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1.
台站温度记录中的城市化信号对于气候变化研究影响重大并仍存在很大争议,尤其是在经历快速城市化的区域。本研究利用遥感影像分类的方法,提取了1980~2009年期间长江三角洲城市群93个气象台站周边10 km×10 km范围的城市土地利用信息,并按照城市土地利用扩张速率对站点进行分类,研究了1980~2009年期间快速城市化站点、中速城市化站点和慢速城市化站点的年和季节平均温度、最低温度和最高温度变化特征,并分析了快速和中速城市化站点城市化影响和城市化影响贡献率。结果表明:全部93个气象站点周边自20世纪80年代起均经历了城市土地利用扩张过程,全部站点周边的平均城市土地利用扩张速率为1.00% a-1;近30年来,各类型站点年和各季节的平均温度、最低温度和最高温度均表现出增加趋势;城市化效应增强因素对快速城市化站点年平均温度贡献率为35.06%,对年平均最低温度的增温贡献率为34.67%,对年平均最高温度增温贡献率最小,仅为18.42%;城市化效应增强因素对中速城市化站点的影响程度小于快速城市化站点,对平均温度、最低温度和最高温度的贡献率分别为19.35%,22.22%和3.13%。在季节变异方面,长江三角洲区域各类型站点冬季的城市化影响贡献率在平均温度、最低温度和最高温度均表现为最低值。  相似文献   

2.
上海气温变化及城市化影响初步分析   总被引:7,自引:1,他引:6  
为研究在全球变化背景下上海市区气温变化规律和城市化进程对其影响,分析了上海市区气温对全球变暖的响应,对比了市区和郊区气温在不同气候背景下的变化趋势,采用与郊区台站对比法分析了上海市区气温城市化效应,研究了城市化进程与气温各分量长期变化趋势之间的关系,将高空与地面观测资料相结合,定量估算了城市化效应对平均气温的贡献,初步讨论了气温的城市化效应成因。研究结果表明:1873~2004年上海市区年平均气温的长期变化趋势为1.31 ℃/(100 a),在1921~1948年和1979~2004年两个时期增温明显,其中第二段增温强于第一段;与郊区站点相比,市区在降温期内降温最小,增温期内升温幅度最大;城市发展导致市区和郊区气温有显著差别且温差逐年加大,其中平均气温和最低气温在秋季的差别最大,最高气温市区和郊区之间差别在夏季最大;城市化进程加快了地面气温升高的速率,其中以最低气温最为明显;在1980年代城市化效应使上海市区年平均温度平均升高0.4 ℃,在1990年代平均升高1.1 ℃。  相似文献   

3.
城市化对深圳气温变化的贡献   总被引:1,自引:0,他引:1  
采用经均一性检验的深圳及其周围台站的地面温度资料和NCEP/DOEAMIP—ⅡReanalysis(R-2)再分析温度数据,通过鲁棒回归(M估计)对气温趋势进行拟合,分析了1967--2005年和1979—2005年两个时间段城市热岛效应对温度的影响,利用再分析数据和地面观测数据的差异估计了1979年以来城市化对气温增暖的贡献。结果表明,20世纪80年代以来的30a里,深圳城市化对当地气温增暖贡献非常显著:1979年以来,城市热岛效应导致年平均气温增暖0.243℃·(10a)^-1,占深圳总体增暖的36.3%;与再分析资料对比得到的城市化对深圳年平均气温增暖的贡献达到0.315℃·(10a)^-1,大于分析观测资料得到的结果,占总体增暖的47.1%。说明城市化的快速发展是导致深圳城市气候增暖的重要因子之一。  相似文献   

