The fabled Northwest Passage and Northern Sea Route that were once the quests of early Western explorers are now increasingly sea ice–free, with routine vessel transits expected by midcentury. The potential impacts of this novel vessel traffic on endemic Arctic marine mammal (AMM) species are unknown despite their critical social and ecological roles in the ecosystem and widely recognized susceptibility to ice loss. We developed a vulnerability assessment of 80 subpopulations of seven AMM species to vessel traffic during the ice-free season. Vulnerability scores were based on the combined influence of spatially explicit exposure to the sea routes and a suite of sensitivity variables. More than half of AMM subpopulations (42/80) are exposed to open-water vessel transits in the Arctic sea routes. Narwhals (Monodon monoceros) were estimated to be most vulnerable to vessel impacts, given their high exposure and sensitivity, and polar bears (Ursus maritimus) were estimated to be the least vulnerable because of their low exposure and sensitivity. Regions with geographic bottlenecks, such as the Bering Strait and eastern Canadian Arctic, were characterized by two to three times higher vulnerability than more remote regions. These pinch points are obligatory pathways for both vessels and migratory AMMs, and so represent potentially high conflict areas but also opportunities for conservation-informed planning. Some of the species and regions identified as least vulnerable were also characterized by high uncertainty, highlighting additional data and monitoring needs. Our quantification of the heterogeneity of risk across AMM species provides a necessary first step toward developing best practices for maritime industries poised to advance into this rapidly changing seascape.

Climate change is fundamentally altering habitats, with complex consequences for species across the globe. The Arctic has warmed 2–3 times faster than the global average, and unprecedented sea ice loss can have multiple outcomes for ice‐associated marine predators. Our goal was to assess impacts of sea ice loss on population‐specific habitat and behaviour of a migratory Arctic cetacean.

Using satellite telemetry data collected during summer–fall from sympatric beluga whale (Delphinapterus leucas) populations ("Chukchi" and "Beaufort" belugas), we applied generalized estimating equations to evaluate shifts in sea ice habitat associations and diving behaviour during two periods: 1993–2002 ("early") and 2004–2012 ("late"). We used resource selection functions to assess changes in sea ice selection as well as predict trends in habitat selection and "optimal" habitat, based on satellite‐derived sea ice data from 1990 to 2014.

Sea ice cover declined substantially between periods, and Chukchi belugas specifically used significantly lower sea ice concentrations during the late than early period. Use of bathymetric features did not change between periods for either population. Population‐specific sea ice selection, predicted habitat and the amount of optimal habitat also generally did not change during 1990–2014. Chukchi belugas tracked during 2007–2012 made significantly more long‐duration and deeper dives than those tracked during 1998–2002.

Taken together, our results suggest bathymetric parameters are consistent predictors of summer–fall beluga habitat rather than selection for specific sea ice conditions during recent sea ice loss. Beluga whales were able to mediate habitat change despite their sea ice associations. However, trends towards prolonged and deeper diving possibly indicate shifting foraging opportunities associated with ecological changes that occur in concert with sea ice loss. Our results highlight that responses by some Arctic marine wildlife can be indirect and variable among populations, which could be included in predictions for the future.

Climate change is causing physical and biological changes in the polar marine environment, which may impact higher trophic level predators such as the bowhead whale (Balaena mysticetus) and the structure of their food webs. We used bulk stable isotope analysis and compound-specific isotope analysis (CSIA) of individual amino acids (AA) to examine bowhead whale trophic position and the biogeochemistry of one of their feeding grounds, Disko Bay, West Greenland, over a period of 7 years (2007–2013). We also examined whether environmental conditions such as sea ice concentration and sea surface temperature were causing any interannual variation in isotope data. Bulk δ¹⁵N values were consistent across the 7 years of sampling and were similar between sex classes. Bulk δ¹³C and essential-AAs δ¹³C values displayed an overall temporal decline of 1.0 and 1.4%, respectively. A significant positive linear relationship was found between δ¹³C of bulk skin and essential-AAs suggesting that some of the observed isotopic variation in bowhead whales between years reflect changes in the carbon at the base of the food web. There were no correlations between the δ¹³C and δ¹⁵N values of isotopic tracers with sea ice concentrations or sea surface temperatures. The trophic level of bowhead whales remained stable over time despite large interannual variability in ice and temperature regimes. Our results indicate that the recent environmental changes in West Greenland resulted in no trophic perturbation being transferred to bowhead whales during that time period. Our study shows that the novel approach of CSIA-AA can be used effectively to study the combined temporal variation of bowhead whale food web structure and ecosystem isotopic baseline values and detect changes at the species and ecosystem levels.

