Gisela Carvajal

Variability of Warm Deep Water Inflow in a Submarine Trough on the Amundsen Sea Shelf

The ice shelves in the Amundsen Sea are thinning rapidly, and the main reason for their decline appears to be warm ocean currents circulating below the ice shelves and melting these from below. Ocean currents transportwarm densewater ontothe shelf,channeledby bathymetric troughs leadingto the deep inner basins. A hydrographic mooring equipped with an upward-looking ADCP has been placed in one of these troughs on the central Amundsen shelf. The two years (2010/11) of mooring data are here used to characterize the inflow of warm deep water to the deep shelf basins. During both years, the warm layer thickness and temperature peaked in austral fall. The along-trough velocity is dominated by strong fluctuations that do not vary in the vertical. These fluctuations are correlated with the local wind, with eastward wind over the shelf and shelf break giving flow toward the ice shelves. In addition, there is a persistent flow of dense lower Circumpolar Deep Water (CDW) toward the ice shelves in the bottom layer. This bottom-intensified flow appears to be driven by buoyancy forces rather than the shelfbreak wind. The years of 2010 and 2011 were characterized by a comparatively stationary Amundsen Sea low, and hence there were no strong eastward winds during winter that could drive an upwelling of warm water along the shelf break. Regardless of this, there was a persistent flow of lower CDW in the bottom layer during the two years. The average heat transport toward the ice shelves in the trough was estimated from the mooring data to be 0.95 TW.

Correlation between Synthetic Aperture Radar Surface Winds and Deep Water Velocity in the Amundsen Sea, Antarctica

The recent observed thinning of the glacier ice shelves in the Amundsen Sea (Antarctica) has been attributed to warm deep currents, possibly induced by along-coast winds in the vicinity of the glacial ice sheet. Here, high resolution maps of wind fields derived from Synthetic Aperture Radar (SAR) data have been studied and correlated with subsurface measurements of the deep water velocities in the Amundsen Sea area. Focus is on periods with low ice coverage in 2010 and 2011. In 2010, which had comparatively low ice coverage, the results indicate a more rapid response to wind forcing in the deep currents than in 2011. The SAR wind speed maps have better spatial resolution than available reanalysis data, and higher maximum correlation was obtained with SAR data than with reanalysis data despite the lower temporal resolution. The maximum correlation was R = 0.71, in a direction that is consistent with wind-driven Ekman theory. This is significantly larger than in previous studies. The larger correlation could be due to the better spatial resolution or the restriction to months with minimum ice coverage. The results indicate that SAR is a useful complement to infer the subsurface variability of the ocean circulation in remote areas in polar oceans.

Sea Surface Currents Estimated from Spaceborne Infrared Images Validated against Reanalysis Data and Drifters in the Mediterranean Sea

Near-real time sea surface current information is needed for ocean operations. On a global scale, only satellites can provide such measurements. This can be done with data from infrared radiometers, available on several satellites, thus giving several images a day. This work analyses the accuracy of such an estimation of surface current fields retrieved with the maximum cross correlation (MCC) method, here used to track patterns of Advanced Very High Resolution Radiometer (AVHRR) brightness temperature between 224 pairs of consecutive images taken between January and December 2015 in the western Mediterranean Sea. Comparison with in-situ drifters shows that relatively small patterns, moving at a slow speed, tracked between images separated by less than four hours give the best agreement. The agreement was strongest in summer, and consistent with low wind, non-eddying situations. When compared to a daily reanalysis field, the averaged satellite-retrieved fields showed good agreement, but not the in-situ drifter data. Drifter data should hence be used to complement satellite-retrieved currents rather than to validate them, since they may measure different components of the surface currents.

Optimization of Sea Surface Current Retrieval Using a Maximum Cross-Correlation Technique on Modeled Sea Surface Temperature

Using sea surface temperature from satellite images to retrieve sea surface currents is not a new idea, but so far its operational near-real-time implementation has not been possible. Validation studies are too region specific or uncertain, sometimes because of the satellite images themselves. Moreover, the sensitivity of the most common retrieval method, the maximum cross correlation, to the parameters that have to be set is unknown. Using model outputs instead of satellite images, biases induced by this method are assessed here, for four different seas of western Europe, and the best of nine settings and eight temporal resolutions are determined. The regions with strong currents return the most accurate results when tracking a 20-km pattern between two images separated by 6-9 h. The regions with weak currents favor a smaller pattern and a shorter time interval, although their main problem is not inaccurate results but missing results: where the velocity is too low to be picked by the retrieval. The results are not impaired by the restrictions imposed by ocean surface current dynamics and available satellite technology, indicating that automated sea surface current retrieval from sea surface temperature images is feasible, for pollution confinement, search and rescue, and even for more energy-efficient and comfortable ship navigation.

