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Within a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic‐wide scale is based on passive microwave satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N‐ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower‐frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher‐frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong. Key Points: The uncertainty of snow depth retrievals from satellite microwave radiometers is estimated using field measurements and model simulationsSnow properties have the strongest contribution to the uncertainty of snow depth retrievals compared to the influence of atmosphere and iceThe snow depth retrieval based on 19 and 7 GHz has a lower uncertainty compared to the 37 and 19 GHz one [ABSTRACT FROM AUTHOR]

This paper investigates automatic segmentation and classification of C-band, polarimetric synthetic aperture radar (SAR) satellite images of Arctic sea ice under freezing conditions prior to melt. The objective is to investigate the robustness of the results obtained under slightly varying environmental conditions and different viewing geometries. Initially, three geographically overlapping SAR images from consecutive days are incidence-angle corrected and segmented into unknown classes. The segmentation is performed by an unsupervised mixture-of-Gaussian segmentation algorithm utilizing six features extracted from the polarimetric data. After segmentation, the segments are contextually smoothed. One segmented image is manually labelled based on in situ data and expert knowledge. Using this scene as reference, we consider two strategies for class labelling of the other scenes. The first manually labels the classes based on visual inspection of the reference; the second utilizes various statistical distance measures to automatically assign each unknown class to the statistically nearest reference class. These two scenes are also classified pixel-wise by a supervised classification algorithm based on the reference data. Poor classification results are obtained when the incidence angle is very different from the reference scene. Similar viewing geometries reveal good classification and labelling results, the latter regardless of the distance measure used. [ABSTRACT FROM AUTHOR]

The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3°N, from 26 July to 3 August 2012. A set of>10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈0.29 and open-water fraction of ≈0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover. [ABSTRACT FROM AUTHOR]

In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Utilizing polarimetric information in sea ice charting will increase the efficiency and exactness of the maps. The number of classes used in the segmentation has shown to be of significant importance. Thus, studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work. [ABSTRACT FROM AUTHOR]

The article looks at the annual extent of the sea ice cover in the Arctic in 2013. Topics discussed include the use of satellite-based passive microwave instruments to determine the sea ice extent, the age and thickness of the Arctic sea ice, and the changes in sea ice extent during the months of March and September. Also mentioned are the estimates produced by the National Snow and Ice Data Center (NSIDC).

Abstract: This paper presents the data collected during an expedition from the marginal ice zone into the multi year sea ice in the Fram Strait in May–June 2005 to measure the variance in sea-ice types, albedo and thickness. It also describes the techniques used to analyze the data. The principal information from the methodologies applied derives the sea-ice types from digital photography, the spectral and broadband reflectance from spectrometer measurements and the total sea-ice thickness profile from an electromagnetic-probe. A combination of methods was used to extract more information from each data set compared to what traditionally are obtained. The digital images were standardized, textural features extracted and a trained neural network was used for classification, while the optical measurements were normalized and standardized to minimize effects from the set-up and atmospheric conditions. Measurements from June 3rd (before the onset of summer melt) showed that the fractional sea-ice types had large spatial variability, with average fractions for snow-covered sea ice of 81.0%, thick bare ice 4.0%, thin ice 5.3% and open water 9.6%, hence an average ice concentration of 90.3%. The average broadband reflectance factor was 0.73, while the average total sea-ice thickness (including snow) was 2.1 m. Relative high correlations were found between the measured albedo and sea-ice concentration (0.69). The paper also addresses the lessons learned for future fusion of data from large field campaigns. [Copyright &y& Elsevier]

Abstract: During the 4th International Polar Year 2007–2009 (IPY), it has become increasingly obvious that we need to prepare for a new era in the Arctic. IPY occurred during the time of the largest retreat of Arctic sea ice since satellite observations started in 1979. This minimum in September sea ice coverage was accompanied by other signs of a changing Arctic, including the unexpectedly rapid transpolar drift of the Tara schooner, a general thinning of Arctic sea ice and a double-dip minimum of the Arctic Oscillation at the end of 2009. Thanks to the lucky timing of the IPY, those recent phenomena are well documented as they have been scrutinized by the international research community, taking advantage of the dedicated observing systems that were deployed during IPY. However, understanding changes in the Arctic System likely requires monitoring over decades, not years. Many IPY projects have contributed to the pilot phase of a future, sustained, observing system for the Arctic. We now know that many of the technical challenges can be overcome. The Norwegian projects iAOOS-Norway, POLEWARD and MEOP were significant ocean monitoring/research contributions during the IPY. A large variety of techniques were used in these programs, ranging from oceanographic cruises to animal-borne platforms, autonomous gliders, helicopter surveys, surface drifters and current meter arrays. Our research approach was interdisciplinary from the outset, merging ocean dynamics, hydrography, biology, sea ice studies, as well as forecasting. The datasets are tremendously rich, and they will surely yield numerous findings in the years to come. Here, we present a status report at the end of the official period for IPY. Highlights of the research include: a quantification of the Meridional Overturning Circulation in the Nordic Seas (“the loop”) in thermal space, based on a set of up to 15-year-long series of current measurements; a detailed map of the surface circulation as well as characterization of eddy dispersion based on drifter data; transport monitoring of Atlantic Water using gliders; a view of the water mass exchanges in the Norwegian Atlantic Current from both Eulerian and Lagrangian data; an integrated physical–biological view of the ice-influenced ecosystem in the East Greenland Current, showing for instance nutrient-limited primary production as a consequence of decreasing ice cover for larger regions of the Arctic Ocean. Our sea ice studies show that the albedo of snow on ice is lower when snow cover is thinner and suggest that reductions in sea ice thickness, without changes in sea ice extent, will have a significant impact on the arctic atmosphere. We present up-to-date freshwater transport numbers for the East Greenland Current in the Fram Strait, as well as the first map of the annual cycle of freshwater layer thickness in the East Greenland Current along the east coast of Greenland, from data obtained by CTDs mounted on seals that traveled back and forth across the Nordic Seas. We have taken advantage of the real-time transmission of some of these platforms and demonstrate the use of ice-tethered profilers in validating satellite products of sea ice motion, as well as the use of Seagliders in validating ocean forecasts, and we present a sea ice drift product – significantly improved both in space and time – for use in operational ice-forecasting applications. We consider real-time acquisition of data from the ocean interior to be a vital component of a sustained Arctic Ocean Observing System, and we conclude by presenting an outline for an observing system for the European sector of the Arctic Ocean. [Copyright &y& Elsevier]