High Salinity Shelf Water production rates in Terra Nova Bay, Ross Sea from high-resolution salinity observations

High Salinity Shelf Water (HSSW) formed in the Ross Sea of Antarctica is a precursor to Antarctic Bottom Water (AABW), a water mass that constitutes the bottom limb of the global overturning circulation. HSSW production rates are poorly constrained, as in-situ observations are scarce. Here, we present high-vertical-and-temporal-resolution salinity time series collected in austral winter 2017 from a mooring in Terra Nova Bay (TNB), one of two major sites of HSSW production in the Ross Sea. We calculate an annual-average HSSW production rate of ~0.4 Sv (106 m3 s-1), which we use to ground truth additional estimates across 2012-2021 made from parametrized net surface heat fluxes. We find sub-seasonal and interannual variability on the order of [Formula: see text] [Formula: see text], with a strong dependence on variability in open-water area that suggests a sensitivity of TNB HSSW production rates to changes in the local wind regime and offshore sea ice pack.

Passive microwave Arctic sea ice melt onset dates from the advanced horizontal range algorithm 1979-2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979-2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Physiographic Controls on Landfast Ice Variability from 20 Years of Maximum Extents across the Northwest Canadian Arctic

Landfast ice is a defining feature among Arctic coasts, providing a critical transport route for communities and exerting control over the exposure of Arctic coasts to marine erosion processes. Despite its significance, there remains a paucity of data on the spatial variability of landfast ice and limited understanding of the environmental processes’ controls since the beginning of the 21st century. We present a new high spatiotemporal record (2000–2019) across the Northwest Canadian Arctic, using MODIS Terra satellite imagery to determine maximum landfast ice extent (MLIE) at the start of each melt season. Average MLIE across the Northwest Canadian Arctic declined by 73% in a direct comparison between the first and last year of the study period, but this was highly variable across regional to community scales, ranging from 14% around North Banks Island to 81% in the Amundsen Gulf. The variability was largely a reflection of 5–8-year cycles between landfast ice rich and poor periods with no discernible trend in MLIE. Interannual variability over the 20-year record of MLIE extent was more constrained across open, relatively uniform, and shallower sloping coastlines such as West Banks Island, in contrast with a more varied pattern across the numerous bays, headlands, and straits enclosed within the deep Amundsen Gulf. Static physiographic controls (namely, topography and bathymetry) were found to influence MLIE change across regional sites, but no association was found with dynamic environmental controls (storm duration, mean air temperature, and freezing and thawing degree day occurrence). For example, despite an exponential increase in storm duration from 2014 to 2019 (from 30 h to 140 h or a 350% increase) across the Mackenzie Delta, MLIE extents remained relatively consistent. Mean air temperatures and freezing and thawing degree day occurrences (over 1, 3, and 12-month periods) also reflected progressive northwards warming influences over the last two decades, but none showed a statistically significant relationship with MLIE interannual variability. These results indicate inferences of landfast ice variations commonly taken from wider sea ice trends may misrepresent more complex and variable sensitivity to process controls. The influences of different physiographic coastal settings need to be considered at process level scales to adequately account for community impacts and decision making or coastal erosion exposure.

A Physical Mechanism for the Indian Summer Monsoon—Arctic Sea-Ice Teleconnection

Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951–2000) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic.

Biogenic aerosol in central East Antarctic Plateau as a proxy for the ocean-atmosphere interaction in the Southern Ocean

Ten years of data of biogenic aerosol (methane sulfonic acid, MSA, and non-sea salt sulfate, nssSO₄^2-) collected at Concordia Station in the East Antarctic plateau (75° 06' S, 123° 20' E) are interpreted as a function of the Southern Annular Mode (SAM), Chlorophyll-a concentration (Chl-a; a proxy for phytoplankton biomass), sea ice extent and area. It is possible to draw three different scenarios that link these parameters in early, middle, and late summer. In early summer, the biogenic aerosol is significantly correlated to sea ice retreats through the phytoplankton biomass increases. Chl-a shows a significant correlation with nssSO₄^2- in the finest fraction (< 1 μm). In contrast, only Chl-a in West Pacific and Indian Ocean sectors correlates with MSA in the coarse fraction. The transport routes towards the inner Antarctic plateau and aerosol formation processes could explain the different correlation patterns of the two compounds both resulting from the DMS oxidation. In mid-summer, Chl-a concentrations are at the maximum and are not related to sea ice melting. Due to the complexity of transport processes of air masses towards the Antarctic plateau, the MSA concentrations are low and not related to Chl-a concentration. In late summer, MSA and nssSO₄^2- present the highest concentrations in their submicrometric aerosol fraction, and both are significantly correlated with Chl-a but not with the sea ice. In early and mid-summer, the enhanced efficiency of transport processes from all the surrounding oceanic sectors with air masses traveling at low elevation can explain the highest concentrations of nssSO₄^2- and especially MSA. Finally, considering the entire time series, MSA shows significant year-to-year variability. This variability is significantly correlated with SAM but with a different time lag in early (0-month lag) and late summer (4-months lag). This correlation likely occurs through the effect of the SAM on phytoplankton blooms.

