MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature (Ta) and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature (Ts) to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years (2001 to 2007) are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta (R2 ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃). Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ (in January, the coldest month) and 17.29 ℃ (in July, the warmest month), and for the warm season (May-September), it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.     

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.     

中国城市扩展对气温观测的影响及其高估程度(英文)

Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ’entered’ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observations of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overestimation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural stations that ’entered’ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air temperature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations’ and rural stations’ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.     

1951–2010年中国农业水热气候条件的时空变化（英文）

Based on the daily observation data of 824 meteorological stations during 1951-2010 released by the National Meteorological Information Center, this paper evaluated the changes in the heat and moisture conditions of crop growth. An average value of ten years was used to analyze the spatio-temporal variation in the agricultural hydrothermal conditions within a 1 km2 grid. Next, the inter-annual changing trend was simulated by regression analysis of the agricultural hydrothermal conditions. The results showed that the contour lines for temperature and accumulated temperatures(the daily mean temperature ≥0°C) increased significantly in most parts of China, and that the temperature contour lines had all moved northwards over the past 60 years. At the same time, the annual precipitation showed a decreasing trend, though more than half of the meteorological stations did not pass the significance test. However, the mean temperatures in the hottest month and the coldest month exhibited a decreasing trend from 1951 to 2010. In addition, the 0°C contour line gradually moved from the Qinling Mountains and Huaihe River Basin to the Yellow River Basin. All these changes would have a significant impact on the distribution of crops and farming systems. Although the mechanisms influencing the interactive temperature and precipitation changes on crops were complex and hard to distinguish, the fact remained that these changes would directly cause corresponding changes in crop characteristics.     

1910-2014年湖南省均一化气温序列构建及其变化特征（英文）

Based on the statistical method and the historical evolution of meteorological stations,the temperature time series for each station in Hunan Province during 1910–2014 are tested for their homogeneity and then corrected.The missing data caused by war and other reasons at the 8 meteorological stations which had records before 1950 is filled by interpolation using adjacent observations,and complete temperature time series since the establishment of stations are constructed.After that,according to the representative analysis of each station in different time periods,the temperature series of Hunan Province during 1910–2014 are built and their changes are analyzed.The results indicate that the annual mean temperature has a significant warming trend during 1910–2014 and the seasonal mean temperature has the largest rising amplitude in winter and spring,followed by autumn,but no significant change in summer.Temperature variation over Hunan Province has several significant warm-cold alternations and more frequent than that in whole China.Annual and seasonal mean temperatures except summer and autumn have abrupt warming changes in the recent 100 years.The wavelet analysis suggests that the annual and four seasonal mean temperatures in recent 100 years have experienced two climatic shifts from cold to warm.     

Climate change on the southern slope of Mt. Qomolangma （Everest） Region in Nepal since 1971

Based on monthly mean, maximum, and minimum air temperature and monthly mean precipitation data from 10 meteorological stations on the southern slope of the Mt. Qomolangma region in Nepal between 1971 and 2009, the spatial and temporal characteristics of climatic change in this region were analyzed using climatic linear trend, Sen＇s Slope Estimates and Mann-Kendall Test analysis methods. This paper focuses only on the southern slope and attempts to compare the results with those from the northern slope to clarify the characteristics and trends of climatic change in the Mt. Qomolangma region. The results showed that： （1） between 1971 and 2009, the annual mean temperature in the study area was 20.0℃, the rising rate of annual mean temperature was 0.25℃/10a, and the temperature increases were highly influenced by the maximum temperature in this region. On the other hand, the temperature increases on the northern slope of Mt. Qomolangma region were highly influenced by the minimum temperature. In 1974 and 1992, the temperature rose noticeably in February and September in the southern region when the increment passed 0.9℃. （2） Precipitation had an asymmetric distribution; between 1971 and 2009, the annual precipitation was 1729.01 mm. In this region, precipitation showed an increasing trend of 4.27 mm/a, but this was not statistically significant. In addition, the increase in rainfall was mainly concentrated in the period from April to October, including the entire monsoon period （from June to September） when precipitation accounts for about 78.9% of the annual total. （3） The influence of altitude on climate warming was not clear in the southern region, whereas the trend of climate warming was obvious on the northern slope of Mt. Qomolangma. The annual mean precipitation in the southern region was much higher than that of the northern slope of the Mt. Qomolangma region. This shows the barrier effect of the Himalayas as a whole and Mt. Qomolangma in particular.     

