Snow and sea ice thermodynamics in the Arctic： Model validation and sensitivity study against SHEBA data

Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model （HIGHTSI）. Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance.     

Status of the Recent Declining of Arctic Sea Ice Studies

In the past 30 years, a large-scale change occurred in the Arctic climatic system, which had never been observed before 1980s. At the same time, the Arctic sea ice experienced a special evolution with more and more rapidly dramatic declining. In this circumstance, the Arctic sea ice became a new focus of the Arctic research. The recent advancements about abrupt change of the Arctic sea ice are reviewed in this paper .The previous analyses have demonstrated the accelerated declining trend of Arctic sea ice extent in the past 30 years, based on in-situ and satellite-based observations of atmosphere, as well as the results of global and regional climate simulations. Especially in summer, the rate of decrease for the ice extents was above 10% per decade. In present paper, the evolution characteristics of the arctic sea ice and its possible cause are discussed in three aspects, i.e. the sea ice physical properties, the interaction process of sea ice, ocean and atmosphere and its response and feedback mechanism to global and arctic climate system.     

北极海冰范围时空变化及其与海温气温间的数值分析

本文利用美国国家冰雪中心提供的1989-2014年海冰范围资料,分析了北极海冰范围的年际变化和季节变化规律。分析发现,北极海冰范围呈减少趋势,每年减小\begin{array}{c}5.91\times {10}^4\mathit{k}{m}^2\end{array},夏季减少趋势显著,冬季减少趋势弱。北极海冰范围显现相对稳定的季节变化规律,海冰的结冰和融化主要发生在各个边缘海,夏季期间的海冰具有融化快、冻结快的特征。结合海温、气温数据,进行北极海冰范围与海温、气温间的数值分析,结果表明北极海冰范围变化通过影响北极海温变化进而影响北极气温变化。海冰范围的季节变化滞后于海温和气温的季节变化。基于北极考察走航海温气温数据,进行楚科奇海海冰范围线与海温气温间的数值分析,发现楚科奇海海冰范围线所在区域的海温、气温与纬度高低、离陆地远近有关。     

Increasing Winter Conductive Heat Transfer in the Arctic Sea-ice-covered Areas:1979–2014

Sea ice is a quite sensitive indicator in response to regional and global climate changes. Based on monthly mean PanArctic Ice Ocean Modeling and Assimilation System(PIOMAS) sea ice thickness fields, we computed the conductive heat flux(CHF) in the Arctic Ocean in the four winter months(November–February) for a long period of 36 years(1979–2014). The calculated results for each month manifest the increasing extension of the domain with high CHF values since 1979 till 2014. In 2014, regions of roughly 90% of the central Arctic Ocean have been dominated by the CHF values larger than 18 Wm~(-2)(November–December) and 12 Wm~(-2)(January–February), especially significant in the shelf seas around the Arctic Ocean. Moreover, the population distribution frequency(PDF) patterns of the CHF with time show gradually peak shifting toward increased CHF values. The spatiotemporal patterns in terms of the trends in sea ice thickness and other three geophysical parameters, surface air temperature(SAT), sea ice thickness(SIT), and CHF, are well coupled. This suggests that the thinner sea ice cover preconditions for the more oceanic heat loss into atmosphere(as suggested by increased CHF values), which probably contributes to warmer atmosphere which in turn in the long run will cause thinner ice cover. This represents a positive feedback mechanism of which the overall effects would amplify the Arctic climate changes.     

