Sea Ice Decline Research Articles

Biomarkers in Ringed Seals Reveal Recent Onset of Borealization in the High- Compared to the Mid-Latitude Canadian Arctic

Warming of the Arctic has resulted in environmental and ecological changes, termed borealization, leading to the northward shift of temperate species. Borealization has occurred across all trophic levels, altering the structure of the food web. The onset and rate of borealization likely varies with latitude, depending on local warming and advection of warmer water into the Arctic. In order to assess latitudinal trends in food web structure in the Arctic, we analyzed stable nitrogen isotopes of specific amino acids alongside bulk stable carbon isotopes in ringed seal muscle tissue from the Canadian Arctic Archipelago (high-Arctic) and Southern Baffin Bay (mid-Arctic) from 1990 to 2016. Our results indicate a shift in food web structure in the high-Arctic that has occurred more recently when compared with the mid-Arctic. Specifically, over the past 25 years, the trophic position of ringed seals from the mid-Arctic was largely constant, whereas the trophic position of ringed seals decreased in the high-Arctic, reaching similar values observed in the mid-Arctic in 2015–2016. This suggests a potential shortening of the food chain length in the high-Arctic, possibly driven by changes in zooplankton communities feeding complexity in association with sea ice decline. This study identifies a temporal offset in the timing of borealization in the Canadian Arctic, resulting in different response of food webs to ecological changes, depending on latitude.

Arctic sea ice motion change and response to atmospheric forcing between 1979 and 2019

AbstractQuantification of the spatial variability and long‐term changes of Arctic sea ice motion is important for understanding the mechanisms of rapid Arctic sea ice decline because sea ice motion determines ice mass advection, outflow, thickness redistribution, as well as the formation of leads and ridges associated with ice deformation. The spatiotemporal changes in Arctic sea ice motion between 1979 and 2019 and their responses to atmospheric forcing were analysed using satellite‐derived sea ice motion products and atmospheric reanalysis data. The pan‐Arctic average sea ice drift speed increased significantly for all seasons between 1979 and 2019 (p < .001). Rates of increase were higher in autumn and winter than in spring and summer. Spatially, rates of increase in the peripheral seas in the Pacific sector—the Beaufort, Chukchi and East Siberian Seas—were higher than in the central Arctic Ocean and the peripheral seas in the Atlantic sector—the Kara and Laptev Seas. On the contrary, Arctic wind speed increased significantly only in autumn (p < .01). However, the correlation between wind speed and ice speed was the lowest in this season, suggesting that wind forcing is unable to completely account for drift speed increase. In general, the trends in above‐average drift speeds—retrieved from grid cells with the relatively high drift speeds—were statistically significant and were larger than that in average drift speeds probably because of enhanced response of ice motion to extreme wind forcing. The influence of the Arctic Oscillation, Beaufort High, and North Atlantic Oscillation on the zonal ice speed was symmetrical between the Pacific and Atlantic sectors of the Arctic Ocean, while the influence of the Dipole Anomaly and the east–west surface air pressure gradient in central Arctic on the meridional ice speed was distributed in an annular pattern and was the strongest along the Transpolar Drift Stream.

Inter-model spread in the wintertime Arctic amplification in the CMIP6 models and the important role of internal climate variability

We study the characteristics of wintertime Arctic amplification (AA) and the inter-model spread in model output from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the Community Earth System Model Large Ensemble Project (CESM-LE). The CMIP6 models exhibit a large spread in AA intensity (as measured by an AA index, ranging from about −0.2 to about 1.1 °C/decade in the Historical simulations) and in its spatial/vertical structure (no AA to AA extending to upper-troposphere). A large model spread in Arctic sea-ice trends (from about −3.1 to about 0.3 %/decade) is also found. Intra-model spread in the ensemble simulations of six selected CMIP6 models, which we interpret as a measure of internal climate variability, is similarly large. We further find that CMIP6 model integrations where AA extends to the upper-troposphere, show weaker warming in the midlatitudes. The observed recent midlatitude cooling trends are generally not seen in the one-realization CMIP6 models but are seen in some ensemble members in the CMIP6 ensemble simulations; They are likely not a response to Arctic sea ice decline according to our analysis and are more likely caused by internal climate variability. By comparing the CMIP6 inter-model spread to the intra-model spread in the six selected CMIP6 ensembles and CESM-LE simulations, we argue that a large part of the CMIP6 inter-model spread in AA index, surface air temperature (SAT) trends and Arctic sea ice trends can be attributed to internal climate variability: about 67% for AA index, 58% for Arctic SAT and 22% for Arctic sea-ice. However, the inferred internal climate variability has been relatively stable in the recent several decades, while the inter-model spread in Arctic temperature trends and particularly Arctic sea-ice trends has been increasing in the CMIP6 models. This highlights the importance of constraining CMIP6 model discrepancies in simulating recent climate variations, which will potentially benefit the future multimodel climate projections. • There is a large spread in CMIP6 models in simulating the recent wintertime Arctic amplification. • Internal climate variability can account for a large part of the CMIP6 model spread. • The influences of Arctic sea ice loss on Eurasian temperature as hypothesized in existing studies are not found based on CMIP6 models/ensembles. • Full-AA is associated with weaker warming trends in midlatitudes compared to surface-only AA.

