Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

Promoting China-U.S.Arctic education cooperation:challenges,opportunities and recommendations

Arctic education refers not only to the teaching,but also to research,communication,dissemination as well as popularization of knowledge related to the Arctic.This article reviews joint efforts between Chinese and American educators and researchers to promote cooperation and understanding in Arctic education and research,and examines the facing challenges of China-U.S.Arctic education cooperation which include current political or economic tensions between the two countries,the differing perspectives and priorities on Arctic policy,the disproportion in Arctic scientific research,different research methodologies and discourse system in social science.This article also argues that there are opportunities for the two countries to cooperate in Arctic education.Common goals and interests in the Arctic,Arctic-dedicated institutions with significant Arctic research capabilities and partnerships around the world provide foundations for Arctic education cooperation.The implementation of a new science-based Arctic treaty of the Arctic Council is an opportunity for China-U.S.Arctic education cooperation.As for future cooperation,it suggests that in addition to promoting the direct bilateral cooperation,cooperation within international cooperation platforms and mechanisms,especially within the Arctic Council also needs to be further promoted.

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

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

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

A progress review of black carbon deposition on Arctic snow and ice and its impact on climate change

The rapid warming of the Arctic,accompanied by glacier and sea ice melt,has significant consequences for the Earth’s climate,ecosystems,and economy.Black carbon(BC)deposition on snow and ice can trigger a significant reduction in snow albedo and accelerate melting of snow and ice in the Arctic.By reviewing the published literatures over the past decades,this work provides an overview of the progress in both the measurement and modeling of BC deposition and its impact on Arctic climate change.In summary,the maximum value of BC deposition appears in the western Russian Arctic(26 ng·g^(–1)),and the minimum value appears in Greenland(3 ng·g^(–1)).BC records in the Arctic ice core already peaked in 1920s and 1970s,and shows a regional difference between Greenland and Canadian Arctic.The different temporal variations of Arctic BC ice core records in different regions are closely related to the large variability of BC emissions and transportation processes across the Arctic region.Model simulations usually underestimate the concentration of BC in snow and ice by 2–3 times,and cannot accurately reflect the seasonal and regional changes in BC deposition.Wet deposition is the main removal mechanism of BC in the Arctic,and observations show different seasonal variations in BC wet deposition in Ny-Ålesund and Barrow.This discrepancy may result from varying contributions of anthropogenic and biomass burning(BB)emissions,given the strong influence by BC from BB emissions at Barrow.Arctic BC deposition significantly influences regional climate change in the Arctic,increasing fire activities in the Arctic have made BB source of Arctic BC more crucial.On average,BC in Arctic snow and ice causes an increase of+0.17 W·m^(–2)in radiative forcing and 8 Gt·a^(–1)in runoff in Greenland.As stressed in the latest Arctic Monitoring and Assessment Programme report,reliable source information and long-term and high-resolution observations on Arctic BC deposition will be crucial for a more comprehensive understanding and a better mitigation strategy of Arctic BC.In the future,it is necessary to collect more observations on BC deposition and the corresponding physical processes(e.g.,snow/ice melting,surface energy balance)in the Arctic to provide reliable data for understanding and clarifying the mechanism of the climatic impacts of BC deposition on Arctic snow and ice.

Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

Representation of the Stratospheric Circulation in CRA-40 Reanalysis:The Arctic Polar Vortex and the Quasi-Biennial Oscillation

