QUALITY INFORMATION DOCUMENT For Global Ocean … · 2.0 25/09/13 All Update for MyOcean V3.1...

QUALITY INFORMATION DOCUMENT

For Global Ocean Observation-based Products

GLOBAL_REP_PHYS_001_013

Issue: 2.8

Contributors: Stéphanie Guinehut, Sandrine Mulet, Laurent Bessières, Marie-Hélène Rio, Nathalie Verbrugge

Approval Date by Quality Assurance Review Group : 02/05/2016

QUID for Global Ocean Observation-based Products


Ref: CMEMS-GLO-QUID-001-013

Date : 22/03/2016

Issue : 2.8

© EU Copernicus Marine Service – Public / 60

CHANGE RECORD

When the quality of the products change, 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

1.0

1.1

15/02/13

25/02/13

All

All

Creation of the document

Corrections to take into account review remarks by QUARG

Sandrine Mulet

Sandrine Mulet

2.0 25/09/13 All Update for MyOcean V3.1 Sandrine Mulet, Guinehut Stéphanie, Rio Marie-Hélène, Bessières Laurent

2.1 12/11/13 All Corrections to take into account review remarks by QUARG

Sandrine Mulet

2.2 24/04/14 I.1, II.2, V.1

Slightly modified because we add the year 2012 for V4.1

Sandrine Mulet

2.3 12/12/14 V Add section V.2 about validation of the year 2012

Sandrine Mulet

2.4 17/12/14 MyOF modifications Yann Drillet Yann Drillet

2.5 09/03/15 I.1,I.3, V.2

Revision after V5 acceptance Sandrine Mulet

2.6 May 1 2015

all Change format to fit CMEMS graphical rules

L. Crosnier

2.7 23/12/15 All + addV.3

Remove reference to MyOcean project and add validation results of the years 2013 and 2014 distributed for CMEMS V2

Sandrine Mulet & Nathalie Verbrugge




Date : 22/03/2016

Issue : 2.8


2.8 22/03/16 I.1 and II.3

Revision after Acceptance review CMEMS V2.

Clarification of the velocities estimation method at the equator + absolute height variable description

Nathalie Verbrugge A. Melet




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TABLE OF CONTENTS

I Executive summary ....................................................................................................................................... 6

I.1 Products covered by this document ........................................................................................................... 6

I.2 Summary of the results ............................................................................................................................... 7

I.3 Estimated Accuracy Numbers .................................................................................................................... 7

II Production Subsystem description ................................................................................................................ 9

II.1 Short description........................................................................................................................................ 9

II.2 Input data sets ............................................................................................................................................ 9

II.3 Method ...................................................................................................................................................... 10

III Validation framework ............................................................................................................................. 13

Temperature.................................................................................................................................................... 13

Salinity: ............................................................................................................................................................ 14

Currents: ......................................................................................................................................................... 14

Transports: ...................................................................................................................................................... 15

Mixed layer depth: .......................................................................................................................................... 15

IV Validation results ......................................................................................................................................... 16

IV.1 Temperature ........................................................................................................................................... 16

IV.1.1 T-CLASS1-T3D-MEAN............................................................................................................. 16

IV.1.2 T-CLASS1-SST-MEAN ............................................................................................................. 18

IV.1.3 T-CLASS1-SST-TREND ........................................................................................................... 19

IV.1.4 T-CLASS2-MOORINGS ............................................................................................................ 19

IV.1.5 T-CLASS3-HC_LAYER ............................................................................................................ 21

IV.1.6 T-CLASS4-LAYER .................................................................................................................... 23

IV.2 Salinity ..................................................................................................................................................... 25

IV.2.1 S-CLASS1-S3D_MEAN ............................................................................................................ 25

IV.2.2 S-CLASS1-SSS-TREND ............................................................................................................ 28

IV.2.3 S-CLASS2-MOORINGS ............................................................................................................ 28

IV.2.4 S-CLASS3-SC_LAYER ............................................................................................................. 30

IV.2.5 S-CLASS4-LAYER .................................................................................................................... 31

IV.3 Currents .................................................................................................................................................. 33

IV.3.1 UV-CLASS1-15m_MEAN ......................................................................................................... 33

IV.3.2 UV-CLASS2-MOORINGS ........................................................................................................ 34

IV.3.3 UV-CLASS2-MKE_1000m ........................................................................................................ 36




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IV.4 Transports ............................................................................................................................................... 37

IV.4.1 UV-CLASS3-AMOC .................................................................................................................. 37

IV.5 Other ....................................................................................................................................................... 37

IV.5.1 OTH-CLASS1-MLD_CLIM_SEASON ..................................................................................... 37

V Quality changes since previous version ...................................................................................................... 39

V.1 Change done at the MyOceanV3.1 (huge improvement of the product) ............................................. 39

V.1.1 Summary of changes since previous version .............................................................................. 39

V.1.2 Temperature and salinity fields ................................................................................................... 39

V.1.2.1 Horizontal resolution .............................................................................................................. 39

a. Temperature field .................................................................................................................................. 40

b. Salinity field ............................................................................................................................................ 42

V.1.2.2 Comparison with in-situ data .................................................................................................. 44

V.1.3 Current fields .............................................................................................................................. 46

V.2 Change done at the MyOceanV4.1 (add year 2012) .............................................................................. 47

V.3 Change done at the CMEMSV2 (add years 2013 and 2014) ................................................................ 49

V.3.1 Current fields at 100-m depth vs Yomaha velocities database ................................................... 50

V.3.2 Temperature and Salinity fields vs in-situ profiles ..................................................................... 51

VI Complementarity of in-situ and satellite observations ............................................................................... 54

VI.1 In-situ data improve information given by satellite data .................................................................... 54

VI.2 Degrees of Freedom of Signal on T/S fields .......................................................................................... 55

VII References ............................................................................................................................................... 58




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I EXECUTIVE SUMMARY

I.1 Products covered by this document

This document describes the External products GLOBAL_REP_PHYS_001_013 which details are given in the Table 1. It contains two datasets:

- dataset-armor-3d-rep-weekly-v3-1-myocean which corresponds to the weekly fields (full temporal resolution),

- dataset-armor-3d-rep-monthly-v3-1-myocean which corresponds to monthly mean fields.

As mention in Table 1, the reprocessing covers the 1993-2014 time period. The years 1993-2011 were produced first, in September 2013 (§IV and §V.1). Then, in April 2014, the year 2012 was produced in a fully consistent way with previous years (§V.2). Finally, in December 2015, the years 2013 and 2014 were produced, also in a fully consistent way with previous years (§V.3).

Note the existence of a similar near real time product: GLOBAL_ANALYSIS_PHYS_001_020. For more information see documentation on the catalogue: http://marine.copernicus.eu/web/69-interactive-catalogue.php?option=com_csw&view=details&product_id=GLOBAL_REP_PHYS_001_020.