A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC.

OC4-SO: A New Chlorophyll-a Algorithm for the Western Antarctic Peninsula Using Multi-Sensor Satellite Data

Chlorophyll-a (Chl-a) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl-a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl-a. Our study indicates that a global algorithm, on average, underestimates in-situ Chl-a by ~59%, although underestimation was only observed for waters with Chl-a > 0.5 mg m−3. In high Chl-a waters (>1 mg m−3), Chl-a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl-a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl-a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce.

An Analysis of Arctic Sea Ice Leads Retrieved from AMSR-E/AMSR2

In this study, we retrieve an Arctic sea ice lead fraction from AMSR2 passive microwave data in winter from 2012 to 2020 based on an algorithm developed for AMSR-E data. The derived AMSR2 sea ice lead fraction is validated against MODIS images. The results show that the derived AMSR2 sea ice lead detects approximately 50% of the ice leads shown in the MODIS images, which is close to the amount of sea ice lead detected from the AMSR-E data from 2002 to 2011. Utilizing the retrievals from both the AMSR-E and AMSR2, our analysis shows no significant trend, but moderate interannual variation exists for the ice lead fraction in the Arctic basin scale over the past two decades. The maximum width and total length of sea ice lead show a significant decreasing trend for the whole Arctic, but the mean width does not exhibit a significant change over the studied period. In the Beaufort Sea the lead fraction varies from 2.06% to 12.35%, with a mean value of 5.72%. In the Greenland Sea the mean lead fraction over the studied period is 5.77%, and there is a significant increase in the lead fraction, with a rate of 0.13% per year. The maximum width in the Greenland Sea is substantially higher than that of other regions, and the mean width increases significantly.

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends.

Long-term increases in pathogen seroprevalence in polar bears (Ursus maritimus) influenced by climate change

The influence of climate change on wildlife disease dynamics is a burgeoning conservation and human health issue, but few long-term studies empirically link climate to pathogen prevalence. Polar bears (Ursus maritimus) are vulnerable to the negative impacts of sea ice loss as a result of accelerated Arctic warming. While studies have associated changes in polar bear body condition, reproductive output, survival, and abundance to reductions in sea ice, no long-term studies have documented the impact of climate change on pathogen exposure. We examined 425 serum samples from 381 adult polar bears, collected in western Hudson Bay (WH), Canada, for antibodies to selected pathogens across three time periods: 1986-1989 (n = 157), 1995-1998 (n = 159) and 2015-2017 (n = 109). We ran serological assays for antibodies to seven pathogens: Toxoplasma gondii, Neospora caninum, Trichinella spp., Francisella tularensis, Bordetella bronchiseptica, canine morbillivirus (CDV) and canine parvovirus (CPV). Seroprevalence of zoonotic parasites (T. gondii, Trichinella spp.) and bacterial pathogens (F. tularensis, B. bronchiseptica) increased significantly between 1986-1989 and 1995-1998, ranging from +6.2% to +20.8%, with T. gondii continuing to increase into 2015-2017 (+25.8% overall). Seroprevalence of viral pathogens (CDV, CPV) and N. caninum did not change with time. Toxoplasma gondii seroprevalence was higher following wetter summers, while seroprevalences of Trichinella spp. and B. bronchiseptica were positively correlated with hotter summers. Seroprevalence of antibodies to F. tularensis increased following years polar bears spent more days on land, and polar bears previously captured in human settlements were more likely to be seropositive for Trichinella spp. As the Arctic has warmed due to climate change, zoonotic pathogen exposure in WH polar bears has increased, driven by numerous altered ecosystem pathways.

