Second-year Ice Research Articles

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Temporospatial variability of snow's thermal conductivity on Arctic sea ice

Abstract. Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parameterizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow dataset from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have evaluated for the first time the seasonal evolution of the snow's and denser snow-ice interface layers' thermal conductivity above different ice ages (refrozen leads, first-year ice, and second-year ice) and topographic features (ridges). Our dataset has a density range of snow and ice between 50 and 900 kg m−3, and corresponding anisotropy measurements, meaning we can test the current parameterizations of thermal conductivity for this density range. Combining different measurement parameterizations and assessing the robustness against spatial heterogeneity, we found the average thermal conductivity of snow (<550 kg m−3) on sea ice remains approximately constant (0.26 ± 0.05 WK-1m-1) over time irrespective of underlying ice type, with substantial spatial and vertical variability. Due to this consistency, we can state that the thermal resistance is mainly influenced by snow height, resulting in a 2.7 times higher average thermal resistance on ridges (1.42 m2 K W−1) compared to first-year level ice (0.51 m2 K W−1). Our findings explain how the scatter of thermal conductivity values directly results from structural properties. Now, the only step is to find a quick method to measure snow anisotropy in the field. Suggestions to do this are listed in the discussion.

Sea ice heat and mass balance measurements from four autonomous buoys during the MOSAiC drift campaign

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day−1, with summer melt rates (June to July) as large as 5 cm day−1. Air temperatures changed as much as 2°C hour−1 but were dampened to <0.3°C hour−1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m−2, with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m−1 °C−1, higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.

Temporal evolution of under-ice meltwater layers and false bottoms and their impact on summer Arctic sea ice mass balance

Low-salinity meltwater from Arctic sea ice and its snow cover accumulates and creates under-ice meltwater layers below sea ice. These meltwater layers can result in the formation of new ice layers, or false bottoms, at the interface of this low-salinity meltwater and colder seawater. As part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), we used a combination of sea ice coring, temperature profiles from thermistor strings and underwater multibeam sonar surveys with a remotely operated vehicle (ROV) to study the areal coverage and temporal evolution of under-ice meltwater layers and false bottoms during the summer melt season from mid-June until late July. ROV surveys indicated that the areal coverage of false bottoms for a part of the MOSAiC Central Observatory (350 by 200 m2) was 21%. Presence of false bottoms reduced bottom ice melt by 7–8% due to the local decrease in the ocean heat flux, which can be described by a thermodynamic model. Under-ice meltwater layer thickness was larger below first-year ice and thinner below thicker second-year ice. We also found that thick ice and ridge keels confined the areas in which under-ice meltwater accumulated, preventing its mixing with underlying seawater. While a thermodynamic model could reproduce false bottom growth and melt, it could not describe the observed bottom melt rates of the ice above false bottoms. We also show that the evolution of under-ice meltwater-layer salinity below first-year ice is linked to brine flushing from the above sea ice and accumulating in the meltwater layer above the false bottom. The results of this study aid in estimating the contribution of under-ice meltwater layers and false bottoms to the mass balance and salt budget for Arctic summer sea ice.

Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development.

Deciphering the Properties of Different Arctic Ice Types During the Growth Phase of MOSAiC: Implications for Future Studies on Gas Pathways