Migrations are often influenced by seasonal environmental gradients that are increasingly being altered by climate change. The consequences of rapid changes in Arctic sea ice have the potential to affect migrations of a number of marine species whose timing is temporally matched to seasonal sea ice cover. This topic has not been investigated for Pacific Arctic beluga whales (Delphinapterus leucas) that follow matrilineally maintained autumn migrations in the waters around Alaska and Russia. For the sympatric Eastern Chukchi Sea ('Chukchi') and Eastern Beaufort Sea ('Beaufort') beluga populations, we examined changes in autumn migration timing as related to delayed regional sea ice freeze-up since the 1990s, using two independent data sources (satellite telemetry data and passive acoustics) for both populations. We compared dates of migration between 'early' (1993–2002) and 'late' (2004–2012) tagging periods. During the late tagging period, Chukchi belugas had significantly delayed migrations (by 2 to >4 weeks, depending on location) from the Beaufort and Chukchi seas. Spatial analyses also revealed that departure from Beaufort Sea foraging regions by Chukchi whales was postponed in the late period. Chukchi beluga autumn migration timing occurred significantly later as regional sea ice freeze-up timing became later in the Beaufort, Chukchi, and Bering seas. In contrast, Beaufort belugas did not shift migration timing between periods, nor was migration timing related to freeze-up timing, other than for southward migration at the Bering Strait. Passive acoustic data from 2008 to 2014 provided independent and supplementary support for delayed migration from the Beaufort Sea (4 day yr) by Chukchi belugas. Here, we report the first phenological study examining beluga whale migrations within the context of their rapidly transforming Pacific Arctic ecosystem, suggesting flexible responses that may enable their persistence yet also complicate predictions of how belugas may fare in the future.

Decisions about sustainable exploitation levels of marine resources are often based on inadequate data, but are nevertheless required for practical purposes. We describe one exception where abundance estimates spanning 30 years and catch data spanning more than 40 years were used in a Bayesian assessment model of belugas Delphinapterus leucas off West Greenland. The model was updated with data from a visual aerial survey on the wintering ground in 2012. Methods that take account of stochastic animal availability by using independent estimates of forward and perpendicular sighting distances were used to estimate beluga abundance. A model that appears to be robust to the presence of a few large groups yielded an estimate of 7456 belugas (cv = 0.44), similar to a conventional distance-sampling estimate. A mark–recapture distance analysis that corrects for perception and availability bias estimated the abundance to be 9072 whales (cv = 0.32). Increasing distance of beluga sightings from shore was correlated with decreasing sea ice cover, suggesting that belugas expand their distribution offshore (i.e. westward in this context) with the reduction of coastal sea ice. A model with high (0.98) adult survival estimated a decline from 18 600 (90% CI: 13 400, 26 000) whales in 1970 to 8000 (90% CI: 5830, 11 200) in 2004. The decline was probably a result of a period with exceptionally large catches. Following the introduction of catch limits in 2004, the model projects an increase to 11 600 (90% CI: 6760, 17 600) individuals in 2020 (assuming annual removals of 294 belugas after 2014). If the annual removal level is fixed at 300 individuals, a low-survival (0.97) model predicts a 75% probability of an increasing population during 2015–2020. Reduced removal rates due to catch limits and the more offshore, less accessible distribution of the whales are believed to be responsible for the initial signs of population recovery.