青藏高原各拉丹冬冰芯记录的季节气温变化

A 70-year history of precipitation δ18O record has been retrieved using an ice core drilled from a plat portion of the firn area in the Guoqu Glacier (33o34′37.8″ N, 91o10′35.3″ E, 5720 m a.s.l.) on Mt. Geladaindong (the source region of Yangtze River) during October and November, 2005. Based on the seasonality of δ18O records and the significant positive relationships between monsoon/non-monsoon δ18O values and summer/spring air temperature from the nearby meteorological stations, the history of summer and spring air temperature have been reconstructed for the last 70 years. The results show that both summer and spring air temperature variations present similar trends during the last 70 years. Regression analysis indicates that the slope of the temperature-δ18O relationship is 1.3℃/‰ for non-monsoon δ18O values and spring air temperature, and 0.4℃/‰ for monsoon δ18O values and summer air temperature. Variation of air temperature recorded in the ice core is consistent with that in the Northern Hemisphere (NH), however, the warming trend in the Geladaindong region is more intense than that in the NH, reflecting a higher sensitivity to global warming in the high elevation regions. In addition, warming trend is greater in spring than in summer.     

长江三角洲城市带扩展对区域温度变化的影响

Based on non-radiance-calibrated DMSP/OLS nighttime light imagery from 1992 to 2003, urban land area statistical data, meteorological data and land surface temperature data retrieved by MODIS and NOAA/AVHRR data, the influence of urbanization on regional cli- matic trend of temperature in the Yangtze River Delta (YRD) was analyzed. Conclusions are as follows: 1) There is a significant urbanization process from 1992 to 2003 in the YRD. Four city clusters of Nanjing–Zhenjiang–Yangzhou, Suzhou–Wuxi–Changzhou, Shanghai and Hangzhou Bay form a zigzag city belt. The increase rate of annual mean air temperature in city-belt is 0.28–0.44℃/10a from 1991 to 2005, which is far larger than that of non-city-belt. 2) The urban heat island (UHI) effect on regional mean air temperature in different seasons is summer>autumn>spring>winter. 3) The UHI intensity and the urban total population logarithm are creditably correlated. 4) The UHI effect made the regional annual mean air temperature increased 0.072℃ from 1961 to 2005, of which 0.047℃ from 1991 to 2005, and the annual maximum air temperature increased 0.162℃, of which 0.083℃ from 1991 to 2005. All these indicating that the urban expansion in the YRD from 1991 to 2005 may be regarded as a serious climate signal.     

Impact of urban expansion on regional tempera-ture change in the Yangtze River Delta

Based on non-radiance-calibrated DMSP/OLS nighttime light imagery from 1992 to 2003, urban land area statistical data, meteorological data and land surface temperature data retrieved by MODIS and NOAA/AVHRR data, the influence of urbanization on regional cli-matic trend of temperature in the Yangtze River Delta (YRD) was analyzed. Conclusions are as follows: 1) There is a significant urbanization process from 1992 to 2003 in the YRD. Four city clusters of Nanjing–Zhenjiang–Yangzhou, Suzhou–Wuxi–Changzhou, Shanghai and Hangzhou Bay form a zigzag city belt. The increase rate of annual mean air temperature in city-belt is 0.28–0.44℃/10a from 1991 to 2005, which is far larger than that of non-city-belt. 2) The urban heat island (UHI) effect on regional mean air temperature in different seasons is summer>autumn>spring>winter. 3) The UHI intensity and the urban total population logarithm are creditably correlated. 4) The UHI effect made the regional annual mean air temperature increased 0.072℃ from 1961 to 2005, of which 0.047℃ from 1991 to 2005, and the annual maximum air temperature increased 0.162℃, of which 0.083℃ from 1991 to 2005. All these indicating that the urban expansion in the YRD from 1991 to 2005 may be regarded as a serious climate signal.     

Interdecadal change of snow depth in winter over the Tibetan Plateau and its effect on summer precipitation in China

This paper obtained a set of consecutive and long-recorded observational snow depth data from 51 observation stations by choosing, removing and interpolating original observation data over the Tibetan Plateau for 1961–2006. We used monthly precipitation and temperature data from 160 stations in China for 1951–2006, which was collected by the National Climate Center. Through calculating and analyzing the correlation coefficient, significance test, polynomial trend fitting, composite analysis and abrupt change test, this paper studied the interdecadal change of winter snow over the Tibetan Plateau and its relationship to summer precipitation and temperature in China, and to tropospheric atmospheric temperature. This paper also studied general circulation and East Asian summer monsoon under the background of global warming.     