4.
With the surface air temperature (SAT) data at 37 stations on Central Yunnan Plateau (CYP) for 1961–2010 and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, the temporal-spatial patterns of the SAT trends are detected using Sen’s Nonparametric Estimator of Slope approach and MK test, and the impact of urbanization on surface warming is analyzed by comparing the differences between the air temperature change trends of urban stations and their corresponding rural stations. Results indicated that annual mean air temperature showed a significant warming trend, which is equivalent to a rate of 0.17 °C/decade during the past 50 years. Seasonal mean air temperature presents a rising trend, and the trend was more significant in winter (0.31 °C/decade) than in other seasons. Annual/seasonal mean air temperature tends to increase in most areas, and higher warming trend appeared in urban areas, notably in Kunming city. The regional mean air temperature series was significantly impacted by urban warming, and the urbanization-induced warming contributed to approximately 32.3–62.9 % of the total regional warming during the past 50 years. Meantime, the urbanization-induced warming trend in winter and spring was more significant than that in summer and autumn. Since 1985, the urban heat island (UHI) intensity has gradually increased. And the urban temperatures always rise faster than rural temperatures on the CYP.  相似文献   

5.
In the study of global warming, one of the main issues is the quantification of the urbanization effect in climate records. Previous studies have contributed much to removing the impact of urbanization from surface air temperature by carefully selecting reference stations. However, due to the insufficient number of stations free from the influence of urbanization and the different criteria used to select reference stations, there are still significant controversies about the intensity of the impact of urbanization on temperature records. This study proposes a dynamic method for quantifying natural warming using information on urbanization from every station acquired from remote sensing (RS) data instead of selecting reference stations. Two different spatial scales were applied to examine the impact of urbanization, but little difference was found, indicating the stability of this method. The results showed a significant difference in original temperature data and the homogenized data—urban warming accounted for approximately 64% in the original temperature warming but only approximately 20% in the homogenized temperature records.  相似文献   

6.
西南地区城市热岛强度变化对地面气温序列影响   总被引:16,自引:2,他引:14       下载免费PDF全文
利用1961—2004年我国西南地区322个站的气温观测资料, 分析了乡村站、小城市站、大中城市站和国家基准/基本站气温变化趋势特点, 着重研究了城市化对城镇站和国家站地面气温记录的影响程度和相对贡献比例。结果显示:区域平均的各类台站年平均气温呈现不同程度的上升趋势, 城市站、国家站的增温速率均高于乡村站。大中城市站和国家站的年平均热岛增温率分别为0.086 ℃/ 10a和0.052 ℃/10a, 其增温贡献率分别达57.6%和45.3%。与大多数地区不同, 西南地区的增温速率明显偏小。因此, 尽管平均热岛强度变化比许多地区弱, 但其相对贡献明显, 表明城市化对该区域气温趋势的绝对影响较弱, 但相对影响较强。另外, 城市热岛增温有明显的季节变化, 表现为秋季最强, 春季或冬季次之, 夏季最弱。热岛增温贡献率则为春季最大 (100%), 夏季次之 (73%以上), 秋季和冬季相对较小。这主要是因为春、夏两季背景气候变凉或趋势微弱, 热岛增温在实际增温中占有更高的比例。  相似文献   

