康普顿化的计算-推广的Kompaneets方程与Monte Carlo方法比较

在X射线天文学以及辐射物理学中,当硬X射线穿过" 冷"的等离子体时所发生的Compton软化是一种重要的辐射转移过程.简要介绍推广的Kompaneets方程,该方程在hv〈mec2及kTe〈mec2较宽松的条件下广泛成立,因此不仅能处理Compton硬化过程,而且也适用于Compton软化过程,后者在目前快速发展的X射线和γ射线天文学中十分重要.基于此方程,我们对天体物理中4种常见辐射谱(Gauss型发射谱线、黑体辐射谱、幂律辐射谱和热轫致辐射谱)在Compton软化情况下的谱演化进行了数值求解,并同Monte Carlo模拟结果做比较,证实了推广的Kompaneets方程的正确性和有效性.最后指出此方程在X射线天文学和γ射线天文学中的重要意义和潜在应用.     

三组火山强迫资料差异及其对模式模拟结果的影响

Gao2008、Crowley2013和Sigl2015火山强迫资料,均基于极地冰芯重建.由于每组重建使用的冰芯数据和分析方法等不同,因此结果存在差异,从而影响气候模式应用.文中详细梳理三组火山强迫资料在原始冰芯数据、信号识别提取和沉积通量计算等方面的差异;介绍重建中涉及的对未知火山事件发生季节、纬度及从极地硫酸盐沉积...     

A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System

How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts. In this study, a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Predi...     

云顶以下臭氧对TOMS反演臭氧总量的影响及反演方法的理论研究

目前紫外后向散射反演臭氧总量的所有算法,都将云考虑成不透明的Lambertian反射体,并假定云顶有效反照率不随波长的变化而变化.然而本文通过模拟计算发现,由于云散射、瑞利散射、臭氧吸收三种作用的综合结果,云顶的有效反照率是与波长相关的,即使光学厚度比较大的云,辐射也可由云顶继续向下传输,因而会受到云顶以下臭氧吸收的影响.用V7方法进行反演,模拟计算结果表明：云的出现使得云顶以下,特别是云内光程增强,导致紫外波段的臭氧吸收衰减增大,所以反演出的臭氧总量值比真实值偏大,本文称这种现象为“云吸收效应”,并讨论了该效应的影响因子.最后,在辐射传输模拟的基础上建立一套反演算法,大大减弱了“云吸收效应”的影响.     

陕西黄土高原人工林土壤干层及形成原因

1 Introduction Abroad, a lot of research on soil water removal, seeping and evaporation has been done (Ole, 1998; Rapp, 2000). A seeping model of soil water was suggested (Yuin, 1998) and it was known that water removal is very slow in the unsaturated zon…     

Paleoclimate studies and natural-resource management in the Murray-Darling Basin I: past,present and future climates

This paper provides an incisive review of paleoclimate science and its relevance to natural-resource management within the Murray-Darling Basin (MDB). The drought of 1997–2010 focussed scientific, public and media attention on intrinsic climate variability and the confounding effect of human activity, especially in terms of water-resource management. Many policy and research reviews make statements about future planning with little consideration of climate change and without useful actionable knowledge. In order to understand future climate changes, modellers need, and demand, better paleoclimate data to constrain their model projections. Here, we present an insight into a number of existing long-term paleoclimate studies relevant to the MDB. Past records of climate, in response to orbital forcing (glacial–interglacial cycles) are found within, and immediately outside, the MDB. High-resolution temperature records, spanning the last 10⁵ years, exist from floodplains and cave speleothems, as well as evidence from lakes and their associated lunettes. More recently, historical climate records show major changes in relation to El Niño–Southern Oscillation cycles and decadal shifts in rainfall regimes. A considerable body of research currently exists on the past climates of southeastern Australia but, this has not been collated and validated over large spatial scales. It is clear that a number of knowledge gaps still exist, and there is a pressing need for the establishment of new paleoclimatic research within the MDB catchment and within adjacent, sensitive catchments if past climate science is to fulfil its potential to provide policy-relevant information to natural-resource management into the future.     

Can Eddy Forcing be Responsible for the Spatial Structure of the Northern Hemisphere Annular Mode?

In order to study the origin of the spatial structure of the Northern Hemisphere Annular Mode (NAM),a linear stochastic model is constructed empirically from the output of a GCM run.Optimal stochastic forcing in terms of the maximum variance contribution,which may be potentially related to the maintenance of the NAM,is investigated.Theoretical analysis on the dominant non-modal response to the stochastic forcing shows that this dominance is jointly decided by the properties of forcing and the non-modal grow...     

Toward an improved global network for determination of tropospheric ozone climatology and trends

An examination of typical tropospheric ozone variability on daily, monthly, annual and interannual timescales and instrumental precision indicates that the current ozonesonde network is insufficient to detect a trend in tropospheric ozone of 1% per year at the 2 level even at stations with records a decade in length. From a trend prediction analysis we conclude that in order to detect a 1% per year trend in a decade or less it will be necessary to decrease the time between observations from its present value of 3–7 days to 1 day or less. The spatial distribution of the current ozonesonde stations is also inadequate for determining the global climatology of ozone. We present a quantitative theory taking into account photochemistry, surface deposition, and wind climatology to define the effectively sampled region for an observing station which, used in conjunction with the instrumental precision and the above prediction analysis, forms the basis for defining a suitable global network for determining regional and global ozone climatology and trends. At least a doubling of the present number of stations is necessary, and the oceans, most of Asia, Africa, and South America are areas where more stations are most needed. Differential absorption lidar ozone instruments have the potential for far more frequent measurements of ozone vertical profiles and hence potentially more accurate climatology and trend determinations than feasible with ozonesondes but may produce a (fair weather) biased data set above the cloud base. A strategy for cloudy regions in which either each station utilizes both lidars and sondes or each station is in fact a doublet comprised of a near-sea-level lidar and a proximal-mountain-top lidar could serve to minimize this bias.