There has been extensive sea ice loss in the Chukchi and Beaufort seas where two beluga whale (Delphinapterus leucas) populations occur between July–November. Our goal was to develop population-specific beluga habitat selection models that quantify relative use of sea ice and bathymetric features related to oceanographic processes, which can provide context to the importance of changing sea ice conditions. We established habitat selection models that incorporated daily sea ice measures (sea ice concentration, proximity to ice edge and dense ice) and bathymetric features (slope, depth, proximity to the continental slope, Barrow Canyon, and shore) to establish quantitative estimates of habitat use for the Eastern Chukchi Sea ('Chukchi') and Eastern Beaufort Sea ('Beaufort') populations. We applied 'used v. available' resource selection functions to locations of 65 whales tagged from 1993–2012, revealing large variations in seasonal habitat selection that were distinct between sex and population groups. Chukchi whales of both sexes were predicted to use areas in close proximity to Barrow Canyon (typically <200 km) as well as the continental slope in summer, although deeper water and denser ice were stronger predictors for males than females. Habitat selection differed more between sexes for Beaufort belugas. Beaufort males selected higher ice concentrations (≥40%) than females (0–40%) in July–August. Proximity to shore (<200 km) strongly predicted summer habitat of Beaufort females, while distance to the ice edge was important for male habitat selection, especially during westward migration in September. Overall, our results indicate that sea ice variables were rarely the primary drivers of beluga summer-fall habitat selection. While diminished sea ice may indirectly affect belugas through changes in the ecosystem, associations with bathymetric features that affect prey availability seemed key to habitat selection during summer and fall. These results provide a benchmark by which to assess future changes in beluga habitat use of the Pacific Arctic.

On 11 August 1778, Captain James Cook, the pre-eminent explorer of his age, sailed north through Bering Strait with two ships, Resolution and Discovery, in search of the Northwest Passage. Working his way along the coast of Alaska, his progress was halted a week later by 'ice which was as compact as a Wall and seemed to be ten or twelve feet high at least'. In this text, all quotations from original journals and manuscripts are taken from Beaglehole (1967). This marked Cook’s farthest north, at 70°44N, about 30 km north of Icy Cape. Turning southwest and then west, Cook sailed close to the ice edge for the next 11 days, trying to find an opening to the north. On 29 August, having reached the coast of Siberia at 69°N without finding a break in the ice, he abandoned the search, since 'so little was the prospect of succeeding', but he resolved 'to return to the North in further search of a Passage the ensuing summer'.He sailed south through Bering Strait on 2 September.

Nineteen subpopulations of polar bears (Ursus maritimus) are found throughout the circumpolar Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology — the cycle of biological events — is linked to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas.

In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from –3 to –9 days decade^-1 in spring and from +3 to +9 days decade^-1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days) and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of –7 to –19 days decade^-1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from –1 to –9 percent decade^-1. These sea-ice metrics (or indicators of habitat change) were designed to be useful for management agencies and for comparative purposes among subpopulations. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

To better simulate the seasonal evolution of sea ice in the Arctic, with particular attention to the marginal ice zone, a sea ice model of the distribution of ice thickness, floe size, and enthalpy was implemented into the Pan-arctic IceOcean Modeling and Assimilation System (PIOMAS). Theories on floe size distribution (FSD) and ice thickness distribution (ITD) were coupled in order to explicitly simulate multicategory FSD and ITD distributions simultaneously. The expanded PIOMAS was then used to estimate the seasonal evolution of the Arctic FSD in 2014 when FSD observations are available for model calibration and validation.

Results indicate that the simulated FSD, commonly described equivalently as cumulative floe number distribution (CFND), generally follows a power law across space and time and agrees with the CFND observations derived from TerraSAR-X satellite images. The simulated power-law exponents also correlate with those derived using MODIS images, with a low mean bias of 2%. In the marginal ice zone, the modeled CFND shows a large number of small floes in winter because of stronger winds acting on thin, weak first-year ice in the ice edge region. In mid-spring and summer, the CFND resembles an upper truncated power law, with the largest floes mostly broken into smaller ones; however, the number of small floes is lower than in winter because floes of small sizes or first-year ice are easily melted away. In the ice pack interior there are fewer floes in late fall and winter than in summer because many of the floes are welded together into larger floes in freezing conditions, leading to a relatively flat CFND with low power-law exponents.

The simulated mean floe size averaged over all ice-covered areas shows a clear annual cycle, large in winter and smaller in summer. However, there is no obvious annual cycle of mean floe size averaged over the marginal ice zone. The incorporation of FSD into PIOMAS results in reduced ice thickness, mainly in the marginal ice zone, which improves the simulation of ice extent and yields an earlier ice retreat.