基于Modis地表温度的横断山区气温估算及其时空规律分析

横断山区气象观测站稀少且多分布在河谷之中,气温资料极度匮乏,严重影响山区地理与生态研究。随着遥感技术的发展,热红外遥感数据,结合地面观测数据,可以用来推测山区气温。本文通过对横断山区2001 年-2007 年间64 个气象台站的多年月平均气温数据(Ta) 与Modis地表温度多年月平均值(Ts) 进行了时序分析和回归分析,并取得如下研究结果：(1) Ts 与Ta 具有非常好的线性相关关系,89%的台站的决定系数高于0.5;95%的台站的标准误差都低于3 oC,84.4%的台站标准误差低于2.5 oC;12 个月份的Ts 与Ta 的决定系数R²在0.63~0.90 之间,标准误差在2.22~3.05 oC之间。(2) 研究区内月均气温的变化范围在-2.25~15.64 oC之间;生长季(5-9 月份) 的月均气温变化范围为：10.44~15.64 oC。(3) 等温线的海拔高度自山体外围向内部逐渐升高,与山体效应的增温效应相吻合;0 oC等温线自10 月份从海拔4700±500 m左右逐渐降低,至1月份降至最低点,约在3500±500 m左右,此后,逐渐回升,至次年5 月份再次达到4700±500 m左右,也就是说横断山区5200 m以下的广大山区全年至少有6~12 个月的气温在0 oC以上。研究表明：可以利用Modis月均地表温度结合地面观测台站的数据较精确的估算山区月均气温。     

基于MODIS数据的青藏高原气温与增温效应估算

利用2001-2007 年MODIS地表温度数据、137 个气象观测台站数据和ASTERGDEM数据, 采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR), 研究了高原月均地表温度与气温的相关关系, 最终选择精度较高的GWR分析方法, 建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(Adjusted R²) 都达到了0.91 以上(0.91~0.95), 标准误差(RMSE) 介于1.16~1.58℃;约70%以上的台站各月残差介于-1.5~1.5℃之间, 80%以上的台站的残差介于-2~2℃之间。根据该模型, 估算了青藏高原气温, 并在此基础上, 将高原及周边地区7 月份月均气温转换到4500 m和5000 m海拔高度上, 对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1) 利用GWR方法, 结合地面台站的观测数据和MODIS Ts、DEM等, 对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2~3℃), 精度可以提高到1.58℃;(2) 高原夏半年海拔5000 m左右的高山区气温能达到0℃以上, 尤其是7 月份, 海拔4000~5500 m的高山区的气温仍能达到10℃左右, 为山地森林的发育提供了温度条件, 使高原成为北半球林线分布最高的地方;(3) 高原的增温效应非常突出, 初步估算, 在相同的海拔高度上高原内部气温要比外围地区高6~10℃。     

Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data

Air temperature is an important climatological variable and is usually measured in meteorological stations. Accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines, but low station density and complexity of the terrain usually lead to significant errors and unrepresentative spatial patterns over large areas. Fortunately the current studies have shown that the regression models can help overcome the problem with the help of time series remote sensing data. However, noise induced by cloud contamination and other atmospheric disturbances variability impedes the application of LST data. An improved Savizky-Golay (SG) algorithm based on the LST background library is used in this paper to reconstruct MODIS LST product. Data statistical analysis included 12 meteorological stations and 120 reconstructed MODIS LST images of the period from 2001 to 2010. The coeffificient of correlations (R²) for 80% of the stations was higher than 0.5 (below 0.5 for only 2 stations) which illustrated that there is a considerably close agreement between monthly mean TA (air temperature) and the reconstructed LST in the Lancang River basin. Comparing to the regression model for every month with only LST data, the regression model with LST and NDVI had higher R² and RMSE. Finally, the LST-NDVI regression method was applied as an estimate model to produce distributed maps of air temperature with month intervals and 1 km spatial in the Lancang River basin of 2010.     