Effects of Wave-Induced Sea Ice Break-Up and Mixing in a High-Resolution Coupled Ice-Ocean Model

Arctic sea ice plays a vital role in modulating the global climate. In the most recent decades, the rapid decline of the Arctic summer sea ice cover has exposed increasing areas of ice-free ocean, with sufficient fetch for waves to develop. This has highlighted the complex and not well-understood nature of wave-ice interactions, requiring modeling effort. Here, we introduce two independent parameterizations in a high-resolution coupled ice-ocean model to investigate the effects of wave-induced sea ice break-up (through albedo change) and mixing on the Arctic sea ice simulation. Our results show that wave-induced sea ice break-up leads to increases in sea ice concentration and thickness in the Bering Sea, the Baffin Sea and the Barents Sea during the ice growth season, but accelerates the sea ice melt in the Chukchi Sea and the East Siberian Sea in summer. Further, wave-induced mixing can decelerate the sea ice formation in winter and the sea ice melt in summer by exchanging the heat fluxes between the surface and subsurface layer. As our baseline model underestimates sea ice cover in winter and produces more sea ice in summer, wave-induced sea ice break-up plays a positive role in improving the sea ice simulation. This study provides two independent parameterizations to directly include the wave effects into the sea ice models, with important implications for the future sea ice model development.

Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.

Satellite-based decadal change assessments of pan-Arctic environments

Remote sensing can advance the work of the Circumpolar Biodiversity Monitoring Program through monitoring of satellite-derived terrestrial and marine physical and ecological variables. Standardized data facilitate an unbiased comparison across variables and environments. Using MODIS standard products of land surface temperature, percent snow covered area, NDVI, EVI, phenology, burned area, marine chlorophyll, CDOM, sea surface temperature, and marine primary productivity, significant trends were observed in almost all variables between 2000 and 2017. Analysis of seasonal data revealed significant breakpoints in temporal trends. Within the terrestrial environment, data showed significant increasing trends in land surface temperature and NDVI. In the marine environment, significant increasing trends were detected in primary productivity. Significantly earlier onset of green up date was observed in bioclimate subzones C&E and longer end of growing season in B&E. Terrestrial and marine parameters showed similar rates of change with unidirectional change in terrestrial and significant directional and magnitude shifts in marine.

Variability in Antarctic sea ice from 1998 to 2017

This study was based on the daily sea ice concentration data from the NASA Team algorithm from 1998 to 2017. The Antarctic sea ice was analyzed from the total sea ice area (SIA), first-year ice area, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years. The results showed that the total SIA increased by 0.0087 × 10⁶ km² year^-1 (+ 2.08% dec^-1) between 1998 and 2017. The multiyear ice area increased by 0.0141 × 10⁶ km² year^-1 from 1998 to 2017. The first-year ice area decreased by - 0.0058 × 10⁶ km² year^-1 between 1998 and 2017. On a spatial scale, the entire Antarctic was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The total sea ice and multiyear ice areas showed a decreasing trend in WA. However, the total SIA and multiyear ice area all showed an increasing trend in EA. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA.

A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic

A newly completed 40-y record of satellite observations is used to quantify changes in Antarctic sea ice coverage since the late 1970s. Sea ice spreads over vast areas and has major impacts on the rest of the climate system, reflecting solar radiation and restricting ocean/atmosphere exchanges. The satellite record reveals that a gradual, decades-long overall increase in Antarctic sea ice extents reversed in 2014, with subsequent rates of decrease in 2014–2017 far exceeding the more widely publicized decay rates experienced in the Arctic. The rapid decreases reduced the Antarctic sea ice extents to their lowest values in the 40-y record, both on a yearly average basis (record low in 2017) and on a monthly basis (record low in February 2017).

Following over 3 decades of gradual but uneven increases in sea ice coverage, the yearly average Antarctic sea ice extents reached a record high of 12.8 × 10⁶ km² in 2014, followed by a decline so precipitous that they reached their lowest value in the 40-y 1979–2018 satellite multichannel passive-microwave record, 10.7 × 10⁶ km², in 2017. In contrast, it took the Arctic sea ice cover a full 3 decades to register a loss that great in yearly average ice extents. Still, when considering the 40-y record as a whole, the Antarctic sea ice continues to have a positive overall trend in yearly average ice extents, although at 11,300 ± 5,300 km²⋅y⁻¹, this trend is only 50% of the trend for 1979–2014, before the precipitous decline. Four of the 5 sectors into which the Antarctic sea ice cover is divided all also have 40-y positive trends that are well reduced from their 2014–2017 values. The one anomalous sector in this regard, the Bellingshausen/Amundsen Seas, has a 40-y negative trend, with the yearly average ice extents decreasing overall in the first 3 decades, reaching a minimum in 2007, and exhibiting an overall upward trend since 2007 (i.e., reflecting a reversal in the opposite direction from the other 4 sectors and the Antarctic sea ice cover as a whole).