Clustering analysis of a yearlong record of ambient sound on the Chukchi Shelf in the 40 Hz to 4 kHz frequency range.

Changes in the Arctic environment with regard to declining sea ice are expected to alter the ambient sound field, affecting both the sound generating processes and the sound propagation. This paper presents acoustic recordings collected on the 150-m isobath on the Chukchi Shelf over a yearlong period spanning October 2016 to October 2017. The analysis uses sections of recordings approximately 12 min long collected six times daily. The measurements were collected on a vertical line array spanning the lower 110 m of the water column. The 25th percentile level is used to characterize the spectral shape of the background sound between 40 Hz and 4 kHz. The ambient sound data are analyzed using k-means clustering to quantify the occurrence of six spectral shapes over the yearlong experiment. Each cluster type is associated with a different sound generation process based on the correlations with environmental observations. The cluster observed most frequently was associated with wind-generated sound based on a correlation of sound level with wind speed as well as occurrence during the open water season. The cluster with the smallest number of observations was attributed to wind effects on frazil ice forming in open leads during the ice-covered season.

Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere.

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.

Arctic Cloud Response to a Perturbation in Sea Ice Concentration: The North Water Polynya

AbstractSurface and atmosphere energy exchanges play an important role in the Arctic climate system by influencing the lower atmospheric stability and humidity, sea ice melt and growth, and surface temperature. Sea ice significantly alters the character of these energy exchanges relative to ice‐free ocean. The observed decline in Arctic sea ice since 1979 motivates questions related to the evolving role of surface‐atmosphere coupling and potential feedbacks on the Arctic system. Due to the strong wintertime cloud warming effect, a critical question concerns the potential response of low clouds to Arctic sea ice decline. Previous approaches relied on interannual variability to investigate the cloud response to sea ice decline. However, the covariation between atmospheric conditions and sea ice makes it difficult to define an observational control when using interannual variability. To circumvent this difficulty, we exploit the recurring North Water polynya, an episodic opening in the northern Baffin Bay sea ice, as a natural laboratory to isolate the cloud response to a rapid, near‐step perturbation in sea ice. Our results show that during the event, (a) low‐cloud cover is 10%–33% larger over the polynya than nearby sea ice, (b) cloud liquid water content is up to 400% larger over the polynya than nearby sea ice, and (c) the surface cloud radiative effect is 18 W m−2 larger over the polynya than nearby sea ice. Our results provide evidence that the low‐cloud response during a polynya is a positive feedback lengthening the event.

Female Pacific walruses (Odobenus rosmarus divergens) show greater partitioning of sea ice organic carbon than males: Evidence from ice algae trophic markers.

The expected reduction of ice algae with declining sea ice may prove to be detrimental to the Pacific Arctic ecosystem. Benthic organisms that rely on sea ice organic carbon (iPOC) sustain benthic predators such as the Pacific walrus (Odobenus rosmarus divergens). The ability to track the trophic transfer of iPOC is critical to understanding its value in the food web, but prior methods have lacked the required source specificity. We analyzed the H-Print index, based on biomarkers of ice algae versus phytoplankton contributions to organic carbon in marine predators, in Pacific walrus livers collected in 2012, 2014 and 2016 from the Northern Bering Sea (NBS) and Chukchi Sea. We paired these measurements with stable nitrogen isotopes (δ15N) to estimate trophic position. We observed differences in the contribution of iPOC in Pacific walrus diet between regions, sexes, and age classes. Specifically, the contribution of iPOC to the diet of Pacific walruses was higher in the Chukchi Sea (52%) compared to the NBS (30%). This regional difference is consistent with longer annual sea ice persistence in the Chukchi Sea. Within the NBS, the contribution of iPOC to walrus spring diet was higher in females (~45%) compared to males (~30%) for each year (p < 0.001), likely due to specific foraging behavior of females to support energetic demands associated with pregnancy and lactation. Within the Chukchi Sea, the iPOC contribution was similar between males and females, yet higher in juveniles than in adults. Despite differences in the origin of organic carbon fueling the system (sea ice versus pelagic derived carbon), the trophic position of adult female Pacific walruses was similar between the NBS and Chukchi Sea (3.2 and 3.5, respectively), supporting similar diets (i.e. clams). Given the higher quality of organic carbon from ice algae, the retreat of seasonal sea ice in recent decades may create an additional vulnerability for female and juvenile Pacific walruses and should be considered in management of the species.

Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multi-model Large Ensemble

AbstractTo examine the atmospheric responses to Arctic sea-ice variability in the Northern Hemisphere cold season (October to following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily-varying sea-ice, sea-surface temperature, and radiative forcings prescribed during the 1979-2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multi-model ensemble mean (MMEM) shows decreasing sea-level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea-ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drives a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual co-variability between sea-ice extent in the Barents-Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the co-variability in MMEMs. The interannual sea-ice decline followed by a negative North Atlantic Oscillation-like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea-ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

Species and spatial variation in the effects of sea ice on Arctic seabird populations

AbstractAimThe Arctic is warming rapidly, and sea ice is disappearing. This is expected to have profound effects on Arctic wildlife. However, empirical evidence that this decline in sea ice is associated with a decline in Arctic wildlife populations is lacking.LocationSvalbard Archipelago.MethodsUsing long‐term time series data (1988–2018) from two fjords in West Spitsbergen (Svalbard), we tested whether or not sea ice concentration was associated with the population size of two of the most common Arctic seabirds, the Brünnich's guillemot (Uria lomvia) and black‐legged kittiwake (Rissa tridactyla).ResultsWe found that the size of guillemot and kittiwake colonies has declined on Svalbard from the mid‐1990s onwards, though the shapes of these trajectories were not linear and kittiwake colony size has stabilized or even increased in recent years. sea ice concentration in West Spitsbergen also declined during the study period. Independent of these long‐term trends, sea ice concentration was positively and significantly associated with seabird colony size with a 2‐year lag, though variations in sea ice explained only a small proportion of the changes in colony size. One likely mechanism linking sea ice and seabird population size involves changes in the food chain, with poor sea ice conditions in a given year leading to low food availability 2 years later. This would affect breeding probability and hence colony size for kittiwakes and guillemots. This relationship between sea ice and colony size was the same in both fjords for guillemots. In the case of kittiwakes, it was not apparent in the fjord where productive glacier fronts, intensely used by kittiwakes to forage, may have buffered the effects of changes in sea ice.Main conclusionsOur study provides evidence that the ongoing decline in Arctic sea ice plays a role in Arctic seabird population trajectories. However, sea ice disappearance on the breeding grounds was likely not the main driver of changes in seabird populations.

Water mass distribution in the northern Bering and southern Chukchi seas using light absorption of chromophoric dissolved organic matter

The northern Bering Sea (NBS) and southern Chukchi Sea (SCS) contain several water masses with different characteristics that have been conventionally classified using temperature and salinity data. However, recent warming and sea ice decline can change these water properties, which suggests that classifying water masses using temperature–salinity diagrams could be problematic for this region. We used the light absorption coefficient of chromophoric dissolved organic matter (CDOM), aCDOM, as an alternative way to classify water masses. The aCDOM spectra of several water depths were measured in the NBS, the Gulf of Anadyr included, and SCS in July 2017 and 2018, and August 2018. Optical parameters that indicate CDOM quantity and quality, aCDOM(350) and spectral slopes (S275–295 and S350–400), were calculated for each sample; water masses were classified using cluster analysis. When surface waters in the NBS and SCS are classified on the basis of the conventional method using a temperature–salinity diagram, they are mistakenly identified as Alaskan Coastal Water because of warmer temperatures. However, our cluster analysis using CDOM parameters evenly classified seven water masses with reasonable distributions. A water mass with the highest aCDOM(350) and lowest S275–295 was found along the coast of the Gulf of Anadyr and Alaska mainland, which suggests that freshwater originates from the Anadyr and Yukon rivers and is transported by the Anadyr Current and Alaskan Coastal Current, respectively. A CDOM-based water mass with high S275–295, indicating CDOM degradation by ultraviolet radiation, was present at the sea surface. A water mass with low S275–295 was found at deeper water depths and river mouths. These results suggest that classification with CDOM parameters is consistent with geographical features. In addition, we recognized a water mass with high nitrate concentrations, which is likely Anadyr Water that originates in the region from the Bering Slope to the Gulf of Anadyr. Overall, this study reveals that water mass classification using CDOM parameters is useful in coastal sea areas in which water mass mixing is complex.

