Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

NOAA IMS雪冰产品在青藏高原积雪监测中的适用性分析

在系统评估青藏高原积雪观测典型气象站历史定位坐标精度基础上,利用站点雪深资料对NOAA IMS 4 km和1 km分辨率雪冰产品在青藏高原的精度和适用性进行了验证和评估,定量分析了IMS 4 km到1 km空间分辨率提高和气象站历史定位与GPS定位坐标之间的差异对青藏高原IMS积雪监测精度的影响。结果表明：青藏高原个别气象站历史坐标与当前GPS接收机定位之间存在较大的差异,如安多气象站经度偏小0.6°,纬度偏大0.08°。IMS 4 km雪冰产品在青藏高原的总精度介于76.4%~83.2%,平均为80.1%,积雪分类精度介于35.8%~60.7%,平均为47.2%,平均误判率为17.1%,平均漏判率为45.5%,总体上呈现地面观测的积雪日数越多、平均雪深越大,其总体监测精度越低,而积雪分类精度越高的特点。IMS分辨率从4 km到1 km总体精度平均提高了2.9%,积雪分类精度平均提高了0.9%,主要是由于个别站点的精度提升较大引起的,对高原多数台站积雪监测精度的改进和提升很小。除个别台站外,目前气象站历史坐标和GPS定位坐标之间的差异,对IMS 4 km积雪监测精度验证结果没有影响。然而,今后随着卫星遥感技术的发展,更高时空分辨率的遥感积雪产品将用于积雪监测和研究,精确的地面观测站坐标信息是对这些遥感数据开展精度验证与实际应用的前提。     

积雪变化对中国森林凋落物分解影响研究进展

森林凋落物的分解对于维持生态系统物质循环和养分平衡具有重要意义,并受到不同积雪厚度下冻融格局的影响。冻融期（包括冻结过程期、完全冻结期、融化过程期）是冻土区凋落物分解的重要时期,该时期分解的凋落物量约占全年分解总量的一半。积雪减少通常会导致土壤温度降低、冻融循环次数增加,进而影响凋落物分解。通过综述近10年来积雪变化对我国森林凋落物分解影响的研究成果发现,积雪厚度减少在冻融期通常会抑制凋落物质量损失、碳元素释放和纤维素降解,生长季则起到促进作用,从全年来看多数表现为抑制作用。因此,冻融作用造成凋落物的物理破坏,对其分解的促进作用主要发生在后续生长季。积雪厚度减少在冻融期通常抑制氮元素释放,生长季和全年则无明显规律;磷元素和木质素目前研究还存在很大差异。最后,进一步阐述了积雪变化对凋落物分解影响研究存在的问题及未来研究发展方向。     

Modeling of in-situ crystallization processes in the Permian mafic layered intrusion of Mont Collon (Dent Blanche nappe, western Alps)

The Mont Collon mafic complex is one of the best preserved examples of the Early Permian magmatism in the Central Alps, related to the intra-continental collapse of the Variscan belt. It mostly consists (> 95 vol.%) of ol + hy-normative plagioclase-wehrlites, olivine- and cpx-gabbros with cumulitic structures, crosscut by acid dikes. Pegmatitic gabbros, troctolites and anorthosites outcrop locally. A well-preserved cumulative sequence is exposed in the Dents de Bertol area (center of intrusion). P–T calculations indicate that this layered magma chamber emplaced at mid-crustal levels at about 0.5 GPa and 1100 °C. The Mont Collon cumulitic rocks record little magmatic differentiation, as illustrated by the restricted range of clinopyroxene mg-number (Mg#_cpx = 83–89). Whole-rock incompatible trace-element contents (e.g. Nb, Zr, Ba) vary largely and without correlation with major-element composition. These features are characteristic of an in-situ crystallization process with variable amounts of interstitial liquid L trapped between the cumulus mineral phases. LA-ICPMS measurements show that trace-element distribution in the latter is homogeneous, pointing to subsolidus re-equilibration between crystals and interstitial melts. A quantitative modeling based on Langmuir's in-situ crystallization equation successfully duplicated the REE concentrations in cumulitic minerals of all rock facies of the intrusion. The calculated amounts of interstitial liquid L vary between 0 and 35% for degrees of differentiation F of 0 to 20%, relative to the least evolved facies of the intrusion. L values are well correlated with the modal proportions of interstitial amphibole and whole-rock incompatible trace-element concentrations (e.g. Zr, Nb) of the tested samples. However, the in-situ crystallization model reaches its limitations with rock containing high modal content of REE-bearing minerals (i.e. zircon), such as pegmatitic gabbros. Dikes of anorthositic composition, locally crosscutting the layered lithologies, evidence that the Mont Collon rocks evolved in open system with mixing of intercumulus liquids of different origins and possibly contrasting compositions. The proposed model is not able to resolve these complex open systems, but migrating liquids could be partly responsible for the observed dispersion of points in some correlation diagrams. Absence of significant differentiation with recurrent lithologies in the cumulitic pile of Dents de Bertol points to an efficiently convective magma chamber, with possible periodic replenishment.     

