1. SMOS mission: a new way for monitoring Sea Surface Salinity? J. Boutin (1) (1) Laboratoire d’Oceanographie et du Climat- Expérimentation et Applications Numériques (LOCEAN), PARIS, FRANCE Thanks to T. Delcroix (LEGOS/IRD) and F. Petitcolin (ACRI-st) How GOSUD and SAMOS data could help for SMOS Cal/Val? (SMOS/GLOSCAL (Global Ocean sea surface Salinity : CALibration and validation for SMOS) project) (IFREMER, LEGOS/IRD, LOCEAN/SA/IPSL, Meteo-France, CLS, ACRI-st)

2. SMOS (Soil Moisture and Ocean Salinity) Should be launched in 2007 Goal: SSS accuracy: 0.1-0.2psu over 200x200km 2 10days L-band radiometer ( =21cm) =>SSS of upper 1cm depth Synthetic Aperture radiometer => spatial resolution  40km 3 arms => bidimensional field of view

3. animation S1T0 S1024s S2048s S3072s S4096s S50120s S60144s S70168s S80192s S90216s SEPSv3 simulations SMOS 2-D FIELD OF VIEW (one over 10 FOV), (F. Petitcolin, Acri-st) Satellite passes at 6AM and 6PM UTC

4. Sensitivity of L-band Tb to SSS (flat sea) 0.5K/psu (15°C) 0.7K/psu (30°C) 0.2K/psu (0°C) Flat sea (Klein and Swift model) Sensitivity of Tb to SSS is: -small: always less than 1K/psu (SMOS radiometric precision of 1 Tb: several K) -Higher in warm water NB: L-band radiometer measurements are representative of top 1cm surface ocean

5. Brightness temperature of the sea surface for a rough sea surface 0.5K/psu (15°C) 0.7K/psu (30°C) 0.2K/psu (0°C) Flat sea Rough sea (without foam) ~0.2K/m/s 2 scale emissivity model: small waves superimposed on large tilted waves Dinnat et al., IJRS, 2002, Radio Science, 2003 At 15°C, a 0.1K Tb variation can be generated by : -0.2psu SSS variation or - 0.5m/s wind speed variation 10m equivalent neutral wind speed (m/s)

6. SSS retrieved from multiangular Tb measurements Cost Function to be minimized: Tb mod : Tb estimated with a direct forward model N: number of Tb observations P: geophysical parameters responsible for Tb variations (e.g.: SSS, SST, wind ; depend on forward model)  i : errors on Tb meas  k : Prescribed errors on auxiliary parameters (typical values):  U = 2m/s;  SST = 1°C Retrieved parameters: SSS, SST, equivalent neutral wind speed (depend on forward model) Minimization: Levenberg-Marquardt algorithm An iterative retrieval algorithm is used to retrieve SSS, SST, surface roughness parameters the most consistent with Tb measurements

7. 1 satellite pass - 40x40km pixels (1 to 3K random error on individual Tb, U error 2m/s, SST error 1°C) SSS Error Error on retrieved SSS (estimated with Dinnat et al. Model) Boutin et al., 2004 Distance across track (km) SST (°C)

8. Error on retrieved SSS averaged in ‘Godae’ boxes (200x200km 2 ; 10 days ) 0.15 PSU Encouraging simulation but ‘optimistic’ hypothesis: -random noise on Tb and auxiliary parameters -knowledge of the true forward model To be checked during Cal/Val in 2007!!!! Boutin et al, JAOT,2004 0.20.1 Number of retrieved SSS in GODAE box (200km x 200km over 10 days) 400 1000 0.040.3 Error on mean SSS (computed as 2  /  N)

9. 1)Estimate SMOS SSS accuracy and precision: -Compare SMOS SSS with in-situ SSSNeed for SSS data 2) Identify error and biases sources: -flaws in direct emissivity models / instrument drifts -Compare SMOS Tb with Tb derived from direct forward models Data needed to compute Tb: SSS, wind, SST, atmospheric pressure, Tair Other useful information: Rain, wave, swell, currents -flaws in auxiliary parameters (coming from ECMWF model/Reynolds analysis) used in the SSS inversion -Compare them with in situ data Need for wind,SST,Patm,Tair Goals of CAL/VAL using in situ data Sampling, Precision and Accuracy of in situ data well adapted for SMOS Cal/Val depends on: Sensitivity of SMOS retrieved SSS to biases on auxiliary parameters Natural variability of SSS (and auxiliary parameters)

10. SSS bias as a function of wind speed bias Simulations of SSS retrieved from biased wind speeds SSS bias mostly related to wind speed bias (at 15°C, 1psu bias 2m/s bias); WIND SPEED BIAS (m/s) In order to get SSS bias<0.1psu, need for bias on wind speed data < 0.2m/s SSS BIAS (m/s) -8 8 -10 10