Product Specification Customer Name


Geographical coverage Global (82°S, 90°N, 0-360°E)

Variables Temperature, Salinity, Absolute Height, Eastward and Northward geostrophic Velocities

Reprocessing Yes

Available time series 06/01/1993 – 24/12/2014 (the years 1993-2011 were produced in September 2013, the year 2012 was produced in April 2014 and the years 2013 and 2014 were produced in December 2015)

Temporal resolution Weekly and monthly fields

Target delivery time -

Delivery mechanism CMEMS Information Service

Horizontal resolution 1/4° on a regular grid

Number of vertical levels 33 levels from 0 to 5500-m depth

Format Netcdf CF1.0

Table 1: GLOBAL_REP_PHYS_001_013 product specification

http://marine.copernicus.eu/web/69-interactive-catalogue.php?option=com_csw&view=details&product_id=GLOBAL_REP_PHYS_001_013

http://marine.copernicus.eu/web/69-interactive-catalogue.php?option=com_csw&view=details&product_id=GLOBAL_REP_PHYS_001_013




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I.2 Summary of the results

These products are Global Ocean Observation-based Products (GOOP) that uses Sea Surface Temperature (SST), Sea Level Anomalies (SLA), Mean Dynamic Topography (MDT) and Temperature (T) and Salinity (S) in-situ vertical profiles. The quality of the T/S and current fields is assessed thanks to comparison to in-situ data.

Temperature: The SST used as an input of the product helps to have good results for the temperature at the surface. The largest errors are found in the mixed layer depth (0.65 °C). Then, the errors decrease with depth.

Salinity: The largest errors are found near the surface and in the mixed layer depth (<0.12 PSU). Then, like temperature field, errors decrease with depth.

Current: Comparison with in-situ current estimated (zonal and meridional components) at 15 and 1000 meter depth and with volumic transport estimated at 26.5°N show good agreement.

I.3 Estimated Accuracy Numbers

Table 2 gives the accuracy estimated for the T/S reprocessed fields:

Depth RMS error temperature (°C) RMS error salinity (psu)

GLOBAL_REP_PHYS_001_013 WOA09 GLOBAL_REP_PHYS_001_013 WOA09

surface 0.40 1.15 0.120 0.170

100 m 0.65 1.30 0.100 0.150

500 m 0.40 0.70 0.050 0.075

1000 m 0.20 0.35 0.025 0.040

1500 m 0.10 0.15 0.015 0.025

Table 2: RMS error in reconstructing subsurface temperature and salinity fields.

There is no quantitative accuracy estimate for 3D ocean currents in this document: further studies are necessary and planed. But comparison with Argo drifting at depth gives an estimate of the error at 1000 m: Table 3 gives RMS difference in 2006 with ANDRO current estimate (http://wwz.ifremer.fr/lpo/Produits/ANDRO ; Ollitrault et Rannou, 2013).

http://wwz.ifremer.fr/lpo/Produits/ANDRO




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Depth RMS zonal velocity (cm.s-1) RMS meridional velocity (cm.s-1)

GLOBAL_REP_PHYS_001_013 GLOBAL_REP_PHYS_001_013

1000 m 4.9 4.7

Table 3: RMS error in reconstructing current at 1000 m.




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II PRODUCTION SUBSYSTEM DESCRIPTION

II.1 Short description

This Quality Information Document describes the quality of the external product GLOBAL_REP_PHYS_001_013 provided by the CLS Monitoring System: GLO-CLS-TOULOUSE-FR, as part of the GLOBAL Production Center.

The GLOBAL_REP_PHYS_001_013 reprocessing consists of a long time series of global 3D temperature (T), salinity (S), absolute height and current fields defined on a 1/4° regular grid, from the surface down to 5500-m depth and at a weekly period (monthly averaged fields are also available).

This Global Ocean Observations-based Product (GOOP) is a combination between satellite and in-situ data processed in three steps:

(1) Satellite data (SLA + SST) are projected onto the vertical via a multiple linear regression method and covariances deduced from historical observations. This step gives synthetic fields,

(2) Combination between these synthetic fields with T/S in situ profiles via an optimal interpolation method. This leads to Armor3D combined fields,

(3) Use of the thermal wind equation to combine current fields from satellite altimetry with the Armor3D T/S fields. This last step generates global 3D current fields and absolute height fields.

II.2 Input data sets

Five sources of data are used:

- In-situ T and S profiles are from the INSITU TAC (Coriolis centre) including Argo profiling floats, XBT, CTD and moorings measurements. For the years 1993-2011, we used the reprocessing CORA3.4 that covers the same time period. While for the year 2012, reprocessing was not available, thus we used Near Real Time products;

- A T/S climatology from Ifremer referenced to 2004-2010 period: ARV11 (http://wwz.ifremer.fr/lpo/SO-Argo/Products/Global-Ocean-T-S/ARV11-climatology ; see also Gaillard et al., 2008, 2012 and Kolodziejczyk and Gaillard 2012);

- Altimeter sea level anomalies (SLA) are from the SL TAC (SSALTO/DUACS centre) and are weekly combined to compute maps of all processed altimeters;

- The Mean Dynamic Topography CNES_CLS13 is also used with SLA to compute surface geostrophic current fields in step (3). This MDT use the same method as CNES_CLS09 (Rio et al., 2011) but include GOCE data and more in-situ data (T/S profiles and surface drifters);

- SST data are from daily Reynolds analyses with a 1/4° horizontal resolution, combining AVHRR, AMSR and in-situ observations and distributed by the National Climatic Data Center at NOAA.

http://wwz.ifremer.fr/lpo/SO-Argo/Products/Global-Ocean-T-S/ARV11-climatology




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II.3 Method

The merging method has been first developed using simulated data sets (Guinehut et al., 2004) and has two steps to compute T/S field. Guinehut et al., 2012 describe the two steps like done for the GLOBAL_ANALYSIS_PHYS_001_020 and GLOBAL_REP_PHYS_001_013 products.

The first step of the method consists in deriving synthetic temperature (T) profiles from the surface down to 1500-meter depth from altimeter and SST data through a multiple linear regression method and covariances calculated from historical data. For synthetic salinity (S) profiles, the method uses only altimeter data. Pre-processing of altimeter SLA includes the extraction of the steric part of the SLA using regression coefficients deduced from an altimeter/in-situ comparison study (Guinehut et al., 2006 ; Dhomps et al., 2011). This first step is implemented as anomalies from the Arivo monthly climatology ARV11.

The second step of the method consists in combining the synthetic profiles with in-situ temperature and salinity profiles using an optimal interpolation method (Bretherton et al., 1976). As the main objective of the combination is to correct the large-scale part of the synthetic fields using surrounding in-situ profiles, signal correlation scales are set to twice those used to compute the AVISO gridded altimeter maps. They vary from 700 km (resp. 500 km) at the Equator to 300 km north of 60°N in the zonal (resp. meridional) directions. To gain maximum benefit from the qualities of both data sets, namely the accurate information given by in-situ T/S profiles and the mesoscale variability given by the T/S synthetic profiles, a precise statistical description of the errors of these observations has been introduced in the optimal interpolation method. For the in-situ profiles, since these observations are considered almost perfect, a very low white noise is applied. For the synthetic profiles, simulating remote-sensing (altimeter and SST) observations, since these observations are not direct measurements but are derived from the regression method, correlated errors have to be applied to correct long-wavelength errors or biases present in the synthetic fields and introduced by the regression method. The correlation scales of the error are the same than the signal ones. This second step is implemented as anomalies from synthetic fields.

Analyses are performed at a weekly period on a 1/4° regular horizontal grid on each Levitus vertical level from the surface down to 1500-meter depth.

An example of the input and output fields of the Armor3D system is given on Figure 1 for the 4th of July 2007. Thanks to the mesoscale structures available in the altimetry and SST fields, the synthetic estimate shows also mesoscale structures in most part of the ocean with T anomalies ranging from -2 to 2 °C at 100-meter depth (Figure 1). The combination of the synthetic estimates with all available in-situ temperature allows correcting the field in some regions like in the North-East Indian Ocean where the in-situ temperature are much colder than the synthetic ones. Amplitudes of the combined fields are thus more similar to the in-situ observations but with still small scale structure.

