Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function(EOF) analysis. The predictors used in this study are Arctic sea ice concentration(ASIC) and regional sea surface temperature(SST) in selected optimal time periods. The most important role that Arctic sea ice(ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC(PA), SST(PS), and the two together(PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years(2005-2015). The correlation coefficient(COR) skills between the observed and cross-validation reforecast principal components(PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980-2004. The COR skill of the PA model during the independent forecast period of 2004-2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.     

1997—2017年塔克拉玛干沙漠腹地降水特征

利用塔克拉玛干沙漠腹地塔中气象站1997—2017年逐日和逐时降水资料,分析塔克拉玛干沙漠腹地降水日变化特征、极端强降水特征及其天气背景。结果表明：1997—2017年研究区降水量呈增加趋势、降水日数呈减少趋势,大雨雨量和雨日明显增加,降水呈增强演变。降水多见于6月,最大雨强为8.4 mm·h^-1。降水量日变化呈多峰特征,降水量最大值出现在23：00,06：00是降水频次最多时刻。降水强度和降水频次对降水量作用不同,午后至前半夜强度大,频次少;而后半夜至清晨频次多,强度小。降水以短历时降水为主,其中1～3 h的短历时降水对总降水量贡献率高达61.76%。日降水和小时降水的99百分位强度阈值分别为15.3 mm·d^-1和6.0 mm·h^-1,大于90百分位极端降水量占总降水量贡献率近半。极端强降水天气发生在南疆盆地受北纬40°以南低槽、切变槽或弱的气旋式风场控制地区,南疆盆地提前增湿,民丰850 hPa比湿接近或超过10 g·kg^-1的背景下,降水连续性较差,多中小尺度引发局地短时降水。     

The Belt and Road: Geographical pattern and regional risks

Journal of Geographical Sciences - Building the Belt and Road is initiatives of China to promote win-win international cooperation in the new era, aiming at green, health, intellect and peace and...     

Effects of wave directionality on extreme response for a long end-anchored floating bridge

Reliable design codes are of great importance when constructing new civil engineering concepts such as floating bridges. Previously only a scarce number of floating bridges have been built in rough wave conditions and only limited knowledge of the extreme environmental conditions and the associated extreme response exists. To form a better design basis an increased understanding of the sensitivity in the structural response towards changes in short-crested sea parameters is needed. Furthermore, acquiring the necessary accuracy in simulated extreme response is often a computationally expensive endeavour and the number of simulations needed is often based on experience. The present study investigates the wave-induced short-term extreme response of a simplified end-anchored floating bridge concept for several wave environments with a return period of 100 years. The study includes convergence of the coefficient of variation for the extreme response for different realization lengths as well as number of realizations. The sensitivity in the structural response towards different main wave directions and spreading exponents is investigated and includes both transverse and vertical displacement response spectra and extreme Von Mises stress in the bridge girder cross-section. The extreme response is based on an accuracy of 2% in the coefficient of variation equivalent to 40 3-h realizations and a low sensitivity in the response is found for natural occurring spreading exponents and for main wave directions within 15° from beam sea.     

一种基于M估计的Robust-ELM滑坡位移预测方法

采用传统ELM算法进行滑坡位移预测时,其网络输出权值由最小二乘估计得出,导致ELM抗差能力较差,从而造成网络训练参数不准确。为此,将M估计与ELM相结合,提出一种基于M估计的RobustELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值,以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例,将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明,该方法能够有效降低粗差对预测的影响,具有良好的抗差能力。     

气候系统预测:基础创新和集成应用

随着全球变暖,极端天气气候事件增强,天气气候灾害造成的损失也愈发严重。当前气候预测的准确性远远不能满足社会需要,气候系统预测理论和方法面临着众多挑战性问题。为提档气候预测科学水平和准确率,由南京信息工程大学和中山大学承担的“气候系统预测研究中心”获得国家自然科学基金基础科学中心项目支持(2021年1月—2025年12月)。在该项目执行的前三年,项目团队开展了大量深入系统的研究,并取得了若干重要进展:1)揭示了气候系统的若干关键变化、驱动力和机制;2)剖析了海-陆-冰-气相互作用对我国重大极端气候事件的影响;3)在气候系统数值模式研发和预测系统集成方面取得重要进展;4)发展了延伸期-S2S-年代际的气候系统预测理论和方法。本文对这些进展作了扼要介绍,并针对气候与环境变化归因、古今气候环境研究融合、跨时空气候系统变异和极端气候、人工智能与气候科学、年代际预测和风险应对体系等关键科学问题做了展望。     

