Sea Ice Age Research Articles

Seasonal and Interannual Variations in the Greenland Sea Ice Age Composition in the Winter Period

Seasonal and Interannual Variations in the Greenland Sea Ice Age Composition in the Winter Period

СЕЗОННЫЕ И МЕЖГОДОВЫЕ ИЗМЕНЕНИЯ ВОЗРАСТНОЙ СТРУКТУРЫ ЛЕДЯНОГО ПОКРОВА ГРЕНЛАНДСКОГО МОРЯ В ЗИМНИЙ ПЕРИОД

Seasonal and interannual changes in the Greenland Sea ice age composition during winter months for seven standard age categories are analyzed. To estimate the amount of the ice of different age categories, the digital archive of the region’s ice charts for the period 1997-2022 produced by the Arctic and Antarctic Research Institute is used. Statistical insignificance of linear trends has been revealed for the main age categories of the Greenland Sea ice. A comparison of the obtained estimates of the variability in the amount of the ice of different age categories and earlier studies shows that the quantitative changes in the Greenland Sea ice age composition started before 1997. The data on the ice age composition over a 25-year observation period alone are insufficient to draw an unambiguous conclusion about a decrease in the Greenland Sea ice thickness.

Variability of Arctic Sea ice age and its relationship with atmospheric circulation patterns

Since at least the 1990s, global warming has caused the decline of multi-year ice (MYI) in the Arctic, which has made the Arctic Ocean more susceptible to the effects of climate change and weather processes. In this study, an analysis of the variations in Arctic sea ice age was carried out based on the results of an Empirical Orthogonal Function (EOF) analysis. Variations in three time periods were investigated. The first period (1984-1995) is characterized by high sea ice age, with antiphase variation processes in the eastern and western parts of the Arctic Ocean. The second period (1996-2012) represents a rapid reduction in ice age, characterized by a gradual replacement of MYI in the Arctic Ocean with seasonal sea ice, resulting in a decreased extent of MYI. The third period (2013-2022) is characterized by low sea ice age, with ice age being in a significantly negative phase. Furthermore, trend-like changes were weak during this period. During the first period, the spatial distribution and significant variations in ice age were driven by cyclonic wind anomalies associated with the Arctic Oscillation (AO) atmospheric pressure modes. The cyclonic wind fields in winter and anticyclonic wind fields in summer collectively influenced the sea ice. This led to a decrease/increase in ice age in the eastern/western Arctic, and antiphase variations were apparent. During the second study period, winter ice age variations were driven by cyclonic wind anomalies associated with the AO atmospheric pressure modes, leading to ice divergence and a decrease in ice age. In summer, both thermodynamic (Arctic warming) and dynamic (DA) mechanisms play a role in modulating the changes of sea ice age, while Arctic warming is the primary driver. The DA generated anomalous wind patterns, characterized by the Beaufort Gyre and Transpolar Drift, which resulted in the outflow of MYI from the Arctic under the influence of meridional winds. During the third study period, the influence of AO/DA and air temperature on sea ice age changes is greatly weakened.

Noise levels in a changing Arctic Ocean and its implications for security

Arctic Ocean is undergoing an “Atlantification” of its oceanographic properties. Sea ice retreat and reduction of sea ice age will affect its underwater soundscape, with anthropogenic noise expected to increase due to the exploitation of new maritime routes. The CMRE’s Environmental and Operational Effectiveness Programme conducted a study of the new Arctic oceanographic conditions and ambient noise by deploying in 2021 and in 2022 different moorings equipped with passive acoustic recorders and oceanographic sensors. Data did not show a clear relation between sea-ice concentration and noise levels in the marginal ice zone. However, noise levels indicate that the possible presence of transmissions ducts might increase high frequency noise in the subsurface area. Seasonality and variations in the overall soundscapes were also evident by the presence of sounds from biological sources. In 2023, with the support of the NATO Office of the Chief Scientist, CMRE started a long-term scientific endeavor to study how climate change might affect the Alliance’s security in the maritime domain. A significant research effort is focused on the Arctic, and in June–July 2023 CMRE deployed three deep moorings for monitoring the acoustic-oceanographic conditions in the long term. [Work supported by the NATO Allied Command Transformation.]

