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).

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.

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.

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.

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.

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.

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.

Definition of Arctic and Antarctic Sea Ice Variation Index

It is well known that varying of the sea ice not only in the Antarctic but also in the Arctic has an active influence on the globe atmosphere and ocean. In order to understand the sea ice variation in detail, for the first time, an objective index of the Arctic and Antarctic sea ice variation is defined by projecting the monthly sea ice concentration anomalies poleward of 20°N or 20°S onto the EOF (empirical orthogonal function)-1 spatial pattern. Comparing with some work in former studies of polar sea ice, the index has the potential for clarifying the variability of sea ice in northern and southern high latitudes.

On the Association between Spring Arctic Sea Ice Concentration and Chinese Summer Rainfall:A Further Study

In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.

Arctic Sea Ice and Eurasian Climate:A Review

The Arctic plays a fundamental role in the climate system and has shown significant climate change in recent decades,including the Arctic warming and decline of Arctic sea-ice extent and thickness. In contrast to the Arctic warming and reduction of Arctic sea ice, Europe, East Asia and North America have experienced anomalously cold conditions, with record snowfall during recent years. In this paper, we review current understanding of the sea-ice impacts on the Eurasian climate.Paleo, observational and modelling studies are covered to summarize several major themes, including： the variability of Arctic sea ice and its controls; the likely causes and apparent impacts of the Arctic sea-ice decline during the satellite era,as well as past and projected future impacts and trends; the links and feedback mechanisms between the Arctic sea ice and the Arctic Oscillation/North Atlantic Oscillation, the recent Eurasian cooling, winter atmospheric circulation, summer precipitation in East Asia, spring snowfall over Eurasia, East Asian winter monsoon, and midlatitude extreme weather; and the remote climate response（e.g., atmospheric circulation, air temperature） to changes in Arctic sea ice. We conclude with a brief summary and suggestions for future research.

Arctic sea ice in CMIP5 climate model projections and their seasonal variability

This paper is focused on the seasonality change of Arctic sea ice extent （SIE） from 1979 to 2100 using newly available simulations from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. A new approach to compare the simulation metric of Arctic SIE between observation and 31 CMIP5 models was established. The approach is based on four factors including the climatological average, linear trend of SIE, span of melting season and annual range of SIE. It is more objective and can be popularized to other comparison of models. Six good models （GFDL-CM3, CESM1-BGC, MPI-ESM-LR, ACCESS-1.0, HadGEM2-CC, and HadGEM2-AO in turn） are found which meet the criterion closely based on above approach. Based on ensemble mean of the six models, we found that the Arctic sea ice will continue declining in each season and firstly drop below 1 million km2 （defined as the ice-free state） in September 2065 under RCP4.5 scenario and in September 2053 under RCP8.5 scenario. We also study the seasonal cycle of the Arctic SIE and find out the duration of Arctic summer （melting season） will increase by about I00 days under RCP4.5 scenario and about 200 days under RCPS.5 scenario relative to current circumstance by the end of the 21st century. Asymmetry of the Arctic SIE seasonal cycle with later freezing in fall and early melting in spring, would be more apparent in the future when the Arctic climate approaches to ＂tipping point＂, or when the ice-free Arctic Ocean appears. Annual range of SIE （seasonal melting ice extent） will increase almost linearly in the near future 30-40 years before the Arctic appears ice-free ocean, indicating the more ice melting in summer, the more ice freezing in winter, which may cause more extreme weather events in both winter and summer in the future years.

The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China:2020/21 La Niña and 2020 Arctic Sea Ice Loss

Several consecutive extreme cold events impacted China during the first half of winter 2020/21,breaking the low-temperature records in many cities.How to make accurate climate predictions of extreme cold events is still an urgent issue.The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes,further influencing the cold conditions in China.However,climate models failed to predict these two ocean environments at expected lead times.Most seasonal climate forecasts only predicted the 2020/21 La Niña after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1-2 month advancement.In this work,the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored.For the 2020/21 La Niña prediction,through sensitivity experiments involving different atmospheric-oceanic initial conditions,the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event.A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Niña development from the early spring of 2020.For predicting the Arctic sea ice loss in autumn 2020,an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model,which swept abnormally hot air over Siberia into the Arctic Ocean,is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.

