SMOS and « La Niña » signature….

Category : CATDS, L2, Model, Ocean, Satellite

Analyzing the 2010-2011 La Niña signature in the tropical Pacific sea surface salinity using in situ data, SMOS observations and a numerical simulation

Audrey Hasson(1, *), Thierry Delcroix(1), Jacqueline Boutin(2),

Raphael Dussin(3), Joaquim Ballabrera-Poy(4)

The tropical Pacific Ocean remained in a La Niña phase from mid 2010 to mid-2012. The near-surface salinity signature of this cold El Niño-Southern Oscillation (ENSO) phase is shown in the figure below and analysed in Hasson et. al (2014) using a combination of numerical model output, in situ data and SMOS satellite salinity products.

boutin-la nina

Figure: Sea Surface Salinity anomalies relative to each product 2010-2011 monthly climatology (pss) in July 2010 (left panels) and July 2011 (right panels) for (a, d) ISAS in situ product (b, e) SMOS and (c, f) the model. Blue lines represent the Voluntary Observing Ship routes and the 170°E-180° hatched areas computation zones. (Figure from Hasson et al., 2014)

Comparisons of all salinity products show a good agreement between them, with a RMS error of 0.2-0.3 between the thermosalinograph (TSG) and SMOS data and between the TSG and model data. The last 6 months of 2010 (La Niña) are characterized by an unusually strong tri-polar anomaly captured by the three salinity products in the western half of the tropical Pacific. A positive SSS anomaly sits north of 10ºS (>0.5), a negative tilted anomaly lies between 10ºS and 20ºS and a positive one south of 20ºS. In 2011, anomalies shift south and amplify up to 0.8, except for the one south of 20ºS. Equatorial SSS changes are mainly the result of anomalous zonal advection, resulting in negative anomalies during El Niño (early 2010), and positive ones thereafter during La Niña. The mean seasonal and interannual poleward drift then exports those anomalies toward the south in the southern hemisphere, resulting in the aforementioned tripolar anomaly. The vertical salinity flux at the bottom of the mixed layer tends to resist the surface salinity changes. The observed basin-scale La Niña SSS signal is then compared in Hasson et al. (2014) with the historical 1998-1999 La Niña event using both observations and modelling.

for more details see Hasson, A., T. Delcroix, J. Boutin, R. Dussin, and J. Ballabrera-Poy (2014), Analyzing the 2010–2011 La Niña signature in the tropical Pacific sea surface salinity using in situ data, SMOS observations, and a numerical simulation, Journal of Geophysical Research: Oceans, 119(6), 3855-3867, doi:10.1002/2013JC009388.

(1) LEGOS, UMR 5566, CNES, CNRS, IRD, Université de Toulouse 14 avenue Edouard Belin, 31400 Toulouse, France

(2) LOCEAN, UMR7159, CNRS, UPMC, IRD, MNHN, Paris, France

(3) LEGI, Grenoble, France

(4) ICM/CSIC, Barcelona, Spain

(*) Corresponding author, Currently at the Jet Propulsion Laboratory,California Institute of Technology, Pasadena, California, USA

A SSS trip from the surface to the thermocline…

Category : L2, Non classé, Ocean

By Christophe Maes

Retrievals of the Sea Surface Salinity from space-borne mission like SMOS or Aquarius SAC-D provide for the first time an essential variable in the determination of ocean mass. If the field will reveal a lot of new signal at the surface its influence on the ocean dynamics is even more important at depths where it participates to the stratification of the water column. Concomitant with temperature profiles, reliable in situ observations of salinity at depth are now available at the global ocean scales. Above the main pycnocline (50-250m in the Tropics), Maes and O’Kane (2014) have recently shown that the stabilizing effect due to salinity could be isolated from its thermal counterpart by separating its role in the computation of the buoyancy frequency. In addition, relationships between such salinity stratification at depths and the SSS are shown to be well defined and quasi-linear in the tropics (see figure), providing some indication that in the future, analyses that consider both satellite surface salinity measurements at the surface and vertical profiles at depth will result in a better determination of the role of the salinity stratification in climate prediction systems.