Possibilities of investigating ice development of the Kara sea based on CryoSat-2 and SMOS data

Sea ice age is a proxy for thickness, which can be obtained through the use of satellite data. The paper presents the results of comparison of CryoSat-2, SMOS and CryoSat-2 & SMOS fusion data with detailed ice charts (October–April, 2010–2018). The AARI ice charts were chosen as references for comparison because they integrate knowledge and data from various sources, including expert analysis of operational satellite information, in-situ measurements at coastal stations, data on ice conditions from ships of the Northern Sea Route (NSR). The division of satellite data elements into classes (stages of development) was performed according to the maximum likelihood classifier. The recognition result for each stage of development was evaluated by means of three criteria, in accordance with the class value of Mode, Median and Mean. The effectiveness of satellite data in determining the Kara Sea ice thickness varies depending on the sea ice stage of development and winter season time. Four stages of development (old ice, thick first-year ice, medium first-year ice, nilas) showed the best recognition results. Although the CryoSat-2 mission was designed primarily to detect climate-dependent variations of the thickness of floating ice, in terms of statistical recognition of the Kara Sea ice stages of development, CryoSat-2 data can also be used to retrieve the thickness of thick first-year ice (January–April) and the thickness of medium first-year ice (January–February). For the remaining stages within the study area, the altimetry method shows a significant uncertainty, which can be resolved for nilas, thin first-year ice and medium first-year ice (March) by using SMOS data and CryoSat-2 & SMOS fusion data. In general, altimetric data, radiometric data and combination thereof can be applied in the complex analysis of all available information to ensure hydrometeorological and navigation support. Also, it is proposed to use the data of the ICESat-2 laser altimeter and to make a general comparison with in-situ measurements.

Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

Impact of Changing Arctic Sea Ice Extent, Sea Ice Age, and Snow Depth on Sea Salt Aerosol From Blowing Snow and the Open Ocean for 1980–2017

AbstractWe evaluate the effects of rapidly changing Arctic sea ice conditions on sea salt aerosols (SSA) produced by oceanic wave‐breaking and the sublimation of wind‐lofted salty blowing snow on sea ice. We use the GEOS‐Chem chemical transport model to assess the influence of changing extent of the open ocean, multi‐year sea ice (MYI), first‐year sea ice (FYI), and snow depths on SSA emissions for 1980–2017. We combine snow depths from the Lagrangian snow‐evolution model (SnowModel‐LG) together with an empirically‐derived snow salinity function of snow depth to derive spatially and temporally varying snow surface salinity over Arctic FYI. We find that pan‐Arctic SSA surface mass concentrations have increased by 6%–12% decade−1 during the cold season (November–April) and by 7%–11% decade−1 during the warm season (May–October). The cold season trend is due to increasing blowing snow SSA originating from FYI: as MYI is replaced by FYI with thinning snow depths, snow surface salinity increases by more than 11% decade−1. During the warm season, rapid sea ice loss and thus increasing open ocean SSA are the cause of modeled SSA trends. Observations of SSA mass concentrations at Alert, Canada display positive trends during the cold season (10%–12% decade−1), consistent with our pan‐Arctic simulations. During fall, Alert observations show a negative trend (−18% decade−1), due to locally decreasing wind speeds and thus lower open ocean emissions. These significant changes in SSA concentrations could potentially affect past and future bromine explosions and Arctic climate feedbacks.

Inter-comparison and evaluation of Arctic sea ice type products

Abstract. Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×106 to 0.49×106 km2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of -0.001×106 km2 in first-year ice (FYI) extent and -0.02×106 km2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscatter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.

