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 SI...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.展开更多
Arctic sea ice loss and the associated enhanced warming has been related to midlatitude weather and climate changes through modulate meridional temperature gradients linked to circulation. However, contrasting lines o...Arctic sea ice loss and the associated enhanced warming has been related to midlatitude weather and climate changes through modulate meridional temperature gradients linked to circulation. However, contrasting lines of evidence result in low confidence in the influence of Arctic warming on midlatitude climate. This study examines the additional perspectives that palaeoclimate evidence provides on the decadal relationship between autumn sea ice extent (SIE) in the Barents-Kara (B-K) Seas and extreme cold wave events (ECWEs) in southern China. Reconstruction of the winter Cold Index and SIE in the B-K Seas from 1289 to 2017 shows that a significant anti-phase relationship occurred during most periods of decreasing SIE, indicating that cold winters are more likely in low SIE years due to the “bridge” role of the North Atlantic Oscillation and Siberian High. It is confirmed that the recent increase in ECWEs in southern China is closely related to the sea ice decline in the B-K Seas. However, our results show that the linkage is unstable, especially in high SIE periods, and it is probably modulated by atmospheric internal variability.展开更多
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 vertica...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.展开更多
Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM...Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.展开更多
Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents,Arctic ecosystems,and fisheries,as well as Arctic maritime ...Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents,Arctic ecosystems,and fisheries,as well as Arctic maritime navigation.Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6(CMIP6)are investigated in this study.Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend.The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend.The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea.Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042-2089 under the Shared Socioeconomic Pathway 585(SSP5-8.5).Even in the observation-based selected models,the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed,indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.展开更多
The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NC...The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.展开更多
To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregress...To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.展开更多
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 da...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.展开更多
Extratropical cyclones are critical weather systems that affect large-scale weather and climate changes at mid-high latitudes.However,prior research shows that there are still great difficulties in predicting extratro...Extratropical cyclones are critical weather systems that affect large-scale weather and climate changes at mid-high latitudes.However,prior research shows that there are still great difficulties in predicting extratropical cyclones for occurrence,frequency,and position.In this study,mean sea level pressure(MSLP)data from the European Centre for Medium-Range Weather Forecasts(ECMWF)reanalysis(ERA5)are used to calculate the variance statistics of the MSLP to reveal extratropical cyclone activity(ECA).Based on the analysis of the change characteristics of ECA in the Northern Hemisphere,the intrinsic link between ECA in the Northern Hemisphere and Arctic sea ice is explored.The results show that the maximum ECA mainly occurs in winter over the mid-high latitudes in the Northern Hemisphere.The maximum ECA changes in the North Pacific and the North Atlantic,which are the largest variations in the Northern Hemisphere,are independent of each other,and their mechanisms may be different.Furthermore,MSLP is a significant physical variable that affects ECA.The North Atlantic Oscillation(NAO)and North Pacific Index(NPI)are significant indices that impact ECA in the North Atlantic and North Pacific,respectively.The innovation of this paper is to explore the relationship between the activity of extratropical cyclones in the Northern Hemisphere and the abnormal changes in Arctic sea ice for the first time.The mechanism is that the abnormal changes in summer-autumn and winter Arctic sea ice lead to the phase transition of the NPI and NAO,respectively,and then cause the occurrence of ECA in the North Pacific and North Atlantic,respectively.Arctic sea ice plays a crucial role in the ECA in the Northern Hemisphere by influencing the polar vortex and westerly jets.This is the first exploration of ECAs in the Northern Hemisphere using Arctic sea ice,which can provide some references for the in-depth study and prediction of ECAs in the Northern Hemisphere.展开更多
Satellite observations over the past four decades have shown that the long-term trend of Antarctic sea ice extent(SIE)is opposite to the trend of sea ice extent in the Arctic.Arctic sea ice extent continues to decline...Satellite observations over the past four decades have shown that the long-term trend of Antarctic sea ice extent(SIE)is opposite to the trend of sea ice extent in the Arctic.Arctic sea ice extent continues to decline while Antarctic SIE is generally on the rise except for a dramatic decline in 2015–2016.Based on the 40-year climatology from 1981 to 2020,Antarctic SIE anomaly in December 2016 is–2.1×10^(6) km^(2),reaching the minimum since 1979.There are many studies on the cause of this record decline.This present review summarizes the spatial and temporal characters of Antarctic sea ice and recaps major findings on the causes of record decline in 2015–2016 from the perspective of direct thermodynamic and dynamic process of atmosphere and ocean as well as the modulation of climate modes.Finally,the challenges and key scientific problems to be solved in the future of Antarctic sea ice research are presented.展开更多
Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the...Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.展开更多
The successful holding of"Nihao!China"--2024 China Ice&Snow Tourism Overseas Promotion Season has not only deepened the understanding of the people of various countries on the ice&snow cultural and t...The successful holding of"Nihao!China"--2024 China Ice&Snow Tourism Overseas Promotion Season has not only deepened the understanding of the people of various countries on the ice&snow cultural and tourist resources with Chinese characteristics,enhanced the exchanges and cooperation between Chinese and foreign cultural and tourist industries,but also accumulated rich experience for the brand construction of the series of event"Themed Tourism Overseas Promotion Season".展开更多
This study investigated atmospheric responses in mid late winter and early spring to sea ice loss in the Barents and Kara seas using regressions of the January March mean atmosphere on Barents and Kara sea ice area in...This study investigated atmospheric responses in mid late winter and early spring to sea ice loss in the Barents and Kara seas using regressions of the January March mean atmosphere on Barents and Kara sea ice area in November and December.Similar atmospheric circulation responses were obtained from reanalysis data and multimodel ensemble results from the Coupled Model Intercomparison Project Phase 5,i.e.,sea ice anomalies are the dominant factor driving the overlying atmosphere.The results showed that an Arctic Asia dipole structure,with opposite anomalies over the mid-latitudes of Asia and over the adjoining Arctic,appears to be the key atmospheric circulation anomaly influencing the East Asian climate in mid late winter and early spring.展开更多
Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts.Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate,the mechanism c...Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts.Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate,the mechanism connecting sea-ice loss to extensive snow cover is still up for debate.In this study,a significant relationship between sea ice concentration(SIC)in the Barents-Kara(B-K)seas in November and snow cover extent over Eurasia in winter(November-January)has been found based in observational datasets and through numerical experiments.The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation(AO),a deepened East Asia trough,and a shallow trough over Europe.These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures,providing favorable conditions for snowfall.In addition,two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence,which results in increased precipitation due to moisture advection and wind convergence.Furthermore,anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air.This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.展开更多
The global wave model WAVEWATCH III®works well in open water.To simulate the propagation and attenuation of waves through ice-covered water,existing simulations have considered the influence of sea ice by adding ...The global wave model WAVEWATCH III®works well in open water.To simulate the propagation and attenuation of waves through ice-covered water,existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module;however,they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover.To improve the simulation performance of wind wave modules in the marginal ice zone(MIZ),this study proposes a parameterization scheme by directly including the sea ice thickness.Instead of scaling the wind input with the fraction of open water,this new scheme allows partial wind input in ice-covered areas based on the ice thickness.Compared with observations in the Barents Sea in 2016,the new scheme appears to improve the modeled waves in the high-frequency band.Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.展开更多
Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice v...Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.展开更多
Possible influences of the Barents Sea ice anomalies on the Eurasian atmospheric circulation and the East China precipitation distribution in the late spring and early summer (May-June) are investigated by analyzing t...Possible influences of the Barents Sea ice anomalies on the Eurasian atmospheric circulation and the East China precipitation distribution in the late spring and early summer (May-June) are investigated by analyzing the observational data and the output of an atmospheric general circulation model (AGCM). The study indicates that the sea ice condition of the Barents Sea from May to July may be interrelated with the atmospheric circulation of June. When there is more than average sea ice in the Barents Sea, the local geopotential height of the 500-hPa level will decrease, and the same height in the Lake Baikal and Okhotsk regions will increase and decrease respectively to form a wave-chain structure over North Eurasia. This kind of anomalous height pattern is beneficial to more precipitation in the south part of East China and less in the north.展开更多
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...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.展开更多
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)p...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.展开更多
基金The National Natural Science Foundation of China under contract Nos 41830536 and 41925027the Guangdong Natural Science Foundation under contract No.2023A1515011235the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021008.
