Thin Sea Ice Research Articles

Vertical distribution of brine and volume structure thin one year ice Amur bay on base NMR and MRT dates

It was studying distribution of liquid and solid phases in pores one year sea ice on Amur Bay with using NMR and MRT methods in period 20132016. According to findings predominant factor in ice structure formation is snow cover. The patterns of brine content and solid phase distribution are considered in interdependence with air temperature and corresponding ice layer temperatures in compliance with preceding weather conditions. Differences in vertical profiles for temperature and salinity for winter and spring periods was marked. The main features of spatial phase dictribution in thin ice in comparision Arctic ice are presented. Just snow cover effects on ice parameters was demonstrated using data 20132016. The relationship of interlayings number in ice with its thickness, air temperature, and snow cover thickness is discussed. The main features of spatial phase dictribution in thin ice in comparision Arctic ice are presented. The empirical relation for calculations thin sea ice thickness was suggested.

Incorporation of satellite-derived thin-ice data into a global OGCM simulation

The presence of thin sea ice is indicative of active freezing conditions in the polar ocean. We propose a simple yet effective method to incorporate information of thin-ice category into coupled ocean–sea-ice model simulations. In our approach, the thin-ice distribution restricts thick-ice extent and constrains atmosphere–ocean heat exchange through the sea ice. Our model simulation with the incorporation of satellite-derived thin-ice data for the Arctic Ocean showed much improved representation of sea-ice and upper-ocean fields, including sea-ice thickness in the Canadian Archipelago and the region north of Greenland, mixed-layer depth over the Central Arctic, and surface-layer salinity over the open ocean. Enhanced sea-ice production by the thin-ice data constraint increased the total sea-ice volume of the Arctic Ocean by 5 times 10^{3}–10 times 10^{3} km3. Subsequent sea-ice melting was also enhanced, leading to the greater amplitude of the seasonal cycle by approximately 2 times 10^{3} km3 (15% of the baseline value from the experiment without the thin-ice data incorporation). Overall, our results demonstrate that the incorporation of satellite-derived information on thin sea ice has great potential for the improvement of coupled ocean–sea-ice simulations.

Contrasted Summer Processes in the Sea Ice for Two Neighboring Floes North of 84°N: Surface and Basal Melt and False Bottom Formation

AbstractWe report continuous observations in the high Arctic (north of 84°N) over the full 2013 summer season at two nearby sites with distinct initial snow depth, ice thickness, and altitude with respect to the local ice topography. The two sites, subject to similar atmospheric conditions that did not favor strong ice melt, showed contrasting evolutions. One site, with initially thin sea ice (1.40 m) at a relatively low location of the floe, witnessed the formation of a spectacular 1.20‐m‐deep melt pond, a pond‐enhanced erosion of the ice surface, and a sudden pond drainage into the ocean. Then, the outpoured fresh water rapidly froze, heated the old ice from below, and also acted as a temporary shield from the ocean heat flux while it was progressively ablated through dissolution. Eventually, the site almost recovered its initial ice thickness. In contrast, the other site, with initially thicker sea ice (1.75 m) at a relatively high location on its floe, did not support any significant meltwater and underwent over 0.5 m of continuous basal ablation. The two sites experienced formation of superimposed and interposed ice. Sea ice survived summer melt at the two sites, which entered the refreezing season with similar snow and ice thicknesses. For the first time, processes associated with the formation of a deep melt pond and subsequent false bottom evolution are continuously documented with ice mass balance instruments.

Impacts of SIS and CICE as Sea Ice Components in BCC_CSM on the Simulation of the Arctic Climate

Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0 (CICE5.0) was introduced to the Beijing Climate Center Climate System Model (BCC_CSM) as a new alternative to the Sea Ice Simulator (SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation (AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further.

Combined SMAP–SMOS thin sea ice thickness retrieval

Abstract. The spaceborne passive microwave sensors Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) provide brightness temperature data in the L band (1.4 GHz). At this low frequency the atmosphere is close to transparent and in polar regions the thickness of thin sea ice can be derived. SMOS measurements cover a large incidence angle range, whereas SMAP observes at a fixed 40∘ incidence angle. By using brightness temperatures at a fixed incidence angle obtained directly (SMAP), or through interpolation (SMOS), thin sea ice thickness retrieval is more consistent as the incidence angle effects do not have to be taken into account. Here we transfer a retrieval algorithm for the thickness of thin sea ice (up to 50 cm) from SMOS data at 40 to 50∘ incidence angle to the fixed incidence angle of SMAP. The SMOS brightness temperatures (TBs) at a given incidence angle are estimated using empirical fit functions. SMAP TBs are calibrated to SMOS to provide a merged SMOS–SMAP sea ice thickness product. The new merged SMOS–SMAP thin ice thickness product was improved upon in several ways compared to previous thin ice thickness retrievals. (i) The combined product provides a better temporal and spatial coverage of the polar regions due to the usage of two sensors. (ii) The radio frequency interference (RFI) filtering method was improved, which results in higher data availability over both ocean and sea ice areas. (iii) For the intercalibration between SMOS and SMAP brightness temperatures the root mean square difference (RMSD) was reduced by 30 % relative to a prior attempt. (iv) The algorithm presented here allows also for separate retrieval from any of the two sensors, which makes the ice thickness dataset more resistant against failure of one of the sensors. A new way to estimate the uncertainty of ice thickness retrieval was implemented, which is based on the brightness temperature sensitivities.

Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

Ocean circulation, ice shelf, and sea ice interactions explain Dansgaard–Oeschger cycles

The last glacial interval experienced abrupt climatic changes called Dansgaard-Oeschger (DO) events. These events manifest themselves as rapid increases followed by slow decreases of oxygen isotope ratios in Greenland ice core records. Despite promising advances, a comprehensive theory of the DO cycles, with their repeated ups and downs of isotope ratios, is still lacking. Here, based on earlier hypotheses, we introduce a dynamical model that explains the DO variability by rapid retreat and slow regrowth of thick ice shelves and thin sea ice in conjunction with changing subsurface water temperatures due to insulation by the ice cover. Our model successfully reproduces observed features of the records, such as the sawtooth shape of the DO cycles, waiting times between DO events across the last glacial, and the shifted antiphase relationship between Greenland and Antarctic ice cores. Our results show that these features can be obtained via internal feedbacks alone. Warming subsurface waters could have also contributed to the triggering of Heinrich events. Our model thus offers a unified framework for explaining major features of multimillennial climate variability during glacial intervals.

A Controlled Experiment on Oil Release Beneath Thin Sea Ice and Its Electromagnetic Detection

This paper presents a multidisciplinary research on the thermodynamic and geophysical effects of crude oil released underneath thin sea ice, and further evaluates the ability of a combined frequency- and time-domain approach toward its detection. To this end, a controlled oil release experiment in an artificially grown sea ice mesocosm was performed during the winter of 2017 at the Sea-Ice Research Environmental Facility located at the University of Manitoba. Ice cores extracted during the evolution of the sea ice prior and post oil injection allowed the investigation of the profile’s properties and the oil distribution. Furthermore, chemical composition and microstructure analysis were performed via a gas chromatography-time-of-flight mass spectrometry and X-ray, respectively. The time-series radar signature of the profile was measured utilizing ground penetration radar at 500 MHz and a C-band scatterometer. For this experiment, it was shown that the retrieval of the oil presence underneath the young sea ice layer was feasible, provided that the measured data were utilized simultaneously in a unified cost function.

Shipboard Observations of the Meteorology and Near‐Surface Environment During Autumn Freezeup in the Beaufort/Chukchi Seas

AbstractThe collection and processing of shipboard air, ice, and ocean measurements from the Sea State field campaign in the Beaufort/Chukchi Seas in autumn 2015 are described and the data used to characterize the near‐surface freezeup environment. The number of parameters measured or derived is large and the location and time of year are unique. Analysis was done of transits through the new, growing ice and of ice edge periods. Through differential surface energy fluxes, the presence of new, thin sea ice (<50 cm) produces lower tropospheric air temperatures in the ice interior that average ~4°C colder than those over open water near the ice edge, resulting in an ice edge baroclinic zone. This temperature difference doubles by late October and produces thermodynamic and dynamic feedbacks. These include off‐ice, cold‐air advection leading to enhanced surface heat loss averaging ~200 W/m2 over the open water, formation of low‐level jets, suppression of the ice edge baroclinic zone, and enhanced ice drift. The interior ice growth rate is thermodynamically consistent with a surface heat loss of ~65 W/m2 to the atmosphere and a heat flux of several tens of W/m2 from the ocean below. Ice drift at times contributes to the southward advance of the autumn ice edge through off‐ice winds. The ocean thermohaline structure is highly variable and appears associated with bathymetric features, small‐scale upper‐ocean eddies, and the growing ice cover. Lower salinity under the ice interior compared to the nearby ice edge is an upper‐ocean impact of this thin ice cover.

