Underestimated relationship between westerly wind bursts and ENSO in CMIP6 models

观测表明,热带太平洋的西风爆发(WWBs)在厄尔尼诺南方涛动(ENSO)的发生,发展和多样性中起着关键作用.因此,在耦合模式中真实地再现WWBs对于改进ENSO的模拟和预测有重要意义.在本研究中,作者发现CMIP6的耦合模式的集合平均能很好地再现了热带太平洋WWB发生频率的纬向分布及其年际变动.然而,大多数CMIP6模型极大低估了WWB和ENSO的线性关系.这可能是因为大多数CMIP6模式里海气耦合强度低于观测:海气耦合强度与WWB-ENSO关系的模式间相关系数高达0.91.

Decadal Variations of Winter Extreme Cold Days in Northern China

Weather and climate extremes often lead to dramatic losses in our society and warrant improvement of their understanding. In this study, the decadal variations in the first two dominant empirical orthogonal function (EOF) modes of winter extreme cold days (WECDs) in northern China (NC) have been investigated. Results show that both EOF modes show distinct decadal variations that together explain around 24% of total variances. At the decadal time scale, the EOF1 is closely related to the decadal Arctic Oscillation (AO);the negative AO can lead to spatially consistent increase of WECDs in NC. On the other hand, the decadal EOF2 can be influenced by the decadal El Niño-Southern Oscillation (ENSO). The decadal El Niño can result in the large-scale negative sea level pressure (SLP) anomalies in the Eurasian continent west of the western NC and the positive ones over western China. The anomalous southwesterlies between the contrasted SLP anomalies can advect the warmer air from the lower latitudes to the western NC, decrease the WECDs there, and contribute to the east-west asymmetric WECD anomalies in NC. The impacts of El Niño are confirmed by the numerical simulations in the Atmospheric Model 2.1 (AM2.1) when forced by the El Niño-related sea surface temperature (SST) anomalies in the tropical Pacific.

Temporal and Spatial Characteristics of Summer Extreme Precipitation in Eastern China and Possible Causalities

In the past decades, with the increasing frequency of extreme weather and climate events, the world has suffered huge losses. Based on NCEP/NCAR reanalysis data and China regional precipitation data provided by China Meteorological Administration, the extreme precipitation events in eastern China are defined by relative threshold method, and the temporal and spatial characteristics of summer extreme precipitation in eastern China from 1961 to 2016 are analyzed by empirical orthogonal function (EOF), and the reverse distribution of extreme precipitation in the middle and lower reaches of the Yangtze River and south China by Indian Ocean warm pool is revealed influence. The results show that the total amount and frequency of extreme precipitation in summer are concentrated in the Yangtze River Basin and south China. EOF1 decomposition of extreme precipitation reflects the interannual oscillation characteristics of reverse spatial distribution in the Yangtze River Basin and south China. The time series corresponding to EOF1 has significant interannual characteristics. The Pacific-Japan (PJ) teleconnection pattern is a circulation system that significantly affects the spatial-temporal pattern of extreme precipitation in southern China. When the PJ pattern is in the positive phase, the anticyclone controls the south China region, and restrains the convective activity, which results in the decrease of extreme precipitation. The anomalous southwest wind to the south of 30°N and the anomalous northerly wind to the north of 30°N converge in the middle and lower reaches of the Yangtze River. Combining with the sufficient water vapor carried by the anomalous southwest airflow at the edge of anticyclone, it is more conducive to the formation of extreme precipitation. The east propagating Kelvin wave in the warm pool of the Indian Ocean is an important reason for the formation of the PJ pattern and finally the formation of extreme precipitation anomalies in China.

North Atlantic forcing of autumn drought in Southwest China

干旱持续时间长,因此往往和缓慢变化的海温异常相关.本文利用观测降水和NCEP-DOE再分析资料,探讨了北大西洋中高纬地区东北-西南走向的偶极子海温异常对我国西南地区秋季干旱的影响.结果显示该偶极子海温异常可激发向东南方向传播的Rossby波,在印度北部和青藏高原西部引起正压气旋性环流和上升运动异常,并在我国西南地区引起补偿性下沉运动,导致降水减少,引发干旱.

Model averaging for generalized linear models in fragmentary data prediction

Fragmentary data is becoming more and more popular in many areas which brings big chal-lenges to researchers and data analysts.Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response variable is discrete.In this paper,we propose a model averaging method for generalized linear models in fragmentary data prediction.The candidate models are fitted based on different combinations of covariate availability and sample size.The optimal weight is selected by minimizing the Kullback-Leibler loss in the completed cases and its asymptotic optimality is established.Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.