010 0.116 0.003 0.132 0.004 0.134 0.003 0.136 0.004 My Ocean QUID Page 83/ 101

GLO 300-800m GLO 800-2000m GLO 2000-5000m GLO 0-5000m 0.057 0.003 0.065 0.004 0.066 0.004 0.068 0.004 0.025-0.001 0.027-0.001 0.027-0.001 0.028-0.001 0.056-0.032 0.052-0.028 0.053-0.028 0.053-0.028 0.065 0.000 0.074 0.000 0.075 0.001 0.077 0.001 Table 45: GLO RMS and average salinity s in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. In GLO zone hindcast and forecast are produced with IR global. The intercomparison of HR global and IR global RMS errors in the 100 300 m layer points out the better accuracy of IR global salinity products thanks to the bias correction. Figure 47: Intercomparison of RMS salinity in psu, on average in the 100 300 m layer and in the GLO region. Orange: reference climatology Levitus (2009) WOA09, black: IR global hindcast, blue: HR global hindcast, cyan: IR global 1-day forecast, green: IR global 3-day forecast, red: HR global 3-day forecast. My Ocean QUID Page 84/ 101

IV.3.3 SST Figure 48: daily SST ( C) and salinity (psu) spatia l mean for a one year period ending in JFM 2011, for Mercator-Ocean systems (in black) and RTG-SST observations (in red). Left: IR global, right: HR global. Polar regions have been removed from statistics. As illustrated in Figure 48, a cold bias can be diagnosed at the global scale in IR global mainly during boreal summer. This bias can reach 0.1 K on average at this period of the year. In consequence the statistics of Table 46 underestimate the maximum SST bias over the year. We note that a global average 0.1 K cold bias is diagnosed in HR global, consistently with previous sections. The time evolution of sea surface salinity on global average is very different between the two systems, illustrating the fact that the two systems have different dynamics. Sea Surface Temperature (K) Hindcast 3-day forecast RMS of RMS of GLO 0.65 K +0.02 K 0.65 K +0.02 K Table 46: GLO RMS and average SST s in K (observation-model) with respect to RTG-SST 1/2 observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 85/ 101

IV.3.4 SLA Figure 49:SLA data assimilation statistics for the IR global system with respect to Jason 2 (black), Jason 1 (blue) and Envisat (orange) along track observations. The stability over time of the accuracy in terms of SLA is illustrated in Figure 49. The RMS error is 7 cm on average and the average oscillates between positive and negative values resulting in a small -0.1 cm bias in Table 47. Sea Level Anomaly (cm) Hindcast 3-day forecast RMS of RMS of GLO 7 cm -0.1 cm 7 cm -0.1 cm Table 47: GLO RMS and average SLA s in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from January 2011 to August 2011 are used to build these regional estimates. My Ocean QUID Page 86/ 101

IV.3.5 Sea Ice Sea ACC and ARC regions comments for IR global forecast. IV.3.6 Biogeochemical variables Modelled mean annual chlorophyll-a field(figure 50a) shows a good agreement with satellite derived estimates at the global scale (Figure 50b). The large scale structures are well reproduced (e.g. double-gyres, Antarctic Circumpolar Current). The concentrations of modelled chlorophyll are slightly too high. In the northern hemisphere, the double-gyres are correctly modelled, both in terms of chlorophyll-a magnitude and of latitudinal position of the transition zone between high productive waters to the North and oligotrophic waters to the South. This transition zone corresponds to the position of the Gulf Stream in the Atlantic Ocean and of the Kurushio in the Pacific Ocean. In the southern hemisphere, the subtropical gyres and the Antarctic Circumpolar Current are also well reproduced. At the equator, however, there are significant differences, with BIOMER_GLORYS1V1_BIO1 overestimating observed chlorophyll-a levels. Moreover, the tropical productive zone stretches too much over the subtropical gyres, and in particular east of the basins. (a) (b) Figure 50: Log10 of the chl-a annual mean in year 2002 at sea surface(mg Chl.m-3); (a) BIOMER_GLORYS1V1_BIO1; (b) Chl-a data from Seawifs Meris Modis sensors (Globcolour) Coming back to the overestimation of chlorophyll-a levels simulated by BIOMER_GLORYS1V1_BIO1 at the equator, two potential underlying causes can be identified. (1) The model data can be at least partly attributed to the CLIO aerodynamic bulk formulae. The later is at the origin of a cool bias in surface temperature leading to an overestimation of upwelling and hence nutrient input at the equator. (2) In BIOMER_GLORYS1V1_BIO1, there is moreover a bias introduced by the assimilation scheme. Preliminary outputs of gravimetric GOCE mission suggest that there are significant errors in the mean sea surface height (MSSH) used to assimilate the satellite altimetry. Regional biases in MSSH are typically of 100km and 5cm (resp. horizontal and vertical scales). The system response to the bias in MSSH is a bias in vertical velocity near the equator, thus introducing anomalous level of nitrate and in chlorophyll. My Ocean QUID Page 87/ 101

