QUALITY INFORMATION DOCUMENT For Global Sea Physical Analysis and Forecasting Product GLOBAL_ANALYSIS_FORECAST_PHYS_001_002

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1 QUALITY INFORMATION DOCUMENT For Global Sea Physical Analysis and Forecasting WP leader: GlobalMFC WP05, Eric Dombrowsky, Mercator- Ocean France Issue: 1.2 Contributors : C.REGNIER, J-M. LELLOUCHE, O. LEGALLOUDEC, C. DESPORTES, M.DREVILLON Approval Date by Quality Assurance Review Group : 06 August 2013 Project N : FP7-SPACE Work programme topic: SPA Prototype Operational continuity of GMES services in the Marine Area. Duration: 30 Months

2 CHANGE RECORD When the quality of the products changes, the QuID is updated, and a row is added to this table. The third column specifies which sections or sub-sections have been updated. The fourth column should mention the version of the product to which the change applies. Issue Date Description of Change Author Validated By /01/13 All Creation of the document C. REGNIER E. Dombrowsky /02/13 Several corrections to take into acount review remarks by QUARG C Regnier /07/2013 take into account M.Drévillon SARAL/altika altimetry observations for assimilation II.1.1 and verification I.2.3 (RFC MYO- 394) My Ocean Public Page 2/ 84

3 TABLE OF CONTENTS I Executive summary... 6 I.1 s covered by this document... 6 I.2 Executive summary... 6 I.2.1 Temperature and salinity... 6 I.2.2 SST... 7 I.2.3 SLA... 7 I.2.4 Ocean currents... 7 I.2.5 Sea Ice... 7 I.3 Estimated Accuracy Numbers... 7 I.3.1 Temperature... 8 I.3.2 Salinity... 8 I.3.3 SST... 9 I.3.4 SLA... 9 II ion Subsystem description II.1.1 The physical high resolution (HR) global system III Validation framework IV Validation results 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 My Ocean Public Page 3/ 84

4 IV.1.4 South Atlantic: SAT IV Temperature IV Salinity IV SST IV SLA IV.1.5 Indian Ocean: IND IV Temperature IV Salinity IV SST IV SLA IV.1.6 Arctic Ocean: ARC IV Temperature IV Salinity IV.1.7 Southern Ocean: ACC IV Temperature IV Salinity IV SST IV SLA IV Sea ice variables IV.1.8 South Pacific Ocean: SPA IV Temperature IV Salinity IV SST IV SLA IV.1.9 Tropical Pacific Ocean: TPA IV Temperature IV Salinity IV SST IV SLA IV.1.10 North Pacific Ocean NPA IV Temperature IV Salinity IV SST IV SLA IV.1.11 GLOBAL IV Temperature My Ocean Public Page 4/ 84

5 IV Salinity IV SST IV SLA IV Sea Ice V Quality changes since previous version VI References VII Maps of regions for data assimilation statistics VII.1 North and tropical Atlantic VII.2 Mediterranean Sea VII.3 Global ocean VII.4 GODAE Regions My Ocean Public Page 5/ 84

6 I EXECUTIVE SUMMARY I.1 s covered by this document The products assessed in this document are referenced in the EPST as: for the physical 14-day hindcast (analyses, updated weekly) and for the physical -7day forecast (updated daily). These products contain global nominal daily mean fields, on global standard grid in 1/12 ( lat x lon) with 50 levels from 0 to 5000m, 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 I.2 Executive summary The quality of the Global high resolution system has been assessed using one year hindcast and forecast. A unique system is now providing consistent products for all areas. The product quality over the globe has been improved by comparing the V3 system with the V2 system. The headline results for each of the variables assessed are as follows. I.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. Small biases persist in hindcast products with an overall cold bias of 0.03 K near the surface and a warm bias in the same order near 150m. For the salinity, small biases appear: a fresh bias occurs near the surface and a salty bias near 200m (of the order of 0.05 psu). Deep biases may develop in the systems and have to be investigated further and better monitored. My Ocean Public Page 6/ 84

7 I.2.2 SST A warm SST bias of 0.03 K on average is observed in the V3 global products since November This warm bias seems to be stable along time and not seasonal (present in boreal winter and spring 2011) but the calibration period is too short to be more precise.the V3 products are less biased than the V2 version thanks to the assimilation of AVHRR-AMSRE ¼ Reynolds analysis. This satellite SST assimilated product displays more consistent SST results than RTG-SST product used in the V2 version. I.2.3 SLA The V3 system is generally very close to altimetric observations (global average of 6cm RMS error). V3 update of the Mean Dynamic Topography correct the local biases observed in the V2 products (for instance in the Indonesian seas) and hopefully will prevent the degradation of the subsurface currents at the Equator. In July 2013, only two satellites (Jason 2 and Cryosat 2) provide along track altimetry measurements, which can lead to local decrease of the products quality (especially surface currents). The SLA performance is estimated in this document with respect to Jason 1, Jason 2 and Cryosat 2, but the performance is of the same order using SARAL/Altika observations instead of Jason 1 observations (for products after August 2013). I.2.4 Ocean currents No additional diagnostics have been added in this document. 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., 2011) and comparisons with these observations have to be re-processed with a corrected dataset. The V3 version with a higher resolution than V2 for the global product displays some improvements in near surface currents (not shown here). I.2.5 Sea Ice The Sea ice concentration is not assimilated in the system. The sea ice concentrations are slightly overestimated in the Arctic during boreal summer (due to atmospheric forcing errors and too much sea ice accumulation). Sea ice concentration is slightly overestimated in the Antarctic during austral winter (also due to atmospheric forcing errors) and underestimated during austral summer (too much sea ice melt and errors caused by the rheology parametrisation of the sea ice model). I.3 Estimated Accuracy Numbers The following 3D T and S, SLA and SST accuracy numbers are an estimate based on data assimilation statistics (instantaneous and at the locations of observations). 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. Here are presented only the accuracy numbers for the global area (error statistics for each region are detailed in section IV). Accuracy values are given by RMS and Mean errors for 3D Temperature; 3D salinity; for the following vertical layers: 0-5m, 5-100m, m, m, m, m; and for SLA and SST. There is no quantitative accuracy estimate for 3D ocean currents in My Ocean Public Page 7/ 84

8 this document: further studies are necessary on the potential bias of near surface drifter observations and a general evaluation of surface currents is given in the summary. There is no quantitative accuracy estimate for Sea Ice variables but orders of magnitude of departures from observations are given. In order to calculate these accuracy numbers we defined a one year calibration period from June 2010 to May Results are presented following each variable. I.3.1 Temperature 3D T (K) vs in situ Observations Hindcast Forecast day 3 RMS Mean RMS Mean 0-5m 0,66 0,015 0,8 0, m 0,73 0,01 0,88 0, m 0,67-0,03 0,829-0, m 0,35-0,01 0,447-0, m 0,13-0,003 0,158-0, m 0,03-0,02 0,03-0, m 0,496-0,003 0,6-0,005 Table 1: GLO RMS and mean temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. I.3.2 Salinity 3D S (PSU) vs in situ Observations Hindcast Forecast day 3 RMS Mean RMS Mean 0-5m 0,296 0,003 0,33 0, m 0, , m 0,101-0,005 0,121-0, m 0, , m 0, , m 0, , m 0,118 0,001 0,13 0,002 Table 2: GLO RMS and mean salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 8/ 84

