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Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r20.15 or lower. r2for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters. Data collected using a surface‐based radar instrument on sea ice during the MOSAiC Arctic expedition were used to develop new techniques to estimate the depth of the overlying snow. We used different polarizations of the radiation to detect the depths of the upper and lower snow surfaces, and subtracted them to give snow depth. These depths agreed well with an independently collected snow depth data set. Estimates of snow depth using two different radar frequencies were less accurate, whilst using information of the shape of the returning pulse of radiation also showed a relationship with the independent snow depths, though not as strong as the polarization method. These results indicate that polarimetry (using a new satellite mission) and/or waveform shape (using existing missions) could be used to estimate snow depth on sea ice from airborne or satellite platforms. Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter dataThe dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequenciesRadar‐derived snow depths agreed with independent measurements, with r2up to 0.77 and accuracy of 1 cm for best‐performing techniques Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter data The dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequencies Radar‐derived snow depths agreed with independent measurements, with r2up to 0.77 and accuracy of 1 cm for best‐performing techniques

State‐of‐the‐art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high‐frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along‐track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer‐term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state‐of‐the‐art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat‐2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03–0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03–0.06 m. A major advancement in mapping the Arctic Ocean mean sea surface (MSS) is realized through the inclusion of CryoSat‐2 dataSea ice freeboard retrievals from airborne/satellite altimeter measurements are impacted by the choice of MSS/GGM model used in the retrieval algorithmDepending on which MSS/GGM is used, the standard deviation of freeboard differences ranges 0.03–0.06 m, corresponding to an ice thickness uncertainty of 0.24–0.54 m

The Barents Sea is one of the fastest changing regions of the Arctic, and has experienced the strongest decline in winter‐time sea‐ice area in the Arctic, at −23±4%decade−1. Sea‐ice thickness in the Barents Sea is not well studied. We present two previously unpublished helicopter‐borne electromagnetic (HEM) ice thickness measurements from the northwestern Barents Sea acquired in March 2003 and 2014. The HEM data are compared to ice thickness calculated from ice draft measured by ULS deployed between 1994 and 1996. These data show that ice thickness varies greatly from year to year; influenced by the thermodynamic and dynamic processes that govern local formation vs long‐range advection. In a year with a large inflow of sea‐ice from the Arctic Basin, the Barents Sea ice cover is dominated by thick multiyear ice; as was the case in 2003 and 1995. In a year with an ice cover that was mainly grown in situ, the ice will be thin and mechanically unstable; as was the case in 2014. The HEM data allow us to explore the spatial and temporal variability in ice thickness. In 2003 the dominant ice class was more than 2 years old; and modal sea‐ice thickness varied regionally from 0.6 to 1.4 m, with the thinner ice being either first‐year ice, or multiyear ice which had come into contact with warm Atlantic water. In 2014 the ice cover was predominantly locally grown ice less than 1 month old (regional modes of 0.5–0.8 m). These two situations represent two extremes of a range of possible ice thickness distributions that can present very different conditions for shipping traffic; or have a different impact on heat transport from ocean to atmosphere. Sea‐ice thickness in the northwestern Barents Sea is strongly variable, both interannually and regionallySea‐ice thickness depends on the balance between local ice formation and long‐range advection of old iceRelations between ice age and thickness can be complicated by specific environmental factors; e.g., warm Atlantic water

Radar altimetry measurements of the current satellite mission CryoSat‐2 show an increase of Arctic sea ice thickness in autumn 2013, compared to previous years but also related to March 2013. Such an increase over the melting season seems unlikely and needs to be investigated. Recent studies show that the influence of the snow cover is not negligible and can highly affect the CryoSat‐2 range retrievals if it is assumed that the main scattering horizon is given by the snow‐ice interface. Our analysis of Arctic ice mass balance buoy records and coincident CryoSat‐2 data between 2012 and 2014 adds observational evidence to these findings. Linear trends of snow and ice freeboard measurements from buoys and nearby CryoSat‐2 freeboard retrievals are calculated during accumulation events. We find a positive correlation between buoy snow freeboard and CryoSat‐2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat‐2 sea ice thickness in autumn 2013. CryoSat‐2 range measurements correlate with buoy‐measured snow depth changesAssuming that CryoSat senses the ice freeboard is not justified for thick snowArctic wide snow depth data are required

