Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Our study is based on an extensive gridded dataset of winter salinity in the upper 50 m layer of the Arctic Ocean for the periods 1950–1993 and 2007–2012, obtained from ~20 000 profiles. We investigate the interannual variability of the salinity fields, identify predominant patterns of anomalous behavior and leading modes of variability, and develop a statistical model for the prediction of surface-layer salinity. The statistical model is based on linear regression equations linking the principal components of surface-layer salinity obtained through empirical orthogonal function decomposition with environmental factors, such as atmospheric circulation, river runoff, ice processes and water exchange with neighboring oceans. Using this model, we obtain prognostic fields of the surface-layer salinity for the winter period 2013–2014. The prognostic fields generated by the model show tendencies of surface-layer salinification, which were also observed in previous years. Although the used data are proprietary and have gaps, they provide the most spatiotemporally detailed observational resource for studying multidecadal variations in basin-wide Arctic salinity. Thus, there is community value in the identification, dissemination and modeling of the principal modes of variability in this salinity record.

Sea-ice thickness in the Sea of Okhotsk is estimated for 2004–2008 from ICESat derived freeboard under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004–2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50–60 cm (2007 and 2008) to 70–80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of htot is similar every year. Ice volume of 6.3 × 1011 m3 is estimated from the ICESat derived htot and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

In this paper an algorithm for ice/water classification of C- and L-band dual polarization synthetic aperture radar data is presented. A comparison of the two different frequencies is made in order to investigate the potential to improve classification results with multi-frequency data. The algorithm is based on backscatter intensities in co- and cross-polarization and autocorrelation as a texture feature. The mapping between image features and ice/water classification is made with a neural network. Accurate ice/water maps for both frequencies are produced by the algorithm and the results of two frequencies generally agree very well. Differences are found in the marginal ice zone, where the time difference between acquisitions causes motion of the ice pack. C-band reliably reproduces the outline of the ice edge, while L-band has its strengths for thin ice/calm water areas within the icepack. The classification shows good agreement with ice/water maps derived from met.no ice-charts and radiometer data from AMSR-2. Variations are found in the marginal ice zone where the generalization of the ice charts and lower accuracy of ice concentration from radiometer data introduce deviations. Usage of high-resolution dual frequency data could be beneficial for improving ice cover information for navigation and modelling.

The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data.

Pressure ridges impact the mass, energy and momentum budgets of the sea-ice cover and present an obstacle to transportation through ice-infested waters. Quantifying ridge characteristics is important for understanding total sea-ice mass and for improving the representation of sea-ice dynamics in high-resolution models. Multi-sensor measurements collected during annual Operation IceBridge (OIB) airborne surveys of the Arctic provide new opportunities to assess the sea ice at the end of winter. We present a new methodology to derive ridge sail height from high-resolution OIB Digital Mapping System (DMS) visible imagery. We assess the efficacy of the methodology by mapping the full sail height distribution along 12 pressure ridges in the western and central Arctic. Comparisons against coincident Airborne Topographic Mapper (ATM) elevation anomalies are used to demonstrate the methodology and evaluate DMS-derived sail heights. Sail heights and elevation anomalies were correlated at 0.81 or above. On average mean and maximum sail height agreed with ATM elevation to within 0.11 and 0.49 m, respectively. Of the ridges mapped, mean sail height ranged from 0.99 to 2.16 m, while maximum sail height ranged from 2.1 to 4.8 m. DMS also delivered higher sampling along ridge crests than coincident ATM data.

Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction fp has been shown to be correlated with the September minimum ice extent due to its impact on ice albedo and heat uptake. Ice forecasts should benefit from knowledge of fp as melt ponds form several months in advance of ice retreat. This study goes further back by examining the potential to predict fp during winter using backscatter data from the commonly available Sentinel-1 synthetic aperture radar. An object-based image analysis links the winter and spring thermodynamic states of first-year and multiyear sea-ice types. Strong correlations between winter backscatter and spring fp, detected from high-resolution visible to near infrared imagery, are observed, and models for the retrieval of fp from Sentinel-1 data are provided (r2 ≥ 0.72). The models utilize HH polarization channel backscatter that is routinely acquired over the Arctic from the two-satellite Sentinel-1 constellation mission, as well as other past, current and future SAR missions operating in the same C-band frequency. Predicted fp is generally representative of major ice types first-year ice and multiyear ice during the stage in seasonal melt pond evolution where fp is closely related to spatial variations in ice topography.

Knowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

Research on young thin sea ice is essential to understand the changes in the Arctic. But it is also the most challenging to investigate, both in situ and from satellites. If satellite remote-sensing techniques are developing rapidly, fieldwork remains crucial for the mandatory validation of such data. In April 2016, an Arctic fieldwork campaign was conducted at Kongsfjorden, Svalbard. This campaign provided an opportunity to combine various techniques to record the fjord ice properties ranging from local field sampling to broader ground-based and satellite radar remote sensing of the fjord. Tracking the boat used to access the field sites with hand-held GPS devices offered a good opportunity to map fjord ice and assess the limits of radar identification of small icebergs and thin ice. During 1 week, 17 icebergs and the thin ice edges in two different locations were mapped. The GPS tracks present a good agreement with the Radarsat-2 data analysis for one of the two ice edges. The second ice edge track only partly corresponds to the radar scene. Ice movement, recorded by a ground-based radar, is likely to explain this result. Grounded icebergs could be identified in both Radarsat-2 and ground-based radar.

The Arctic marine environment is undergoing a transition from thick multi-year to first-year sea-ice cover with coincident lengthening of the melt season. Such changes are evident in the Baffin Bay-Davis Strait-Labrador Sea (BDL) region where melt onset has occurred ~8 days decade−1 earlier from 1979 to 2015. A series of anomalously early events has occurred since the mid-1990s, overlapping a period of increased upper-air ridging across Greenland and the northwestern North Atlantic. We investigate an extreme early melt event observed in spring 2013. (~6σ below the 1981–2010 melt climatology), with respect to preceding sub-seasonal mid-tropospheric circulation conditions as described by a daily Greenland Blocking Index (GBI). The 40-days prior to the 2013 BDL melt onset are characterized by a persistent, strong 500 hPa anticyclone over the region (GBI >+1 on >75% of days). This circulation pattern advected warm air from northeastern Canada and the northwestern Atlantic poleward onto the thin, first-year sea ice and caused melt ~50 days earlier than normal. The episodic increase in the ridging atmospheric pattern near western Greenland as in 2013, exemplified by large positive GBI values, is an important recent process impacting the atmospheric circulation over a North Atlantic cryosphere undergoing accelerated regional climate change.

With rapid and accelerated Arctic sea-ice loss, it is beneficial to update and baseline historical change on the regional scales from a consistent, intercalibrated, long-term time series of sea-ice data for understanding regional vulnerability and monitoring ice state for climate adaptation and risk mitigation. In this paper, monthly sea-ice extents (SIEs) derived from a passive microwave sea-ice concentration climate data record for the period of 1979–2015, are used to examine Arctic-wide and regional temporal variability of sea-ice cover and their decadal trends for 15 regions of the Arctic. Three unique types of SIE annual cycles are described. Regions of vulnerability within each of three types to further warming are identified. For the Arctic as a whole, the analysis has found significant changes in both annual SIE maximum and minimum, with −2.41 ± 0.56% per decade and −13.5 ± 2.93% per decade change relative to the 1979–2015 climate average, respectively. On the regional scale, the calculated trends for the annual SIE maximum range from +2.48 to −10.8% decade−1, while the trends for the annual SIE minimum range from 0 to up to −42% decade−1.

Large discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

The response of the atmospheric boundary layer (ABL) to subgrid-scale variations of sea ice properties and fracturing is poorly understood and not taken into account in mesoscale Numerical Weather Prediction (NWP) model parametrizations. In this paper we analyze three-dimensional air circulation within the ABL over fragmented sea ice. A series of idealized high-resolution simulations with the Weather Research and Forecasting (WRF) model is performed for several spatial distributions of ice floes and leads for two values of sea ice concentration (0.5 and 0.9) and several ambient wind speed profiles. The results show that the convective circulation within the ABL is sensitive to the subgrid-scale spatial distribution of sea ice. Considerable variability of several domain-averaged quantities – cloud liquid water content, surface turbulent heat flux (THF) – is found for different arrangements of floes. Moreover, the organized structure of air circulation leads to spatial covariance of variables characterizing the ABL. Based on the example of THF, it is demonstrated that this covariance may lead to substantial errors when THF values are estimated from area-averaged quantities, as it is done in mesoscale NWP models. This suggests the need for developing suitable parametrizations of ABL effects related to subgrid-scale sea ice features for these models.