Maes, C., and T. J. O’Kane (2014), Seasonal variations of the upper ocean salinity stratification in the Tropics, J. Geophys. Res. Oceans, 119, 1706–1722, doi:10.1002/2013JC009366.

Haiyan Typhoon: the brightest natural source of L-band radiation ever measured over the oceans

Category : CATDS, Ocean

N. Reul


Typhoon Haiyan (known in the Philippines as Typhoon Yolanda) slammed into the Philippines less than 2 weeks ago with sustained winds of 310 kilometers per hour, making it one of the strongest tropical storms to date and the second-deadliest Philippine typhoon on record. Haiyan originated from an area of low pressure in the Federated States of Micronesia on November 2. Tracking generally westward, environmental conditions favoured tropical cyclogenesis and the system developed into a tropical depression the following day. After becoming a tropical storm and attaining the name Haiyan at 0000 UTC on November 4, the system began a period of rapid intensification that brought it to typhoon intensity by 1800 UTC on November 5. By November 6, the Joint Typhoon Warning Center (JTWC) assessed the system as a Category 5-equivalent super typhoon on the Saffir-Simpson hurricane wind scale; the storm passed over the Palau shortly after attaining this strength.


Figure 1: SMOS retrieved surface wind speed [km/h] along the eye track of super typhoon Haiyan from 4 to 9 Nov 2013.

SMOS intercepted the typhoon several times along its track. We selected only those passes where the signal was well detected and not too contaminated by RFI or land masses. As illustrated by Figure 1, this let one pass on the 4 as Haiyan was still a Tropical Storm, two on the 6th Nov (with the morning pass capturing only a small portion of the typhoon), one on the 7 prior landing towards Philippines and one interception on the 9, just before it passed over Vietnam. Passes on the evening of the 6 and during the 7th morning were close in time to the maximum intensity reached by that super storm (reached on the evening of the 7th).

As illustrated by Figure 2 top panel, the estimated excess brightness signal (First stokes parameter/2) due to surface roughness and foam-formation processes under the cyclone on the 7th morning overpass (i.e., after correcting for atmosphere, extra-terrestrial sources, salinity and temperature contributions) reached a record value of 41 K. To put such value in perspective of other natural oceanic signals, we plotted together the Tb jump measured during the passage of Hurricane Category 4-5 Igor in 2010, which was only 22 K! In contrast, global changes of surface salinity (32-38 pss) and temperature (0°C-30°C) only modify the Tb by ~5 K. So we believe such signal is very likely a natural extreme of sea surface emission at L-band over the oceans.



Figure 2: Top: North-south section trough the Haiyan Typhoon showing the change of residual brightness temperature (Th+Tv)/2 reconstructed from SMOS data at longitude of 130.05°E on the 7 Nov 2013 at 09:15Z Typhoon (black). The Blue curve is showing an equivalent section through the Igor Category 4 hurricane in 2010. The red line is illustrating the range of brightness temperature variation expected on earth due to sea surface salinity and temperature changes. Bottom: surface wind speed deduced from the excess brightness temperature.

Application of the wind-speed bi-linear retrieval algorithm that we derived in 2012 based on the established relationship between surface wind speed estimates during IGOR and the excess brightness temperature, we obtained the wind speed module shown in Figure 2, bottom. One can easily see that around the cyclone eye, wind speeds largely exceed the 64 knots threshold for typhoons within a more than 50 km radius. The spatial resolution of SMOS however does not allow to resolve the detailed wind speed structure around the eye. The maximum wind estimated from SMOS reaches 142 knots !


