QWG-12 Ocean studies (v5 reprocessed SSS) -South Pacific Maximum Salinity -North Atlantic Maximum Salinity (SPURS) -Variability of SSS: effects of rain/roughness/interpolation smoothing Several topics Version 6 versus version 5 -v602 in May 2011 (land/ice contamination; ARGO colocations) -v610 in December/descending (sun?) Ice coverage Rain-Roughness effect/ 2-step algo

QWG-12 Version 6 versus version 5 J. Boutin, N. Martin, X. Yin -L1v602 (land & ice contamination worse in L1v602 than in v5.5 not shown in this presentation); colocs with ARGO. -Sun impact on descending orbits in December with L1v610?

QWG-12 Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed Land and ice contamination worse in L1 v602!

QWG-12 Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed Far from continents (>700km), comparisons with ARGO : colocs +/-9days, +/-50km ; Mai 2011

QWG-12 Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed Far from continents, comparisons with ARGO : colocs +/-9days, +/-50km ; Mai 2011 Orbit A [45S;45N] : v5 : N= 1674 mean=-0.16 std=0.417 v6ref : N= 1674 mean=-0.13 std=0.396 v6a3tec : N= 1674 mean=-0.13 std=0.396 Orbit D [45S;45N] : v5 : N= 1605 mean= 0.04 std=0.448 v6ref : N= 1605 mean= 0.06 std=0.456 v6a3tec : N= 1605 mean=-0.05 std=0.398 Orbit A+D [45S;45N] : v5 : N= 1943 mean=-0.07 std=0.360 v6ref : N= 1943 mean=-0.05 std=0.346 v6a3tec : N= 1943 mean=-0.10 std=0.321 L1 v602 gives equal or very slightly improved performance wrt v5; L2 v6a3tec improves much more

QWG-12 Are descending orbits in the northern hemisphere in December less noisy in v610 than in v5 repr. ? X. Yin, J. Boutin Look at std(Tbsmos-Tbmodel) wrt radiometric accuracy over one descending orbit T032055_ T between 5°N and 35°N Flags of border, sun point and suntails are applied. The gridpoints in the FOV with number of measurements less than 30 are forced to be blank.

QWG-12 (in v5, most retrieved SSS north of 5°N in Dec are flagged December/Descending! )

QWG-12 Std(Txsmos-Txmodel)/Radiometric accuracy V5 V6 V6 less noisy than v5 but still some remaining anomalous signatures

QWG-12 Std(Tysmos-Tymodel)/Radiometric accuracy V5 V6 V6 less noisy than v5 but still some remaining anomalous signatures

QWG-12 Std(Txsmos-Txmodel) V5 V6 V6 less noisy than v5 but still some remaining anomalous signatures

QWG-12 Std(Tysmos-Tymodel) V5 V6 V6 less noisy than v5 but still some remaining anomalous signatures

QWG-12 SMOS-ECMWF ice coverage wrt OSTIA ice coverage J. Boutin, N. Martin,C. Banks

QWG-12 Motivation At the ESA LPS 2013 Chris Banks suggests that SMOS was biased close to ice as detected by OSTIA In v5 (and v600), in L2OS processor threshold for ice concentration is not put to 0% but to 30% =>look at difference between considering a threshold=0% or 30% => look at difference between SMOS-ECMWF & OSTIA ice concentration

QWG-12 Data & Method All tests are performed on 15 May 2011 All SMOS-ECMWF ice concentrations along SMOS orbits on 15 May are averaged

QWG-12 SMOS-ECMWF & OSTIA coverage around Antarctica

QWG-12 SMOS-ECMWF – OSTIA ice concentration

QWG-12 Zoom on some regions of 0% & 30% threshold on SMOS/ECMWF ice concentration Ice concentration between 30% and 0% concerns often a band of ~1° in latitude or even larger; this is not negligible and from a physical point of view it is necessary to consider 0%!

