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。 ;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.
