祁连山冰川:正在远去     

窦贤 《国土资源》2004,(6):36-39

横亘在甘肃与青海交界处的祁连山，在中国西部算不上大山，但由于其特殊的生态作用而成为人们关注的焦点。  相似文献   

水晶:那一滴珍藏亿年的天使泪     

李君洲  谭力 《国土资源导刊(湖南)》2009,6(5)

传说欧洲人最早在阿尔卑斯山找到了水晶,那里山岭高峻、四季积雪,古人相信水晶是冰的化石,是上帝用冰造的.在中国古时亦有"千年冰化为水精"之说;南宋诗人杨万里有诗"西湖野僧夸藏冰,半年化作真水精".因为水晶的晶莹,人们相信其中隐藏有神灵.  相似文献   

青藏高原积雪和季节冻融层的突变特征及其对中国降水的影响   总被引：7，自引：5，他引：2  

高荣  钟海玲  董文杰  韦志刚 《冰川冻土》2010,32(3)

利用青藏高原气象台站观测的积雪和冻土资料,建立了高原积雪和季节冻融层1965—2004年的变化序列,通过滑动T平均、M-K检验、动力学分割算法(BG算法)等方法检验出高原积雪没有发生明显的突变过程,而高原季节冻融层在1987年前后有一次明显的突变,冻结深度减少比较显著.当高原积雪偏少时,华南和西南降水偏多,而当高原冻结较厚时,全国的降水几乎都偏少.通过计算高原积雪和季节冻融层与全国夏季降水的单因子相关和复相关发现,积雪和季节性冻土对中国夏季降水都有一定的可预测性,但是如果共同考虑两个因子的影响,则能够提高夏季降水预测的准确率.考虑两个因子的共同影响,有3个明显的相关带,分别是北部沿大兴安岭经太行山北部到陕北最后到河西走廊,中部在长江中下游地区,南部则是沿武夷山经南岭到云贵高原中部.  相似文献   

常数和变化积雪密度方案诊断计算积雪厚度的敏感性研究     

张慧敏  金梅兵  祁第 《海洋学报》2022,44(7):47-57

海冰上积雪的分布是影响海冰与大气能量交换以及气候变化的重要因素。当前的CMIP6气候模式（如CESM2和NESM3）采用定常的积雪密度,而专注于模拟雪厚度和密度变化的模式（如SnowModel-LG）则采用经验的变化雪密度公式。对比CryoSat-2卫星观测的积雪厚度发现,从积雪厚度的空间分布与平均值难以判断出变化雪密度对北冰洋积雪厚度模拟产生何种影响,对于变化雪密度模拟积雪厚度的改进及机制有待进一步研究。本文采用随气温、风速等因子变化的雪密度经验公式模型,并利用SNOTEL单站的长时间序列观测资料,对不同影响因子设计如下敏感性实验:A. 考虑所有气象因子的变化雪密度模型;B. 常数雪密度模型;C. 在A中不考虑风对密实化的影响;D. 在A中不考虑气温对密实化的影响。实验A、B、C和D诊断计算的2018年11月1日至2019年5月10日积雪厚度的均方根误差分别为4.2 cm、4.8 cm、25.9 cm和4.2 cm。结果表明,变化雪密度方案A模拟的积雪密度、厚度在平均值上与常数雪密度的结果接近,但其模拟的积雪厚度均方根误差最小,并且能够模拟出积雪厚度在几天到十几天时间尺度上的高频变化,同时减小了这种高频变化对应时段雪厚模拟结果的相对误差,二者具有一定的相关性。此外,还发现气温变化对积雪密实化的影响远小于风。  相似文献   

中国积雪资源时空演变特征及其旅游开发关键区识别     

刘志林  王磊  丁银平  角媛梅  周司洋  徐秋娥  唐承财 《地理研究》2023,42(2):352-370

积雪资源深刻影响着滑雪旅游的发展与布局,刻画中国积雪资源时空特征,识别旅游开发关键区,对中国冰雪旅游高质量发展具有重要意义。本文通过Mann-Kendall突变检验、热点分析、重要-感知实绩分析（IPA）等方法刻画中国1979—2020年积雪资源时空演变特征,构建资源稳定性-旅游开发适宜性指标体系,识别中国滑雪旅游开发关键区。结果表明：(1)中国积雪资源过去40年变化可划为3个阶段,1980—1995年（Ⅰ）积雪资源下降,1995—2010年（Ⅱ）增加,2010—2020年（Ⅲ）下降。在空间上,变化面积呈现Ⅰ-Ⅱ（33.2%）>Ⅱ-Ⅲ（31.1%）>Ⅰ-Ⅲ（29.3%）的特征;(2)中国积雪资源在月尺度上,1—3月（Ⅰ）积雪资源基本维持稳定,3—10月（Ⅱ）显著下降,10—12月（Ⅲ）恢复。在空间上,变化面积呈Ⅰ-Ⅲ（30.3%）>Ⅱ-Ⅲ（28.3%）>Ⅰ-Ⅱ（26.8%）的变化规律;(3)中国境内滑雪旅游开发极关键区面积占比为4.90%、关键区为11.69%、一般区为31.57%、不关键区为25.73%、极不关键区为26.11%,总体来看中国境内85%的区域不...  相似文献   

