气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬...气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬度坐标关联匹配,对季节气候条件如何影响人们的抑郁情绪展开研究,关键气候因子选取基本气象因素(气温、相对湿度)以及气候舒适度(温湿指数、寒冷指数和人体舒适度指数),得到以下结论。(1)春夏季节气象因素对抑郁情绪有显著影响,其中,气温和气候舒适度对抑郁情绪的影响程度较大。具体为适宜的气温、相对湿度、温湿指数和人体舒适度指标可以显著降低抑郁情绪,但是寒冷指数升高显著加重抑郁。人体舒适度指数每增加一个单位,抑郁情绪显著降低3%。(2)在春夏季节,当同时控制气温和相对湿度两个气象因素时,各自对抑郁情绪的影响比控制单个气象因素时更为明显。(3)春夏季节南北地区的气候因素对抑郁情绪存在明显差异。北方气候因素对抑郁情绪影响显著,而南方地区未发现显著影响。(4)在春夏季节,不同社会人口因素对抑郁情绪的影响也存在差异,比如未婚人口占比越大,公众抑郁情绪水平可能越高。本研究结果强调了非极端的气候条件对身心健康的潜在影响,以期推动我国对公众气候变化心理问题的关注,并为气候学、心理学相关学科和政府部门制定政策提供参考,助力构建全民参与的气候适应型社会。展开更多
利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,...利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。展开更多
在全球气候变化背景下,极端高温事件频发,且自21世纪以来其发生频率明显增加,对农业生产及人体健康造成深远影响。为探究地形复杂的伊犁河流域极端高温时空演变规律,本文利用1991—2020年该流域11个气象站逐日气温数据,计算夏季日数(SU...在全球气候变化背景下,极端高温事件频发,且自21世纪以来其发生频率明显增加,对农业生产及人体健康造成深远影响。为探究地形复杂的伊犁河流域极端高温时空演变规律,本文利用1991—2020年该流域11个气象站逐日气温数据,计算夏季日数(SU25)、热夜日数(TR20)、暖昼日数(TX90p)、暖夜日数(TN90p)、日最高气温极高值(TXx)和日最低气温极高值(TNx)6个极端高温指数,并通过线性趋势分析、Mann-Kendall突变检验、经验正交函数分解(Empirical Orthogonal Function decomposition,EOF)以及克里金插值法(Kriging),系统分析伊犁河流域6个极端高温指数的时空变化特征。结果表明:伊犁河流域大部分极端高温指数整体呈快速增长趋势,且在20世纪90年代至21世纪初发生显著突变。其中SU25、TX90p、TXx和TNx的增长速率突出,2015年后各指数处于加速增长阶段。空间分布上,流域大部分极端高温指数呈现明显的“西北高、东南低”格局:西北部为高温指数高值区,昭苏东北部、特克斯、巩留及尼勒克西南部形成稳定低值中心。由EOF分解得到TXx与TNx的空间分布存在两种典型模态,其演变特征与流域极端高温整体变化趋势具有高度一致性。展开更多
The variation of land surface temperature(LST)has a vital impact on the energy balance of the land surface process and the ecosystem stability.Based on MDO11C3,we mainly used regression analysis,GIS spatial analysis,c...The variation of land surface temperature(LST)has a vital impact on the energy balance of the land surface process and the ecosystem stability.Based on MDO11C3,we mainly used regression analysis,GIS spatial analysis,correlation analysis,and center-of-gravity model,to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020.Furthermore,we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern.The results indicate the following:(1)From 2001 to 2020,the LST climate average in China is 9.6℃,with a general pattern of higher temperatures in the southeast and northwest regions,lower temperatures in the northeast and Qinghai-Tibet Plateau,and higher temperatures in plains compared to lower temperatures in mountainous areas.Generally,LST has a significant negative correlation with elevation,with a correlation coefficient of–0.66.China’s First Ladder has the most pronounced negative correlation,with a correlation coefficient of–0.76 and the lapse rate of LST is 0.57℃/100 m.(2)The change rate of LST in China during the study is 0.21℃/10 a,and the warming area accounts for 78%,demonstrating the overall spatial pattern a“multi-core warming and axial cooling”.(3)LST’s variation exhibits prominent seasonal characteristics in the whole country.The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations.The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement.Cooling areas exhibit more substantial centroid movement,indicating greater regional variation and seasonal variability.(4)China’s LST variation is driven by both natural influences and human activities,of which natural factors contribute more,with sunshine duration and altitude being key factors.The boundary trend between the two dominant type areas is highly consistent with the“Heihe-Tengchong Line”.The eastern region is mostly dominated by human activity in conjunction with terrain factors,while the western region is predominantly influenced by natural factors,which enhance/weaken the change range of LST through mutual coupling with climate,terrain,vegetation,and other factors.This study offers valuable scientific references for addressing climate change,analyzing surface environmental patterns,and protecting the ecological environment.展开更多
