本篇論文的主要目的是利用從觀測資料所發現的季節循環間交互作用機制做為篩選條件，來選取比較合適的耦合環流模式所提供的情境模擬結果，來探討人類的活動對於季節循環與聖嬰現象的影響。
我們所採用的資料除了美國國家海洋和氣象管理局標準化過的海溫資料(ERSST)與美國氣象環境預報中心和美國國家大氣研究中心的重新分析數據資料(NCEP-NCAR reanalysis)。我們著重於世界氣候研究計畫（WCRP）的耦合模式比較計畫—階段３(CMIP3)的多模式相關模擬資料的數據分析與比較。所採用的CMIP3資料包括(1)20世紀的；(2)工業革命前；(3)二倍二氧化碳；和(4)四倍二氧化碳。觀測與這四種情境模擬所選取的海溫資料其全球經緯度網格解析度統一調整成4°(lat.) × 4°(long.)，選取的資料涵蓋範圍是40°S-40°N。
我們對原始資料都會先行去除其線性趨勢。去除線性趨勢後所得的資料再分別去除其所含的(a)氣候平均與(b)氣候季節循環得到一組是有包含季節循環一組未包含季節循環的兩組變異資料。然後對資料做主分量分析得到季節循環模。然後對季節循環模進行波譜分析。
經由分析比較觀測與CMIP3的20世紀模擬資料的季節循環間交互作用機制，我們認為CSIRO MK3.5, GFDL CM2.0, GFDL CM2.1, MPI 這幾個耦合環流模式與真實地球系統運行方式比較接近，而CNRM, GISS AOM, IAP FGOALS, MIROC 3.2 HIRES, MIUB ECHO這幾個模式則與觀測的結果差異很大。我們主要比較分析GFDL CM2.0 ＆ GFDL CM2.1模式在不同的情境所得的模擬資料。結果顯示ENSO現象的主要週期與特徵並不會因為二氧化碳的增加而有劇烈的改變。

The main purpose of this paper is to use observation data from the seasonal cycle interactions among mechanisms discovered by as a filter criteria, to select appropriate coupled general circulation models provide the situational simulation results, to explore human activity seasonal cycle and the impact of the El Niño phenomenon.
Our data apart from the National Oceanic and Atmospheric Administration (NOAA) standardized the SST data (ERSST) and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP-NCAR reanalysis).We focus on The World Climate Research Programme''s (WCRP''s) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset. By used of CMIP3 data reported enclosed (1) climate of the 20th Century experiment (20c3m) ; (2) pre-industrial control experiment (pre-industrial) ; (3) 1%/year CO2 increase experiment to doubling (1pctto2x) ; (4) 1%/year CO2 increase experiment to quadrupling (1pctto4x). Observation and simulation of these four situations selected its global sea surface temperature data uniformly into a 4°(lat.) × 4°(long.), select the data coverage is 40°S-40°N.
We first remove the linear trend on the raw data. After you remove the linear trend data derived from respectively remove it contains (a) average climate and (b) climatological seasonal cycle by one group contains seasonal cycle group does not contain a seasonal cycle variation of two sets of data. And then master component analysis to data you have seasonal cycle mode. The spectrum analysis of the seasonal cycle mode.
By analyzing and comparing they coupled seasonal cycle interaction mechanism among observed and CMIP3 20c3m data, we identified CSIRO MK3.5, GFDL CM2.0, GFDL CM2.1, MPI as CGCMs having similar behavior as the current earth’s climate system, and the CNRM, GISS AOM, IAP FGOALS, MIROC 3.2 HIRES, MIUB ECHO a few models with observations of the results varied. Furthermore, we compared results among different scenario runs from GFDL CM2.0 ＆ GFDL CM2.1. They showed that the ENSO and seasonal cycle modes do not have any significant variation among these runs. Therefore, it appears that ENSO may not change much in the future.

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