安徽庐枞盆地矾山酸性蚀变岩帽形成时代及其地质意义

酸性蚀变岩帽是岩浆热液流体和围岩在近地表相互作用的产物,是斑岩-浅成低温热液成矿系统的重要指标。发育在长江中下游成矿带庐枞盆地内的矾山酸性蚀变岩帽产出面积较大( 20km~2)。前人对该酸性蚀变岩帽中的明矾石矿床的地质和地化特征进行了相关研究,但详细的年代学研究工作尚未开展。为精确厘定矾山酸性蚀变岩帽的形成时代,本文开展了明矾石~(40)Ar-~(39)Ar法和金红石原位U-Pb法定年。矾山酸性蚀变岩帽中明矾石共有三种类型:ⅠA型明矾石主要呈交代蚀变发生在热液蚀变早阶段,与石英、粒状黄铁矿或赤铁矿、少量金红石共生;ⅠB型明矾石形成于热液蚀变晚阶段,主要呈叶片状集合体充填在开放空间中,与石英、星点状赤铁矿、粒状金红石集合体共生,少量金红石和赤铁矿沿明矾石解理裂隙分布;Ⅱ型明矾石是表生明矾石,主要呈细粒集合体沿裂隙分布,与赤铁矿、高岭石、地开石共生。三类明矾石形成于不同环境下:ⅠA和ⅠB型明矾石形成于岩浆热液环境下,是大矾山明矾石矿区的主要产物;Ⅱ型细粒明矾石分布在矾山酸性蚀变岩帽的非明矾石矿区,是表生环境下的产物。ⅠA型明矾石的~(40)Ar-~(39)Ar定年的坪年龄为131±6Ma,代表了矾山酸性蚀变岩帽的形成时代。与Ⅱ型明矾石密切共生的金红石U-Pb定年结果为32. 7±4Ma,在该期间,整个盆地内无岩浆活动发生,该年龄反映了矾山酸性蚀变岩帽经历表生氧化作用的时间。明矾石和金红石定年结果分别对应岩浆热液和表生明矾石的形成时代。在利用明矾石进行找矿工作时需先明确明矾石成因,矾山酸性蚀变岩帽中深成明矾石是下一阶段的找矿研究的基础。     

GPS探测与FY-2反演大气可降水量对比分析

为深入了解FY-2卫星大气可降水量(PW)的反演质量,文章选取2012和2015年地基GPS水汽观测数据,与FY-2的PW反演产品进行了对比分析。结果表明:(1)北京、武汉和海口三站GPS/PW(PW_(GPS))与FY-2/PW(PW_(FY-2))在夏季存在显著正相关,三站的相关系数都达到0.67以上,夏季PW的均方根误差值、月平均偏差绝对值均小于冬季。北京与武汉站PW平均偏差和均方根误差在四季均具有明显日变化特征;(2)当PW_(GPS)20 mm时,北京、武汉、海口和拉萨站FY-2/PW与GPS/PW比较一致,PW偏差均值的绝对值和均方根误差较小,当PW_(GPS)20 mm时,PW偏差均值绝对值和均方根误差随PW_(GPS)值减小而迅速变大。FY-2的PW产品在夏季可以为大部分区域提供高时空分辨率、高精度的大气可降水量,在大气湿度非常低、冬季和夜间条件,反演结果精度有待提高。     

Uncertainty in projections of the South Asian summer monsoon under global warming by CMIP6 models: Role of tropospheric meridional thermal contrast

本文基于第六次国际耦合模式比较计划共18个模式的工业革命前实验和CO₂浓度突然四倍实验,发现在CO₂四倍强迫下,南亚夏季风环流呈显著减弱趋势,但减弱强度存在较大模式间差异.利用Webster-Yang指数和经向哈得莱环流指数的下降趋势表征SASM减弱强度,发现该下降趋势与欧亚大陆-印度洋之间对流层上层经向温度梯度的变化值(EUTT-IUTT)高度相关.进一步利用气候反馈-响应分析方法进行分析,发现EUTT-IUTT变化的模式间差异主要来自于大气动力过程,其次是云的短波辐射效应的贡献.地表潜热通量和云的长波辐射效应缩小了EUTT-IUTT变化的模式间差异.     

用选支CO激光研究NO,NO_2气体的吸收特性

本文利用国产XZJ—1型CO选支激光器测量了NO 5.2微米带和NO_2 6.2微米带49个激光频率处的吸收系数,对吸收变化特征进行了初步分析并且讨论了实验误差及实际大气监测中水汽干扰等问题.     

华南地区城市化对区域气候变化的影响

按照人口数将华南地区站点分为大城市站、一般城市站、郊区站,并利用华南地区1960~2011年的站点观测资料分别计算了3类站点年平均、季节平均的气温、高温日数、降水、相对湿度、风速、日照时数距平序列的变化,分析了城市化对华南地区区域气候的影响。结果表明:相较于背景场,大城市的平均气温有更明显的上升趋势;高温日数在3类站点中均有增加的趋势,在城市化的影响下,大城市的高温日数有明显的增加;平均气温日较差在整个华南地区均有下降趋势,特别是在大城市中。在3类站点中,降雨总量均有减少的趋势,且降雨更多的以中雨及以上的形式表现。该地区的相对湿度、风速、日照时数均呈现减少趋势,在城市化影响下,大城市的相对湿度、风速、日照时数均有明显的减少。华南地区处于我国最大的城市群之一——珠江三角洲地区,同时处于气候系统复杂的热带季风区,因此有必要研究城市化对该地区多个气象变量的可能影响。     

2009/2010年冬季云南严重干旱的原因分析

2009/2010年冬季我国云南省出现严重干旱,这次大范围严重干旱是较长时期降水稀少所造成的。首先讨论云南省冬季降水偏多和偏少时大气环流和海温的统计特征,基于它们的统计关系,再对2009/2010年冬季我国云南省的严重干旱进行个例对比分析。研究表明西风带环流系统异常是造成这次干旱灾害的主要成因。贝加尔湖为高度负距平,东亚沿海为高度正距平,从贝加尔湖以西到东亚中高纬度西风带较平直,冬季冷空气偏弱,很难影响西南地区。尤其是副热带中东急流减弱,从欧洲东部到里海为高压脊控制,西风带的扰动系统不易东移到东亚上空;青藏高原上空为稳定的高压脊,孟加拉湾南支槽减弱,云南省受异常西北气流控制。对太平洋和印度洋海温的分析表明,虽然海温异常可以影响冬季的云南降水,但海温异常并不是2009/2010年冬季云南省降水偏少的最重要原因。     

一种新的统计回归模型及其建模方案

根据经典门限自回归模型的基本思想,引入半截多项式变换,导出了一种新的统计回归模型,并提出了相应的一整套建模方案。这种模型和建模方案的特点是:（1）解决了统计回归分析中逐段线性化模型的检验问题;（2）在确定统计回归模型中各待估参数和变量——包括门限变量、门限值、阶数、时滞和回归系数时显得十分方便、快捷。试验结果表明,根据此方案建立的统计回归模型具有较高的拟合和预报精度,同时具有良好的稳定性。     

Effect of Stochastic MJO Forcing on ENSO Predictability

Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation(MJO) on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing.The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation(CNOP);compared to CNOP-type initial error,the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO,and its influence over the ENSO predictability was not significant.This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction,which could provide a theoretical foundation for the data assimilation of the ENSO forecast.     

Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.