The representation of the Arctic stratospheric circulation and the quasi-biennial oscillation(QBO)during the period 1981–2019 in a 40-yr Chinese global reanalysis dataset(CRA-40)is evaluated by comparing two widely used reanalysis datasets,ERA-5 and MERRA-2.CRA-40 demonstrates a comparable performance with ERA-5 and MERRA-2 in characterizing the winter and spring circulation in the lower and middle Arctic stratosphere.Specifically,differences in the climatological polar-mean temperature and polar night jet among the three reanalyses are within±0.5 K and±0.5 m s^(–1),respectively.The onset dates of the stratospheric sudden warming and stratospheric final warming events at 10 hPa in CRA-40,together with the dynamics and circulation anomalies during the onset process of warming events,are nearly identical to the other two reanalyses with slight differences.By contrast,the CRA-40 dataset demonstrates a deteriorated performance in describing the QBO below 10 hPa compared to the other two reanalysis products,manifested by the larger easterly biases of the QBO index,the remarkably weaker amplitude of the QBO,and the weaker wavelet power of the QBO period.Such pronounced biases are mainly concentrated in the period 1981–98 and largely reduced by at least 39%in 1999–2019.Thus,particular caution is needed in studying the QBO based on CRA-40.All three reanalyses exhibit greater disagreement in the upper stratosphere compared to the lower and middle stratosphere for both the polar region and the tropics.

Wintertime Arctic Sea-Ice Decline Related to Multi-Year La Niña Events

Arctic sea ice has undergone a significant decline in the Barents-Kara Sea(BKS)since the late 1990s.Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes(mainly referring to La Niña events).The occurrence of multi-year La Niña(MYLA)events has increased significantly in recent decades,and their impact on Arctic sea ice needs to be further explored.In this study,we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña(OTLA)years.The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years.This is because MYLA events tend to be accompanied by a warm Arctic-cold continent pattern with a barotropic high pressure blocked over the Urals region.Consequently,more frequent northward atmospheric rivers intrude into the BKS,intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice.However,in the early winter of OTLA years,a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere,which obstructs the atmospheric rivers to the south of Iceland.We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes.Considering the rapid climate changes in the past,more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

Changes in area fraction of sediment-laden sea ice in the Arctic Ocean during 2000 to 2021

Sediment-laden sea ice plays an important role in Arctic sediment transport and biogeochemical cycles,as well as the shortwave radiation budget and melt onset of ice surface.However,at present,there is a lack of efficient observation approach from both space and in situ for the coverage of Arctic sediment-laden sea ice.Thus,both spatial distribution and long-term changes in area fraction of such ice floes are still unclear.This study proposes a new classification method to extract Arctic sediment-laden sea ice on the basic of the difference in spectral characteristics between sediment-laden sea ice and clean sea ice in the visible band using the MOD09A1 data with the resolution of 500 m,and obtains its area fraction over the pan Arctic Ocean during 2000−2021.Compared with Landsat-8 true color verification images with a resolution of 30 m,the overall accuracy of our classification method is 92.3%,and the Kappa coefficient is 0.84.The impact of clouds on the results of recognition and spatiotemporal changes of sediment-laden sea ice is relatively small from June to July,compared to that in May or August.Spatially,sediment-laden sea ice mostly appears over the marginal seas of the Arctic Ocean,especially the continental shelf of Chukchi Sea and the Siberian seas.Associated with the retreat of Arctic sea ice extent,the total area of sediment-laden sea ice in June-July also shows a significant decreasing trend of 8.99×10^(4) km^(2) per year.The occurrence of sediment-laden sea ice over the Arctic Ocean in June-July leads to the reduce of surface albedo over the ice-covered ocean by 14.1%.This study will help thoroughly understanding of the role of sediment-laden sea ice in the evolution of Arctic climate system and marine ecological environment,as well as the heat budget and mass balance of sea ice itself.

