This work presents the results of physical and biological investigations at 27 biogeochemical stations of early winter sea ice in the Ross Sea during the 2017 PIPERS cruise. Only two similar cruises occurred in the past, in 1995 and 1998. The year 2017 was a specific year, in that ice growth in the Central Ross Sea was considerably delayed, compared to previous years. These conditions resulted in lower ice thicknesses and Chl-a burdens, as compared to those observed during the previous cruises. It also resulted in a different structure of the sympagic algal community, unusually dominated by Phaeocystis rather than diatoms. Compared to autumn-winter sea ice in the Weddell Sea (AWECS cruise), the 2017 Ross Sea pack ice displayed similar thickness distribution, but much lower snow cover and therefore nearly no flooding conditions. It is shown that contrasted dynamics of autumnal-winter sea-ice growth between the Weddell Sea and the Ross Sea impacted the development of the sympagic community. Mean/median ice Chl-a concentrations were 3–5 times lower at PIPERS, and the community status there appeared to be more mature (decaying?), based on Phaeopigments/Chl-a ratios. These contrasts are discussed in the light of temporal and spatial differences between the two cruises.

Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.

A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

Two satellite datasets are used to characterize winter landfast first-year sea-ice (FYI), deformed FYI (DFYI) and multiyear sea-ice (MYI) roughness in the Canadian Arctic Archipelago (CAA): (1) optical Multi-angle Imaging SpectroRadiometer (MISR) and (2) synthetic aperture radar Sentinel-1. The Normalized Difference Angular Index (NDAI) roughness proxy derived from MISR, and backscatter from Sentinel-1 are intercompared. NDAI and backscatter are also compared to surface roughness derived from an airborne LiDAR track covering a subset of FYI and MYI (no DFYI). Overall, NDAI and backscatter are significantly positively correlated when all ice type samples are considered. When individual ice types are evaluated, NDAI and backscatter are only significantly correlated for DFYI. Both NDAI and backscatter are correlated with LiDAR-derived roughness (r = 0.71 and r = 0.74, respectively). The relationship between NDAI and roughness is greater for MYI than FYI, whereas for backscatter and ice roughness, the relationship is greater for FYI than MYI. Linear regression models are created for the estimation of FYI and MYI roughness from NDAI, and FYI roughness from backscatter. Results suggest that using a combination of Sentinel-1 backscatter for FYI and MISR NDAI for MYI may be optimal for mapping winter sea-ice roughness in the CAA.

Although the effects of snow during sea-ice growth have been investigated for sea ice which is thick enough to accommodate dry snow, those for thin sea ice have not been paid much attention due to the difficulty in observing them. Observations are complicated by the presence of slush and its subsequent freeze-up, and the surface heat budget might be sensitive to the additional ice thickness. An onsite short-term land fast sea-ice freeze-up experiment in the Saroma-ko Lagoon, Hokkaido, Japan was carried out to examine the effects of snowfall on the structure and surface heat budget of thin sea ice, based on observational results and a 1-D thermodynamic model. We found that snowfall contributes to the solidification of the surface slush layer, contributing ice thickness that is comparable to the snowfall amount and affecting the crystal texture significantly. On the other hand, the basal ice growth rate and turbulent heat flux were not significantly affected, being <3.1 × 10−8 m s−1 and 3 W m−2, respectively. This finding may validate the omission in past studies of snow effect in estimating ice production rates in polynyas and has implications about the reconstruction of growth history from sample analysis.

Snow and ice were monitored by thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) in Lake Orajärvi in northern Finland. An existing automatic SIMBA-algorithm was further developed to derive air/snow, snow/ice and ice/water interfaces based on the SIMBA environment temperature (ET) profiles. The identified interfaces agreed with in situ observations made in 2011/12 winter season. The method was capable to identify upward-moving snow/ice interface that was also visible from SIMBA heating temperature (HT) profiles, which responds to differences in the thermal diffusivities of air, snow, ice and water. The SIMBA data obtained in winters 2017/18 and 2018/19 were used to investigate snow and ice mass balance. An upward-moving snow/ice interface was detected as a result of meteoric ice (snow ice and superimposed ice) formation. Snow contributed to granular lake ice formation up to 40–55% of the total ice thickness on the seasonal mean. Heavy snowfalls and low air temperature in early winter are favourable for granular ice formation. The seasonal mean snow depth on nearby land was 2.7–2.9 times of that on the lake. The estimation of freeboard from snow and ice mass-balance measurement is sensitive to the snow density. Accurate ice freeboard calculation is still a challenge.

