Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

Sea ice thickness analyses for the Bohai Sea using MODIS thermal infrared imagery

Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009-2010 was investigated in this paper using MODIS night-time thermal infrared imagery. The cloud cover in the imagery was masked out manually. Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation. Weather forcing data was from the European Centre for Medium-Range Weather Forecasts （ECMWF） analyses. The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms. The overall bias and the root mean square error of the MODIS ice thickness are -1.4 cm and 3.9 cm, respectively. The MODIS results under cold conditions （air temperature 〈 -10~C） also agree with the estimated ice growth from Lebedev and Zubov models. The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature, in particular when the sea ice is relatively thin. It is less sensitive to the wind speed. Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

The Arctic Sea Ice Thickness Change in CMIP6’s Historical Simulations

This study assesses sea ice thickness(SIT)from the historical run of the Coupled Model Inter-comparison Project Phase 6(CMIP6).The SIT reanalysis from the Pan-Arctic Ice Ocean Modeling and Assimilation System(PIOMAS)product is chosen as the validation reference data.Results show that most models can adequately reproduce the climatological mean,seasonal cycle,and long-term trend of Arctic Ocean SIT during 1979-2014,but significant inter-model spread exists.Differences in simulated SIT patterns among the CMIP6 models may be related to model resolution and sea ice model components.By comparing the climatological mean and trend for SIT among all models,the Arctic SIT change in different seas during 1979-2014 is evaluated.Under the scenario of historical radiative forcing,the Arctic SIT will probably exponentially decay at-18%(10 yr)-1 and plausibly reach its minimum(equilibrium)of 0.47 m since the 2070s.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Estimation of Design Sea Ice Thickness with Maximum Entropy Distribution by Particle Swarm Optimization Method

The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.

Fhe application of electromagnetic-induction on the measurement of sea ice thickness in the Antarctic

As an important component of the cryosphere, sea ice is very sensitive to climate change. The study of sea ice physics needs accurate sea ice thickness. This paper presents an electromagnetic induction （EM） technique which can be used to measure the sea ice thickness distribution efficiently and its successful application in the Antarctic Neila Fjord. Based on the electrical properties of sea ice and seawater and the application of electromagnetic field theory, this technique can accurately detect the distance between the EM instrument and the ice/water interface to measure the sea ice thickness. Analyzing the apparent conductivity data obtained by the electromagnetic induction technique and drill-hole measurements at same location allows the construction of a transform equation for the apparent conductivity and sea ice thickness. The verification of the calculated sea ice thickness using this equation indicates that the electromagnetic induction technique is able to determine reliable sea ice thickness with an average relative error of only 5.5%. The ice thickness profiles show the sea ice distribution in Neila Fjord is basically level with a thickness of 0.8 - 1.4 m.

Regional characteristics of sea ice thickness in Canadian shelf and Arctic Archipelago measured by Ground Penetrating Radar

Ground Penetrating Radar （GPR） measurements of sea ice thickness including undeformed ice and ridged ice were carried out in the central north Canadian Archipelago in spring 2010. Results have shown a significant spatial heterogeneity of sea ice thickness across the shelf. The undeformed multi-year fast ice of （2.05±0.09） m thick was investigated southern inshore zone of Borden island located at middle of the observational section, which was the observed maximum thickness in the field work. The less thick sea ice was sampled across a flaw lead with the thicknesses of （1.05±0.11） m for the pack ice and （1.24±0.13） m for the fast ice. At the northernmost spot of the section, the undeformed multi-year pack ice was （1.54±0.22） m thick with a ridged ice of 2.5 to 3 m, comparing to the multi-year fast ice with the thickness of （1.67±0.16） m at the southernmost station in the Prince Gustaf Adolf Sea.

Assessment and application of electromagnetic induction method to measure Arctic sea ice thickness

The electromagnetic induction method is widely used to measure sea ice thickness. Based on the electrical properties of sea ice and seawater, the method measures the apparent conductivity, which represents the conductivity of the half-space, and calculates the thickness of the sea ice. During the fourth Chinese National Arctic Research Expedition in summer 2010, an integrated electromagnetic induction system was set up on the icebreaker R/V XUE LONG to measure sea ice thickness along the ship＇s tracks to the north of the Chukchi Sea. The conductivities of sea ice, seawater, and brine were measured and a simple forward model was used to explain the effect of changes in those conductivities on the apparent conductivity over a horizontal layered structure. The results of this analysis indicated that when using the electromagnetic induction method to measure sea ice thickness, the conductivity of sea ice could be neglected and the conductivity of seawater could be treated as a constant. The ice distribution results derived from the electromagnetic induction method showed that the typical sea ice thickness was 160 cm and 90 cm during the outbound and the return legs of the voyage, respectively.