基于MODIS的秦巴山地气温估算与山体效应分析

秦巴山地作为横亘在中国南北过渡带的巨大山脉,其山体效应对中国中部植被和气候的非地带性分布产生了重要的影响,山体内外同海拔的温差是表征山体效应大小较为理想的指标。本研究结合MODIS地表温度（LST）数据、STRM-1 DEM数据和秦巴山地的118个气象站点的观测数据,分别采用普通线性回归（OLS）和地理加权回归（GWR）两种分析方法对秦巴山地的气温进行估算,在此基础上将秦巴山地各月气温转换为同海拔（1500 m,秦巴山地平均海拔）气温,对比分析秦巴山地的山体效应。结果表明：① 相比OLS分析,GWR分析方法的精度更高,各月回归模型的R ²均在0.89以上,均方根误差（RMSE）在0.68~0.98 ℃之间。② 利用GWR估算得到的同海拔气温,从东向西随海拔升高呈现了明显的升高的趋势,秦岭西部山地比东段升高约6 ℃和4.5 ℃;大巴山西部山地年均和7月份同海拔的气温较东段升高约8 ℃和5 ℃。③ 从南向北,以汉江为分界,秦岭与大巴山的同海拔的气温均呈现出由山体边缘向内部升高的趋势。④ 秦巴山地西部大起伏高山,秦岭大起伏高中山和大巴山大起伏中山,相比豫西汉中中山谷地,各月均同海拔气温分别升高了约3.85~9.28 ℃、1.49~3.34 ℃和0.43~3.05 ℃,平均温差约为3.50 ℃,说明秦巴山地大起伏中高山的山体效应十分明显。     

青藏高原气温空间分布规律及其生态意义

作为世界第三极的青藏高原,其巨大的块体产生了显著的夏季增温作用,对亚洲乃至全球气候都具有重大影响。但由于高原自然条件严酷,山区气象观测台站很少,气象资料极度匮乏;如果依靠台站数据进行空间插值获得高原气温的空间分布数据,会由于插值点过少而产生较大误差并可能掩盖一些空间信息,因而难以全面反映高原气温的空间分布规律。利用基于MODIS地表温度数据估算的青藏高原气温数据,详细分析各月气温及重要等温线的空间分布格局,并结合林线和雪线数据,初步探讨了高原气温空间分布格局对高原地理生态格局的重要影响。研究表明：① 等温线的海拔高度自高原东北部、东部边缘向内部逐渐升高,等温线在高原内部比东部边缘高500~2000 m,表明相同海拔高度上气温自边缘向高原内部逐渐升高。② 高原西北部的羌塘高原、可可西里为高原的寒冷区,全年有7个月的气温低于0 ℃,3~4个月的气温低于-10 ℃;青藏高原南部（喜马拉雅山北坡—冈底斯山南坡）和中部（冈底斯山北坡—唐古拉山南坡）是高原的温暖区,全年有5个月的气温能达到5~10 ℃,有3个月的气温能超过10 ℃,尤其是拉萨—林芝—左贡一带在3500~4000 m以下的地区最冷月均温也能高于0 ℃。③ 北半球最高雪线和林线分别分布于高原的西南部和东南部,表明高原气温空间分布特征对本地的地理生态格局具有重要影响。     

念青唐古拉山南坡气温分布及其垂直梯度

利用架设在念青唐古拉山南坡9个海拔高度(4 300~5 500 m)的自动气象站1 a(2006年8月1日至2007年7月31日)的实测数据,对山坡1.5 m高度的近地面气温随海拔梯度和时间的分布进行了分析。表明念青南坡4 300~4 950 m冷季(10~4月)存在逆温。利用高山各观测高度的温度与当雄气象站气温具有良好相关,推算出多年平均情况下念青唐古拉山南坡各观测高度的年平均气温和各月平均气温。并由此推测念青唐古拉山南坡海拔5 100 m以上存在高山多年冻土,此多年冻土下界高度比《中国冻土》指出的高度高约200 m。     

东北区夏季低温事件概率空间分布与亚洲阻塞流型域及其冷空气活动路径

采用国家气候中心的全国160个和东北88个测站1960~2010年的逐月观测资料、美国NCEP/NCAR的再分析资料,对东北区夏季低温事件概率的空间分布与冷空气活动路径及其环流特征进行了分析,结果表明:东北区测站严重冷夏A事件≥20%概率区主要分布在黑龙江省的大部和吉林省的中东部,概率大值轴呈东北-西南向分布;东北区测站冷夏B事件≥20%概率区主要分布在东北区的中西部和长白山东部区,概率大值轴呈西北-东南向分布;5 个A事件典型年的500 hPa 亚洲地区呈现乌拉尔山与鄂霍次克海阻高,与东北区均为负距平区构成双阻塞偶极子流型,偏东和偏北区域为温度场负距平极值中心;5 个B事件典型年则巴尔喀什湖和贝加尔湖为正异常区,东北亚至北太平洋阿留申群岛附近上空为负距平区,构成亚洲地区东西向偶极子阻塞流型,温度场负距平分布为偏西北-东南走向;选择850 hPa 的假相当位温θse 的316 K候平均等值线的变化,反映来源于极地或超极地的冷空气活动路径的时空范围,其A和B事件典型年冷空气活动路径均分别与与东北区A、B事件大概率区的主要轴向分布是吻合的.