7.
武汉市城市热岛强度非对称性变化   总被引:15,自引:0,他引:15  
利用武汉市区气象站及其周边4个县气象站1960-2005年的气温资料,计算了46 a及分时段的季节和年平均气温、平均最高和最低气温倾向率,城市热岛强度倾向率及其贡献率。结果表明:46 a来,城区和郊区的平均气温均以上升趋势为主,最低气温增幅最大,最高气温增幅最小,甚至下降;冬季增幅最快,夏季增幅最慢,甚至下降,这是第一类非对称性。 城市热岛效应也存在增强趋势,以年平均、最低和最高气温表示的城市热岛强度倾向率分别为0.235℃/10 a、0.425℃/10 a和0.034℃/10 a,热岛效应贡献率分别达到60.4%、67.7%和21.8%,这是第二类非对称性。 46 a来的增温和城市热岛强度加强主要是最近23 a快速增温所致,进入本世纪增温进一步加剧。 摘要 计算了武汉市气象站、周边4县气象站平均的1960~2005年间以及前后两半时段四季和年平均、最高、最低气温倾向率,城市热岛强度倾向率和贡献率。结果表明:1)46年来,城区和郊区的平均气温均以增趋势为主,平均气温倾向率为正,最低气温增幅最大,最高气温增幅最小甚至下降,冬季增幅最快,夏季增幅最慢甚至下降,这是第一类非对称性;2)城市热岛效应也存在增趋势,以年平均、最低、最高气温表示的城市热岛强度倾向率分别为0.235、0.425、0.034 ℃/10a,热岛效应贡献率分别达到60.4%、67.7%、21.8%,这是第二类非对称性,3)46年来的增温和城市热岛强度加强主要是后23年快速增温所致,前23年气温变化不明显。武汉市气象站气温资料严重地保留着城市化影响,建议尽快迁站。 关键词 城市热岛强度 最高气温 最低气温 非对称性变化  相似文献   

8.
当前的地面气候观测资料普遍存在非气候性因素导致的非均一性,对气候变化监测和研究结论可靠性造成重要影响。结合观测台站的历史沿革数据,使用ACMANT和Pairwise Comparisons方法以及RHtest V4软件,对北京地区20个台站均一化前的月平均气温序列进行了非均一性检验和订正,最后评估了均一化对北京地区气温序列变化趋势及其城市化偏差估算的影响。结果表明:除元数据中记录的断点外,无元数据记录的断点也会对序列的趋势变化造成明显影响,其中乡村站最显著;经过订正,1958—2018年整个北京地区、乡村站以及城市站增温趋势分别为0.27℃/(10 a)、0.10℃/(10 a)和0.32℃/(10 a),较订正前分别上升了0.03℃/(10 a)、0.06℃/(10 a)和0.02℃/(10 a)。利用均一化资料估算,1958—2018年北京观象台的城市化影响为0.24℃/(10 a),城市化贡献率为70.2%,评估结果较前人结论有所降低。可见,在现有的北京地区气温资料序列中,仍可能存在较明显的非均一性和未被记录的断点,对区域平均气温趋势估算具有显著影响。  相似文献   

9.
多种方法分析城市化对保定气温变化的贡献   总被引:1,自引:0,他引:1  
文章通过三种方法构造不同的背景气温序列,分析近33a(1979—2011年)城市化对保定气温变化的影响。结果表明:(1)对比保定站与郊区背景站气温资料得到城市热岛效应导致的增暖幅度为0.15℃/10a,城市化贡献率为30.3%。(2)用NCEP/DOE的2m气温再分析资料为背景得到的城市化增温幅度为0.238℃/10a,分离出的城市化贡献率为48.08%。(3)比较城市站与山区背景站资料得出年均气温的城市化增暖幅度为0.216℃/10a,贡献率为43.64%。(4)三种方法计算得出的城市化增温幅度及贡献率各不相同,却一致表明城市化对保定年气温的增暖贡献较为显著。  相似文献   

10.
为深入认识城市对其附近气象站气温的影响,采用位于长沙市区东部和西部两个气象观测站的2007-2009年的逐日气温、风向和风速资料,结合地表覆盖特征数据,对比分析了两站气象记录差异,并通过改进的城市影响指数模型估算了气温资料中的城市影响偏差。分析显示:(1)2007-2009年东、西气象站逐月平均气温(Tmean)、最高气温(Tmax)和最低气温(Tmin)差异很大,最大差异分别可达0.90℃、0.83℃和1.34℃;(2)受城市及风向的影响,两气象站的逐月城市影响指数(K)差异较大,东、西站平均K值分别为2.01和1.50,年内同一台站的K值存在季节变化规律;(3)两站逐月△K与△T之间存在极显著正相关关系;(4)东、西两站2007年Tmean中的城市增温最大,分别达0.63℃和0.45℃。城市附近气象站气温记录受城市规模、风向和风速等因素影响明显,在分析长历时气候变化特征和利用站点记录数据进行空间分析时,有必要对气温数据进行订正。  相似文献   