The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs

Sea ice export through the Fram Strait is crucial in the dynamic evolution of Arctic sea ice and can further modulate Arctic sea ice mass balance as well as the ocean thermohaline circulation. In this study, based on outputs from a parameter-optimized and fully physical ocean–sea ice coupled model and sea ice age observation, we estimate sea ice volume (SIV) flux and its age evolution via the Fram Strait. The estimate of mean annual SIV flux is about 1605 315 km3 yr−1 without a significant trend for 1979–2021. Combining with sea ice age data, the variation of the sea ice age and its corresponding SIV flux are obtained for 1984–2020. The SIV flux of 1st-year ice significantly increases as expected, but it still contributes very little to the total flux in the 2010s with a proportion of 3.5%. SIV fluxes of different ages in multi-year ice present diverse variations. The proportions of 2nd-year ice and 3rd-year ice in the annual SIV flux show an extreme increase from 6.8% and 25.0% in the 1980s to 49.0% and 38.8% in the 2010s, respectively, while the proportions of 4th-year ice and 5th-year and older (5+ year) ice significantly decrease from 22.8% and 45.0% in the 1980s to 7.1% and 1.6% in the 2010s, respectively. Meanwhile, the prevailing age of annual volume export via Fram Strait shifts from 4th-year and 5+ year ice to 2nd-year and 3rd-year ice around 2007/2008. It is worth noticing that the variation in Fram Strait ice export modulates the variation in Arctic SIV prior to 2008, but the reverse is true after 2008, indicating a decreasing influence of Fram Strait SIV export on Artic SIV variability with decreasing sea ice age. The results are beneficial to promote the understanding of the evolution of Fram Strait SIV export under the warming Arctic.

Wintertime Emissivities of the Arctic Sea Ice Types at the AMSR2 Frequencies

The surface effective emissivities of Arctic sea ice are calculated using Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. These emissivities are analyzed for stable winter conditions during the months of January–May and November and December of 2020 for several main sea ice types defined with the sea ice maps of the Arctic and Antarctic Research Institute (AARI). The sea ice emissivities are derived from the AMSR2 data using the radiation transfer model for a non-scattering atmosphere and ERA5 reanalysis data. The emissivities are analyzed only for areas of totally consolidated sea ice of definite types. Probability distribution functions are built for the emissivities and their functions for such sea ice types as nilas, young ice, thin first-year (FY) ice, medium FY ice, thick FY ice and multi-year ice. The emissivity variations with frequency are estimated for each of the considered sea ice type for all seven months. The variations are calculated both for the emissivities and for their gradients at the AMSR2 channel frequencies. Obtained emissivities turned out to be generally lower than reported previously in scientific studies, whereas the emissivity variability values proved to be much larger than was known before. For all FY ice types, at all the frequencies, an increase in the emissivity at the beginning of winter and its decrease by the end of May are observed. The emissivity gradients demonstrate noticeable decreases with sea ice age, and their values may be used in sea ice classification algorithms based on the AMSR2 data.

Deep neural network-based spatial gap-filling of MODIS ice surface temperatures over the Arctic using satellite and reanalysis data

ABSTRACT Because ice surface temperature (IST) controls snow melt, sea ice growth and air–ocean heat exchange, it is a key parameter in analyses of the Arctic climate system. However, optical satellite-based IST data often have missing values due to the satellite orbit, clouds and polar night. In this study, we estimated IST using a deep neural network (DNN) algorithm based on meteorological, sea ice and geometric variables that are strongly correlated with IST. Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra IST data were used, and the input data were 2 m air temperature (Ta), 30-year averaged Ta (Ta climatology), total column water vapour (TCWV), solar zenith angle (SZA), local solar noon angle (LSN) and latitude. The data were classified into six cases according to sea ice age (SIA) and Ta to create an efficient DNN model that supplemented the IST data missing from the existing dataset. Model validation based on the MODIS IST data revealed a correlation coefficient of 0.94, root mean square error of 3.54 K and relative root mean square error of 1.35%, showing high accuracy.

