An Actively Controlled Wind Tunnel And Its Application To The Reproduction Of The Atmospheric Boundary Layer

An actively controlled wind tunnel equipped with multiple fansand airfoils has been developed, mainly for the purpose of reproducing the atmospheric boundary layer (ABL) for wind engineering applications. Various fluctuating flows can be achieved in this wind tunnel by altering the input data of the fans and airfoils through computer control. In this study, the ABL is physically simulated in this wind tunnel, and particular attention ispaid to the simulation of the profile of Reynolds stress. The method of generating the fluctuating flow and the experimental results of reproducing the ABL are presented. As the results show, the spatial distribution of Reynolds stress is satisfactorily simulated, and the profiles of other statisticalturbulent parameters, such as mean velocity, turbulent intensity, integral scale and power spectrum are successfully reproduced simultaneously.     

Extremely high temperatures in France at the end of the century

Power plant construction requires anticipation to achieve a liable dimensioning on the long functioning time of the installation. In the present climate change context, dimensioning towards extremely high temperature for installations intended to run until the 2070s or later implies an evaluation of plausible extreme values at this time scale. This study is devoted to such an estimation for France, using both observation series and climate model simulation results. The climate model results are taken from the European PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects) project database of regional climate change scenarios for Europe. Comparison of high summer temperature distributions given by observations and climate models under current climate conditions, conducted using Generalized Extreme Value distribution, reveals that only a few models are able to correctly reproduce it. For these models, climate change under IPCC A2 and B2 scenarios leads to differences in the variability of high values, whose proportion has an important impact on future 100-year return levels. This study was first presented at the EGU General Assembly in Vienna, 2–7 April 2006.     

Modeling endogenous learning and imperfect competition effects in climate change economics

In this two-part paper we evaluate the effect of “endogenizing” technological learning and strategic behavior of agents in economic models used to assess climate change policies. In the first part we show the potential impact of R&D policies or demonstration and deployment (D&D) programs in the context of stringent stabilization scenarios. In the second part we show how game-theoretic methods can be implemented in climate change economic models to take into account three types of strategic interactions: (i) the market power of the countries benefiting from very low abatement costs on international markets for CO₂ emissions, (ii) the strategic behavior of governments in the domestic allocation of CO₂ emissions quotas, and (iii) the non-cooperative behavior of countries and regions in the burden sharing of CO₂ concentration stabilization. The two topics of endogenous learning and game-theoretic approach to economic modeling are two manifestations of the need to take into account the strategic behavior of agents in the evaluation of climate change policies. In the first case an R&D policy or a demonstration and deployment (D&D) program are put in place in order to attain a cost reduction through the learning effect; in the second case the agents (countries) reply optimally to the actions decided by the other agents by exploiting their strategic advantages. Simulations based on integrated assessment models illustrate the approaches. These studies have been conducted under the Swiss NCCR-Climate program.     

Aerosol concentration measurement with multiple light scattering system and laser aerosol spectrometer

The performance of two different optical concentration-measuring techniques was investigated over a concentration range starting with about 10² cm⁻³ and extending over more than four decades. Both instruments are capable of real-time counting, however due to their particular design-single particle counter and ensemble particle-measuring system—they operate in overlapping, but different concentration ranges. The upper, coincidence-free counting limit for the single particle counter used in this study was established to be in the order of 10⁴ cm⁻³. The ensemble technique was found to be functional and stable for concentrations of about 10³ cm⁻³ and limited by the onset of multiple scattering at concentrations nearby 2×10⁶ cm⁻³. Within the determined boundaries, both techniques proved to provide reliable aerosol concentration data.     

气溶胶对云中水成物粒子荷电情况的影响

利用已有的二维雷暴云起电模式,加入气溶胶模块,建立一个完善的雷暴云起电模式.结合SEET个例,初步探讨了气溶胶浓度对雷暴云内各种水成物粒子荷电情况的影响.发现气溶胶的浓度与雷暴云内云滴、霰粒、冰雹以及雨滴等水成物粒子在空间所携带的最大电荷面密度值以及电荷量有很好的正相关性;同时气溶胶粒子浓度的增加使得雨滴在空间携带电荷量达到峰值的时间有一定提前.     