The last 15 years have brought major innovations in helicopter towed time domain electromagnetics (EM), while few further developments have been made within the classic frequency domain segment. Operational use of frequency domain EM for sea ice thickness mapping acted as a driving force to develop new concepts such as the system under our consideration. Since its introduction we have implemented new concepts aiming at noise reduction and drift elimination. We decreased signal noise base levels by one to two orders of magnitude with changes to the signal transmission concept. Further, we increased the receiver coil dynamic range creating an EM setup without the need for primary field bucking. Finally, we implemented control signals inside the receiver coils to potentially eliminate system drift. Ground tests demonstrate the desired noise reduction and demonstrate drift control, leading to essentially drift free data. Airborne field data confirm these results, yet also show that the procedures can still be improved. The remaining quest is whether these specialised system improvements could also be implemented in exploration helicopter EM (HEM) systems to increase accuracy and efficiency.

Background The short-wavelength-sensitive (SWS) cone-mediated sensitivity is a sensitive indicator of functional changes of the macula in diabetic maculopathy. This study was performed to investigate whether functional losses of the macula are detectable in patients without a significant macular edema. Methods In 45 patients with diabetes mellitus with clear optical media and no macular edema, conventional white-on-white perimetry (WWP) and short-wavelength automated perimetry (SWAP) were performed in the central 10-deg field. Fifty-eight healthy subjects ranging in age from 16 to 62 years served as controls. The two groups did not differ in age. Results Variance analysis (ANOVA) revealed significantly lower sensitivity in patients with diabetes than in controls. SWAP thresholds were significantly more greatly reduced by diabetes than those of WWP (ANOVA interaction: P=0.003). Post-hoc testing revealed a sensitivity reduction of 2.8 dB ( P=0.0003) in patients with diabetes for SWAP versus 0.46 for WWP ( P=0.15). Subgroup analysis revealed that mean thresholds of SWAP and WWP predominantly were reduced in patients with advanced disease. In patients with no retinopathy, sensitivity was not affected at all. Conclusion SWS sensitivity may be affected in patients with diabetic retinopathy without clinically significant macular edema. Sensitivity loss was pronounced with increasing severity of retinopathy, reflecting the global status of the eye.

In April 2017, we collected unique, extensive in situ data of sea ice and snow thickness. At 10 sampling sites, located under a CryoSat‐2 overpass, between Ellesmere Island and 87.1°N mean and modal total ice thicknesses ranged between 2 to 3.4 m and 1.8 to 2.9 m, respectively. Coincident snow thicknesses ranged between 0.3 to 0.47 m (mean) and 0.1 to 0.5 m (mode). The profile spanned the complete multiyear ice zone in the Lincoln Sea, into the first‐year ice zone farther north. Complementary snow thickness measurements near the North Pole showed a mean thickness of 0.31 m. Compared with scarce measurements from other years, multiyear ice was up to 0.75 m thinner than in 2004, but not significantly different from 2011 and 2014. We found excellent agreement with a commonly used snow climatology and with published long‐term ice thinning rates. There was reasonable agreement with CryoSat‐2 thickness retrievals. Snow and ice thickness and northern extent of multiyear ice were observed in April 2017 with unique in situ measurementsModal, total ice thickness ranged between 1.8 and 3.2 m, little different from 2011 and 2014, but 0.75 m thinner than in 2004, and consistent with thinning rates over 2000–2012Mean and modal snow thicknesses varied strongly between 0.3 to 0.47 m and 0.1 to 0.5 m, respectively, in close agreement with climatology