基于相机摄影测量的冰面反照率反演方法研究

雪冰反照率是冰川与积雪能量平衡计算的关键参数,也是观测的难点. 为测量冰面反照率,试验采用相机摄影测量技术,在祁连山十一冰川开展了自动拍摄、并利用共线方程进行冰川正射制图,在此基础上于2012年7月28日-8月18日在冰川表面开展了不同海拔梯度的反照率观测. 通过统计分析晴天与阴天观测反照率与对应点相机拍摄获取的RGB归一化指数之间的关系,对十一冰川进行冰面反照率参数化方法研究. 结果表明：晴天冰面反照率与RGB归一化指数存在明显的指数关系,拟合R²达到0.79;而阴天的统计关系较差,线性拟合R²仅为0.52. 据此尝试利用晴天的拟合公式对整个冰面进行反照率参数化,计算表明：在晴天冰川表面地形阴影区的反照率存在低估现象,而无阴影区的反照率估算结果可信度较高. 利用2013年的反照率观测数据进行了参数化方案的验证,结果表明：观测值与模拟值平均误差仅为0.037,表明利用RGB影像对反照率参数化具有一定的可用性和可靠性.     

基于MODIS数据的北疆积雪黑碳和雪粒径反演及时空变化分析

积雪是地球上反射率较高的自然表面,对于中高纬度地区的水文和能量收支平衡发挥着重要作用。表层积雪中的黑碳和雪粒径变化可以显著影响积雪反照率,造成积雪对太阳辐射吸收的变化,进而对区域气候变化和水文循环产生反馈作用。利用遥感技术对季节性积雪表层黑碳和雪粒径进行定量评估,可以获取时空上连续系统的雪表黑碳浓度和雪粒径变化情况,这也是许多气候和水文模型的输入因子。以中国主要季节性积雪区北疆为研究区,基于MODIS（Moderate Resolution Imaging Spectroradiometer）数据的3（0.47 μm）、2（0.86 μm）和5（1.24 μm）波段,采用SGSP（Snow Grain Size and Pollution Amount）算法反演2000-2018年积雪期的雪表黑碳浓度和雪粒径,并结合地面观测数据对于反演结果进行了精度验证,综合分析北疆雪表黑碳浓度和雪粒径时空变化趋势。结果显示,SGSP算法能够同时反演雪表黑碳浓度和雪粒径,并且验证结果表明纯雪像元上反演结果具有较好的精度;2000-2018年北疆雪表年均黑碳浓度和年均雪粒径都随时间变化呈现微弱下降趋势;受地理位置和局部污染源的影响,北疆积雪黑碳浓度空间分布复杂,天山北坡经济带平均黑碳浓度最高,伊犁地区平均黑碳浓度最低,雪粒径的空间分布显示塔城地区平均雪粒径最大,伊犁地区最小。     

1965-2012年甘肃平凉地区大雪天气气候特征

利用甘肃平凉地区7个气象自动站1965-2012年的逐日气象整编资料, 统计出近48 a来大雪天气出现次数, 分析了平凉大雪天气的统计特征和气候变化规律, 然后用近14 a历史天气图资料对31次大雪过程进行对比分型, 总结出三类天气形势的特征. 结果表明: 1965-2012年48 a来, 平凉大雪天气出现次数总体变化趋势不显著; 大雪天气多集中出现在秋冬、冬春冷暖季节交替的时期, 冬春交替期间出现次数多于秋冬交替期间, 隆冬季节出现次数相对较少. 平凉产生大雪天气的主要天气形势有三类: 高原低槽型、西风带小槽型和阻高-横槽型, 三类形势中最多的是高原低槽型, 其他两类出现概率相差不到10个百分点.     

青海高原雪灾风险区划及对策建议

利用青海省50个气象台站逐日积雪深度资料、遥感监测积雪深度资料和牲畜死亡率资料,对遥感监测积雪数据进行了验证,证实遥感监测积雪数据能很好的反映青海积雪状况.利用积雪指标分析青海各地致灾强度大小.结果表明:青海三江源地区和祁连山区的部分地区致灾因子危险性最高,柴达木盆地的西部和东部农业区以及环湖的部分地区致灾因子危险性较低.通过分析积雪指标和牲畜死亡率的相关关系,确定了不同雪灾等级临界气象指标,对青海地区进行了雪灾风险区划.区划结果为:轻灾主要发生在柴达木盆地、东部农业区的大部和环湖的部分地区,这些地区发生轻灾的频率大都在50%以上;中灾和重灾在青海发生频率均不高,都在20%以下;三江源的大部尤其是囊谦、玉树和称多一带是特大雪灾的高发区,发生频率均在50%以上.     

气候变化感知对城市居民冰雪旅游环境责任行为的影响机制——以北京市为例

冰雪旅游是推动冰雪经济与旅游产业高质量融合发展的重要途径,现已成为文旅产业中最具潜力的领域之一。然而在全球气候变化的影响下,冰雪旅游发展面临严峻挑战。文章以北京市为例,运用结构方程模型对冰雪旅游情境中城市居民的气候变化感知、感知价值、旅游意向和环境责任行为等变量间的影响关系及其形成机制进行分析。研究发现：(1)气候变化感知分为气候变化表征和气候变化风险两个维度,且两者显著正向影响冰雪旅游感知价值。同时,气候变化风险显著正向影响旅游者环境责任行为。(2)冰雪旅游感知价值显著正向影响冰雪旅游意向和环境责任行为。(3)冰雪旅游意向显著正向影响环境责任行为。(4)旅游经历在气候变化感知对冰雪旅游意向和环境责任行为的影响关系中具有显著的正向调节作用。本研究对厘清冰雪旅游情境下气候变化感知对游客环境责任行为的复杂影响机理具有重要的理论价值,同时也能够在一定程度上为相关部门把握“后冬奥”黄金机遇,推动冰雪旅游高质量发展提供决策参考。     

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%.