基于NSIDC海冰产品的FY北极海冰数据集优化

北极海冰对全球气候起着非常重要的调制作用,海冰范围是海冰监测的基本参数。近40年,北极地区持续变暖,北极海冰显著减少,进而引发北极自然环境恶化、北半球极端天气频发、全球海平面上升等一系列环境和气候问题。准确获取北极海冰范围及其演变趋势,确定海冰变化对全球气候系统的响应,是研究和预测全球气候变化趋势的关键之一。HasISST和OISST海冰数据集在海冰监测中应用最为广泛,可为北极地区长时间序列海冰变化研究提供基础数据,但这2套数据集空间分辨率相对较低,应用于北极关键区对中国气候响应研究方面存在很大的局限,为解决这一问题和弥补国内海冰监测微波遥感数据的空白,2011年6月27日,国家卫星气象中心（National Satellite Meteorological Center, NSMC）发布了FY（Fengyun, FY）北极海冰数据集,该数据集利用搭载在FY卫星上的微波成像仪（Microwave Radiation Imager, MWRI）数据,使用Enhance NASA Team算法制作,该算法利用前向辐射传输模型模拟北极地区4种海表类型（海水、新生冰、一年冰和多年冰）在不同大气条件下MWRI辐射亮温,进而得到每种大气条件下0~100%的海冰覆盖度查找表（海冰覆盖度每次增加1%）,通过观测值与模拟值的比对得到海冰覆盖度,由该数据集计算得到的北极海冰范围在大部分区域与实际情况相符。该产品虽已进行通道间匹配误差修正和定位精度偏差订正,但由于其搭载的微波成像仪（Microwave Radiation Imager, MWRI）天线长度有限,造成传感器探测到的地物回波信号相对较弱,难以区分海冰和近岸附近的陆地,影响了该数据集的精度和应用。为解决这一问题,本文基于美国冰雪中心（National Snow and Ice Data Center, NSIDC）发布的海冰产品对FY海冰数据集进行优化,NSIDC产品利用判断矩阵对海岸线附近的像元进行识别,并对误差像元进行不同程度的修正,由NSIDC产品计算得到的北极海冰范围与实际情况更为符合。数据集优化大大提高了FY海冰数据集的精度,研究结果表明,优化后FY海冰数据集与NSIDC产品相关系数高达0.9997,且二者日、月、年平均最大海冰范围偏差仅为3.5%、1.9%、0.9%,且FY海冰数据集优化过程对其较好的空间分异特征无明显影响。该数据集可正确地反映北极海冰范围及其变化情况,且海岸线附近海冰的分布情况更准确,可为北极海冰变化研究提供可靠的基础数据。     

Contribution of a pathway through the Arctic Ocean to the re cent reduction in the ice cover

The sea ice cover in the Arctic Ocean has been reducing and hit the low record in the summer of 2007. The anomaly was extremely large in the Pacific sector. The sea level height in the Bering Sea vs. the Greenland Sea has been analyzed and compared with the current meter data through the Bering Strait. A recent peak existed as a consequence of atmospheric circulation and is considered to contribute to inflow of the Pacific Water into the Arctic Basin. The timing of the Pacific Water inflow matched with the sea ice reduction in the Pacific sector and suggests a significant increase in heat flux. This component should be included in the model prediction for answering the question when the Arctic sea ice becomes a seasonal ice cover.     

A coupled regional Arctic sea ice-ocean model: configuration and application

A regional sea ice-ocean coupled model for the Arctic Ocean was developed, based on the MITgcm ocean circulation model and classical Hibler79 type two categorythermodynamics-dynamics sea ice model. The sea ice dynamics and thermodynamicswere considered based on Viscous-Plastic (VP) and Winton three-layer models, respectively. A detailed configuration of coupled model has been introduced. Special attention has been paid to the model grid setup, subgrid paramerization, ice-ocean coupling and open boundary treatment. The coupled model was then applied and two test run examples were presented. The first model run was a climatology simulation with 10 years (1992?002) averaged NCAR/NCEP reanalysis data as atmospheric forcing. The second model run was a seasonal simulation for the period of 1992?007. The atmospheric forcing was daily NCAR/NCEP reanalysis. The climatology simulation captured the general pattern of the sea ice thickness distribution of the Arctic, i.e., the thickest sea ice is situated around the CanadaArchipelago and the north coast of the Greenland. For the second model run, themodeled September Sea ice extent anomaly from 1992?007 was highly correlated with the observations, with a linear correlation coefficient of 0.88. Theminimum of the Arctic sea ice area in the September of 2007 was unprecedented. The modeled sea ice area and extent for this minimum was overestimated relative to the observations. However, it captured the general pattern of the sea ice retreat.     

The role of Pacific water in the dramatic retreat of arctic sea ice during summer 2007

A model study is conducted to examine the role of Pacific water in the dramatic retreat of arctic sea ice during summer 2007. The model generally agrees with the observations in showing considerable seasonal and interannual variability of the Pacific water inflow at Bering Strait in response to changes in atmospheric circulation. During summer 2007 anomalously strong southerly winds over the PaCific sector of the Arctic Ocean strengthen the ocean circulation and bring more Pacific water into the Arctic than the recent （2000-2006） average. The simulated summer （3 months ） 2007 mean Pacific water inflow at Bering Strait is 1.2 Sv, which is the highest in the past three decades of the simulation and is 20% higher than the recent average. Particularly, the Pacific water inflow in September 2007 is about 0.5 Sv or 50% above the 2000-2006 average. The strengthened warm Pacific water inflow carries an additional 1.0 x 1020 Joules of heat into the Arctic, enough to melt an additional 0.5 m of ice over the whole Chukchi Sea. In the model the extra summer oceanic heat brought in by the Pacific water mainly stays in the Chukchi and Beaufort region, contributing to the warming of surface waters in that region. The heat is in constant contact with the ice cover in the region in July through September. Thus the Pacific water plays a role in ice melting in the Chukchi and Beaufort region all summer long in 2007, likely contributing to up to O. 5 m per month additional ice melting in some area of that region.     

