北极海冰的基本特征

本文应用1953～1984年的北极海冰资料,分析各区海冰的季节变化、年际变化、自相关特性及互相关特性。认为Ⅰ区海冰占有最大权重,又具有较大的方差,在全区海冰中起着重耍作用。冬季,各区海冰相互关联,其余季节,基本上相互独立。各区海冰均提供了气候“贮存”机制,一个季节的冰能影响下一个季节冰的特性;冬季的贮存能力大于夏季,春秋次之;Ⅱ区和Ⅳ区冰的持续性优于Ⅰ区 。     

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.     

Numerical simulation of seasonal snow in Tianshan Mountains

Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environment,considerably affect the stability of snow on slopes.Therefore,a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches.This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components.The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated.Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover.Temperature drives water movement in snow.The low-density area of the bottom snow is the result of temperature gradient.The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation.Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.     

Snow cover influences the thermal regime of active layer in Urumqi River Source,Tianshan Mountains,China

Snow cover is characterized by the high albedo, low thermal conductivity, and notable heat transition during phase changes. Thus, snow cover significantly affects the ground thermal regime. A comparison of the snow cover in high latitudes or high-altitude snowy mountain regions indicates that the eastern Tianshan Mountains (China) show a characteristically thin snow cover (snow depth below 15 cm) with remarkable temporal variability. Based on snow depth, heat flux, and ground temperature from 2014 to 2015 in the Urumqi River source, the spatialtemporal characteristics of snow cover and snow cover influences on the thermal conditions of active layer in the permafrost area were analyzed. During the autumn (Sept. - Oct.), thin and discontinuous snow cover can noticeably accelerate the exothermic process of the ground, producing a cooling effect on the shallow soil. During the winter (Nov. - Mar.), it is inferred that the effective thermal insulation starts with snow depth exceeding 10 cm during early winter. However, the snow depth in this area is generally below 15 cm, and the resulting snow-induced thermal insulation during the winter is very limited. Due to common heavy snowfalls in the spring (Apr. to May), the monthly mean snow thickness in April reached to 15 cm and remained until mid-May. Snow cover during the spring significantly retarded the ground warming. Broadly, snow cover in the study area exerts a cooling effect on the active layer and plays a positive role in the development and preservation of permafrost.     

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…     

基于CryoSat-2数据反演2011～2017年波弗特海海冰厚度变化

利用CryoSat-2卫星测高数据反演波弗特海的海冰厚度,并利用2010~2013年10月份仰视声呐(ULS)和2011年冰桥计划（IceBridge）数据对结果进行精度评估。结果表明,测高反演的海冰吃水深度与ULS吃水深度差值的最大值和标准差分别为14 cm和4 cm;测高反演的海冰厚度与冰桥计划海冰厚度差值的平均值和标准差分别为2.7 cm和65.7 cm,优于Laxon(2013)研究结果（分别优化2.1 cm和6.6 cm）。在此基础上,研究2011~2017年波弗特海夏冬两季的海冰厚度变化,发现二者具有类似的分布特征,且冬季3月海冰覆盖范围更广,厚度更大;进一步分析2011~2017年3月份冬季海冰厚度年际变化,发现其呈整体下降趋势,且2012年最小,2014年最大。     

Interannual and seasonal changes in the ice cover of glacial lakes in the Snowy Mountains of Australia

Seasonal ice cover is uncommon on Australian lakes. In the Snowy Mountains, there are five natural, seasonally ice-covered lakes including Lake Cootapatamba, the highest lake in Australia. Blue Lake is the only one of the five lakes with sufficient volume to be relatively independent of short-term changes in ambient temperature, and therefore is the lake most likely to be of use in tracking long-term regional climate change. Ice forms on Blue Lake near the winter solstice and ice-breakup occurs from late September to November. Timing of breakup is related to spring temperature and, as such, mirrors the timing of general snow thaw in the mountains. The existence of historic photographs taken of the lake at about the time of ice breakup allows for the possibility of reconstructing a history of alpine climate and in 1905 ice breakup was probably as late as mid-December.     

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.     

