青岛市海滩表层砂的干密度特征研究

通过对青岛市主要旅游海滩(第一海水浴场、第二海水浴场、金沙滩、石老人、仰口和流清河海滩)进行野外调查,并对162个表层砂样品进行室内实验,分析了各海滩表层砂的干密度变化特征。结果表明:研究区的6个海滩表层砂的干密度在高潮线至低潮线方向由小变大,具有良好的一致性;而在剖面A至剖面C方向上每个海滩表层砂的干密度具有自己独特的变化特征;平均粒径是影响表层砂干密度的重要因素,干密度的大小与平均粒径呈负相关关系。     

杭州湾北岸一次极端高温过程分析

利用实时探空场和欧洲中心20:00细网格、NCEP再分析资料对2013年8月6—10日杭州湾北岸极端高温过程进行分析,结果表明:副高脊线稳定在30°N、高层有辐散气流、低层26℃暖气团处于强副高中心影响是持续40℃极端高温发生的大尺度环流背景。副高与南亚高压"相向而行",在垂直方向上产生深厚的次级环流圈,是2013年极端高温持续时间长、影响范围大的一个重要因素。极端高温的出现与空气异常干燥有密切关系,北方高层干空气南下叠加在中低层副高暖干气团之上,从高层到近地层受一致下沉气流的绝热压缩增温,以及低空暖平流输送的综合影响,是导致7—9日杭州湾北岸多地最高气温屡破记录的原因。经检验,极端高温期间,业务参考使用的4个主要数值模式对最高气温的预报,EC模式误差较小且稳定,绝对误差为1℃,而GFS、JMA和T639误差分别达3℃、4℃和5℃,应用时需订正,以上结果可供夏季高温预报参考。     

压实黄土非饱和渗透系数试验研究

压实黄土非饱和渗透特性具有重要的工程意义。对不同干密度的压实黄土试样,采用非饱和导水率测定系统进行非饱和渗透系数量测,得到不同干密度压实黄土的渗透系数与吸力的关系。试验结果表明：渗透系数随吸力的增大而成非线性减小,随干密度的减小而增大;在低吸力时,干密度对渗透系数的影响较大;在较高吸力时,干密度对渗透系数的影响较小。进一步给出了压实黄土的渗透系数模型并进行回归分析,得到各Wind幂函数模型参数与干密度的关系,建立了基于干密度的压实黄土渗透系数的确定方法。     

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

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。     

中国积雪资源时空演变特征及其旅游开发关键区识别

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...     

河北省重点城市干湿季PM_(2.5)输送路径和来源分析

为深入研究河北省石家庄、邯郸、唐山以及邢台4座“退后十”重点城市干湿季PM_(2.5)的重污染传输潜在源区,结合GDAS数据,基于HYSPLIT后向轨迹,通过聚类分析、潜在污染源以及浓度权重的计算,对4座重点城市进行大气污染来源的分析。结果表明:1)PM_(2.5)质量浓度呈现为干季浓度较高、湿季浓度较低,同时地域差异较大;2)干季气团4个城市主要受山西省、陕西省、河南省和山东省重工业城市春冬季大量的燃煤、机动车尾气及二次反应携带高浓度气团的影响,该类气团轨迹传输路径较短,传输速度快,影响显著;3)湿季气团大多来源于沿海地区和北部内陆地区,由蒙古国传输至内蒙古地区,以自然源排放为主,受北部冷空气持续影响,使得大气污染物浓度稀释;4)PM_(2.5)较强潜在源区都集中在内蒙古西南部、陕西省东北部、山西省中南部、河南省北部,其中强潜在源区相似,主要集中在延安市、吕梁市、临汾市。     

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.     

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

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.     

双齿围沙蚕对温度、盐度和干露的耐受性研究

本论文主要研究双齿围沙蚕(Perinereis aibuhitensis)对盐度突变、温度突变和盐度渐变、温度渐变及干露的耐受性。实验结果表明:双齿围沙蚕耐受盐度的范围比较广泛,为5~45,适宜的盐度范围为20~30,求得双齿围沙蚕96 h半致死盐度上限为33.1。适宜温度范围为20~30℃,对低温有很强的耐受力。双齿围沙蚕在低温保湿干露10 d未发生死亡,大量死亡集中在13~14 d。本研究结果表明:双齿围沙蚕对环境的适应性较强,耐温、耐盐范围较广,且具有很强的耐干露能力。     

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

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.