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.

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.

Joint Probability Analysis and Prediction of Sea Ice Conditions in Liaodong Bay

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.

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

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.

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.

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.

Temporal and spatial variation characteristics of Antarctic sea ice and the causes of its record decline during 2015–2016: a review

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.

Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation

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.

Simulations and Projections of Winter Sea Ice in the Barents Sea by CMIP6 Climate Models

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.

Impact of the Shrinkage of Arctic Sea Ice on Eurasian Snow Cover Changes in 1979-2021

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.

A Statistical Linkage between Extreme Cold Wave Events in Southern China and Sea Ice Extent in the Barents-Kara Seas from 1289 to 2017

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.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

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.

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.

A Cross-Seasonal Linkage between Arctic Sea Ice and Eurasian Summertime Temperature Fluctuations

This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high- pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic-Barents-Kara Sea-Central Asia regions and manifests a more evident southward propagation from the Barents- Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region.

Separation of Atmospheric Circulation Patterns Governing Regional Variability of Arctic Sea Ice in Summer

In recent decades,Arctic summer sea ice extent(SIE)has shown a rapid decline overlaid with large interannual variations,both of which are influenced by geopotential height anomalies over Greenland(GL-high)and the central Arctic(CA-high).In this study,SIE along coastal Siberia(Sib-SIE)and Alaska(Ala-SIE)is found to account for about 65%and 21%of the Arctic SIE interannual variability,respectively.Variability in Ala-SIE is related to the GL-high,whereas variability in Sib-SIE is related to the CA-high.A decreased Ala-SIE is associated with decreased cloud cover and increased easterly winds along the Alaskan coast,promoting ice-albedo feedback.A decreased Sib-SIE is associated with a significant increase in water vapor and downward longwave radiation(DLR)along the Siberian coast.The years 2012 and 2020 with minimum recorded ASIE are used as examples.Compared to climatology,summer 2012 is characterized by a significantly enhanced GL-high with major sea ice loss along the Alaskan coast,while summer 2020 is characterized by an enhanced CA-high with sea ice loss focused along the Siberian coast.In 2012,the lack of cloud cover along the Alaskan coast contributed to an increase in incoming solar radiation,amplifying ice-albedo feedback there;while in 2020,the opposite occurs with an increase in cloud cover along the Alaskan coast,resulting in a slight increase in sea ice there.Along the Siberian coast,increased DLR in 2020 plays a dominant role in sea ice loss,and increased cloud cover and water vapor both contribute to the increased DLR.

Will the summer sea ice in the Arctic reach a tipping point?

The Arctic sea-ice cover has decreased in extent,area,and thickness over the last six decades.Most global climate models project that the summer sea-ice extent(SIE)will decline to less than 1 million(mill.)km^(2) in this century,ranging from 2030 to the end of the century,indicating large uncertainty.However,some models,using the same emission scenarios as required by the Paris Agreement to keep the global temperature below 2°C,indicate that the SIE could be about 2 mill.km^(2) in 2100 but with a large uncertainty of±1.5 mill.km^(2).Here,the authors take another approach by exploring the direct relationship between the SIE and atmospheric CO_(2) concentration for the summer-fall months.The authors correlate the SIE and In(CO_(2)/CO_(2)r)during the period 1979-2022,where CO_(2)r is the reference value in 1979.Using these transient regression equations with an R2 between 0.78 and 0.87,the authors calculate the value that the CO_(2) concentration needs to reach for zero SIE.The results are that,for July,the CO_(2) concentration needs to reach 691±16.5 ppm,for August 604±16.5 ppm,for September 563±17.5 ppm,and for October 620±21 ppm.These values of CO_(2)for an ice-free Arctic are much higher than the targets of the Paris Agreement,which are 450 ppm in 2060 and 425 ppm in 2100,under the IPCC SSP1-2.6 scenario.If these targets can be reached or even almost reached,the "no tipping point"hypothesis for the summer SIE may be valid.

Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data

The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

Characteristics of extratropical cyclone variability in the Northern Hemisphere and their response to rapid changes in Arctic sea ice

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.

Investigating the impact of atmospheric parameters on sea ice variability in and around Svalbard

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.

Arctic summertime anticyclonic circulation mode and its influence on substantial sea ice depletion:a review

The summertime anticyclonic circulation mode(SACM)is related to recent substantial loss of sea ice in the Arctic.This review outlines the potential causes of the SACM and considers its influence on sea ice depletion.Local triggers(i.e.,sea ice loss and sea surface temperature(SST)variation)and spatiotemporal teleconnections(i.e.,extratropical cyclone intrusion,tropical and mid-latitude SST anomalies,and winter atmospheric circulation preconditions)are discussed.The influence of the SACM on the dramatic loss of sea ice is emphasized through inspection of relevant dynamic(i.e.,Ekman drift and export)and thermodynamic(i.e.,moisture content,cloudiness,and associated changes in radiation)mechanisms.Moreover,the motivation for investigation of the underlying physical mechanisms of the SACM in response to the recent substantial sea ice depletionis also clarified through an attempt to better understand the shifting ice-atmosphere interaction in the Arctic during summer.Therecord low extent of sea ice in September 2012 could be reset in the near future if the SACM-like scenario continues to exist during summer in the Arctic troposphere.