Ice age composition in the Barents sea

The paper presents the key results of investigating Barents Sea ice age composition during the winter season, from the beginning of ice formation in October to its termination in May. To analyze the seasonal and interannual changes in the amount of ice of different age categories, we used ice charts for the Barents Sea for the period 1997–2021, produced by the Arctic and Antarctic Research Institute. The age composition of the ice cover in the Barents Sea is represented by seven standard ice categories (thickness ranges). The areas of ice of different age categories were calculated for a ten-day time interval (in percentage of the total ice area). The results are provided for three parts of the Barents Sea: western, northeastern and southeastern.The interannual changes in the amount of ice in relative fractions of ice of different age categories in the ice cover of the Barents Sea do not show significant trends for the period 1997–2021. Thus, with the observed reduction in the Barents Sea total ice area, the amount of ice of different age categories ranges within the limits of its own natural variability. Therefore, it is impossible to draw a definite conclusion about a decrease in ice cover thickness in the Barents Sea based only on data on the ice age composition over a 24-year period of observations analyzed in this study. On comparing the estimates obtained in this study of the age structure of the ice cover in the Barents Sea with those of the previous studies on this subject, we can argue that its average thickness at the beginning of the 21st century decreased, compared to the period 1971–1976. Taking into account the statistical insignificance of the trends in interannual variations in the amount of ice of different age gradations, one can maintain that quantitative changes in the age structure of the Barents Sea ice cover began earlier than 1997.

Thick and old sea ice in the Beaufort Sea during summer 2020/21 was associated with enhanced transport

The Arctic Ocean has seen a remarkable reduction in sea ice coverage, thickness and age since the 1980s. These changes are most pronounced in the Beaufort Sea, with a transition around 2007 from a regime dominated by multi-year sea ice to one with large expanses of open water during the summer. Using satellite-based observations of sea ice, an atmospheric reanalysis and a coupled ice-ocean model, we show that during the summers of 2020 and 2021, the Beaufort Sea hosted anomalously large concentrations of thick and old ice. We show that ice advection contributed to these anomalies, with 2020 dominated by eastward transport from the Chukchi Sea, and 2021 dominated by transport from the Last Ice Area to the north of Canada and Greenland. Since 2007, cool season (fall, winter, and spring) ice volume transport into the Beaufort Sea accounts for ~45% of the variability in early summer ice volume—a threefold increase from that associated with conditions prior to 2007. This variability is likely to impact marine infrastructure and ecosystems.

A New Structure for the Sea Ice Essential Climate Variables of the Global Climate Observing System

Abstract Climate observations inform about the past and present state of the climate system. They underpin climate science, feed into policies for adaptation and mitigation, and increase awareness of the impacts of climate change. The Global Climate Observing System (GCOS), a body of the World Meteorological Organization (WMO), assesses the maturity of the required observing system and gives guidance for its development. The Essential Climate Variables (ECVs) are central to GCOS, and the global community must monitor them with the highest standards in the form of Climate Data Records (CDR). Today, a single ECV—the sea ice ECV—encapsulates all aspects of the sea ice environment. In the early 1990s it was a single variable (sea ice concentration) but is today an umbrella for four variables (adding thickness, edge/extent, and drift). In this contribution, we argue that GCOS should from now on consider a set of seven ECVs (sea ice concentration, thickness, snow depth, surface temperature, surface albedo, age, and drift). These seven ECVs are critical and cost effective to monitor with existing satellite Earth observation capability. We advise against placing these new variables under the umbrella of the single sea ice ECV. To start a set of distinct ECVs is indeed critical to avoid adding to the suboptimal situation we experience today and to reconcile the sea ice variables with the practice in other ECV domains.