Figure 51: Temporal correlation between model and data Figure 51 presents the temporal correlation of surface chlorophyll-a between model and data. It gives an assessment of the model capacity to represent the chlorophyll seasonal cycles. We can see that our model is doing a good job at mid-latitudes. At high latitudes, the low correlations reflect that the model does not manage to capture the timing of the bloom, there is a time-lag of about 2 months between model and data. At low latitudes, in the equatorial band, the seasonal cycle is not very marked so we expect lower correlations. Moreover, the low values in the low latitudes also reflect the too high chlorophyll concentrations predicted by the model. (a) (b) Figure 52: Concentrations of nitrate in a 10 m deep layer (µmol N.L-1); (a) BIOMER_GLORYS1V1_BIO1; (b) WOA 2005 Figure 52 shows a global comparison of the nitrate concentration derived from data (WOA 2005) and predicted by the model. Globally, there is a good accordance between them except at the equator where the upwelling is too strong. My Ocean QUID Page 88/ 101

(a) (b) Figure 53: Annual mean of O2 concentration in sea water in µmol.l-1. Section in the Atlantic Ocean at 20 W; (a) BIOMER_GLORYS1V1_BIO1; (b) WOA 2 005 (a) (b) Figure 54: Annual mean of O2 concentration in sea water in µmol.l-1. Section in the Pacific Ocean at 205 E; (a) BIOMER_GLORYS1V1_BIO1; (b) WOA 2005 Sections of oxygen concentration are presented on Figure 53 and Figure 54, respectively in the Atlantic Ocean and in the Pacific Ocean. They show a good adequacy between model and climatologies (annual mean). My Ocean QUID Page 89/ 101

Figure 55: Log10 of the chlorophyll-a (mg Chl.m-3) at the BATS station during 2002-2007 period between 0 and 900 m depth ; (top) BIOMER_GLORYS1V1_BIO1; (bottom) bottle data Figure 56: Normalized taylor diagram at the BATS station at sea surface (empty symbols), 100 m (filled symbols) and 200 m (filled symbols with black contour) depth for chlorophyll-a (circles), nitrates (squares) and oxygen (triangles) parameters. My Ocean QUID Page 90/ 101