9 I.3.3 SST SST (K) vs Satellite data Hindcast Forecast day 3 RMS Mean RMS Mean GLO 0.5-0,01 0,63-0,01 Table 3: GLO RMS and mean SST misfits in K (observation-model) with respect to AVHRR-AMSRE 1/4 observations. I.3.4 SLA Seal Level Anomaly (cm) vs Satellite Along track data Hindcast Forecast day 3 RMS Mean RMS Mean GLO 6,05-0,14 6,67-0,13 Table 4: GLO RMS and mean SLA misfits in cm (observation-model) with respect to altimetric along tracks observations (Sea Level TAC). My Ocean Public Page 9/ 84

10 II PRODUCTION SUBSYSTEM DESCRIPTION These products are provided by the production centre MFC-GLOBAL in the subsystem WP05 by GLO MERCATOR-TOULOUSE-FR II.1.1 The physical high resolution (HR) global system This system, called PSY4V2R1, produces (see part I.1): GLO weekly 7-day hindcast, nowcast and forecast GLO daily 7-day forecast The high resolution global analysis and forecasting V3 system uses version 3.1 of NEMO ocean model (Madec et al., 2008). The physical configuration is based on the tripolar ORCA grid type (Madec and Imbard, 1996) with a horizontal resolution of 9 km at the equator, 7 km at Cape Hatteras (midlatitudes) and 2 km toward the Ross and Weddell seas. The 50-level vertical discretization retained for these system has 1 m resolution at the surface decreasing to 450 m at the bottom, and 22 levels within the upper 100 m. Partial cells parameterization was chosen for a better representation of the topographic floor (Barnier et al., 2006) and the momentum advection term is computed with the energy and enstrophy conserving scheme proposed by Arakawa and Lamb (1981). The advection of the tracers (temperature and salinity) is computed with a total variance diminishing (TVD) advection scheme (Lévy et al., 2001; Cravatte et al., 2007). The high frequency gravity waves are filtered out by a free surface (Roullet and Madec, 2000). A laplacian lateral isopycnal diffusion on tracers and a horizontal biharmonic viscosity for momentum are used. In addition, the vertical mixing is parameterized according to a turbulent closure model (order 1.5) adapted by Blanke and Delecluse (1993), the lateral friction condition is a partial-slip condition with a regionalisation of a no-slip condition (over the Mediterranean Sea) and the Elastic-Viscous-Plastic rheology formulation for the LIM2 ice model (hereafter called LIM2_EVP, Fichefet and Maqueda, 1997) has been activated (Hunke and Dukowicz, 1997). The bathymetry used in the system comes from a combination of interpolated ETOPO (Amante et al., 2009) and GEBCO8 (Becker et al., 2009) databases. ETOPO datasets are used in regions deeper than 300 m and GEBCO8 is used in regions shallower than 200 m with a linear interpolation in the 200 m 300 m layer. The monthly runoff climatology is built with data on coastal runoffs and 100 major rivers from Dai and Trenberth (2002) together with an annual estimate of Antarctica ice sheets melting given by Jacobs et al. (1992). Barotropic mixing due to tidal currents in the semi-enclosed Indonesian throughflow region has been parameterized following Koch-Larrouy et al. (2008). The atmospheric fields forcing the ocean model are taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) Integrated Forecast System. A 3 h sampling is used to reproduce the diurnal cycle, in order to force the upper layers of the ocean model, with a thickness of 1 m for the uppermost level. Momentum and heat turbulent surface fluxes are computed from CORE bulk formulae (Large and Yeager, 2009) using the usual set of atmospheric variables: surface air temperature at a height of 2 m, surface humidity at a height of 2 m, mean sea level pressure and wind at a height of 10 m. Downward longwave and shortwave radiative fluxes and rainfall (solid + liquid) fluxes are also used in the surface heat and freshwater budgets. An analytical formulation (Bernie et al., 2005) is applied to the shortwave flux in order to reproduce an ideal diurnal cycle. Lastly, the system does not include tides and so does not intend to simulate continental shelf areas with large tides. My Ocean Public Page 10/ 84

11 The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. MyOcean TAC s altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out an internal quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. Note that in addition to Jason 2 and Cryosat 2 altimetry observations, Jason 1 altimetry observations are assimilated until June 2013 (until the end of the mission on June 21 st ) and SARAL/Altika observations start being assimilated in August 2013 (2013/08/07). The analysis is not performed at the end of the assimilation window but at the middle of the 7-day assimilation cycle. The objective is to take into account both past and future information and to provide the best estimate of the ocean centred in time. With such an approach, the analysis, to some extent, acts like a Smoother algorithm. After each analysis, the data assimilation produces increments of barotropic height, temperature, salinity and zonal and meridional velocity. The SSH increment is the sum of barotropic and dynamic height increments. Dynamic height increment is calculated from the temperature and salinity increments. All these increments are applied progressively using the Incremental Analysis Update (IAU) method (Bloom et al., 1996; Benkiran and Greiner, 2008) which makes it possible to avoid model shock every week due to the imbalance between the analysis increments and the model physics. In this way, the IAU reduces spin-up effects. It is fairly similar to a nudging technique but does not exhibit weaknesses such as frequency aliasing and signal damping. Following the analysis performed at the end of the forecast (or background) model trajectory (referred to as FORECAST first trajectory, with analysis time at the 4 th day of the cycle), a classical forward scheme would continue straight on from this analysis, integrating from day 7 until day 14. Instead, the IAU scheme rewinds the model and starts again from the beginning of the assimilation cycle, integrating the model for 7 days (referred to as BEST second trajectory) with a tendency term added in the model prognostics equations for temperature, salinity, sea surface height and horizontal velocities. The tendency term (which is equal to the increment divided by the length of the cycle) is modulated by an increment distribution function. The time integral of this function equals 1 over the cycle length. In practice, the IAU scheme is more costly than the classical model correction (increment applied on one time step) because of the additional model integration ( BEST trajectory) over the assimilation window. The first guess at appropriate time (FGAT) method (Huang et al., 2002) is used, which means that the forecast model equivalent of the observation for the innovation computation is taken at the time for which the data is available, even if the analysis is delayed. The concept of pseudo-observations or Observed-No Change (innovation equal to zero) has also been used to overcome the deficiencies of the background errors, in particular for extrapolated and/or poorly observed variables. We apply this approach to the barotropic height and the 3-D coastal salinity at river mouths and all along the coasts (run off rivers). Pseudo-observations are also used for the 3-D variables T, S, U and V under the ice, between 6 S and 6 N below a depth of 200 m. These observations are geographically positioned on the analysis grid points rather than on a coarser grid in order to avoid generating aliasing on the horizontal dimension. The time of these observations is the same as for the analysis, namely the fourth day of a 7-day assimilation cycle. Lastly, the Mean Dynamic Topography (MDT) named CNES-CLS09 derived from observations and described in Rio et al. (2011) is used as a reference for SLA assimilation. My Ocean Public Page 11/ 84