In the third step, the T/S profiles are completed from 1500 to 5500 meter depth with the annual climatology ARV11. . Then, the thermal wind equation with a reference level at the surface is used combine geostrophic current fields at the surface with the Armor3D T/S fields and thus to generate global 3D geostrophic current (Equation 3) and 3D absolute height fields (Equation 1 and 2) (Mulet al., 2012). The geostrophic surface current fields are computed from the Absolute Dynamic Topography (ADT) resulting from the addition of the SLA and the MDT CNES-CLS13 (




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Figure 2). At the equator, the thermal wind equation in no more valid because the Coriolis parameter is zero. Consequently, the velocities between 5°S and 5°N are estimated with the Lagerloef et al. (1999) method.

ℎ�𝑧𝑖 = 𝐴𝐷𝑇 + ℎ𝑠𝑡𝑟𝑞𝑧=0

(𝑧𝑖)

Equation 1

𝑤𝑖𝑡ℎ ℎ𝑠𝑞𝑟𝑡𝑧=0

�𝑧𝑖 = 𝜌′

𝜌𝑠𝑡𝑑

𝑑𝑧0

𝑧𝑖

Equation 2

𝑢� �𝑧𝑖 =

𝑔

𝑓𝑘� ∧ 𝑔𝑟𝑎𝑑�� (ℎ(𝑧𝑖))

Equation 3

In these equations, hstrq (zi) is the steric height at depth zi referenced to the surface, ADT = SLA + MDT

and std is the standard density (O°C, 35 psu).

Altimeter SLA – 04/07/2007 SST – 04/07/2007




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Arivo climatology for July

Synthetic T anomalies at 100-m depth – 04/07/2007

In-situ temperature anomalies at 100-m around the 04/07/2007

T anomalies at 100-m depth from the combined estimates – 04/07/2007

Figure 1: Input and outputs from the Armor3D systems for the 4th of July 2007.




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Figure 2: Schematic view of the computation of the ARMOR3D current fields.




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III VALIDATION FRAMEWORK

The method itself was first assessed thanks to simulated observations (results are described in Guinehut et al., 2004). Then the product was validated with independent and no-independent in-situ data and literature (results are described in Guinehut et al., 2012 ; Mulet et al. 2012 and in this document).

The validation methodology used to validate the global ocean multi year products is described by [1] and [2] and consists in a series of diagnostics defined in common by all global ocean reanalysis / reference forced simulation / reprocessing producers. Following this calibration/validation plan, this document present the results for the reprocessing product ARMOR3D_REPv3.1, the metrics used are listed in Table 4Table 8. The validation period is 1st January 1993 – 31 December 2011 (period spanned by the reanalysis/reprocessing). The times series are based on monthly fields.

Note that a common QUID was done for all the numerical model reanalyses

(http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-004-009-010-011-017.pdf). To harmonize the validation framework, we decided to add some diagnostics from it.

Temperature

Metric name Description

T-CLASS1-T3D_MEAN

Maps of long-term annual mean difference between reference climatological data set and REA temperature at different depths (see reference data sets table for the chosen depths).

T-CLASS1-SST_MEAN Maps of long-term annual mean difference between SST reference data set and REA SST.

T-CLASS1-SST_TREND Map of SST trend (computed from monthly average) over the REA period

T-CLASS2-MOORINGS Monthly mean values of REA temperature profiles collocated on observed temperature profiles for reference moorings (TAO/TRITON, PIRATA, RAMA). The following quantities are displayed: - Model and observation mean profile - Correlation between model and observations as a function of depth - Model – Observation std dev as a function of depth

T-CLASS3-HC_LAYER Domain average of temperature monthly fields as a function of time in different layers ([0-700m], [0-2000m], [2000m-bottom], [0-bottom])

T-CLASS4-LAYER

Time series of the bias and std dev of the observation minus REA (collocated on obs.) difference for in situ temperature profiles averaged in different layers ([0m; 100m], [100m; 300m], [300m; 800m], [800m; 2000m] using reanalysis monthly fields) and for each month using a reference in situ profile observation data set. The time series are calculated over the whole domain.

Table 4: Description of metrics used for temperature validation.

http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-004-009-010-011-017.pdf





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Salinity:


S-CLASS1-S3D_MEAN

Maps of long-term annual mean difference between reference climatological data set and REA salinity at different depths (see reference data sets table for the chosen depths).

WOA 2009 annual mean interpolated on the model grid at 0m, 100m, 300m, 800m and 2000m (closest model vertical levels).

S-CLASS1-SSS_TREND Map of SSS trend (computed from monthly average) over the REA period

S-CLASS2-MOORINGS Monthly mean values of salinity profiles collocated on observed salinity profiles for reference moorings (TAO/TRITON, RAMA,PIRATA). The following quantities are displayed: - Model and observation mean profile - Correlation between model and observations as a function of depth (the trend is not removed) - Model – Observation std dev as a function of depth

S-CLASS3-SC_LAYER Domain average of salinity monthly fields as a function of time in different layers ( [0-700m], [0-2000m], [2000m-bottom], [0-bottom])

S-CLASS4-LAYER Time series of the bias and std dev of the observation minus REA (collocated on obs.) difference for in situ salinity averaged in different layers ([0m; 100m], [100m; 300m], [300m; 800m], [800m; 2000m]) and for each month using a reference in situ profile observation data set. The time series are calculated over the whole domain.

Table 5: Description of the different metrics used for salinity validation.

Currents:


UV-CLASS1-15m_MEAN Zonal and meridional 15m depth current climatology.

Ref data: NOAA AOML drifter-derived climatology for total current, including Ekman component (see http://www.aoml.noaa.gov/phod/dac/drifter_climatology.html)

UV-CLASS2-MOORINGS Monthly mean values of zonal velocity profiles collocated on observations for reference moorings (TAO/TRITON, RAMA, PIRATA). The following quantities are displayed: - Model and observation mean profile - Correlation between model and observations as a function of depth - Model – Observation std dev as a function of depth

UV-CLASS2-MKE_1000m Mean value of kinetic energy at 1000m depth estimated from monthly means.

Ref data: Mean value of kinetic energy at 100M depth estimated from ANDRO Argo drift data base (http://wwz.ifremer.fr/lpo/Produits/ANDRO ; Ollitrault et Rannou, 2013) over the years 2002-2009. To be consistent, the averaging period for the reanalysis will also be 2002-2009.

Table 6: Description of the different metrics used for currents validation.

http://www.aoml.noaa.gov/phod/dac/drifter_climatology.html

http://wwz.ifremer.fr/lpo/Produits/ANDRO




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Transports:


UV-CLASS3-AMOC ONLY FOR GLOBAL OCEAN REANALYSES Maximum of the Atlantic MOC at 26.5°N (to compare with monthly RAPIDMOC data):

mean value, error std dev, correlation, plot of the 2 time series (monthly values)

MOC(z) is computed in the following way:

West

East

Zz

z

dzdxvZMOC

0

)(

, then, we take the maximum value of MOC(Z) (Z<0). Note that this requires masks of the Atlantic ocean (without Med sea)

Ref data: RAPID-MOC data, see http://www.noc.soton.ac.uk/soes/research/groups/ocean_climate/rapidmoc/

Table 7: Description of the different metrics used for transport validation.

Mixed layer depth:


OTH-CLASS1-MLD_CLIM_SEASON Maps of climatological mixed layer depth in March and September. Temperature criterion is 0.2°C.