Experiment of near surface layer parameters in ice camp over Arctic Ocean

Estimates of near surface layer parameters over 78°N drifting ice in ice camp over the Arctic ocean are made using bulk transfer methods with the data from the experiments operated by the Chinese Arctic Scientific Expedition in August 22-September 3,2003.The results show that the net radiation received by the snow surface is only 3.6 W/m2,among which the main part transported into atmosphere in term of sensible heat and latent heat,which account for 52% and 31% respectively,and less part being transported to deep ice in the conductive process.The bulk transfer coefficient of momentum is about 1.16×10-3 in the near neutral layer,which is a little smaller than that obtained over 75°N drifting ice.However,to compare with the results observed over 75°N drifting ice over the Arctic Ocean in 1999,it can be found that the thermodynamic and momentum of interactions between sea and air are significant different with latitudes,concentration and the scale of sea ice.It is very important on considering the effect of sea-air-ice interaction over the Arctic Ocean when studying climate modeling.     

北极熊栖息地稳定性的遥感评估方法

北极熊是北极最重要的哺乳动物之一,近年来数量却在减少。海冰作为北极熊狩猎、活动和繁殖的平台,是其栖息地的重要组成部分。因此其种群栖息地变化主要依赖于海冰变化。本文基于美国雪冰中心的海冰密集度和NOAA提供的ETOPO1基岩数据,分析了北极海冰密集度、开阔水域面积、海冰消退时间、海冰出现时间、开阔水域季节长度的年际变化,进而评价北极熊栖息地的稳定性。结果表明,海冰密集度呈现降低的趋势,开阔水域面积增大,多年冰数量减少,大多变为一年冰。海冰消退时间提前,海冰出现时间延后,开阔水域季节长度大幅增加,与1992年相比增加了72 d。19个栖息地中,巴伦支海是开阔水域面积和季节长度变化贡献最大的海域,增加速度分别为9.71×10³ km²/a和71.69 d/10a。以开阔水域季节长度变化率为依据,将北极熊栖息地划分为稳定、次稳定和不稳定3个等级。总共有3个稳定栖息地,包括分布在相对其他栖息地而言纬度较低的楚科奇海、西哈得孙湾和南哈得孙湾。13个次稳定栖息地,包括拉普捷夫海、喀拉海、东格陵兰、巴芬湾、戴维斯海峡、福克斯湾、布西亚湾、麦克林托克海峡、梅尔维尔子爵海峡、挪威湾、北波弗特、南波弗特和兰开斯特海峡。3个不稳定栖息地,均位于70°N以北,包括北极盆地、巴伦支海和凯恩盆地。稳定区主要位于低纬度,不稳定区全部位于高纬度。该分级结果表明高纬度地区虽然海冰覆盖多,但是年际变化十分显著,不稳定的3个区域内北极熊对海冰变化适应时间更少,年际迁移变化大,对北极熊的生存发展更为不利。     

Definition of Arctic and Antarctic Sea Ice Variation Index

1　Introduction Itiswellknownthatseaiceinthepolarregionplaysanimportantroleintheglobal climatechangesasapartofclimatesystem(Carleton1989;YuanandMartinson2000, 2001;ChengandBian2002;LiuandMartinson2002;LiuandZhang2004;Gigorand Wallace2002etal).Infact,numerousmodelingstudiessuggestanimportantinfluence throughtheseaicefieldsalone(Grumbine1994,Meehl1990,Rindetal.1995).Inor dertounderstandthevariabilityofArcticandAntarcticseaicealongwiththepossiblecon nectionswithclimaticanomaliesindetail…     

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.     

