近年受到氣候變遷的影響導致海洋波浪行為的改變，海岸地區首當其衝，為因應未來波浪的改變，在海岸防護與設計上須加以改善，因此需要可靠的相關預測與評估數據。政府間氣候變遷專門委員會(Intergovernmental Panel on Climate Change, IPCC)提供多個全球氣候模式(global climate model, GCM)，但其數據中的空間解析度並不適用台灣海域，在大尺度模擬中可能只涵蓋少數個格點不足以作為海岸評估的依據，因此本研究引入氣候型態統計降尺度之方法架構(Camus et al., 2014)提高波浪資料的空間解析度，並對數個GCM大氣數據在不同情境條件下預測台灣海域之示性波高(significant wave height)。
本模式以海平面壓力作為預測因子(predictor)、示性波高作為預測值(predictand)透過K-Means方法分類大氣資料決定氣候型態，以波浪資料作為條件連結氣候型態建立兩者間的統計關係。驗證顯示在模式中，台灣西、南部海域有較好的成果。本研究使用RCP4.5與RCP8.5未來情境之GCM數據，利用Perez等人(2015)提出的篩選方法評估GCM的不確定性，最後以10個GCM進行模擬，並從未來短期、中期、長期預測，然而台灣氣候受季節影響甚大，因此再從其中細分季節加以分析，由結果顯示，西部海域之示性波高變化趨勢顯著，尤其與過去相比在春季增加幅度最大。

In recent years, due to the impact of climate change, the strength of ocean waves and the occurrence frequency of giant waves make coastal areas become more vulnerable. In order to respond to changes in future waves, coastal protection and design must be im-proved. Therefore, reliable projection and assessment are needed. The Intergovernmental Panel on Climate Change (IPCC) provides multiple global climate models (GCMs). How-ever, the coarse spatial resolution isn’t enough to apply to sea of Taiwan because of cover-ing a few grid points near coasts, which is not sufficient as a basis for coastal assessment. Therefore, this study introduces a framework for weather type statistical downscaling (Camus et al., 2014) to improve the spatial resolution of wave data. The significant wave height of sea of Taiwan is projected under the conditions of assumptions of future scenari-os from several GCMs results.
In this model, sea level pressure is used as predictor and significant wave height as predictant. The K-Means method is used to cluster the atmospheric data to determine weather types, and the wave climate parameter is associated weather types to establish the statistical relation. Verification shows that in the model, there are good results in the west-ern and southern waters of Taiwan. This study used GCMs data of RCP4.5 and RCP8.5 scenarios and the method proposed by Perez et al. (2015) to evaluate the uncertainty of GCMs, and finally simulated in the short-term, medium-term and long-term with 10 GCMs. On the other hand, Taiwan's climate is greatly affected by the seasons. Therefore, it is analyzed from four seasons. The results show that the trend of the increase in the signif-icant wave height in the western seas is more obvious, especially in the spring.

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