The comparison between model output and biogeochemical data from eulerian observatories provides another way to assess the quality of our simulations. In complement with climatologies (e.g. nitrates), observed data time series constitute independent data against which the model can be validated. The BATS (Bermuda Atlantic Time-series Study) station is one of them. It is situated in the Sargasso Sea (31 40'N, 64 10'W). At this station, an exhaustiv e set of biogeochemical, along with physical variables are measured through bottle samples all year round (Steinberg et al. 2001). This sustained data collections are carried out monthly or even twice a month during bloom events. These data open the possibility for assessing the ability of the biogeochemical model to reproduce the seasonal and interannual variability of the ecosystem. Moreover, they give a valuable insight of the ocean biogeochemical state at a given location characteristic of a particular large-scale biogeographic region (Longhurst, 1998). BATS is situated in the western North Atlantic subtropical gyre, in a highly-turbulent region, between the Gulf Stream (north) and the North Atlantic equatorial current (Steinberg et al. 2001). BATS is characterized by a deep mixed-layer in winter in which nutrients are injected by entrainment and immediately consumed by phytoplankton. In summer, after spring restratification, nutrients are rapidly depleted and phytoplankton declines in the shallow mixed-layer. A subsurface chlorophyll maximum develops at the base of the mixed layer. Figure 55 (bottom) presents the concentration of chlorophyll-a as a function of depth and time measured at the BATS station between 2002-2007. It illustrates the seasonal cycle of phytoplankton. Figure 55 (top) shows the results of BIOMER_GLORYS1V1_BIO1. The seasonal cycle is in general well reproduced by the models. BIOMER_GLORYS1V1_BIO1 succeeds well in capturing the interannual variability as demonstrated by the deepening of the mixed-layer in summer 2005. However, the model predicts spring blooms that are not present in the observations. This is due to the nutricline that is too shallow (valid for nitrates, silicates, phosphates) in our simulations. Variable Observations BIOMER Error (%) Chlorophyll Mean = 0.323 mg/m 3 Std = 0.516 mg/m 3 Mean = 0.389 mg/m 3 Std = 0.451 mg/m 3 +20% Dissolved oxygen Mean = 277.52 µmol/l Mean = 281.35 µmol/l +1% Std = 65.794 µmol/l Std = 65.127 µmol/l Nitrates Mean = 6.841 µmol/l Mean = 7.091 µmol/l +4% Std = 9.179 µmol/l Std = 8.303 µmol/l Phosphates Mean = 0.675 µmol/l Mean = 0.721 µmol/l +7% Std = 0.596 µmol/l Std = 0.553 µmol/l Table 48: Mean and standard deviation values over the whole GLO domain in 2002 at sea surface computed for model and observations on a 1 regular grid. Figure 56 is a Taylor diagram, allowing to sum up a few basic statistics (standard deviation, error RMS and temporal correlation) performed on the model and data, here for three key-variables of the system: chlorophyll-a, nitrate and oxygen at three different depth of the water column: surface, 100 m and 200 m. It shows a good data model correlation at sea surface for oxygen and chlorophyll-a. For nitrates, at sea surface, the correlation is not good, but it is not surprising because nitrates are quasi- My Ocean QUID Page 91/ 101

exhausted during all year at surface. The concentrations are almost negligible. However, the corresponding standard deviation is close to data. It shows that their magnitudes of variations are similar. The statistical scores are lower at 100 m and 200 m depth, because there is a shift between model and observed nitracline. My Ocean QUID Page 92/ 101

V VALIDATION SYNTHESIS V.1 Validation methodology The quality of WP04 V2 products was assessed in 11 regions corresponding to the main basins of the global ocean. The modelled physical and biogeochemical variables were compared to assimilated and independent observations when available. Integrated quantities were compared to standards from the literature. When possible, the various model solutions were intercompared to ensure the consistency of the results. This document displays the main synthetic results of the accuracy assessment of operational systems on several months (from 2 boreal summer months to 8 months in 2011). V.2 Validation summary Accuracy numbers by variables, regions and layers are given in synthetic tables in the results section IV. V.2.1 Temperature and salinity The systems description of the ocean water masses is very accurate on average and departures from in situ observations rarely exceed 1 C and 0.1 psu (mostly in the thermocline, and in high variability regions like the Gulf Stream or the Eastern Tropical Pacific).The temperature and salinity forecast have significant forecast skill in many regions of the ocean in the 0-500m layer. Biases persist mainly in the HR zoom hindcast products with an overall cold bias of 0.1 K. In IR global products the cold bias is reduced and a warm bias appears near 100m. In HR global products fresh biases occur near the surface and between 300 m and 800 m, between 5 and 300 m salty biases dominate (of the order of 0.05 psu). The bias is much smaller in IR global products, still fresh at the surface, then salty near 100m and fresh between 100 m and 800m No deep biases develop in the systems, but biased observations deeper than 2000m alter the statistics (to be corrected in a further version of QUID) V.2.2 SST A cold SST bias of 0.1 K on average is observed in HR global products. IR global products are less biased with respect to the same assimilated RTG-SST product, and the cold bias is mainly seasonal (present in summer). HR zoom assimilate AVHRR-AMSRE ¼ Reynolds analysis and display the more consistent SST results and best accuracy. In the future either this product or the MyOcean OSTIA SST product will be assimilated in all systems. V.2.3 SLA The monitoring system is generally very close to altimetric observations (global average of 6cm RMS error). Future updates of the Mean Dynamic Topography will correct the local biases that are currently My Ocean QUID Page 93/ 101

