Ocean Processes

It is well known that rain events leave footprints on the sea surface that sometimes become visible on synthetic aperture radar (SAR) images. Rain cells can easily be detected on SAR images at all radar frequencies when they are associated with a downdraft pattern. But rain cells are not always associated with downdraft and rain can also occur in other forms: stratified rain, rain bands, and squall lines. It turns out that C-band radar signatures of rain are much more complex than at L- or X-band radar and it is particularly difficult at this radar band to identify unambiguously rain events on SAR images of the sea surface. This is because C-band lies in the transition region where raindrops impinging  onto the sea surface can increase (usually) or decrease the backscattered radar power and where volume scattering and attenuation by rain drops in the atmosphere are not always be negligible (at very high rain rates). In order to get an insight into the physical mechanism causing the C-band radar signatures of rain, we first revisit results obtained from historic laboratory and field experiments and from Spaceshuttle SIR-C/X-SAR multi-frequency/multi-polarization SAR data. Then we analyze several C-band (5.3 GHz) SAR images acquired by the European satellites ERS, Envisat and Sentinel 1A, and the Canadian satellite Radarsat 2 and compare them, whenever possible, with quasi-coincident and collocated weather radar images. The observational data show that, at low to medium rain rates, the main physical mechanism causing C-band radar signatures of rain is Bragg scattering at ring causing an increase of the backscattered radar power. However, areas of increased radar backscattered power are often accompanied by adjacent areas of decreased radar backscattered power, which are caused by attenuation of the Bragg waves by turbulence generated by impinging rain drops onto the sea surface and which we interpret as being associated with the time evolution of the rain event. Finally, we present a Radarsat 2 image of a rain cell, where the polarimetric decomposition seems to indicate non- Bragg scattering contributions. Possible mechanism causing these contributions, like volume scattering and scattering at splash products, are discussed.

The European Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors sea surface salinity (SSS) over the global ocean for more than 5 years since 2010.

In tropical regions, SMOS SSS contains a large imprint of atmospheric rainfall, but is also likely affected by oceanographic processes (advection and diffusion). At local and short time scales, Boutin et al. (2013, 2014) have shown that the spatio-temporal variability of SSS is dominated by rainfall as detected by satellite microwave radiometers and have demonstrated a close to linear relationship between SMOS SSS freshening under rain cells and satellite rain rate. The order of magnitude agrees with the theoretical renewal model of Schlussel et al. (1997) and is compatible with AQUARIUS SSS observations, as well as with in situ drifters observations although the latter are local and taken at 45cm depth while satellite L-band SSS roughly correspond to the top 1cm depth and are spatially integrated over 43-150km.

The MADRAS microwave radiometer carried by the French (CNES) Indian (ISRO) mission Megha-Tropiques provided an excellent sampling of the 30° N-30° S region end of 2011 and in 2012, complementary to other Global Precipitation Measurement(GPM) missions.

In this presentation we will examine to which extent the combined information of satellite rain rates and satellite SSS brings new constraints on the precipitation budget. We first look at the consistency between the spatial structures of SMOS SSS decrease and of rain rates derived either from the MADRAS microwave radiometer or from the CMORPH (NOAA Climate Prediction Center Morphing technique) combined products that do not use MADRAS rain rates. This provides an indirect validation of the rain rates estimates. We then investigate the impact of rain history and of wind speed on the observed SMOS SSS freshening. Based on these results, we discuss the precision on various precipitation estimates over 2012 in the ITCZ region and the major sources of uncertainties that a campaign like SPURS2 should help to resolve.

Salinity and temperature gradients drive the thermohaline circulation of the oceans, and play a key role in the ocean-atmosphere coupling. The strong and direct interactions between the ocean and the cryosphere (primarily through sea ice and ice shelves) is also a key ingredient of the thermohaline circulation.

The launch of the soil Moisture and Ocean Salinity (SMOS) mission, in 2009, marked the dawn of a new type of space-based microwave observations. Although the mission was originally conceived for hydrological and oceanographic studies [1,2], SMOS is also making inroads in  cryospheric science. SMOS carries  an L-band (1.4 GHz, or 21-cm wavelength), passive interferometric radiometer (the so-called MIRAS) that measures the electromagnetic radiation emitted by the Earth’s surface, at about 50 km spatial resolution, full polarization, continuous multi-angle viewing, wide swath (1200-km), and with a 3-day revisit time at the equator, but more frequently at the poles.

Although the SMOS radiometer operating frequency offers almost the maximum sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) variations, such sensitivity is rather low, even lower at cold waters [3]. 90% of ocean SSS values span a range of brightness temperatures of just 5K. This implies that the SMOS SSS retrieval requires a high performance of the MIRAS interferometric radiometer [4].

Since the SMOS launch, SSS Level 3 maps have been distributed by several expert laboratories including the Barcelona Expert Centre (BEC) team. However, since the TB sensitivity to SSS decreases with decreasing sea surface temperature (SST), no specific attention has been dedicated the retrieval of surface salinity from Arctic regions (i.e., above 50⁰N)).

