Ice thickness measurements on South Tyrolean glaciers 1996-2014

The ice thickness of 15 South Tyrolean glaciers has been surveyed with two different types of radar systems between 1996 and 2014 within various research projects. For all glaciers apart from Weissbrunnferner, the Laser scan DEMs of the South Tyrolean glacier inventories had been taken as basis for the data processing. Earlier data has been measured with the Narod Sensor using a central frequency of 6.5 MHz, later data was recorded with a GSSI SIR 3000 system. The positions have been measured with a Garmin handheld GPS. The snow height at the time of the measurement was recorded by snow probing. The majority of the glaciers have been surveyed between 2009 and 2014, 9 glaciers in the year 2013. The methods for measurements and calculation of ice thickness are described in the various reports.

Landfast sea-ice and platelet-layer thickness and conductivity of Atka Bay, Antarctica, December 2012

Ice shelves strongly impact coastal Antarctic sea-ice and the associated ecosystem through the formation of a sub-sea-ice platelet layer. Although progress has been made in determining and understanding its spatio-temporal variability based on point measurements, an investigation of this phenomenon on a larger scale remains a challenge due to logistical constraints and a lack of suitable methodology. In this study, we applied a laterally-constrained Marquardt-Levenberg inversion to a unique multi-frequency electromagnetic (EM) induction sounding dataset obtained on the landfast sea ice of Atka Bay, eastern Weddell Sea, in 2012. In addition to consistent fast-ice thickness and -conductivities along > 100 km transects; we present the first comprehensive, high resolution platelet-layer thickness and -conductivity dataset recorded on Antarctic sea ice. The reliability of the algorithm was confirmed by using synthetic data, and the inverted platelet-layer thicknesses agreed within the data uncertainty to drill-hole measurements. Ice-volume fractions were calculated from platelet-layer conductivities, revealing that an older and thicker platelet layer is denser and more compacted than a loosely attached, young platelet layer. The overall platelet-layer volume below Atka Bay fast ice suggests that the contribution of ocean/ice-shelf interaction to sea-ice volume in this region is even higher than previously thought. This study also implies that multi-frequency EM induction sounding is an effective approach in determining platelet layer volume on a larger scale than previously feasible. When applied to airborne multi-frequency EM, this method could provide a step towards an Antarctic-wide quantification of ocean/ice-shelf interaction.

AWI Bathymetric Chart of the Gakkel Ridge (AWI BCGR) (Scale 1:150,000)

The 1 : 1,500,000 AWI Bathymetric Chart of the Gakkel Ridge (AWI BCGR) has been developed from multibeam data measured during the Arctic Mid-Ocean Ridge Expedition in 2001 (AMORE 2001, ARK-XVII/2). This expedition was conducted to investigate the Gakkel Ridge in the Arctic Ocean and was carried out by the icebreaking research vessels RV Polarstern and USCGC Healy. Polarstern is equipped with the multibeam sonar system Hydrosweep DS-2, whereas Healy carries Seabeam 2112. During the expedition an area of 8890 km length and 18 - 46 km width, situated between 82°N/8°W and 87°N/75°E, was surveyed simultaneously by both vessels. Water depths ranged from 566 to 5673 meters. Dense sea ice cover derogated the sonar measurements and decreased data quality. Data errors were corrected in an extensive post-processing. The data of two different sonar systems had to be consolidated in order to derive a high resolution bathymetry of the Gakkel Ridge. Final result was a digital terrain model (DTM) with a grid spacing of 100 meters, which was utilized for generating the map series AWI Bathymetric Chart of the Gakkel Ridge, consisting of ten map sheets.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.