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Gérard Caudal, Emmanuel Dinnat, and Jacqueline Boutin

Abstract

Empirical Ku-band altimeter model functions of near-nadir normalized radar cross-sectional σ° are compared to electromagnetic two-scale quasi-specular theory in the context of a standard sea wave spectral model. Three empirical model functions are tested: (i) the modified Chelton and Wentz model (WCM) using data from Geosat, (ii) the Callahan et al. model using data from TOPEX, and (iii) the Freilich and Vanhoff model using data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). These three models are basically very similar, except that they differ in terms of the level of absolute calibration. The difference between the absolute calibrations of the two extreme models (MCW and Freilich and Vanhoff) is as high as 1.9 dB. Assuming a sea wave spectrum similar to that used by Elfouhaily et al., the two-scale quasi-specular electromagnetic model is run, with a wave separation wavenumber kd adjusted so as to minimize the rms difference between the theoretical σ°(θ) function and the empirical near-nadir model function. The quality of the best-fit solution is not perfect, however, because the shape and absolute level of the function σ°(θ) cannot usually be adjusted simultaneously by the electromagnetic model. Taking the model function used by Freilich and Vanhoff as a reference, an offset is then introduced to the empirical model function, and the residual error is computed as a function of the offset. The overall quality of the fit is shown to be best when a −1.1 dB offset is introduced into the Freilich and Vanhoff model function. To within 0.1 dB, this corresponds to the offset that would be required to match Callahan et al.’s model function. This result is obtained in a context where the effect of the peakedness of the sea surface was assumed negligible. When this effect is introduced, with a peakedness parameter Δ assumed to be independent of wind speed and taken tentatively as Δ = 0.23, as suggested by Chapron et al., the optimal offset is then found to be −0.2 dB, thus indicating that for this example the best consistency with electromagnetic modeling is closer to Freilich and Vanhoff’s calibration. A more refined assessment would require accurate measurements of the parameter Δ involving both magnitude and variability with wind speed. Such accurate measurements are, unfortunately, not available at this time.

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Jacqueline Boutin, Philippe Waldteufel, Nicolas Martin, Gérard Caudal, and Emmanuel Dinnat

Abstract

The Soil Moisture and Ocean Salinity (SMOS) mission recently led by the European Space Agency (ESA) intends to monitor soil moisture and sea surface salinity (SSS). Since the sensitivity of radiometric L-band signal to SSS is weak, measuring SSS with an acceptable accuracy is challenging: it requires both a very stable instrument and very precise corrections of other geophysical signals than the SSS affecting the L-band signal. Concentration is on the sea surface roughness and temperature (SST) effects and the extent to which they need to be corrected to optimize both SSS precision and retrieval complexity. In addition to uncertainties regarding SST and wind speed (W), realistic noise on the SMOS brightness temperatures (Tb's) are considered and possible consequences of Tb biases are examined.

In most oceanic regions, random noise in W, SST, and Tb should not hamper the SMOS SSS retrieval within the Global Ocean Data Assimilation Experiment (GODAE) requirements (a precision better than 0.1 pss over 200 km × 200 km and 10 days). However, minimizing systematic bias errors over the time scale at which the SSS products will be averaged is critical: the GODAE requirement will not be met if Tb's or W is biased in warm waters (25°C) by 0.07 K and 0.3 m s−1, respectively, and in cold waters (5°C) by 0.03 K and 0.15 m s−1, respectively, or if no a priori information on W is available. In order to minimize errors coming from the W natural variability, it is essential to use high-temporal-resolution wind data. The use of the first Stokes parameter instead of bipolarized Tb degrades the SSS precision by less than 10% in most regions, showing that Faraday rotation should not hamper SMOS SSS retrieval.

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Claire Henocq, Jacqueline Boutin, Gilles Reverdin, François Petitcolin, Sabine Arnault, and Philippe Lattes

Abstract

Two satellite missions are planned to be launched in the next two years; the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Aquarius missions aim at detecting sea surface salinity (SSS) using L-band radiometry (1.4 GHz). At that frequency, the skin depth is on the order of 1 cm. However, the calibration and validation of L-band-retrieved SSS will be done with in situ measurements, mainly taken at 5-m depth. To anticipate and understand vertical salinity differences in the first 10 m of the ocean surface layer, in situ vertical profiles are analyzed. The influence of rain events is studied. Tropical Atmosphere Ocean (TAO) moorings, the most comprehensive dataset, provide measurements of salinity taken simultaneously at 1, 5, and 10 m and measurements of rain rate. Then, observations of vertical salinity differences, sorted according to their vertical levels, are expanded through the tropical band (30°S–30°N) using thermosalinographs (TSG), floats, expendable conductivity–temperature–depth (XCTD), and CTD data. Vertical salinity differences higher than 0.1 pss are observed in the Pacific, Atlantic, and Indian Oceans, mainly between 0° and 15°N, which coincides with the average position of the intertropical convergence zone (ITCZ). Some differences exceed 0.5 pss locally and persist for more than 10 days. A statistical approach is developed for the detection of large vertical salinity differences, knowing the history of rain events and the simultaneous wind intensity, as estimated from satellite measurements.

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Nicolas Kolodziejczyk, Mathieu Hamon, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Alexandre Supply, and Nicolas Reul

Abstract

Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

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Stephen English, Catherine Prigent, Ben Johnson, Simon Yueh, Emmanuel Dinnat, Jacqueline Boutin, Stuart Newman, Magdalena Anguelova, Thomas Meissner, Masahiro Kazumori, Fuzhong Weng, Alexandre Supply, Lise Kilic, Michael Bettenhausen, Ad Stoffelen, and Christophe Accadia
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Thomas Popp, Michaela I. Hegglin, Rainer Hollmann, Fabrice Ardhuin, Annett Bartsch, Ana Bastos, Victoria Bennett, Jacqueline Boutin, Carsten Brockmann, Michael Buchwitz, Emilio Chuvieco, Philippe Ciais, Wouter Dorigo, Darren Ghent, Richard Jones, Thomas Lavergne, Christopher J. Merchant, Benoit Meyssignac, Frank Paul, Shaun Quegan, Shubha Sathyendranath, Tracy Scanlon, Marc Schröder, Stefan G. H. Simis, and Ulrika Willén

Abstract

Climate data records (CDRs) of essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon, and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: 1) consistency in format and metadata to facilitate their synergetic use (technical level); 2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and 3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analyzing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The interdependencies of the satellite-based CDRs derived within the CCI program are analyzed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it.

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