Modeling of Arctic Sea Ice Variability During 1948–2009: Validation of Two Versions of the Los Alamos Sea Ice Model(CICE)

The Los Alamos sea ice model(CICE) is used to simulate the Arctic sea ice variability from 1948 to 2009. Two versions of CICE are validated through comparison with Hadley Centre Global Sea Ice and Sea Surface Temperature(Had ISST) observations. Version 5.0 of CICE with elastic-viscous-plastic(EVP) dynamics simulates a September Arctic sea ice concentration(SASIC) trend of –0.619 × 1012 m2 per decade from 1969 to 2009, which is very close to the observed trend(-0.585 × 1012 m2 per decade). Version 4.0 of CICE with EVP dynamics underestimates the SASIC trend(-0.470 × 1012 m2 per decade). Version 5.0 has a higher correlation(0.742) with observation than version 4.0(0.653). Both versions of CICE simulate the seasonal cycle of the Arctic sea ice, but version 5.0 outperforms version 4.0 in both phase and amplitude. The timing of the minimum and maximum sea ice coverage occurs a little earlier(phase advancing) in both versions. Simulations also show that the September Arctic sea ice volume(SASIV) has a faster decreasing trend than SASIC.

Distinct Modes of Winter Arctic Sea Ice Motion and Their Associations with Surface Wind Variability

Using monthly mean sea ice velocity data obtained from the International Arctic Buoy Programme （IABP） for the period of 1979–1998 and the monthly mean NCEP/NCAR re-analysis dataset （1960–2002）, we investigated the spatiotemporal evolution of the leading sea ice motion mode （based on a complex correlation matrix constructed of normalized sea ice motion velocity） and their association with sea level pressure （SLP） and the predominant modes of surface wind field variability. The results indicate that the leading winter sea ice motion mode’s spatial evolution is characterized by two alternating and distinct sea ice modes, or their linear combination. One mode （M1） shows a nearly closed cyclonic or anti-cyclonic circulation anomaly in the Arctic Basin and its marginal seas, resembling to a large extent the response of sea ice motion to the Arctic Oscillation （AO）, as many previous studies have revealed. The other mode （M2） displays a coherent cyclonic or anti-cyclonic circulation anomaly with its center close to the Laptev Sea, which has not been identified in previous observational studies. In fact, M1 and M2 respectively reflect the responses of sea ice motion to two predominant modes of winter surface wind variability north of 70 ？ N, which well correspond, with slight differences, to the first two modes of EOF analysis of winter monthly mean SLP north of 70 ？ N. These slight differences in SLP anomalies lead to a difference of M2 from the response of sea ice motion to the dipole anomaly. Although the AO significantly influences sea ice motion, it is not crucial for the existence of M1. The new sea ice motion mode （M2） has the largest variance and clearly differs from the response of winter monthly mean sea ice motion to the dipole anomaly in SLP fields, and corresponding SLP anomalies also show differences compared to the dipole anomaly. This study indicates that in the Arctic Basin and its marginal seas, slight differences in SLP anomaly patterns can force distinctly different sea ice motion anomalies.

A Preliminary Investigation of Arctic Sea Ice Negative Freeboard from in-situ Observations and Radar Altimetry

The negative freeboard of sea ice(i.e., the height of ice surface below sea level) with subsequent flooding is widespread in the Southern Ocean, as opposed to the Arctic, due to the relatively thicker ice and thinner snow. In this study, we used the observations of snow and ice thickness from 103 ice mass balance buoys(IMBs) and NASA Operation IceBridge Aircraft Missions to investigate the spatial distribution of negative freeboard of Arctic sea ice. The Result showed that seven IMBs recorded negative freeboards, which were sporadically located in the seas around Northeast Greenland, the Central Arctic Ocean, and the marginal areas of the Chukchi–Beaufort Sea. The observed maximum values of negative freeboard could reach-0.12 m in the seas around Northeast Greenland. The observations from IceBridge campaigns also revealed negative freeboard comparable to those of IMBs in the seas around North Greenland and the Beaufort Sea. We further investigated the large-scale distribution of negative freeboard using NASA CryoSat-2 radar altimeter data, and the result indicates that except for the negative freeboard areas observed by IMBs and IceBridge, there are negative freeboards in other marginal seas of the Arctic Ocean. However, the comparison of the satellite data with the IMB data and IceBridge data shows that the Cryosat-2 data generally overestimate the extent and magnitude of the negative freeboard in the Arctic.

