辐射和积云对流过程对大气辐射通量的影响

基于中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室发展的全球大气环流谱模式(SAMIL-R42L26),研究了澳大利亚气象局研究中心(BMRC)新辐射方案和新Zhang-McFaflane积云对流方案对大气辐射通量模拟的影响.新辐射方案相比原辐射方案在辐射计算光谱分辨率、气体吸收和计算效率等方面作了很多改进,其对大气辐射通量的模拟能力相应提高.在晴空条件下,大气顶出射长波、大气吸收短波和地表入射短波等与观测的偏差较原辐射方案明显减小,尤其是在对流活跃区域.在云天条件下大气辐射通量与观测的偏差也较原辐射方案减小,但其偏差依然较大,这与模式中积云对流参数化方案模拟能力不足引起的辐射通量偏差有关.为此,换用了新Zhang-McFarlane积云对流方案,其结果表明,对流活跃区水汽含量显著增加,原对流方案中偏强的"双赤道辐合带"现象明显减弱,赤道辐合带地区的大气辐射通量偏差有明显减小,在海洋地区晴空大气顶出射长波和地表入射短波的量值及空间分布均接近观测结果,同时大气顶全球平均能量收支的年变化和观测结果趋于一致,其中模拟的伞球年平均大气顶能量收支和观测的偏差不到0.6 W/m2.试验结果同时表明,在未来研究中引入气溶胶分布、调整相关的云物理和陆面过程等物理参数化方案是进一步提高SAMIL-R42L26辐射通最模拟性能的关键.     

A Deep-Learning-Based Geological Parameterization for History Matching Complex Models

A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.

     

城市轨道交通对土地利用变化的时空效应

定量分析轨道交通对城市内部精细土地利用复杂变化影响的时空效应,对于预测未来新建站点对土地利用的影响以及规划方案的调整与优化具有重要的指导意义。本文提出结合历史高分遥感影像与POI数据获取多时间节点精细土地利用信息的方法,并以广州市二号和八号地铁线为例,结合逐步回归模型与站点用地功能分类,从时间和空间两个维度定量研究地铁对土地利用转变的复杂影响过程与空间差异规律。结果表明,轨道交通促使站点周边低密度居住用地向商业用地、高密度居住用地等高效益土地利用方式的转变;轨道交通在规划、建设、运营不同阶段对于周边土地利用的影响呈现显著的差异规律;地铁站点对土地利用变化影响规律具有明显的空间异质性特征,与站点在城市里的位置以及站点周边的土地利用有关,对城市中心区域的影响较小,对郊区的影响主要与可用的非建设用地面积相关,而工业用地对土地利用变化具有限制作用;轨道交通还带来用地功能和性质的转变,这样的转变大多发生在地铁站点开通运营的时间点。     

洞庭湖流域降水同位素与ENSO关系研究

基于洞庭湖流域内长沙市2010年1月至2012年12月降水事件、GNIP(Global Network for Isotopes in Precipitation)长沙站1988~1992年月降水同位素资料及ENSO（厄尔尼诺/拉尼娜和南方涛动）的2个常用指标（南方涛动指数SOI和Nino3区海面温度SST）,分析了流域降水同位素与ENSO关系。结果表明：洞庭湖流域降水中δ¹⁸O与降水量、气温在日、月尺度上均呈负相关且只有月度上与降水量的负相关不显著。洞庭湖流域降水中δ¹⁸O与ENSO的SST指标则呈显著正相关。ENSO对洞庭湖流域降水同位素的影响机制可能如下：春季,La Nina年源自西太平洋的东南风强盛,其转向为西南风的区域达到印度洋,而El Nino年,东南风转向为西南风的发生区域位于印度洋以东,前者有利于挟带印度洋远源水汽向中国东部区域输送,进而造成降水同位素的贫化;夏季,La Nina年印度洋水汽输送在中国南海转为经向继而向北延伸,而El Nino年,源自印度洋的水汽沿纬向穿过南海,在东海转向往北延伸,后者有利于挟带西太平洋的近源水汽输送到中国东部季风区,进而引起降水同位素的富集。     

基于GIS建模的海水环境质量可变模糊识别评价

基于对立统一与质量互变定理的可变模糊评价方法引入海水水质综合评价,构建基于对立统一与质量互变定理的海洋环境质量可变模糊评价模型,首先分析可变模糊评价方法的原理和方法,然后结合GIS栅格数据在表达空间信息方面具有的独特优势,以栅格数据为基础,利用地理信息系统空间叠置分析、地理信息系统空间建模等计算,建立基于多源栅格数据的海水环境可变模糊综合评价模型,得到莱州湾2004~2010年海水环境空间分布图。实践证明,该模型应用于海水水质综合评价是完全可行的,为海洋环境领域的多目标综合评价与决策提供了新的思路与方法。     

