Significant Variations of Surface Albedo during a Snowy Period at Xianghe Observatory,China

Surface albedo over typical types of surfaces in the North China Plain was observed using a Multi-field Albedo Observation System before and after several snowfalls from 13 to 27 February 2005. Dramatic variations of the surface albedos of bare land, a frozen pond, and withered grassland during that period were analyzed. Under cloudy sky, the mean surface albedo of bare land was about 0.23, but it immediately rose to 0.85 when the surface was covered by fresh snow. The albedo decreased gradually to normal levels afterwards. The melting processes were different depending on the characteristics of the underlying surfaces. For example, over grassland the surface albedo was relatively lower after snowfall, and as a result, more solar energy could be absorbed and consequently the snow melting process was accelerated. Significant variations of surface albedo cannot be easily captured by satellite observations; therefore, detailed measurements of surface albedo and related parameters are essential for determining the impact of snow on the energy budget of the Earth.

Spectral Reflectance Characteristics of Different Snow and Snow-Covered Land Surface Objects and Mixed Spectrum Fitting

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area.The result showed that for a pure snow spectrum,the snow reflectance peaks appeared from visible to 800 nm band locations;there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength.Compared with fresh snow,the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300～1 300,1 700～1 800 and 2 200～2 300 nm,the lowest was from the compacted snow and frozen ice.For the vegetation and snow mixed spectral characteristics,it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350～1 300 nm.However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic.In the end,based on the spectrum analysis of snow,vegetation,and mixed snow/vegetation pixels,the mixed spectral fitting equations were established,and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.950 9).

Understanding the Soil Temperature Variability at Different Depths:Effects of Surface Air Temperature,Snow Cover,and the Soil Memory

The soil temperature(ST)is closely related to the surface air temperature(AT),but their coupling may be affected by other factors.In this study,significant effects of the AT on the underlying ST were found,and the time taken to propagate downward to 320 cm can be up to 10 months.Besides the AT,the ST is also affected by memory effects-namely,its prior thermal conditions.At deeper depth(i.e.,320 cm),the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season.At shallower layers(i.e.,<80 cm),the effects of the AT may be blocked by the snow cover,resulting in a poorly synchronous correlation between the AT and the ST.In northeastern China,this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring.Due to the thermal insulation effect of the snow cover,the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period.These findings may be instructive for better understanding ST variations,as well as land−atmosphere interactions.

Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Surface albedo is a quantitative indicator for land surface processes and climate modeling,and plays an important role in surface radiation balance and climate change.In this study,by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS),we analyzed the spatiotemporal variation,persistence status,land cover type differences,and annual and seasonal differences of surface albedo,as well as the relationship between surface albedo and various influencing factors(including Normalized Difference Snow Index(NDSI),precipitation,Normalized Difference Vegetation Index(NDVI),land surface temperature,soil moisture,air temperature,and digital elevation model(DEM))in the north of Xinjiang Uygur Autonomous Region(northern Xinjiang)of Northwest China from 2010 to 2020 based on the unary linear regression,Hurst index,and Pearson's correlation coefficient analyses.Combined with the random forest(RF)model and geographical detector(Geodetector),the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated.The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer.The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south,showing a weak decreasing trend and a small and stable overall variation.Land cover types had a significant impact on the variation of surface albedo.The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation,and negatively correlated with NDVI,land surface temperature,soil moisture,and air temperature.In addition,the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons.To be specific,NDSI had the largest influence on surface albedo,followed by precipitation,land surface temperature,and soil moisture;whereas NDVI,air temperature,and DEM showed relatively weak influences.However,the interactions of any two influencing factors on surface albedo were enhanced,especially the interaction of air temperature and DEM.NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation,with an explanatory power greater than 92.00%.This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Seasonal evolution of the dominant modes of the Eurasian snowpack and atmospheric circulation from autumn to the subsequent spring and the associated surface heat budget