Table 8: Other metric.

http://www.noc.soton.ac.uk/soes/research/groups/ocean_climate/rapidmoc/




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IV VALIDATION RESULTS

IV.1 Temperature

IV.1.1 T-CLASS1-T3D-MEAN

ARMOR3D_REPv3.1 GLORYS2V3 MJM105b

0 m

100

m




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30

0 m

80

0 m

20

00

m

Figure 3: Mean differences in temperature between (left) ARMOR3D_REPv3.1 /(middle) GLORYS2V3 /(right) MJM105b and the World Ocean Atlas 2009 climatology (WOA09) at different

depths (product minus climatology). For this comparison, model data have been averaged over the period 1993 – 2011.

The differences of ARMOR3D-REPv3.1 with WOA09 climatology are less than 2°C and do not show any unexpected bias but only pattern due to inter-annual variability. At 2000m the differences are very small (less than 0.1°C) without inter-annual variability pattern because the temperature in ARMOR3D-REPv3.1 at that depth is from the climatology ARV11 (see details about the construction of ARMOR3D-REPv3.1 in section II.3) so we only see differences between the climatologies ARV11 and WOA09.




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For this diagnostic we also show results from a free run MJM105b and a model reanalysis with data assimilation GLORYS2V3 (for more information about MJM105b and GLORYS2V3 see

http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-004-009-010-011-017.pdf). Thus we can see data against model dynamic contributions.

The differences from WOA09 climatology are much more important for the free run MJM105b. Data assimilation forces the model to be close to the observation and difference for GLORYS2V3 shows similar pattern and intensity than for ARMOR3D_REPv3.1 (except at high latitude and at 2000 m).

IV.1.2 T-CLASS1-SST-MEAN

Figure 4: Mean differences in sea surface temperature between ARMOR3D_REPv3.1 and the AVHRR/AMSR Reynolds SST data. For this comparison, data have been averaged over the period

1993-2011.

The SST in ARMOR3D_REPv3.1 is a combination of the Reynolds SST and in-situ observation, as there is few in-situ data in the surface layer, main contribution is from Reynolds SST. Thus, the differences with Reynolds SST (Figure 4) are low (<0.1°C) except at very high latitude.

To understand what happens at high latitude, let’s remember the method used to compute ARMOR3D_REPv3.1. In the first step (see section II.3), the SST and SLA are projected on statistical vertical profiles. Mainly the SST contributes in the first layers and the SLA contribution is dominant deeper. To be consistent along the vertical profiles, the projection is not done if neither SST nor SLA are defined. Thus, at high latitudes where SLA are not defined because of sea ice, the result of the first step is only climatology (no projection of satellite data). This is why we can observe higher differences at these latitudes on Figure 4.






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IV.1.3 T-CLASS1-SST-TREND

Figure 5: SST trend of (left) ARMOR3D_REPv3.1 and (right) Reynolds AMSR-AVHRR data. Covered time period: 1993 – 2011.

For the same raison than in the previous paragraph (IV.1.2) ARMOR3D_REPv3.1 and Reynolds SST trends show similar pattern with little differences north of Siberia. SST shows a global increase over 1993-2011 except in the eastern Pacific.

IV.1.4 T-CLASS2-MOORINGS

Profile RMS Correlation

TAO

/TR

ITO

N 0

N-1

47

E




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TAO

/TR

ITO

N 0

N-1

65

E

TAO

/TR

ITO

N 0

N-1

40

W

TAO

/TR

ITO

N 0

N-1

10

W

PIR

ATA

0

N-2

3W

PIR

ATA

0

N-0

E




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RA

MA

:

0N

-90

E

Figure 6: Mean equatorial temperature profile (left), RMS (middle) and correlation (right) estimated at TAO/TRITON, RAMA and PIRATA moorings location over the period 1993 – 2011: observations (black), GLORYS2V3 (green), MJM105b (red) and ARMOR3D-REP (blue). Retained

moorings location: for the TAO/TRITON array: 0°N-140°W, 0°N-110°W, 0°N-165°E, 0°N-147°E, the PIRATA array: 0°N-23°W, 0°N-0°E and the RAMA array: 0°N-90°E. Units in °C.

In this section the ARMOR3D-REP temperature along the equator is compared to the one of the TOGA/TAO, PIRATA and RAMA moorings array during the 1993-2011 period. The mean vertical temperature profiles are displayed on Figure 6 (right panels) as the RMS (middle panels) and the correlation (left panels) between the model and the observations. The GLORYS2V3 and the MJM105b profiles have been added for comparison purposes.

At first sight, all the temperature profiles are close to each other (right panels on Figure 6). Yet, the ARMOR3D-REP RMS (resp. correlation) are approximately everywhere smaller (resp. higher) than the ones of GLORYS2V3 and MJM105b on the whole water column. The ARMOR3D-REP RMS (resp. correlation) is approximately of the same order as the one of GLORYS2V3 in the 50 first meters and roughly in the layer 150-250m (resp. in the 150 first meters), while the ARMOR3D-REP RMS (resp. correlation) is everywhere far smaller (resp. higher) than the one of MJM105b on the whole water column. A peak of low correlation is observed in the ARMOR3D-REP model in the Pacific ocean at 110W. Reasons of such a peak have to be investigated.

One of the reasons of the relatively good agreement between ARMOR3D-REP and the mooring array temperatures is that the array observations are taken into account in the ARMOR3D-REP model.

IV.1.5 T-CLASS3-HC_LAYER




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Figure 7: Time evolution of ARMOR3D-REP and ARIVO temperature monthly fields averaged on the global domain in different layers: [0-700m] (top right), [0-2000m] (top left), [2000m-bottom]

(bottom left) and [0-bottom] (bottom right). ARMOR3D-REP with the method 1 restrictive layers computation (green curve) and the method 2 extensive layers computation (red curve) and ARV11

monthly climatology also computed with method 2 (black curve). Units in °C.

In this section the time evolution of ARMOR3D-REP temperature monthly fields is compared to the one of ARV11 during the 1993-2011 period. The temperature monthly fields are averaged on the global domain (including the Mediterranean Sea) in different layers: [0-700m], [0-2000m], [2000m-bottom] and [0-bottom] (cf. Figure 7). Moreover, two methods have been tested for the layer computation: the first one (method 1) takes into account of all the oceanic points for which the total depth of the associated water column is at least equal to the maximal depth of the layer, while the second one (method 2) takes into account of all the oceanic points for which the total depth of the associated water column is equal or lower than the maximal depth of the layer. As a consequence, the layers surfaces (e.g [0-bottom]) are far smaller with method 1 than with method 2 but less biased towards the top of the layers on the shelf breaks.




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IV.1.6 T-CLASS4-LAYER

Figure 8: Time evolution of the bias between products and CORA3.4 in-situ temperature profiles over the period 1993-2011 (in °C) ; in red ARMOR3D_REPv3.1, in black ARV11 climatology.

Statistics are computed over different layers: [0-100m], [100-300m], [300-800m], [800-2000m].

1993 2011

300-800 m

1993 2011

800-2000 m

1993 2011

0-100 m

1993 2011

100-300 m

1993 2011

0-100 m

1993 2011

100-300 m




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Figure 9: Time evolution of the RMS difference between products and CORA3.4 in-situ temperature profiles over the period 1993-2011 (in °C) ; in red ARMOR3D_REPv3.1, in black ARV11 climatology.

