宜蘭冬季長期氣候降水特徵變化與未來推估

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题名:	宜蘭冬季長期氣候降水特徵變化與未來推估 The Long-term Variations and Future Projections of The Winter Rainfall Features in Yilan
作者:	吳津享 WU, CHIN-HSIANG
贡献者:	地學研究所大氣科學組
关键词:	宜蘭階層式群聚分析 K-近鄰演算法 Yilan Hierarchical Clustering K-Nearest Neighbor
日期:	2025
上传时间:	2025-03-14 14:46:04 (UTC+8)
摘要:	本研究針對宜蘭地區冬季降水特徵進行長期氣候分析與未來推估。透過分析過去40年(1981-2020年)的TCCIP網格化日降水資料，我們發現宜蘭地區降水熱區集中於南部山區，並向平原遞減。為瞭解風場對宜蘭地區局地環流的影響導致降水位置發生改變，使用TaiwanVVM理想化模式模擬資料，發現風向從東北風偏向東風時，宜蘭地區南側山區對流發展越顯著。為瞭解宜蘭地區在不同降水形態下其風場特徵，我們使用階層式群聚法分析網格化降水資料，將宜蘭地區降水分布分成5種群集，透過分析上游的背景風場與水氣場特徵發現，當上游風場偏北風且水氣含量較低時，宜蘭地區降水較少，降水集中於南側山區與北海岸。相反地，當上游風場偏東風且水氣量高時，宜蘭地區容易發生強降水事件，此時降水熱區會從南側山區擴散至蘭陽平原。由此我們得知降水強度與頻率的變化與上游低層風場的水氣傳輸具有顯著相關性。在未來宜蘭降水特徵推估中，我們使用KNN(K-Nearest Neighbor)模型，並結合CMIP6全球氣候模型中TaiESM1模式模擬結果。我們發現宜蘭地區的強降水事件並未隨著暖化加劇而全面增加，而是在不同暖化情境與時間段中呈現不同的變化。在輕度暖化情境(SSP1-2.6)下，分群中降水量最顯著的群集其發生頻率在所有時期呈現全面下降；中度暖化情境(SSP2-4.5)下，近未來與世紀末降水頻率上升，但在世紀中出現顯著下降；而在重度暖化情境(SSP5-8.5)下，僅在世紀中頻率呈現上降。本研究提出了一種新的方法推估未來區域降水特徵，並且發現低層大氣之水氣與風向為造成降水特徵變化的主要因素之一。然而目前KNN模型尚未能夠完整解釋其頻率變化趨勢，未來強降水事件的變化並非只有受到大尺度風場的風向與水氣影響，還有其他因素如風速、穩定度與季節性偏移等。因此為提高未來氣候推估的準確性，後續研究將納入CMIP6其他氣候模式資料進行對比分析，並深入探討更多大氣熱力與動力變數，進一步提升對宜蘭地區推估未來降水特徵變化的能力。 This study conducts a long-term climatological analysis and future projection of winter precipitation patterns in Yilan, Taiwan. By analyzing 40 years (1981-2020) of gridded daily precipitation data, we noticed that the rainfall hotspot in Yilan was located in the southern mountainous region and decreased toward the Lan-Yang Plain. We used a cloud-resolving numerical model - TaiwanVVM simulation outputs to understand the interaction of upstream background wind and terrain effect, which induced local circulation variations and affected rainfall distributions. We note that when the easterly component of the background wind was stronger, the convection became more significant in the southern mountainous areas of Yilan. We analyzed gridded precipitation data using a hierarchical clustering method. We grouped Yilan's rainfall distribution into five clusters. We found that when the upstream wind field is dominated by northerly winds with a low mixing ratio, precipitation in Yilan decreases. The rainfall was concentrated in the southern mountainous areas and the northern coast. Conversely, heavy rainfall events are more likely when the upstream wind field shifts to easterly winds with high moisture content, with precipitation hotspots expanding from the southern mountainous areas to the Lanyang Plain. Therefore, we noticed that precipitation intensity and frequency variations are strongly related to moisture transport in the upstream lower-level wind fields. We used the K-Nearest Neighbor algorithm to project future rainfall features and combined one of the CMIP6 global climate models, TaiESM1, with simulation outputs. Despite significant global warming scenarios, we observed that heavy rainfall events in Yilan have not generally increased. Instead, the changes in rainfall patterns vary under different warming scenarios and time periods. Under the warming scenario (SSP1-2.6), the occurrence frequency of the significant rainfall clusters will be decreased across all periods. In the warming scenario (SSP2-4.5), precipitation frequency increases in the "near future period" and "end of the century" but decreases significantly in the "middle of the century." In the scenario (SSP5-8.5), frequencies increases in the " near future period ".
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