12 To correct some deficiencies of the previous systems, trials were carried out with the following additions and changes: (1) Instead of being constant, the depth of light extinction is separated in Red-Green-Blue bands depending on the chlorophyll data distribution from mean monthly SeaWIFS climatology. (2) The estimation of Silva et al. (2006) has been implemented in the system to represent the amount and distribution of meltwater which can be attributed to giant and small icebergs calving from Antarctica, in the form of a monthly climatological runoff at the southern ocean surface. (3) Despite the previous correction and updates, the freshwater budget is far from balanced. In order to avoid any mean sea-surface-height drift due to the poor water budget closure, the surface freshwater budget is set to zero at each time step with a superimposed seasonal cycle (Chen et al., 2005). It helps to reduce errors in SLA assimilation. However, the concentration/dilution water flux term is not set to zero. (4) AVHRR+AMSRE SST has been assimilated in the V3 system, instead of RTG SST in V2. (5) An error map based on the maximum of sea-ice extent was applied to correctly assimilate the Envisat altimeter data for the Arctic. (7) In October 2010, the Envisat altimeter was brought to a lower orbit, which has led to a slight degradation of data quality (Ollivier and Faugere, 2010). This degradation is due to the fact that SLA is computed with respect to a Mean Sea Surface of lower quality because it falls outside the historical repeat track. This is particularly true at high latitudes where no tracks from other missions are available. For this reason, the Envisat error was increased by 2 cm over the entire domain and by 5 cm above 66 N. (7) The observation error variance was increased for the assimilation of SLA near the coast and on the shelves, and for the assimilation of SST near the coast (within 50 km of the coast). (8) The spatial correlation radii were modified everywhere in order to improve the analysis particularly near the European coast. (9) New temperature and salinity vertical profiles from the sea mammal (elephant seals) database (Roquet et al., 2011) were assimilated to compensate for the lack of such data at high latitudes. (10) A Quality Control (QC) of T/S vertical profiles has been implemented to discard suspicious temperature and salinity vertical profiles. This is done in addition to the quality control procedures performed by the INS TAC CORIOLIS data centre. (11) The updates include a modification of the multivariate data assimilation scheme in order to use an adjusted version of CNES-CLS09 MDT including GOCE observations and bias correction. A simulation for calibration of the V3 system was started in October 2009 from a 3D climatology of temperature and salinity (Levitus 2009). The V3 system starts operation in April System name Domain Resolution Model version Assimilation software version Assimilated observations Status production of HR global global 1/12 on the horizontal + 50 levels on the vertical ORCA12 + LIM2 EVP +NEMO hourly atmospheric forcing from ECMWF + Bulk CORE + New SAM2 (SEEK Kernel) + IAU + 3D-Var bias correction + Observation error s higher near the coast (SST and SLA) and Reynolds SST AVHRR/AMSR + SLA from available altimetric satellites + in situ profile from CORIOLIS + MDT CNES/CLS09 Weekly + Daily update of atmospheric forcing for 7-day forecast My Ocean Public Page 12/ 84

13 parameterization of vertical mixing + Taking into account ocean colour for depth of light extinction + Adding runoff for iceberg melting + Adding seasonal cycle for surface mass budget on shelves (SLA) + MDT error adjusted + New correlation radii (minimum =130km) + Increase of Envisat error + QC on T/S vertical profiles + Procedure to avoid the damping of SST increments via the bulk forcing function adjusted + Sea Mammals T/S profiles Table 5: Synthetic description of WP05 V3 production system My Ocean Public Page 13/ 84

14 III VALIDATION FRAMEWORK Most of the scientific assessment of HR global system is documented in Lellouche et al. (2012) and is summarized by the table of Metrics (Table 6). In this document we will emphasize accuracy metrics (data assimilation scores) when available. Results will be presented in section 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, and regional basins Error=obs-model Time evolution of RMS and mean error Data statistics assimilation MyOcean: On track AVISO sla observations from Jason1, Jason2, Envisat, Cryosat 2 and SARAL/Altika Sea surface height At tide gauges (Global but near coastal regions) Error=model-obs Time series correlation and RMS error CLASS4 Tide gauges sea level time series from GLOSS, BODC, Imedea, WOCE, OPPE and SONEL Sea Surface Temperature SST Global, and regional basins Error=obs-model Time evolution of RMS and mean error Data statistics assimilation Reynolds AVHRR ¼ Surface layer zonal current U Global, and regional basins Error=model-obs Mean error and vector correlation CLASS4 SVP drifting buoys from CORIOLIS Surface layer meridional current V Global, and regional basins Error=model-obs Mean error and vector correlation CLASS4 SVP drifting buoys from CORIOLIS Sea concentration ice Antarctic and Arctic regions Visual inspection of seasonal and interannual signal CLASS1 CERSAT Sea ice concentration Sea concentration ice Antarctic and Arctic regions Error=obs-model Time evolution of RMS CLASS4 CERSAT Sea ice concentration My Ocean Public Page 14/ 84

15 and mean error Sea concentration ice 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 2009 monthly climatology of temperature and salinity T, S, U, V, SSH, atmospheric forcing 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 AVHRR 3D U and V Global Visual inspection of volume transports through sections CLASS3 litterature Table 6: List of metrics that were computed to assess the system that produce WP05 V3 products: HR global 1/12. My Ocean Public Page 15/ 84

16 IV VALIDATION RESULTS As defined in Figure 47 for GODAE project, 11 regions were calibrated corresponding to the main basins of the global ocean: NAT, TAT, MED, SAT, IND, SPA, TPA, NPA, ARC, ACC, GLO. The modeled physical variables were compared to assimilated and independent observations when available. When possible, the V3 and V2 model solutions were inter-compared to ensure the consistency of the results. This document displays the main synthetic results of the calibration of the systems. The non-regression is shown by comparing V3 statistics with V2 ones. We defined a period of one year for the V3 calibration from June 2010 to May In the next sections of the document, we present hindcast and forecast 3-day statistics by region and depth layers. IV.1.1 North Atlantic: NAT IV Temperature 3D temperature accuracy results are synthesized in Table 7. - Small biases (< 0.05K) reduced from V2 to V3. - RMS errors are large near the thermocline where the variability is higher. - Surface slightly too cold on average. - Sub-surface (until 300m) generally warmer than the observations - Warm biases in the m layer but the statistics are not very reliable in this layer because of the few number of data 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,767 0,821-0,06 0,022 0,912 1,008-0,085 0, m 0,814 0,848-0,046-0,017 0,987 1,02-0,062-0, m 0,663 0,684-0,069-0,035 0,831 0,864-0,087-0, m 0,485 0,494-0,012-0,003 0,623 0,634-0,014-0, m 0,192 0,258-0,01 0 0,234 0,293-0,014-0, m 0,052 0,09 0,044-0,058 0,066 0,101-0,057-0, m 0,54 0,597-0,03-0,01 0,672 0,725-0,044-0,013 Table 7: NAT RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 16/ 84

17 Figure 1: V2 and V3 forecast accuracy intercomparison during V3 calibration period: NAT RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table 8. - Small biases (< 0.02 psu). - Fresh bias at the surface. - Salty bias at the subsurface until 300m My Ocean Public Page 17/ 84

18 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,405 0,42 0,016-0,007 0,468 0,473 0,024 0, m 0,197 0,216 0,013-0,012 0,235 0,253 0,016-0, m 0,116 0,13-0,007-0,015 0,142 0,158-0,009-0, m 0,069 0,07-0,002-0,003 0,084 0,087-0,003-0, m 0,03 0, ,035 0,046-0, m 0,005 0,008-0,004-0,002 0,006 0,009-0,005-0, m 0,153 0,169 0,006-0,005 0,182 0,197 0,008-0,003 Table 8: NAT RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 18/ 84