QWG-12 SMOS SSS close to ECMWF-SMOS ice mask (purple 0%, black 30%) Probably due to other sortings, SMOS SSS is not retrieved very close to SMOS- ECMWF ice on 15 May asc orbits

QWG-12 SMOS SSS close to ECMWF-SMOS ice mask (purple 0%, black 30%) The southern limit of SMOS retrieval is closer to OSTIA than to ECMWF ice concentration =0%

QWG-12 Conclusion Study in progress; some strange across track biases close to ice (TBC) but it was v6!!! It would be safer to put threshold to 0% instead of 30% on percentage of ECMWF ice coverage (although it seems that other tests in L2OS already discard data within 0 and 30%)

QWG-12 Rain-Roughness effect : what two- step algorithm tells us X. Yin, J. Boutin, N. Martin

QWG-12 Two-step retrieval

QWG-12

rWS OP - WS SSMIS rWS twostep - WS SSMIS 24 In regions with large ECMWF-SSMI WS differences, 2-step retrieved SMOS WS closer to SSMI WS; 2-step method more sensitive to problems near continents and RFI regions WS ECMWF - WS SSMIS North Eq Pac East Eq Pac

QWG-12 rWS OP - WS SSMIS rWS twostep - WS SSMIS 25 In regions with large ECMWF-SSMI WS differences, 2-step retrieved SMOS WS closer to SSMI WS; 2-step method more sensitive to problems near continents and RFI regions WS ECMWF - WS SSMIS East Eq Pac region

QWG-12 Statistics in August 2010 –Ascending orbits (EEP contains low WS that may create an additional problem)

QWG-12 SMOS SSS – ARGO SSS 5°N-15°N Pacific Ocean Operational SSSTwo step SSS SMOS SSS [1cm]– ARGO SSS [5m] correlated with SSM/I rain rates (within - 80mn;+60mn from SMOS passes) Boutin et al., Ocean Science, 2013 Still strong correlation with RR when an alternative SMOS roughness correction scheme is used (poster Yin et al., 4P-117) Method described in 4P-117 Change in roughness by raindrop: a second order effect wrt vertical stratification (account for less than 20% of observed SSSsmos-SSSargo difference)

QWG-12 Conclusions As suggested by 2-step algorithm, rain induced roughness is an order of magnitude smaller than rain- stratification effect 2-step algorithm very useful to detect peculiar roughness (not wel modelled with ECMWF WS) but very sensitive to land contamination, RFI...

QWG-12 Scientific studies that used v5 reprocessed SSS -South Pacific Maximum Salinity -North Atlantic Maximum Salinity (SPURS) -Variability of SSS: effects of rain/roughness/interpolation smoothing

QWG-12 In press Figure 1. (a) Mean modelled mixed-layer salinity. The blue lines represent the Matisse Ship routes of 2010 and 2011 discussed in the main text. (b) Mean Evaporation – Precipitation (E-P) based on ERAi; units are mm/day. Overplotted as arrows are the mean modelled surface currents. The 0 isohyet is shown on both panels with a bold black solid line.

QWG-12 (Hasson et al, JGR, 2013 in press) Comparison between near-surface salinity data derived from (black line) the TSG instrument installed on board M/V Matisse and the collocated SSS: (dashed line) modelled and (dotted line) SMOS values. The Matisse salinity values were obtained during February 2011 along the northern shipping line shown in Figure 1a. SMOS SSS, modelled SSS and ship SSS Model: ORCA025.L75-MRD911 run by french Drakkar group Over 8 VOS TSG transects (averaged over km): For a collocation radius of 9 days and 50 km, std difference : Between SMOS and in-situ SSS : 0.20 Between modelled and in-situ SSS : 0.26 (mean biases and 0.07, respectively)

QWG-12 (Hasson et al, JGR, 2013 in press) Mean mixed-layer salinity budgets in the high-salinity regions bounded by the 35.6, 36 and 36.4 isohalines Salinity budget as derived from model analysis Evaporation-Precipitation (~0.7pss yr -1 ) balanced by the horizontal salinity advection (~ pss yr -1 ) and processes occurring at the mixed layer base (~-0.35 pss yr -1 ).

