Solar radiation over and under sea ice and photographic quantification of Ice algal aggregates during the POLARSTERN cruise ARK-XXVII/3 (IceArc) in summer 2012

The ice cover of the Arctic Ocean has been changing dramatically in the last decades and the consequences for the sea-ice associated ecosystem remain difficult to assess. Algal aggregates underneath sea ice have been described sporadically but the frequency and distribution of their occurrence is not well quantified. We used upward looking images obtained by a remotely operated vehicle (ROV) to derive estimates of ice algal aggregate biomass and to investigate their spatial distribution. During the IceArc expedition (ARK-XXVII/3) of RV Polarstern in late summer 2012, different types of algal aggregates were observed floating underneath various ice types in the Central Arctic basins. Our results show that the floe scale distribution of algal aggregates in late summer is very patchy and determined by the topography of the ice underside, with aggregates collecting in dome shaped structures and at the edges of pressure ridges. The buoyancy of the aggregates was also evident from analysis of the aggregate size distribution. Different approaches used to estimate aggregate biomass yield a wide range of results. This highlights that special care must be taken when upscaling observations and comparing results from surveys conducted using different methods or on different spatial scales.

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.