19 Figure 2 : V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: NAT RMS (upper panel) and average (lower panel) salinity misfits in PSU(observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST The intercomparison of SST data assimilation statistics during calibration period shows similar results between V3 and V2 products. Note that the V2 system performed well in this region because it was a regional high resolution model (1/12 ) specially tuned in the Atlantic and Mediterranean basins. A warm bias seems to appear in most of the regions. My Ocean Public Page 19/ 84

20 Figure 3: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the North Atlantic NAT region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Bias are reduced and RMS errors are of the same order on average over the region (Table 9). SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 NAT 0,546 0,566 0,015 0,002 0,68 0,71 0,02 0 Table 9: NAT RMS and average SST misfits in K (observation-model) with respect to AVHRR-AMSRE Reynolds ¼ observations. IV SLA The same consistency is observed on Figure 4 in terms of SLA data assimilation. On average between the three available satellites (Jason1, Jason2, Envisat), HR global biases do not exceed 1.5 cm in all regions except in Gulf of Mexico and Florida straits 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). V3 results are similar to V2 except in the Gulf stream region V3 seems to be better. My Ocean Public Page 20/ 84

21 Figure 4: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the Tropical and North Atlantic regions. 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. On average over the NAT zone (Table 10) we note a small negative bias of around 1 cm consistent with a warm SST bias at the surface (Figure 3). Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 NAT 6,78 7,54-0,62-1,72 7,57 8,17-0,62-1,7 Table 10:NAT RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from calibration period are used to build these regional estimates. IV.1.2 Tropical Atlantic: TAT IV Temperature 3D temperature accuracy results are synthesized in Table 11. My Ocean Public Page 21/ 84

22 - Warm biases in the whole water column but especially at the surface and in the m layer. - Small biases (< 0.1K) reduced from V2 to V3. - RMS errors are large near the thermocline where the variability is higher. - Surface bias is stronger than in NAT region. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,406 0,379 0,062-0,03 0,5 0,483 0,097-0, m 0,876 0,853-0,015-0,01 1,04 1,02-0,005-0, m 0,612 0,608-0,053-0,045 0,748 0,746-0,064-0, m 0,275 0,303-0,008-0,006 0,323 0,344-0,007-0, m 0,079 0,095-0,011-0,004 0,085 0,098-0,011-0, m 0,039 0,113-0,037-0,037 0,043 0,119-0,041-0, m 0,447 0,453-0,007-0,018 0,54 0,542 0,001-0,032 Table 11: TAT RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 22/ 84

23 -0,1-0,05 0 0,05 0,1 0-5m 5-100m m m V2 V m m m Figure 5: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: TAT RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table Very small biases (< 0.01 psu). - V3 clearly better than V2 in the 5-100m layer - Very slightly salty bias from surface to subsurface until 300m 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,334 0,321-0,009-0,01 0,385 0,364-0,001-0, m 0,199 0,202 0,067-0,003 0,245 0,244 0,086-0, m 0,112 0,114-0,003-0,008 0,137 0,14-0,003-0, m 0,044 0,044 0, ,051 0,051 0, m 0,022 0,021 0,002 0,003 0,023 0,021 0,002 0, m 0,005 0,013-0,005 0,003 0,005 0,013 0,005 0, m 0,145 0,152 0,03 0,007 0,174 0,18 0,039 0,01 Table 12: TAT RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 23/ 84

24 Figure 6: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: TAT RMS (upper panel) and average (lower panel) salinity misfits 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 negative and less than 0.25 K as can be seen on Figure 7. RMS errors vary around 0.5 K, and the average over the zone is 0.4 K as indicated in Table 13. On average over the TAT zone the warm bias is around 0.1 K. My Ocean Public Page 24/ 84

25 Figure 7: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the Tropical Atlantic TAT region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 TAT 0,33 0,33 0,016-0,08 0,42 0,44 0,03-0,12 Table 13: TAT RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. My Ocean Public Page 25/ 84

26 IV SLA Figure 8: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the Tropical Atlantic 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. - V3 version is better than V2 in the whole domaine (except in Sao Tome Tide) for mean and rms statistics. - SLA innovations of the Tropical Atlantic exhibit a 1 cm bias on average (Table 14,Figure 8). Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 TAT 3,13 3,19 0,16 0,09 3,14 3,43 0,17 0,07 Table 14: TAT RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from calibration period are used to build these regional estimates. IV.1.3 Mediterranean Sea: MED My Ocean Public Page 26/ 84

27 IV Temperature 3D temperature accuracy results are synthesized in Table Cold biases from the surface (0.2K) to 800m (0.05K) - Warm bias in the m layer (0.08) - Biases <0.2K but higher than V2, RMS error is higher in V3 - RMS errors are large near the thermocline where the variability is higher. Subsurface biases are stronger in V3 compare to V2 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,52 0,811 0,129 0,337 0,587 0,847 0,147 0, m 0,665 1,01-0,112 0,147 0,805 1,087-0,166 0, m 0,318 0,554-0,002 0,103 0,395 0,608 0,003 0, m 0,178 0,28 0,047 0,027 0,237 0,293 0,069 0, m 0,057 0,127-0,01-0,08 0,061 0,122-0,009-0, m No data No data No data No data No data No data No data No data m 0,448 0,74-0,009 0,19 0,535 0,776-0,021 0,036 Table 15: MED RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. The lack of stratification can also be diagnosed in the Mediterranean Sea, as can be seen in Table 15 and in Figure 9. The cold bias of around 0.2 K at the surface is stronger than in the Atlantic. In the MED region, the V2 version is significantly more accurate than the V3 version. My Ocean Public Page 27/ 84

28 Figure 9: V2 and V3 forecast accuracy intercomparison during V3 calibration period: MED RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table Small biases (< 0.05 psu). - V3 biases smaller than V2 in 0-100m layer but higher in m layer - A fresh bias of 0.05 psu can be diagnosed at the surface up to 800m 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,242 0,275 0,051 0,024 0,263 0,298 0,064 0, m 0,168 0,214 0,044 0,026 0,199 0,239 0,055 0, m 0,088 0,131 0,001 0,04 0,105 0,141 0,003 0, m 0,044 0,078 0,017 0,053 0,053 0,08 0,021 0, m 0,018 0,021-0,004-0,009 0,019 0,02-0,004-0, m No data No data No data No data No data No data No data No data m 0,136 0,174 0,034 0,029 0,155 0,188 0,042 0,036 Table 16 : MED RMS and average salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 28/ 84

29 Figure 10: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: MED RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST Except in Aegean Seas a warm bias (Table 17) of 0.01 K on average (Figure 11) is diagnosed at the surface with Reynolds ¼ SST assimilation statistics in the MED region. The Ionian Sea and the Aegean Seas display more bias and higher RMS errors. The RMS error is higher in V3 compare to V2 products. My Ocean Public Page 29/ 84

30 Figure 11: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the Mediterranean MED region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 MED 0,6 0,98-0,078 0,058 0,66 0,88-0,06-0,09 Table 17: MED RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. My Ocean Public Page 30/ 84

31 IV SLA Figure 12: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the Mediterranean 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 MED 6,78 8,3 1,51 2,06 7 8,21 1,48 1,68 Table 18: MED RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from calibration period are used to build these regional estimates. The SLA bias is more important in the MED region than in the Atlantic, and stay below 4 cm on average. This high value is due to strong biases of more than 3 cm in the Ionian Sea and Sicily regions. These regions are shallow and close to the coast, in consequence the SLA observation error is high these zones in the HR global. Thus, these regions are poorly constrained and biases are observed. My Ocean Public Page 31/ 84

