Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

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.

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.

Impacts of Ice-Ocean Stress on the Subpolar Southern Ocean:Role of the Ocean Surface Current

The mechanical influences involved in the interaction between the Antarctic sea ice and ocean surface current(OSC)on the subpolar Southern Ocean have been systematically investigated for the first time by conducting two simulations that include and exclude the OSC in the calculation of the ice-ocean stress(IOS), using an eddy-permitting coupled ocean-sea ice global model. By comparing the results of these two experiments, significant increases of 5%, 27%, and 24%, were found in the subpolar Southern Ocean when excluding the OSC in the IOS calculation for the ocean surface stress,upwelling, and downwelling, respectively. Excluding the OSC in the IOS calculation also visibly strengthens the total mechanical energy input to the OSC by about 16%, and increases the eddy kinetic energy and mean kinetic energy by about38% and 12%, respectively. Moreover, the response of the meridional overturning circulation in the Southern Ocean yields respective increases of about 16% and 15% for the upper and lower branches;and the subpolar gyres are also found to considerably intensify, by about 12%, 11%, and 11% in the Weddell Gyre, the Ross Gyre, and the Australian-Antarctic Gyre, respectively. The strengthened ocean circulations and Ekman pumping result in a warmer sea surface temperature(SST), and hence an incremental surface heat loss. The increased sea ice drift and warm SST lead to an expansion of the sea ice area and a reduction of sea ice volume. These results emphasize the importance of OSCs in the air-sea-ice interactions on the global ocean circulations and the mass balance of Antarctic ice shelves, and this component may become more significant as the rapid change of Antarctic sea ice.

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.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

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.

Experimental Research of Hull Vibration of a Full-Scale River Icebreaker

A series of tests of a full-scale river icebreaker was conducted to investigate the characteristics of ice-induced hull vibration.The test was conducted when the river icebreaker was operating in an ice-covered river at temperatures of-4 to 0°C with ice thicknesses of 300 to 400 mm.In the tests,the ice condition and icebreaker speed were chosen as the main influence factors.By analyzing the measured test data,we identified some important points regarding ice-induced hull vibration.When the river icebreaker navigates in an area with complete ice coverage,the peak value of the acceleration amplitude spectrum is highest.Also,the vibration response excited by the icebreaking load in an area with complete ice coverage exhibits more frequencies.With an increase in icebreaker speed,the vibration acceleration response gradually increases.However,habitability of the bow region on the main and driving decks is maintained due to the harmless vibration levels.In addition,the icebreaking operation of the river icebreaker causes violent local vibration of the grillage on the main deck.

Some recent advances in remote sensing-based monitoring of changes in the Greenland Ice Sheet

The mass balance of the Greenland Ice Sheet(GrIS)plays a crucial role in global sea level change.Since the 1960s,remote sensing missions have been providing extensive and continuous observation data for change monitoring of the GrIS.In this paper,we present our recent research results from remote sensing-based GrIS change monitoring.First,historical satellite data are processed and used to fill data gaps and are combined with existing partial maps,completing an ice velocity map of the GrIS from the 1960s to 1980s.This map provides valuable data for estimating the historical mass balance of Greenland.Second,the monthly gravimetry-based mass balance of the GrIS from 2002 to 2020 is estimated by combining Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow On(GRACE-FO)data.It is found that the GrIS has lost a total mass of approximately 4443±75 Gt during this period.Third,based on Global Land Ice Measurements from Space(GLIMS),an updated Greenland glacier inventory is achieved utilizing data collected between 2006 and 2020.This inventory provides more detailed and up-to-data glacier boundaries of Greenland.Overall,these advances provide essential data support for estimating the mass balance of the GrIS,contributing to the advancement of research on global sea level change.

Brightness temperature simulation of subarctic lake-ice evolution by HIGHTSI and SMRT model

Microwave brightness temperature(TB)can be used to retrieve lake ice thickness in the Arctic and subarctic regions.However,the accuracy of the retrieval is affected by the physical properties of lake ice.To improve the understanding of how lake ice affects TB,numerical modeling was applied.This study combined a physical thermodynamic ice model HIGHTSI with a microwave radiation transfer model SMRT to simulate the TB and lake ice evolution in 2002-2011 in Hulun Lake,China.The reanalyzed meteorological data were used as atmospheric forcing.The ice season was divided into the growth period,the slow growth period,and the ablation period.The simulations revealed that TB was highly sensitive to ice thickness during the ice season,especially vertical polarization measurement at 18.7 GHz.The quadratic polynomial fit for ice thickness to TB outperformed the linear fit,regardless of whether lake ice contained bubbles or not.A comparison of the simulated TB with space-borne TB showed that the simulated TB had the best accuracy during the slow growth period,with a minimum RMSE of 4.6 K.The results were influenced by the bubble radius and salinity.These findings enhance comprehension of the interaction between lake ice properties(including ice thickness,bubbles,and salinity)and TB during ice seasons,offering insights to sea ice in the Arctic and subarctic freshwater observations.