文摘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.
基金the National Natural Science Foundation of China(Grant No.42101142)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070103)the Young Elite Scientists Sponsorship Program by CAST(Grant No.2022QNRC001).
文摘Arctic sea ice loss and the associated enhanced warming has been related to midlatitude weather and climate changes through modulate meridional temperature gradients linked to circulation. However, contrasting lines of evidence result in low confidence in the influence of Arctic warming on midlatitude climate. This study examines the additional perspectives that palaeoclimate evidence provides on the decadal relationship between autumn sea ice extent (SIE) in the Barents-Kara (B-K) Seas and extreme cold wave events (ECWEs) in southern China. Reconstruction of the winter Cold Index and SIE in the B-K Seas from 1289 to 2017 shows that a significant anti-phase relationship occurred during most periods of decreasing SIE, indicating that cold winters are more likely in low SIE years due to the “bridge” role of the North Atlantic Oscillation and Siberian High. It is confirmed that the recent increase in ECWEs in southern China is closely related to the sea ice decline in the B-K Seas. However, our results show that the linkage is unstable, especially in high SIE periods, and it is probably modulated by atmospheric internal variability.
基金The Chinese Academy of Sciences(CAS)Key Deployment Project of Centre for Ocean Mega-Research of Science under contract No.COMS2020Q07the Open Fund Project of Key Laboratory of Marine Environmental Information Technology,Ministry of Natural Resourcesthe National Natural Science Foundation of China under contract No.41901133.
文摘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.
基金supported by the National Natural Science Foundation of China(No.52171284).
文摘Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.
基金the Chinese Natural Science Foundation(Grant No.41941012)the Basic Scienti fic Fund for National Public Research Institute of China(ShuXingbei Young Talent Program)under contract No.2019S06,Shandong Provincial Natural Science Foundation(ZR2022JQ17)the Tais-han Scholars Program(No.tsqn202211264).
文摘Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents,Arctic ecosystems,and fisheries,as well as Arctic maritime navigation.Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6(CMIP6)are investigated in this study.Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend.The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend.The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea.Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042-2089 under the Shared Socioeconomic Pathway 585(SSP5-8.5).Even in the observation-based selected models,the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed,indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.
基金the National Natural Science Foundation of China[grant number 42088101]the Postgraduate Research and Practice Innovation Program of Jiangsu Province[grant number KYCX22_1147].
基金jointly supported by the National Natural Science Foundation of China (Grant No. 42005037)Special Project of Innovative Development, CMA (CXFZ2021J022, CXFZ2022J008, and CXFZ2021J028)+1 种基金Liaoning Provincial Natural Science Foundation Project (Ph.D. Start-up Research Fund 2019-BS214)Research Project of the Institute of Atmospheric Environment, CMA (2021SYIAEKFMS08, 2020SYIAE08 and 2021SYIAEKFMS09)
文摘The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.
基金The National Key Research and Development Program of China under contract No.2023YFC3107701the National Natural Science Foundation of China under contract No.42375143.
文摘To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.
基金The National Natural Science Foundation of China under contract No.42076235.
文摘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.
基金The National Key Research and Development Program of China under contract No.2022YFF0802002.