To better describe the state of sea ice in the marginal ice zone (MIZ) with floes of varying thicknesses and sizes, both an ice thickness distribution (ITD) and a floe size distribution (FSD) are needed. In this work, we have developed a FSD theory that is coupled to the ITD theory of Thorndike et al. (1975) in order to explicitly simulate the evolution of FSD and ITD jointly. The FSD theory includes a FSD function and a FSD conservation equation in parallel with the ITD equation. The FSD equation takes into account changes in FSD due to ice advection, thermodynamic growth, and lateral melting. It also includes changes in FSD because of mechanical redistribution of floe size due to ice ridging and, particularly, ice fragmentation induced by stochastic ocean surface waves. The floe size redistribution due to ice fragmentation is based on the assumption that wave-induced breakup is a random process such that when an ice floe is broken, floes of any smaller sizes have an equal opportunity to form, without being either favored or excluded. To focus only on the properties of mechanical floe size redistribution, the FSD theory is implemented in a simplified ITD and FSD sea ice model for idealized numerical experiments. Model results show that the simulated cumulative floe number distribution (CFND) follows a power law as observed by satellites and airborne surveys. The simulated values of the exponent of the power law, with varying levels of ice breakups, are also in the range of the observations. It is found that floe size redistribution and the resulting FSD and mean floe size do not depend on how floe size categories are partitioned over a given floe size range. The ability to explicitly simulate multicategory FSD and ITD together may help to incorporate additional model physics, such as FSD-dependent ice mechanics, surface exchange of heat, mass, and momentum, and wave-ice interactions.

Arctic marine mammals (AMMs) are icons of climate change, largely because of their close association with sea ice. However, neither a circumpolar assessment of AMM status nor a standardized metric of sea ice habitat change is available. We summarized available data on abundance and trend for each AMM species and recognized subpopulation. We also examined species diversity, the extent of human use, and temporal trends in sea ice habitat for 12 regions of the Arctic by calculating the dates of spring sea ice retreat and fall sea ice advance from satellite data (1979–2013). Estimates of AMM abundance varied greatly in quality, and few studies were long enough for trend analysis. Of the AMM subpopulations, 78% (61 of 78) are legally harvested for subsistence purposes. Changes in sea ice phenology have been profound. In all regions except the Bering Sea, the duration of the summer (i.e., reduced ice) period increased by 5–10 weeks and by >20 weeks in the Barents Sea between 1979 and 2013. In light of generally poor data, the importance of human use, and forecasted environmental changes in the 21st century, we recommend the following for effective AMM conservation: maintain and improve comanagement by local, federal, and international partners; recognize spatial and temporal variability in AMM subpopulation response to climate change; implement monitoring programs with clear goals; mitigate cumulative impacts of increased human activity; and recognize the limits of current protected species legislation.

Few studies have investigated the impacts of climate change on polar bears (Ursus maritimus) in East Greenland (EG), where some of the largest rates of sea ice loss have occurred. We used remotely sensed sea ice data to quantify changes in timing of sea ice freeze-up and breakup in EG polar bear habitat between 1979 and 2012. We then quantified movement rates, area use, habitat selection, and distribution and phenology of maternity denning using data from adult female polar bears tracked with satellite transmitters between 2007 and 2010 (n = 7). We compared results to historical data collected from adult females in the 1990s (n = 4). Adult females in the 2000s used areas with significantly lower sea ice concentrations (10–15% lower) than bears in the 1990s during winter, a pattern influenced by delayed freeze-up in October–December. Adult females in the 2000s were located significantly closer (100–150 km) to open water in all seasons and spent approximately 2 months longer in areas with <60% sea ice concentration than bears in the 1990s. Multivariate resource selection models contrasting preference between decades showed that there was a statistically significant and stronger winter preference in the 2000s for adult females to select for higher sea ice concentrations. Timing of maternity denning did not significantly differ between decades. Results indicate that multi-decadal loss of sea ice has resulted in shifts in polar bear habitat use in EG.

The surface of Arctic sea ice presents complex patterns of cracks and ridges that change with the seasons according to the external forces acting on the ice and the internal stresses within the ice. We propose a new statistical tool for analysis of these patterns based on a two-dimensional Maximal Overlap Discrete Wavelet Transform (MODWT) of Synthetic Aperture Radar (SAR) images, which can be used to track how ice conditions change over the course of the year. Here we give details on an extended pyramid algorithm that efficiently computes the MODWT coefficients for all combinations of vertical and horizontal scales. We show how to use these coefficients to form mean- and median-based wavelet variance estimates along with confidence intervals for the true unknown variances. We demonstrate the usefulness of the statistical tool on images acquired by the SAR sensor onboard RADARSAT, but the tool is of potential use in other geoscience applications and in other areas (e.g. medical imaging). We provide a Matlab implementation of this tool but also give sufficient details so that it can be encoded in other languages.