The effect of freshwater sources on wintertime sea-ice CO2 processes was studied from the glacier front to the outer Tempelfjorden, Svalbard, in sea ice, glacier ice, brine and snow. March–April 2012 was mild, and the fjord was mainly covered with drift ice, in contrast to the observed thicker fast ice in the colder April 2013. This resulted in different physical and chemical properties of the sea ice and under-ice water. Data from stable oxygen isotopic ratios and salinity showed that the sea ice at the glacier front in April 2012 contained on average 54% of frozen-in glacial meltwater. This was five times higher than in April 2013, where the ice was frozen seawater. In April 2012, the largest excess of sea-ice total alkalinity (AT), carbonate ion ([CO32−]) and bicarbonate ion concentrations ([HCO3−]) relative to salinity was mainly related to dissolved dolomite and calcite incorporated during freezing of mineral-enriched glacial water. In April 2013, the excess of these variables was mainly due to ikaite dissolution as a result of sea-ice processes. Dolomite dissolution increased sea-ice AT twice as much as ikaite and calcite dissolution, implying different buffering capacity and potential for ocean CO2 uptake in a changing climate.

Basal melt of ice shelves is not only an important part of Antarctica's ice sheet mass budget, but it is also the origin of platelet ice, one of the most distinctive types of sea ice. In many coastal Antarctic regions, ice crystals form and grow in supercooled plumes of Ice Shelf Water. They usually rise towards the surface, becoming trapped under an ice shelf as marine ice or forming a semi-consolidated layer, known as the sub-ice platelet layer, below an overlying sea ice cover. In the latter, sea ice growth consolidates loose crystals to form incorporated platelet ice. These phenomena have numerous and profound impacts on the physical properties, biological processes and biogeochemical cycles associated with Antarctic fast ice: platelet ice contributes to sea ice mass balance and may indicate the extent of ice-shelf basal melting. It can also host a highly productive and uniquely adapted ecosystem. This paper clarifies the terminology and reviews platelet ice formation, observational methods as well as the geographical and seasonal occurrence of this ice type. The physical properties and ecological implications are presented in a way understandable for physicists and biologists alike, thereby providing the background for much needed interdisciplinary research on this topic.

The widespread occurrence of snow-ice formation on the pack ice plays a critical role in the mass balance of Antarctic sea ice. The stable isotope composition, ice texture and salinity of eight ice cores, obtained from the Amundsen Sea during the Oden Southern Ocean 2010/11 expedition from late December 2010 to January 2011, were investigated to illustrate the snow-ice growth process and its contribution to sea-ice development. Most previous research has utilized δ18O as an index tracer to determine the percentages of core length that contain meteoric water, i.e. snow ice. However, this standard practice of snow-ice identification might be biased due to normally low-resolution isotopic measurements and mixing/diffusion processes between the snow ice and underlying ice layers. Snow-ice contributions in these ice cores based instead on an updated isotope mixing model are also presented. Depth profiles of ice texture and salinity are described to serve as representations of the structures of these ice cores. Our isotope mixing model produced an average of 15.9% snow-ice contribution for pack ice in the Amundsen Sea, and meteoric water occupying 40% of snow-ice mass for all ice stations. These results are compared to previous investigations of snow-ice occurrence around Antarctica.

Airborne electromagnetic induction sensors have demonstrated their extensive capacities to measure sea-ice thickness distributions. However, biases can emerge when comparing these 1-D measurements to a broader 2-D regional scale due to the spatial anisotropy inherent to sea-ice cover. Automated processing of available sea-ice maps could significantly ease the decision on how to set up an optimised flight pattern, which would result in representative ice thickness numbers for the region. In this study, first we investigate the extent to which the sea-ice anisotropy can influence the representativeness of an airborne survey compared to the regional situation. Second, we propose a method to process sea-ice maps prior to flights to help preparing the most representative flight plan possible for the local area. The method is based on automated segmentation of radar satellite images and extensive simulation of flight transects over the image. The spatial analysis of these transects enables for the identification of the most representative survey trajectories for the area. The method was applied for seven different synthetic aperture radar satellite images over Arctic sea ice north of Svalbard. The results indicate that the proposed method improved the representativeness of the airborne survey by identifying the most suitable transect over the ice pack.