本文研究了欧亚大陆地区雪水当量、积雪覆盖率以及500 hPa位势高度的主模态从秋季到次年春季的演变情况。结果表明,欧亚大陆地区的积雪与大气环流主模态从秋季至次年春季呈稳定而显著的相关关系。欧亚地区雪水当量和大气环流主模态的季节演变表现出了良好的连贯性,而积雪覆盖率的主模态则在秋-冬过渡时期表现出了较差的连贯性。在相应的表面热量收支方面,由近表面风驱动的热平流是影响表面热量收支的重要因素,在冬季尤为显著。此外,由积雪引起的短波辐射异常在春季扮演了同样重要甚至更加重要的角色。

Sensitivity of the Number of Snow Cover Days to Surface Air Temperature over the Qinghai-Tibetan Plateau

基于雪盖子天(NSCD ) 的数字并且一致为时期的表面空气温度数据 19512004，这研究在 Qinghai 西藏的高原(QTP ) 上在 NSCD 的敏感上执行定量分析到表面空气温度。结果证明在当前的气候下面的极端敏感和敏感比在 QTP 的中央区域在边是更高的。在那里存在在车站举起和批评温度，敏感在到达极值之间的强壮的否定关联。在举起和极端敏感之间的否定关联不象以前的一样强壮。当前，在许多车站的 climatological 温度不到达批评阶段。在这些车站的敏感将在温暖的气候的当前的背景下面变得更大，它意味着 NSCD 将对表面空气温度更敏感。引证妈， L. ， D。Qin， L。Bian，等， 2010：到在 Qinghai 西藏的高原上的表面空气温度的雪盖子天的数字的敏感。副词。Clim。变化物件， 1， doi：10.3724/SP .J.1248.2010.00076。

Spatial variations of Pb,As,and Cu in surface snow along the transect from the Zhongshan Station to Dome A,East Antarctica

The spatial distributions of lead, arsenic, and copper （Pb, As, and Cu, respectively） in surface snow along the transect from the Zhongshan Station to Dome A, East Antarctica, are presented. The mean concentrations of Pb, As, and Cu are 1.04±1.56 pg/g, 0.39±0.08 pg/g, and 11.2±14.4 pg/g, respectively. It is estimated that anthropogenic contributions are dominant for Pb, As, and Cu. Spatially, Pb concentrations show an exponentially decreasing trend from the coast inland, while a moderate decreasing trend is observed for Cu concentrations in the coastal area （below 2,000 m above sea level （a.s.1.））. In the intermediate area （2,000-3,000 m a.s.1.）, the concentrations and enrichment factors of all these elements show high variability due to the complicated characteristics of climate and environment. On the inland plateau （above 3,000 m a.s.1.）, the high concentrations of As and Pb are induced by high deposition efficiency, the existence of polar stratospheric precipitation, and the different fraction deposition to East Antarctica. The extremely high concentrations with maximum values of 9.59 pg/g and 69.9 pg/g for Pb and Cu, respectively, are suggested to result mainly from local human activities at the station. Our results suggest that source, transport pathway, and deposition pattern, rather than distance from the coast or altitude, lead to the spatial distributions of Pb, As, and Cu; and it is further confirmed by spatial variations of the three metals deposited over the whole continent of Antarctica.

Impact of forcing data and land surface properties on snow simulation in a regional climate model:a case study over the Tianshan Mountains,Central Asia

Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertainties in modeling snow resources over complex terrain such as mountains.This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting(WRF)model coupled with the Noah-Multiparameterization(Noah-MP)land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains.Six tests were conducted based on different reanalysis forcing datasets and different land surface properties.The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests.The model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing datasets.Additionally,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters.However,no significant improvement in accuracy was found in the date of snow depth maximum and melt rate.The best performance was achieved using ERA5 with modified land cover and vegetation parameters(mean bias=−4.03 mm and−1.441 mm for snow depth and SWE,respectively).This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.