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

Bhogendra Mishra  Nitin K. Tripathi  Mukand S. Babel 《山地科学学报》2014,11(4):825-837

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.  相似文献   

A new MODIS daffy cloud free snow cover mapping algorithm on the Tibetan Plateau     

XiaoDong Huang  XiaoHua Hao  QiSheng Feng  Wei Wang  TianGang Liang 《寒旱区科学》2014,(2):116-123

Because of similar reflective characteristics of snow and cloud, the weather status seriously affects snow monitoring using optical remote sensing data. Cloud amount analysis during 2010 to 2011 snow seasons shows that cloud cover is the major limitation for snow cover monitoring using MOD10A1 and MYD10A1. By use of MODIS daily snow cover products and AMSR-E snow wa- ter equivalent products （SWE）, several cloud elimination methods were integrated to produce a new daily cloud flee snow cover product, and information of snow depth from 85 climate stations in Tibetan Plateau area （TP） were used to validate the accuracy of the new composite snow cover product. The results indicate that snow classification accuracy of the new daily snow cover product reaches 91.7% when snow depth is over 3 cm. This suggests that the new daily snow cover mapping algorithm is suitable for monitoring snow cover dynamic changes in TP.  相似文献   

春夏季青藏高原积雪对中国夏末秋初 降水的影响及其可能机制   总被引：5，自引：1，他引：4  

霍飞  江志红  刘征宇 《大气科学》2014,38(2):352-362

本文首先利用最大协方差分析方法,探讨青藏高原积雪与中国降水之间的联系,发现中国夏末秋初（8～10月,简称ASO）降水与前期及同期高原积雪有着显著联系,当春夏季青藏高原西部多雪时,其后ASO中国长江及其以南地区多雨,而东部沿海的狭长区域少雨。进一步引入最大响应估计等方法,研究中国区域降水对高原积雪异常的响应及其可能的物理机制,结果表明,冬春季高原多雪异常可持续到夏季,并通过改变地表热力状况,导致ASO南亚高压减弱,同时在高、低空激发出两支波列:高层200 hPa波列沿中高纬西风急流传播,自高原经蒙古到达日本呈现明显的“负—正—负”位势高度异常传播,日本上空为气旋性异常环流;低层850 hPa波列起于高原,经孟加拉湾至中国南海,沿着西南气流传播,导致台湾附近的反气旋性异常环流,其西侧的偏南气流,将南海丰富的水汽输送至中国南部湖南、广西;而高层中心位于日本的气旋性异常环流西侧的偏北气流利于北方天气尺度扰动向南移动,它们为长江中下游及其以南地区多雨提供了有利条件。进一步计算定常波波数也表明,高层西风急流与低层西南季风气流作为波导,有利于高原上空的扰动沿着高、低空2支通道向东传播。由于东部沿海浙江、福建为正位势高度异常区,低层反气旋性异常环流则抑制了该区域的降水。  相似文献   

Thickness distribution and extent of sea ice and snow in Prydz Bay and surrounding waters observed in 2012/2013 austral summer     

CHENG Peng  HAN Hongwei  LEI Ruibo  LI Zhijun  LU Peng 《极地研究(英文版)》2014,(1):1-9

Three ship-based observational campaigns were conducted to survey sea ice and snow in Prydz Bay and the surrounding waters(64.40°S–69.40°S, 76.11°E–81.29°E) from 28 November 2012 to 3 February 2013. In this paper, we present the sea ice extent and its variation, and the ice and snow thickness distributions and their variations with time in the observed zone. In the pack ice zone, the southern edge of the pack ice changed little, whereas the northern edge retreated significantly during the two earlier observation periods. Compared with the pack ice, the fast ice exhibited a significantly slower variation in extent with its northernmost edge retreating southwards by 6.7 km at a rate of 0.37 km?d-1. Generally, ice showed an increment in thickness with increasing latitude from the end of November to the middle of December. Ice and snow thickness followed an approximate normal distribution during the two earlier observations(79.7±28.9 cm, 79.1±19.1 cm for ice thickness, and 11.6±6.1 cm, 9.6±3.4 cm for snow thickness, respectively), and the distribution tended to be more concentrated in mid-December than in late November. The expected value of ice thickness decreased by 0.6 cm, whereas that of snow thickness decreased by 2 cm from 28 November to 18 December 2012. Ice thickness distribution showed no obvious regularity between 31 January and 3 February, 2013.  相似文献   

Thermodynamic properties of snow cover on sea ice during the austral summer in Prydz Bay,East Antarctica     

HAN Hongwei  CHENG Peng  LI Zhijun  LEI Ruibo  LU Peng 《极地研究(英文版)》2014,(1):10-16

The thermodynamic properties of snow cover on sea ice play a key role in the ice-ocean-atmosphere system and have been a focus of recent scientific research. In this study, we investigated the thermodynamic properties of snow cover on sea ice in the Nella Fjord, Prydz Bay, East Antarctica(69°20′S, 76°07′E), near the Chinese Antarctic Zhongshan Station. Our observations were carried out during the 29th Chinese National Antarctic Research Expedition. We found that the vertical temperature profile of snow cover changed considerably in response to changes in air temperature and solar radiation during the summer. Associated with the changes in the temperature profile were fluctuations in the temperature gradient within the upper 10 cm of the snow cover. Results of previous research have shown that the thermal conductivity of snow is strongly correlated with snow density. To calculate the thermal conductivity in this study, we measured densities in three snow pits. The calculated thermal conductivity ranged from 0.258–0.569 W?m-1?K-1. We present these datasets to show how involved parameters changed, and to contribute to a better understanding of melting processes in the snow cover on sea ice.  相似文献