Clouds play an important role in the Arctic surface radiative budget, impacting the seasonal evolution of Arctic sea-ice cover. We explore the large-scale impacts of springtime and early summer (March through July) cloud and radiative fluxes on sea ice by comparing these fluxes to seasonal ice volume losses over the central Arctic basin, calculated for available observational years 2004–2007 (ICESat) and 2011–2017 (CryoSat-2). We also supplement observation data with sea-ice volume computed from the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS) during summer months. We find that the volume of sea ice lost over the melt season is most closely related to observed downwelling longwave radiation in March and early summer (June and July) longwave cloud radiative forcing, which together explain a large fraction of interannual variability in seasonal sea-ice volume loss (R2 = 0.71, p = 0.007). We show that downwelling longwave fluxes likely impact the timing of melt onset near the sea-ice edge, and can limit the magnitude of ice thickening from March to April. Radiative fluxes in June and July are likely critical to seasonal volume loss because modeled data show the greatest ice volume reductions occur during these months.

The Arctic sea-ice cover has undergone a significant decline in recent decades. The melt season is starting earlier, ice is thinner and seasonal ice dominates. Here we examine the effects of these changes on the solar heat input to the upper ocean in ice-covered Arctic waters from 1985 to 2014. Satellite observations of ice concentration, onset dates of melt and freeze-up and ice age, are combined with ice thicknesses from the PIOMAS model and incident solar irradiance from reanalysis products to calculate the contributions of open ocean and ice to the solar heat input in the upper ocean. Of the total, 86% of the area has positive trends for solar heat input to the ocean through leads due to decreases in ice concentration. Only 62% of the area shows positive trends of solar heat input to the ocean explicitly through the ice. Positive trends are due to thinning ice, while negative trends occur in regions where the ice-free season has lengthened. The annual total solar heat input to the ocean exhibits positive trends in 82% of the area. The spatial pattern of the cumulative annual total solar heat input is similar to the pattern of solar heat input directly to leads.

Columnar sea ice grows with an interface of tiny parallel ice plates, the distance of which is known as plate spacing. While it has been proposed as a fundamental microstructure scale of sea ice, the physics behind its formation has not been fully understood. Here the problem is analysed on the basis of morphological stability theory to propose a model that results in a physically consistent prediction of the relationship between the plate spacing a0 and growth velocity V. The relationship may be divided into two regimes. In the diffusive regime, for V above ≈2 × 10−4 cm s−1 one finds a0 ~ V−2/3 to first order. In the convective regime, the extent of diffusive boundary layer is controlled by solutal convection near the interface, which leads to the proportionality a0 ~ V−1/3. From a comparison to observations it is evident that the plate spacing is predictable over 5 orders of magnitude in the growth velocity, covering the range from fast laboratory ice growth to slow accretion at the bottom of marine ice shelves. The predictability opens new paths towards concise modelling of marine and sea-ice microstructure and physical properties.

Spatial and temporal trends of remotely sensed sea-ice cover, sea surface temperatures, chlorophyll-a concentration and primary production in the Baffin Bay, Davis Strait and Labrador Sea were analyzed for the 1998–2017 period. We found spatial variability in the trends of these cryospheric, biologic and oceanographic phenomena. For example, in the northern Baffin Bay, we observed decreases in annual sea-ice persistence, yet increases along the Labrador Sea-ice edge during winter, with the latter having significant correlations with broader atmospheric patterns. In general, we observed increases in summer sea surface temperatures across the study region, except a small area of cooling along the southern Greenlandic coast. We also found significant negative trends in April chlorophyll-a and primary production followed by significant positive trends for both biological phenomena in May, owing to anomalously high values in 2014 and 2015. Notably, we found a significant positive correlation between days of monthly sea ice presence in April with May primary production quantities. Finally, we found a significant positive trend in total annual primary production over the study period. This novel finding suggests an important relationship between the timing of breakup along the sea-ice edge and peaks in biological production.