Statistics are computed over different layers: [0-100m], [100-300m], [300-800m], [800-2000m]. Blue line gives the number of in-situ data used to compute the statistics.

In this section we compare the weekly temperature from ARMOR3D_REPv3.1 to in-situ profiles from INSITU TAC and to climatological estimates from the ARV11. Figure 8 andFigure 9 show the validation results that are given at various layers as mean and root mean square (RMS) differences from in-situ data. Note that comparisons of in-situ T profiles with ARMOR3D_REPv3.1 fields are not independent since the in-situ data set has been used to create the fields. These kinds of comparisons are nevertheless useful in order to check that the in-situ T profiles have been taken into account correctly.

There is no bias in ARMOR3D-REPv3.1 product (Figure 8). The SST used as an input of the product helps to have good results for the temperature at the surface. The largest errors are found at the bottom of the mixed layer (≈ 0.4 °C). Then, the RMS decrease with depth (Figure 9).

Our product is qualitatively and quantitatively very similar to in-situ observations. RMS and BIAS are always lower than those obtained using climatological fields. Note that the statistics improve with time thanks to the increase of number of in-situ data.

1993 2011

300-800 m

1993 2011

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IV.2 Salinity

IV.2.1 S-CLASS1-S3D_MEAN

ARMOR3D_REPv3.1 GLORYS2V3 MJM105b

0 m

100

m

300

m




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80

0 m

20

00

m

Figure 10: Mean differences in salinity (psu) between (left) ARMOR3D_REPv3.1 / (middle) GLORYS2V3 / (right) MJM105b and the World Ocean Atlas 2009 climatology (WOA09) at different

depths. For this comparison, model data have been averaged over the period 1993 – 2011.

Except at high latitude at the surface, the differences of ARMOR3D-REPv3.1 with WOA09 climatology are less than 0.5 psu and decrease with depth. At 2000 m there are almost no differences.

ARMOR3D_REPv3.1 is computed from a first guess that is the ARV11 climatology (see section II.3) and not the WOA09 one. Thus the main differences are due to differences between WOA09 and ARV11 that use in-situ data from 2004 to 2010. Indeed the huge difference (>2 psu) seen in the Arctic on Figure 10 is similar than difference between ARV11 and WOA09 (top-left panel on Figure 11).

The other panels of Figure 11 show the differences between ARMOR3D_REPv3.1 and ARV11. As expected, the differences are smaller than with WOA09 (Figure 10). The patterns do not show any unexpected bias but only pattern due to inter-annual variability. At 800 m and deeper there is almost no difference from climatology.

As for T-CLASS1-S3D_MEAN diagnostic (IV.1.1), we also show results from a free run MJM105b and a

model reanalysis with data assimilation GLORYS2V3 (Figure 10). The differences from WOA09 climatology are the most important for the free run MJM105b. Unlike for temperature diagnostics, differences between ARMOR3D_REPv3.1 and GLORYS2V3 are significant; restitution of salinity field




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still more problematic than temperature in Global Ocean Observed-based Product like ARMOR3D_REPv3.1 but also in numerical model.

Figure 11: Mean differences in salinity (psu) between ARMOR3D_REPv3.1/WOA09 and the ARV11 climatology at different depths.




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IV.2.2 S-CLASS1-SSS-TREND

Figure 12: SSS trend of ARMOR3D_REPv3.1. Covered time period: 1993 - 2011. The colour scale ranges from -0.05 to 0.05 psu.

The SSS trend shows absolute value less than 0.025 psu. SSS decrease in the tropics while increase at mid-latitudes.

IV.2.3 S-CLASS2-MOORINGS

Profile RMS Correlation

TAO

/TR

ITO

N 0

N-1

47

E




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TAO

/TR

ITO

N 0

N-1

40

W

TAO

/TR

ITO

N 0

N-1

10

W

PIR

ATA

0

N-2

3W

PIR

ATA

0

N-0

E

RA

MA

:

0N

-90

E




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Figure 13: Mean equatorial salinity profile (left), RMS (middle) and correlation (right) estimated at TAO/TRITON, RAMA and PIRATA moorings location over the period 1993 – 2011: observations

(black), GLORYS2V3 (green), MJM105b (red) and ARMOR3D-REP (blue). Retained moorings location: for the TAO/TRITON array: 0°N-140°W, 0°N-110°W, 0°N-165°E, 0°N-147°E, the PIRATA array: 0°N-23°W, 0°N-0°E and the RAMA array: 0°N-90°E. Units in PSU. Note that at 0N-165E, the

time series from the mooring have gaps and there is few data in the first layers, that why the profile is noisy.

In this section the ARMOR3D-REP salinity along the equator is compared to the one of the TOGA/TAO, PIRATA and RAMA moorings array during the 1993-2011 period. The mean vertical salinity profiles are displayed on Figure 6 (right panels) as the RMS (middle panels) and the correlation (left panels) between the model and the observations. The GLORYS2V3 and the MJM105b profiles have been added for comparison purposes.

At first sight, all the salinity profiles are close to each other (right panels on Figure 13). The ARMOR3D-REP RMS (resp. correlation) is approximately of the same order as the one of GLORYS2V3, while the ARMOR3D-REP RMS (resp. correlation) is everywhere far smaller (resp. higher) than the one of MJM105b on the whole water column. The presence of close and repeated peaks of low and high correlation is observed in the ARMOR3D-REP model in the Pacific ocean at 165E. Reasons of such significant oscillation have to be investigated, as for the ones observed in the profile and the RMS of GLORYS2V3 and ARMOR3D-REP at the same moorings.

Excepted for the 165E moorings, the reasons of the relatively good agreement between ARMOR3D-REP and the mooring array temperatures is that the array observations are correctly taken into account in the ARMOR3D-REPV3.1 reprocessing.

IV.2.4 S-CLASS3-SC_LAYER




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Figure 14: Time evolution of ARMOR3D-REP and ARIVO salinity monthly fields averaged on the global domain in different layers: [0-700m] (top right), [0-2000m] (top left), [2000m-bottom]

(bottom left) and [0-bottom] (bottom right). ARMOR3D-REP with the method 1 restrictive layers computation (green curve) and the method 2 extensive layers computation (red curve) and ARV11

monthly climatology also computed with method 2 (black curve). Units in PSU.

In this section the time evolution of ARMOR3D-REP salinity monthly fields is compared to the one of the climatology ARV11 during the 1993-2011 period. The salinity monthly fields are averaged on the global domain (including the Mediterranean Sea) in different layers: [0-700m], [0-2000m], [2000m-bottom] and [0-bottom] (cf. Figure 7). Moreover, two methods have been tested for the layer computation: the first one (method 1) takes into account of all the oceanic points for which the total depth of the associated water column is at least equal to the maximal depth of the layer, while the second one (method 2) takes into account of all the oceanic points for which the total depth of the associated water column is equal or lower than the maximal depth of the layer. As a consequence, the layers surfaces (e.g [0-bottom]) are far smaller with method 1 than with method 2.

IV.2.5 S-CLASS4-LAYER

1993 2011

0-100 m

1993 2011

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Figure 15: Time evolution of the bias between products and CORA3.4 in-situ temperature profiles over the period 1993-2011 (in psu) ; in red ARMOR3D_REPv3.1, in black ARV11 climatology.

Statistics are computed over different layers: [0-100m], [100-300m], [300-800m], [800-2000m].

