In the marginal ice zone, surface waves drive motion of sea ice floes. The motion of floes relative to each other can cause periodic collisions, and drives the formation of pancake sea ice. Additionally, the motion of floes relative to the water results in turbulence generation at the interface between the ice and ocean below. These are important processes for the formation and growth of pancakes, and likely contribute to wave energy loss. Models and laboratory studies have been used to describe these motions, but there have been no in situ observations of relative ice velocities in a natural wave field. Here, we use shipboard stereo video to measure wave motion and relative motion of pancake floes simultaneously. The relative velocities of pancake floes are typically small compared to wave orbital motion (i.e. floes mostly follow the wave orbits). We find that relative velocities are well-captured by existing phase-resolved models, and are only somewhat over-estimated by using bulk wave parameters. Under the conditions observed, estimates of wave energy loss from ice–ocean turbulence are much larger than from pancake collisions. Increased relative pancake floe velocities in steeper wave fields may then result in more wave attenuation by increasing ice–ocean shear.

The surface energy budget over the Antarctic sea ice from 8 April 2016 through 26 November 2016 are presented. From April to October, Sensible heat flux (SH) and subsurface conductive heat flux (G) were the heat source of surface while latent heat flux (LE) and net radiation flux (Rn) were the heat sink of surface. Our results showed larger downward SH (due to the warmer air in our site) and upward LE (due to the drier air and higher wind speed in our site) compared with SHEBA data. However, the values of SH in N-ICE2015 campaign, which located at a zone with stronger winds and more advection of heat in the Arctic, were comparable to our results under clear skies. The values of aerodynamic roughness length (z0m) and scalar roughness length for temperature (z0h), being 1.9 × 10−3 m and 3.7 × 10−5 m, were suggested in this study. It is found that snow melting might increase z0m. Our results also indicate that the value of log(z0h/z0m) was related to the stability of stratification. In addition, several representative parameterization schemes for z0h have been tested and a couple of schemes were found to make a better performance.

NASA's Operation IceBridge mission flew over the Ross Sea, Antarctica (20 and 27 November 2013) and collected data with Airborne Topographic Mapper (ATM) and Digital Mapping System (DMS). Using the DMS and reflectivity of ATM L1B, leads are detected to define local sea level height. The total freeboard is then obtained and converted to ice thickness. The estimated mean sea-ice thickness values are found to be in the 0.48–0.99 m range. Along the N-S track, sea ice was thinner southward rather than northward of the fluxgate, resulting in two peaks of modal thickness: 0.35 m (south) and 0.7 m (north). This supports that new ice produced in coastal polynyas is transported northward by katabatic winds off the ice-shelf. The lowest (2%) elevation method used for freeboard retrieval for ICESat is also tested for ATM data. It is found that the lowest elevation method tends to overestimate freeboard, but mean values are less affected than mode values. Using mean thickness values of ICESat and ATM along the ‘fluxgate’, separating the shelf from the deep ocean, the exported ice volume at this ‘fluxgate’ is found to be higher during the ICESat years (2003–2008) than during the IceBridge year (2013).

Synthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006.

We present the analysis of global sympagic primary production (PP) from 300 years of pre-industrial and historical simulations of the E3SMv1.1-BGC model. The model includes a novel, eight-element sea ice biogeochemical component, MPAS-Seaice zbgc, which is resolved in three spatial dimensions and uses a vertical transport scheme based on internal brine dynamics. Modeled ice algal chlorophyll-a concentrations and column-integrated values are broadly consistent with observations, though chl-a profile fractions indicate that upper ice communities of the Southern Ocean are underestimated. Simulations of polar integrated sea ice PP support the lower bound in published estimates for both polar regions with mean Arctic values of 7.5 and 15.5 TgC/a in the Southern Ocean. However, comparisons of the polar climate state with observations, using a maximal bound for ice algal growth rates, suggest that the Arctic lower bound is a significant underestimation driven by biases in ocean surface nitrate, and that correction of these biases supports as much as 60.7 TgC/a of net Arctic PP. Simulated Southern Ocean sympagic PP is predominantly light-limited, and regional patterns, particularly in the coastal high production band, are found to be negatively correlated with snow thickness.

Ponds that form on sea ice can cause it to thin or break-up, which can promote calving from an adjacent ice shelf. Studies of sea ice ponds have predominantly focused on Arctic ponds formed by in situ melting/ponding. Our study documents another mechanism for the formation of sea ice ponds. Using Landsat 8 and Sentinel-2 images from the 2015–16 to 2018–19 austral summers, we analyze the evolution of sea ice ponds that form adjacent to the McMurdo Ice Shelf, Antarctica. We find that each summer, meltwater flows from the ice shelf onto the sea ice and forms large (up to 9 km2) ponds. These ponds decrease the sea ice's albedo, thinning it. We suggest the added mass of runoff causes the ice to flex, potentially promoting sea-ice instability by the ice-shelf front. As surface melting on ice shelves increases, we suggest that ice-shelf surface hydrology will have a greater effect on sea-ice stability.