observed for instance in the Indonesian seas, and hopefully will prevent the degradation of the subsurface currents at the Equator. V.2.4 Ocean currents No additional diagnostics have been added in this document (see DR5). We recall that the surface currents are underestimated with respect to in situ measurements of drifting buoys. The underestimation ranges from 20% in strong currents to 60% in weak currents. On the contrary the orientation of the current vectors is well represented. However, drifter velocities happen to be biased towards high velocities (Grodsky et al., GRL, May 2011) and comparisons with these observations have to be re-processed with a corrected dataset. A relative improvement of the MyOcean near surface currents is thus expected. V.2.5 Sea Ice The sea ice concentrations are overestimated in the Arctic all through the year in HR global and in summer in IR global (unrealistic rheology for HR global and generally too much accumulation of ice in the Arctic). Sea ice concentration is underestimated in the Antarctic in IR global in austral summer (atmospheric forcings problem) and overestimated all through the year in HR global (rheology problems). V.2.6 Biogeochemical variables This first calibration phase revealed a good accordance at large scale between annual mean fields from our model and from observations. The large scale structures corresponding to specific biogeographic regions (double-gyres, ACC, etc) are well reproduced. However, there are serious discrepancies in the tropical band. This problem has been thoroughly studied and is attributed to a bias in the Mean Dynamic Topography which is combined to Sea Level Anomalies in the assimilation process. This induces overestimated vertical velocities which are the source of anomalous levels of nitrates in equatorial shallow waters.table 48 shows a comparison of the global mean and the global standard deviation for the main variables: Chl-a, 02, NO3, PO4 (computed for year 2002 at sea surface). It illustrates the global overestimation of our model and especially of the chlorophyll. This reflects both the problem of equator where there is a too strong biogeochemical activity and a global tendency of our model to overestimate the chlorophyll. O2, however, is very close to climatological estimation. This is due to the intrinsic link of O2 concentration with temperature and salinity (and especially at sea surface), which are constrained to be as close as possible from observations via the assimilation process. Concerning the temporal monitoring, our model manages well to reproduce the seasonal cycle in most part of the ocean (spring bloom at mid-latitudes, two monsoon blooms in the Indian Ocean etc.). However, the timing of the blooms is not yet in phase with observations (a one or two-month lag). It will need more work and in particular we will have to improve the photo-adaptive model of Geider et al. (1996, 1998). We also detected a shift in the depth of the nutricline between model and observations, which induces a too strong spring bloom (between 30 and 40 N in the west part of the Atlantic Ocean). In conclusion, the model displays a good behavior considering the present state-of-the-art of biogeochemical modeling at the global scale (Yool et al., 2011, Ford et al., in prep). In the near future, we will improve the physical part, with a focus on the MDT bias at the equator and we will work on the biogeochemical parameterization to improve the timing of the bloom and the magnitude of chlorophyll concentrations. My Ocean QUID Page 94/ 101

