a

c

b

ISAS SMOS OI

SATELLITE SSS (level 3, monthly maps)

● SMOS LOCEAN derived from ESA L2OS v5 (wind-model 1) SSS:

- SMOS LOCEAN 0.25°: SMOS SSS maps averaged over 0.25x0.25°

- SMOS OI: SMOS LOCEAN 0.25° interpolated with similiar method as the one

applied to ISAS maps.

- SMOS CEC LOCEAN: SMOS SSS (originally at ~43km resolution) maps are

averaged over 100x100km2 and oversampled every 0.25° (Boutin et al. 2013).

● SMOS IFREMER derived from ESA Level 1 product

- SMOS CEC IFREMER: SMOS-CATDS CEC IFREMER SSS maps, over a regular

grid of 0.25° x 0.25°, with a daily 5°x 5° adjustment with respect to World Ocean

Atlas 2001 climatology.

● AQUARIUS level 3 maps: 2 products V2.0 and V2 CAP versions 1x1°resolution

Sea surface salinity as detected by SMOS, AQUARIUS and by in situ sensors: In and around the SPURS / STRASSE experiment

In the frame of the Salinity Processes in the Upper Ocean Regional Study (SPURS) experiments, the STRASSE (Subtropical Atlantic surface salinity experiment,

PI G. Reverdin) campaign took place in August-September 2012, providing a very high resolution monitoring of the salinity in the high salinity region of the

subtropical North Atlantic. In addition to this survey, Sea Surface Salinity (SSS) monitoring across the high salinity region is performed every month by ships of

opportunity (Toucan and Colibri). Here, we take advantage of this wide in situ SSS monitoring for validating the SMOS (ESA version 5) and AQUARIUS (level 3

version 2 ) SSS and evidence the gain of coverage and resolution of satellite product over in situ interpolated products (Argo, ISAS, WOA,...).

SSS seasonal variability in the SPURS

region from SMOS and Aquarius is not

well consistent with observations (Fig 2).

Satellites do not retrieve properly absolute

SSS values: on SMOS SSS (except CEC

IFREMER adjusted to WOA), year to year

consistent seasonal bias are observed.

In order to study the spatial variability

of SSS, all datasets (SMOS and

Aquarius) are corrected from a bias B1

calculated for each month on the SPURS

region, as following:

Fig. 2: Mean SSS variability over SPURS region from

various product.

B1 = <SSS>SPURS - < SSS ISAS >SPURS

.

.

3 - Large scale variability

Fig 10: Scatter plot of SSS anomaly for SMOS OI and ISAS versus TSG SSS. TSG SSS was filtered at 300km. All products are gridded to 1°.

● SSS variability at scales of several hundreds of km is at first order consistent between SMOS, AQUARIUS and ISAS, but seasonal biases on satellite SSS first need to be

corrected. After correction, the calculated RMSE of SMOS CEC LOCEAN, AQUARIUS V2CAP and ISAS in respect to 14 transects of SSS TSG from 09-2011 to 12-2012 was

estimated to be 0.14. Global correlation coefficient r was estimated to be 0.93.

● Satellite products better reproduce SSS mesoscale variability (around ̴ 100 km) than interpolated in situ data. SMOS performs better than AQUARIUS.

References: Boutin, J., Martin, N., Reverdin, G., Yin, X., and Gaillard, F. (2013). Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain. Ocean

Sci. 9, 183-192.

Gaillard, F., Autret, E., Thierry, V., Galaup, P., Coatanoan, C., and Loubrieu, T. (2009).Quality control of large argo datasets. Journal of Atmospheric and

Oceanic Technology.

Reul, N., Fournier, S., Boutin, J., Hernandez, O., Maes, C., Chapron, B., Alory, G., Quilfen, Tenerelli, J., Morisset, S., Kerr, Y., and Mecklenburg, S. (2013).

Sea surface salinity observations from space with SMOS satellite: a new tool to better monitor the marine branch of the water cycle. Surveys in Geophysics.

SMOS SSS CATDS CEC are available at www.catds.fr

Acknowledgements:

This study is supported by the ESA SMOS+SOS ('OSMOSIX') projet. It is part of the ESA SMOS/GLOSCAL Cal/Val

project funded by CNES It is also partly performed and funded in the frame of the ESA level 2 Ocean Salinity expert

support laboratory (ESL) projects. Sea surface salinity data derived from thermosalinograph instruments installed

onboard Toucan and Colibri voluntary observing ships were collected, validated, archived, and made freely available by

the French Sea Surface Salinity Observation Service (http://www.legos.obs-mip.fr/observations/sss/). AQUARIUS V2.0

version and AQUARIUS V2.0 CAP version were obtained from the Physical Oceanography Distributed Active Archive

Center (PO.DAAC) at the NASA Jet Propulsion Laboratory, Pasadena, CA. http://podaac.jpl.nasa.gov."

Collocation SSS products with ship data :

Level 3 SSS products at 0.25° (bilinear

interpolation) are compared to TSG SSS, binned

at 0.25°, except in Fig 10.

Statistics are done during the overlapping period

of SMOS and AQUARIUS.