Figure 16: Time evolution of the RMS difference between products and CORA3.4 in-situ salinity profiles over the period 1993-2011 (in psu) ; in red ARMOR3D_REPv3.1, in black ARV11

1993 2011

300-800 m

1993 2011

800-2000 m

1993 2011

300-800 m

1993 2011

800-2000 m

1993 2011

0-100 m

1993 2011

100-300 m




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climatology. Statistics are computed over different layers: [0-100m], [100-300m], [300-800m], [800-2000m]. The blue line shows the number of in-situ data used to compute the statistics.

IV.3 Currents

IV.3.1 UV-CLASS1-15m_MEAN

Figure 17: Zonal (top) and meridional (bottom) 15m depth current climatology from (left) NOAA AOML drifter-derived climatology and (right) ARMOR3D_REPv3.1. For this comparison, data have

been averaged over the period 1993 – 2011.

ARMOR3D_REPv3.1 and drifters show similar pattern. In the Antarctic Circumpolar Current (ACC), intensity of drifter zonal velocities are much higher than ARMOR3D_REPV3.1 ones. Difference in ACC could be explained by a likely overestimate of the drifter current estimate because of an anomaly in the drogue’s loss detectors (Lumpkin et al. 2013). Indeed, drifters are designed to have drogues,




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Issue : 2.8


centered at 15-m depth, to minimize direct wind forcing. However, Lumpkin et al. (2013) explain that a significant fraction of drifters believed to be drogued have actually lost their drogues. Note also that ARMOR3D_REPv3.1 current are consistent with reanalyses results even in the ACC

(see: http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-004-009-010-011-017.pdf).

IV.3.2 UV-CLASS2-MOORINGS

TAO

/TR

ITO

N 0

N-1

47

E

TAO

/TR

ITO

N 0

N-1

65

E

TAO

/TR

ITO

N 0

N-1

40

W






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TAO

/TR

ITO

N 0

N-1

10

W

PIR

ATA

0

N-2

3W

RA

MA

:

0N

-90

E

Figure 18: Mean equatorial zonal velocity profile (left), RMS (middle) and correlation (right) estimated at TAO/TRITON, RAMA and PIRATA moorings location over the period 1993 – 2011: observations (black), GLORYS2V3 (green), MJM105b (red) and ARMOR3D-REP (blue). Retained

moorings location: for the TAO/TRITON array: 0°N-140°W, 0°N-110°W, 0°N-165°E, 0°N-147°E, the PIRATA array: 0°N-23°W, 0°N-0°E and the RAMA array: 0°N-90°E. Units in m.s-1.

In this section the ARMOR3D-REP zonal velocity along the equator is compared to the one of the TOGA/TAO, PIRATA and RAMA moorings array during the 1993-2011 period. The mean vertical zonal velocity profiles are displayed on Figure 6 (right panels) as the RMS (middle panels) and the correlation (left panels) between the model and the observations. The GLORYS2V3 and the MJM105b profiles have been added for comparison purposes.




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Intensity of the ARMOR3D_REPv3.1 current is consistent with other estimates. However the vertical structure is to smooth. This could be explained by two points:

- Limitation of geostrophic approximation used to construct ARMOR3D_REPv3.1 current in the equatorial area;

- a weak vertical density gradient shear as explained in Mulet et al. (2012).

IV.3.3 UV-CLASS2-MKE_1000m

Figure 19: Mean value of kinetic energy at 1000m from (left) ANDRO Argo drift data base and (right) ARMOR3D_REPv3.1. Covered time period: 2002-2009.

Figure 19 shows kinetic energy at 1000 m of currents averaged over 2002-2009 period from ANDRO (Ollitrault et Rannou, 2013) and ARMOR3D_REPv3.1. ARMOR3D_REPv3.1 is more energetic than ANDRO. This can be explained by two points:

- First ANDRO current are computed from Argo float drifting at depth over about 10 days. The mean displacement of the float depend of the area, in energetic areas (ACC, equatorial current, western boundary current) it can be more than 1°. Thus, in these areas the spatial resolution of ANDRO current is much lower than ARMOR3D_REPv3.1 one (1/4°)

- Second, like already mention in the previous section and explained in Mulet et al. (2012), penetration of surface velocity are overestimate in ARMOR3D_REPv3.1 because of a too small vertical density gradient shear.




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IV.4 Transports

IV.4.1 UV-CLASS3-AMOC

Figure 20: Maximum of the Atlantic Meridional Overturning Circulation at 26.5°N (in Sverdrup)

for (red) ARMOR3D_REP and (blue) RAPID-MOCHA. Covered time period: 2004 - 2010.

ARMOR3D_REPv3.1 GLORYS2V3 MJM105b C-GLORS

0.56 0.82 0.81 0.6

Table 9: Correlation of AMOC time series from reprocessing/reanalyses with RAPID-MOCHA estimate (results for GLORS2V3, MJM105b and C-GLORS are from

http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-004-009-010-011-017.pdf).

IV.5 Other

IV.5.1 OTH-CLASS1-MLD_CLIM_SEASON

Mixed Layer Depths (MLD) are estimated using a fixed temperature criterion as in de Boyer Montégut et al., (2004). The MLD is defined as follows: it is the depth where temperature decrease or increase equals 0.2 °C, compared to temperature at 10 m depth.

Weekly temperature fields from the ARMOR3D_REPv3-1 fields are used to compute MLD. 1993 to 2011 March and September means are then compared to estimates from de Boyer Montégut et al., (2004) (http://www.ifremer.fr/cerweb/deboyer/mld/Surface_Mixed_Layer_Depth.php). Results are very similar in terms of amplitude and spatial structures between the two products and for both



http://www.ifremer.fr/cerweb/deboyer/mld/Surface_Mixed_Layer_Depth.php




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Issue : 2.8


months. MLD are maximum in the Northern Hemisphere in winter with the highest values in the Northern Atlantic Ocean. They are maximums in the Southern Hemisphere in summer with a clear signature of the Antarctic Circumpolar Current.

March

September

Figure 21: Mixed Layer Depth calculated using a fixed temperature criterion of 0.2°C for March (top) and September (bottom), and from de Boyer Montegut et al., (2003) (left) and ARMOR3D-

REPv3.1 (right). The colour scale ranges from 0 to 800 m.




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V QUALITY CHANGES SINCE PREVIOUS VERSION

V.1 Change done at the MyOceanV3.1 (huge improvement of the product)

V.1.1 Summary of changes since previous version

V1 V3.1

First guess of step 1 climatology ARIVO 08 climatology ARIVO 11

First guess of step 2 climatology ARIVO 08 synthetic fields

In-situ data used in step 2 CORA 3.1 CORA3.4 (except for 2012 because reprocessing was not available, we used Near Real

Time T/S profiles)

Mean Dynamic topography used to compute surface current

CNES-CLS09 preliminary version of CNES-CLS13 that include GOCE data

currents in equatorial band (5°S-5°N)

NO YES

Available time series Jan 1993 – Dec 2010 Jan 1993 – Dec 2012

Horizontal resolution 1/3° on a Mercator grid 1/4° on regular grid

Number of vertical levels 24 levels from 0 to 1500-m 33 levels from 0 to 5500-m

Horizontal coverage 82°S-82°N 82°S-90°N

Table 10: Summary of improvements of the new version V3.1. The first column refers to the different steps of the method described in section II.3.

