Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/94057
Title: 臺灣地區氣溫之統計特性及其長期變遷
The Statistical Properties of Temperatures and Their Long-term Trends in Taiwan
Authors: 翁叔平
Weng, Shu-Ping
徐紹青
Hsu, Shao-Ching
Keywords: 氣溫
氣候變遷
長期趨勢
Issue Date: 2018
Abstract:   本研究利用中央氣象局的8個測站,臺北、臺中、澎湖、臺南、恆春、臺東、花蓮和阿里山的長期地表氣溫記錄,包括日均溫、最高溫,以及最低溫資料,個別分析它們在以下5種天氣情境:雨日、無雨日、雨前一日,雨後一日,以及一般日(不論有無降雨)下的4階統計動量:平均值、標準差、偏度、以及峰度,在氣候基期(1961-1990)的統計特性及對應的長期(1897-2014)線性趨勢。   分析上述情境的各類組合於氣候基期的4階統計動量後發現,雖然平均值及標準差代表季節平均狀態及其振幅,但是偏度和峰度,因其計算利用平均值及標準差的3或4次的冪次,能將前者的訊號放大,而提供吾人檢視上述各類情境組合的統計特性之變化細節。例如,冬季雨日的迎風地區,受東北季風影響會有明顯的降溫。雖然分析平均值能獲得其位移變化,但偏度更能表現出其傾向低溫的統計特徵(即右偏的機率密度函數分佈)。同樣的,秋季的臺南測站,也可從雨後一日的分析中,觀察到較高的最高溫(即左偏的機率密度函數分佈)及正值的峰度,顯示洗刷作用可增強陽光短波入射,更能凸顯出秋老虎現象的氣候特徵。   與前人研究一致,本文也發現全球暖化對臺灣地表氣溫的影響,主要表現在夜間最低溫的明顯上升。分析澎湖、恆春以及阿里山等背景測站的結果發現,暖化下,增加的水氣含量加強向下長波輻射,且夜間較低的氣溫又使得水氣容易飽和,增加的雲量可能抑制向上長波輻射的冷卻作用,因此,最低溫出現較高幅度的成長。   研究也發現,人類活動產生的懸浮微粒和汙染物,可能會增強而非減弱夜間的暖化作用。分析臺南和臺北測站最低溫的長期趨勢後發現,臺南冬天雲量較少,但冬季的最低溫卻有像臺北多雲環境時一樣的增溫趨勢。分析進一步發現,臺南秋季雨後一日的偏度下降但峰度增加,因而顯示,該地秋夜溫度有高於平均值而集中的長期現象。但是,大多數的其他測站並沒有高溫集中的表現,再者,大部分測站的各項氣溫的綜合表現,均呈現出長期趨勢的轉折時間點,大致發生在都市化以及工業蓬勃發展的1970年代左右。因此本研究認為人類活動所產生的懸浮微粒和汙染物,對於區域氣候的長期趨勢,有一定的影響。   全球暖化下,各季節之長期趨勢變化並不一致,分析冬季雨日及無雨日的溫度趨勢表現後發現,1970年代前(後),臺北測站無雨日的夜間氣溫小(大)於有雨日夜間氣溫,暗示降雨過程和氣候暖化的交互作用機制,可能發生變化。此外,分析9月份最低溫的長期趨勢也發現,其各項氣溫綜合數值及趨勢,和6月相較下,更接近7、8月的表現,建議夏季有延長的現象。根據上述針對氣溫的分析,本文建議臺灣季節氣候應分為,春季:2、3、4月;梅雨季:5、6月;夏季:7、8、9月;秋季:10、11月以及冬季:12、1月,上述季節的劃分,也大致與降雨的時序一致。
This study uses the long-term surface temperature records of eight stations: Taipei, Taichung, Penghu, Tainan, Hengchun, Taitung, Hualien and Alishan, including daily average temperature, maximum temperature and minimum temperature data archived at Central Weather Bureau, to analyze the following five weather scenarios: rainy days, clear days, days before rain, days after rain, and normal day (whether or not there is rainfall). Their first 4 statistical moments, namely the mean, standard deviation, skewness and kurtosis are calculated and the related statistical characteristics during the base period (1961-1990) and the corresponding long-term (1897-2014) linear trend are explored. After analyzing the statistical moments of the base period for all kinds of combinations of the above situations, it is found that, although the mean and standard deviation represent the seasonal average state and its amplitude, skewness and kurtosis are calculated using the standard deviation of 3 or 4 times the power of the former signal can be amplified, and provide us the opportunity to examine the combination of various types of changes in the statistical characteristics in detail. For example, during the wintertime, there is a clear cooling effect at the windward stations due to the northeast monsoon. Although analyzing the mean can obtain its displacement change, analyzing the skewness is more able to show its tendency towards the low temperature statistical characteristics (i.e. right-sided probability density function distribution). Similarly, during the autumn season, the Tainan station, in the days after the rain, is observed to have the higher maximum temperature (i.e. the left-sided probability density function) and the positive kurtosis, suggesting that the scrubbing effect after the rainy day probably enhances the insolation, and highlights the autumn tiger phenomenon. Consistent with the previous studies, this study also finds that the effect of global warming on the surface air temperature is mainly reflected in increasing the night temperature. Analyzes of the background stations (Penghu, Hengchun and Alishan) found that the increased moisture content enhances the downward longwave radiation under a warming world, and the lower degree of the night temperatures can make the moisture saturated and the resulting increased cloudiness inhibits the upward long-wave radiation. Therefore, the nighttime temperature appears to have a higher degree of growing amplitude. The study also finds that aerosols and pollutants due to human activities may increase the nighttime warming instead of decreasing it. After analyzing the long-term trend of the minimum temperature at Tainan and Taipei stations, it is found that there is less winter cloud in Tainan, but the minimum temperature has the same warming trend as Taipei which is normally situated in an overcast environment in winters. The autumn analysis at Tainan station further finds that, on the first days after rain, the skewness decreased but the kurtosis increased. It thus suggests that there is a long-term phenomenon that the temperature of the autumn night falls above the average and becomes more concentrated. However, most of the other stations are not concentrated in high temperatures. Moreover, the comprehensive performance of temperature at most of the stations shows a turning point in the long-term trend, which generally occurs in the period when urbanization and industrial development were flourishing around the 1970s. Therefore, this study argues that the suspended particles and pollutants due to human activities have impacts on the long-term trend of regional climate. Under the global warming, the changes in the long-term trend at different seasons are not consistent. Based on the wintertime trend analysis for the nighttime temperature at Taipei station during the rainy and no-rain days, it is found that the nighttime temperature of the no-rain days is smaller (larger) than that of the rainy days before (after) the 1970s, suggesting the changing interactions between the rainfall processes and warming. In addition, the analysis of the long-term trend of the minimum temperature in September finds that the integrated values and trends of temperatures are closer to those in July and August as compared with those in June, suggesting the extension of the summer season. Based on the above analysis of the temperature, this study suggests that the seasonal climate in Taiwan should be divided into Spring season: February, March and April, Mei-Yu season: May-June, Summer season: July, August and September, Autumn season: October, November and Winter season: December and January. The above seasonal division based on the temperature analysis in this study is consistent with the timing of seasonal rainfall in Taiwan.
URI: http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=%22http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G0503231102%22.&%22.id.&
http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/94057
Other Identifiers: G0503231102
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