Abstract. In this paper, we evaluate the neXtSIM sea ice model with respect to the observed scaling invariance properties of sea ice deformation in the spatial and temporal domains. Using an Arctic setup with realistic initial conditions, state-of-the-art atmospheric reanalysis forcing and geostrophic currents retrieved from satellite data, we show that the model is able to reproduce the observed properties of this scaling in both the spatial and temporal domains over a wide range of scales, as well as their multi-fractality. The variability of these properties during the winter season is also captured by the model. We also show that the simulated scaling exhibits a space–time coupling, a suggested property of brittle deformation at geophysical scales. The ability to reproduce the multi-fractality of this scaling is crucial in the context of downscaling model simulation outputs to infer sea ice variables at the sub-grid scale and also has implications for modeling the statistical properties of deformation-related quantities, such as lead fractions and heat and salt fluxes.

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In this study a 1-yr dataset of a convective-scale atmospheric prediction system of the European Arctic (AROME-Arctic) is compared with the ECMWF’s medium-range forecasting, ensemble forecasting, and reanalysis systems, by using surface and radiosonde observations of wind and temperature. The focus is on the characteristics of the model systems in the very short-term forecast range (6–15 h), but without a specific focus on lead-time dependencies. In general, AROME-Arctic adds value to the representation of the surface characteristics. The atmospheric boundary layer thickness, during stable conditions, is overestimated in the global models, presumably because of a too diffusive turbulence scheme. Instead, AROME-Arctic shows a realistic mean thickness compared to the radiosonde observations. All models behave similarly for the upper-air verification and surprisingly, as well, in forecasting the location of a polar low in the short-range forecasts. However, when comparing with the largest wind speeds from ocean surface winds and at coastal synoptic weather stations during landfall of a polar low, AROME-Arctic shows the most realistic values. In addition to the model intercomparison, the limitation of the representation of sea ice and ocean surface characteristics on kilometer scales are discussed in detail. This major challenge is illustrated by showing the rapid drift and development of sea ice leads during a cold-air outbreak. As well, the available sea surface temperature products and a high-resolution ocean model result are compared qualitatively. New developments of satellite products, ocean–sea ice prediction models, or parameterizations, tailored toward high-resolution atmospheric Arctic prediction, are necessary to overcome this limitation.

Abstract. A satellite database including 16 555 satellite images and ice charts displaying the area of Isfjorden, Hornsund, and the Svalbard region has been established with focus on the time period 2000–2014. 3319 manual interpretations of sea ice conditions have been conducted, resulting in two time series dividing the area of Isfjorden and Hornsund into "fast ice" (sea ice attached to the coastline), "drift ice", and "open water". The maximum fast ice coverage of Isfjorden is  >  40 % in the periods 2000–2005 and 2009–2011 and stays  <  30 % in 2006–2008 and 2012–2014. Fast ice cover in Hornsund reaches  >  40 % in all considered years, except for 2012 and 2014, where the maximum stays  <  20 %. The mean seasonal cycles of fast ice in Isfjorden and Hornsund show monthly averaged values of less than 1 % between July and November and maxima in March (Isfjorden, 35.7 %) and April (Hornsund, 42.1 %), respectively. A significant reduction of the monthly averaged fast ice coverage is found when comparing the time periods 2000–2005 and 2006–2014. The seasonal maximum decreases from 57.5 to 23.2 % in Isfjorden and from 52.6 to 35.2 % in Hornsund. A new index, called "days of fast ice" (DFI), is introduced for quantification of the interannual variation of fast ice cover, allowing for comparison between different fjords and winter seasons. Considering the time period from 1 March until end of the sea ice season, the mean DFI values for 2000–2014 are 33.1 ± 18.2 DFI (Isfjorden) and 42.9 ± 18.2 DFI (Hornsund). A distinct shift to lower DFI values is observed in 2006. Calculating a mean before and after 2006 yields a decrease from 50 to 22 DFI for Isfjorden and from 56 to 34 DFI for Hornsund. Fast ice coverage generally correlates well with remote-sensing sea surface temperature and in situ air temperature. An increase of autumn ocean heat content is observed during the last few years when the DFI values decrease. The presented sea ice time series can be utilized for various climate effect studies linked to, e.g. glacier dynamics, ocean chemistry, and marine biology.

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available.

Abstract. We propose a satellite mission that uses a near-nadir Ka-band Doppler radar to measure surface currents, ice drift and ocean waves at spatial scales of 40 km and more, with snapshots at least every day for latitudes 75 to 82°, and every few days for other latitudes. The use of incidence angles of 6 and 12° allows for measurement of the directional wave spectrum, which yields accurate corrections of the wave-induced bias in the current measurements. The instrument's design, an algorithm for current vector retrieval and the expected mission performance are presented here. The instrument proposed can reveal features of tropical ocean and marginal ice zone (MIZ) dynamics that are inaccessible to other measurement systems, and providing global monitoring of the ocean mesoscale that surpasses the capability of today's nadir altimeters. Measuring ocean wave properties has many applications, including examining wave–current interactions, air–sea fluxes, the transport and convergence of marine plastic debris and assessment of marine and coastal hazards.

Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

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