Methods for extraction of microorganism DNA from glacier surface snow

In order to thoroughly investigate the diversity of glacier microorganisms, four DNA extraction methods with differem lysis pat- terns were tested and two screened methods （the Bosshard-Bano method and the Zhou method） were optimized for the most ef- fective form of the filter membrane （cut vs. uncut）, the DNA extraction method, and the precipitation method. The two optimized methods were then compared with the commercial Mo-Bio DNA extraction kit, and the results showed that the kit was generally suitable for extraction of microorganism DNA fi＇om glacier surface snow. Procedurally, it was found that a modified Boss- hard-Bano method （i.e., cutting the filter membrane into pieces, using a specific lysis pattern [lysozyme （5 mg/mL）-protease K （ 1 mg/mL）-CTAB （ 1%）-SDS （ 1%）], performing the extraction only once by chloroform-isoamyl alcohol （24： 1）, and conducting DNA precipitation by pure ethanol） was also an effective and less expensive method for extraction of microorganism DNA from glacier surface snow.

Micro-particle in surface snow at Princess Elizabeth Land,East Antarctic

During the Austral summer of 1996/1997, the First Chinese Antarctic Inland Expedition reached the inland area about 330 km along the direction around 76°E from Zhongshan Station, and collected 84 surface snow samples at an interval of 4 km . Micro particle analysis of the samples indicates that the micro particle concentration apparently decreases with the increasing of altitude, and the amplitudes of micro particle concentration is much larger in the lower altitude than in the higher altitude. Further analysis of grain size distributions of micro particle, percentage of micro particles from different sources and variations with altitude suggest that micro particles in this area are from a considerably dominant source. Although this area is controlled by polar easterly wind and katabatic wind, transportation and deposition of the micro particles are mainly influenced by marine transportation in coastal area.

An Improved Method for Modeling Spatial Distribution of δD in Surface Snow over Antarctic Ice Sheet

Using the recent compilation of the isotopic composition data of surface snow of Antarctic ice sheet, we proposed an improved interpolation method of δD, which utilizes geographical factors (i.e., latitude and altitude) as the primary predictors and incorporates inverse distance weighting (IDW) technique. The method was applied to a high-resolution digital elevation model (DEM) to produce a grid map of multi-year mean δD values with 1km spatial resolution for Antarctica. The mean absolute deviation between observed and estimated data in the map is about 5.4‰, and the standard deviation is 9‰. The resulting δD pattern resembles well known characteristics such as the depletion of the heavy isotopes with increasing latitude and distance from coast line, but also reveals the complex topographic effects.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Velocity of Surface Ice Flow on Amery Ice Shelf Determined with PPP

The main activities in the joint expedition between CHINARE and ANARE on Amery ice shelf are introduced. Five day continuous GPS observation data collected on the site which locates at the frontal part of Amery ice shelf was processed with precise point positioning (PPP) technology based on precise products from IGS. Velocity of the surface ice flow on Amery can be derived from the PPP solution. Preliminary result shows that the surface ice flow velocity of the site is 2.25 meters per day, the motion direction is northeastward. Semidiurnal oceanic tide and diurnal oceanic tide signal of that site can be recovered from the height variation series of PPP solution. These above solutions can be used to the consequent mass balance calculation.