The Interdecadal Variation Characteristics of Arctic Sea Ice Cover-ENSO-East Asian Monsoon and Their Interrelationshipat Quasi-Four Years Time Scale

Interdecadal and quasi-four years variation characterstics of Arctic sea for cover, ENSO and East Asian monsoon index(EAMI) are analyzed based on Singular Spectrum Analys. (SSA), lead-lag correlation and EOF for the past four decades. Results show that the Arctic sea for cover decreased in the early 1970s, several years earlier than that of global SSTA increase in the mid 1970s, which indicates that recent warming over the Northern Hemisphere firstly begins in the Arctic region in the 1970s. Great change of the East Asian monsoon intensity from stronger to weaker in summer (from weaker to stronger in winter) took place in the mid 1970s response to the abrupt modulation of SSTA particularly in the tropical eastern Pacific.Focus on the quasi-four years oscillation,close relationship is found among the sea ice cover, ENSO and EAMI based on lead-lag correlation. In which, the correlation coefficient reaches its maximum when the index of NINO3 SSTA variation takes 6 and 9 months lead of the western Pacific subtropical high and sea for cover index in Section-Ⅲ. Their interaction can be explained in the framework of asymmetric Walker circulation anomaly and Western Pacific Northern Pole (WPN) teleconnection pattern in the context of quasi-four years oscillation.

Delving into the relationship between autumn Arctic sea ice and central–eastern Eurasian winter climate

Whether recent Arctic sea ice loss is responsible for recent severe winters over mid-latitude continents has emerged as a major debate among climate scientists owing to short records of observations and large internal variability in mid- and high-latitudes. In this study, the authors divide the evolution of autumn Arctic sea ice extent during 1979–2014 into three epochs, 1979–1986（high）, 1987–2006（moderate）, and 2007–2014（low）, using a regime shift identification method. The authors then compare the associations between autumn Arctic sea ice and winter climate anomalies over central and eastern Eurasia for the three epochs with a focus on extreme events. The results show robust and detectable signals of Arctic sea ice loss in weather and climate over western Siberia and East Asia. Associated with sea ice loss, the latitude（speed） of the jet stream shifts southward（reduces）,the wave extent amplifies, and blocking high events increase over the Ural Mountains, leading to increased frequency of cold air outbreaks extending from central Asia to northeast China. These associations bear a high degree of similarity to the observed atmospheric anomalies during the low sea ice epoch. By contrast, the patterns of atmospheric anomalies for the high sea ice epoch are different from those congruent with sea ice variability, which is related to the persistent negative phase of the Arctic Oscillation.

The role of diminishing Arctic sea ice in increased winter snowfall over northern high-latitude continents in a warming environment

Large parts of North America, Europe, Siberia, and East Asia have experienced cold snaps and heavy snowfalls for the past few winters, which have been linked to rapid decline of Arctic sea ice. Although the role of reduction in Arctic sea ice in recent cold and snowy winters is still a matter of debate, there is considerable interest in determining whether such an emerging climate feedback will persist into the future in a warming environment. Here we show that increased winter snowfall would be a robust feature throughout the 21st century in the northeastern Europe, central and northern Asia and northern North America as projected by current-day climate model simulations under the medium mitigation scenario. We argue that the increased winter snowfall in these regions during the 21st century is due primarily to the diminishing autumn Arctic sea ice （largely externally forced）. Variability of the winter Arctic Oscillation （dominant mode of natural variability in the Northern Hemisphere）, in contrast, has little contribution to the increased winter snowfall. This is evident in not only the multi-model ensemble mean, but also each individual model （not model-dependent）. Our findings reinforce suggestions that a strong sea ice-snowfall feedback might have emerged, and would be enhanced in coming decades, increasing the chance of heavy snowfall events in northern high-latitude continents.

Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China

The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979-2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6)are examined by comparison with observational and reanalysis datasets.Most of the models reasonably represent the Beaufort Gyre(BG)and Transpolar Drift Stream(TDS)in the spatial patterns of their long-term mean sea ice drift,while the detailed location,extent,and strength of the BG and TDS vary among the models.About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern.About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern.In the observation/reanalysis,however,the sea ice drift pattern does not match well with the surface ocean current pattern.All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic.For the Arctic basin-wide spatial average,five of the nine models overestimate the Arctic long-term(1979-2014)mean sea ice drift speed in all months.Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn.The increases are weaker than those in the observation.This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China.