11. SSS bias as a function of SST (SST bias=5°C) Influence of SST bias on retrieved SSS SSS bias strongly dependent on SST: almost no bias around 15°C; >0 biases at low SST and <0 biases at high SST In order to get SSS bias < 0.1psu, need unbiased SST especially at low and high SST: -at SST=30°C: SST bias<0.5°C -at SST=0°C: SST bias<0.3°C (extreme value!) Estimated – reference SSS (psu) -8 -6 -4 -2 0 2 4 6 8 10 2 7 17 12 22 27 SST (°C)

12. 10day-horizontal variability of SSS as detected by ARGO floats At 10 day interval ARGO floats drift over 56km on average (up to 200km in frontal regions) => difference between SSS recorded at 10 day interval by the same float represents SSS variability at 10 day-20km to 200km scale 020060140

13. 10day-horizontal variability of ARGO measurements  (  SSS 10days )=0.2psu; largest differences in tropical regions; similar results for 10days-20-50km and 10days-50-200km drift => Number of measurements needed to achieve an accuracy of (a=0.1psu) on a 10day- 20-200km mean: N = 4  2 / a 2 => N = 16 observations Quadratic mean of  SSS 10days in 2°x2° pixels (N>10) July 2004-July 2005 00.30.10.2 Difference in SSS measured by the same float at 10 days interval 0.4 0.2 -0.4 -0.2 0  SSS 10days Boutin and Martin, 2006

14. 0 Delcroix et al., 2005 SSS variability derived from ships and moorings measurements Estimate spatial SSS variability from ship measurements and temporal SSS variability from mooring measurements

15. SMALL SCALE VARIABILITY IN THE TIME DOMAIN, 0-165E 0 Delcroix et al., 2005 SMALL SCALE VARIABILITY IN THE SPACE (N-S) DOMAIN (PX04; Fiji-Japan line) SSS variability derived from ships and moorings measurements => The mean expected variability within a box of 1°x2°x10 days is  0.2 psu => Nmin to achieve 0.1psu accuracy: N = 4  2 / a 2 => N = 16 observations The mean standard deviation of SSS over : -1° latitude is 0.1 psu -2° longitude is 0.12 psu -10 days is 0.10 psu (such values are variable in space and time)

16. Summary: requirements on in situ measurements for SMOS/Cal Val SamplingAccuracyPrecisionRemarks Parameters needed to compute Tb with present forward models (resolution ~40km): SSSNmeas=16 in 40x40km pixel (optimal) 0.05psu0.1psuDepth: Upper layer (same as SST) SSTSimilar to SSS0.3°C0.5°CDepth: Upper layer (diurnal cycle) (L-band signal coming from 1cm depth) Wind speed (direction) hourly0.2m/s1m/sComputed at 10m height (equivalent neutral) Additional information very useful for interpreting SSS differences: Rain; Surface roughness: Currents; Waves and swell

17. Conclusions/Remarks Advantages of GOSUD/SAMOS measurements w/r to other measurements : -w/r to moorings: almost ‘global ocean’coverage : sampling of very variable meteorological and oceanographic conditions -w/r to ARGO floats: provide meteorological measurements and complementary information necessary for interpreting differences between in situ and SMOS SSS. Remarks: It would be very convenient to get colocated ocean surface and meteorological parameters or software generating colocated measurements. Colocations useful for other applications? -Study of air-sea interactions (e.g. CO 2 air-sea flux in case ocean CO 2 measurements, see Lefevre et al. poster) …

18. SSS vertical variability determined from ARGO measurements S 5-10 -S 0-5 is small: <0.01 in 80% of cases but systematic differences with fresher S in upper layer observed in the tropics Boutin and Martin, 2006 Difference between 2 successive measurements on the same profile (S 5-10 – S 0-5 ) - July 2004-July 2005 0m 5m 10m S 0-5 S 5-10 -0.020.10 S 5-10 -S 0-5

19. SSS bias as a function of wind speed bias (constant bias put on zonal wind speed) Simulations of SSS retrieved from biased wind speeds SSS bias mostly related to wind speed bias (at 15°C, 1psu bias 2m/s bias); SSS bias generated by wind speed bias is slightly more important at low SST WIND SPEED BIAS (m/s) In order to get SSS bias<0.1psu, need for bias on wind speed data < 0.2m/s SSS BIAS (m/s) -8 8 -10 10 WIND SPEED BIAS (m/s) SSS BIAS (m/s) -8 8 -1010 0°C<SST<10°C 10°C<SST<20°C 20°C<SST<30°C

20. SSS bias as a function of SST (SST bias=5°C) Influence of SST bias on retrieved SSS SSS bias strongly dependent on SST: almost no bias around 15°C; >0 biases at low SST and <0 biases at high SST In order to get SSS bias < 0.1psu, need unbiased SST especially at low and high SST: -at SST=30°C: SST bias<0.5°C -at SST=0°C: SST bias<0.3°C (extreme value!) Estimated – reference SSS (psu) -8 -6 -4 -2 0 2 4 6 8 10 2 7 17 12 22 27 SST (°C)