阿克达拉大气本底站地表辐射收支特征

地表辐射收支变化影响区域乃至全球气候,利用阿克达拉大气本底站基准辐射观测系统的2021年11月1日至2022年10月31日地表辐射四分量数据,分析阿克达拉大气本底站地表辐射与反照率的变化特征。结果表明:(1)太阳总辐射年曝辐量为5772.74 MJ·m^(-2)。太阳总辐射曝辐量的季节变化表现为夏季>春季>秋季>冬季,不同月份太阳总辐射和反射短波辐射日峰值集中在11:00—12:00,地面长波辐射日峰值集中在12:00—13:00,大气逆辐射日峰值出现时间没有明显规律。(2)净收入辐射最大值主要出现在正午,净支出辐射最大值出现时间主要在日落时。(3)降雨对太阳总辐射、反射短波辐射、地面长波辐射、地表反照率具有削弱作用,降雪过程太阳总辐射受到削弱,降雪后地面积雪导致地表反照率增大,地面反射短波辐射增强,日间地面长波辐射和地面净辐射减弱,由于雪对地面的保温作用,夜间地面长波辐射较降雪前增强;降雨与降雪均对大气逆辐射具有增强作用。(4)雨天地表反照率最小值为0.16;当积雪深度达到5 cm时,地表反照率可达0.96,新雪地表反照率大于老雪,且稳定积雪的地表反照率日均值在0.72~0.88。

1974—2020年大兴安岭北部多年冻土区积雪变化及其与地面、空气温度的关系

基于大兴安岭北部多年冻土区5个气象站1974—2020年逐日气温、地面温度、积雪深度资料,利用气象统计方法分析了积雪气候特征及长期变化、积雪物候变化及积雪对温度的影响,结果表明:大兴安岭北部冻土区积雪深度年内变化呈单峰型,积雪深度最大出现在2月,平均17.9 cm,年积雪日数为161.5 d,积雪日数和积雪深度最大月份不重合。年均积雪深度为10.6 cm,最大积雪深度平均为22.6 cm,近47 a年均积雪深度呈弱的上升趋势。积雪初日显著推迟,终日显著提前,使得积雪持续日数明显缩短。研究区年均积雪深度和冷季地面−雪面温差呈较好的正相关,年最大积雪深度和冷季地面−雪面温差亦呈较好的正相关,积雪深度越大其对地面的隔热作用越大,年均积雪深度每增加1 cm,地面−雪面温差升高0.4211℃,年最大积雪深度每增加1 cm,地面−雪面温差升高0.2889℃,年均积雪深度对冷季地面−雪面温差的影响更大。

“23·12”山东半岛特大海效应暴雪特征及成因

采用地面气象观测站、多普勒天气雷达、闪电、积雪深度人工加密观测资料、常规观测及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析(ECMWF Reanalysis v5,ERA5)资料,对2023年12月15—22日山东半岛特大海效应暴雪过程的降雪特征及极端性成因进行了分析。结果表明:(1)此次过程有4站积雪深度突破本站历史极值,有1站2 d的日降雪量为山东半岛海效应降雪有气象记录以来的最大值,文登积雪深度达74 cm,超过山东所有国家级地面气象观测站纪录,是一次极端海效应暴雪事件。(2)欧亚中高纬度阻塞形势下两次异常强冷空气持续影响渤海和山东半岛地区,850 hPa温度最低降至-21~-20℃,冷空气强度明显强于往年12月海效应暴雪过程,造成降雪持续时间长、累计降雪量大。异常强冷空气是此次极端暴雪过程产生的关键因素,渤海海面温度(简称“海温”)异常偏高是有利的海温背景。(3)冷空气强、海温偏高造成海气温差偏大,700 hPa以下产生对流不稳定,使得降雪强度大;强降雪发生在海气温差快速增大阶段。(4)925 hPa以下存在来自渤海的北—东北风与内陆地区的西北风构成的切变线,产生强上升运动,切变线长时间维持形成“列车效应”。(5)主要降雪时段强垂直上升运动、高相对湿度层的温度为-20~-12℃,适宜树枝状冰晶形成和维持,有利于产生大的积雪和降雪含水比;2 m气温持续低于-5℃,0 cm地温在降雪开始时即降至0℃以下,且两次强降雪过程仅间隔1 d,均有利于降雪累积产生极端积雪深度。

Persistent Variations in the East Asian Trough from March to April and the Possible Mechanism