Distribution of ice thickness and subglacial topography of the "Chinese Wall" around Kunlun Station,East Antarctica

As fundamental parameters of the Antarctic Ice Sheet,ice thickness and subglacial topography are critical factors for studying the basal conditions and mass balance in Antarctica.During CHINARE 24（the 24 th Chinese National Antarctic Research Expedition,2007/08）,the research team used a deep ice-penetrating radar system to measure the ice thickness and subglacial topography of the ＂Chinese Wall＂ around Kunlun Station,East Antarctica.Preliminary results show that the ice thickness varies mostly from 1600 m to 2800 m along the ＂Chinese Wall＂,with the thickest ice being 3444 m,and the thinnest ice 1255 m.The average bedrock elevation is 1722 m,while the minimum is just 604 m.Compared with the northern side of the ice divide,the ice thickness is a little greater and the subglacial topography lower on the southern side,which is also characterized by four deep valleys.We found no basal freeze-on ice in the Gamburtsev Subglacial Mountains area,subglacial lakes,or water bodies along the ＂Chinese Wall＂.Ice thickness and subglacial topography data extracted from the Bedmap 2 database along the ＂Chinese Wall＂ are consistent with our results,but their resolution and accuracy are very limited in areas where the bedrock fluctuates intensely.The distribution of ice thickness and subglacial topography detected by ice-penetrating radar clarifies the features of the ice sheet in this ＂inaccessible＂ region.These results will help to advance the study of ice sheet dynamics and the determination of future locations of the GSM＇s geological and deep ice core drilling sites in the Dome A region.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Advances in ice avalanches on the Tibetan Plateau

As some of the greatest natural disasters in the cryosphere,ice avalanches(IAs)seriously threaten lives and cause catastrophic damage to the resource environment,but a comprehensive overview of the state of knowledge on IAs remains lacking.We summarized 63 IAs on the Tibetan Plateau(TP)since the 20th century,of which,over 20 IAs occurred after the 21st century.The distributions of IAs are mainly concentrated in the southeastern and northwestern TP,and the occurrence time of IAs is mostly concentrated from July to September.We highlight recent advances in mechanical properties and genetic mechanisms of IAs and emphasize that temperature,rainfall,and seismicity are the inducing factors.The failure modes of IAs are summarized into 6 categories by examples:slip pulling type,slip toppling type,slip breaking type,water level collapse type,cave roof collapse type,and wedge failure type.Finally,we deliver recommendations concerning the risk assessment and prediction of IAs.The results provide important scientific value for addressing climate change and resisting glacier-related hazards.

毛竹ICE基因家族的全基因组鉴定及低温胁迫下的表达模式分析

【目的】对毛竹Phyllostachys edulis ICE基因家族进行鉴定及分析,找出响应毛竹抗寒关键家族成员,研究毛竹ICE基因的生物学功能、响应低温胁迫的分子机制及遗传转化,为提高毛竹抗寒性奠定理论基础。【方法】利用生物信息学方法分析毛竹ICE基因家族成员,并对4、0、−2℃低温处理0(对照)、0.5、1.0、24.0、48.0 h的毛竹生理指标和ICE基因的表达模式进行分析。【结果】共鉴定了4个毛竹ICE基因。保守结构域和多重序列比对分析表明:PeICE基因结构高度相似。系统发育关系及启动子顺式作用元件分析显示:PeICE基因与水稻Oryza sativa亲缘关系更近,同时存在大量与非生物胁迫相关的顺式作用元件。活性氧自由基(ROS)染色发现随着处理时间增长,ROS染色逐渐加深,但是其0℃处理24.0 h、−2℃处理1.0 h后染色逐渐减弱。脯氨酸(Pro)质量摩尔浓度、超氧化物歧化酶(SOD)活性显示:4和0℃条件下,Pro质量摩尔浓度和SOD活性整体增加,但−2℃时低于对照。过氧化物酶(POD)活性显示:在3个低温处理下均增加。ICE基因表达模式分析发现:4、0℃处理时PeICE表达量整体增加,且都以PeICE3增量最明显;而−2℃处理下PeICE整体表达量水平低于对照。【结论】随着温度降低和处理时间增强,毛竹受到的损伤不断增强,其内酶活系统以及ICE基因积极响应低温胁迫,其中,PeICE3对低温胁迫最为敏感,但在−2℃时,ICE基因表达量并未增加,推测该基因家族响应了寒冷胁迫而非冷冻胁迫。

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.

Experimental Study on the Resistance of a Transport Ship Navigating in Level Ice

This study investigates the resistance of a transport ship navigating in level ice by conducting a series of model tests in an ice tank at Tianjin University. The laboratory-scale model ship was mounted on a rigid carriage using a one-directional load cell and then towed through an ice sheet at different speeds. We observed the ice-breaking process at different parts of the ship and motion of the ice floes and measured the resistances under different speeds to determine the relationship between the ice-breaking process and ice resistance. The bending failure at the shoulder area was found to cause maximum resistance. Furthermore, we introduced the analytical method of Lindqvist （1989） for estimating ice resistance and then compared these calculated results with those from our model tests. The results indicate that the calculated total resistances are higher than those we determined in the model tests.

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).

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.

Interaction between sea ice/iceberg and ship structures: A review

For ship structural design and good maneuverability in an ice-covered sea, the local and global load of ice cover on ships should be well understood. This paper reviews the extensive work done on ice loads on ships, including： （a） Ice pressure and local load determination based on field and model tests; （b） Global ice loads on ships from full-scale field observations, model tests and numerical models under different ice conditions （level ice and pack ice） and ship operations （maneuvering and mooring）. Spe- cial attention is paid to the discrete element simulation of global ice loads on ships; and （c） Analytical solutions and numerical models of impact loads of icebergs on ships for polar navigation. Finally, research potential in these areas is discussed.