QWG-12 (Hasson et al, JGR, 2013 in press) (a) Monthly and (b) annual mean positions of the modelled 36-isohaline. On both panels the colored dots and stars show respectively the barycentre of the modelled and SMOS-derived 36 isohalines Seasonal and interannual displacement of 36 isohalines Model: ORCA025.L75-MRD911 run by french Drakkar group Very consistent seasonal variation of longitudinal displacement between SMOS and modelled SSS (according to ship SSS, max of modelled SSS could be too north)

QWG-12 SMOS-LOCEAN activities in SPURS region (Hernandez et al., Kolodziejczyk et al., 2013, in prep. JGR-SMOS- AQUARIUS)

QWG-12 SMOS senses mesoscale variability (≠ ISAS) (Hernandez et al., Kolodziejczyk et al., 2013, in prep. JGR) SMOS vs TSG SSS anomaly (0.25° resol.)ISAS vs TSG SSS anomaly (0.25° resol.) Ship data from 07/2011 to 12/2012: 14 transects SMOS –climato SSS ISAS –climato SSS TSG –climato SSS Once monthly biases are corrected, SMOS senses variability with a RMSE= 0.14

QWG-12 SMOS and in situ salinity: rain, roughness and interpolation effects J. Boutin 1, N. Martin 1, J.L. Vergely 2, X. Yin 1, G. Reverdin 1, F. Gaillard 3, O. Hernandez 1, N. Reul 4 1 LOCEAN/CNRS, Paris, 2 ACRI-st, Paris, 3 LPO/IFREMER, Brest, 4 LOS/IFREMER, Toulon, France

QWG-12 ISAS ~5m depth ~3°smoothing SMOS 1cm depth 100x100km 2 averages ITCZ SPCZ Motivation: SMOS S 1cm maps versus in situ S ~5m maps CATDS-CEC/LOCEAN_v2013 product SSS August 2010 Rain Rate (SSMI)

QWG-12 Outline -Assessment of Levenberg and Marcquardt (LM) retrieved SSS error: SMOS SSS variability within 100km-1month in and out of rainy regions: comparison with SMOS SSS error predicted by LM. -Alternative roughness retrieval: the 2-step algorithm of X.Yin =>weak effect of rain-roughness on SMOS-ARGO SSS: first order is vertical stratification -Monthly SMOS SSS maps wrt in situ derived maps (ISAS): -differences linked to rain-stratification -differences linked to interpolation smoothing

QWG-12 Data & Methods SATELLITE SMOS SSS ESA v5 reprocessing SSS at 1cm depth ; ~40km resolution or averaged (CATDS-CEC/LOCEAN_v2013 product available at ; focus on August 2010 monthly mapswww.catds.fr Rain Rate: -SSM/I (RemSSS: ° resolution; SMOS SSS colocated within -80mn, +40mnwww.ssmi.com IN SITU SSS ARGO INDIVIDUAL PROFILES ‘SSS’ between 10m and 4m depth; Colocation with SMOS within +/-5days, +/-50km CORIOLIS GDAAC: ARGO + TSG OPTIMAL INTERPOLATED SSS MAPS (ISAS) Monthly maps from In-Situ Analysis System v6 – Correlation scale ~300km

QWG-12 Method for building SMOS SSS maps (CATDS LOCEAN_CEC_v2013) Monthly SSS average over 100x100km 2 – weighted by each SSSj resolution (Rj) and theoretical uncertainty on retrieved SSS (ej) derived by the L.M. algorithm ( depends on NTb, dTb/dSSS,...) Mean theoretical error on single SMOS SSS is computed with same weights ESSS =eSSS e e e