11.
Gridded temperature data are necessary to run ecological models at regional scales for climate impact studies and have been generated by spatially interpolating measured values at synoptic stations. Because there are few synoptic stations with long-term records in rural areas in Korea, data from urban stations have been used for this purpose. Due to the overlapping of the rapid urbanization-industrialization period with the global warming era in Korea, climate data from these urbanized areas might be contaminated with urban heat island effect. This study was conducted to differentiate urbanization and regional climate change effects on apparent temperature change. Monthly averages of daily minimum, maximum, and mean temperature at 14 synoptic stations were prepared for 1951-1980 (past normal) and 1971-2000 (current normal) periods, respectively.Differences in two temperature normals were regressed to the logarithm of the population increase at 14 corresponding cities from 1966 to 1985. The regression equations were used to determine potential effects of urbanization and to extract the net contribution of regional climate change to the apparent temperature change. According to the model calculation, urbanization effect was common in all months except April. Up to 0.5° warming of nighttime temperature was induced by urbanization in the current normal period compared with the past normal period. There was little effect of regional climate change on local warming in the warm season (May through November). The cool season was warmed mainly by regionally increased daytime temperature. The results could be used to remove urbanization effects embedded in raw data, helping restore unbiased rural temperature trends in South Korea.  相似文献   

12.
利用1964—2011年廊坊9个气象观测站的日平均气温数据,分别利用可行性倾向估计、累计距平、M-K检验三种分析方法,分析了廊坊地区近50 a气温的气候变化特征;并通过统计分析,研究了廊坊地区年平均气温与全社会固定资产投资总额、城市总人口数、国内生产总值三项城市发展指标的关系。结果表明:廊坊市人口、GDP、全社会固定资产投资总额均与年平均气温显著相关。近48 a来,廊坊城区站与郊区站的温度差成逐步减小趋势,受城市化影响,廊坊地区年平均温度呈缓慢上升趋势,空间分布特征郊区站低于城区站,城区站每10 a增温0.5℃,郊区站每10 a增温0.41℃,10 a总增温0.42℃。  相似文献   

13.
周雅清  任国玉 《高原气象》2009,28(5):1158-1166
利用华北地区255个一般站和国家基本、 基准站1961\_2000年的实测资料, 经过质量检验和均一性订正后, 将所有台站根据人口和台站地理位置分为5个类别, 分析了这5个类别台站和国家基本、 基准站地面平均气温、 最高、 最低气温的年和季节变化趋势以及城市化影响。结果表明: 华北全部台站的年平均气温、 最高、 最低气温均呈增加趋势, 且以最低气温上升最为明显, 导致年平均日较差呈现明显下降。就城市化影响而言, 平均气温、 最低气温变化趋势中城市热岛效应加强因素的影响明显, 但城市化对最高气温趋势影响微弱, 个别台站和季节甚至可能造成降温。在国家基本、 基准站观测的年平均气温和年平均最低气温上升趋势中, 城市化造成的增温分别为0.11℃·(10a)-1和0.20℃·(10a)-1, 对全部增温的贡献率分别达39.3%和52.6%。各类台站的四季平均气温和最低气温序列中城市化影响均造成增温。城市化增温以冬季为最大, 夏季最小。城市化还导致乡村站以外的各类台站日较差减小, 近40年华北地区国家基本、 基准站年平均和秋、 冬季平均气温日较差明显下降均由城市化影响造成的。  相似文献   