Revisiting Trans-Arctic Maritime Navigability in 2011–2016 from the Perspective of Sea Ice Thickness

Arctic navigation has become operational in recent decades with the decline in summer sea ice. To assess the navigability of trans-Arctic passages, combined model and satellite sea ice thickness (CMST) data covering both freezing seasons and melting seasons are integrated with the Arctic Transportation Accessibility Model (ATAM). The trans-Arctic navigation window and transit time are thereby obtained daily from modeled sea ice fields constrained by satellite observations. Our results indicate that the poorest navigability conditions for the maritime Arctic occurred in 2013 and 2014, particularly in the Northwest Passage (NWP) with sea ice blockage. The NWP has generally exhibited less favorable navigation conditions and shorter navigable windows than the Northern Sea Route (NSR). For instance, in 2013, Open Water (OW) vessels that can only safely resist ice with a thickness under 15 cm had navigation windows of 47 days along the NSR (45% shorter than the 2011–2016 mean) and only 13 days along the NWP (80% shorter than the 2011–2016 mean). The longest navigation windows were in 2011 and 2015, with lengths of 103 and 107 days, respectively. The minimum transit time occurred in 2012, when more northward routes were accessible, especially in the Laptev Sea and East Siberian Sea with the sea ice edge retreated. The longest navigation windows for Polar Class 6 (PC6) vessels with a resistance to ice thickness up to 120 cm reached more than 200 days. PC6 vessels cost less transit time and exhibit less fluctuation in their navigation windows compared with OW vessels because of their ice-breaking capability. Finally, we found that restricted navigation along the NSR in 2013 and 2014 was related to the shorter periods of navigable days in the East Siberian Sea and Vilkitskogo Strait, with local blockages of thick ice having a disproportionate impact on the total transit. Shorter than usual navigable windows in the Canadian Arctic Archipelago and Beaufort Sea shortened the windows for entire routes of the NWP in 2013 and 2014.

Accelerating Ice Mass Loss Across Arctic Russia in Response to Atmospheric Warming, Sea Ice Decline, and Atlantification of the Eurasian Arctic Shelf Seas

AbstractGlaciers and ice caps of the Russian Arctic are currently experiencing accelerating mass loss as a result of strong atmospheric and oceanic warming in the Barents and Kara Sea (BKS) region. Since 2010, this loss has been driven by both increased surface ablation, and dramatic shifts in ice dynamics at individual drainage basins across the entire Eurasian High Arctic. Here, we provide a high‐resolution spatial and temporal overview of ice surface elevation change and mass imbalance across both Novaya Zemlya and Severnaya Zemlya using CryoSat‐2 interferometric swath altimetry from 2010 to 2018. We find a total mass imbalance of −300 kg m−2 a−1, marked by a strong east‐to‐west gradient, with higher rates of loss over Novaya Zemlya (9.7 ± 0.5 Gt a−1 at 431 ± 22 kg m−2 a−1) in the west compared to Severnaya Zemlya (1.7 ± 0.1 Gt a−1 at 97 ± 8 kg m−2 a−1) in the east. Correlation between time series of surface elevation change and climate forcing reveals a quasi‐linear relationship between coupled ocean‐atmospheric forcing and glacier change over Novaya Zemlya, in agreement with similar findings from east Greenland. We further discern the likely role of ocean warming as the key factor driving dynamic ice loss in Severnaya Zemlya, owing to increasingly warm Atlantic Waters circulating along the Eurasian continental margin. We conclude that simple, linear relationships between environmental forcing and glacier change may be sufficiently accurate to parametrize ice loss in regions where synchronous, coupled ocean‐atmosphere forcing prevails.