Northern-Hemisphere snow cover patterns and formation conditions in winter 2007 and 2012

The Arctic sea ice minimum records appeared in the Septembers of 2007 and 2012, followed by high snow cover areas in the Northern Hemisphere winters. The snow cover distributions show different spatial patterns in these two years: increased snow cover in Central Asia and Central North America in 2007, while increased snow cover in East Asia and northwestern Europe in 2012. The high snow cover anomaly shifted to higher latitudes in winter of 2012 compared to 2007. It is noticed that the snow cover had positive anomaly in 2007 and 2012 with the following conditions: the negative geopotential height and the related cyclonic wind anomaly were favorable for upwelling, and, with the above conditions, the low troposphere and surface air temperature anomaly and water vapor anomaly were favorable for the formation and maintenance of snowfalls. The negative geopotential height, cyclonic wind and low air temperature conditions were satisfied in different locations in 2007 and 2012, resulting in different spatial snow cover patterns. The cross section of lower air temperature move to higher latitudes in winter of 2012 compared to 2007.     

西藏高原积雪覆盖空间分布及地形影响

利用2000-2014年MOD10A2积雪产品和数字高程模型DEM数据,以积雪覆盖率为指标,在分析西藏高原积雪空间分布特点的基础上,定量研究了高程、坡度和坡向等地形要素对高原积雪时空分布的影响。主要结论有：① 西藏高原积雪的空间分布差异显著,具有中东部念青唐古拉山和周边高山积雪丰富,覆盖率高,而南部河谷和羌塘高原中西部积雪少,覆盖率低的特点。② 海拔越高积雪覆盖率越高,积雪持续时间越长,年内变化越稳定。海拔2 km以下积雪覆盖率不足4%,海拔6 km以上覆盖率达75%。海拔4 km以下年内积雪覆盖呈单峰型分布特点,海拔越高,单峰型越明显;而海拔4 km以上则为双峰型,海拔越高,双峰型越明显。海拔6 km以下积雪覆盖率最低值出现在夏季,而6 km以上则出现在冬季。③ 总体上,高原地形坡度越高积雪覆盖率越高。不同坡向中,北坡积雪覆盖率最高,南坡最低,年内分布呈双峰型,而无坡向的平地积雪覆盖率要小于有坡向的山地,其年内变化呈单峰型分布特点。     

A coupled multi-category sea ice model and POM for Baffin Bay and the Labrador Sea

An overview of the seasonal variation of sea-ice cover in Baffin Bay and the Labrador Sea is given. A coupled ice-ocean model, CECOM, has been developed to study the seasonal variation and associated ice-ocean processes. The sea-ice component of the model is a multi-category ice model in which mean concentration and thickness are expressed in terms of a thickness distribution function. Ten categories of ice thickness are specified in the model. Sea ice is coupled dynamically and thermodynamically to the Princeton Ocean Model. Selected results from the model including the seasonal variation of sea ice in Baffin Bay, the North Water polynya and ice growth and melt over the Labrador Shelf are presented.     

An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.     

Detecting Arctic snow and ice cover with FY-1D global data

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.     

The research of Polar sea ice and its role in climate change

As an important part of global climate system, the Polar sea ice is effccting on global climate changes through ocean surface radiation balance, mass balance, energy balance as well as the circulating of sea water temperature and salinity. Sea ice research has a centuries - old history. The many correlative sea ice projects were established through the extensive international cooperation during the period from the primary research of intensity and the boaring capacity of sea ice to the development of sea/ice/air coupled model. Based on these reseamhes, the sea ice variety was combined with the global climate change. All research about sea ice includes： the physical properties and processes of sea ice and its snow cover, the ecosystem of sea ice regions, sea ice and upper snow albedo, mass balance of sea ice regions, sea ice and climate coupled model. The simulation suggests that the both of the area and volume of polar sea ice would be reduced in next century. With the developing of the sea ice research, more scientific issues are mentioned. Such as the interaction between sea ice and the other factors of global climate system, the seasonal and regional distribution of polar sea ice thickness, polar sea ice boundary and area variety trends, the growth and melt as well as their influencing factors, the role of the polynya and the sea/air interactions. We should give the best solutions to all of the issues in future sea ice studying.     