Intraspecific differences in movement behaviour reflect different tactics used by individuals or sexes to favour strategies that maximize fitness. We report movement data collected from n = 23 adult male polar bears with novel ear-attached transmitters in two separate pack ice subpopulations over five breeding seasons. We compared movements with n = 26 concurrently tagged adult females, and analysed velocities, movement tortuosity, range sizes and habitat selection with respect to sex, reproductive status and body mass. There were no differences in 4-day displacements or sea ice habitat selection for sex or population. By contrast, adult females in all years and both populations had significantly more linear movements and significantly larger breeding range sizes than males. We hypothesized that differences were related to encounter rates, and used observed movement metrics to parametrize a simulation model of male–male and male–female encounter. The simulation showed that the more tortuous movement of males leads to significantly longer times to male–male encounter, while having little impact on male–female encounter. By contrast, linear movements of females are consistent with a prioritized search for sparsely distributed prey. These results suggest a possible mechanism for explaining the smaller breeding range sizes of some solitary male carnivores compared to females.

Sea ice entrapments of narwhals (Monodon monoceros) occur when rapid changes in weather and wind conditions create a formation of fast ice in bays or passages used by whales. Between 2008 and 2010, four entrapments of narwhals were reported in Canada and Greenland. In each case, large groups (40–600 individuals) succumbed in the sea ice at three separate summering localities, two of these where entrapments had never before been reported. We examined long-term trends in autumn freeze-up timing (date when sea ice concentration rises above some threshold) on the 6 largest narwhal summering areas using sea ice concentration from satellite passive microwave data (1979–2009). We found strongly positive and significant trends (P < 0.001) in progressively later dates of autumn freeze-up in all summering areas. Autumn freeze-up occurs between 0.5 and 1 day later per year, or roughly 2–4 weeks later, over the 31-year time series. This indicates that sea ice conditions on narwhal summering areas are changing rapidly. The question remains whether entrapment events on summering areas are random or whether narwhals are adapting to changes in sea ice freeze-up by prolonging their summer residence time.

The effects of climate change on marine ecosystems and in particular on marine top predators are difficult to assess due to, among other things, spatial variability, and lack of clear delineation of marine habitats. The banks of West Greenland are located in a climate sensitive area and are likely to elicit pronounced responses to oceanographic changes in the North Atlantic. The recent increase in sea temperatures on the banks of West Greenland has had cascading effects on sea ice coverage, residency of top predators, and abundance of important prey species like Atlantic cod (Gadus morhua). Here, we report on the response of one of the top predators in West Greenland; the harbour porpoise (Phocoena phocoena). The porpoises depend on locating high densities of prey species with high nutritive value and they have apparently responded to the general warming on the banks of West Greenland by longer residence times, increased consumption of Atlantic cod resulting in improved body condition in the form of larger fat deposits in blubber, compared to the situation during a cold period in the 1990s. This is one of the few examples of a measurable effect of climate change on a marine mammal population.

Uncertainty in the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) Arctic sea ice volume record is characterized. A range of observations and approaches, including in situ ice thickness measurements, ICESat retrieved ice thickness, and model sensitivity studies, yields a conservative estimate for October Arctic ice volume uncertainty of 1.35 x 10^3 km^3 and an uncertainty of the ice volume trend over the 1979-2010 period of 1.0 x 10^3 km^3 decade^-1. A conservative estimate of the trend over this period is ~2.8 x 10^3 km^3 decade^-1. PIOMAS ice thickness estimates agree well with ICESat ice thickness retrievals (<0.1 m mean difference) for the area for which submarine data are available, while difference outside this area are larger. PIOMAS spatial thickness patterns agree well with ICESat thickness estimates with pattern correlations of above 0.8. PIOMAS appears to overestimate thin ice thickness and underestimate thick ice, yielding a smaller downward trend than apparent in reconstructions from observations. PIOMAS ice volume uncertainties and trends are examined in the context of climate change attribution and the declaration of record minima. The distribution of 32 year trends in a preindustrial coupled model simulation shows no trends comparable to those seen in the PIOMAS retrospective, even when the trend uncertainty is accounted for. Attempts to label September minima as new record lows are sensitive to modeling error. However, the September 2010 ice volume anomaly did in fact exceed the previous 2007 minimum by a large enough margin to establish a statistically significant new record.