The Polar Radiant Energy in the Far Infrared Experiment: A New Perspective on Polar Longwave Energy Exchanges

AbstractEarth’s climate is strongly influenced by energy deficits at the poles that emit more thermal energy than they receive from the sun. Energy exchanges between the surface and atmosphere influence the local environment while heat transport from lower latitudes drives midlatitude atmospheric and oceanic circulations. In the Arctic, in particular, local energy imbalances induce strong seasonality in surface–atmosphere heat exchanges and an acute sensitivity to forced climate variations. Despite these important local and global influences, the largest contributions to the polar atmospheric and surface energy budgets have not been fully characterized. The spectral variation of far-infrared radiation that makes up 60% of polar thermal emission has never been systematically measured impeding progress toward consensus in predicted rates of Arctic warming, sea ice decline, and ice sheet melt. Enabled by recent advances in sensor miniaturization and CubeSat technology, the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will document, for the first time, the spectral, spatial, and temporal variations of polar far-infrared emission. Selected under NASA’s Earth Ventures Instrument (EVI) program, PREFIRE will utilize new lightweight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 μm to quantify Earth’s far-infrared spectrum. Estimates of spectral surface emissivity, water vapor, cloud properties, and the atmospheric greenhouse effect derived from these measurements offer the potential to advance our understanding of the factors that modulate thermal fluxes in the cold, dry conditions characteristic of the polar regions.

Factors contributing to rapid decline of Arctic sea ice in autumn

Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations. In this study, we examined the factors contributing to the decline of autumn sea ice concentration. From the Beaufort Sea to the Barents Sea, autumn sea ice concentration has decreased considerably between 1982 and 2020, and the rates of decline were the highest around the Beaufort Sea. We calculated the correlation coefficients between sea ice extent (SIE) anomalies and anomalies of sea surface temperature (SST), surface air temperature (SAT) and specific humidity (SH). Among these coefficients, the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October, which has a value of −0.9446. The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November, which has a value of −0.9436. Among the correlation coefficients between SIE and SST anomalies, the largest absolute value was found in the coefficient for August to October, which has a value of −0.9410. We conducted empirical orthogonal function (EOF) analyses of sea ice, SST, SAT, SH, sea level pressure (SLP) and the wind field for the months where the absolute values of the correlation coefficient were the largest. The first EOFs of SST, SAT and SH account for 39.07%, 63.54% and 47.60% of the total variances, respectively, and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea. The corresponding principal component time series also indicate positive trends. The first EOF of SLP explains 41.57% of the total variance. It is mostly negative in the central Arctic. Over the Beaufort, Chukchi and East Siberian seas, the zonal wind weakened while the meridional wind strengthened. Results from the correlation and EOF analyses further verified the effects of the ice–temperature, ice–SH and ice–SLP feedback mechanisms in the Arctic. These mechanisms accelerate melting and decrease the rate of formation of sea ice. In addition, stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region, further promoting Arctic sea ice decline. Citation :Li S Y, Cui H Y, Xu J L, et al. Factors contributing to rapid decline of Arctic sea ice in autumn. Adv Polar Sci, 2021, 32(2): 96-104, doi: 10.13679/j.advps.2020.0039

Microbial metagenome-assembled genomes of the Fram Strait from short and long read sequencing platforms.

The impacts of climate change on the Arctic Ocean are manifesting throughout the ecosystem at an unprecedented rate. Of global importance are the impacts on heat and freshwater exchange between the Arctic and North Atlantic Oceans. An expanding Atlantic influence in the Arctic has accelerated sea-ice decline, weakened water column stability and supported the northward shift of temperate species. The only deep-water gateway connecting the Arctic and North Atlantic and thus, fundamental for these exchange processes is the Fram Strait. Previous research in this region is extensive, however, data on the ecology of microbial communities is limited, reflecting the wider bias towards temperate and tropical latitudes. Therefore, we present 14 metagenomes, 11 short-read from Illumina and three long-read from PacBio Sequel II, of the 0.2–3 µm fraction to help alleviate such biases and support future analyses on changing ecological patterns. Additionally, we provide 136 species-representative, manually refined metagenome-assembled genomes which can be used for comparative genomics analyses and addressing questions regarding functionality or distribution of taxa.

Rapid decline in Antarctic sea ice in recent years hints at future change

Rapid decline in Antarctic sea ice in recent years hints at future change

Persistent freshening of the Arctic Ocean and changes in the North Atlantic salinity caused by Arctic sea ice decline

Arctic sea ice has been declining over past several decades with the largest ice loss occurring in summer. This implies a strengthening of the sea ice seasonal cycle. Here, we examine global ocean salinity response to such changes of Arctic sea ice using simulations wherein we impose a radiative heat imbalance at the sea ice surface, inducing a sea ice decline comparable to the observed. The imposed perturbation leads to enhanced seasonal melting and a rapid retreat of Arctic sea ice within the first 5–10 years. We then observe a gradual freshening of the upper Arctic ocean that continues for about a century. The freshening is most pronounced within the central Arctic, including the Beaufort gyre, and is attributed to excess surface freshwater associated with the stronger seasonal sea ice melting, as well as a greater upper-ocean freshwater storage due to changes in ocean circulation. The freshening of the Nordic Seas can also occur via a distillation-like process in which denser saline waters with increased salinity are exported to the subtropical/tropical North Atlantic by meridional overturning circulation. Thus, enhanced seasonal sea ice melting in a warmer climate can lead to a persistent Arctic freshening with large impacts on the global salinity distribution.