32 IV.1.4 South Atlantic: SAT IV Temperature 3D temperature accuracy results are synthesized in Table Cold biases from the surface to 100m (0.05K) - Warm bias in the m layer (0.1K in m layer) - Small biases (< 0.05K) reduced from V2 to V3. - Small RMS error (<0.8) reduced from V2 to V3. - RMS errors are large near the thermocline where the variability is higher. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,393 0,279 0,099 0,041 0,419 0,365 0,107 0, m 0,952 0,679 0,203 0,013 1,027 0,85 0,227 0, m 0,92 0,64 0,209-0,084 1,007 0,81 0,236-0, m 0,554 0,372 0,14-0,042 0,607 0,474 0,153-0, m 0,172 0,11-0,008-0,006 0,186 0,135-0,008-0, m 0,09 No data -0,073 No data 0,091 No data -0,07 No data m 0,63 0,485 0,114 0,004 0,681 0,614 0,129 0,002 Table 19: SAT RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 32/ 84

33 Figure 13: : V2 and V3 forecast accuracy intercomparison during V3 calibration period: SAT RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table fresh bias (less than psu) at the surface - small salty bias in the m layer (less than 0.01 psu) - Small biases (< 0.05K) reduced from V2 to V3. - Small rms error (<0.15) reduced from V2 to V3. - RMS errors are large near the thermocline where the variability is higher. 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,169 0,079 0,033 0,011 0,177 0,092 0,036 0, m 0,179 0,129 0,015-0,003 0,191 0,159 0,019-0, m 0,163 0,104 0,025-0,006 0,177 0,131 0,029-0, m 0,088 0,051 0, ,094 0,062 0,029-0, m 0,044 0,024 0,013 0,002 0,045 0,026 0,013 0, m 0,002 No data 0 No data 0,002 No data 0 No data m 0,132 0,103 0,024 0,002 0,14 0,125 0,027 0,002 My Ocean Public Page 33/ 84

34 Table 20: SAT RMS and average salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. Figure 14: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: SAT RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 34/ 84

35 IV SST As can be seen in Figure 15, most subregions of the SAT zone display a warm bias at the surface of around 0.2 K. We can notice that the HR global has a reduce bias in region with high mesoscale variability: the Aghulas region. The RMS error reaches 0.6 K. Figure 15: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the South Atlantic SAT region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 SAT 0,458 0,477 0,07-0,03 0,55 0,64 0,08-0,04 Table 21: SAT RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE Reynolds ¼ observations. IV SLA As can be seen in Table 22 and Figure 16, the forecast is close to the SLA observations. A very small positive bias of 0.1 cm (consistent with a cold bias) can be diagnosed, while the RMS error is less than My Ocean Public Page 35/ 84

36 5 cm in most of the South Atlantic, and higher (near 15 cm) in regions of mesoscale activity such as Agulhas region. The RMS error seems to be lower is the V3 version than in V2 Figure 16: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the South Atlantic 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 SAT 8,59 6,81 0 0,28 9,14 7,85-0,001 0,337 Table 22: SAT RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. Statistics from calibration period are used to build these regional estimates. My Ocean Public Page 36/ 84

37 IV.1.5 IV Indian Ocean: IND Temperature 3D temperature accuracy results are synthesized in Table warm bias at the surface (0.01K) - Biases reduced from V2 to V3 in the whole water column. (<0.1) - Rms error reduced from V2 to V3 in the whole water column (<0.8) - RMS errors are large near the thermocline where the variability is higher. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,505 0,324 0,08-0,059 0,535 0,413 0,084-0, m 0,896 0,686 0,176 0,009 0,973 0,846 0,193 0, m 0,804 0,636 0,153-0,006 0,89 0,815 0,169-0, m 0,445 0,335 0,076 0,007 0,473 0,415 0,081 0, m 0,22 0,156 0, ,229 0,148 0,055 0, m 0,082 No data -0,07 No data 0,082 No data -0,074 No data m 0,566 0,458 0, ,61 0,573 0,104 0,002 Table 23: IND RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 37/ 84

38 Figure 17: V2 and V3 forecast accuracy intercomparison during V3 calibration period: IND RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table fresh bias at the surface (0.02 psu) until 100m - salty bias (-0.01 psu) in subsurface. - Biases reduced from V2 to V3 in the whole water column. (<0.02) - RMS error reduced from V2 to V3 in the whole water column (<0.2) 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,188 0,073-0,002 0,01 0,197 0,097-0,003 0, m 0,192 0,158-0,02 0,006 0,205 0,191-0,028 0, m 0,124 0,105-0,017-0,007 0,133 0,128-0,017-0, m 0,063 0,049-0,003 0,001 0,066 0,058-0,003 0, m 0,028 0,019 0, ,028 0,021 0, m 0,001 No data -0,001 No data 0,001 No data -0,005 No data m 0,13 0,116-0,011 0,005 0,137 0,141-0,011 0,006 Table 24: IND RMS and average salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 38/ 84

39 Figure 18: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: IND RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST As seen in Table 25 and Figure 19, in the Indian Ocean the cold bias is very small (<0.01K) and lower than V2 version. In South Indian Ocean the cold bias is much stronger than in other part of the domain. In the Tropical Region the mean error is close to zero and the RMS error of V3 is lower than V2 version. In the whole domain the RMS error is close to 0.5 K. My Ocean Public Page 39/ 84

40 Figure 19: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the Indian IND region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 IND 0,43 0,39 0,08 0,02 0,5 0,5 0,1 0,03 Table 25: IND RMS and average SST misfits in K (observation-model) with respect to AVHRR-AMSRE 1/4 observations. IV SLA The SLA bias is close to zero or negative in the Indian ocean, as seen in Figure 20 and Table 26.The RMS error is around 5 cm. The V3 version performs better than V2 in RMS and Average misfit. My Ocean Public Page 40/ 84

41 Figure 20: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) 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. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 IND 6,54 5,41-0,09-0,16 7,01 6,22-0,1-0,15 Table 26: IND RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2, Envisat observations. IV.1.6 IV Arctic Ocean: ARC Temperature 3D temperature accuracy results are synthesized in Table warm bias from the surface up to 300m (0.05K to 0.1K) - cold bias at the subsurface between 300 and 2000m (<0.1K) - Biases reduced from V2 to V3 in the whole water column. (<0.1) - RMS error reduced from V2 to V3 in the whole water column (<1) - RMS errors are large near the thermocline where the variability is higher. My Ocean Public Page 41/ 84

42 However one must keep in mind that very few in situ observations are available to constrain the systems in this region. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,58 0,376 0,141 0,009 0,593 0,442 0,132-0, m 0,86 0,821 0,226-0,085 0,932 0,975 0,251-0, m 0,69 0,602 0,224-0,05 0,763 0,723 0,258-0, m 0,556 0,466 0,145 0,061 0,596 0,591 0,165 0, m 0,138 0,09 0,069 0,01 0,144 0,099 0,072 0, m No data No data No data No data No data No data No data No data m 0,751 0,6 0,212-0,025 0,8 0,714 0,227-0,04 Table 27: ARC RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 42/ 84

