Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.

Changes in Climatic Factors and Extreme Climate Events in Northeast China during 1961-2010

This study focuses on examining the characteristics of climate factors and extreme climate events in Northeast China during 1961- 2010 by using daily data from 104 stations, including surface air temperature, precipitation, wind speed, sunshine duration, and snow depth. Results show that annual mean temperature increased at a significant rate of 0.35℃ per decade, most notably in the Lesser Khingan Mountains and in winter. Annual rainfall had no obvious linear trend, while rainy days had a significant decreasing trend. So, the rain intensity increased. High-temperature days had a weak increasing trend, and low-temperature days and cold wave showed significant decreasing trends with rates of 3.9 d per decade and -0.64 times per decade, respectively. Frequency and spatial scope of low-temperature hazard reduced significantly. Warm days and warm nights significantly increased at 1.0 and 2.4 d per decade, while cold days and cold nights decreased significantly at -1.8 and -4.1 d per decade, respectively. The nighttime warming rate was much higher than that for daytime, indicating that nighttime warming had a greater contribution to the overall warming trend than daytime warming. The annual mean wind speed, gale days, and sunshine duration had significant decreasing trends at rates of-0.21 m s-1 per decade, -4.0 d per decade and -43.3 h per decade, respectively. The snow cover onset dates postponed at a rate of 1.2 d per decade, and the snow cover end date advanced at 1.5 d per decade, which leads to shorter snow cover duration by -2.7 d per decade. Meanwhile, the maximum snow depth decreased at -0.52 cm per decade. In addition, the snow cover duration shows a higher correlation with precipitation than with temperature, which suggests that precipitation plays a more important role in maintaining snow cover duration than temperature.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

Modulation of the late summer Northeast China cold vortex by previous-winter ENSO

本文利用再分析资料,研究了前冬ENSO对夏末东北冷涡(NCCV)的调制作用.结果表明,前冬ENSO与夏末NCCV强度之间存在显著的相关性,El Nino(La Ni?a)对应于弱(强)的NCCV.印度洋海盆模态(IOBM)在前冬ENSO对夏末东北亚地区大气环流的影响中起着至关重要的作用.作为东部型El Nino的被动响应,IOBM可以从前冬一直持续至夏末,并在夏末激发“中国中部上空气旋—东北亚地区上空反气旋”的经向遥相关模态,从而不利于NCCV增强.反之亦然.此外,印度洋的信号在中部型El Nino和中性年份相对较弱,使得它们对于NCCV的影响不显著.

Risk Evaluation of Rainstorm and Flood in the Upper Reaches of Hunhe River (Qingyuan Section) Based on the FloodArea Model

By using data of hourly rainfall with all of the meteorological stations in the upper reaches of Hunhe River (Qingyuan Section), the digital elevation model, the land use and the disasters data etc., the storm flood process which happened on 16 August 2013 is reproduced simulated based on the rainstorm flooding of FloodArea model, also the flooding simulation and its effectiveness have been carried out. Conclusions are drawn as follows: there is a sharp rise of the flood depth falling behind the change of rainfall three or four hours. As the accumulation of the precipitation, the water level has increased, and when the precipitation process stops, the flood pooled into midstream and downstream gradually. According to the disaster investigation, in both the aspects of flooding scope and flooding depth of warning spots, the simulation result of FloodArea model agrees with the fact. It indicates that the FloodArea model generates good simulation effect in the upper reaches of Hunhe River (Qingyuan Section). Combined with population and GDP information, in the whole river, about eighty-three thousand people were affected by flooding, also one billion and five hundred seventy five million RMB of gross domestic product was lost. When the flood depth deepens and the flooding scope increases, correspondingly the loss rate of population and GDP rises and the flood risk increases.

Risk Assessment and Simulation on Storm Flood of the 100-Year Return Period in Hunhe River Basin

Based on the meteorological and geographic information data, with statistical method and the FloodArea model, the extreme daily rainfall of the 100-year return period in Hunhe River basin was established, through the simulation of rainstorm and flood disaster, characteristics of flood depth in warning spot Cangshi village in the upstream of the river were analysed, and possible effect on community economy was also evaluated. Results showed that, the precipitation of 100-year return period occurred, the flood depth has been below 1.0 meter in the most areas of Hunhe River basin, the depth was between 1.0 meter and 2.5 meters in the part areas of Hunhe River basin, and the flood depth has been exceed 2.5 meters in a small part of Hunhe River basin. After the beginning of precipitation, the flood was concentrated in the upper reaches of the river. With the accumulation of precipitation and the passage of time, the flood pools into midstream and downstream. Precipitation lasted for 24 hours, the warning spot was flooded in the beginning of precipitation. With the accumulation of precipitation, water level of the river increases gradually. The depth of warning spot has passed 1.0 meter at the 07 time of the whole process, and the maximum value of flood depth at warning spot was 1.083 meters that occurred at the 19 time. The flood depth of warning spot decreased gradually after the precipitation stopping, and the depth has been below 0.2 meters, the flood of upstream ended. Up to the end of the upstream flood process, in the whole river, about one million five hundred and sixty thousand people were affected by flooding, and thirty-eight billion and two hundred million RMB of gross domestic product were lost, in addition, dry land and paddy field were affected greatly, but woodland and grassland were less affected.

Objective identification research on cold vortex and mid-summer rainy periods in Northeast China

Considering the differences between the Northeast China Cold Vortex （CV） and the Mid-Summer （MS） rainy period and their corresponding atmospheric circulations are comprehensively analyzed, and the objective identification methods of defining the annual beginning and ending dates of Northeast China CV and MS rainy periods are developed respectively. The annual beginning date of the CV （MS） rainy period is as follows. In a period from April to August, if daily regional mean precipitation ryi is larger than yearly regional mean precipitation R （or 2R） on a certain day, the station precipitation rs is larger than the station yearly mean precipitation （r/ （or 2（r）） in at least 50% of stations in Northeast China, and this condition is satisfied in the following 2 （7） days, then this date is defined as the beginning date of the CV （MS） rainy period. While the definition of the ending date of the MS rainy period shows the opposite process to its beginning date. With this objective identification method, the multi-year average （1981-2010） beginning date of the CV rainy period is May 3, the beginning date of the MS rainy period is June 27, the ending day of the CV rainy period is defined as the day before the beginning date of the MS rainy period, and the ending date of the MS rainy period is August 29. Meanwhile, corresponding anomaly analysis at a 500-hPa geopotential height, 850-hPa wind, Omega and relative humidity fields all show that the definitions of the average beginning and ending dates of the CV and MS rainy periods have a certain circulation meaning. Furthermore, the daily evolution of the CV index, meridional and zonal wind index, etc. all show that these objectively defined beginning and ending dates of the CV and MS rainy periods have climate significance.

Multi-Scheme Corrected Dynamic–Analogue Prediction of Summer Precipitation in Northeastern China Based on BCC_CSM

Based on summer precipitation hindcasts for 1991-2013 produced by the Beijing Climate Center Climate System Model （BCC_CSM）, the relationship between precipitation prediction error in northeastern China （NEC） and global sea surface temperature is analyzed, and dynamic-analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic-analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient （ACC） of summer precipitation is respectively improved from -0. 13/0.15 to 0.16/0.24 for 2009-13/1991-95 when using the equally weighted dynamic-analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.