V.1.2 Temperature and salinity fields

V.1.2.1 Horizontal resolution

V1 and V3.1 versions of Armor3D fields are compared below for the temperature and salinity fields at different depths and for the 30th of May 2007 (Figure 22 and Figure 23). As the synthetic field of the date of analysis is added back to the result of the optimal interpolation(step 2), the new version of Armor3D (left) contain exactly the same mesoscale structures (position and size) as the one present in the synthetic fields and thus as the one present originally in the altimeter and SST fields. The mesoscale structures restored by the combined Armor3D fields are now much more consistent with those originally available in the altimeter and SST fields. It is clearly visible for the temperature and salinity fields and for all depths.




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a. Temperature field

Step1 as first guess Climatology as first guess

0 m

50 m

100 m




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200 m

500 m

1000 m




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1500 m

Figure 22: Temperature fields at different depths for the 30th of May 2007 calculated when using step 1 as first guess (left) or the climatology as first guess (right).

b. Salinity field

Step1 as first guess Climatology as first guess

0 m




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50 m

100 m

200 m




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500 m

1000 m

1500 m

Figure 23: Same as Figure 22 for the salinity fields.

V.1.2.2 Comparison with in-situ data

In this section we test the non-regression of V3.1 over V1. The scientific validation has consisted in comparing the in situ T/S profiles to the Armor3D (called COMBI) and ARIVO fields. Results are expressed as a function of depth as mean, rms and rms as percentage of in situ minus ARIVO variance of the in situ minus fields for the temperature and salinity fields. These comparisons of in situ T/S profiles with Armor3D combined fields are not independent since the in situ data set has




Date : 22/03/2016

Issue : 2.8


been used to create the fields. These kinds of comparisons are nevertheless useful, in the one hand, in order to check that the in situ T/S profiles have been taken into account correctly, in the other hand; two compared the old and new version of the method.

The scientific validation has been performed for the year 2007 and results are available on Figure 24 and Figure 25. They show very similar values when the climatology is used as first guess (green lines) than when the synthetic field (step 1) is used as first guess (blue lines). The two curves are almost indistinguishable for temperature. Degradation is found with the new method for salinity but it is very small and of the order of 1 to 2 %.

Figure 24: Mean (dotted lines) and RMS (full lines) error in predicting subsurface T anomalies using the ARIVO monthly climatology (red), the new combined field (blue) and the old combined field

(green) (left). Results are also expressed as % of in-situ minus ARIVO variance (right).




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Figure 25: Same as Figure 24 but for the salinity field.

V.1.3 Current fields

In this section we compare current fields at 1000 m from V1 and V3.1 with independent data from Argo floats drifting at depth (ANDRO atlas, Ollitrault and Rannou, 2013). Statistics are computed over the year 2006, the results are sum up in Table 11 and Figure 26 shows the ANDRO data used in this study.

Results are very similar for zonal and meridian velocities. The velocities were too energetic in V1 with a standard deviation of ≈10 cm.s-1. This was corrected in the version V3.1: for this version the standard deviation (≈6 cm.s-1) is in much better agreement with ANDRO (≈ 5.5 cm.s-1). All other statistics were also improved.




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Figure 26: Zonal velocities (cm.s-1) from ANDRO atlas between 950 and 1150 meter depth in 2006.

Fields Mean field (cm.s-1) Mean difference with ANDRO (cm.s-1)

Standard deviation (cm.s-1)

Standard deviation of the difference

with ANDRO (cm.s-1)

zonal meridian zonal meridian zonal meridian zonal meridian

ANDRO 0.43 0.01 - - 5.25 4.68 - -

V3.1 0.44 -0.03 0.01 -0.04 6.03 5.64 4.92 4.74

V1 0.17 0.01 -0.26 0.01 9.50 8.89 7.76 7.33

Table 11: Results of statistical comparisons of ARMOR3D_REP V3.1 and V1 current fields to Argo floats drifting at 1000 m in 2006 and computed from 52916 data (ANDRO atlas).

V.2 Change done at the MyOceanV4.1 (add year 2012)

As mention above, for the MyOceanV4.1, the time series of the reprocessed product has been extended with the computation of the year 2012. At that time, the reprocessed T/S in-situ profiles were not available in 2012, thus the reprocessed ARMOR3D was computed with Near Real time T/S profiles. Note that the other inputs are the same as the ones used for the previous years.

Figure 27 and Figure 28 show results of the validation of the year 2012.




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Figure 27 shows comparison to in-situ T/S profiles. The results are very similar to the ones obtained for the previous years (not shown), for instance the ones obtained in 2007 (Figure 35 and Figure 36).

Figure 28 shows that there is no anomaly on the current fields. As expected, the intensity of the current decreases with depth. As year 2012 has been computed in a consistent way with the previous years (1993-2011), the accuracy of 2012 is similar to the period 1993-2011 validated in section IV.

Figure 27: Mean (dotted lines) and RMS (full lines) difference between (red) climatology ARV11 / (blue) regm = synthetic field (step 1) / (green) combi = ARMOR3D_REPv3.1 (step 2) and in-situ

temperature profiles (°C) for the year 2012.




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Figure 28: Intensity of the ARMOR3D currents at 0, 100, 500 and 2000 meter depth averaged over the year 2012.

V.3 Change done at the CMEMSV2 (add years 2013 and 2014)

For the CMEMSV2, the time series of the reprocessed product ARMOR3D_REPv3.1 has been extended with the computation of the years 2013 and 2014. At that time, the reprocessed T/S in-situ profiles were not available in 2014, thus the reprocessed ARMOR3D was computed in 2014 with Near Real time T/S profiles. For the year 2013, we have used the CMEMS reprocessed T/S profiles (CORA4.1). Also, since the first production in 2013 (see V.1), the Sea Level Anomaly products have changed, we thus use the new reprocessed maps from SEALEVEL_GLO_SLA_L4_REP_OBSERVATIONS_008_027. Note that the other inputs are the same as the ones used for the previous years.

The following figures show results of the validation of the years 2013 and 2014.




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V.3.1 Current fields at 100-m depth vs Yomaha velocities database

The current fields are validated at 1000-meter depth against velocity estimates from Argo drift (YoMaHa atlas from Lebedev et al, 2007). Figure 29 and Figure 31 shows the Argo velocity estimates used to validate respectively the years 2013 and 2014. Figure 30 and Figure 32 shows the Root Mean Square (RMS) of the difference between ARMOR3D_REPv3.1 currents and the Argo velocity estimates. At the equator, because the geostrophic assumption used to compute ARMOR3D_REPv3.1 currents (cf II.3) is less valid, the ARMOR3D_REPv3.1 currents have less good performances with RMS difference higher than 15cm/s. Away from the equator, the RMS difference is almost everywhere less than 5cm/s.

Zonal component

Meridional component

Figure 29: Zonal and meridional component of the velocity computed from Argo drift at 1000 m (YoMaHa atlas, Lebedev et al, 2007) in 2013 (cm/s).

Zonal component


Figure 30: Root Mean Square (RMS) of the difference between ARMOR3D_REPv3.1 current and the velocity computed from Argo drift at 1000 m (YoMaHa atlas, Lebedev et al, 2007) in 2013 (cm.s-1).




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Zonal component


Figure 31: Zonal and meridional component of the velocity computed from Argo drift at 1000 m (YoMaHa atlas, Lebedev et al, 2007) in 2014 (cm/s).

Zonal component


Figure 32: Root Mean Square (RMS) of the difference between ARMOR3D_REPv3.1 current and the velocity computed from Argo drift at 1000 m (YoMaHa atlas, Lebedev et al, 2007) in 2014 (cm.s-1).