43 Figure 21: V2 and V3 forecast accuracy intercomparison during V3 calibration period: ARC RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table Salty bias is diagnosed in the whole water column. (up to psu) - RMS error is reduced in the V3 version with respect to the V2 (cf Figure 22) 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,074 0,082 0,006-0,017 0,075 0,09 0,006-0, m 0,19 0,171-0,013-0,03 0,196 0,187-0,01-0, m 0,088 0,082-0,007-0,016 0,09 0,089-0,006-0, m 0,037 0,033-0,008-0,005 0,038 0,039-0,007-0, m 0,008 0,015-0,002-0,007 0,008 0,015-0,002-0, m No data No data No data No data No data No data No data No data m 0,141 0,116-0,01-0,026 0,146 0,126-0,008-0,027 Table 28: ARC RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 43/ 84

44 Figure 22: HR global (blue) and IR global (brown) hindcast accuracy intercomparison: ARC RMS (upper panel) and average (lower panel) salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST No observations IV SLA No observations My Ocean Public Page 44/ 84

45 IV Sea ice variables In the Arctic Ocean, Sea ice products from the HR global V3 are generally more realistic than HR global products V2. The HR global Sea ice cover is slightly overestimated throughout the year, as can be seen in Figure 23. HR global V3 slightly overestimate the sea ice cover in boreal summer, the signal in the Marginal Seas being well reproduced 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 29. Figure 23: Sea ice area (upper panel, 10 3 km2) and extent (lower panel, 10 3 km2) in the Arctic in HR global products V2 (blue line), HR global products V3 (black line) and SSM/I observations (red line) for a one year period ending in June 2011 Sea ice concentration (%) Hindcast Forecast day 3 Description of misfit Description of misfit ARC +50 to locally 100% in marginal seas, +20% all year long in central Arctic. +50 to locally 100% marginal seas, + 10% in Central Arctic in boreal summer My Ocean Public Page 45/ 84

46 Table 29: qualitative estimate of the Sea Ice concentration accuracy in the ARC region IV.1.7 IV Southern Ocean: ACC Temperature 3D temperature accuracy results are synthesized in Table RMS error and average misfit error are reduced in the V3 version with respect to the V2 (cf Figure 24) - As in the Arctic, the High latitudes of the Southern Hemisphere display a warm bias of around 0.15 K on the whole water column. - RMS errors reach 0.5 K on average. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,574 0,319 0,196 0,009 0,62 0,447 0,211-0, m 0,803 0,559 0,118-0,008 0,882 0,725 0,13-0, m 0,801 0,578 0,039-0,08 0,887 0,746 0,044-0, m 0,527 0,359 0,071-0,034 0,581 0,471 0,078-0, m 0,192 0,114-0,043-0,0053 0,205 0,141-0,043-0, m No data 0,014 No data -0,012 No data 0,014 No data -0, m 0,647 0,399 0,093-0,01 0,71 0,52 0,102-0,022 Table 30: ACC RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 46/ 84

47 Figure 24: V2 and V3 forecast accuracy intercomparison during V3 calibration period: ACC RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table RMS and salinity average salinity misfits are reduced in the V3 version with respect to the V2 (cf Figure 22) - Very small biases (< 0.02 psu). - Very small RMS error (< 0.15 psu). My Ocean Public Page 47/ 84

48 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,148 0,07 0,029 0,01 0,153 0,089 0,031 0, m 0,15 0,103-0,02 0 0,16 0,125-0,02-0, m 0,136 0,094-0,026-0,004 0,145 0,114-0,02-0, m 0,077 0,052 0,01 0 0,082 0,063 0, m 0,034 0,019 0,006 0,001 0,035 0,021 0,006 0, m No data 0,007 No data 0,007 No data 0,007 No data 0, m 0,126 0,077-0,001 0,002 0,133 0, ,003 Table 31: ACC RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 48/ 84

49 Figure 25: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: ACC RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST - The RMS error is around 0.5 to 0.6 K. - Mean error is lower than 0.05K My Ocean Public Page 49/ 84

50 Figure 26: Comparison of SST data assimilation scores (left: average misfit in K, right: RMS misfit in K) averaged ) averaged on calibration period 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. SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 ACC 0,455 0,5 0,023-0,02 0,54 0,63 0,03-0,02 Table 32: ACC RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE 1/4 observations. IV SLA - SLA bias in this region is close to 0.4cm - The RMS error, around 8 cm on average (Figure 27 and Table 33), is reduced in the V3 version with respect to the V2. My Ocean Public Page 50/ 84

51 Figure 27: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit 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. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 ACC 8,08 6,62 0,01 0,33 8,58 7,42 0,01 0,37 Table 33: ACC RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. IV Sea ice variables The Sea Ice cover is slightly underestimated by the HR global in the austral summer and overestimated during the austral winter as can be seen in Figure 28 and Table 34. The HR global V3 performs better than HF global V2 version for ice extend and ice area. My Ocean Public Page 51/ 84

52 Figure 28 : Sea ice area (upper panel, 10 3 km2) and extent (lower panel, 10 3 km2) in the Antarctic Ocean in HR global products V2 (blue line), HR global products V3 (black line) and SSM/I observations (red line) for a one year period ending in June 2011 Sea ice concentration (m) Hindcast Forecast day 3 Description of misfit Description of misfit ACC +10% all year long, +50 to locally 100% in austral summer -50 to locally -100% in austral summer Table 34: qualitative estimate of the Sea Ice concentration accuracy in the ACC region My Ocean Public Page 52/ 84

53 IV.1.8 South Pacific Ocean: SPA IV Temperature 3D temperature accuracy results are synthesized in Table Cold biases from the surface to 100m (0.05K) - Biases reduced from V2 to V3 in the whole water column. (<0.05K) - RMS error reduced from V2 to V3 in the whole water column (<0.8K) - RMS errors are large near the thermocline where the variability is higher. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,355 0,271 0,178 0,02 0,39 0,362 0,197 0, m 0,658 0,471 0,189 0,044 0,736 0,611 0,213 0, m 0,859 0,65 0,219-0,008 0,935 0,785 0,246-0, m 0,464 0,368 0,096 0,013 0,495 0,436 0,102 0, m 0,143 0,121-0,015-0,002 0,148 0,132-0,015-0, m No data 0,014 No data -0,012 No data 0,014 No data -0, m 0,536 0,356 0,154 0,022 0,589 0,447 0,171 0,03 Table 35: SPA RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 53/ 84

54 Figure 29: V2 and V3 forecast accuracy intercomparison during V3 calibration period: SPA RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table Fresh biases from the surface to 100m (up to 0.02 PSU) - Biases reduced from V2 to V3 in the whole water column. (<0.02 psu) - RMS error reduced from V2 to V3 in the whole water column (<0.15 psu) - RMS errors are large near the thermocline where the variability is higher. 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,107 0,076 0,028 0,013 0,112 0,092 0,029 0, m 0,158 0,12-0,009 0,005 0,167 0,146-0,01 0, m 0,119 0,084-0,033-0,005 0,124 0,098-0,033-0, m 0,054 0,04-0, ,057 0,048-0,004-0, m 0,02 0,014-0, ,02 0,015-0, m No data 0,007 No data 0,007 No data 0,007 No data 0, m 0,121 0,083 0,002 0,007 0,127 0,099 0,002 0,008 Table 36: SPA RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 54/ 84