文摘Extratropical cyclones are critical weather systems that affect large-scale weather and climate changes at mid-high latitudes.However,prior research shows that there are still great difficulties in predicting extratropical cyclones for occurrence,frequency,and position.In this study,mean sea level pressure(MSLP)data from the European Centre for Medium-Range Weather Forecasts(ECMWF)reanalysis(ERA5)are used to calculate the variance statistics of the MSLP to reveal extratropical cyclone activity(ECA).Based on the analysis of the change characteristics of ECA in the Northern Hemisphere,the intrinsic link between ECA in the Northern Hemisphere and Arctic sea ice is explored.The results show that the maximum ECA mainly occurs in winter over the mid-high latitudes in the Northern Hemisphere.The maximum ECA changes in the North Pacific and the North Atlantic,which are the largest variations in the Northern Hemisphere,are independent of each other,and their mechanisms may be different.Furthermore,MSLP is a significant physical variable that affects ECA.The North Atlantic Oscillation(NAO)and North Pacific Index(NPI)are significant indices that impact ECA in the North Atlantic and North Pacific,respectively.The innovation of this paper is to explore the relationship between the activity of extratropical cyclones in the Northern Hemisphere and the abnormal changes in Arctic sea ice for the first time.The mechanism is that the abnormal changes in summer-autumn and winter Arctic sea ice lead to the phase transition of the NPI and NAO,respectively,and then cause the occurrence of ECA in the North Pacific and North Atlantic,respectively.Arctic sea ice plays a crucial role in the ECA in the Northern Hemisphere by influencing the polar vortex and westerly jets.This is the first exploration of ECAs in the Northern Hemisphere using Arctic sea ice,which can provide some references for the in-depth study and prediction of ECAs in the Northern Hemisphere.
基金supported by Sino-German Mobility Program(Grant no.M0333)Deep Blue Program of Shanghai Jiao Tong University(Grant no.SL2021ZD204)Grant of Shanghai Frontiers Science Center of Polar Science(SCOPS)。
文摘Satellite observations over the past four decades have shown that the long-term trend of Antarctic sea ice extent(SIE)is opposite to the trend of sea ice extent in the Arctic.Arctic sea ice extent continues to decline while Antarctic SIE is generally on the rise except for a dramatic decline in 2015–2016.Based on the 40-year climatology from 1981 to 2020,Antarctic SIE anomaly in December 2016 is–2.1×10^(6) km^(2),reaching the minimum since 1979.There are many studies on the cause of this record decline.This present review summarizes the spatial and temporal characters of Antarctic sea ice and recaps major findings on the causes of record decline in 2015–2016 from the perspective of direct thermodynamic and dynamic process of atmosphere and ocean as well as the modulation of climate modes.Finally,the challenges and key scientific problems to be solved in the future of Antarctic sea ice research are presented.
文摘Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.
文摘The successful holding of"Nihao!China"--2024 China Ice&Snow Tourism Overseas Promotion Season has not only deepened the understanding of the people of various countries on the ice&snow cultural and tourist resources with Chinese characteristics,enhanced the exchanges and cooperation between Chinese and foreign cultural and tourist industries,but also accumulated rich experience for the brand construction of the series of event"Themed Tourism Overseas Promotion Season".
基金The authors are very grateful to the two anonymous reviewers for their comments and suggestions.The monthly reanalysis data were provided by the ECMWF(http://apps.ecmwf.int/datasets)the monthly SST and sea ice data were provided by the Hadley Centre,United Kingdom(http://www.metoffice.gov.uk/hadobs/hadisst/data/download.html)+2 种基金We thank the climate modeling groups attending CMIP5 for making their model output availableThe model data are available from the following website:http://cmip-pcmdi.llnl.gov/cmip5/data_portal.htmlThis work was supported by the Natural Science Foundation of China(Grant nos.41790475 and 41305064).