Thin Arctic sea ice in L-band observations and an ocean reanalysis

Abstract. L-band radiance measurements of the Earth's surface such as those from the SMOS satellite can be used to retrieve the thickness of thin sea ice in the range 0–1 m under cold surface conditions. However, retrieval uncertainties can be large due to assumptions in the forward model, which converts brightness temperatures into ice thickness and due to uncertainties in auxiliary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data before using sea-ice thickness products from L-band radiometry for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2011 to autumn 2017 and compare it to the global ocean reanalysis ORAS5, which does not assimilate the SMOS-SIT data. ORAS5 currently provides the ocean and sea-ice initial conditions for all coupled weather, monthly and seasonal forecasts issued by ECMWF. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter and can under certain conditions be used to expose deficiencies in the reanalysis. Overall, there is a promising match between sea-ice thicknesses from ORAS5 and SMOS-SIT early in the freezing season (October–December), while later in winter, sea ice is consistently modelled thicker than observed. This is mostly attributable to refrozen polynyas and fracture zones, which are poorly represented in ORAS5 but easily detected by SMOS-SIT. However, there are other regions like Baffin Bay, where biases in the observational data seem to be substantial, as comparisons with independent observational data suggest. Despite considerable uncertainties and discrepancies between thin sea ice in SMOS-SIT and ORAS5 on local scales, interannual variability and trends of its large-scale distribution are in good agreement. This gives some confidence in our current ability to monitor climate variability and change in thin sea ice. With further improvements in retrieval methods, forecast models and data assimilation methods, the huge potential of L-band radiometry to derive the thickness of thin sea ice in winter will be realised and will provide an important building block for improved predictions in polar regions.

CO<sub>2</sub> flux over young and snow-covered Arctic pack ice in winter and spring

Abstract. Rare CO2 flux measurements from Arctic pack ice show that two types of ice contribute to the release of CO2 from the ice to the atmosphere during winter and spring: young, thin ice with a thin layer of snow and older (several weeks), thicker ice with thick snow cover. Young, thin sea ice is characterized by high salinity and high porosity, and snow-covered thick ice remains relatively warm ( > −7.5 °C) due to the insulating snow cover despite air temperatures as low as −40 °C. Therefore, brine volume fractions of these two ice types are high enough to provide favorable conditions for gas exchange between sea ice and the atmosphere even in mid-winter. Although the potential CO2 flux from sea ice decreased due to the presence of the snow, the snow surface is still a CO2 source to the atmosphere for low snow density and thin snow conditions. We found that young sea ice that is formed in leads without snow cover produces CO2 fluxes an order of magnitude higher than those in snow-covered older ice (+1.0 ± 0.6 mmol C m−2 day−1 for young ice and +0.2 ± 0.2 mmol C m−2 day−1 for older ice).

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING MODIS DATA

Abstract. Sea ice has an important role of reflecting the solar radiation back into space. However, once the sea ice area melts, the area starts to absorb the solar radiation which accelerates the global warming. This means that the trend of global warming is likely to be enhanced in sea ice areas. In this study, the authors have developed a method to extract thin ice area using reflectance data of MODIS onboard Terra and Aqua satellites of NASA. The reflectance of thin sea ice in the visible region is rather low. Moreover, since the surface of thin sea ice is likely to be wet, the reflectance of thin sea ice in the near infrared region is much lower than that of visible region. Considering these characteristics, the authors have developed a method to extract thin sea ice areas by using the reflectance data of MODIS (NASA MYD09 product, 2017) derived from MODIS L1B. By using the scatter plots of the reflectance of Band 1 (620 nm–670 nm) and Band 2 (841 nm–876 nm)) of MODIS, equations for extracting thin ice area were derived. By using those equations, most of the thin ice areas which could be recognized from MODIS images were well extracted in the seasonal sea ice zones in the Northern Hemisphere, namely the Sea of Okhotsk, the Bering Sea and the Gulf of Saint Lawrence. For some limited areas, Landsat-8 OLI images were also used for validation.

Collapse of the 2017 Winter Beaufort High: A Response to Thinning Sea Ice?

AbstractThe winter Arctic atmosphere is under the influence of two very different circulation systems: extratropical cyclones travel along the primary North Atlantic storm track from Iceland toward the eastern Arctic, while the western Arctic is characterized by a quasi‐stationary region of high pressure known as the Beaufort High. The winter (January through March) of 2017 featured an anomalous reversal of the normally anticyclonic surface winds and sea ice motion in the western Arctic. This reversal can be traced to a collapse of the Beaufort High as the result of the intrusion of low‐pressure systems from the North Atlantic, along the East Siberian Coast, into the Arctic Basin. Thin sea ice as the result of an extremely warm autumn (October through December) of 2016 contributed to the formation of an anomalous thermal low over the Barents Sea that, along with a northward shift of the tropospheric polar vortex, permitted this intrusion. The collapse of the Beaufort High during the winter of 2017 was associated with simultaneous 2‐sigma sea level pressure, surface wind, and sea ice circulation anomalies in the western Arctic. As the Arctic sea ice continues to thin, such reversals may become more common and impact ocean circulation, sea ice, and biology.