VI ANNEX VI.1 References Antoine D., Morel A. 2006: Oceanic primary production: 1. Adaptation of a spectral lightphotosynthesis model in view of application to satellite chlorophyll observations. Global Biogeochemical Cycles. 10 (1): 43-55 Aumont O. 2005: PISCES biogeochemical model. Unpublished report. Aumont O., Orr, J.C., Jamous, D., Monfray, P., Marti, O., and Madec, G. 1998: A degradation approach to accelerate simulationsto steady-state in a 3-D tracer transport model of the global ocean. Climate Dynamics. 14: 101-116. Aumont, O. and Bopp, L. 2006: Globalizing results from ocean in situ iron fertilization studies. Global Biogeochem. Cycles. 20 (2):10 1029. Behrenfeld M., Falkowski P. 1997: Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42 (1):1-20 Bloom, S.C., Takacs, L.L., Da Silva, A.M. and Ledvina, D. 1996 : Data assimilation using incremental analysis updates. MonthlyWeather Review. 1265-1271. Bopp L., Aumont, O., Cadule, P., Alvain, S. and Gehlen, M. 2005: Response of diatoms distribution to global warming andpotential implications: A global model study. Geophys. Res. Lett.. 32, L19606, doi:10.1029/2005gl023653. Brasseur, P. and Verron, J. 2006 : The SEEK filter method for data assimilation in oceanography: a synthesis. Ocean Dynamics.56 (5): 650-661 Conkright, M.E., Locarnini, R.A., Garcia, H.E., O Brien, T.D., Boyer, T.P., Stephens, C. and Antonov, J.I. 2002: WorldOcean Atlas2001: Objective Analyses, Data Statistics, and Figures, CD-ROM ation. National Oceanographic DataCenter, SilverSpring, MD, 17 pp. Elmoussaoui A., Perruche C., Greiner E., Ethé C. and Gehlen M. 2011 : Integration of biogeochemistry into MercatorOcean system. Mercator Ocean Newsletter #40, January 2011, http://www.mercator-ocean.fr/fre/actualites-agenda/newsletter/newsletter-newsletter-40-les-modelesnumeriques-des-ecosystemes-myocean Ferry, N.,, L. Parent, G. Garric, B. Barnier, N. C. Jourdain and the Mercator Ocean team, 2010: Mercator Global Eddy PermittingOcean Reanalysis GLORYS1V1: Description and Results, Mercator Ocean Newsletter #36, January 2010, http://www.mercator.eu.org/documents/lettre/lettre_36_en.pdf Ford DA, Edwards KP, Lea D, Barciela RM, Mertin MJ, Demaria J. 2011: Assimilating GlobColour ocean colour data into a pre-operational model. in prep. Gehlen, M., Bopp, L., Emprin, N., Aumont, O., Heinze, C. and Ragueneau, O. 2006: Reconciling surface ocean productivity,export fluxes and sediment composition in a global biogeochemical ocean model. Biogeosciences. 1726-4189/bg/2006-3-521,521-537. Gehlen, M., Gangstø, R., Schneider, B., Bopp, L., Aumont, O. and Ethé, C. 2007: The fate of pelagic CaCO3 production in a highco2 ocean: A model study. Biogeoscience., 4: 505-519. Geider M.J., MacIntyre H.L., Kana T.M. 1996: A dynamic model of photoadaptation in phytoplankton. Limnol. Oceanogr. 41: 1-15 My Ocean QUID Page 95/ 101