An aerial survey was conducted to estimate the abundance of belugas (Delphinapterus leucas) on their wintering ground in West Greenland in March–April 2006 and 2008. The survey was conducted as a double platform aerial line transect survey, and sampled approximately 17% of the total survey area of ca. 125 000 km². The abundance of belugas was 10 595 (95% confidence interval 4904–24 650). The largest abundance was found at the northern part of Store Hellefiske Bank, at the eastern edge of the Baffin Bay pack ice, a pattern similar to that found in eight systematic surveys conducted since 1981.

A clear relationship between decreasing sea-ice cover and increasing offshore distance of beluga sightings was established from all previous surveys, suggesting that belugas expand their distribution westward as new areas on the banks of West Greenland open up earlier in spring with reduced sea-ice coverage or early annual ice recession. This is in contrast to the relatively confined distribution of belugas near the coast in limited open areas in the early 1980s, when sea-ice cover was greater. However, the effects of the changes in coastal availability of belugas can also be observed with the correlation between catches from the local Inuit hunt and sea-ice cover, where the catches increased significantly with increasing sea-ice coverage during the period 1954–2006. These results, based on nearly 30 years of dedicated survey effort, are among the first available evidence showing a shift in distribution of an Arctic cetacean in response to changes in sea-ice coverage.

Arctic sea ice deformation arises from spatial gradients in the ice velocity field. This deformation occurs across a wide range of spatial scales, from meters to thousands of kilometers. We analyze 7 years of sea ice deformation data from the RADARSAT Geophysical Processor System (RGPS) covering the western Arctic Ocean. We find that the mean deformation rate is related to the spatial scale over which it is measured according to a power law with exponent ~ –0.2, over a scale range from 10 to 1000 km (e.g., deformation rate doubles for a 30-fold reduction in scale). Both the exponent and the deformation rate have distinct annual cycles. The exponent becomes more negative in summer as the ice pack weakens and internal stresses are not as readily transmitted over long distances. The deformation rate reaches a minimum in late winter when the ice pack is strongest. The deformation also exhibits considerable localization, in which the largest deformation rates are confined to smaller and smaller areas as the scale of measurement decreases.

This supports a model for sea ice based on granular or fracture mechanics. The scaling exponent in the power law relationship tends to be larger in magnitude where the concentration of multiyear ice is low, consistent with a thinner and weaker ice pack. With decreasing multiyear ice in the Arctic and a thinning ice pack, an increase in the deformation rate has already been documented (from buoy data). However, the net effect of several deformation/thickness feedbacks is still uncertain.

The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of –0.57 m decade^-1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

Several new data sets reveal that thinning and speedup of Jakobshavn Isbrae continue, following its recent rapid increase in speed as its floating ice tongue disintegrated. The present speedup rate of –5% a^-1 over much of the fast-moving region appears to be a diffusive response to the initial much larger speedup near the front. There is strong seasonality in speed over much of the fast-flowing main trunk that shows a good inverse correlation with the seasonally varying length of a short (typically ~6 km) floating ice tongue. This modulation of speed with ice front position supports the hypothesis that the major speedup was caused by loss of the larger floating ice tongue from 1998 to 2003. Analysis of image time series suggests that the transient winter ice tongue is formed when sea ice bonds glacier ice in the fjord to produce a nearly rigid mass that almost entirely suppresses calving. Major calving only resumes in late winter when much of this ice clears from the fjord. The collapse of the ice tongue in the late 1990s followed almost immediately after a sharp decline in winter sea-ice concentration in Disko Bay. This decline may have extended the length of the calving season for several consecutive years, leading to the ice tongue's collapse.

A temporal and spatial scaling analysis of Arctic sea ice deformation is performed over timescales from 3 h to 3 months and over spatial scales from 300 m to 300 km. The deformation is derived from the dispersion of pairs of drifting buoys, using the IABP (International Arctic Buoy Program) buoy data sets. This study characterizes the deformation of a very large solid plate (the Arctic sea ice cover) stressed by heterogeneous forcing terms like winds and ocean currents. It shows that the sea ice deformation rate depends on the scales of observation following specific space and time scaling laws. These scaling properties share similarities with those observed for turbulent fluids, especially for the ocean and the atmosphere. However, in our case, the time scaling exponent depends on the spatial scale, and the spatial exponent on the temporal scale, which implies a time/space coupling. An analysis of the exponent values shows that Arctic sea ice deformation is very heterogeneous and intermittent whatever the scales, i.e., it cannot be considered as viscous-like, even at very large time and/or spatial scales. Instead, it suggests a deformation accommodated by a multiscale fracturing/faulting processes.