全球及中国区域气候变化预估研究主要进展简述

自IPCC评估报告发表以来,气候模式对未来气候变化的预估成为人们重视的问题。因此,本文回顾了IPCC前4次报告对全球未来气候变化预估的主要结论,并对全球及区域气候模式对中国地区的模拟和预估结果进行了分类总结,得出区域气候模式由于分辨率更高、对特殊地形的模拟能力更强,因此比全球气候模式的模拟和预估结果更准确;同时讨论了目前存在的问题及今后研究的方向。     

Policy uncertainty and diffusion of carbon capture and storage in an optimal region

Abstract

The social cost of carbon (SCC) is the value of the climate change impacts from 1 tonne of carbon emitted today as CO₂, aggregated over time and discounted back to the present day. We used PAGE2002, the same probabilistic integrated assessment model as used by the Stern Review (Stern et al., 2006), to calculate the SCC and to examine how it varies with discount rate; and find that it is not sensitive to the path of emissions on which the tonne of carbon is superimposed. The mean value of the SCC is $43 per tonne under both a business-as-usual scenario, and under a scenario aimed at stabilizing CO₂ concentrations at 550 ppm. This counter-intuitive result is caused by the interplay between the logarithmic relationship between forcing and concentration, the nonlinear relationship of damage to temperature, and discounting. However, the SCC is sensitive to a number of scientific and economic inputs to the model. Two recent distributions for the sensitivity of climate to a doubling of atmospheric CO₂ (Murphy et al., 2004; Stainforth et al., 2005) increase the mean value of the SCC from $43 to $68 and $90 per tonne. Using a pure rate of time preference of 0.1% per year, as in the Stern Review, gives a mean SCC of $365 per tonne.     

夏季南海台风移动路径的一种客观预报方法

以1960—2003年共44a夏季的7月、8月、9月西行进入南海海域的台风样本为基础,综合考虑南海台风移动路径的气候持续因子和数值预报产品物理量因子,运用条件数方法选取因子并建立回归方程,进行台风路径预报模型的预报建模研究。通过对比分析发现,基于条件数方法的南海台风移动路径模型具有较好的预报效果,7月、8月、9月3个月24h台风路径预报的平均距离误差为153．9km,预报能力明显高于目前国内外的其他一些台风路径客观预报方法。该方法的预报精度相对于逐步回归方法有了很大的提高,相对于气候持续法也为正的预报技巧水平。     

基于历史地震数据的地震烈度与人员死亡率相关性研究

基于历史震例数据,通过对不同烈度的人员死亡率的分析发现,人员死亡率与地震烈度存在着正相关关系,即人员死亡率随烈度的增加而增大,不同烈度的人员死亡率有各自的分布区间范围,而通过对相邻烈度的人员死亡率的分析发现,相邻烈度人员死亡率存在着1个数量级的倍数关系,一般在10倍左右,在低烈度区域,倍数关系集中于偏向大于10倍的区间范围;在高烈度区域,倍数关系集中于小于10倍的区间范围。在此结果上,基于地震烈度、震级等因素构建了烈度人员死亡率模型,决定系数R²值为0.8667,拟合结果相对较好,能够为后续基于分烈度人员死亡率的地震人员死亡评估模型提供参考。     

Stochastic ground motion models to NGA-West2 and NGA-Sub databases using Bayesian neural network

Ground motion models (GMMs) are traditionally developed from a frequentist approach. The Bayesian framework has received recent attention in developing nonergodic models, measuring uncertainty, or updating the model with additional data. However, no neural networks are developed to date in this framework to predict ground motion parameters or spectra. Hence, the present work develops a probabilistic Bayesian neural network (PBNN) to next-generation attenuation – West2 and Subduction databases using variational inference with mean-field assumption. Network inputs are magnitude, rupture distance, hypocentral depth, shear wave velocity, style of faulting, and region flags; outputs are peak ground values and response spectra. Both models have two hidden layers with seven neurons in each hidden layer. The models are verified for potential overfit, and their performance is validated through the parametric study by varying inputs. The output of a deterministic model is a point estimate. Considering probabilistic layers in hidden and output layers enables the model to capture within-model epistemic uncertainty and aleatory variability. Obtained aleatory standard deviations are consistent with other models. Mean epistemic uncertainty and aleatory variability are in the range 0.07–0.10 and 0.62–0.78 (ln units) for NGA-West2 and 0.09–0.16 and 0.67–0.95 for NGA-Sub models, respectively. The correlation coefficients between recorded and overall mean predictions ranged from 0.94 to 0.97 for NGA-the West2 model and from 0.91 to 0.95 for the NGA-Sub models. Network performance for out-of-training inputs showed increased epistemic deviations with no effect on aleatory deviations.