Hydrographic Characteristics and Oceanic Heat Flux in the Upper Arctic Ocean over the Alpha Ridge Observed by the DTOP Platform in 2018 and 2021

In 2018 and 2021,the Drift-Towing Ocean Profilers(DTOP)provided extensive temperature and salinity data on the upper 120m ocean through their drifts over the Alpha Ridge north of the Canada Basin.The thickness and temperature maximum of Alaska Coastal Water(ACW)ranged from 20m to 40m and-1.5℃to-0.8℃,respectively,and the salinity generally maintained from 30.2 to 32.5.Comparison with World Ocean Atlas 2018’s climatology manifested a 40m-thick and warm ACW roughly ex-ceeding the temperature maximum by 0.4–0.5℃in June–August 2021.This anomalously warm ACW was highly related to the ex-pansion of the Beaufort Gyre in the negative Arctic Oscillation phase.During summer,the under-ice oceanic heat flux F_(w)^(OHF)was elevated,with a maximum value of above 25Wm^(-2).F_(w)^(OHF)was typically low in the freezing season,with an average value of 1.2Wm^(-2).The estimates of upward heat flux contributed by ACW to the sea ice bottom F_(w)^(OHF)were in the range of 3–4Wm^(-2)in June–August 2021,when ACW contained a heat content of more than 80MJm^(-2).The heat loss over this period was driven by a weak stratification upon the ACW layer associated with a surface mixed layer(SML)approaching the ACW core.After autumn,F_(w)^(OHF)was reduced(<2 Wm^(-2))except during rare events when it elevated F_(w)^(OHF)slightly.In addition,the intensive and widespread Ekman suction,which created a violent upwelling north of the Canada Basin,was largely responsible for the substantial cooling and thinning of the ACW layer in the summer of 2021.

Four-to Six-Year Periodic Variation of Arctic Sea-Ice Extent and Its Three Main Driving Factors

Besides the rapid retreating trend of Arctic sea-ice extent(SIE),this study found the most outstanding low-frequency variation of SIE to be a 4-6-year periodic variation.Using a clustering analysis algorithm,the SIE in most ice-covered regions was clustered into two special regions:Region-1 around the Barents Sea and Region-2 around the Canadian Basin,which were located on either side of the Arctic Transpolar Drift.Clear 4-6-year periodic variation in these two regions was identified using a novel method called“running linear fitting algorithm”.The rate of temporal variation of the Arctic SIE was related to three driving factors:the regional air temperature,the sea-ice areal flux across the Arctic Transpolar Drift,and the divergence of sea-ice drift.The 4-6-year periodic variation was found to have always been present since 1979,but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005.The joint contribution of the three factors to SIE variation exceeded 83%and 59%in the two regions,respectively,remarkably reflecting their dynamic mechanism.It is proven that the process of El Niño-Southern Oscillation(ENSO)is closely associated with the three factors,being the fundamental source of the 4-6-year periodic variations of Arctic SIE.

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.

Contribution of Surface Waves to Sea Surface Temperatures in the Arctic Ocean

The aim of our study was to examine the contribution of surface waves from WAVEWATCH-III(WW3)to the variation in sea surface temperature(SST)in the Arctic Ocean.The simulated significant wave height(SWH)were validated against the products from Haiyang-2B(HY-2B)in 2021,obtaining a root mean squared error(RMSE)of 0.45 with a correlation of 0.96 and scatter index of 0.18.The wave-induced effects,i.e.,wave breaking and mixing induced by nonbearing waves resulting in changes in radiation stress and Stokes drift,were calculated from WW3,ERA-5 wind,SST,and salinity data from the National Centers for Environmental Prediction and were taken as forcing fields in the Stony Brook Parallel Ocean Model.The results showed that an RMSE of 0.81℃ with wave-induced effects was less than the RMSE of 1.11℃ achieved without the wave term compared with the simulated SST with the measurements from Argos.Considering the four wave effects and sea ice freezing,the SST in the Arctic Ocean decreased by up to 1℃ in winter.Regression analysis revealed that the SWH was linear in SST(values without subtraction of waves)in summer and autumn,but this behavior was not observed in spring or winter due to the presence of sea ice.The interannual variation also presented a negative relationship between the difference in SST and SWH.

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.

Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

Observation of Arctic surface currents using data from a surface drifting buoy

During the 10th Chinese Arctic scientific expedition carried out in the summer of 2019,the surface current in the high-latitude areas of the Arctic Ocean was observed using a self-developed surface drifting buoy,which was initially deployed in the Chukchi Sea.The buoy traversed the Chukchi Sea,Chukchi Abyssal Plain,Mendeleev Ridge,Makarov Basin,and Canada Basin over a period of 632 d.After returning to the Mendeleev Ridge,it continued to drift toward the pole.Overall,the track of the buoy reflected the characteristics of the transpolar drift and Chukchi Slope Current,as well as the inertial flow,cross-ridge surface flow,and even the surface disorganized flow for some time intervals.The results showed that:(1)the transpolar drift mainly occurs in the Chukchi Abyssal Plain,Mendeleev Ridge,and western Canada Basin to the east of the ridge where sea ice concentration is high,and the average northward flow velocity in the region between 79.41°N and 86.32°N was 5.1 cm/s;(2)the average surface velocity of the Chukchi Slope Current was 13.5 cm/s,and while this current moves westward along the continental slope,it also extends northwestward across the continental slope and flows to the deep sea;and(3)when sea ice concentration was less than 50%,the inertial flow was more significant(the maximum observed inertial flow was 26 cm/s,and the radius of the inertia circle was 3.6 km).

Potential of the Arkhangelsk seismic network for European Arctic monitoring

The Arkhangelsk Seismic Network(ASN)of the N.Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences,founded in 2003,includes 10 permanent seismic stations located on the coasts of the White,Barents,and Kara Seas and on the Arctic archipelagos of Novaya Zemlya,Franz Josef Land,and Severnaya Zemlya.The network is registered with the International Federation of Digital Seismograph Networks and the International Seismological Center.We used not only ASN data to process earthquakes but also the waveforms of various international seismic stations.The 13,000 seismic events were registered using ASN data for 2012-2022,and for 5,500 of them,we determined the parameters of the earthquake epicenters from the European Arctic.The spatial distribution of epicenters shows that the ASN monitors not only the main seismically active zones but also weak seismicity on the shelf of the Barents and Kara Seas.The representative magnitude of ASN was ML,rep=3.5.The level of microseismic noise has seasonal variations that affect the registration capabilities of each station included in the ASN and the overall sensitivity of the network as a whole.In summer,the sensitivity of the ASN decreased owing to the increasing microseismic and ambient noises,whereas in winter,the sensitivity of the ASN increased significantly because of the decrease.

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.

Recent advances in studies on changes in Arctic sea ice microstructure and implications to thermodynamic modeling

The study of Arctic sea ice has traditionally been focused on large-scale such as reductions of ice coverage,thickness,volumes and sea ice regime shift.Research has primarily concentrated on the impact of large-scale external factors such as atmospheric and oceanic circulations,and solar radiation.Additionally,Arctic sea ice also undergoes rapid micro-scale evolution such as gas bubbles formation,brine pockets migration and massive formation of surface scattering layer.Field studies like CHINARE(2008-2018)and MOSAiC(2019-2020)have confirmed these observations,yet the full understanding of those changes remain insufficient and superficial.In order to cope better with the rapidly changing Arctic Ocean,this study reviews the recent advances in the microstructure of Arctic sea ice in both field observations and laboratory experiments,and looks forward to the future objectives on the microscale processes of sea ice.The significant porosity and the cyclical annual and seasonal shifts likely modify the ice's thermal,optical,and mechanical characteristics,impacting its energy dynamics and mass balance.Current thermodynamic models,both single-phase and dual-phase,fail to accurately capture these microstructural changes in sea ice,leading to uncertainties in the results.The discrepancy between model predictions and actual observations strongly motivates the parameterization on the evolution in ice microstructure and development of next-generation sea ice models,accounting for changes in ice crystals,brine pockets,and gas bubbles under the background of global warming.It helps to finally achieve a thorough comprehension of Arctic sea ice changes,encompassing both macro and micro perspectives,as well as externaland internal factors.