Figure 3: Maximum sustained 1 minute wind speed estimated during Haiyan Typhoon. From SMOS data (black filled dots) compared to Advanced Dvorak Technic (ADT=blue diamond), CIMSS (yellow filled dots), SATCON (red) and Best Track from NHC (cyan). Note the empty circle correspond to the SMOS measurements for the 11/06 morning for which only a small portion of the cyclone signal was intercepted. Maximum 10 minutes wind speed deduced from SMOS algorithm were multiplied by 1/0.93, adopting the conversion factor proposed in (Harper et al., 2010) between one minute winds and 10 min winds.

Given the spatial resolution of SMOS, the wind speed measured is more equivalent to a 10 minute sustained wind than to a 1 minute one, traditionally used by forecasters in the US. Using a 0.93 conversion factor from 1 mn to 10 mn winds (Harper et al., 2010), 1 minutes sustained winds can be estimated from SMOS. The evolution of the maximum sustained wind speed deduced from SMOS is compared to other estimates in Figure 3. SMOS estimate compares well with standard methods. The Advanced Dvorak Technique (ADT) uses longwave-infrared, temperature measurements from geostationary satellites to estimate tropical cyclone (TC) intensity. This step-by-step technique relies upon the user to determine a primary cloud pattern and measure various TC cloud top parameters in order to derive an initial intensity estimate. It continues to be the standard method for estimating TC intensity where aircraft reconnaissance is not available (all tropical regions outside the North Atlantic and Caribbean Sea), however it has several important limitations and flaws. The primary issue centers upon the inherent subjectivity of the storm center selection and scene type determination proceedures. Secondly, learning the Dvorak Technique and its regional nuances and adjustments can take a significant time to master. SMOS winds in TC will be produced operationally next year so as to be ingested by numerical weather forecast model within 6 hours from acquisition. In regions not too polluted by RFI, this new impressive example shows again that SMOS new information shall well complement existing satellite observation to better forecast TC intensification and evolution.

Using SMOS to analyze the variability of the South Pacific Sea Surface Salinity maximum

Category : L3, Ocean

By Jacqueline BOUTIN

Understanding the variability of high-salinity surface waters, as shown in Fig. 1 for the south-eastern tropical Pacific, is important to improve our interpretation of climate and hydrological cycle changes at different time scales. SMOS CATDS-CEC LOCEAN SSS products have been used , in complement to Voluntary Observing Ships (VOS) thermo-salinograph data obtained from the French SSS Observation Service, to validate and understand the seasonal variability of the South Pacific Sea Surface Salinity maximum simulated by an ocean general circulation model with no direct SSS relaxation.


Fig. 1. Mean 1990-2011 modelled mixed-layer salinity. The blue lines represent the Matisse Ship routes of 2010 and 2011.

All products reveal a consistent seasonal cycle of the displacement of the 36-isohaline barycenter (Fig. 2; about +/-400 km in longitude) in response to changes in the South Pacific Convergence Zone location and Easterly winds intensity respectively associated with changes in precipitation and evaporation.


Fig.2. Location of isohaline 36 (simulated) and of its barycentre (dots: model; stars: SMOS) for various months (colors).

The SSS from 8 VOS transects compare remarkably well with collocated SMOS SSS averaged over 100km, 18 days (std difference=0.2), as exemplified in Fig. 3 along a shipping track running from New Zealand to Panama ; the comparison with simulated SSS is slightly degraded due to a few degrees latitudinal shift of the simulated SSS maximum (std difference=0.26).


Fig. 3. Example of comparison between SMOS (dots), VOS (straight line), and simulated (dashed line) SSS as a function of latitude.

Model results and in situ measurements further indicate a low frequency westward shift of the 36-isohaline barycenter (about 1400 km since 1992) that could not be linked to ENSO and may reflect the signature of decadal changes and/or global warming.

Details can be found in: Hasson, A., T. Delcroix, and J. Boutin (2013), Formation and variability of the South Pacific Sea Surface Salinity maximum in recent decades, J. Geophys. Res. Oceans, 118, doi:10.1002/jgrc.20367.