An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator

Abstract. The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat to biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved sea ice extent indicator covering the European regional seas, the new IceMap500 algorithm has been developed to classify sea ice and water at a resolution of 500 m at nadir. IceMap500 features a classification strategy built upon previous MODIS sea ice extent algorithms and a new method to reclassify areas affected by resolution-breaking features inherited from the MODIS cloud mask. This approach results in an enlargement of mapped area, a reduction of potential error sources and a better delineation of the sea ice edge, while still systematically achieving accuracies above 90 %, as obtained by manual validation. Swath maps have been aggregated at a monthly scale to obtain sea ice extent with a method that is sensitive to spatio-temporal variations in the sea ice cover and that can be used as an additional error filter. The resulting dataset, covering the months of maximum and minimum sea ice extent (i.e. March and September) over 2 decades (from 2000 to 2019), demonstrates the algorithm's applicability as a monitoring tool and as an indicator, illustrating the sea ice decline at a regional scale. The European sea regions located in the Arctic, NE Atlantic and Barents seas display clear negative trends in both March (−27.98 ± 6.01 × 103 km2yr−1) and September (−16.47 ± 5.66 × 103 km2yr−1). Such trends indicate that the sea ice cover is shrinking at a rate of ∼ 9 % and ∼ 13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼ 70 % greater in March.

Arctic Cyclones and Their Interactions With the Declining Sea Ice: A Recent Climatology

AbstractIn this study, we applied a cyclone tracking algorithm to multiple reanalyses and categorized the detected cyclones based on season, intensity, and sea ice state to provide an Arctic cyclone climatology with emphasis on cyclone‐sea ice interactions over the Arctic Ocean and adjacent waters. Our study period is from 1979 to 2015 and the reanalyses we are using are ERA‐Interim, ERA5, and CFSR. As for our intensity variable, we use Accumulated Cyclone Energy (ACE), which has commonly been used as an intensity metric in the tropics but has not been utilized for high latitudes before. ACE has the advantage that it relates the cyclone intensity to the kinetic energy of the cyclone and therefore better represents the cyclone impact/interaction with the surface. We performed comparisons with the other more commonly used intensity metrics to assess the use of the ACE metric in the Arctic. Our main findings include increased cyclone counts with higher trends toward the end of the study period. Cold season cyclone counts were also found to be related to decreased sea ice concentration throughout the year. We also show that there is a relationship between the cyclone intensity measured by ACE and the surface state. Less sea ice was shown to be related to higher cyclone intensities. Comparisons between the the three reanalysis data sets are also presented.

Probabilistic estimation of level ice resistance on ships based on sea ice properties measured along summer Arctic cruise paths

The decline of Arctic sea ice makes trans-Arctic transport possible in the summer, which increases the requirement of ice-strengthened vessels. Accurate estimation of ice resistance is necessary for ice-strengthened ship design and this process requires data on various ice properties. The objective of this study was to investigate the probabilistic characteristics of Arctic sea ice properties associated with estimations of ice resistance on ships. To achieve this aim, measurements of ice properties were collected from Arctic voyages conducted in the summers of 2008−2016. Ice thickness and ice concentration were observed along ship routes, and ice flexural strength and effective modulus were determined based on the physical properties of ice. The ice–hull kinetic friction coefficient was estimated according to ship velocity. Statistical analyses were conducted on these ice properties. The results showed that most ice properties can be described using existing statistical models (Burr XII, Beta, Gamma, and generalized extreme value distributions), with the exceptions of ice concentration, ice salinity and temperature, as well as friction coefficient. Furthermore, a Monte Carlo simulation was employed to estimate the ice resistance of a ship moving forward in a level ice zone based on a well-established empirical equation. The results showed that the distribution of ice resistance conformed to a Gamma distribution. The correlation coefficients between ice density and flexural strength as well as effective modulus had negligible effects on the simulation results. This study provides a helpful guide for the design of ice-strengthened ships and related numerical simulations required for summer trans-Arctic navigation.