Role of sea ice in air-sea exchange and its relation to sea fog

Synchronous or quasi-synchronous stereoscopic sea-ice-air comprehensive observation was conducted during the First China Arctic Expedition in summer of 1999. Based on these data, the role of sea ice in sea-air exchange was studied. The study shows that the kinds, distribution and thickness of sea ice and their variation significantly influence the air-sea heat exchange. In floating ice area, the heat momentum transferred from ocean to atmosphere is in form of latent heat; latent heat flux is closely related to floating ice concentration; if floating ice is less, the heat flux would be larger. Latent heat flux is about 21 23.6 W*m-2, which is greater than sensible heat flux. On ice field or giant floating ice, heat momentum transferred from atmosphere to sea ice or snow surface is in form of sensible heat. In the floating ice area or polynya, sea-air exchange is the most active, and also the most sensible for climate. Also this area is the most important condition for the creation of Arctic vapor fog. The heat exchange of a large-scale vapor fog process of about 500000 km2 on Aug. 21 22,1999 was calculated; the heat momentum transferred from ocean to air was about 14.8×109 kW. There are various kinds of sea fog, radiation fog, vapor fog and advection fog, forming in the Arctic Ocean in summer. One important cause is the existence of sea ice and its resultant complexity of both underlying surface and sea-air exchange.     

Fine-resolution simulation of surface current and sea ice in the Arctic Mediterranean Seas

A fine-resolution model is developed for ocean circulation simulation in the National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Chinese Academy of Sciences, and is applied to simulate surface current and sea ice variations in the Arctic Mediterranean Seas. A dynamic sea ice model in elastic-viscous-plastic rheology and a thermodynamic sea ice model are employed. A 200-year simulation is performed and a dimatological average of a 10-year period (141st-150th) is presented with focus on sea ice concentration and surface current variations in the Arctic Mediterranean Seas. The model is able to simulate well the East Greenland Current, Beaufort Gyre and the Transpolar Drift, but the simulated West Spitsbergen Current is small and weak. In the March climatology, the sea ice coverage can be simulated well except for a bit more ice in east of Spitsbergen Island. The result is also good for the September scenario except for less ice concentration east of Greenland and greater ice concentration near the ice margin. The extra ice east of Spitsbergen Island is caused by sea ice current convergence forced by atmospheric wind stress.     

Seasonal changes in sea ice conditions along the Northeast Passage in 2007 and 2012

Remote sensing data from passive microwave and satellite-based altimeters, associated with the data measured underway, were used to characterize seasonal and spatial changes in sea ice conditions along...     

Numerical simulation of the impact of underlying surface changes on Arctic climate

Using a regional atmospheric model for Arctic climate simulation, two groups of numerical experiments were carried out to study the inlfuence of changes in the underlying surface (land surface, sea sur...     

THE FEATURES OF SEA-ICE COVER, SNOW DISTRIBUTION AND ITS DENSIFICATION IN THE CENTRAL ARCTIC OCEAN

I.INTKODUCTIONTheArcticOcean,withanareaofapproximately9.5X106krnZ,ispredominantlysea--icecoveredthroughouttheyearinitscentralarea,whilethesouthedgeofmarginalicezone(MIZ)variesseasonally.ThemaxinltlmofIcecoverextentoccursbetweenFebruaryandMarch,whilethemininlunlisbetweenAugustandseptember.Placingtheiceedgeto8%iceconcentration(percentarealcoveragesofseaice)isopleths,variationofextentofsea--icecoveroftheArcticOceanisI)etween9X106--16X106kmZIbytheobservationofasatellite--bornescanningm…     

Satellite-observed trends in the Arctic sea ice concentration for the period 1979–2016

Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land(S-FJL),and the passage between Franz Josef Land and Severnaya Zemlya(FJL-SZ).     

Response of the Dominant Modes of Atmospheric Circulation in the Northern Hemisphere to a Projected Arctic Sea Ice Loss in 2007

This study revisits the Arctic sea ice extent(SIE) for the extended period of 1979-2015 based on satellite measurements and finds that the Arctic SIE experienced three different periods: a moderate sea ice decline period for 1979-1996, an accelerated sea ice decline period from 1997 to 2006, and large interannual variation period after 2007, when Arctic sea ice reached its tipping point reported by Livina and Lenton(2013). To address the response of atmospheric circulation to the lowest sea ice conditions with a large interannual variation, we investigated the dominant modes for large atmospheric circulation responses to the projected 2007 Arctic sea ice loss using an atmospheric general circulation model(ECHAM5). The response was obtained from two 50-yr simulations: one with a repeating seasonal cycle of specified sea ice concentration for the period of 1979-1996 and one with that of sea ice conditions in 2007. The results suggest more occurrences of a negative Arctic Oscillation(AO) response to the 2007 Arctic sea ice conditions, accompanied by an North Atlantic Oscillation(NAO)-type atmospheric circulation response under the largest sea ice loss, and more occurrences of the positive Arctic Dipole(AD) mode under the 2007 sea ice conditions, with an across-Arctic wave train pattern response to the largest sea ice loss in the Arctic. This study offers a new perspective for addressing the response of atmospheric circulation to sea ice changes after the Arctic reached the tipping point in 2007.