受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊...受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊的热浪特征进行平均,而青海湖的热浪特征尚不清楚。因此,本研究使用青海湖水温和湖表温度的原位观测数据、刚察气象站观测数据、MODIS地表温度观测数据、第三极地区长时间序列高分辨率地面气象要素驱动数据集(A high-resolution near-surface meteorological forcing dataset for the Third Pole region,TPMFD)和一维湖泊模式(Freshwater Lake Model,FLake)研究了青海湖1980-2022年湖表温度的变化和热浪特征,利用相关性分析和去趋势分析法揭示了湖表温度和湖泊热浪变化的原因。研究表明:(1)TPMFD再分析数据的气温、比湿和风速与刚察气象站观测的气温、比湿和风速相关性较好且偏差和均方根误差较小,两者的相关系数分别为0.96、0.84、0.74,偏差分别为0.55℃、0.00068 g·g^(-1)、-0.31 m·s^(-1),均方根误差分别为0.59℃、0.00069 g·g^(-1)、0.38 m·s^(-1),TPMFD气温的变化速率[0.48℃·(10a)^(-1)]与观测气温的变化速率[0.44℃·(10a)^(-1)]接近,TPMFD比湿的变率[0.0001 g·g^(-1)·(10a)^(-1)]与观测值一致,TPMFD风速的变率[-0.1 m·s^(-1)·(10a)^(-1)]较观测[-0.25 m·s^(-1)·(10a)^(-1)]略小,并且TPMFD和刚察气象站的气温、比湿和风速的变化速率均通过了95%的显著性检验。模拟的青海湖湖水、湖表温度与青海湖原位观测的湖水和湖表温度相关性很好且偏差及均方根误差较小,长时间序列的模拟湖表温度与MODIS地表温度的相关性也较好且偏差和均方根误差均在合理范围,模拟结果与三种观测的相关系数分别为0.99、0.96、0.98,偏差分别为0.25℃、-0.1℃、0.87℃,均方根误差分别为0.58℃、2.65℃、2.20℃。(2)1980-2022年的青海湖湖表温度和湖泊热浪特征均呈现出显著的升高趋势(p<0.05),湖泊热浪的频次在0~6次之间波动,每年发生湖泊热浪的总天数明显增多,2022年的总天数达到150天,多数年份的平均持续时间都超过了10 d·time^(-1),2022年的热浪最长持续时间甚至达到76天,平均强度也显著增强,其中2016年和2022年的青海湖热浪强度等级已处于比多年平均强度等级(“中等”等级)强两个量级的“严重”等级状态。(3)气温、比湿、向下长波辐射、向下短波辐射及气压与模拟湖表温度、湖泊热浪总天数和平均强度呈现正相关关系,而风速则与之呈负相关,与湖泊热浪总天数的增加和平均强度的增强呈正相关。对湖表温度的升高呈正贡献的气象要素从大到小依次为气温(23.83%)、比湿(20.52%)、风速(16.05%)、向下长波辐射(14.79%)和向下短波辐射(10.68%);对湖泊热浪总天数的增加呈正贡献的气象要素分别为气温(37.54%)、风速(35.86%)、比湿(30.03%)、向下长波辐射(28.27%)、向下短波辐射(27.72%);对湖泊热浪强度的增强呈正贡献的气象要素分别为气温(13.25%)、风速(13.07%)、比湿(12.35%)、向下长波辐射(11.05%)、向下短波辐射(10.98%),气压则对湖表温度、湖泊热浪总天数和平均强度的升高呈现抑制作用。展开更多
Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(EN...Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(ENSO)on summer precipitation in China.The inverse phase spatial distribution of sea surface temperature anomalies(SSTAs)in the southwest and northeast of the southern Indian Ocean is defined as the SIOD.Positive SIOD events(positive SSTAs in the southwest,negative SSTAs in the northeast)are associated with La Niña events(Central Pacific(CP)type),while negative SIOD events(negative SSTAs in the southwest,positive SSTAs in the northeast)are associated with El Niño events(Eastern Pacific(EP)type).Both SIOD and ENSO have certain impacts on summer precipitation in China.Precipitation in the Yangtze River basin decreases,while precipitation in southern China increases during pure positive SIOD(P_PSIOD)events.During pure negative SIOD(P_NSIOD)events,the changes in precipitation are exactly the opposite of those during P_PSIOD events,which may be due to differences in the cross-equatorial flow in the southern Indian Ocean,particularly in low-level Australian cross-equatorial flow.When positive SIOD and CP-type La Niña events occur simultaneously(PSIOD+La_Niña),precipitation increases in the Yangtze-Huaihe River basin,while it decreases in northern China.When negative SIOD and EP-type El Niño events occur simultaneously(NSIOD+El_Niño),precipitation in the Yangtze-Huaihe River basin is significantly lower than during P_NSIOD events.This is caused by differences in water vapor originating from the Pacific Ocean during different events.展开更多