V.3.2 Temperature and Salinity fields vs in-situ profiles

Figure 33 and Figure 34 show, resp. for the years 2013 and 2014, statistical comparison with in-situ profiles for the climatology ARV11 (used as first guess of step 1), the synthetic fields and ARMOR3D_REPv3.1 (see section II.3).

Synthetics fields give better results than the climatology. This underlines that step 1 (projection of satellite data) manage to resolve variability. Synthetic field resolves around 40% of the temperature signal and 20% of the salinity one.




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Then the combination with in-situ data improves the comparison (especially for salinity fields). ARMOR3D_REPv3.1 resolves 80 to 90% of the temperature and salinity signal.

These results are consistent with the ones of the previous years. As an example, the year 2007 is shown on Figure 35.

Figure 33: Mean (dotted lines) and RMS (full lines) difference between (red) climatology ARV11 / (blue) regm = synthetic field (step 1) / (green) combi = ARMOR3D_REPv3.1 (step 2) and in-situ

temperature profiles (°C) (top) or in-situ salinity (down) for the year 2013. [right]: same but results are expressed as % of in-situ minus ARV11 variance.




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Figure 34: Mean (dotted lines) and RMS (full lines) difference between (red) climatology ARV11 / (blue) regm = synthetic field (step 1) / (green) combi = ARMOR3D_REPv3.1 (step 2) and in-situ temperature profiles (°C) (top) or in-situ salinity (down) for 2014. [right]: same but results are

expressed as % of in-situ minus ARV11 variance.




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VI COMPLEMENTARITY OF IN-SITU AND SATELLITE OBSERVATIONS

VI.1 In-situ data improve information given by satellite data

As explained in section II.3, the construction of T/S fields in ARMOR3D_REPv3.1 has two step. The first one gives synthetics fields and used only satellites data while the second gives ARMOR3D_REPv3.1 fields and combined synthetics fields with in-situ data.

Figure 35 and Figure 36 show statistical comparison with in-situ profiles for the climatology ARV11 (used as first guess of step 1), the synthetic fields and ARMOR3D_REPv3.1.

Synthetics fields gives better results than the climatology. This underlines that step 1 (projection of satellite data) manage to resolve variability. Synthetic field resolves ≈40% of the temperature signal and ≈20% of the salinity one.

Then the combination with in-situ data improves again the comparison (especially for salinity fields). ARMOR3D_REPv3.1 resolves ≈80% of the temperature and salinity signal.

This shows the complementarity of in-situ and satellite data. In our method, satellite data are used to resolve meso-scale structures while in-situ data are indispensable to resolve correctly the vertical structure and variability.

Figure 35: [left]: Mean (dotted lines) and RMS (full lines) difference between (red) climatology ARV11 / (blue) regm = synthetic field (step 1) / (green) combi = ARMOR3D_REPv3.1 (step 2) and in-




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situ temperature profiles (°C) for the year 2007. [right]: same but results are expressed as % of in-situ minus ARV11 variance.

Figure 36: [left]: Mean (dotted lines) and RMS (full lines) difference between (red) climatology ARV11 / (blue) regm = synthetic field (step 1) / (green) combi = ARMOR3D_REPv3.1 (step 2) and in-situ salinity profiles (psu) for the year 2007. [right]: same but results are expressed as % of in-situ

minus ARV11 variance.

VI.2 Degrees of Freedom of Signal on T/S fields

In order to assess the impact of in situ observations to map temperature and salinity fields with satellite observations, Degree of Freedom of Signal (DFS) are derived from the present Global Ocean Observation-based reprocessing. DFS is an influence matrix diagnostics, first developed for the atmosphere (Cardinali et al., 2004), and now used for the ocean in data assimilation systems (Oke et al., 2009; Sakov et al., 2012) and also in the altimeter DUACS system (Dibarboure et al., 2011). It provides a measurement of the gain in information brought by the observations. It is thus a complementary approach to the one already developed in the ARMOR3D system (see section VI.1).

DFS is calculated as the trace of the HK matrix, H being the observation operator and K the kriging weight matrix. The optimal interpolation method used in the ARMOR3D system uses a Gauss-Markov estimator that provides a direct access to the HK matrix as it is explicitly computed along with the error covariance matrix or formal mapping error (Bretherton et al., 1976). DFS are computed on each




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HK matrix, meaning each grid point in case of a suboptimal optimal interpolation method. It is thus possible to use this metric to access the local mapping gain in information provided by each dataset (in situ, satellite…). Partial DFS are associated with a particular dataset and are computed from the partial trace of the HK matrix, taking only elements associated with the dataset to be analyzed. Partial DFS associated with the dataset i is written DFS(i). Two metrics have been particularly studied:

1- The fraction of the overall information coming from a given dataset (DFS(i)/ΣiDFS(i)),

2- The fraction of the information from the dataset actually exploited by the optimal interpolation method (i.e. the amount of information not lost to duplicate data and measurement error) (DFS(i)/N(i), N(i) being the actual number of observations from dataset i).

Time series of those two metrics are available on Figure 37, as global means, for the temperature field at 100-meter depth. Two datasets are considered: the in situ observations including Argo, moorings, XBTs, CTDs, ..., and the synthetic field that has been computed from satellite observations (altimeter and SST) and the ARV11 climatology (http://wwz.ifremer.fr/lpo/SO-Argo/Products/Global-Ocean-T-S/ARV11-climatology), see section II. Results indicate that 1/3 of the overall information comes from the in situ dataset at the beginning of the period and that this number increase to 2/3 when the Argo observing system is fully deployed. The synthetic field dataset completes the information with 2/3 at the beginning of the period and then 1/3.

The fraction of the information from the in situ dataset actually exploited by the optimal interpolation method is quite constant over time with mean values around 65 %. This number is really dictated by the correlation scales used in the optimal interpolation method and by the space/time distribution of observations. Associated mean standard deviation is of the order of 20 %. In some area, redundant in situ observations show indeed lower values and isolated observations (like in the Southern Ocean) have values close to 100 %.

The fraction of the information from the synthetic field dataset actually exploited by the optimal interpolation method is also quite constant over time with mean values around 20 % and associated mean standard deviation of the order of 6 %. These numbers are dictated by the way the synthetic fields are used (i.e. as first guess for step 2 of the method) and the measurement errors applied to those fields.

Results described here are very consistent with the parameters used in the optimal interpolation method and at this stage, we learn more on our Global Ocean Observation-based reprocessing than on the observing system itself. Test of the sensitivity of the results to the optimal interpolation method parameters (correlation scales, error covariance matrix,...) will be part of future work.






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Dataset Fraction of the overall information coming from a specific dataset

Fraction of the information from a specific dataset actually exploited by the optimal interpolation

method

In situ

Synthetic field (satellite&clim)

Figure 37: DFS metrics for the 1993-2001 periods and the temperature field at 100-meter depth (Global means +/- 1 std) (Units: x100%).




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VII REFERENCES

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[2] F. Hernandez et al., 2007, List of internal metrics for the MERSEA GODAE Global Ocean: Specification for Implementation. MERSEA IP report MERSEA-WP05-MERCA-STR-0015-01C.doc, 71pp. http://www.clivar.org/organization/wgomd/reos/references/GODAE_MERSEA-report.pdf

Bretherton, F. P., R. E. Davis and C. B. Fandry, A technique for objective analysis and design of oceanographic experiments applied to MODE-73, 1976 / Deep-Sea Res., 23, 559-582.

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http://archimer.ifremer.fr/doc/00112/22354/

http://archimer.ifremer.fr/doc/00115/22583/




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