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.

Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N‐ICE2015 North of Svalbard

AbstractIn recent years, sea‐ice conditions in the Arctic Ocean changed substantially toward a younger and thinner sea‐ice cover. To capture the scope of these changes and identify the differences between individual regions, in situ observations from expeditions are a valuable data source. We present a continuous time series of in situ measurements from the N‐ICE2015 expedition from January to June 2015 in the Arctic Basin north of Svalbard, comprising snow buoy and ice mass balance buoy data and local and regional data gained from electromagnetic induction (EM) surveys and snow probe measurements from four distinct drifts. The observed mean snow depth of 0.53 m for April to early June is 73% above the average value of 0.30 m from historical and recent observations in this region, covering the years 1955–2017. The modal total ice and snow thicknesses, of 1.6 and 1.7 m measured with ground‐based EM and airborne EM measurements in April, May, and June 2015, respectively, lie below the values ranging from 1.8 to 2.7 m, reported in historical observations from the same region and time of year. The thick snow cover slows thermodynamic growth of the underlying sea ice. In combination with a thin sea‐ice cover this leads to an imbalance between snow and ice thickness, which causes widespread negative freeboard with subsequent flooding and a potential for snow‐ice formation. With certainty, 29% of randomly located drill holes on level ice had negative freeboard.

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp.

On the potential of hand-held GPS tracking of fjord ice features for remote-sensing validation

ABSTRACTResearch 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.

Vertical thermodynamic structure of the troposphere during the Norwegian young sea ICE expedition (N‐ICE2015)

AbstractThe Norwegian young sea ICE (N‐ICE2015) expedition was designed to investigate the atmosphere‐snow‐ice‐ocean interactions in the young and thin sea ice regime north of Svalbard. Radiosondes were launched twice daily during the expedition from January to June 2015. Here we use these upper air measurements to study the multiple cyclonic events observed during N‐ICE2015 with respect to changes in the vertical thermodynamic structure, moisture content, and boundary layer characteristics. We provide statistics of temperature inversion characteristics, static stability, and boundary layer extent. During winter, when radiative cooling is most effective, we find the strongest impact of synoptic cyclones. Changes to thermodynamic characteristics of the boundary layer are associated with transitions between the radiatively “clear” and “opaque” atmospheric states. In spring, radiative fluxes warm the surface leading to lifted temperature inversions and a statically unstable boundary layer. Further, we compare the N‐ICE2015 static stability distributions to corresponding profiles from ERA‐Interim reanalysis, from the closest land station in the Arctic North Atlantic sector, Ny‐Ålesund, and to soundings from the SHEBA expedition (1997/1998). We find similar stability characteristics for N‐ICE2015 and SHEBA throughout the troposphere, despite differences in location, sea ice thickness, and snow cover. For Ny‐Ålesund, we observe similar characteristics above 1000 m, while the topography and ice‐free fjord surrounding Ny‐Ålesund generate great differences below. The long‐term radiosonde record (1993–2014) from Ny‐Ålesund indicates that during the N‐ICE2015 spring period, temperatures were close to the climatological mean, while the lowest 3000 m were 1–3°C warmer than the climatology during winter.

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

Abstract. Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness ≲ 0.6 m), the method underestimates the actual sea ice concentration.

Atmospheric components of the surface energy budget over young sea ice: Results from the N‐ICE2015 campaign

AbstractThe Norwegian young sea ice campaign obtained the first measurements of the surface energy budget over young, thin Arctic sea ice through the seasonal transition from winter to summer. This campaign was the first of its kind in the North Atlantic sector of the Arctic. This study describes the atmospheric and surface conditions and the radiative and turbulent heat fluxes over young, thin sea ice. The shortwave albedo of the snow surface ranged from about 0.85 in winter to 0.72–0.80 in early summer. The near‐surface atmosphere was typically stable in winter, unstable in spring, and near neutral in summer once the surface skin temperature reached 0°C. The daily average radiative and turbulent heat fluxes typically sum to negative values (−40 to 0 W m−2) in winter but then transition toward positive values of up to nearly +60 W m−2 as solar radiation contributes significantly to the surface energy budget. The sensible heat flux typically ranges from +20–30 W m−2 in winter (into the surface) to negative values between 0 and −20 W m−2 in spring and summer. A winter case study highlights the significant effect of synoptic storms and demonstrates the complex interplay of wind, clouds, and heat and moisture advection on the surface energy components over sea ice in winter. A spring case study contrasts a rare period of 24 h of clear‐sky conditions with typical overcast conditions and highlights the impact of clouds on the surface radiation and energy budgets over young, thin sea ice.