极限波浪运动特性的非线性数值模拟

利用时域高阶边界元方法建立了模拟极限波浪运动的完全非线性数值模型,其中自由水面满足完全非线性自由水面条件.采用半混合欧拉-拉格朗日方法追踪流体瞬时水面,运用四阶Runge-Kutta方法更新下一时间步的波面和速度势,同时应用镜像格林函数消除水槽两个侧面和底面上的积分.研究中利用波浪聚焦的方法产生极限波浪,并且在水槽中开展了物理模型实验,将测点试验数据与数值结果进行了对比,两者吻合得很好.对极限波浪运动的非线性和流域内速度分布进行了研究.     

Nonlinear numerical simulation on extreme-wave kinematics

A fully nonlinear numerical model based on a time-domain higher-order boundary element method (HOBEM) is founded to simulate the kinematics of extreme waves. In the model, the fully nonlinear free surface boundary conditions are satisfied and a semi-mixed Euler-Lagrange method is used to track free surface; a fourth-order Runga-Kutta technique is adopted to refresh the wave elevation and velocity potential on the free surface at each time step; an image Green function is used in the numerical wave tank so that the integrations on the lateral surfaces and bottom are excluded. The extreme waves are generated by the method of wave focusing. The physical experiments are carried out in a wave flume. On the horizontal velocity of the measured point, numerical solutions agree well with experimental results. The characteristics of the nonlinear extreme-wave kinematics and the velocity distribution are studied here.     

海洋极端环境微生物活动与油气资源关系

为了弄清海洋极端环境下微生物参与油气资源形成和演化的潜在机制, 进行了现代海洋热泉和冷泉等环境中微生物类型分析和生物量估算, 探讨了极端微生物活动和油气资源的潜在关系.认为海洋极端环境下微生物类型主要为细菌和古细菌, 热泉微生物群落主要为异养发酵菌、硫酸盐还原菌、产甲烷菌等; 冷泉微生物群落主要为ANME-2族的厌氧甲烷氧化古细菌、硫酸盐还原细菌和ANME-1族厌氧甲烷氧化古菌.这些极端微生物利用CH₄和H₂S等气体进行能量固定, 有较高的生物丰度和较低的分异度, 具有垂向和水平分带性, 并能营生一套独特的宏体生物.极端微生物活动直接和间接地参与了油气资源的形成和改造, 示踪海底油气资源的变迁.对于探索地球早期海洋微生物活动与油气资源形成, 寻找地史时期或华南地史早期烃源岩具有重要理论和实践指导意义.      

From China’s Heavy Precipitation in 2020 to a “Glocal” Hydrometeorological Solution for Flood Risk Prediction

The prolonged mei-yu/baiu system with anomalous precipitation in the year 2020 has swollen many rivers and lakes,caused flash flooding,urban flooding and landslides,and consistently wreaked havoc across large swathes of China,particularly in the Yangtze River basin.Significant precipitation and flooding anomalies have already been seen in magnitude and extension so far this year,which have been exerting much higher pressure on emergency responses in flood control and mitigation than in other years,even though a rainy season with multiple ongoing serious flood events in different provinces is not that uncommon in China.Instead of delving into the causes of the uniqueness of this year’s extreme precipitation-flooding situation,which certainly warrants in-depth exploration,in this article we provide a short view toward a more general hydrometeorological solution to this annual nationwide problem.A“glocal”(global to local)hydrometeorological solution for floods(GHS-F)is considered to be critical for better preparedness,mitigation,and management of different types of significant precipitation-caused flooding,which happen extensively almost every year in many countries such as China,India and the United States.Such a GHS-F model is necessary from both scientific and operational perspectives,with the strength in providing spatially consistent flood definitions and spatially distributed flood risk classification considering the heterogeneity in vulnerability and resilience across the entire domain.Priorities in the development of such a GHS-F are suggested,emphasizing the user’s requirements and needs according to practical experiences with various flood response agencies.