21. Principle of SSS retrieval from L-band radiometer measurements At L-band (microwave frequency: 1.4GHz; skin depth: 1cm) radiometric signal is mainly affected by ocean surface emission (atmosphere is almost transparent): Tb = e. SST = (1-R) SST Tb : brightness temperature e : sea surface emissivity = R ( θ, SSS, SST, U…) =reflexion coefficient (may be deduced from Fresnel equations for a flat sea) R depends on sea water permittivity (SSS, SST), on sea surface roughness usually parametrized with U… Main lobe solid angle Antenna pattern Sky Atmospheric emission & absorption Ocean brightness temperature Apparent temperature distribution

22. 2D SMOS Field of View: SSS retrieved from multiangular Tb  SSS in 1 pixel (~40x40km) retrieved from multiangular Tb measurements (~40) Retrieval performed using iterative inverse method Nb ind t. Meas. in 40x40km pixel Boutin et al., JAOT, 2004

23. Vertical variability between 0-5m and 5m-10m Difference is small <0.01 in 80% of cases but systematic differences with fresher S in upper layer observed in the tropics

24. Effect of diurnal SST change in the surface ocean on Tb In case of very low wind speed, solar heating may create a diurnal cycle of temperature at the ocean surface (warm layer effect; few meters depth). Model of Fairall et al. (1996): if solar heating exceeds cooling by net longwave radiation and turbulent heat transfer => development of a warm layer Forcing: ECMWF heat fluxes and wind speed Stage T. Pastier, 2000 Case of moderate wind speed

25. 1 day simulation (1 July 1999) 0 7 m/s 1°C0 ECMWF wind speed T6PM –T6AM at 1cm depth Tb_at_6PM –Tb_at_6AM -0.10.1K

26. 10 days simulation (1-10 July 1999) Diurnal SST variation at 1cm depth: Significant at 6PM LT (descending orbits) Over 10 days, T° at 6PM warmer by up to 1.5°C => Tb variation between -0.2K (tropics-mid lat. in summer) and 0.1K (high latitude) Weak but not negligible effect for SSS retrieval: In the summer hemisphere, bias > 0.1psu in 14% of the rasters if diurnal SST effect is not corrected Need to correct this effect when using evening orbit measurements T6PM –T6AM at 1cm depth Tb_at_6PM –Tb_at_6AM 0 1°C -0.1 0 0.1K

27. Forward Modeling of SMOS apparent temperature Apparent temperature measured by the SMOS radiometer affected by: –the emission of the sea surface : Klein and Swift model + tabulations of Dinnat et al. 2-scale model –the emission and attenuation by the atmosphere Liebe et al (1993) model –the emission of the sky reflected at the sea surface Reich maps + specular reflexion –Ionospheric effects (Faraday rotation) Waldteufel et al. 2004 parametrization –Sunglint Scattering deduced from SSA model (see N. Reul presentation)

28. Expansion of Tb sea in Fourrier serie (no swell): Tb=Tbn (SSS, SST,θ) +Tb0(U,θ) +Tb1(U,θ) cos(φ) +Tb2(U,θ) cos(2φ) Tbn : variations induced by SSS and SST for a flat surface (no wind) : Klein and Swift model Forward Modeling of SMOS apparent temperature: Brightness temperature of the sea surface (1)

29. Expansion of Tb sea in Fourrier serie (no swell): Tb=Tbn (SSS, SST,θ) +Tb0(U,θ) +Tb1(U,θ) cos(φ) +Tb2(U,θ) cos(2φ) Tb0 : isotropic variations induced by the wind speed (Dinnat et al. model ; 2x Durden and Vesecki wave spectrum) Tv0 and Th0 as functions of wind speed at nadir, 30° and 50° incidence angles. At nadir dTb0/dU  0.2K/m/s Forward Modeling of SMOS apparent temperature: Brightness temperature of the sea surface (Dinnat et al. model) (2)

30. Over the Mediterranean Sea on November, 23 rd, 2001 (no rain) Validity of emissivity model ?? Comparison with Eurostarrs measurements QSCAT WIND SPEED Contardo et al, 2002 Dinnat et al., 2002 Coll. J. Miller, D. Long

31. Good agreement of  Tb = Tbmeasured - Tb (SSS,SST) with Dinnat et al. roughness parametrization; scatter partly due to radiometer imperfections Dinnat et al., 2002 0.27 K/m/s ; б = 1.12°K 0.23 K/m/s ; б = 1.01°K Signal induced by roughness at 2 incidence angles (21°) & (38.5°) Validity of emissivity model ?? Comparison with Eurostarrs measurements Thèse Dinnat, 2003 Etcheto et al., TGARS, 2004