The Arctic winter seasonal sea ice (WSSI) concentration from 1979 to 2019 is derived from passive microwave data. Based on Empirical Orthogonal Function (EOF) analysis, the WSSI time series includes regionally different trends, abrupt shifts and interannual variations. The time series of the first EOF mode (PC1) mainly represents the WSSI trend, which is characterized by an increase, particularly in the Pacific sector. PC1 confirms two abrupt shifts in WSSI in 1989 and 2007, with a variance of 31%. After 2007, the large-scale atmospheric circulation anomaly shows a strengthened wavenumber 3 structure at high latitudes associated with a mid-tropospheric low-pressure anomaly in central and western Siberia and a high-pressure anomaly in eastern Siberia in summer and autumn. These patterns have promoted the increased transport of moist static energy to the central Arctic and contributed to increased near-surface air temperatures that may enhance ice melting in summer and reduce ice growth in autumn and winter. The changes in ice melt and growth have had opposite effects in the Pacific and Atlantic sectors: WSSI has increased in the Pacific sector due to the replacement of multi-year ice by WSSI, and decreased in the Atlantic sector due to the replacement of WSSI by open water.

Sea ice, which forms in polar and nonpolar areas, transmits light to ice-associated (sympagic) algal communities. To noninvasively study the distribution of sea-ice algae, empirical relations to estimate its biomass from under-ice hyperspectral irradiance have been developed in the Arctic and Antarctica but lack for nonpolar regions. This study examines relationships between normalised difference indices (NDI) calculated from hyperspectral transmittance and sympagic algal biomass in the nonpolar Saroma-ko Lagoon. We analysed physico-biogeochemical properties of snow and land-fast sea ice supporting 27 paired bio-optical measurements along three transects covering an area of over 250 m × 250 m in February 2019. Snow depth (0.08 ± 0.01 m) and ice-bottom brine volume fraction (0.21 ± 0.02) showed low (0.06) and high (0.58) correlations with sea-ice core bottom section chlorophyll a (Chl. a), respectively. Spatial analyses unveiled the patch size of sea-ice Chl. a to be ~65 m, which is in the same range reported from previous studies. A selected NDI (669, 596 nm) explained 63% of algal biomass variability. This reflects the bio-optical properties and environmental conditions of the lagoon that favour the wavelength pair in the orange/red part of the spectrum and suggests the necessity of a specific bio-optical relationship for Saroma-ko Lagoon.

Geophysical systems are often assumed to follow Gaussian probability density functions; however, deviations from Gaussian behaviour can shed light on the underlying dynamics. For the large-scale motion of the Arctic sea ice, such deviations have been interpreted as signatures of structure in dynamic flow fields. In this study, we use higher-order moments (skewness and kurtosis) to identify spatiotemporal changes in the Beaufort Gyre (BG) and the Transpolar Drift (TPD) sea-ice drift patterns. Higher-order moments of satellite-derived ice drift speeds are examined over the winter period of 2006–2017 to describe the persistence of features like the BG and TPD, and their variation over time. Index patterns indicate that the periphery of the BG can be identified by a combination of high positive skewness and high kurtosis in the ice drift time series on an annual basis.

This paper provides the first evidence for sea-ice formation in the Cordillera Darwin (CD) fjords in southern Chile, which is farther north than sea ice has previously been reported for the Southern Hemisphere. Initially observed from a passenger plane in September 2015, the presence of sea ice was then confirmed by aerial reconnaissance and subsequently identified in satellite imagery. A time series of Sentinel-1 and Landsat-8 images during austral winter 2015 was used to examine the chronology of sea-ice formation in the Cuevas fjord. A longer time series of imagery across the CD was analyzed from 2000 to 2017 and revealed that sea ice had formed in each of the 13 fjords during at least one winter and was present in some fjords during a majority of the years. Sea ice is more common in the northern end of the CD, compared to the south where sea ice is not typically present. Is suggested that surface freshening from melting glaciers and high precipitation reduces surface salinity and promotes sea-ice formation within the semi-enclosed fjord system during prolonged periods of cold air temperatures. This is a unique set of initial observations that identify questions for future research in this remote area.