14.
北京地区城市热岛强度变化对区域温度序列的影响   总被引:57,自引:2,他引:55  
初子莹  任国玉 《气象学报》2005,63(4):534-540
通过对北京地区20个台站1961~2000年月平均温度资料的对比分析,证实热岛效应对城市气象站记录的地表平均气温的绝对影响随时间显著增大,近20 a尤为突出,但其相对影响即热岛增温对全部增暖的贡献却呈下降趋势。近40 a来,北京地区的国家基本、基准站平均温度距平序列与被认为不受城市热岛影响的郊区站平均温度距平序列差异明显,由于热岛效应加强因素引起的国家基本、基准站平均年温度变化速率为0.16℃/(10 a),对整个时期全部增温的贡献达到71%;近20 a来热岛效应加强因素使北京地区国家基本、基准站年平均温度每10 a增暖0.33℃,对该时期全部增温的贡献达到49%。城市热岛效应加强因素对国家基本、基准站季节平均温度上升的贡献在夏、秋季高,冬季最小。本文的结果说明,目前根据国家基本、基准站资料建立的全国或较大区域平均温度序列可能在很大程度上保留着城市化的影响,有必要做进一步的检验和订正。  相似文献   

15.
香港城市与郊区气候差异分析   总被引:6,自引:1,他引:5  
香港天文台近年的研究显示,香港的气温上升是由温室效应增强所导致的全球变暖及本地高密度城市发展的共同影响.除温度外,香港因受城市化影响,相对湿度在城市与郊区之间亦有很明显的差异.选取1989-2006年较能代表香港市区与郊区情况的气象站每小时气温和相对湿度数据,初步比较了香港市郊温度和相对湿度差异的日变化和季节变化,并试图分析这些差异变化与城市化影响的关系.结果显示郊区的气温变化幅度比市区大、变化也较突然,市区晚间至清晨气温较郊区为高;日间情况大致逆转,但气温差别幅度不及晚间.一年之中,城市化效应在冬季最为显著,春季则最不明显.市郊相对湿度的差异同样很明显.晚间至清晨市区相对湿度较郊区为低,日间相反.  相似文献   

16.
While the land-surface temperature (LST) observed at meteorological stations has significantly increased over the previous few decades, it is still unclear to what extent urbanization has affected these positive trends. Based on the LST data recorded at an urban station in Shijiazhuang in North China, and two rural meteorological stations, the effect of urbanization at the Shijiazhuang station for the period 1965–2012 is examined. We find, (1) a statistically-significant linear trend in annual mean urban–rural LST difference of \(0.27\,^{\circ }\hbox {C}\) \(\hbox {(10 year)}^{-1}\), with an urbanization contribution of 100% indicating that the increase in the annual mean LST at the urban station is entirely caused by urbanization. The urbanization effects in spring, summer and autumn on the trends of mean LST are also significant; (2) the urbanization effect is small for time series of the annual mean minimum LST, and statistically marginal for the trend in annual mean maximum LST [\(0.19\,^{\circ }\hbox {C}\,\hbox {(10 year)}^{-1}\)]; (3) the urbanization effect on the annual mean diurnal LST range (\(\Delta {LST}\)) at the urban station is a strongly significant trend of \(0.23\,^{\circ }\hbox {C (10\,year)}^{-1}\), with an urbanization contribution of 21%. The urbanization effects on trends in the spring and autumn mean \(\Delta {LST}\) are also larger and more significant than for the other seasons; (4) the urbanization effects on the long-term LST trends are remarkably different from those on the near-surface air temperature at the same urban station. Nonetheless, the significant warming of the urban boundary layer is expected to affect the urban environment and ecosystems. However, the problem of data representativeness at an urban station for the monitoring and investigation of large-scale climate change remains.  相似文献   

