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

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1 Project QUality Information MyOcean V2 System WP 04 GLO MERCATOR Reference: MYO-WP04-QUID-V2-MERCATOR-v1.0.doc Project N : FP7-SPACE Start Date of project : Work programme topic: SPA 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

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

3 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 III Validation framework III.1 Methodology III.2 Disclaimer note on accuracy numbers determination by variable III.3 List of tested metrics used to assess the system s performance IV Validation results IV.1 High resolution hindcast and forecast (HR zoom products) IV.1.1 North Atlantic: NAT IV Temperature IV Salinity IV SST IV SLA IV.1.2 Tropical Atlantic: TAT IV Temperature IV Salinity IV SST IV SLA IV.1.3 Mediterranean Sea: MED IV Temperature IV Salinity IV SST IV SLA IV.2 High resolution hindcast (HR global) and intermediate resolution forecast (IR global products). 37 IV.2.1 South Atlantic: SAT IV Temperature IV Salinity IV SST IV SLA IV.2.2 Indian Ocean: IND IV Temperature IV Salinity IV SST IV SLA IV.2.3 Arctic Ocean: ARC IV Temperature IV Salinity IV SST IV SLA IV Sea ice variables IV.2.4 Southern Ocean: ACC IV Temperature IV Salinity IV SST My Ocean QUID Page 3/ 101

4 IV SLA IV Sea ice variables IV.2.5 South Pacific Ocean: SPA IV Temperature IV Salinity IV SST IV SLA IV.2.6 Tropical Pacific Ocean: TPA IV Temperature IV Salinity IV SST IV SLA IV.2.7 North Pacific Ocean NPA IV Temperature IV Salinity IV SST IV SLA IV Surface currents: application in the Japan region IV.3 Intermediate resolution hindcast and forecast (IR global products): GLO IV.3.1 Temperature IV.3.2 Salinity IV.3.3 SST IV.3.4 SLA IV.3.5 Sea Ice IV.3.6 Biogeochemical variables V Validation synthesis V.1 Validation methodology V.2 Validation summary V.2.1 Temperature and salinity V.2.2 SST V.2.3 SLA V.2.4 Ocean currents V.2.5 Sea Ice V.2.6 Biogeochemical variables VI annex VI.1 References VI.2 Maps of regions for data assimilation statistics VI.2.1 North and tropical atlantic VI.2.2 Mediterranean Sea VI.2.3 Global ocean My Ocean QUID Page 4/ 101

5 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 m, in Med and Black Sea 40 levels m 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 m, 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

6 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

7 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 m, ½ ). My Ocean QUID Page 7/ 101

8 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 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

9 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

10 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 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

11 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 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

12 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

13 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

14 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

15 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

16 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

17 IV VALIDATION RESULTS IV.1 High resolution hindcast and forecast (HR zoom products) IV.1.1 IV 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 m NAT m NAT m NAT m NAT m 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

18 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

19 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 m 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 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 NAT5-100m NAT m NAT m NAT m NAT m NAT m 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

20 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

21 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 , on the right RMS differences for the same period. My Ocean QUID Page 21/ 101

22 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 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

23 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 K 0.7 K 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 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

24 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

25 IV.1.2 IV 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 m 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 m 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 m TAT m TAT m TAT m TAT m 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

26 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 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 m 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

27 3DS (psu) TAT 0-5m TAT 5-100m TAT m TAT m TAT m TAT m TAT m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 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

28 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 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

29 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

30 IV 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

31 IV.1.3 IV 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 m MED m MED m MED m MED m 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

32 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 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

33 3DS (psu) MED 0-5m MED 5-100m MED m MED m MED m MED m MED m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 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

34 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 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

35 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

36 IV 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

37 IV.2 High resolution hindcast (HR global) and intermediate resolution forecast (IR global products) IV.2.1 IV 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 m SAT m SAT m SAT m SAT m 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

38 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 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

39 3DS (psu) SAT 0-5m SAT 5-100m SAT m SAT m SAT m SAT m SAT m Hindcast 1-day forecast 3-day forecast 5-day forecast RMS of RMS of RMS of RMS of 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

40 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

41 IV 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 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

42 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 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