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风尘堆积常见的同沉积和沉积后改造特征及其环境意义 总被引：2，自引：2，他引：0
黄土堆积作为气下沉积,任何一个深度都曾经暴露于地表,因而必然受到相关地表过程的作用.由于这些过程均发生于特定的环境条件下,形成的特征多数具有明确的环境意义;而黄土在沉积后也可能受到各种地质过程的改造,从而对研究中常用的气候代用指标有一定影响.文章基于野外、微形态等分析,结合前人成果,对我国北方新近纪风尘堆积中常见的同沉积和沉积后改造特征、形成过程及环境意义进行研究.由于一些特征在黄土堆积中具有普遍性,可作为识别风成堆积的标志和环境事件研究的指标,并有助于全面理解常用的替代指标的环境意义. 相似文献
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天水-秦安一带中新世黄土堆积区沉积-地貌演化 总被引：5，自引：0，他引：5
前人的构造地质学研究,将天水-秦安一带的中新世黄土分布区划归两个不同的构造单元.文章基于野外调查和已有年代地层学工作,结合前人成果,对该区新生代沉积-地貌演化历史进行研究,并划分为以下主要阶段:1)古近纪初南部秦岭山地的剥蚀,使本区在原有基岩准平原地形的基础上,形成以冲洪积平原为主的地形.古近纪末-新近纪初的构造活动使冲洪积平原解体,在秦安地区形成基岩台地与沉陷盆地相间、天水-西和地区形成拉分盆地与隆起山地交错的地貌景观,这些高地为中新世黄土堆积提供了地形基础.2)中新世从22Ma到11Ma,基岩台地和相对平缓的高地上堆积典型黄土-古土壤序列,盆地内则主要发育次生黄土等洼地沉积,表明研究区类似于今天的黄土高原.3)中新世晚期约11Ma起发生的一次侵蚀事件,使研究区的一些小盆地内发育河流相和间歇性浅湖相沉积,秦安一带的黄土堆积也遭到侵蚀,形成的洼地内发育黄土状土或洼地静水沉积,其中包含较多哺乳动物化石,而大范围的相对平坦高地上一直继续发育黄土-古土壤序列.这次侵蚀对本区内甘肃群的沉积多样性有重要贡献,但一直没有深水湖泊发育的条件.4)发生于3.5Ma以后的另一次重大侵蚀,奠定了该区今天狭窄长墚地形的基础,是第四纪黄土堆积在本区保存较差的主要原因. 相似文献
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Grain-size features of a Miocene loess-soil sequence at Qinan: Implications on its origin 总被引：4，自引：0，他引：4
QIAO Yansong GUO Zhengtang HAO Qingzhen YIN Qiuzhen YUAN Baoyin LIU Tungsheng 《中国科学D辑(英文版)》2006,49(7):731-738
In northern China, the Quaternary loess-soil se-quences[1] and the Hipparion Red-Earth Formation in the eastern Loess Plateau[2―6] provide a continental climate record for the past 8 Ma. The recently reported Miocene[7] and Pliocene[8] loess-soil sequences near Qinan constitute an eolian record of the western Loess Plateau from 22 to 3.5 Ma. Earlier studies[9] place the Miocene loess deposits into the so-called Gansu Sys-tem. Our investigations show that the Gansu System contains inde… 相似文献
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Ensemble standardization constraints on the influence of the tree growth trends in dendroclimatology
Shi Feng Yang Bao Linderholm Hans W. Seftigen Kristina Yang Fengmei Yin Qiuzhen Shao Xuemei Guo Zhengtang 《Climate Dynamics》2020,54(7):3387-3404
Climate Dynamics - Tree growth trends can affect the interpretation of the response of tree-ring proxies (especially tree-ring width) to climate in the low-frequency band, which in turn may limit... 相似文献
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Helene Muri André Berger Qiuzhen Yin Aurore Voldoire David Salas Y. Mélia Suchithra Sundaram 《Climate Dynamics》2012,39(7-8):1739-1761
As a first qualitative assessment tool, LOVECLIM has been used to investigate the interactions between insolation, ice sheets and the East Asian Monsoon at the Marine Isotopic Stage 13 (MIS–13) in work by Yin et?al. (Clim Past 4:79–90, 2008, Clim Past 5:229–243, 2009). The results are in need of validation with a more sophisticated model, which is done in this work with the ARPEGE atmospheric general circulation model. As in the Earth system Model of Intermediate Complexity, LOVECLIM, ARPEGE shows that the northern hemispheric high insolation in summer leads to strong MIS–13 monsoon precipitation. Data from the Chinese Loess Plateau indicate that MIS–13 was locally a warm and humid period (Guo et?al. in Clim Past 5:21–31, 2009; Yin and Guo in Chin Sci Bull 51(2):213–220, 2006). This is confirmed by these General Circulation Model (GCM) results, where the MIS–13 climate is found to be hotter and more humid both in the presence and absence of any added ice sheets. LOVECLIM found that the combined effects of the ice sheets and their accompanying SSTs contribute to more precipitation in eastern China, whilst in ARPEGE the impact is significant in northeastern China. Nonetheless the results of ARPEGE confirm the counter-intuitive results of LOVECLIM where ice sheets contribute to enhance monsoon precipitation. This happens through a topography induced wave propagating through Eurasia with an ascending branch over northeastern China. A feature which is also seen in LOVECLIM. The SST forcing in ARPEGE results in a strong zonal temperature gradient between the North Atlantic and east Eurasia, which in turn triggers an atmospheric gravity wave. This wave induces a blocking Okhotskian high, preventing the northwards penetration of the Meiyu monsoon front. The synergism between the ice sheets and SST is found through the factor separation method, yielding an increase in the Meiyu precipitation, though a reduction of the Changma precipitation. The synergism between the ice sheets and SST play a non-negligible role and should be taken into consideration in GCM studies. Preliminary fully coupled AOGCM results presented here further substantiate the finding of stronger MIS–13 monsoons and a reinforcement from ice sheets. This work increases our understanding of the signals found in the paleo-observations and the dynamics of the complex East Asian Summer Monsoon. 相似文献