文摘气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬度坐标关联匹配,对季节气候条件如何影响人们的抑郁情绪展开研究,关键气候因子选取基本气象因素(气温、相对湿度)以及气候舒适度(温湿指数、寒冷指数和人体舒适度指数),得到以下结论。(1)春夏季节气象因素对抑郁情绪有显著影响,其中,气温和气候舒适度对抑郁情绪的影响程度较大。具体为适宜的气温、相对湿度、温湿指数和人体舒适度指标可以显著降低抑郁情绪,但是寒冷指数升高显著加重抑郁。人体舒适度指数每增加一个单位,抑郁情绪显著降低3%。(2)在春夏季节,当同时控制气温和相对湿度两个气象因素时,各自对抑郁情绪的影响比控制单个气象因素时更为明显。(3)春夏季节南北地区的气候因素对抑郁情绪存在明显差异。北方气候因素对抑郁情绪影响显著,而南方地区未发现显著影响。(4)在春夏季节,不同社会人口因素对抑郁情绪的影响也存在差异,比如未婚人口占比越大,公众抑郁情绪水平可能越高。本研究结果强调了非极端的气候条件对身心健康的潜在影响,以期推动我国对公众气候变化心理问题的关注,并为气候学、心理学相关学科和政府部门制定政策提供参考,助力构建全民参与的气候适应型社会。
文摘利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。
文摘在全球气候变化背景下,极端高温事件频发,且自21世纪以来其发生频率明显增加,对农业生产及人体健康造成深远影响。为探究地形复杂的伊犁河流域极端高温时空演变规律,本文利用1991—2020年该流域11个气象站逐日气温数据,计算夏季日数(SU25)、热夜日数(TR20)、暖昼日数(TX90p)、暖夜日数(TN90p)、日最高气温极高值(TXx)和日最低气温极高值(TNx)6个极端高温指数,并通过线性趋势分析、Mann-Kendall突变检验、经验正交函数分解(Empirical Orthogonal Function decomposition,EOF)以及克里金插值法(Kriging),系统分析伊犁河流域6个极端高温指数的时空变化特征。结果表明:伊犁河流域大部分极端高温指数整体呈快速增长趋势,且在20世纪90年代至21世纪初发生显著突变。其中SU25、TX90p、TXx和TNx的增长速率突出,2015年后各指数处于加速增长阶段。空间分布上,流域大部分极端高温指数呈现明显的“西北高、东南低”格局:西北部为高温指数高值区,昭苏东北部、特克斯、巩留及尼勒克西南部形成稳定低值中心。由EOF分解得到TXx与TNx的空间分布存在两种典型模态,其演变特征与流域极端高温整体变化趋势具有高度一致性。
基金National Natural Science Foundation of China,No.41461086,No.41761108。
文摘The variation of land surface temperature(LST)has a vital impact on the energy balance of the land surface process and the ecosystem stability.Based on MDO11C3,we mainly used regression analysis,GIS spatial analysis,correlation analysis,and center-of-gravity model,to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020.Furthermore,we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern.The results indicate the following:(1)From 2001 to 2020,the LST climate average in China is 9.6℃,with a general pattern of higher temperatures in the southeast and northwest regions,lower temperatures in the northeast and Qinghai-Tibet Plateau,and higher temperatures in plains compared to lower temperatures in mountainous areas.Generally,LST has a significant negative correlation with elevation,with a correlation coefficient of–0.66.China’s First Ladder has the most pronounced negative correlation,with a correlation coefficient of–0.76 and the lapse rate of LST is 0.57℃/100 m.(2)The change rate of LST in China during the study is 0.21℃/10 a,and the warming area accounts for 78%,demonstrating the overall spatial pattern a“multi-core warming and axial cooling”.(3)LST’s variation exhibits prominent seasonal characteristics in the whole country.The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations.The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement.Cooling areas exhibit more substantial centroid movement,indicating greater regional variation and seasonal variability.(4)China’s LST variation is driven by both natural influences and human activities,of which natural factors contribute more,with sunshine duration and altitude being key factors.The boundary trend between the two dominant type areas is highly consistent with the“Heihe-Tengchong Line”.The eastern region is mostly dominated by human activity in conjunction with terrain factors,while the western region is predominantly influenced by natural factors,which enhance/weaken the change range of LST through mutual coupling with climate,terrain,vegetation,and other factors.This study offers valuable scientific references for addressing climate change,analyzing surface environmental patterns,and protecting the ecological environment.