Comparison between current fields detected with infrared radiometry and modeled currents around Sweden

The understanding of the relationship between surface currents derived from weather models and remote sensing data is essential in order to produce an improved and integrated surface current information of high quality and resolution. The large availability of satellite derived infrared radiometer data at the high latitudes of the coastal waters surrounding Sweden makes the Maximum Cross Correlation (MCC) method a feasible alternative to produce real time measurements of surface currents. This work compares current retrievals from the MCC method and model data around Sweden. Our results indicate a similar magnitude for both sources of current fields in most of the locations. However, there exist small discrepancies in the localization of the larger current values. Also, the MCC retrievals generally present more features than the modeled ones. Possible reasons for these discrepancies might be the MCC detection of circulation patterns not predicted by the model, or the depreciation in the MCC performance due to the influence of diurnal variability of the sea surface temperature, wind driven mixing due to upwelling or tides.

Assessment of satellite and ground-based estimates of surface currents

Estimation of surface currents still presents a challenge. In this work validates surface current estimates from the Maximum Cross Correlation (MCC) method, that uses spaceborne radiometer data, against ground-based retrievals from a High Frequency (HF) radar system. Moreover, these datasets have been compared with surface current data from two assimilated satellite products and four weather prediction models. The comparison shows large differences in the spatial resolution and the location of specific features. It is concluded that the variation of the observations may be due to the difference between the measuring or estimated method used in each case and the forces driving them.

Wind-wave effect on ATI-SAR measurements of ocean surface currents in the Baltic Sea

Along-Track Interferometric (ATI) SAR has demonstrated through several studies a capability to detect ocean surface currents. One of the most challenging problems in ocean surface current retrieval using SAR is the removal of the wind-wave contribution. The phase difference provided by ATI-SAR technique is directly related to the radial velocity of the moving ocean surface. In order to infer the current-only velocity from the total phase the wind-wave contribution need to be removed. This is achieved by simulation of SAR Doppler spectra from given wind fields. This paper investigates the effect of the local wind on ATI-SAR phase. A study case, where the backscatter modulation is dominated by the wind variation, is illustrated using TanDEM-X data over the Baltic Sea. It is shown that retrieving high resolution winds from SAR data using an empirical wind model and using the retrieved winds as input to the SAR imaging model improves the simulated SAR signatures.

Analysis of surface wind retrieval in coastal areas from SAR

There are different parameters inherent to coastal areas that can affect the backscattering of the ocean surface detected by Synthetic Aperture Radar (SAR). The parameters include the influence of land, the influence of the SAR acquisition geometry, and the influence of backscattering features not directly related to wind variations. This work focuses on the study of the influence of those parameters for the performance of surface wind retrieval with C-band SAR data in coastal areas.

Retrieval and assessment of sub-mesoscalewind velocity vectors with Synthetic Aperture Radar

Wind vector fields are currently available from different sources at mesoscale resolutions (2 km to 200 km). The commonly provided wind resolution on 25 km imposes a limit in coastal areas and in the study of small-scale phenomena occurring in the ocean surface. Since wind is the driving force of the ocean, the study of its behavior in sub-mesoscale (< 10 km), improves the understanding of the ocean state. This paper presents a description and assessment of a wind velocity algorithm implemented for C-band (frequency 5.3 GHz) Synthetic Aperture Radar (SAR) data to detect sub-mesoscale wind fields. Results are obtained with the algorithm applied on 14 different SAR images. The quality assessment is performed by the comparison with wind velocity estimates from a numerical weather model and a scatterometer sensor. The statistics of the wind speed retrievals show a bias of about 0.5 m/s, a root mean square (rms) error of 2.6 m/s, and correlation of 79%. For the wind direction, the bias is lower than 8°, with a rms error of about 40° and a correlation of about 85%. The large magnitude of the rms error in the direction is attributed to the differences in variability and resolutions of the data.