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断层逸出气体的δHe／Ar值变化与地震活动的关系 总被引：3，自引：0，他引：3
论述了１９９０年６月底至１９９１年１２月间塔院断层气体中δＨｅ／Ａｒ值变化与几次地震的关系，从已获得的几次震例来看，有的地震δＨｅ／Ａｒ出现负异常，而有的出现正异常。δＨｅ／Ａｒ值具有较好的映震能力。 相似文献
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白浮监测点断层土壤气的变化特征 总被引：1，自引：0，他引：1
文中介绍了北京南口—孙河断裂带上白浮断层土壤气四个不同集气深度H_2/Ar,N_2/Ar,CO_2/Ar,~4He/~(20)Ne,~4He/~(40)/Ar,~(40)Ar/~(36)Ar比值与地震活动,降雨等的关系,得到各项指标的变化与地震的发生具有一定的关系,但受季节变化的影响。通过断层剖面的工作,证明该断层具有较强的活动性。 相似文献
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Various solar wind forecasting methods have been developed during the past decade, such as the Wang?–?Sheeley model and the Hakamada?–?Akasofu?–?Fry Version 2 (HAFv2) model. Also, considerable correlation has been found between the solar wind speed v and the coronal hole (CH) area A M on the visible side of the Sun, showing quantitative improvement of forecasting accuracy in low CME activity periods (e.g., Vr?nak, Temmer, and Veronig, Solar Phys. 240, 315, 2007a). Properties of lower layers of the solar atmosphere are good indications of the subsequent interplanetary and geomagnetic activities. We analyze the SOHO/EIT 284 Å images and construct a new forecasting factor (Pch) from the brightness of the solar EUV emission, and a good correlation is found between the Pch factor and the 3-day-lag solar wind velocity (v) probed by the ACE spacecraft. The main difference between the Pch and A M factor is that Pch does not depend on the CH-boundary estimate and can reflect both the area and brightness of CH. A simple method of forecasting the solar wind speed near Earth in low CME activity periods is presented. Between Pch and v from 21 November until 26 December 2003, the linear correlation coefficient is R=0.89. For comparison we also analyze the data in the same period (DOY 25?–?125, 2005) as Vr?nak, Temmer, and Veronig (Solar Phys. 240, 315, 2007a), who used the CH areas A M for predicting the solar wind parameters. In this period the correlation coefficient between Pch and v is R=0.70, whereas for A M and v the correlation coefficient is R=0.62. The average relative difference between the calculated and the observed values is $\overline{|\delta|}\approx 12.15\%Various solar wind forecasting methods have been developed during the past decade, such as the Wang – Sheeley model and the
Hakamada – Akasofu – Fry Version 2 (HAFv2) model. Also, considerable correlation has been found between the solar wind speed
v and the coronal hole (CH) area A
M on the visible side of the Sun, showing quantitative improvement of forecasting accuracy in low CME activity periods (e.g., Vršnak, Temmer, and Veronig, Solar Phys.
240, 315, 2007a). Properties of lower layers of the solar atmosphere are good indications of the subsequent interplanetary and geomagnetic
activities. We analyze the SOHO/EIT 284 ? images and construct a new forecasting factor (Pch) from the brightness of the solar
EUV emission, and a good correlation is found between the Pch factor and the 3-day-lag solar wind velocity (v) probed by the ACE spacecraft. The main difference between the Pch and A
M factor is that Pch does not depend on the CH-boundary estimate and can reflect both the area and brightness of CH. A simple
method of forecasting the solar wind speed near Earth in low CME activity periods is presented. Between Pch and v from 21 November until 26 December 2003, the linear correlation coefficient is R=0.89. For comparison we also analyze the data in the same period (DOY 25 – 125, 2005) as Vršnak, Temmer, and Veronig (Solar Phys.
240, 315, 2007a), who used the CH areas A
M for predicting the solar wind parameters. In this period the correlation coefficient between Pch and v is R=0.70, whereas for A
M and v the correlation coefficient is R=0.62. The average relative difference between the calculated and the observed values is
. Furthermore, for the ten peaks during the analysis period, Pch and v show a correlation coefficient of R=0.78, and the average relative difference between the calculated and the observed peak values is
. Moreover, the Pch factor can eliminate personal bias in the forecasting process, which existed in the method using CH area
as input parameter, because CH area depends on the CH-boundary estimate but Pch does not. Until now the CH-boundary could
not be easily determined since no quantitative criteria can be used to precisely locate CHs from observations, which led to
differences in forecasting accuracy. 相似文献