CMIP5多模式系集年代際預報實驗對熱帶地區的年際預報能力與偏差校正的探討

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姓名

王俊寓(Chun-Yu Wang) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

CMIP5多模式系集年代際預報實驗對熱帶地區的年際預報能力與偏差校正的探討
(A study on the interannual prediction skills and bias correction of CMIP5 multi-model ensemble of decadal prediction experiments)

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摘要(中)

本研究使用第五期耦合模式比較計畫(CMIP5)多模式系集的年代際預報實驗提供的月平均資料來評估在全球熱帶地區(30°S-30°N)的數個變數的年際預報技術。由於氣候系統具有複雜的時空結構而氣候預報的主要目的是預先得知氣候系統隨著時間演進的變化，因此我們嘗試利用氣候系統中的穩定空間分布做為評估多模式系集的預報技術的量度。首先我們對觀測資料進行型態穩定度分析以獲得在時間變動的情況下仍可保持相當穩定的EOF空間分布。然後讓觀測資料與年代預報實驗資料在相同空間分布的基礎上進行時間序列分析。最後再使用線性迴歸與排序修正法對多模式系集預報的結果進行偏差校正。同時，我們也利用上述研究所獲得的穩定空間分布進行EOF模態資料重組來評估所得到的重組資料是否對於熱帶地區的個別網格尺度的預報技術有所幫助。
　　觀測資料的型態穩定度分析顯示每個變數至少都有4個或以上的穩定EOF空間分布。第一個EOF所代表的是該變數的全球熱帶平均狀態而第二個和以下的EOFs則隨著數目的增加呈現越來越局地化的結構。除了海表溫度的第三個EOF與ENSO有密切相關外，本研究主要著重於與第一個EOF相關的年際預報能力的探討上。結果顯示，除了全球熱帶平均地表氣溫和海表溫度在熱帶地區還有不錯的年際預報技術外，其他變數幾乎都沒有任何年際預報能力。地表氣溫和海表溫度所具有的年際預報能力應該與近幾十年來的氣候暖化有密切的關係。受到ENSO訊號所主導的海表溫度年際變化的預報結果雖然較平均狀態的結果為差，但是仍具有某些預報能力。這可能與氣候模式經由每年的初始化過程得以適當地捕捉ENSO訊號有關。另外，根據對年際預報時間序列資料進行偏差校正的結果顯示使用線性迴歸與排序修正法基本上都可以有效減少MME預報誤差和預報的不確定性。利用穩定空間分布進行EOF模態資料重組則對於CMIP5年代預報實驗資料在大部分的陸地及沿岸地區的預報誤差有很好的校正結果。

摘要(英)

In this study, we use monthly data from the multi-model ensemble (MME) of Coupled Model Intercomparison Project Phase 5 (CMIP5) decadal prediction experiments to assess interannual prediction skills for several atmospheric and oceanic variables in Tropics (30°S-30°N). First, we applied pattern stability analyses to extract persistent empirical orthogonal functions (EOFs) from observations-based data as reference spatial patterns. By projecting CMIP5 MME predictions to the extracted EOFs, then we compared these associated time series to assess the MME prediction skills. Finally, we applied linear regression and rank histogram to calibrate the associated time series of MME predictions. In the meantime, this study also evaluates the grid-point scale prediction capability in Tropics by EOF reconstructed fields.
Pattern stability analyses of the observations-based data indicated that at least 4 persistent EOFs can be found in each examined variable field. The first EOF (EOF1) mainly corresponds to the mean state of the given field, while the second EOF and beyond correspond to more and more localized spatial structures. Except for the third EOF (EOF3) of sea surface temperature (SST) field that has close relation to the El Nino Southern Oscillation (ENSO), most of our efforts focused on the study of interannual prediction skill associated with EOF1. Results indicated that, except for near surface air temperature (SAT) and SST fields, most variable fields did not have any interannual prediction skill. Furthermore, the apparent prediction skill that SAT and SST fields possessed may largely come from the warming trend observed in the last half of the 20th century. As for the ENSO related prediction skill, the EOF3 related time series showed certain prediction skill. This skill may be related to the capability of climate models to better synchronize with ENSO evolution through the adoption of yearly initialization procedure. Additionally, the results of the calibrated MME predicted time series showed that both linear regression and rank histogram calibration methods could effectively reduce the prediction errors and the MME uncertainty. Furthermore, the use of EOF reconstruction reduced MME prediction errors on extensive continent and coastal regions.

關鍵字(中)

★ 年代際預報
★ 偏差校正

關鍵字(英)

★ decadal prediction
★ bias correction

論文目次

摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 v
第一章　緒論 1
1-1　前言 1
1-2　文獻回顧 1
1-3　研究目的 4
第二章　資料來源與處理 5
2-1　資料來源 5
2-2　資料處理 6
第三章　研究方法 7
3-1　主成分分析法 7
3-2　型態穩定度分析 9
3-3　偏差校正 10
3-3-1　線性迴歸 11
3-3-2　排序修正法 12
3-3-3　EOF模態資料重組法 12
3-4　驗證指標 13
3-4-1　相關係數 13
3-4-2　方均根誤差 13
第四章　結果與討論 15
4-1　型態穩定度分析 15
4-2　CMIP5年代際預報實驗的預報技術比較 16
4-3　評估CMIP5 MME年際預報能力與偏差校正結果 18
4-3-1　地表氣溫 18
4-3-2　降水率 19
4-3-3　海平面氣壓 20
4-3-4　海表溫度 21
4-3-5　ENSO事件 22
4-4　EOF模態重組資料的預報技術 24
4-4-1　地表氣溫 24
4-4-2　降水率 25
4-4-3　海平面氣壓 25
4-4-4　海表溫度 26
4-5　 EOF模態的選擇對於資料重組的的影響 27
4-5-1　地表氣溫 27
4-5-2　海表溫度 28
4-6　不同預報領先時間下的預報技術 29
4-6-1　地表氣溫 29
4-6-2　海表溫度 30
第五章　結論與未來展望 31
參考文獻 33
附表 36
附圖 37

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指導教授

李永安(Yung-An Lee)

審核日期

2017-8-24

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