Geider M.J., MacIntyre H.L., Kana T.M. 1998: A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature. Limnol. Oceanogr. 43, 679-694 Grodsky S. A., Lumpkin R., Carton J. A. (2011) Spurious trends in global surface drifter currents Geophysical Research Letters, 38(L10606) doi:10.1029/2011gl047393 Karl D., Lukas R. 1996: The Hawaii Ocean Time-series (HOT) program: Background, rationale and field implementation. Deep-Sea Research II. 43 (2-3): 129-156 Key, R. M., Kozyr, A., Sabine, C.L., Lee, K., Wanninkhof, R., Bullister, J.L., Feely, R.A., Millero, F.J., Mordy, C., and Peng, T.-H.2004: A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Global Biogeochem. Cycles. 18.GB4031, doi:10.1029/2004gb002247. Longhurst, A. 1998: Ecological geography in the sea. Academic Press. Ourmières Y., Brankart J.M., Berline L., Brasseur P. and Verron J. 2006: Incremental Analysis Update implementation into asequential ocean data assimilation system. Journal of Atmospheric and Oceanic Technology, vol 23: 1729-1744. Schneider, B., Bopp, L., Gehlen, M., Segschneider, J., Frölicher, T.L., Cadule, P., Friedlingstein, P., Doney, S.C., Behrenfeld M.J.and Joos, F. 2008: Climate-induced interannual variability of marine primary and export production in three global coupled climatecarbon cycle models. Biogeosciences. 5: 597-614 Steinacher, M., Joos, F., Frölicher, T.L., Bopp, L., Cadule, P., Cocco, V., Doney, S.C., Gehlen, M., Lindsay, K., Moore, J.K.,Schneider, B., and Segschneider, J. 2010: Projected 21st century decrease in marine productivity: a multi-model analysis.biogeoscience. 7: 979-1005. Steinberg, D.K., Carlson, C.A., Bates, N.R., Johnson, R.J., Michaels, A.F. and Knapp, A.H. 2001: Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep-Sea Research II 48:1405-1447 Tagliabue, A., Bopp, L., Dutay, J.-C., Bowie, A.R., Chever, F., Jean-Baptiste, Ph., Bucciarelli, E., Lannuzel, Remenyi, D.T.,Sarthou, G., Aumont, O., Gehlen, M. and Jeandel, C. 2010: On the importance of hydrothermalism to the oceanic dissolved ironinventory. Nature Geoscience. 3: 252 256, doi:10.1038/ngeo818. Yool A., Popova E.E., Anderson T.R. 2011: MEDUSA-1.0: a new intermediate complexity plankton ecosystem model for the global domain My Ocean QUID Page 96/ 101

VI.2 Maps of regions for data assimilation statistics VI.2.1 North and tropical atlantic 1 IrmingerSea 2 IcelandBasin 3 Newfoundland-Iceland 4 Yoyo Pomme 5 Gulf Stream2 6 Gulf Stream1 XBT 7 North Medeira XBT 8 Charleston tide 9 Bermuda tide 10 Gulf of Mexico 11 Florida Straits XBT My Ocean QUID Page 97/ 101

12 Puerto Rico XBT 13 Dakar 14 Cape Verde XBT 15 Rio-La Coruna Woce 16 Belem XBT 17 Cayenne tide 18 Sao Tome tide 19 XBT - central SEC 20 Pirata 21 Rio-La Coruna 22 Ascension tide VI.2.2 Mediterranean Sea My Ocean QUID Page 98/ 101

1 Alboran 2 Algerian 3 Lyon 4 Thyrrhenian 5 Adriatic 6 Otranto 7 Sicily 8 Ionian 9 Egee 10 Ierepetra 11 Rhodes 12 Mersa Matruh 13 Asia Minor VI.2.3 Global ocean My Ocean QUID Page 99/ 101

1 Antarctic Circumpolar Current 2 South Atlantic 3 Falkland current 4 South Atl. gyre 5 Angola 6 Benguela current 7 Aghulas region 8 Pacific Region 9 North Pacific gyre 10 California current 11 North Tropical Pacific 12 Nino1+2 13 Nino3 My Ocean QUID Page 100/ 101

14 Nino4 15 Nino6 16 Nino5 17 South tropical Pacific 18 South Pacific Gyre 19 Peru coast 20 Chile coast 21 Eastern Australia 22 Indian Ocean 23 Tropical indian ocean 24 South indian ocean My Ocean QUID Page 101/ 101