The increased fraction of first year ice (FYI) at the expense of old ice (second-year ice (SYI) and multi-year ice (MYI)) likely affects the permeability of the Arctic ice cover. This in turn influences the pathways of gases circulating therein and the exchange at interfaces with the atmosphere and ocean. We present sea ice temperature and salinity time series from different ice types relevant to temporal development of sea ice permeability and brine drainage efficiency from freeze-up in October to the onset of spring warming in May. Our study is based on a dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 and 2020. These physical properties were used to derive sea ice permeability and Rayleigh numbers. The main sites included FYI and SYI. The latter was composed of an upper layer of residual ice that had desalinated but survived the previous summer melt and became SYI. Below this ice a layer of new first-year ice formed. As the layer of new first-year ice has no direct contact with the atmosphere, we call it insulated first-year ice (IFYI). The residual/SYI-layer also contained refrozen melt ponds in some areas. During the freezing season, the residual/SYI-layer was consistently impermeable, acting as barrier for gas exchange between the atmosphere and ocean. While both FYI and SYI temperatures responded similarly to atmospheric warming events, SYI was more resilient to brine volume fraction changes because of its low salinity (<2). Furthermore, later bottom ice growth during spring warming was observed for SYI in comparison to FYI. The projected increase in the fraction of more permeable FYI in autumn and spring in the coming decades may favor gas exchange at the atmosphere-ice interface when sea ice acts as a source relative to the atmosphere. While the areal extent of old ice is decreasing, so is its thickness at the onset of freeze-up. Our study sets the foundation for studies on gas dynamics within the ice column and the gas exchange at both ice interfaces, i.e. with the atmosphere and the ocean.

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

Measurements of 540–1740 MHz Brightness Temperatures of Sea Ice During the Winter of the MOSAiC Campaign

A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> measurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5–2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.

Sympagic fauna in the northern part of the Barents Sea and adjacent Nansen Basin

Sympagic fauna in the northern Barents Sea and southern Nansen Basin were studied between 1 April 2019 and 5 May 2019 during the expedition Transarctica-2019. Ice cover was represented primarily by thin and medium first-year ice and less by second-year ice. The total abundance of sympagic fauna varied from 0 to 5906.2 ind/m2, with averages of 2244 ± 485 ind/m2 and 3437 ± 1713 ind/m2 recorded for the northern Barents Sea shelf and Nansen Basin, respectively. Sea-ice fauna was dominated by copepod nauplii (66.7%) and Harpacticoida (28.3%), while such taxa as Calanoida (1.8%), Amphipoda (1.5%), Nematoda (1.2%) and Acari (0.5%) were comparatively rare in the lowermost 10 cm of ice cores; Crustaceans were the most diverse group, whereas copepod nauplii was most abundant, especially in April and early May 2019. When compared with published data, diversity of sea-ice fauna in this study was lower for both the Barents Sea and Nansen Basin, and there were no statistically significant differences detected between the community compositions of the different geographic regions. The sea-ice motion reconstruction combined with the composition of sea-ice fauna, particularly the presence of ice nematodes, amphipods, and halacarid mites, suggest that the origin of the sympagic fauna is closely associated with the ice formation area near Franz Josef Land.

Strength–Temperature Equations for First-Year Ice, Second-Year Ice, and Two Categories of Multi-Year Ice

Strength–Temperature Equations for First-Year Ice, Second-Year Ice, and Two Categories of Multi-Year Ice

Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

Basic physical and mechanical characteristics of ice based on the results of the expedition “Transarktika-2019”

On the “Transarktika-2019” expedition, works were carried out for determining the physical and mechanical characteristics of frost field of the first-year sea ice and the field of second-year ice. The thickness of the ice cover was determined by contact and non-contact methods, the temperature, salinity and density of ice, the strength of the samples at central bending and uniaxial compression, as well as the local (borehole) strength of ice were measured. Studies have shown that most of the field is an ice formation formed in the process of dynamic metamorphism. At the beginning of the expedition, an ice floe passed through a section of warm surface waters. This led to the disappearance of the openwork layer on the lower boundary of the ice and stopping the growth of ice from below. During the observation period, the average temperature and salinity of the deformed ice increased, while the average density decreased. The values of mechanical characteristics decreased with increasing temperature and brine volume. The average borehole strength were close to the values obtained by the quadratic approximation for ice in the area of the Ice Station “Cape of Baranov”. The physical and mechanical properties of the level ice are generally similar to the properties of ice, composed mainly of fibrous structures. The ratios between the borehole strength and the strength under uniaxial compression of ice samples drilled parallel to the ice surface were 4.5 and 4.7, which corresponds to the literature data. The thickness of the second-year sea ice at the place of work was 166 — 169 cm, the snow height was 27 cm, the raft of the ice surface above the water surface was 15 cm. The average ice temperature was –4.0 °C. Second-year ice can be divided into three parts that differ in their physical properties. The upper part (0 — 10 cm) was formed in the autumn. The second part (10 — 85 cm) is ice that has undergone seasonal thermometamorphic changes. The lower part was formed during the natural growth of ice from below at the current season.

