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研究生:陳正昌
研究生(外文):Cheng-Chang CHEN
論文名稱:氣候模式極端降雨指標的統計降尺度研究
論文名稱(外文):Statistics downscaling of extreme indices in IPCC AR4 climate models
指導教授:陳正達陳正達引用關係
指導教授(外文):Cheng-Ta CHEN
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:海洋環境科技研究所
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:95
中文關鍵詞:氣候模式極端降雨統計降尺度
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本研究利用長期高解析度逐日觀測資料,嘗試建立極端降雨指標的空間差異變量,並用此變量對IPCC AR4(2007)的氣候模式資料進行降尺度,以提升GCMs針對區域極端降雨的強度及空間分布描述。為了降低氣候模式本身因物理方法及參數設定對於極端降雨模擬的誤差,本文應用Wood(2004)發展的誤差修正方法,將觀測資料排序成累積機率分布(CDF)曲線,利用此曲線為基準,對模式歷史模擬及未來模擬作整體強度範圍的校正,以消除模式本身的誤差。
透過統計降尺度及誤差修正方法,本文探討了下列四種極端降雨指標: 1.年最大單日累積降雨量(RX1DAY);2.年最大五日累積降雨量(RX5DAY);3.年期間雨日日數(RR1),以及4.年平均雨日降雨強度(SDII)。
研究結果顯示,此方法對極端降雨在氣候變遷的區域特徵及強度推估上均有顯著描述。(1)季風亞洲地區:可將氣候模式解析度提升至0.25度X0.25度,在原始解析度下強度不足的大陸東南沿海區域及孟加拉灣北部極端降雨特徵都有明顯的改善。(2)台灣地區:透過本研究方法提升解析度和強度特徵後,可顯示出北部、中部,及南部之極端降雨特徵。另外利用此方法將目前推估未來氣候變遷的低解析度氣候模式,可得到高解析度極端氣候事件長期變遷的推估,而且可以保留多個氣候模式推估結果,以呈現多個氣候模式的不確定性範圍。

致謝 II
摘要 III
圖表目錄 IV
第一章 前言 1
第二章 資料介紹 5
2.1觀測資料 5
2.2模式資料 6
第三章 研究方法 10
3.1降尺度方法 10
3.2海陸邊界選取 11
3.3空間影響因子 12
3.4模式誤差修正 14
3.5統計檢定方法 15
第四章 極端降雨指標探討 17
4.1年最大單日降雨量 17
4.2年最大五日累積降雨量 18
4.3年期間雨日日數 19
4.4年雨日降雨強度 20
第五章 臺灣地區極端降雨推估 22
5.1歷史氣候極端降雨指標降尺度評估 22
5.2氣候變遷趨勢分析 24
第六章 結論 27
參考文獻 30
附錄 34
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