55 Figure 30: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: SPA RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST In Figure 31 and Table 37, on the whole regions the SST average misfit is close to zero. The RMS error is around 0.5 K, also consistent with the other regions. My Ocean Public Page 55/ 84

56 Figure 31: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the South Pacific SPA region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 SPA 0,405 0,406 0,089-0,05 0,47 0,5 0,1-0,06 Table 37: SPA RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE 1/4 observations. IV SLA No significant SLA bias can be diagnosed in the SPA region Figure 32 and Table 38, while the RMS error is 5 cm on average. V3 is better than V2 in RMS. My Ocean Public Page 56/ 84

57 Figure 32: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the SPA 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 SPA 6,27 5,54 0,09-0,31 6,64 6,15 0,097-0,31 Table 38: SPA RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. IV.1.9 Tropical Pacific Ocean: TPA IV Temperature 3D temperature accuracy results are synthesized in Table Cold biases from the surface to 100m (0.05K) - Biases reduced from V2 to V3 in the whole water column. (<0.05K) - RMS error reduced from V2 to V3 in the whole water column (<0.8K) My Ocean Public Page 57/ 84

58 - RMS errors are large near the thermocline where the variability is higher. - V3 product is better adjusted than V2 version to the strong interannual ENSO variability in the region. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,109 0,259 0,157 0,016 0,442 0,325 0,167 0, m 0,89 0,659 0,192 0,04 0,958 0,797 0,213 0, m 0,965 0,744 0,174-0,012 1,026 0,861 0,19-0, m 0,343 0,257 0,06-0,007 0,357 0,286 0,062-0, m 0,131 0,086 0,009-0,005 0,133 0,09 0,009-0, m 0,015 0,006-0,014-0,004 0,015 0,006-0,01-0, m 0,546 0,388 0,098 0,008 0,583 0,4635 0,11 0,014 Table 39: TPA RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 58/ 84

59 Figure 33: V2 and V3 forecast accuracy intercomparison during V3 calibration period: TPA RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table Fresh biases from the surface to 100m (up to 0.02 PSU) - Biases reduced from V2 to V3 in the whole water column. (<0.025 psu) except in surface layer (0-5m) - RMS error reduced from V2 to V3 in the whole water column (<0.18 psu) - RMS errors are large near the thermocline where the variability is higher. 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,169 0,147 0,022 0,023 0,182 0,18 0,025 0, m 0,198 0,152-0,009 0,002 0,209 0,183-0,009 0, m 0,127 0,09-0,034-0,002 0,133 0,104-0,035-0, m 0,039 0,025-0, ,04 0,028-0, m 0,015 0, ,015 0, m m 0,125 0,097-0,002 0,007 0,132 0,117-0,002 0,009 Table 40: TPA RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 59/ 84

60 Figure 34: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: TPA RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST As can be seen in Table 41 and Figure 35 a warm bias between 0.1 and 0.2 K is diagnosed in HR global for all regions. The RMS error is below 0.5K for main regions but could reach 1K in Nino1-2 region. RMS error and bias are reduced from V2 to V3 in the whole domain except in Nino1-2 box. My Ocean Public Page 60/ 84

61 Figure 35: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the Tropical Pacific TPA region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 TPA 0,43 0,348 0,155-0,073 0,52 0,48 0,19-0,1 Table 41: TPA RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE 1/4 observations. IV SLA A strong negative bias (-6 cm) appears in the nino 5 subregion of the Tropical Pacific in Figure 36 and Table 42, consistently with the warm bias already diagnosed Figure 35. 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 10 cm) than in the other subregions (around 4 cm) and is greater than V2 version because of a bigger observation error. The bias is thus slightly negative on average on the region while it is nearly zero in most subregions. RMS error and bias are slightly reduced from V2 to V3 in the whole domain except in Nino5 box. My Ocean Public Page 61/ 84

62 Figure 36: : Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the TPA 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Seal Level Anomaly (cm) Hindcast Forecast RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 TPA 4,28 3,83-0,02-0,17 4,47 4,1-0,007-0,17 Table 42: TPA RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. IV.1.10 North Pacific Ocean NPA IV Temperature 3D temperature accuracy results are synthesized in Table Warm biases from the surface layer (0.04K) to 800m (0.02K) except in 5-100m layer with a cold bias (0.02K) My Ocean Public Page 62/ 84

63 - Biases drastically reduced from V2 to V3 in the whole water column. (<0.05K) - RMS error reduced from V2 to V3 in the whole water column (<0.9K) - RMS errors are large near the thermocline where the variability is higher. 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,617 0,448 0,048-0,028 0,653 0,541 0,054-0, m 1,01 0,788 0,143 0,011 1,092 0,955 0,159 0, m 0,965 0,769 0,15-0,018 1,046 0,92 0,166-0, m 0,391 0,312 0,042-0,005 0,418 0,376 0,044-0, m 0,119 0,078-0,017-0,002 0,121 0,083-0,017-0, m 0,076 0,028-0,068-0,009 0,076 0,028-0,068-0, m 0,624 0,476 0,056 0,004 0,669 0,574 0,065 0,007 Table 43: NPA RMS and average temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 63/ 84

64 Figure 37: V2 and V3 forecast accuracy intercomparison during V3 calibration period: NPA RMS (upper panel) and average (lower panel) temperature misfits in K (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV Salinity 3D salinity results are synthesized in Table biases close to zero in the whole water column ( below PSU) - Biases drastically reduced from V2 to V3 in the whole water column. (<0.004PSU) - RMS error reduced from V2 to V3 in the whole water column (<0.25 psu) - RMS errors are large near the thermocline where the variability is higher. 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,252 0,213-0,01-0,004 0,265 0,244-0,01 0, m 0,2 0,149-0,011 0,001 0,212 0,18-0,016 0, m 0,123 0,088-0,016-0,003 0,13 0,104-0,042-0, m 0,047 0,036-0, ,049 0,04-0, m 0,018 0,012-0, ,018 0, m 0,004 0, ,003 0,004 0,007-0,003 0, m 0,132 0,104-0,003 0,002 0,139 0,123-0,009 0,003 Table 44: NPA RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 64/ 84

65 Figure 38: V2 (blue) and V3 (red) forecast accuracy intercomparison during V3 calibration period: NPA RMS (upper panel) and average (lower panel) salinity misfits in PSU (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. IV SST The SST biases in Table 45 and Figure 39 are consistent between systems and with all other regions, indicating a warm bias of 0.01 K and RMS errors of around 0.6 K. Mean is reduced from V2 to V3 and RMS error in on the same order. My Ocean Public Page 65/ 84

66 Figure 39: Comparison of SST data assimilation forecast scores (left: average misfit in K, right: RMS misfit in K) averaged on calibration period in the Mediterranean MED region. For each region, the bars refer respectively to V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. SST (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 NPA 0,525 0,5 0,102-0,022 0,61 0,64 0,125-0,02 Table 45: NPA RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE 1/4 observations. IV SLA In the North Pacific SLA biases are small (less than 0.6 cm) and the RMS error is 6 cm on average (Figure 40 and Table 46). Mean and RMS error are is the same order for V3 and V2. My Ocean Public Page 66/ 84

67 Figure 40: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in the NPA 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 V2 (blue) and V3 (red). The geographical location of regions is displayed in the annex. Seal Level Anomaly (cm) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 NPA 5,55 5,05-0,12-0,14 5,87 5,51-0,11-0,14 Table 46: NPA RMS and average SLA misfits in cm (observation-model) with respect to Jason 1, Jason 2 and Envisat observations. My Ocean Public Page 67/ 84