文摘This study investigated atmospheric responses in mid late winter and early spring to sea ice loss in the Barents and Kara seas using regressions of the January March mean atmosphere on Barents and Kara sea ice area in November and December.Similar atmospheric circulation responses were obtained from reanalysis data and multimodel ensemble results from the Coupled Model Intercomparison Project Phase 5,i.e.,sea ice anomalies are the dominant factor driving the overlying atmosphere.The results showed that an Arctic Asia dipole structure,with opposite anomalies over the mid-latitudes of Asia and over the adjoining Arctic,appears to be the key atmospheric circulation anomaly influencing the East Asian climate in mid late winter and early spring.
基金financially supported by the International Partnership Program of Chinese Academy of Sciences (Grant No. 131B62KYSB20180003)the Frontier Science Key Project of CAS (Grant No. QYZDY-SSW-DQC021)the State Key Laboratory of Cryospheric Science (Grant No. SKLCSZZ-2022)
文摘Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts.Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate,the mechanism connecting sea-ice loss to extensive snow cover is still up for debate.In this study,a significant relationship between sea ice concentration(SIC)in the Barents-Kara(B-K)seas in November and snow cover extent over Eurasia in winter(November-January)has been found based in observational datasets and through numerical experiments.The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation(AO),a deepened East Asia trough,and a shallow trough over Europe.These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures,providing favorable conditions for snowfall.In addition,two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence,which results in increased precipitation due to moisture advection and wind convergence.Furthermore,anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air.This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.
基金funded by the National Key R&D Program of China (Grant No. 2022YFE0106300)the National Natural Science Foundation of China (Grant Nos. 41922044, 42106226 and 42106233)+4 种基金the Fundamental Research Funds for the Central Universities (Grant No. 3132023133)the China Postdoctoral Science Foundation (Grant No. 2020M683022)the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020B1515020025)the fundamental research funds for the Norges Forskningsråd. (Grant No. 328886)the Research Council of Norway for financial support through the research project “Multi-scale integration and digitalization of Arctic sea ice observations and predic tion models (328960)” and basic funding for research institutes
文摘The global wave model WAVEWATCH III®works well in open water.To simulate the propagation and attenuation of waves through ice-covered water,existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module;however,they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover.To improve the simulation performance of wind wave modules in the marginal ice zone(MIZ),this study proposes a parameterization scheme by directly including the sea ice thickness.Instead of scaling the wind input with the fraction of open water,this new scheme allows partial wind input in ice-covered areas based on the ice thickness.Compared with observations in the Barents Sea in 2016,the new scheme appears to improve the modeled waves in the high-frequency band.Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.
基金Supported by the National Natural Science Foundation of China(Nos.41630969,41941013,41806225)the Tianjin Municipal Natural Science Foundation(No.20JCQNJC01290)。
文摘Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.
基金This study was supported jointly by the Project ZKCX2-SW-210the"National Key Programme for Developing Basic Sciences of China"(G1998040900)the National Natural Science Foundation of China under Grant No.40135020.
文摘Possible influences of the Barents Sea ice anomalies on the Eurasian atmospheric circulation and the East China precipitation distribution in the late spring and early summer (May-June) are investigated by analyzing the observational data and the output of an atmospheric general circulation model (AGCM). The study indicates that the sea ice condition of the Barents Sea from May to July may be interrelated with the atmospheric circulation of June. When there is more than average sea ice in the Barents Sea, the local geopotential height of the 500-hPa level will decrease, and the same height in the Lake Baikal and Okhotsk regions will increase and decrease respectively to form a wave-chain structure over North Eurasia. This kind of anomalous height pattern is beneficial to more precipitation in the south part of East China and less in the north.
基金The National Key Research and Development Program of China under contract No.2021YFC2803301the National Natural Science Foundation of China under contract Nos 41976212 and 41830105the Natural Science Foundation of Jiangsu Province under contract No.BK20210193.
文摘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.
基金the National Natural Science Foundation of China(Grant Nos.41922044 and 41941009)the National Key R&D Program of China(Grant No.2019YFA0607004 and 2022YFE0106300)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2020B1515020025 and 2019A1515110295)the Norges Forskningsråd(Grant No.328886).
文摘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.