Impacts of snow accumulation on air temperature measured by automatic weather stations on the Antarctic ice sheet

The heights of automatic weather station (AWS) sensors over the Antarctic ice sheet are nominal and change with snow accumulation or ablation. Therefore, the measured data may not be used directly. In this study, we analyzed the impact of snow accumulation on AWS observations using continuous measurements from three AWS that were deployed on the traverse route from the Zhongshan Station to Dome A over East Antarctica. We then corrected the measured air temperature to account for changes in the sensor height relative to the snow surface to improve the authenticity and representativeness of the observation data from the AWS. The results show that (i) the annual mean snow accumulations at Dome A, Eagle and LGB69 were approximately 0.11 m, 0.30 m and 0.49 m, respectively, and the corresponding annual mean air temperature differences between the corrected and measured values at 1 m in height were 0.34℃, 0.29℃ and 0.35℃; (ii) the impact on air temperature from accumulation decreases with height from the surface; (iii) the air temperature difference between the corrected and measured values was not directly proportional to the snow accumulation but was related to the seasonal air temperature variations and the intensity of the local surface inversion; and (iv) the averaged corrected air temperature was higher than the measured values except during the summer when there were days without temperature inversion. The magnitude of the temperature difference between the corrected and measured was mainly determined by snow accumulation and the intensity of the local surface inversion.     

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.     

An artificial neural network-based snow cover predictive modeling in the higher Himalayas

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.     

The influence of seasonal snow on soil thermal and water dynamics under different vegetation covers in a permafrost region

Seasonal snow is one of the most important influences on the development and distribution of permafrost and the hydrothermal regime in surface soil. Alpine meadow, which constitutes the main land type in permafrost regions of the Qinghai-Tibet Plateau, was selected to study the influence of seasonal snow on the temperature and moisture in active soil layers under different vegetation coverage. Monitoring sites for soil moisture and temperature were constructed to observe the hydrothermal processes in active soil layers under different vegetation cover with seasonal snow cover variation for three years from 2010 to 2012. Differences in soil temperature and moisture in areas of diverse vegetation coverage with varying levels of snow cover were analyzed using active soil layer water and temperature indices. The results indicated that snow cover greatly influenced the hydrothermal dynamics of the active soil layer in alpine meadows. In the snow manipulation experiment with a snow depth greater than 15 cm, the snow cover postponed both the freeze-fall and thawrise onset times of soil temperature and moisture in alpine LC （lower vegetation coverage） meadows and of soil moisture in alpine HC （higher vegetation coverage） meadows; however, the opposite response occurred for soil temperatures of alpine HC meadows,where the entire melting period was extended by advancing the thaw-rise and delaying the freeze-fall onset time of the soil temperature. Snow cover resulted in a decreased amplitude and rate of variation in soil temperature, for both alpine HC meadows and alpine LC meadows, whereas the distinct influence of snow cover on the amplitude and rate of soil moisture variation occurred at different soil layers with different vegetation coverages. Snow cover increased the soil moisture of alpine grasslands during thawing periods. The results confirmed that the annual hydrothermal dynamics of active layers in permafrost were subject to the synergistic actions of both snow cover and vegetation coverage.     

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.     

Monitoring snow-cover area change in Antarctic coastline region using MODIS product data

Based on MODIS snow products, this article studied the changes of snow cover area during 2003-2006 along the coastline of the Antarctic, and 18 typical regions were chosen for further analysis. The result showed that the change of snow cover area was in a fluctuant downward trend as a whole, and more fluctuated obviously in warm season than in cold season. In temporal scale: for the season cycle, the snow cover extent increased rapidly in cold season (Apr-Oct), while the performance in warm season (Nov-Mar) was not exactly the same during the four years, the snow cover extent decreased in the first and then increased in 2004 and 2006, however, increased firstly and then decreased but reduced as a whole in 2005, for the inter-annual cycle, snow cover extent was the largest in 2003, but reached to the lowest level in 2004, and then increased gradually in 2005 and 2006, whereas, it declined with fluctuant as a whole. In spatial scale, changes mainly centralized along the coastline, moreover, it was more remarkable in the West Antarctic than in the East Antarctic, especially in the Antarctic Peninsula region.