Simulating tropospheric BrO in the Arctic using an artificial neural network

An intriguing natural phenomenon occurs every polar spring, namely the bromine explosion, in which plumes of tropospheric bromine monoxide (BrO) are formed. These plumes are observed in the BrO vertical column densities (VCDs), retrieved from satellite sensors. Tropospheric BrO depletes tropospheric ozone and facilitates the deposition of mercury. Bromine molecules are mainly released from young sea ice, and meteorological parameters determine the formation and evolution of enhanced BrO VCD plumes. Due to the complexity of the physicochemical processes involved in the bromine explosion, the modeling of tropospheric BrO VCDs in chemical transport models is challenging and not yet adequate. The first of its type, this study demonstrates the potential of using an artificial neural network (ANN), which uses meteorological parameters and sea ice age as inputs to simulate and predict tropospheric BrO VCDs in the Arctic. The ANN is trained and validated using a 22-year satellite remote sensing dataset of Arctic tropospheric BrO VCDs. A generally satisfactory spatial agreement between observed and simulated tropospheric BrO VCDs is observed. However, the magnitude of the observed BrO VCD plumes is underestimated. Air temperature and mean sea level pressure are the most important parameters influencing the magnitude of tropospheric BrO VCD simulations. Although the changing spatial distribution of tropospheric BrO VCDs over time is well captured, the trend reported in the observations of tropospheric BrO VCDs is not reproduced by the ANN, suggesting that additional parameters not included in the ANN also influence the formation of tropospheric BrO VCD plumes.

The influence of global climate change on accumulation and toxicity of persistent organic pollutants and chemicals of emerging concern in Arctic food webs.

This review summarizes current understanding of how climate change-driven physical and ecological processes influence the levels of persistent organic pollutants (POPs) and contaminants of emerging Arctic concern (CEACs) in Arctic biota and food webs. The review also highlights how climate change may interact with other stressors to impact contaminant toxicity, and the utility of modeling and newer research tools in closing knowledge gaps on climate change-contaminant interactions. Permafrost thaw is influencing the concentrations of POPs in freshwater ecosystems. Physical climate parameters, including climate oscillation indices, precipitation, water salinity, sea ice age, and sea ice quality show statistical associations with POPs concentrations in multiple Arctic biota. Northward range-shifting species can act as biovectors for POPs and CEACs into Arctic marine food webs. Shifts in trophic position can alter POPs concentrations in populations of Arctic species. Reductions in body condition are associated with increases in levels of POPs in some biota. Although collectively understudied, multiple stressors, including contaminants and climate change, may act to cumulatively impact some populations of Arctic biota. Models are useful for predicting the net result of various contrasting climate-driven processes on POP and CEAC exposures; however, for some parameters, especially food web changes, insufficient data exists with which to populate such models. In addition to the impact of global regulations on POP levels in Arctic biota, this review demonstrates that there are various direct and indirect mechanisms by which climate change can influence contaminant exposure, accumulation, and effects; therefore, it is important to attribute POP variations to the actual contributing factors to inform future regulations and policies. To do so, a broad range of habitats, species, and processes must be considered for a thorough understanding and interpretation of the consequences to the distribution, accumulation, and effects of environmental contaminants. Given the complex interactions between climate change, contaminants, and ecosystems, it is important to plan for long-term, integrated pan-Arctic monitoring of key biota and ecosystems, and to collect ancillary data, including information on climate-related parameters, local meteorology, ecology, and physiology, and when possible, behavior, when carrying out research on POPs and CEACs in biota and food webs of the Arctic.

Analysis of the Spatial and Temporal Variation of Sea Ice and Connectivity in the NEP of the Arctic in Summer in Hot Years