Spatial and temporal evolution of landfast ice near Zhongshan Station, East Antarctica, over an annual cycle in 2011/2012

Annual observations of first-year ice (FYI) and second-year ice (SYI) near Zhongshan Station, East Antarctica, were conducted for the first time from December 2011 to December 2012. Melt ponds appeared from early December 2011. Landfast ice partly broke in late January, 2012 after a strong cyclone. Open water was refrozen to form new ice cover in mid-February, and then FYI and SYI co-existed in March with a growth rate of 0.8 cm/d for FYI and a melting rate of 2.7 cm/d for SYI. This difference was due to the oceanic heat flux and the thickness of ice, with weaker heat flux through thicker ice. From May onward, FYI and SYI showed a similar growth by 0.5 cm/d. Their maximum thickness reached 160.5 cm and 167.0 cm, respectively, in late October. Drillings showed variations of FYI thickness to be generally less than 1.0 cm, but variations were up to 33.0 cm for SYI in March, suggesting that the SYI bottom was particularly uneven. Snow distribution was strongly affected by wind and surface roughness, leading to large thickness differences in the different sites. Snow and ice thickness in Nella Fjord had a similar “east thicker, west thinner” spatial distribution. Easterly prevailing wind and local topography led to this snow pattern. Superimposed ice induced by snow cover melting in summer thickened multi-year ice, causing it to be thicker than the snow-free SYI. The estimated monthly oceanic heat flux was ∼30.0 W/m2 in March-May, reducing to ∼10.0 W/m2 during July–October, and increasing to ∼15.0 W/m2 in November. The seasonal change and mean value of 15.6 W/m2 was similar to the findings of previous research. The results can be used to further our understanding of landfast ice for climate change study and Chinese Antarctic Expedition services.

Seasonal variability of ice temperature upon the results of measuring at “North Pole-38” drifting station

Vertical profiles of ice temperature were measured in November 2010 – September 2011 at “North Pole-38” drifting station. These observations were made at five locations, including young ice, residual first-year ice, second-year ice, multi-year ice. Methods of measurements are considered. Time-temperature profiles for all types of ice as well as time profile for the gradient of temperature of ice, as an example, are shown. A snow cover gives substantial thermal resistance, reducing a heat flux between atmosphere and ocean. The periods of warming-up and cooling of ice in the annual cycle alternate with periods, when vertical distribution of ice temperature close to linear: with a maximal gradient in winter and zero gradient in summer. A general pattern of the seasonal evolution of ice temperature is discussed. A cold front is propagating down through the ice from October to June. Ice growth lasts, on the average, to a middle of May. Warming of ice to the freezing point occurs mainly in August.

Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions.

Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean

AbstractDuring the Norwegian young sea ICE (N‐ICE2015) campaign in early 2015, a deep snowpack was observed, almost double the climatology for the region north of Svalbard. There were significant amounts of snow‐ice in second‐year ice (SYI), while much less in first‐year ice (FYI). Here we use a 1‐D snow/ice thermodynamic model, forced with reanalyses, to show that snow‐ice contributes to thickness growth of SYI in absence of any bottom growth, due to the thick snow. Growth of FYI is tightly controlled by the timing of growth onset relative to precipitation events. A later growth onset can be favorable for FYI growth due to less snow accumulation, which limits snow‐ice formation. We surmise these findings are related to a phenomenon in the Atlantic sector of the Arctic, where frequent storm events bring heavy precipitation during autumn and winter, in a region with a thinning ice cover.

