北太平洋涛动研究

北太平洋涛动是北半球大气中一个显著的、南北向跷跷板式的低频振荡,其演变不仅直接影响北太平洋附近地区的天气和气候,甚至对整个北半球的环流异常、持续以及气候突变都有重要影响.文章综述了北太平洋涛动的时间演变特征、区域变化特征、常用计算方法、北太平洋涛动指数的定义以及它们对天气气候的影响,阐述了需要进一步解决的一些问题,指出了目前关于NPO的研究尚处于初级阶段,对NPO的研究还可以借鉴其他涛动中较为成熟的研究方式和方法.     

基于偏互信息法遴选因子的长江中长期径流预报

为提高流域中长期径流预见期和预报精度,以长江流域代表性水文站为例,研究遥相关气候因子对水文站径流的影响,通过偏互信息方法遴选与逐月径流具有显著相关性的气候因子,并采用多元回归方法建模进行中长期径流预报。研究表明:根据偏互信息法选出的输入因子建立的回归方程在建模和试报阶段的拟合优度值都大于0.6,而且入选的气候因子均具有4个月及以上的预见期。可见利用偏互信息法挑选遥相关气候因子进行中长期径流预报能够延长预见期并提供具有较高精度的预报结果。     

基于随机森林模型的长江上游枯水期径流预报研究

预报因子选取和预报模型构建是长期径流预报的两大难点。本研究采用随机森林模型从当年1月份至10月份长江干流的实测径流和国家气候中心74项水文—气象特征因子共750个变量中选取预报因子集合,对长江上游屏山站、寸滩站枯水期（当年11月～次年5月）径流预报进行了研究。结果显示,随预见期增加,径流自相关关系逐渐减弱,水文—气象遥相关关系逐渐强于径流自相关关系。在屏山站和寸滩站的径流预报中,预报结果与实测结果呈显著线性关系,平均相对误差在20%以内。月径流预报误差还较大,枯水期总径流预报精度优于单月径流预报。不确定性分析结果表明随机森林模型除了预报径流变化趋势,还可以用于预报径流丰枯概率。     

2009年春黑龙江上游冰坝分析

2009年春黑龙江漠河站出现了建站以来最早的开江日期,比历年提前了16 d。黑龙江洛古河站下游6 km,北宏至红旗岭,依西肯和欧浦下游多处发生冰坝。北宏和依西肯的冰坝,两处总水头都在7.0 m以上。依据水文、气象资料,对冰坝成因进行了分析,提出了＂拉尼娜＂是影响冰坝形成的一个重要因素,同时东太平洋的SST对黑龙江上游冰坝具有一定的指示作用,根据动热因素影响指标构建了凌情综合因子,在综合因子高值年多为冰凌灾害年。海温与冰坝的遥相关和冰情综合因子的构建,为冰坝预报分析提供了较为有价值的指标。     

Interdecadal Variability in Surface Solar Radiation over Northwest China and Its Possible Cause

The present study investigates the interdecadal variability of seasonal mean surface solar radiation over Northwest China using station observations from 1961-2003. Spring and summer surface solar radiation over Northwest China was lower in the late 1970s through 1990s than in the 1960s through the mid-1970s, and fall and winter surface solar radiation displayed similar patterns. These results indicate that the decrease in spring and summer surface solar radiation may be associated with increased low-cloud cover over Northwest China. Rainfall anomalies were closely related to the low-cloud cover over Northwest China and with the Northern Hemisphere circumglobal teleconnection in spring, summer, and winter.     

The Interannual Variability of Summer Rainfall in the Arid and Semiarid Regions of Northern China and Its Association with the Northern Hemisphere Circumglobal Teleconnection

Using the latest daily observational rainfall datasets for the period 1961-2008, the present study investigates the interannual variability of June-September (JJAS) mean rainfall in northern China. The regional characteristics of JJAS mean rainfall are revealed by a rotated empirical orthogonal function (REOF) analysis. The analysis identifies three regions of large interannual variability of JJAS rainfall: North China (NC), Northeast China (NEC), and the Taklimakan Desert in Northwest China (TDNWC). Summer rainfall over NC is shown to have displayed a remarkable dry period from the late 1990s; while over NEC, decadal-scale variation with a significant decreasing trend in the last two decades is found, and over TDNWC, evidence of large interannual variability is revealed. Results also show that the interannual variability of JJAS rainfall in northern China is closely associated with the Northern Hemisphere circumglobal teleconnection (CGT). Correlation coefficients between the CGT index and regional-averaged JJAS mean rainfall over NC and NEC were calculated, revealing values of up to 0.50 and 0.53, respectively, both of which exceeded the 99% confidence level.     

Climate change and food-water supply from Africa's drylands: local impacts and teleconnections through global commodity flows

This article uses the WaterWorld Policy Support System, coupled with a global database for commodity flows, to examine the impacts of AR4 SRES climate change scenarios on Africa's drylands and the commodity flows that originate from them. It shows that changes to precipitation and, to a lesser extent, temperature in Africa's drylands can significantly affect the potential to supply water-for-food locally and internationally. By comparing the geographical distribution of climate change with the supply chain–connected distribution of climate change, it shows how food-water impacts of climate change may affect local dryland populations but also those dependent on these flows from afar.     

非稳态条件下的中长期径流耦合预报方法

气候变化会导致水文序列的非稳态性,从而给水文预报带来新的挑战。以疏勒河上游为例,提出了一种适于非稳态条件下的新的中长期径流预报方法。根据疏勒河径流的补给来源及其受气候变化的影响,按照时间序列模型的思路,依次提取趋势项和周期项,对剩余的随机项采用基于水文-气象遥相关模型,构建了时间序列与水文-气象遥相关的耦合模型。对比分析时间序列法、水文-气象遥相关法和耦合预报法对昌马堡站径流预报的结果,发现耦合预报方法不仅精度最高、模型可信度最高,而且可以描述非稳态的趋势性变化。     

基于随机森林遥相关因子选择的月径流预报北大核心CSCD

流域径流过程与大尺度气候因子之间存在遥相关关系,如何从众多的水文、气象、大气环流及洋流等因子中找出与径流密切关联的因子,是中长期径流预报的一个难题。将基于贝叶斯优化的随机森林模型应用于对水文、气象、气候因子构成的高维度因子集进行因子选择,根据变量重要性评分挑选对月径流影响较大的预报因子,构建广义回归神经网络、极限学习机、支持向量回归径流预报模型。将该方法应用到金沙江流域,相较于线性相关法,基于随机森林输入因子选择的方法提高了模型泛化性能;遥相关因子的引入既实现了流域月径流高精度预报,又从物理机制上提供了支撑。