17.
18.
The signal of recent global warming has been detected in meteorological records, borehole temperatures and by several indirect climate indicators. Anthropogenic warming continues to evolve, and various methods are used to study and predict the changes of the global and regional climate. Results derived from GCMs, palaeoclimate reconstructions, and regional climate models differ in detail. An empirical model could be used to predict the spatial pattern of the near-surface air temperature and to narrow the range of regional uncertainties. The idea behind this approach is to study the correlations between regional and global temperature using century-scale meteorological records, and to evaluate the regional pattern of the future climate using regression analysis and the global-mean air temperature as a predictor. This empirical model, however, is only applicable to those parts of the world where regional near-surface air temperature reacts linearly to changes of the global thermal regime. This method and data from a set of approximately 2000 weather stations with continuous century-scale records of the monthly air temperature was applied to develop the empirical map of the regional climate sensitivity. Data analysis indicated that an empirical model could be applied to several large regions of the World, where correlations between local and global air temperature are statistically significant. These regions are the western United States, southern Canada, Alaska, Siberia, south-eastern Asia, southern Africa and Australia, where the correlation coefficient is typically above 0.9. The map of regional climate sensitivity has been constructed using calculated coefficients of linear regression between the global-mean and regional annual air temperature. As long as the correlations between the local and global air temperature are close to those in the last several decades, this map provides an effective tool to scale down the projection of the global air temperature to regional level. According to the results of this study, maximum warming at the beginning of the 21st century will take place in the continental parts of North America and Eurasia. The empirical regional climate sensitivity defined here as the response of the mean-annual regional temperature to 1 °C global warming was found to be 5–6 °C in southern Alaska, central Canada, and over the continental Siberia, 3–4 °C on the North Slope of Alaska and western coast of the U.S.A., and 1–2 °C in most of the central and eastern U.S.A. and eastern Canada. Regions with negative sensitivity are located in the southeastern U.S.A., north-western Europe and Scandinavia. The local tendency towards cooling, although statistically confirmed by modern data, could, however, change in the near future.  相似文献   

19.
ON TEMPERATURE CHANGES OF SHANGHAI AND URBANIZATION IMPACTS   总被引:1,自引:1,他引:0  
To understand how temperature varies in urban Shanghai under the background of global climate change and how it is affected by urbanization, the Shanghai temperature responses to global warming were analyzed, and then the temperature trends of urban and suburb stations under different climatic backgrounds were obtained. The urbanization effects on temperature were studied by comparing urban stations to suburb stations, the relationship between urbanization variables and temperature components were obtained, and observation data of surface and high level were combined to assess the contribution of urbanization effect. In the last part of the paper, the cause of urbanization effects on temperature was discussed. The results indicated: The long term change trend of Shanghai annual mean temperature is 1.31/100a from 1873 to 2004, the periods of 1921 – 1948 and 1979 – 2004 are warmer, and the 1979 – 2004 period is the warmest; compared to suburb stations, the representative urban station has slower decreases in the cool period and faster increases in the warm one; the urban and suburb temperatures have distinct differences resulting from urbanization and the differences are increasing by the year, with the difference of mean temperature and minimum temperature being the greatest in fall and that of maximum temperature being the largest in summer between the urban and suburban areas. The urbanization process accelerates the warming speed, with the minimum temperature being the most obvious; the urbanization effect contributes a 0.4°C increase in 1980s and 1.1°C in 1990s to the annual mean temperature.  相似文献   

20.
根据1979—2010年珠江三角洲24个气象站的气温观测数据以及NCEP/NCAR R1地表气温再分析月资料,运用OMR(observation minus reanalysis)方法分析了珠三角地区平均气温、平均最高气温、平均最低气温的年、季变化趋势。研究结果表明,过去32年珠三角大部分地区呈增温趋势,年平均气温、年平均最高气温、年平均最低气温的OMR趋势分别为0.22/10a、0.19℃/10a、0.23℃/10a,对珠三角地区观测气温增暖的贡献率分别为55.7%、41.7%、57.2%;四季OMR增温趋势冬季最大,夏秋季较小。城市化对区域平均最低气温的影响比对平均最高气温的影响更大。  相似文献   

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