68 IV.1.11 IV GLOBAL Temperature Figure 41: Average temperature misfit in K (observation forecast) from data assimilation statistics over time and depth. Positive anomalies in Figure 41 indicate a cold bias and negative values a warm bias. We note that these biases are stable in time and representative of the instantaneous behaviour of the HR system. To summarize: - cold bias at the surface seems to seems at the end of simulation - warm bias at subsurface near 150m, seems to be weak and stable This is confirmed by Table 47: My Ocean Public Page 68/ 84

69 3DT (K) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,6 0,66 0,044 0,015 0,73 0,8 0,058 0, m 0,73 0,73 0,012 0,01 0,908 0,88 0,017 0, m 0,7 0,67 0,03-0,03 0,879 0,829 0,043-0, m 0,38 0,35 0,02-0,01 0,485 0,447 0,028-0, m 0,13 0,13 0,005-0,003 0,153 0,158 0,005-0, m 0,02 0,03-0,011-0,02 0,021 0,03-0,012-0, m 0,488 0,496 0,02-0,003 0,6 0,6 0,03-0,005 Table 47: GLO RMS and average temperature misfits in K(observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. The quality differences between HR global V2 and V3 are illustrated in Figure 42. These differences are most sensible between 100 and 300m where there are many in situ observations. Finally, all estimates are from 10% to 50% more accurate than the WOA09 reference climatology. Figure 42: Intercomparison of RMS and Mean temperature misfit in K, on average in the m layer and in the GLO region. Black : V3 version, Blue : V2 version and red : reference climatology Levitus (2009) WOA09. My Ocean Public Page 69/ 84

70 IV Salinity Figure 43: Average salinity misfit in psu (observation forecast) from data assimilation statistics over time and depth. As shown in Figure 43 and Table 48, the GLO salinity products display: - fresh bias at the surface (from 0 to 50m) less than 0.01psu - salty bias near the thermocline (from 50m to 150m) less than 0.01 psu. 3DS(PSU) Hindcast Forecast day 3 RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 0-5m 0,269 0,296-0,004 0,003 0,312 0,33-0,003 0, m 0,152 0,154-0, ,183 0,183-0, m 0,102 0, ,005 0,122 0, , m 0,051 0,048 0, ,06 0,057 0, m 0,021 0, ,023 0, m 0,002 0,004-0, ,002 0,004-0, m 0,113 0,118-0,006 0,001 0,134 0,13-0,006 0,002 Table 48: GLO RMS and average salinity misfits in psu (observation-model) with respect to the CORIOLIS in situ observations, in contiguous layers from 0 to 5000m. My Ocean Public Page 70/ 84

71 The intercomparison of HR global V2 and V3 RMS errors in the m layer points out the better accuracy of V3 global salinity products thanks to the bias correction. Figure 44: Intercomparison of RMS and Mean salinity misfit in K, on average in the m layer and in the GLO region. Black : V3 version, Blue : V2 version and red : reference climatology Levitus (2009) WOA09. IV SST Figure 45 : daily SST ( C) spatial mean for a one year period ending in may 2011, for Mercator- Ocean systems (in black) and RTG-SST observations (in red) As illustrated in Figure 45, a cold bias can be diagnosed at the global scale mainly during boreal summer 2010 and a warm bias take place during boreal automn 2010 up to spring This bias is very small and can reach 0.03 K on average. In consequence the statistics of Table 49 underestimate the maximum SST bias over the year. My Ocean Public Page 71/ 84

72 SST (K) Hindcast Forecast RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 GLO Table 49: GLO RMS and average SST misfits in K (observation-model) with respect to AVHRR- AMSRE 1/4 observations. IV SLA Figure 46: SLA data assimilation statistics for the HR global system with respect to along track observations. The stability over time of the accuracy in terms of SLA is illustrated in Figure 46. The RMS error is 6 cm on average and the average misfit oscillates between positive and negative values resulting in a small -0.1 cm bias in Table 50. Seal Level Anomaly (cm) Hindcast Forecast RMS of misfit Average misfit RMS of misfit Average misfit V2 V3 V2 V3 V2 V3 V2 V3 GLO 6,11 6,05 0,001-0,14 6,99 6,67-0,001-0,13 Table 50: GLO RMS and average SLA misfits in cm (observation-model) with respect to altimetric observations. My Ocean Public Page 72/ 84

73 IV Sea Ice Sea ACC and ARC regions comments for IR global forecast. My Ocean Public Page 73/ 84

74 V QUALITY CHANGES SINCE PREVIOUS VERSION The main impacts on product quality induced by technical changes are described in Lellouche et al. (2012) and the technical description is described in part II of this document. The V3 products are generally better than the V2 ones. When we look at the accuracy numbers for the different variables described in part IV we can conclude for a non regression between the V3 and the V2. However in the NAT and MED regions the results are not clearly better and sometimes worse for certain depth layers. Indeed, in these regions, we compare the V3 products with V2 products coming from the Atlantic Regional high resolution system (1/12 ) specially tuned to perform well in Atlantic and Mediterranean basins. Globally the V3 products are more coherent than the V2 because they are produced by a single system at a high resolution instead of a composite of a global intermediate resolution system with a regional high resolution system. These differences allow avoiding artefacts near open boundary. To summarize, the main quality product improvements are the followings: - the bias correction corrects large biases especially in the Pacific region - the update of the MDT (and his error) corrects local biases in the Indonesian throughflow and in the western Tropical Pacific ocean and improves the subsurface currents at the Equator. - earlier studies have shown that RTG SST data suffer from a cold bias near the coasts so the use of the AVHRR+AMSRE product instead of RTG helps to significantly reduce the surface temperature and salinity biases, as well as RMS differences. - the sea-ice concentration in both Arctic and Antarctica also benefits from the assimilation of an SST product improvement, the system appears to be closer to the observations (ice drift and ice concentration) than the V2 version. - the V2 system was not efficient enough to correct the SST because heat fluxes were not included in the estimated state. A detailed study reveals that most of the SST correction provided by the analysis is swept away by the bulk forcing function. The correction of the air temperature prevents the damping of increments via the bulk forcing function. The large scale bias of more than 1 K for the boreal summer in the North Pacific gyre is attenuate. My Ocean Public Page 74/ 84

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78 Tranchant, B., Testut, C. E., Renault, L., Ferry, N., Birol, F., and Brasseur, P.: Expected impact of the future SMOS and Aquarius Ocean surface salinity missions in the Mercator Océan operational systems: New perspectives to monitor ocean circulation, Remote Sens. Environ., 112: , 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 Public Page 78/ 84

79 VII MAPS OF REGIONS FOR DATA ASSIMILATION STATISTICS VII.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 My Ocean Public Page 79/ 84

80 11 Florida Straits XBT 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 VII.2 Mediterranean Sea My Ocean Public Page 80/ 84

81 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 My Ocean Public Page 81/ 84

82 13 Asia Minor VII.3 Global ocean 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 My Ocean Public Page 82/ 84

83 10 California current 11 North Tropical Pacific 12 Nino Nino3 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 Public Page 83/ 84

84 VII.4 GODAE Regions Figure 47: The regional description of GODAE regions My Ocean Public Page 84/ 84