Abstract. Sea ice volume has decreased in the last decades, evoked by changes in sea ice area and thickness. Estimates of sea ice area and thickness rely on a number of geophysical parameters which introduce large uncertainties. To quantify these uncertainties we use freeboard retrievals from ICESat and investigate different assumptions about snow depth, sea ice density and area. We find that uncertainties in ice area are of minor importance for the estimates of sea ice volume during the cold season in the Arctic basin. The choice of mean ice density used when converting sea ice freeboard into thickness mainly influences the resulting mean sea ice thickness, while snow depth on top of the ice is the main driver for the year-to-year variability, particularly in late winter. The absolute uncertainty in the mean sea ice thickness is 0.28 m in February/March and 0.21 m in October/November. The uncertainty in snow depth contributes up to 70 % of the total uncertainty and the ice density 30-35 %, with higher values in October/November. We find large uncertainties in the total sea ice volume and trend. The mean total sea ice volume is 10 120±1280 km 3 in October/November and 13 250 ± 1860 km 3 in February/March for the time period [2005][2006][2007]. Based on these uncertainties we obtain trends in sea ice volume of −1450 ± 530 km 3 a −1 in October/November and −880 ± 260 km 3 a −1 in February/March over the ICESat period (2003ICESat period ( -2008. Our results indicate that, taking into account the uncertainties, the decline in sea ice volume in the Arctic between the ICESat (2003)(2004)(2005)(2006)(2007)(2008) and CryoSat-2 (2010CryoSat-2 ( -2012 periods may have been less dramatic than reported in previous studies. However, more work and validation is required to quantify these changes and analyse possible unresolved biases in the freeboard retrievals.

The use of academic profiling sites is becoming more common, and emerging technologies boost researchers’ visibility and exchange of ideas. In our study we compared profiles at five different profiling sites. These five sites are ResearchGate, Academia.edu, Google Scholar Citations, ResearcherID and ORCID. The data set is enriched by demographic information including age, gender, position and affiliation, which are provided by the national CRIS-system in Norway. We find that approximately 37% of researchers at the University of Bergen have at least one profile, the prevalence being highest (> 40%) for members at the Faculty of Psychology and the Faculty of Social Sciences. Across all disciplines, ResearchGate is the most widely used platform. However, within Faculty of Humanities, Academia.edu is the preferred one. Researchers are reluctant to maintain multiple profiles, and there is little overlap between different services. Age turns out to be a poor indicator for presence in the investigated profiling sites, women are underrepresented and professors together with PhD students are the most likely profile holders. We next investigated the correlation between bibliometric measures, such as publications and citations, and user activities, such as downloads and followers. We find different bibliometric indicators to correlate strongly within individual platforms and across platforms. There is however less agreement between the traditional bibliometric and social activity indicators.

Abstract. Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80 % of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6 % while for the power maximum it is reduced to 6.5 %. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.

Abstract. Clouds regulate the Earth's radiation budget, both by reflecting part of the incoming sunlight leading to cooling and by absorbing and emitting infrared radiation which tends to have a warming effect. Globally averaged, at the top of the atmosphere the cloud radiative effect is to cool the climate, while at the Arctic surface, clouds are thought to be warming. Here we compare a passive instrument, the AVHRR-based retrieval from CM-SAF, with recently launched active instruments onboard CloudSat and CALIPSO and the widely used ERA-Interim reanalysis. We find that in particular in winter months the three data sets differ significantly. While passive satellite instruments have serious difficulties, detecting only half the cloudiness of the modeled clouds in the reanalysis, the active instruments are in between. In summer, the two satellite products agree having monthly means of 70-80 percent, but the reanalysis are approximately ten percent higher. The monthly mean long-and shortwave components of the surface cloud radiative effect obtained from the ERAInterim reanalysis are about twice that calculated on the basis of CloudSat's radar-only retrievals, while ground based measurements from SHEBA are in between. We discuss these differences in terms of instrument-, retrieval-and reanalysis characteristics, which differ substantially between the analyzed datasets.

Clouds regulate the Earth's radiation budget, both by reflecting part of the incoming sunlight leading to cooling and by absorbing and emitting infrared radiation which tends to have a warming effect. Globally averaged, at the top of the atmosphere the cloud radiative effect is to cool the climate, while at the Arctic surface, clouds are thought to be warming. Here we compare a passive instrument, the AVHRR-based retrieval from CM-SAF, with recently launched active instruments onboard CloudSat and CALIPSO and the widely used ERA-Interim reanalysis. We find that in particular in winter months the three data sets differ significantly. While passive satellite instruments have serious difficulties, detecting only half the cloudiness of the modeled clouds in the reanalysis, the active instruments are in between. In summer, the two satellite products agree having monthly means of 70-80 percent, but the reanalysis are approximately ten percent higher. The monthly mean long-and shortwave components of the surface cloud radiative effect obtained from the ERA-Interim reanalysis are about twice that calculated on the basis of CloudSat's radar-only retrievals, while ground based measurements from SHEBA are in between. We discuss these differences in terms of instrument-, retrieval-and reanalysis characteristics, which differ substantially between the analyzed datasets.Published by Copernicus Publications on behalf of the European Geosciences Union.