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.

Solar irradiance over and under seasonal land-fast sea ice off Barrow, Alaska, in 2010

The amount of solar radiation transmitted through Arctic sea ice is determined by the thickness and physical properties of snow and sea ice. Light transmittance is highly variable in space and time since thickness and physical properties of snow and sea ice are highly heterogeneous on variable time and length scales. We present field measurements of under-ice irradiance along transects under undeformed land-fast sea ice at Barrow, Alaska (March, May, and June 2010). The measurements were performed with a spectral radiometer mounted on a floating under-ice sled. The objective was to quantify the spatial variability of light transmittance through snow and sea ice, and to compare this variability along its seasonal evolution. Along with optical measurements, snow depth, sea ice thickness, and freeboard were recorded, and ice cores were analyzed for chlorophyll a and particulate matter. Our results show that snow cover variability prior to onset of snow melt causes as much relative spatial variability of light transmittance as the contrast of ponded and white ice during summer. Both before and after melt onset, measured transmittances fell in a range from one third to three times the mean value. In addition, we found a twentyfold increase of light transmittance as a result of partial snowmelt, showing the seasonal evolution of transmittance through sea ice far exceeds the spatial variability. However, prior melt onset, light transmittance was time invariant and differences in under-ice irradiance were directly related to the spatial variability of the snow cover.

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.

Particulate and phytoplankton absorption during POLARSTERN cruises ANT-XXVI/3 and ANT-XXVIII/3

The euphotic depth (Zeu) is a key parameter in modelling primary production (PP) using satellite ocean colour. However, evaluations of satellite Zeu products are scarce. The objective of this paper is to investigate existing approaches and sensors to estimate Zeu from satellite and to evaluate how different Zeu products might affect the estimation of PP in the Southern Ocean (SO). Euphotic depth was derived from MODIS and SeaWiFS products of (i) surface chlorophyll-a (Zeu-Chla) and (ii) inherent optical properties (Zeu-IOP). They were compared with in situ measurements of Zeu from different regions of the SO. Both approaches and sensors are robust to retrieve Zeu, although the best results were obtained using the IOP approach and SeaWiFS data, with an average percentage of error (E) of 25.43% and mean absolute error (MAE) of 0.10 m (log scale). Nevertheless, differences in the spatial distribution of Zeu-Chla and Zeu-IOP for both sensors were found as large as 30% over specific regions. These differences were also observed in PP. On average, PP based on Zeu-Chla was 8% higher than PP based on Zeu-IOP, but it was up to 30% higher south of 60°S. Satellite phytoplankton absorption coefficients (aph) derived by the Quasi-Analytical Algorithm at different wavelengths were also validated and the results showed that MODIS aph are generally more robust than SeaWiFS. Thus, MODIS aph should be preferred in PP models based on aph in the SO. Further, we reinforce the importance of investigating the spatial differences between satellite products, which might not be detected by the validation with in situ measurements due to the insufficient amount and uneven distribution of the data.

Horizontal profiles of longwave and shortwave radiation components over sea ice near Barrow, Alaska during the 2011 melt

An integrated instrument package for measuring and understanding the surface radiation budget of sea ice is presented, along with results from its first deployment. The setup simultaneously measures broadband fluxes of upwelling and downwelling terrestrial and solar radiation (four components separately), spectral fluxes of incident and reflected solar radiation, and supporting data such as air temperature and humidity, surface temperature, and location (GPS), in addition to photographing the sky and observed surface during each measurement. The instruments are mounted on a small sled, allowing measurements of the radiation budget to be made at many locations in the study area to see the effect of small-scale surface processes on the large-scale radiation budget. Such observations have many applications, from calibration and validation of remote sensing products to improving our understanding of surface processes that affect atmosphere-snow-ice interactions and drive feedbacks, ultimately leading to the potential to improve climate modelling of ice-covered regions of the ocean. The photographs, spectral data, and other observations allow for improved analysis of the broadband data. An example of this is shown by using the observations made during a partly cloudy day, which show erratic variations due to passing clouds, and creating a careful estimate of what the radiation budget along the observed line would have been under uniform sky conditions, clear or overcast. Other data from the setup's first deployment, in June 2011 on fast ice near Point Barrow, Alaska, are also shown; these illustrate the rapid changes of the radiation budget during a cold period that led to refreezing and new snow well into the melt season.