Measurements of sea ice thickness and its subice morphology analysis using ice-penetration radar in the Arctic Ocean

Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The results indicate that the radar wave can penetrate Arctic summer sea ice of over 6 meters thick; and the propagation velocity of the radar wave in sea ice is in the range of 0.142 m·ns -1 to 0.154 m·ns -1 . The radar images display the roughness and micro-relief variation of sea ice bottom surface. These features are closely related to sea ice types, which show that radar survey may be used to identify and classify ice types. Since radar images can simultaneously display the linear profile features of both the upper surface and the underside of sea ice, we use these images to quantify their actual linear length discrepancy. A new length factor is suggested in relation to the actual linear length discrepancy in linear profiles of sea ice, which may be useful in further study of the area difference between the upper surface and bottom surface of sea ice.

A study on remote sensing models of sea ice thickness by microwave radiometry

Extrapolating from the propagation theories of electromagnetic waves in a layered medium, a three-layer medium model is deduced in this paper by using microwave radiometric remote sensing technology which is suitable to first-year sea ice condition of the northern part of China seas. Comparison with in situ data indicates that for microwave wavelength of 10 cm, the coherent model gives a quite good fit result for the thickness of sea ice less than 20 cm, and the incoherent model also works well for thickness within 20 to 40 cm. Based on three theoretical models, the inversion soft ware from microwave remote sensing data for calculating the thickness of sea ice can be set up. The relative complex dielectrical constants of different types of sea ice in the Liaodong Gulf calculated by using these theoretical models and measurement data are given in this paper. The extent of their values is (0. 5-4. 0)-j(0. 07~0. 19).

Retrieval of sea ice thickness using FY-3E/GNOS-II data

Sea ice,a significant component in polar regions,plays a crucial role in climate change through its varying conditions.In Global Navigation Satellite System-Reflectometry(GNSS-R)studies,the observed surface reflectivityΓserves as a tool to examine the physical characteristics of sea ice covers.This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model.However,it is important to note that when Sea Ice Thickness(SIT)becomes thicker,the accuracy of SIT retrieval via this two-layer model begins to decline.In this paper,we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E(FY-3E).Soil Moisture Ocean Salinity(SMOS)data are treated as the reference.The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval.The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m.By comparing the retrieved SITs against reference values,the three-layer model achieved a Root Mean Square Error(RMSE)of 0.149 m and Correlation Coefficient(r)of 0.830,while the two-layer model reported the RMSE of 0.162 m and r value of 0.789.A scheme incorporating both models yielded superior results than either individual model,with the RMSE of 0.137 m and r reaching up to 0.852.This study is the first application of FY-3E for GNSS-R SIT retrieval,combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m.This provides a good reference for the future studies on GNSS-R SIT retrieval.

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Relationship Between Arctic Sea Ice Thickness Distribution and Climate of China

Based on the simulated ice thickness data from 1949 to 1999,monthly mean temperature data from 160 stations,and monthly mean 1 × 1 precipitation data reconstructed from 749 stations in China from 1951 to 2000,the relationship between the Arctic sea ice thickness distribution and the climate of China is analyzed by using the singular value decomposition method.Climate patterns of temperature and precipitation are obtained through the rotated empirical orthogonal function analysis.The results are as follows.（1） Sea ice in Arctic Ocean has a decreasing trend as a whole,and varies with two major periods of 12-14 and 16-20 yr,respectively.（2） When sea ice is thicker in central Arctic Ocean and Beaufort-Chukchi Seas,thinner in Barents-Kara Seas and Baffin Bay-Labrador Sea,precipitation is less in southern China,Tibetan Plateau,and the north part of northeastern China than normal,and vice versa.（3） When sea ice is thinner in the whole Arctic seas,precipitation is less over the middle and lower reaches of Yellow River and north part of northeastern China,more in Tibetan Plateau and south part of northeastern China than normal,and the reverse is also true.（4） When sea ice is thinner in central Arctic Ocean,East Siberian Sea,Beaufort-Chukchi Seas,and Greenland Sea;and thicker in Baffin Bay-Labrador Sea,air temperature is higher in northeastern China,southern Tibetan Plateau,and Hainan Island than normal.（5） When sea ice is thicker in East Siberian Sea 5 months earlier,thinner in Baffin Bay-Labrador Sea 7-15 months earlier,air temperature is lower over the north of Tibetan Plateau and higher in the north part of northwestern China than normal,and a reverse correlation also exists.

Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018

In August 2018,a remarkable polynya was observed off the north coast of Greenland,a perennial ice zone where thick sea ice cover persists.In order to investigate the formation process of this polynya,satellite observations,a coupled iceocean model,ocean profiling data,and atmosphere reanalysis data were applied.We found that the thinnest sea ice cover in August since 1978(mean value of 1.1 m,compared to the average value of 2.8 m during 1978-2017) and the modest southerly wind caused by a positive North Atlantic Oscillation(mean value of 0.82,compared to the climatological value of-0.02) were responsible for the formation and maintenance of this polynya.The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind.Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

Arctic sea ice volume export through the Fram Strait: variation and its effect factors

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.

Arctic sea ice variation in the Northwest Passage in 1979-2017 and its response to surface thermodynamics factors

Sea ice conditions in the Canadian Arctic Archipelago(CAA)play a key role in the navigation of the Northwest Passage(NWP).Limited by observed sea ice thickness data,the research of temporal and spatial variation of sea ice in the NWP is insufficient.Based on the observed sea ice concentration and simulated thickness data,the temporal and spatial characteristics of sea ice concentration,extent and thickness from 1979 to 2017 in the NWP of the CAA were studied.The more specific pathways of the northern and southern routes of the NWP were evaluated.Against the background of the rapid retreat of Arctic sea ice,the 39-year observed sea ice concentration and extent of the NWP exhibited a relatively large decreasing trend in summer and fall,while heavy sea ice conditions were maintained in winter and spring,with slightly increasing trend in some subregions.The sea ice thickness in most subregions of the NWP showed a decreasing trend,with exception of Lancaster Sound.The sea ice thickness was larger along the northern route than the southern routes.The significant correlation(p<0.05)between sea ice and surface air temperature(SAT)and sea surface temperature(SST)in the NWP suggested that the surface thermodynamic factors had a greater impact on sea ice in the summer and fall,and the variations of sea ice concentration were more closely correlated with the surface thermodynamic factors than sea ice thickness.The SST had a higher correlation with sea ice concentration than SAT,while SAT exhibited a higher correlation with sea ice thickness than SST.The remaining sea ice concentration and thickness in the fall,associated with the summer and fall SAT and SST,contributed to the formation of sea ice in the following winter and spring.The heat content and mixed layer depth were also be considered as the vertical thermodynamic factors to the sea ice condition in the NWP.

Navigability of the Northern Sea Route for Arc7 ice-class vessels during winter and spring sea-ice conditions

Sea ice hinders the navigability of the Arctic,especially in winter and spring.However,three Arc7 ice-class Liquefied Natural Gas carrying vessels safely transited the Northern Sea Route(NSR)without icebreaker assistance in January 2021.More and more Arc7 ice-class vessels are putting into the transit services in the NSR.Therefore,it is necessary to analyze sea-ice conditions and their impact on navigation during wintertime,and the future navigability of Arc7 ice-class vessels along the NSR during winter and spring.Based on sea ice datasets from satellite observations and a model using data assimilation,we explored the sea-ice conditions and their impact during the first three successful commercial voyages through the NSR in winter.In addition,we analyzed the sea ice variation and estimated navigability for Arc7 ice-class vessels in the NSR from January to June of the years 2021–2050 using future projections of the sea-ice cover by the Coupled Model Inter-comparison Project Phase 6(CMIP6)models under two emission scenarios(SSP2-4.5 and SSP5-8.5).The results reveal lower sea ice thickness and similar sea ice concentration during these three transits relative to the past 42 years(from 1979 to 2020).We found the thickness has a larger impact on the vessels’speeds than sea ice concentration.Very likely sea ice thickness played a larger role than the sea ice concentration for the successful transit of the NSR in winter 2021.Future projections suggest sea ice thickness will decrease further in most regions of the NSR from January to June under all scenarios enabling increased navigability of the NSR for Arc7 ice-class vessels.Such vessels could transit through the NSR from January to June under all scenarios by 2050,while some areas near the coast of East Siberian Sea remain inaccessible for Arc7 ice-class vessels in spring(April and May).These findings can support the strategic planning of shipping along the NSR in winter and spring.