Winter snow conditions on Arctic sea ice north of Svalbard during the Norwegian young sea ICE (N‐ICE2015) expedition

AbstractSnow is a crucial component of the Arctic sea ice system. Its thickness and thermal properties control heat conduction and radiative fluxes across the ocean, ice, and atmosphere interfaces. Hence, observations of the evolution of snow depth, density, thermal conductivity, and stratigraphy are crucial for the development of detailed snow numerical models predicting energy transfer through the snow pack. Snow depth is also a major uncertainty in predicting ice thickness using remote sensing algorithms. Here we examine the winter spatial and temporal evolution of snow physical properties on first‐year (FYI) and second‐year ice (SYI) in the Atlantic sector of the Arctic Ocean, during the Norwegian young sea ICE (N‐ICE2015) expedition (January to March 2015). During N‐ICE2015, the snow pack consisted of faceted grains (47%), depth hoar (28%), and wind slab (13%), indicating very different snow stratigraphy compared to what was observed in the Pacific sector of the Arctic Ocean during the SHEBA campaign (1997–1998). Average snow bulk density was 345 kg m−3 and it varied with ice type. Snow depth was 41 ± 19 cm in January and 56 ± 17 cm in February, which is significantly greater than earlier suggestions for this region. The snow water equivalent was 14.5 ± 5.3 cm over first‐year ice and 19 ± 5.4 cm over second‐year ice.

Observations and modelling of first-year ice growth and simultaneous second-year ice ablation in the Prydz Bay, East Antarctica

ABSTRACT The seasonal cycle of fast ice thickness in Prydz Bay, East Antarctica, was observed between March and December 2012. In March, we observed a 0.16 m thickness gain of 0.22 m-thick first-year ice (FYI), while 1.16 m-thick second-year ice (SYI) nearby simultaneously ablated by 0.59 m. A 1-D thermodynamic sea-ice model was applied to identify the factors that led to the simultaneous growth of FYI and melt of SYI. The different evolutions were explained by the difference in the conductive heat flux between the FYI and SYI. As the FYI was thin, there was a large temperature gradient between the ice base and the colder ice surface. This generated an upward conductive heat flux, which was larger than the heat flux from the ocean to the ice base, yielding basal growth of ice. In the case of the thicker SYI the temperature gradient and, hence, the conductive heat flux were smaller, and not sufficient to balance the oceanic heat flux at the ice base, yielding basal ablation. Penetration of solar radiation affected the conductive heat flux in both cases, and the model results were sensitive to the initial ice temperature profile and the uncertainty of the oceanic heat flux.

Seasonal changes in the properties of first-year, second-year and multi-year ice

To date, too few measurements have been available to document the seasonal change in the strength for different types of sea ice. The situation is remedied here with nearly 1300 borehole measurements on first-year ice (FYI), second-year ice (SYI) and multi-year ice (MYI) in the Canadian Arctic from the past 15years. The thickness, temperature, salinity and borehole strength are given for five categories of sea ice: FYI, SYI, young multi-year ice (yMYI), thick level multi-year ice (TkMYI) and hummocked multi-year ice (hMYI). Results show that the deterioration of FYI and SYI is offset by about one month in summer, yMYI is more similar to TkMYI than SYI, and deteriorated MYI floes can have strengths comparable to more competent looking MYI floes in summer. The seasonal reduction in strength for different ice types is expressed here as an ‘equivalent floe strength’, which is the depth-averaged strength of all boreholes on a floe normalized by the maximum depth-averaged strength of FYI in winter (32MPa). Seasonal trends in strength show SYI and MYI floes to be stronger than FYI floes in all seasons. The strength of FYI floes decreases from 61% (May) to 9% (July), relative to its 32MPa winter maximum, whereas the equivalent floe strength of SYI decreases from 69% (May) to 16% (August), relative to 32MPa. The equivalent floe strength of the combined category of yMYI & TkMYI decreases from 78% or more (May) to 40% (September). The equivalent floe strength of hMYI decreases from 102% (May) to 51% (September).