气候模拟数据的订正与应用——以北京为例

为了消除气候模拟数据中气候平均值和气候变率的漂移,发展了一种新的数据订正方案。应用该方案对IPCC提供的B2情景下北京未来100 a气候预估值进行了订正试验,证实了方案的可用性。在此基础上分析了北京未来气候变化特征,结果表明：21世纪北京气温将继续上升,升温速率约为0.31℃/10 a,最低、最高气温的非对称变化仍将持续;未来北京年降水量呈微弱下降趋势,下降速率约为1.03 mm/10 a。     

CMA-GFS全球模式海洋边界层高度的主要偏差特征

海洋边界层高度是表征海洋上空大气的水汽、热量、物质等垂直分布的重要特征量,同时在气候、污染、模式预报上有关键作用。然而,利用海洋边界层高度观测对数值天气预报模式进行诊断的研究很少。因此,本文利用2019—2020年GPS掩星资料计算出的海洋边界层高度的分布特征,对CMA-GFS全球模式的预报性能进行分析,同时借助ERA5再分析资料对CMA-GFS模式的偏差进行讨论。主要结论如下：(1)CMA-GFS全球模式在西太平洋、南太平洋、南大西洋绝大部分海域预报的边界层高度比较合理;(2)模式在热带辐合带海域和南太平洋辐合带存在高估预报,初步分析与模式对热带深对流的抬升凝结高度的预报偏高有关。(3)模式和ERA5在南半球层积云所在区域均存在边界层高度预报偏低,初步分析可能是模式对南半球层积云顶辐射冷却驱动的湍流扩散偏小造成。(4)模式在有云的大气下主要呈现为预报偏高,中心值在200 m左右,而在晴空区域模式预报较为合理,偏差值范围较小,ERA5也存在类似的特点。     

偏压分岔隧道施工过程损伤破坏分析与优化研究

正在施工的沪蓉西高速公路八字岭分岔隧道属于偏压隧道,而且分岔隧道包括连拱段和小净距段,施工过程转换复杂,因此,采用正确的施工方案对于隧道的稳定性非常重要。为更真实地模拟岩体的力学特性,研究施工过程中岩体的损伤破坏,根据不可逆热力学理论建立了弹塑性损伤耦合模型,并编制了三维弹塑性损伤有限元程序D-FEM。建立了分岔隧道大型三维数值模型,采用弹塑性损伤有限元D-FEM模拟了分岔隧道不同的施工过程,通过分析施工过程中隧道围岩的位移、应力和损伤屈服区,最终确定采用左洞超前右洞32 m,左右洞同步开挖的施工方法。     

Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Regional climate models(RCMs) participating in the Coordinated Regional Downscaling Experiment(CORDEX)have been widely used for providing detailed climate change information for specific regions under different emissions scenarios. This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical(1980-2005) temperature simulations over East Asia. Future(2006-49) temperature trends under the Representative Concentration Pathway...     

基于Himawari-8卫星的逐时次海表温度融合

Himawari-8卫星是日本气象厅发射的新一代地球同步静止气象卫星,为获取逐时次海表温度产品提供了有力数据支持.本文以Himawari-8 AHI海表温度为基础,利用最优插值法融合GCOM-W1 AMSR2海表温度和NERA-GOOS现场观测资料,生成逐时次海表温度融合产品.为了充分利用邻近时刻的海表温度观测资料,利...     

Air pollutions and loan decision bias

Studies have largely discussed the impact of air pollution on the stock market. However, the latter requires certain financial knowledge and certain income, which may lead to sample selection bias. To address this concern, using unique data from online peer-to-peer lending platform, namely, RenRenDai, this paper analyzes the effects of air pollution on loan decisions in China. In the identification strategy, we use approximate exogenous and random daily level air pollution to alleviate the endogenous problem. In a preferred specification, results show that air pollution has significant positive effects on loan amounts with elasticity of 0.013. Such result is driven by health expenditure and behavioral bias. Specifically, after air pollution rises for two or three days, individuals will then increase loan amounts for health and consumption. However, air pollution has no proof of increasing investment. Our study provides new evidence on the effects of air pollution on cognitive performance, particularly on financial behavior. It then reveals the mechanism how air pollution influences loan decisions. In view of the negative impact of air pollution, we also need to know the risks it poses to the financial industry.