Sea ice concentration derived from satellite data were used to quantify sea ice characteristics in the Baffin Bay–Davis Strait–Labrador Sea area. The ice edge in Davis Strait extends from Disko Bay in West Greenland 2500 km south to Newfoundland. The mean intercept at the West Greenland coast between 1979 and 2002 was located at 66.9°N, assuming the ice edge was 85% ice concentration. The shallow banks of West Greenland (> 200 m) had, on average, an ice extent covering 30 to 100% of the bank area during March for the 24 year time series. This extent varied in concentration between 39 and 100%. However, intermediate ice concentrations (39–85% ice concentration) covered on average 25% of the banks. The Davis Strait ice edge showed considerable interannual variation correlated with the winter index of the North Atlantic Oscillation and the Arctic Oscillation. No temporal trend in ice extent could be detected over the 24 years. In addition to the ice production on the banks of West Greenland, sea ice produced further north in Baffin Bay was advected to the banks as shown by satellite tracked drifting buoys. Both the local sea ice production and the advected sea ice contributed significantly to sea temperatures and salinities measured during summer on the banks. No correlation between sea ice concentration and plankton abundance could be detected but the recruitment of the offshore cod (Gadus morhua) component in South Greenland was negatively correlated to the amount of sea ice in Baffin Bay.

In April 2006, a dedicated survey of bowhead whales (Balaena mysticetus) was conducted on the former whaling ground in West Greenland to determine the current wintering population abundance. This effort included a double platform aerial survey design, satellite tracking of the movements of nine whales, and estimation of high-resolution surface time from 14 whales instrumented with time–depth recorders. Bowhead whales were estimated to spend an average of 24% (cv=0.03) of the time at or above 2 m depth, the maximum depth at which they can be seen on the trackline. This resulted in a fully corrected abundance estimate of 1229 (95% CI: 495–2939) bowhead whales when the availability factor was applied and sightings missed by observers were corrected. This surprisingly large population estimate is puzzling given that the change in abundance cannot be explained by a recent or rapid growth in population size. One possible explanation is that the population, which demonstrates high age and sex segregation, has recently attained a certain threshold size elsewhere, and a higher abundance of mature females appears on the winter and spring feeding ground in West Greenland. This in combination with the latest severe reduction in sea ice facilitating access to coastal areas might explain the surprising increase in bowhead whale abundance in West Greenland.

On the basis of an analysis of in-situ ice stresses and of satellite-derived ice strain rates, as well as of a comparison between field and laboratory behaviour, we describe an alternative viewpoint for modelling sea ice deformation during winter. We propose that fracture and frictional sliding govern inelastic deformation over all spatial and temporal scales, even under small stresses. Consequently, winter and/or perennial sea ice does not behave as a viscous material, even at large scales, the normal flow rule is not obeyed (as observed during laboratory tests on sea ice samples harvested from the field), and stresses are highly intermittent and poorly correlated spatially.

A scaling analysis of the deformation of Arctic sea ice over a 3-day time period is performed for scales of 10 to 1000 km. The deformation field is derived from satellite radar data; it allows us to study how a very large solid body — the Arctic sea-ice cover — deforms under the action of heterogeneous forcing winds and ocean currents. The deformation is strongly localized at small scales, and can be characterized as multifractal. This behavior is well known for turbulent flows, and is here also observed for a deforming solid. A multiscaling extrapolation to the meter scale (laboratory scale) shows that, at the 3-day time scale, about 15% of the deformation is larger than 10^-4 s^-1, implying brittle failure, over 0.2% of the total area.