New algorithms, recently developed at BEC to improve the SMOS quality, allow for the first time to derive cold water SSS maps from SMOS data. These algorithms include: i) improvement of the image reconstruction by a reduction of sidelobe levels and ripples caused by abrupt changes of brightness temperatures [5](at level 1); ii) empirical correction of the instrumental systematic biases (land-sea and ice-sea contamination) (at level 2); iii) use of synergistic and multifractal approaches for increasing the spatial and temporal resolution [6] (at levels 3 and 4). The new maps will be presented at the conference together with a thorough assessment of their quality.

[1] Font, J.; Camps, A.; Borges, A.; Martí andn-Neira, M.; Boutin, J.; Reul, N.; Kerr, Y.; Hahne, A. & Mecklenburg, S. SMOS: The Challenging Sea Surface Salinity Measurement From Space Proceedings of the IEEE, 2010, 98, 649 -665

[2] Kerr, Y.; Waldteufel, P.; Wigneron, J. P..; Delwart, S.; Cabot, F.; Boutin, J.; Escorihuela, M.J..; Font, J.; Reul, N.; Gruhier, C.; Juglea, S.; Drinkwater, M.; Hahne, A.; Martin-Neira, M. & Mecklenburg, S. The SMOS mission: New tool for monitoring key elements of the global water cycle Proc. IGARSS 2010, no. 5., 2010, 98, 666-687

[3] Swift, C. (1980). Passive Microwave Remote Sensing. Boundary-layer Meteorology,18:25–54.

[4] McMullan K., Brown M., Martín-Neira M., Rits W., Ekholm S., Marti J., Lemanzyk J. SMOS: The payload, IEEE T. Geosci. Remote, 46, 594-605, 2008.

[5] Conzález-Gambau, V., Turiel, A., Martínez, J., Olmedo, E., and Corbella, I., (2014), “A novel reconstruction algorithm for the improvement of SMOS brightness temperatures”. In Microwave Radiometry and Remote Sensing of the Enviroment (MicroRad), 2014 13^th Specialist Meeting on, pages 124-127.

[6] Umbert, M., Hoareau, N., Turiel, A., and Ballabrera-Poy, J. (2014). “New blending algorithm to synergize ocean variables: the case of smos sea surface salinity maps”, Remote Sensing of Enviroment, 146, pp. 188-200.

Since the beginning of the industrial revolution humans have released approximately 500 billion metric tons of carbon into the atmosphere from burning fossil fuels, cement production and land-use changes. About 30% of this carbon dioxide has been taken up (or absorbed) by the oceans. The oceanic uptake of carbon dioxide leads to a change in marine carbonate chemistry resulting in a decrease of seawater pH and carbonate ion concentration, a situation that is commonly called ‘Ocean Acidification’ (OA).

To date, the majority of the scientific studies into the potential impacts of OA and efforts for monitoring the effects of OA have focused on the use of models and in situ studies (such as buoys, research cruises and lab or field based mesocosm studies).  The international community through the Global Ocean Acidification Observing Network (GOA-ON) has highlighted the need to utilise space-based observations.  Space observations from satellite Earth observation (EO) have yet to be fully exploited and could play an important role in this area of science through providing quasi-synoptic, reproducible and well-calibrated measurements for investigating processes on global scales. 

The recent advancement of space-based salinity observations will unlock the potential for satellite based carbonate system monitoring.  The European Space Agency (ESA) Pathfinders-OA (http://www.pathfinders-oceanacidification.org ) project is now investigating the role that satellite Earth Observation (EO) can play in supporting and expanding OA research. The project is exploiting satellite observed salinity data from ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Aquarius, along with data from other international satellite sensors and the use of carbonate system models.

The project is focusing its efforts on five study regions: i) Arctic seas; ii) Bay of Bengal, iii) the greater Caribbean, iv) The Amazon plume and iv) the global oceans.  For each region we are implementing and evaluating existing in situ derived carbonate algorithms, along with developing and evaluating novel carbonate system algorithms.  Approaches for estimating pH, total alkalinity, partial pressure of carbon dioxide and dissolved inorganic carbon are being investigated.  We will present the results from the project, showing example satellite derived carbonate system data for each of the study regions focusing on the period 2010-2012.

In the framework of the Italian National Program on Antarctic Research (PNRA), and based also on the output of the ESA OSMOSIS (Ocean ecoSystem Modelling based on Observations from Satellite and In-Situ data) project, remotely-sensed sea surface temperature, salinity and absolute dynamical topography observations have been combined with in situ Argo data to provide mesoscale-resolving 3D tracers and geostrophic velocity fields. The 3D reconstruction has been validated with independent data collected during PNRA surveys. These data have been used to diagnose the vertical exchanges in the Southern Ocean through a generalized version of the Omega equation. Intense vertical motion is found along the ACC, upstream/downstream of its meanders, and within mesoscale eddies, where multipolar vertical velocity patterns are generally observed.