Multiple-Scale Variations of Sea Ice and Ocean Circulation in the Bering Sea Using Remote Sensing Observations and Numerical Modeling

The Bering Sea is located between the Aleutian Low and Siberian High, with strong seasonal variations in the oceanic circulation and the sea ice coverage. Within such a large-scale system, the physical processes in the Bering Sea carry interannual variability. The special topography in the Bering Sea traps a strong jet along the Bering Slope, whose instability enriches the eddy activity in the region. A Regional Oceanic Modeling System (ROMS), coupled with a sea ice module, is employed to study multiple-scale variability in the sea ice and oceanic circulation in the Bering Sea for interannual, seasonal, and intra-seasonal eddy variations. The model domain covers the whole Bering Sea and a part of the Chukchi Sea and south of Aleutian Islands, with an averaged spatial resolution of 5 km. The external forcings are momentum, heat, and freshwater flux at the surface and adaptive nudging to reanalysis fields at the boundaries. The oceanic model starts in an equilibrium state from a multiple year cyclical climatology run, and then it is integrated from years 1990 through 2004. The 15 year simulation is analyzed and assessed against the observational data. The model accurately reproduces the seasonal and interannual variations in the sea ice coverage compared with the satellite-observed sea ice data from the National Snow and Ice Data Center (NSIDC). Sea surface temperature and eddy kinetic energy patterns from the ROMS agree with satellite remote sensing data. The transportation through the Bering Strait is also comparable with the estimate of mooring data. The mechanism for seasonal and interannual variation in the Bering Sea is connected to the Siberia-Aleutian index. Eddy variation along the Bering Slope is discussed. The model also simulates polynya generation and evolution around the St. Lawrence Island.

Variability of Arctic Sea Ice (1979–2016)

This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes.

St. Matthew Island colonized through multiple long-distance red fox (Vulpes vulpes) dispersal events

Expansion of red fox (Vulpes vulpes (L., 1758)) into new arctic habitat and the potential for competition with arctic fox (Vulpes lagopus (L., 1758)) are of considerable conservation concern. Previous work has focused on red fox expanding into contiguous areas with few barriers to dispersal. Here, we examine mitochondrial DNA in red fox on recently colonized St. Matthew Island in the Bering Sea to determine their ultimate origin. Though limited in sample size (n = 7), we found that St. Matthew Island was colonized by North American lineages; surprisingly, despite the >400 km distance to the mainland, we found the island was colonized by at least three mitochondrial matrilines. These results suggest that even extremely isolated places may be colonized by red fox, and that the over-ice or over-ocean dispersal ability of red fox may have been previously underappreciated.

Demographic and traditional knowledge perspectives on the current status of Canadian polar bear subpopulations

Subpopulation growth rates and the probability of decline at current harvest levels were determined for 13 subpopulations of polar bears (Ursus maritimus) that are within or shared with Canada based on mark-recapture estimates of population numbers and vital rates, and harvest statistics using population viability analyses (PVA). Aboriginal traditional ecological knowledge (TEK) on subpopulation trend agreed with the seven stable/increasing results and one of the declining results, but disagreed with PVA status of five other declining subpopulations. The decline in the Baffin Bay subpopulation appeared to be due to over-reporting of harvested numbers from outside Canada. The remaining four disputed subpopulations (Southern Beaufort Sea, Northern Beaufort Sea, Southern Hudson Bay, and Western Hudson Bay) were all incompletely mark-recapture (M-R) sampled, which may have biased their survival and subpopulation estimates. Three of the four incompletely sampled subpopulations were PVA identified as nonviable (i.e., declining even with zero harvest mortality). TEK disagreement was nonrandom with respect to M-R sampling protocols. Cluster analysis also grouped subpopulations with ambiguous demographic and harvest rate estimates separately from those with apparently reliable demographic estimates based on PVA probability of decline and unharvested subpopulation growth rate criteria. We suggest that the correspondence between TEK and scientific results can be used to improve the reliability of information on natural systems and thus improve resource management. Considering both TEK and scientific information, we suggest that the current status of Canadian polar bear subpopulations in 2013 was 12 stable/increasing and one declining (Kane Basin). We do not find support for the perspective that polar bears within or shared with Canada are currently in any sort of climate crisis. We suggest that monitoring the impacts of climate change (including sea ice decline) on polar bear subpopulations should be continued and enhanced and that adaptive management practices are warranted.