Climate warming has enabled the Arctic region to achieve seasonal navigation, and sea ice concentration is an important factor affecting the navigation of the Arctic waterways. This article uses the Arctic sea ice concentration data of the three highest temperatures in 2016, 2019, and 2020, combined with the Arctic summer sea level pressure, wind field, temperature, temperature anomaly, ice age, and sea ice movement data to analyze the spatial and temporal variation of sea ice and connectivity in the Northeast Passage (NEP) of the Arctic in Summer in three hot years, and summarizes the causes of sea ice anomalies. The results show that: (1) the summer Arctic sea ice extent in 2016, 2019 and 2020 were all lower than the multi-year average sea ice extent, and the summer sea ice extent in 2020 had the largest change trend; (2) in October of these three years, the sea ice was all negative anomalies, extending the opening time of the NEP; (3) when the sea ice concentration was 30% as the threshold, the navigation period of the NEP in 2016 was from mid-August to late October, 2019 was from the beginning of August to mid-October, 2020 was from the end of July to the end of October, and 2020 was the longest year since the opening of the NEP; (4) when the sea ice concentration was 10% as the threshold, the navigation period of the NEP in 2016 was from the end of August to the end of October, 2019 was from early August to mid-October, and 2020 was from the beginning of August to the end of October; (5) the key navigable areas of the NEP in the past three years were the central waters of the East Siberian Sea, the New Siberian Islands and the Vilkitsky Strait; (6) the navigation period of the NEP in 2016, 2019 and 2020 was longer. The main reasons were that the temperature of the NEP in the past 3 years was relatively high, the wind was weak, the sea ice movement had little effect, and the sea ice age in the key navigable areas was first year ice, which was easy to melt, which greatly promoted the opening of the NEP.

Analysis of Sea Ice Timing and Navigability along the Arctic Northeast Passage from 2000 to 2019

The ablation of Arctic sea ice makes seasonal navigation possible in the Arctic region, which accounted for the apparent influence of sea ice concentration in the navigation of the Arctic route. This paper uses Arctic sea ice concentration daily data from January 1, 2000, to December 31, 2019. We used a sea ice concentration threshold value of 40% to define the time window for navigating through the Arctic Northeast Passage (NEP). In addition, for the year when the navigation time of the NEP is relatively abnormal, we combined with wind field, temperature, temperature anomaly, sea ice age and sea ice movement data to analyze the sea ice conditions of the NEP and obtain the main factors affecting the navigation of the NEP. The results reveal the following: (1) The sea ice concentration of the NEP varies greatly seasonally. The best month for navigation is September. The opening time of the NEP varies from late July to early September, the end of navigation is concentrated in mid-October, and the navigation time is basically maintained at more than 30 days. (2) The NEP was not navigable in 2000, 2001, 2003 and 2004. The main factors are the high amount of multi-year ice, low temperature and the wind field blowing towards the Vilkitsky Strait and sea ice movement. The navigation time in 2012, 2015 and 2019 was longer, and the driving factors were the high temperature, weak wind and low amount of one-year ice. The navigation time in 2003, 2007 and 2013 was shorter, and the influencing factors were the strong wind field blowing towards the Vilkitsky Strait. (3) The key navigable areas of the NEP are the central part of the East Siberian Sea and the Vilkitsky Strait, and the Vilkitsky Strait has a greater impact on the NEP than the central part of the East Siberian Sea. The main reason for the high concentration of sea ice in the central part of the East Siberian Sea (2000 and 2001) was the large amount of multi-year ice. The main reason for the high concentration of sea ice in the Vilkitsky Strait (2000 to 2004 and 2007, 2013) was the strong offshore wind in summer, all of which were above 4 m s−1, pushing the sea ice near the Vilkitsky Strait to accumulate in the strait, thus affecting the opening of the NEP.

Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

Changes in shipping navigability in the Canadian Arctic between 1972 and 2016

There have been rapid recent reductions in sea ice age and extent in the Canadian Arctic, but little previous analysis of how this has impacted the navigability of Arctic shipping. In this study we analyze how navigability changed over the period 1972–2016 by converting Canadian Ice Service ice charts to shipping navigability charts for different hull strength classifications based on the Arctic Ice Regime Shipping System. Analysis focuses on the southern route of the Northwest Passage, and the Arctic Bridge route across Hudson Bay, for changes in early-season (∼25 June), mid-season (∼3 September), and late-season (∼15 October) conditions. Results reveal that there has been a marked easing in shipping navigability for all vessels over the past decade, driven by reductions in the area and age of sea ice, particularly across the southern route of the Northwest Passage. Both medium (Type B) and little (Type E) ice strengthened vessels were able to transit the full length of this route in the middle part of the shipping season in 2012–2016, but not in 1972–1976 or 1992–1996.