研究模式在月或季節尺度的大氣可預報度時，大氣平均狀態可分為自然變化分量和邊界力分量，自然變化分量是因為大氣內部動力過程所產生，為內部的變化；邊界力分量是外部對大氣所作用，視為外部給予大氣的訊號，而內部的變化是隨機產生無法預測的雜訊，以這二者的變化去評估模式潛在的可預報度。
變異量分析的方法，將因為海表面溫度改變而產生的變異量佔總變異量的比例定義為潛在的可預報度，描繪出季節平均的可預報度分布。熱帶地區受海表面溫度影響較大，存在較高的可預報度，熱帶外地區的大氣主要受到內部動力過程主控，大多為混亂的訊號，可預報度較低。使用距平型態的相關係數方法，分別對El Nino、La Nina、平均年及其他年等，計算平均海平面氣壓場在亞洲地區（0～45N；90～150E）、降水場在東亞地區（20～45N；90～150E）及Z500(500-mb height)高度場在PNA(Pacific-North American)地區（20～70N；180～60W）的可預報度在ENSO及非ENSO年的季節性變化，藉以了解ENSO事件對可預報度的影響。
本研究主要是使用下列四個模式：ECHAM4氣候模式、CWB模式、GFDL新一代大氣海洋耦合模式及NCEP模式，模式模擬的時間都取1955年12月至2000年2月，每個系集模式都有10個個別模擬，針對降水場、海平面氣壓場及z500進行分析、探討可預報度的特性。分析結果顯示，海平面氣壓場和降水場的可預報度主要是集中在熱帶太平洋，降水場甚至更集中在赤道附近，Z500高度場則呈帶狀分佈環繞整個熱帶地區，不論哪一個變數，一般而言，模式在El Nino的可預報度比La Nina年要高，這兩者的可預報度又比非ENSO年的可預報度要高很多；可預報度的值在El Nino年的1-3月達到最高，La Nina年的可預報度比平均年要高，但是在春天時會快速的下降到和平均年差不多，稱之為春天預報障礙(Spring barrier)，主要是這時候的雜訊突然增大的結果。
另外以GFDL模式在不同實驗設計下的結果來探討，在模式裡中考慮海氣交互作用和未考慮海氣交互作用的差異，實驗設計分別有MLM模擬和CTRL模擬，這兩個模擬在東赤道太平洋(15°S-15°N,172°E-South American coast)區域內都使用觀測的海溫資料，也就是說這兩個模擬同時受到ENSO事件的影響，區域外在MLM模擬則使用一個簡單的海洋混合層模式所模擬的海溫資料，而CTRL模擬所使用的SST是從MLM模擬結果長期平均，並不包含年際變化部分，以研究海氣交互作用對氣

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