Spatially mapped reductions in the length of the Arctic sea ice season

Satellite data are used to determine the number of days having sea ice coverage in each year 1979-2013 and to map the trends in these ice-season lengths. Over the majority of the Arctic seasonal sea ice zone, the ice season shortened at an average rate of at least 5 days/decade between 1979 and 2013, and in a small area in the northeastern Barents Sea the rate of shortening reached over 65 days/decade. The only substantial non-coastal area with lengthening sea ice seasons is the Bering Sea, where the ice season lengthened by 5-15 days/decade. Over the Arctic as a whole, the area with ice seasons shortened by at least 5 days/decade is 12.4 × 10⁶ km², while the area with ice seasons lengthened by at least 5 days/decade is only 1.1 × 10⁶ km². The contrast is even greater, percentage-wise, for higher rates.

KEY POINTS

Sea ice seasons have shortened by at least 5 days/decade over most of the ArcticAcross 1.9 million km² ice seasons have shortened by at least 25 days/decadeCounter to most of the Arctic ice seasons have lengthened in the Bering Sea.

Remote sensing of seawater and drifting ice in Svalbard fjords by compact Raman lidar

A compact Raman lidar system for remote sensing of sea and drifting ice was developed at the Wave Research Center at the Prokhorov General Physics Institute of the Russian Academy of Sciences. The developed system is based on a diode-pumped solid-state YVO(4):Nd laser combined with a compact spectrograph equipped with a gated detector. The system exhibits high sensitivity and can be used for mapping or depth profiling of different parameters within many oceanographic problems. Light weight (∼20 kg) and low power consumption (300 W) make it possible to install the device on any vehicle, including unmanned aircraft or submarine systems. The Raman lidar presented was used for study and analysis of the different influence of the open sea and glaciers on water properties in Svalbard fjords. Temperature, phytoplankton, and dissolved organic matter distributions in the seawater were studied in the Ice Fjord, Van Mijen Fjord, and Rinders Fjord. Drifting ice and seawater in the Rinders Fjord were characterized by the Raman spectroscopy and fluorescence. It was found that the Paula Glacier strongly influences the water temperature and chlorophyll distributions in the Van Mijen Fjord and Rinders Fjord. Possible applications of compact lidar systems for express monitoring of seawater in places with high concentrations of floating ice or near cold streams in the Arctic Ocean are discussed.

Health effects from long-range transported contaminants in Arctic top predators: An integrated review based on studies of polar bears and relevant model species

The aim of this review is to provide a thorough overview of the health effects from the complexed biomagnified mixture of long-range transported industrial organochlorines (OCs), polybrominated diphenyl ethers (PBDEs), perfluorinated compounds (PFCs) and mercury (Hg) on polar bear (Ursus maritimus) health. Multiple scientific studies of polar bears indicate negative relationships between exposure to these contaminants and health parameters; however, these are all of a correlative nature and do not represent true cause-and-effects. Therefore, information from controlled studies of farmed Norwegian Arctic foxes (Vulpes lagopus) and housed East and West Greenland sledge dogs (Canis familiaris) were included as supportive weight of evidence in the clarification of contaminant exposure and health effects in polar bears. The review showed that hormone and vitamin concentrations, liver, kidney and thyroid gland morphology as well as reproductive and immune systems of polar bears are likely to be influenced by contaminant exposure. Furthermore, exclusively based on polar bear contaminant studies, bone density reduction and neurochemical disruption and DNA hypomethylation of the brain stem seemed to occur. The range of tissue concentration, at which these alterations were observed in polar bears, were ca. 1-70,000 ng/g lw for OCs (blood plasma concentrations of some PCB metabolites even higher), ca. 1-1000 ng/g lw for PBDEs and for PFCs and Hg 114-3052 ng/g ww and 0.1-50 microg/g ww, respectively. Similar concentrations were found in farmed foxes and housed sledge dogs while the lack of dose response designs did not allow an estimation of threshold levels for oral exposure and accumulated tissue concentrations. Nor was it possible to pinpoint a specific group of contaminants being more important than others nor analyze their interactions. For East Greenland polar bears the corresponding daily SigmaOC and SigmaPBDE oral exposure was estimated to be 35 and 0.34 microg/kg body weight, respectively. Furthermore, PFC concentrations, at which population effect levels could occur, are likely to be reached around year 2012 for the East Greenland polar bear subpopulation if current increasing temporal trends continue. Such proposed reproductive population effects were supported by physiological based pharmacokinetic (PBPK) modelling of critical body residues (CBR) with risk quotients >or=1 for SigmaPCB, dieldrin, SigmaPFC and SigmaOHC (organohalogen contaminant). The estimated daily TEQ for East Greenland polar bears and East Greenland sledge dogs were 32-281-folds above WHO SigmaTEQ guidelines for humans. Compared to human tolerable daily intake (TDI), these were exceeded for PCBs, dieldrin, chlordanes and SigmaHCH in East Greenland polar bears. Comparisons like these should be done with caution, but together with the CBR modelling and T-score estimations, these were the only available tools for polar bear risk evaluation. In conclusion, polar bears seem to be susceptible to contaminant induced stress that may have an overall sub-clinical impact on their health and population status via impacts on their immune and reproductive systems.