The region not affected by coast proximity was

selected for statistics (green square):

[50°W:27°W, 15°N:35°N]

Table 1: Statistics at 0.25°, n = 1723,

standard deviation of TSG =0.36. MBE=

mean bias estimate, RMSE= root mean

square error, r= correlation at 99% interval

confidence, σP: standard deviation of each

SSS product. Results of SMOS and ISAS

statistics remains unchanged by using 16

more transects (from 07-2010 to 08-2011).

● SMOS LOCEAN, LOCEAN OI, ISAS

OI and AQUARIUS V2CAP are the best

products to represent the observed

large scale structures (best RMSE,

best r2, Table 1).

● At 0.25º grid resolution, noise in SMOS

is reduced by averaging to 100x100km2:

SMOS OI shows good correlation but

less variability (lower σP)

Tradeoff between noise effect and

natural variability.

Fig 5: Scatter plot of SSS SMOS CEC

LOCEAN versus ship SSS.

4 – Mesoscale features

Colocation of all SSS products with TSG SSS

Fig 4: Spatial SSS maps from TSG ship (a), SMOS CEC LOCEAN (b), AQUARIUS

V2CAP (c), SMOS OI (d) and ISAS map (+ argo floats) (f). SSS from ships are

superimposed in color over SSS maps. The applied bias correction is B1 = -0.15. (f)

Isohaline contour for ISAS (red), AQUARIUS V2 CAP (cyan) and SMOS (black).

Fig 6: Position of the longitude (top) and latitude

(bottom) of the barycenter of the sea surface salinity

maximum for SMOS, ISAS and AQUARIUS V2CAP.

2- Salinity Data 3 - Methods

Fig 7: SSS anomaly maps from TSG ship (a), SMOS CEC LOCEAN (b) and AQUARIUS V2CAP (c). TSG SSS anomaly are superimposed in color over SSS maps.

● SMOS SSS and AQUARIUS SSS anomaly are significantly correlated with TSG SSS anomaly: r2 SMOS = 0.39, r2 AQUARIUS = 0.23

Fig 3 : Mean of monthly standard deviation of SSS

on 0.25º pixel between 2010 and 2012. Between

27°W and 20°W, high values are observed due to

a coastal effect on SMOS.

(Gaillard et al. 2009, D7CA2S0 re-analysis, at ~5m

depth) : sampled at 0.5° but nominal resolved

resolution of Argo area ~3°

● SSS TSG (at ~5m depth) from Ships of

Opportunity (Toucan, Colibri ship) from 08-2011 to

12- 2012 (data from french ORE-SSS)

● SSS thermosalinograph (TSG) from STRASSE

campaign (08-09 2012)

IN SITU SSS

● Optimal Interpolation of ARGO SSS: IFREMER In Situ Analysis System (ISAS)

● At 1º resolution grid, after filtering TSG SSS at 300km (as interpolated products), SMOS OI have better correlation than ISAS:

r2 SMOS = 0.34, r2 ISAS = 0.28.

O. Hernandez (1), J. Boutin (1) N. Kolodziejczyk(1) , G. Reverdin (1) , N. Martin(1), F. Gaillard(2) , N. Reul(3) , , J.L. Vergely(4)

(1)LOCEAN/IPSL, Paris, France, (2)LPO/IFREMER, Brest, France, (3)LOS/IFREMER, Toulon, France, (4)Acri-st, Paris, France

● SSS maximum present a seasonal

cycle, moving north to south from

summer to spring (Fig 6) from about

0.8º. In longitude, no seasonal cycle

is observed.

● SSS spatial anomalies in each product have been computed removing the monthly climatological lSAS

SSS (Fig 7).

● SMOS and AQUARIUS show higher SSS signal and smaller scales than in ISAS and SMOS OI product.

Despite a high noise level, it suggests that mesoscale features present in TSG ships are retrieved in satellite product.

Example: In south western zone of SPURS area

(red square, Fig. 7 and 9) SMOS and AQUARIUS

retrieved better SSS because lack of ARGO data

in this area.

Fig 1.:

14 transects

of TSG SSS

a

37.5

37.0

a b

d

f

c

e

SMOS also more accurate than ISAS at 300km

resolution, likely due to a better coverage of the

SMOS SSS measurements.

● Illustration of these SSS maps is presented Fig. 3. The

spatial structure of SSS shows difference (Fig. 4), which are

not evidenced in global statistics. Statistics are dominated by

the signal at low frequency.

● On satellite products, the maximum SSS isohaline

contours (Fig. 4f), show mesoscale features as observed by

looking isohaline contours (Fig 4f).

In order to study the ability of satellite products to retrieve

mesoscale feature, spatial SSS anomalies were studied by

removing large scale SSS signal.

SMOS CEC LOCEAN AQUARIUS V2CAP ISAS

SSS TSG ANOMALY SSS TSG ANOMALY SSS TSG ANOMALY

SS

S S

MO

S A

NO

MA

LY

SS

S A

QU

AR

IUS

AN

OM

ALY

SS

S IS

AS

AN

OM

ALY

Fig 8: Scatter plot of SSS anomaly sampled at 0.25° for SMOS CEC LOCEAN, AQUARIUS V2CAP and ISAS versus TSG SSS anomaly.

Fig 9: SMOS OI (a) and ISAS map (+ argo floats) (b). TSG SSS anomaly filtered at 300km are superimposed in color over SSS maps.

Satellite products better reproduce SSS

mesoscale variability than interpolated in situ

data.

b