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.

Sea ice is composed of columnar-shaped grains. To investigate the influence of the loading direction on the uniaxial compressive strength and failure processes of sea ice, field experiments were performed with first-year level ice. Loads were applied both horizontally (parallel to the grain columns) and vertically (across the grain columns) with various nominal strain rates. Two failure modes have been observed: a ductile failure mode at low nominal strain rates, and a brittle failure mode at high nominal strain rates. However, the failure pattern of sea ice was clearly dependent on the loading direction. At low nominal strain rates (ductile failure mode), the sea-ice samples yielded due to the development of wing cracks under horizontal loading and due to splaying out at one end under vertical loading. When sea ice fails in the ductile mode, the deformation is driven by grain boundary sliding under horizontal loading and by grain decohesion and crystal deflection under vertical loading. At high nominal strain rates (brittle failure mode), the sea-ice samples failed in shear faulting under horizontal loading and in cross-column buckling under vertical loading. The nominal strain rate at the brittle–ductile transition zone is about ten times higher under vertical loading.

The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

Our understanding of ice algal responses to the recent changes in Arctic sea ice is impeded by limited field observations. In the present study, environmental characteristics of the landfast sea-ice zone as well as primary production and macromolecular composition of ice algae and phytoplankton were studied in the Kitikmeot Sea near Cambridge Bay in spring 2017. Averaged total chlorophyll-a (Chl-a) concentration was within the lower range reported previously for the same region, while daily carbon uptake rates of bottom-ice algae were significantly lower in this study than previously reported for the Arctic. Based on various indicators, the region's low nutrient concentrations appear to limit carbon uptake rates and associated accumulation of bottom-ice algal biomass. Furthermore, the lipids-dominant biochemical composition of bottom-ice algae suggests strong nutrient limitation relative to the distinctly different carbohydrates-dominant composition of phytoplankton. Together, the results confirm strong nitrate limitation of the local marine system.

The surface flooding of Antarctic sea ice in summer covers 50% or more of the sea-ice area in the major summer ice packs, the western Weddell and the Bellingshausen-Amundsen Seas. Two CRREL ice mass-balance buoys were deployed on the Amundsen Sea pack in late December 2010 from the icebreaker Oden, bridging the summer period (January–February 2011). Temperature records from thermistors embedded vertically in the snow and ice showed progressive increases in the depth of the flooded layer (up to 0.3–0.35 m) on the ice cover during January and February. While the snow depth was relatively unchanged from accumulation (<10 cm), ice thickness decreased by up to a meter from bottom melting during this period. Contemporaneous with the high bottom melting, under-ice water temperatures up to 1°C above the freezing point were found. The high temperature arises from solar heating of the upper mixed layer which can occur when ice concentration in the local area falls and lower albedo ocean water is exposed to radiative heating. The higher proportion of snow ice found in the Amundsen Sea pack ice therefore results from both winter snowfall and summer ice bottom melt found here that can lead to extensive surface flooding.

The Polar Sea Ice Topography REconstruction System, or PSITRES, is a 3D camera system designed to continuously monitor an area of ice and water adjacent to an ice-going vessel. Camera systems aboard ships in the polar regions are common; however, the application of computer vision techniques to extract high-level information from the imagery is infrequent. Many of the existing systems are built for human involvement throughout the process and lack automation necessary for round the clock use. The PSITRES was designed with computer vision in mind. It can capture images continuously for days on end with limited oversight. We have applied the system in different ice observing scenarios. The PSITRES was deployed on three research expeditions in the Arctic and Subarctic, and we present applications in measuring ice concentration, melt pond fraction and presence of algae. Systems like PSITRES and the computer vision algorithms applied represent steps toward automatically observing, evaluating and analyzing ice and the environment around ships in ice-covered waters.

The role of synoptic-scale cyclones in the trends and variability of Arctic sea ice conditions has remained uncertain. In recognition, we conduct a systematic investigation of how sea-ice concentration (SIC) changes with cyclone passage, including all individual storms that pass over any part of the region's ice pack. For all seasons, especially summer and autumn, we find a pattern of higher ice concentration after a region is influenced by a cyclone compared to when it is not, primarily due to thermodynamic effects. During warm months, cyclones appear to slow the general day-to-day decline in concentration; in cold months, cyclones augment the day-to-day increase. These relationships are changing over time, with cyclone-associated concentration changes becoming less distinct from overall changes. Cyclone effects on ice divergence are spatially variable; computed fields are noisy. In summer, these dynamic effects of cyclone passage generally decrease SIC, but are outweighed by the thermodynamic effects (e.g., reductions in air temperature, shortwave radiation). In autumn, cyclone-associated concentration changes are not as easily explained by observed cyclone conditions. Key questions remain regarding the extent to which our findings are influenced by artifacts of surface melt and weather effects on the passive microwave retrievals.

The evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

We examine the relative effect of warming events (storms) and snow cover on thermodynamic growth of Arctic sea ice in winter. We use a 1-D snow and ice thermodynamic model to perform sensitivity experiments. Observations from the winter period of the Norwegian young sea ICE (N-ICE2015) campaign north of Svalbard are used to initiate and force the model. The N-ICE2015 winter was characterized by frequent storm events that brought pulses of heat and moisture, and a thick snow cover atop the sea ice (0.3–0.5 m). By the end of the winter, sea-ice bottom growth was negligible. We show that the thermodynamic effect of storms to the winter sea-ice growth is controlled by the amount of snow on sea ice. For 1.3 m initial ice thickness, the decrease in ice growth caused by the warming events ranged from −1.4% (for 0.5 m of snow) to −7.5% (for snow-free conditions). The decrease in sea-ice growth caused by the thick snow (0.5 m) was more important, ranging from −17% (with storms) to −23% (without storms). The results showcase the critical role of snow on winter Arctic sea-ice growth.

The yearly paired process of slow growth and rapid melt of some 15 million square kilometers of Antarctic sea ice takes place with a regular asymmetry; the process has been linked to the relationship of the position of the ice edge with the band of low pressure that circles the continent between 60° and 70°S. In autumn, winds to the north of the low-pressure band slow the advancing ice edge. In summer, Ekman divergence created by opposing winds on either side of the low-pressure band opens up warm water regions that rapidly melt sea ice. We use the 40 ensemble members from the CESM-LENS historical run (1920–2005) to examine the relationship between the asymmetry in the annual cycle and the position and intensity of the low-pressure band. CESM-LENS reproduces the magnitude of the annual cycle of Antarctic sea ice extent with a short lag (2 weeks). Melt rate is the characteristic of the annual cycle that varies the most. Our results provide evidence that lower pressure leads to increased melt rates, which supports the importance of the role of divergence in increasing the melt rate of Antarctic sea ice. The role of winds during the growing season remains unquantified.

The Ross Sea is known for showing the greatest sea-ice increase, as observed globally, particularly from 1979 to 2015. However, corresponding changes in sea-ice thickness and production in the Ross Sea are not known, nor how these changes have impacted water masses, carbon fluxes, biogeochemical processes and availability of micronutrients. The PIPERS project sought to address these questions during an autumn ship campaign in 2017 and two spring airborne campaigns in 2016 and 2017. PIPERS used a multidisciplinary approach of manned and autonomous platforms to study the coupled air/ice/ocean/biogeochemical interactions during autumn and related those to spring conditions. Unexpectedly, the Ross Sea experienced record low sea ice in spring 2016 and autumn 2017. The delayed ice advance in 2017 contributed to (1) increased ice production and export in coastal polynyas, (2) thinner snow and ice cover in the central pack, (3) lower sea-ice Chl-a burdens and differences in sympagic communities, (4) sustained ocean heat flux delaying ice thickening and (5) a melting, anomalously southward ice edge persisting into winter. Despite these impacts, airborne observations in spring 2017 suggest that winter ice production over the continental shelf was likely not anomalous.

Quantifying the rate of wave attenuation in sea ice is key to understanding trends in the Antarctic marginal ice zone extent. However, a paucity of observations of waves in sea ice limits progress on this front. We deployed 14 waves-in-ice observation systems (WIIOS) on Antarctic sea ice during the Polynyas, Ice Production, and seasonal Evolution in the Ross Sea expedition (PIPERS) in 2017. The WIIOS provide in situ measurement of surface wave characteristics. Two experiments were conducted, one while the ship was inbound and one outbound. The sea ice throughout the experiments generally consisted of pancake and young ice <0.5 m thick. The WIIOS survived a minimum of 4 d and a maximum of 6 weeks. Several large-wave events were captured, with the largest recorded significant wave height over 9 m. We find that the total wave energy measured by the WIIOS generally decays exponentially in the ice and the rate of decay depends on ice concentration.

Results examining variations in the ice extent along the Norwegian coastline based on the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 to 2019, February through May, are presented. A total of 386 fjords and coastal areas were outlined and grouped into ten regions to assess seasonal and long-term trends in ice extent. In addition, three fjords were examined to investigate how ice extent may vary over short distances (<100 km). Of the 386 outlined, 47 fjords/coastal areas held >5 km2 of ice at least once between 2001 and 2019. Over this span of time, no statistically significant trend in ice extent is found for all ten regions; however, variations between regions and years are evident. Ice extent is assessed through comparison to three weather variables – freezing degree days (FDD), daily new snowfall and daily freshwater supply from rainfall plus snowmelt. Six out of ten regions are significantly positively correlated (p < 0.05) to FDD. In addition, ice in two regions is significantly positively correlated to daily new snowfall, and in one region negatively correlated to rainfall plus snowmelt. The importance of fjord geometry and bathymetry as well as other weather variables including wind is discussed.

Although altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.