Loss of sea ice in the Arctic

The Arctic sea ice cover is in decline. The areal extent of the ice cover has been decreasing for the past few decades at an accelerating rate. Evidence also points to a decrease in sea ice thickness and a reduction in the amount of thicker perennial sea ice. A general global warming trend has made the ice cover more vulnerable to natural fluctuations in atmospheric and oceanic forcing. The observed reduction in Arctic sea ice is a consequence of both thermodynamic and dynamic processes, including such factors as preconditioning of the ice cover, overall warming trends, changes in cloud coverage, shifts in atmospheric circulation patterns, increased export of older ice out of the Arctic, advection of ocean heat from the Pacific and North Atlantic, enhanced solar heating of the ocean, and the ice-albedo feedback. The diminishing Arctic sea ice is creating social, political, economic, and ecological challenges.

Climate of the Arctic marine environment

The climate of the Arctic marine environment is characterized by strong seasonality in the incoming solar radiation and by tremendous spatial variations arising from a variety of surface types, including open ocean, sea ice, large islands, and proximity to major landmasses. Interannual and decadal-scale variations are prominent features of Arctic climate, complicating the distinction between natural and anthropogenically driven variations. Nevertheless, climate models consistently indicate that the Arctic is the most climatically sensitive region of the Northern Hemisphere, especially near the sea ice margins. The Arctic marine environment has shown changes over the past several decades, and these changes are part of a broader global warming that exceeds the range of natural variability over the past 1000 years. Record minima of sea ice coverage during the past few summers and increased melt from Greenland have important implications for the hydrographic regime of the Arctic marine environment. The recent changes in the atmosphere (temperature, precipitation, pressure), sea ice, and ocean appear to be a coordinated response to systematic variations of the large-scale atmospheric circulation, superimposed on a general warming that is likely associated with increasing greenhouse gases. The changes have been sufficiently large in some sectors (e.g., the Bering/Chukchi Seas) that consequences for marine ecosystems appear to be underway. Global climate models indicate an additional warming of several degrees Celsius in much of the Arctic marine environment by 2050. However, the warming is seasonal (largest in autumn and winter), spatially variable, and closely associated with further retreat of sea ice. Additional changes predicted for 2050 are a general decrease of sea level pressure (largest in the Bering sector) and an increase of precipitation. While predictions of changes in storminess cannot be made with confidence, the predicted reduction of sea ice cover will almost certainly lead to increased oceanic mixing, ocean wave generation, and coastal flooding.

Quantifying the sensitivity of Arctic marine mammals to climate-induced habitat change

We review seven Arctic and four subarctic marine mammal species, their habitat requirements, and evidence for biological and demographic responses to climate change. We then describe a pan-Arctic quantitative index of species sensitivity to climate change based on population size, geographic range, habitat specificity, diet diversity, migration, site fidelity, sensitivity to changes in sea ice, sensitivity to changes in the trophic web, and maximum population growth potential (R(max)). The index suggests three types of sensitivity based on: (1) narrowness of distribution and specialization in feeding, (2) seasonal dependence on ice, and (3) reliance on sea ice as a structure for access to prey and predator avoidance. Based on the index, the hooded seal, the polar bear, and the narwhal appear to be the three most sensitive Arctic marine mammal species, primarily due to reliance on sea ice and specialized feeding. The least sensitive species were the ringed seal and bearded seal, primarily due to large circumpolar distributions, large population sizes, and flexible habitat requirements. The index provides an objective framework for ranking species and focusing future research on the effects of climate change on Arctic marine mammals. Finally, we distinguish between highly sensitive species and good indicator species and discuss regional variation and species-specific ecology that confounds Arctic-wide generalization regarding the effects of climate change.