选取了一个热浪指数,利用地面2 m气温场和500 h Pa位势高度场的美国环境预报中心和国家大气研究中心（NCEP/NCAR）再分析资料,通过聚类分析发现欧洲大陆容易产生6类热浪：西欧型（WE）、俄罗斯型（RU）、东欧型（EE）、斯堪的纳维亚半...选取了一个热浪指数,利用地面2 m气温场和500 h Pa位势高度场的美国环境预报中心和国家大气研究中心（NCEP/NCAR）再分析资料,通过聚类分析发现欧洲大陆容易产生6类热浪：西欧型（WE）、俄罗斯型（RU）、东欧型（EE）、斯堪的纳维亚半岛型（SC）、北海型（NS）、伊比利亚半岛型（IB）;这些热浪事件都与欧洲大陆阻塞的位置有关。同时我们发现这6类热浪发生的频率出现明显的年代际变化,特别在20世纪80年代以后欧洲大陆热浪发生频率明显的增多趋势可能与欧洲大陆增暖背景有关,而欧洲大陆热浪发生频率的年代际变化可能是夏季北大西洋涛动（NAO）的年代际变化的结果。夏季NAO偶极子通过欧洲地区的阻塞异常对欧洲大陆气温有重要的调制作用。当夏季NAO指数处于正位相阶段时,欧洲大陆容易产生高纬度热浪,反之则容易产生低纬度热浪,并且欧洲大陆增暖趋势并不影响NAO对欧洲气温的调制作用。同时还发现：大西洋夏季NAO事件可以是欧洲热浪发生的前期条件,欧洲大陆阻塞异常落后于NAO事件1～5 d,其中IB型和WE型与NAO同期相关,其余4类型热浪对应阻塞落后于NAO 4～5 d。另外,也发现大西洋—欧洲大陆定常波列正距平的位置通过对欧洲阻塞的影响,而影响欧洲热浪发生的频率和位置。展开更多
In late July and early August 2018,Northeast China suffered from extremely high temperatures,with the maxium temperature anomaly exceeding 6°C.In this study,the large-scale circulation features associated with th...In late July and early August 2018,Northeast China suffered from extremely high temperatures,with the maxium temperature anomaly exceeding 6°C.In this study,the large-scale circulation features associated with this heat wave over Northeast China are analyzed using station temperature data and NCEP–NCAR reanalysis data.The results indicate that strong anomalous positive geopotential height centers existed from the lower to upper levels over Northeast China,and the related downward motions were directly responsible for the extreme high-temperature anomalies.The northwestward shift of the western Pacific subtropical high(WPSH)and the northeastward shift of the South Asian high concurrently reinforced the geopotential height anomalies and descending flow over Northeast China.In addition,an anomalous Pacific–Japan pattern in the lower troposphere led to the northwestward shift of the WPSH,jointly favoring the anomalous geopotential height over Northeast China.Two wave trains emanating from the Atlantic region propagated eastwards along high latitudes and midlatitudes,respectively,and converged over Northeast China,leading to the enhancement of the geopotential height anomalies.展开更多
A machine-learning(ML)model,the light gradient boosting machine(LightGBM),was constructed to simulate the variation in the summer(June-July-August)heatwave frequency(HWF)over eastern Europe(HWFUR)and to analyze the co...A machine-learning(ML)model,the light gradient boosting machine(LightGBM),was constructed to simulate the variation in the summer(June-July-August)heatwave frequency(HWF)over eastern Europe(HWFUR)and to analyze the contributions of various lower-boundary climate factors to the HWFUR variation.The examined lower-boundary climate factors were those that may contribute to the HWFUR variation—namely,the sea surface temperature,soil moisture,snow-cover extent,and sea-ice concentration from the simultaneous summer,preceding spring,and winter.These selected climate factors were significantly correlated to the summer HWFUR variation and were used to construct the ML model.Both the hindcast simulation of HWF EUR for the period 1981-2020 and its real-time simulation for the period 2011-2020,which used the constructed ML model,were investigated.To evaluate the contributions of the climate factors,various model experiments using different combinations of the climate factors were examined and compared.The results indicated that the LightGBM model had comparatively good performance in simulating the HWFUR variation.The sea surface temperature made more contributions to the ML model simulation than the other climate factors.Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter,suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWFUR variation.展开更多
基金supported by the National Natural Science Foundation of China under Grant 41775073
文摘In late July and early August 2018,Northeast China suffered from extremely high temperatures,with the maxium temperature anomaly exceeding 6°C.In this study,the large-scale circulation features associated with this heat wave over Northeast China are analyzed using station temperature data and NCEP–NCAR reanalysis data.The results indicate that strong anomalous positive geopotential height centers existed from the lower to upper levels over Northeast China,and the related downward motions were directly responsible for the extreme high-temperature anomalies.The northwestward shift of the western Pacific subtropical high(WPSH)and the northeastward shift of the South Asian high concurrently reinforced the geopotential height anomalies and descending flow over Northeast China.In addition,an anomalous Pacific–Japan pattern in the lower troposphere led to the northwestward shift of the WPSH,jointly favoring the anomalous geopotential height over Northeast China.Two wave trains emanating from the Atlantic region propagated eastwards along high latitudes and midlatitudes,respectively,and converged over Northeast China,leading to the enhancement of the geopotential height anomalies.
基金supported by the National Natural Science Foundation of China[grant number 42075050]。
文摘A machine-learning(ML)model,the light gradient boosting machine(LightGBM),was constructed to simulate the variation in the summer(June-July-August)heatwave frequency(HWF)over eastern Europe(HWFUR)and to analyze the contributions of various lower-boundary climate factors to the HWFUR variation.The examined lower-boundary climate factors were those that may contribute to the HWFUR variation—namely,the sea surface temperature,soil moisture,snow-cover extent,and sea-ice concentration from the simultaneous summer,preceding spring,and winter.These selected climate factors were significantly correlated to the summer HWFUR variation and were used to construct the ML model.Both the hindcast simulation of HWF EUR for the period 1981-2020 and its real-time simulation for the period 2011-2020,which used the constructed ML model,were investigated.To evaluate the contributions of the climate factors,various model experiments using different combinations of the climate factors were examined and compared.The results indicated that the LightGBM model had comparatively good performance in simulating the HWFUR variation.The sea surface temperature made more contributions to the ML model simulation than the other climate factors.Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter,suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWFUR variation.