QWG-12 SMOS SSS theoretical error & observed variability Mean theoretical error on single SMOS SSS (~43km resolution): eSSS Standard deviation of L2OS SMOS retrieved SSS over 1 month in 100x100km 2 :  SSS (if no natural variability, no bias on SMOS SSS, errorSSS~  SSS)

QWG-12 SMOS SSS theoretical error & variability:  SSS - eSSS Large  SSS close to the coast (large and variable SMOS SSS biases) 750km

QWG-12 SMOS SSS theoretical error & variability:  SSS - eSSS Large  SSS close to the coast (large and variable SMOS SSS biases) but also in tropical regions Influence of rain on  SSS – eSSS ? Several possible causes: -natural variability (rain is an intermittent process) -rain-roughness artefact on SSS retrieval

QWG-12 SMOS SSS theoretical error & variability: Correlation with rain Tropical Pacific 5N-15N  SSS – eSSS ~0.6 * RR : - SSSvar 2 ~  SSS 2 - eSSS 2 For RR=0.5mm/hr & eSSS=0.6 => SSS natural variability ~0.6 order of magnitude need to be further checked with in situ ground truths  SSS – eSSS versus monthly SSMI Rain Rate

QWG-12 SMOS SSS – ISAS SSS After masking areas close to continents and RFIS: SMOS SSS-ISAS SSS Global Ocean: moy=-0.16 (std=0.36) 30°S-30°N: moy=-0.18 (std=0.28) What is the impact of a correction for rain stratification effect as seen on SSSsmos- SSSargo?

QWG-12 SMOS S 1cm – ARGO S ~5m =fn(RR) Zone ITCZ (Jul-Sep2010)RainaRR+brN SsmosA-SargoSSMI -80mn +60mn-0,17RR-0,16-0, Zone SPCZ (Jun10-Feb11) SsmosA-SargoSSMI -80mn +60mn-0,22RR-0,26-0, SsmosD-SargoSSMI -80mn +60mn-0,19RR-0,29-0, SsmosA-SargoTRMM 3h before-0,15RR-0,20-0, SsmosD-SargoTRMM 3h before-0,16RR-0,23-0, SMOS S ~5m ~ SMOS S 1cm RR Boutin et al Region close to ITCZ Region close to SPCZ

QWG-12 SSScorr-SSSSSScorr-SSSisas Effect of Rain correction on monthly SSS A small correction (not the same color scales!!) Without rain correction: moy=-0.16 (std=0.36) moy=-0.18 (std=0.28) With rain correction: moy=-0.15 (std=0.36) moy=-0.17 (std=0.28) SSSsmos – SSSisas : Global Ocean: 30°S-30°N: Vertical stratification of S linked to rain is not responsible for the main differences between SSSsmos-SSSisas – Influence of ISAS optimal interpolation (~300km)???

QWG-12 Global: moy=-0.09 (std=0.22) 30°N-30°S: moy=-0.09 (std=0.21) moy=-0.16 (std=0.36) moy=-0.18 (std=0.28) SSSsmos_like_ISAS - SSSisas SSSsmos- SSSisas Effect of ISAS interpolation

QWG-12 Summary -SMOS SSS theoretical error in very good agreement with the SMOS SSS observed variability within 100x100km 2 and one month, except close to continents, RFIs and in rainy regions. -In rainy regions, salinity natural variability is expected to be large (temporally, spatially and vertically): -SMOS suggests S 1cm variability due to time/space variability over one month ~0.6 for a monthly RR=0.5mm/hr. This order of magnitude should be checked in future studies with surface ground truth. -When averaged over one month, the rain induced vertical salinity difference between 5m & 1cm is small (<0.1). -When SMOS SSS interpolated similarly to in situ SSS : precision on monthly SMOS SSS smoothed at ~3° resolution ~0.2 between 30°N-30°S. -