祁连造山带研究概况——历史,现状及展望

回顾了祁连造山带的研究历史，对研究现状作了简要总结。认为在祁连造山带已初步建立起岩石圈结构及动力学模型，划分了板块构造单元，祁连山自震理纪以来先后出现大陆裂谷和板块构造两种构造体制及三种不同类型的造山作用（俯冲造山，碰撞造山和陆内造山）而构造体制变更，盆地类型转换以及造山作用类型变化的地球动力学机制是地幔柱构造。提出了造山带研究现存在的关键问题，对未来的研究进行了展望，作者认为祁连山造山理论的研究     

青藏高原地表位涡密度强迫对2008年1月中国南方降水过程的影响Ⅰ:资料分析

位涡外部源汇是驱动大气环流的原动力。文中详细介绍了地表位涡制造和位涡密度强迫的联系,讨论了不同坐标系中位涡密度方程的特点及其在应用中应当注意的问题。还以2008年初南方低温雨雪冰冻灾害为例,探讨了青藏高原地表位涡密度强迫及东传对下游地区对流性天气发生的影响,拟由此揭示青藏高原位涡密度强迫激发中国东部激烈天气发生的一种新机制。伴随着青藏高原地表正位涡密度的东传,下游地区对流层中高层出现纬向正绝对涡度平流,气旋性环流增强,从而促使低空南风发展,为南方地区提供充沛的水汽条件。另外,南风的增强有利于低空经向负绝对涡度平流的加强,从而使南方地区高、低空形成绝对涡度平流随高度增大的大尺度环流背景,有利于上升运动的发展。上升运动的加强又促进低空南风气流的增强,使高、低空绝对涡度平流随高度增大的环流背景进一步增强,最终导致降水的产生。      

地震危险性分析中确定潜在震源的反思

在区域稳定性评价中,其主要内容之一就是地震稳定性评价。近年来,国内外多采用地震危险性分析的概率方法作为区域地震稳定性评价的主要方法。 在地震危险性分析中,首要问题是确定区域潜在震源,但至今尚缺乏完整、科学的理论和实践。作者认为有必要从地震地质角度进行一次深刻的地质反思。 本文从构造变形的动力源、区域及局部应力场、地震断裂活动带的地震重复周期,地震断裂的活动特征及区域地震强度衰减规律等几个方面提出了若干看法,作为地震危险性分析的基础地震地质工作加以考虑。至于常见的非发震断裂在区域地震稳定性评为中所起的作用,则需对这些断裂的地质特征与周围地质体的介质特征进行具体的对比分析,才能判定其对地震波传播的影响,不能一概而论。     

Statistical downscaling of reference evapotranspiration in Haihe River Basin: applicability assessment and application to future projection

Future changes in reference evapotranspiration (ET₀) are of increasing importance in assessing the potential impacts on hydrology and water resources systems of more pronounced climate change. This study assesses the applicability of the Statistical Downscaling Model (SDSM) in projecting ET₀, and investigates the seasonal and spatial patterns of future ET₀ based on general circulation models (GCMs) across the Haihe River Basin. The results indicate that SDSM can downscale ET₀ well in term of different basin-averaged measures for the HadCM3 and CGCM3 GCMs. HadCM3 has a much superior capability in capturing inter-annual variability compared to CGCM3 and thus is chosen as the sole model to assess the changes in future ET₀. There are three homogeneous sub-regions of the Haihe River Basin: Northwest, Northeast and Southeast. Change points are detected at around 2050 and 2080 under the A2 and B2 scenarios, respectively. The Northwest is revealed to have a slight to strong increase in ET₀, while the Northeast and the Southeast tend to experience a pattern change from decrease to increase in ET₀.

EDITOR M.C. Acreman

ASSOCIATE EDITOR J. Thompson     

Bivariate frequency analysis of flood and extreme precipitation under changing environment: case study in catchments of the Loess Plateau,China

Floods have changed in a complex manner, triggered by the changing environment (i.e., intensified human activities and global warming). Hence, for better flood control and mitigation in the future, bivariate frequency analysis of flood and extreme precipitation events is of great necessity to be performed within the context of changing environment. Given this, in this paper, the Pettitt test and wavelet coherence transform analysis are used in combination to identify the period with transformed flood-generating mechanism. Subsequently, the primary and secondary return periods of annual maximum flood (AMF) discharge and extreme precipitation (Pr) during the identified period are derived based on the copula. Meanwhile, the conditional probability of occurring different flood discharge magnitudes under various extreme precipitation scenarios are estimated using the joint dependence structure between AMF and Pr. Moreover, Monte Carlo-based algorithm is performed to evaluate the uncertainties of the above copula-based analyses robustly. Two catchments located on the Loess plateau are selected as study regions, which are Weihe River Basin (WRB) and Jinghe River Basin (JRB). Results indicate that: (1) the 1994–2014 and 1981–2014 are identified as periods with transformed flood-generating mechanism in the WRB and JRB, respectively; (2) the primary and secondary return periods for AMF and Pr are examined. Furthermore, chance of occurring different AMF under varying Pr scenarios also be elucidated according to the joint distribution of AMF and Pr. Despite these, one thing to notice is that the associate uncertainties are considerable, thus greatly challenges measures of future flood mitigation. Results of this study offer technical reference for copula-based frequency analysis under changing environment at regional and global scales.