The East Asian trough(EAT)profoundly influences the East Asian spring climate.In this study,the relationship of the EATs among the three spring months is investigated.Correlation analysis shows that the variation in March EAT is closely related to that of April EAT.Extended empirical orthogonal function(EEOF)analysis also confirms the co-variation of the March and April EATs.The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April.Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature(SST)pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean.The dipole SST pattern over the North Atlantic,with one center east of Newfoundland Island and another east of Bermuda,could trigger a Rossby wave train to influence the EAT in March−April.The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific,subsequently impacting the southern part of the EAT in March−April.Besides the SST factors,the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April.These three impact factors are generally independent of each other,jointly explaining large variations in the EAT EEOF1.Moreover,the signals of the three factors could be traced back to February,consequently providing a potential prediction source for the EAT variation in March and April.

基于GPS-IR的复杂寒区地表冻融状态监测研究

针对复杂寒区地表冻融状态情况,基于GPS-IR(Global Positioning System-Interference Reflectometry)技术进行地表冻融状态监测精度研究。采用2017—2022年PBO(The Plate Boundary Observatory)GPS站点数据,结合SNOTEL气象站地表实测数据,充分考虑了复杂寒区地表积雪深度、土壤湿度和地表冻融状态对GPS(Global Positioning System)多路径观测值的影响,基于GPS-IR技术利用卫星反射信号来进行地表冻融状态监测。结果表明:在积雪深度和土壤湿度时间序列平稳的状态下,GPS-IR技术的冻融监测精度为90.63%,积雪深度和土壤湿度的波动都会导致监测精度下降。实验表明GPS-IR技术的监测精度会受到积雪深度和土壤湿度的影响,在地球物理参数小范围变化的情况下监测精度较好,对地表冻融状态响应敏感,是一种有效监测复杂寒区冻融特性的有效手段。

低热量非浓缩还原澳洲青苹雪梨复合果汁配方优化

利用响应面法对低热量非浓缩还原澳洲青苹雪梨复合果汁(以下简称NFC复合果汁)配方进行优化,建立了物料配比、木糖醇添加量、亚麻籽胶添加量与产品感官评分的数学模型,用建立的数学模型对NFC复合果汁最优配方进行预测,确定了最佳的产品配方,即澳洲青苹与雪梨复配比例1∶0.65、木糖醇添加量2%和亚麻籽胶添加量0.061%。该NFC复合果汁弥补了我国营养型、复合型和功能型等新型果蔬汁产业技术开发不足的现状,更对NFC果汁市场提供新的产品类型,对满足消费者健康营养低热量的需求具有重要意义。

基于FT-ICR MS表征达古冰川冬春季节表层雪中DOM分子组成变化

分析冰川中溶解性有机质(dissolved organic matter,DOM)的迁移转化特征是评估冰川消融对下游生态环境效应的重要依据,但冰川表层雪中DOM在冬春季节的迁移转化特征尚不明确。本文以达古冰川冬春季节表层雪和春季冰川径流为研究对象,使用傅里叶变换离子回旋共振质谱(FT-ICR MS)对表层雪和径流中DOM进行分子层次表征。结果显示:达古冰川冬春季节表层雪中DOM化学组成非常丰富,主要包括脂类、多肽类、不饱和烃类,还含有少量的酚类、多环芳烃类和糖类,来源包括微生物活动和陆源性输入。春季径流中DOM的主要来源是陆源性输入,受人类活动影响较大。分析不同季节表层雪DOM的分子组成变化,发现冬季低温环境有利于表层雪中脂类、多肽类和含S类DOM分子的富集,春季气温升高则有利于表层雪中多肽类、不饱和烃类和含N类DOM分子的富集。比较春季径流和表雪中DOM的分子组成变化,发现径流中杂原子类DOM含量降低,微生物来源的脂类和多肽类DOM含量降低,陆源性的酚类和多环芳烃类含量显著增加,说明径流中DOM的生物活性降低而光反应活性增加。因此随着冰川持续消融大量的冰川融水将DOM带入下游水环境,在微生物活动和光反应过程的共同作用下增加温室气体CO_(2)的排放,增强温室效应。