NASA's RADARSAT Geophysical Processor System (RGPS) uses sequential synthetic aperture radar (SAR) images to track the trajectories of some 30 000 points on the Arctic sea ice for periods of up to 6 months. Much of the Arctic basin is imaged and tracked every 3 days. The result is a highly detailed picture of how the sea ice moves and deforms. The points are initially spaced 10 km apart and are organized into four-cornered cells. The area and the strain rates are calculated for each cell for each new observation of its corners. The accuracy of the RGPS ice tracking, area changes, and deformation estimates is needed to make the dataset useful for analysis, model validation, and data assimilation. Two comparisons are made to assess the accuracy. The first compares the tracking performed at two different facilities (the Jet Propulsion Laboratory in Pasadena, California, and the Alaska SAR Facility in Fairbanks, Alaska), between which the primary difference is the operator intervention. The error standard deviation of the tracking, not including geolocation errors, is 100 m, which is the pixel size of the SAR images. The second comparison is made with buoy trajectories from the International Arctic Buoy Program. The squared correlation coefficient for RGPS and buoy displacements is 0.996. The median magnitude of the displacement differences is 323 m. The tracking errors give rise to error standard deviations of 0.5% day^-1 in the divergence, shear, and vorticity. The uncertainty in the area change of a cell is 1.4% due to tracking errors and 3.2% due to resolving the cell boundary with only four points. The uncertainties in the area change and deformation invariants can be reduced substantially by averaging over a number of cells, at the expense of spatial resolution.

The extent and duration of sea ice in Baffin Bay and Davis Strait has a major impact on the timing and strength of the marine production along West Greenland. The advance and retreat of the sea ice follows a predictable pattern, with maximum extent typically in March. We examine the area of sea ice in March in three overlapping study regions centred on Disko Bay on the west coast of Greenland. Sea ice concentration estimates derived from satellite passive microwave data are available for the years 1979-2001. We extend the record back in time by digitizing ice charts from the Danish Meteorological Institute, 1953-1981. There is reasonable agreement between the chart data and the satellite data during the three years of overlap: 1979-1981. We find a significant increasing trend in sea ice for the 49-year period (1953-2001) for the study regions that extend into Davis Strait and Baffin Bay. The cyclical nature of the wintertime ice area is also evident, with a period of about 8 to 9 years. Correlation of the winter sea ice concentration with the winter North Atlantic Oscillation (NAO) index shows moderately high values in Baffin Bay. The correlation of ice concentration with the previous winter's NAO is high in Davis Strait and suggests that next winter's ice conditions can be predicted to some extent by this winter's NAO index.

Satellite data are important for providing the large-scale context of the Surface Heat Budget of the Arctic Ocean (SHEBA) station and for characterizing the spatial variability of the sea ice in its vicinity. The Canadian RADARSAT satellite collected 195 synthetic aperture radar (SAR) images of the SHEBA site over the course of the 1 year drift. The RADARSAT Geophysical Processor System (RGPS) used these images to compute the spatial pattern of ice motion within 100 km of the SHEBA station by tracking features in sequential images. From the ice motion data the divergence and shear of the pack ice are estimated. The divergence is large from November to January, followed by a gradual convergence from February through July. The character of the ice motion changes at the end of July, from piecewise rigid motion to free drift. The ice motion reverts to its winterlike character in late September. Thus the "kinematic" summer runs from late July to late September. The radar backscatter also goes through seasonal transitions, capturing the abrupt onset of melt (29 May) and freeze-up (15 August). The concentration of multiyear ice is about 94% in the fall, and its backscatter signature remains stable through spring. Multiyear and first-year ice cannot be distinguished during the summer melt season, when the mean backscatter is negatively correlated with the surface air temperature. The "thermodynamic" summer runs from late May to mid-August.

A summary is presented of the Surface Heat Budget of the Arctic Ocean (SHEBA) project, with a focus on the field experiment that was conducted from October 1997 to October 1998. The primary objective of the field work was to collect ocean, ice, and atmospheric datasets over a full annual cycle that could be used to understand the processes controlling surface heat exchanges—in particular, the ice-albedo feedback and cloud-radiation feedback. This information is being used to improve formulations of arctic ice-ocean-atmosphere processes in climate models and thereby improve simulations of present and future arctic climate. The experiment was deployed from an ice breaker that was frozen into the ice pack and allowed to drift for the duration of the experiment. This research platform allowed the use of an extensive suite of instruments that directly measured ocean, atmosphere, and ice properties from both the ship and the ice pack in the immediate vicinity of the ship. This summary describes the project goals, experimental design, instrumentation, and the resulting datasets. Examples of various data products available from the SHEBA project are presented.