文摘受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊的热浪特征进行平均,而青海湖的热浪特征尚不清楚。因此,本研究使用青海湖水温和湖表温度的原位观测数据、刚察气象站观测数据、MODIS地表温度观测数据、第三极地区长时间序列高分辨率地面气象要素驱动数据集(A high-resolution near-surface meteorological forcing dataset for the Third Pole region,TPMFD)和一维湖泊模式(Freshwater Lake Model,FLake)研究了青海湖1980-2022年湖表温度的变化和热浪特征,利用相关性分析和去趋势分析法揭示了湖表温度和湖泊热浪变化的原因。研究表明:(1)TPMFD再分析数据的气温、比湿和风速与刚察气象站观测的气温、比湿和风速相关性较好且偏差和均方根误差较小,两者的相关系数分别为0.96、0.84、0.74,偏差分别为0.55℃、0.00068 g·g^(-1)、-0.31 m·s^(-1),均方根误差分别为0.59℃、0.00069 g·g^(-1)、0.38 m·s^(-1),TPMFD气温的变化速率[0.48℃·(10a)^(-1)]与观测气温的变化速率[0.44℃·(10a)^(-1)]接近,TPMFD比湿的变率[0.0001 g·g^(-1)·(10a)^(-1)]与观测值一致,TPMFD风速的变率[-0.1 m·s^(-1)·(10a)^(-1)]较观测[-0.25 m·s^(-1)·(10a)^(-1)]略小,并且TPMFD和刚察气象站的气温、比湿和风速的变化速率均通过了95%的显著性检验。模拟的青海湖湖水、湖表温度与青海湖原位观测的湖水和湖表温度相关性很好且偏差及均方根误差较小,长时间序列的模拟湖表温度与MODIS地表温度的相关性也较好且偏差和均方根误差均在合理范围,模拟结果与三种观测的相关系数分别为0.99、0.96、0.98,偏差分别为0.25℃、-0.1℃、0.87℃,均方根误差分别为0.58℃、2.65℃、2.20℃。(2)1980-2022年的青海湖湖表温度和湖泊热浪特征均呈现出显著的升高趋势(p<0.05),湖泊热浪的频次在0~6次之间波动,每年发生湖泊热浪的总天数明显增多,2022年的总天数达到150天,多数年份的平均持续时间都超过了10 d·time^(-1),2022年的热浪最长持续时间甚至达到76天,平均强度也显著增强,其中2016年和2022年的青海湖热浪强度等级已处于比多年平均强度等级(“中等”等级)强两个量级的“严重”等级状态。(3)气温、比湿、向下长波辐射、向下短波辐射及气压与模拟湖表温度、湖泊热浪总天数和平均强度呈现正相关关系,而风速则与之呈负相关,与湖泊热浪总天数的增加和平均强度的增强呈正相关。对湖表温度的升高呈正贡献的气象要素从大到小依次为气温(23.83%)、比湿(20.52%)、风速(16.05%)、向下长波辐射(14.79%)和向下短波辐射(10.68%);对湖泊热浪总天数的增加呈正贡献的气象要素分别为气温(37.54%)、风速(35.86%)、比湿(30.03%)、向下长波辐射(28.27%)、向下短波辐射(27.72%);对湖泊热浪强度的增强呈正贡献的气象要素分别为气温(13.25%)、风速(13.07%)、比湿(12.35%)、向下长波辐射(11.05%)、向下短波辐射(10.98%),气压则对湖表温度、湖泊热浪总天数和平均强度的升高呈现抑制作用。
基金supported by the National Natural Science Foundation of China[grant numbers 41975087,U2242212,and 41975085]supported by the National Natural Science Foundation of China[grant number U2242212]。
文摘Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(ENSO)on summer precipitation in China.The inverse phase spatial distribution of sea surface temperature anomalies(SSTAs)in the southwest and northeast of the southern Indian Ocean is defined as the SIOD.Positive SIOD events(positive SSTAs in the southwest,negative SSTAs in the northeast)are associated with La Niña events(Central Pacific(CP)type),while negative SIOD events(negative SSTAs in the southwest,positive SSTAs in the northeast)are associated with El Niño events(Eastern Pacific(EP)type).Both SIOD and ENSO have certain impacts on summer precipitation in China.Precipitation in the Yangtze River basin decreases,while precipitation in southern China increases during pure positive SIOD(P_PSIOD)events.During pure negative SIOD(P_NSIOD)events,the changes in precipitation are exactly the opposite of those during P_PSIOD events,which may be due to differences in the cross-equatorial flow in the southern Indian Ocean,particularly in low-level Australian cross-equatorial flow.When positive SIOD and CP-type La Niña events occur simultaneously(PSIOD+La_Niña),precipitation increases in the Yangtze-Huaihe River basin,while it decreases in northern China.When negative SIOD and EP-type El Niño events occur simultaneously(NSIOD+El_Niño),precipitation in the Yangtze-Huaihe River basin is significantly lower than during P_NSIOD events.This is caused by differences in water vapor originating from the Pacific Ocean during different events.
