臺灣地區日溫度網格化資料庫之建置和長期變化趨勢分析

The Construction and Long-term Trend Analysis of Daily Gridded Temperature Dataset in Taiwan

10.6234/JGR.202311_(78).0004

楊承道(Chen-Dau Yang)；張容慈(Rong-Cih Chang)；翁叔平(Shu-Ping Weng)

網格化資料庫 ； 氣溫極端值 ； 水文循環 ； 氣候變遷 ； 氣候暖化 ； gridded datasets ； temperature extremes ； hydrological cycle ； climate change ； climatic warming

地理研究

78期（2023 / 11 / 01）

75 - 109

繁體中文;英文

因應國家科學及技術委員會政策推動的臺灣氣候變遷推估資訊與調適知識平台計畫（Taiwan Climate Change projection and adaptation Information Platform; TCCIP）的需求，本論文蒐集整合散佈在交通部氣象局、經濟部水利署、行政院農業委員會農業試驗所和林業試驗所等單位的測站溫度資料，產製出1960至2017年之1公里解析度溫度網格化資料庫，並分析日溫度網格化資料的長期變化趨勢。分析結果顯示，受到全球暖化影響，日均溫和日最低溫為增溫的趨勢，特別是日最低溫，暖化幅度最大；但是日最高溫則在部份山區呈現冷化的趨勢，和動力氣候模式的分析結果不同（林秉毅等，2021）。為了解日最高溫在山區降溫的原因，透過分析ISCCP（International Satellite Cloud Climatology Project）的雲量、雲含水量、雲光學厚度的變化趨勢顯示，臺灣山區日最高溫的下降趨勢和白天山區的雲量增加有關。本文還分析了歐洲中期天氣預報中心（European Centre for Medium-Range Weather Forecasts，ECMWF）的ERA5資料，包含均溫、最高溫、最低溫、太陽短波輻射、降雨、水氣輻散（合）場、500mb的重力位高度場，其分析結果表示ERA5資料支持了日最高溫網格資料在臺灣部份山區降溫的觀測趨勢。雖然臺灣山區的測站豐富度不足，使得降雨和溫度的網格化資料存在不確定性，但是ISCCP的雲資料和ERA5的資料分析結果，指出氣候暖化下，水汽量的增加，可能因輻合增強，加強水文循環，使得雲量增加，並造成太陽入射輻射減少，進一步影響臺灣山區日最高溫的下降趨勢。換句話說，臺灣在全球暖化下，可能產生水文循環的負回饋機制，會造成山區最高溫下降的長期趨勢，上述結果和動力模式模擬的單調增溫現象明顯不同。

In response to the needs of the Taiwan Climate Change Projection and Adaptation Information Platform (TCCIP) promoted by the National Science and Technology Council, we collected temperature data and integrated them from stations of the Central Weather Bureau of the Ministry of Transportation and Communication, Water Resources Agency of Ministry of Economic Affairs, and Taiwan Agricultural Research Institute and Forestry Research Institute of the Council of Agriculture of Executive Yuan. We used these station data and produced a temperature grid database with a resolution of 1 km from 1960 to 2017. This long-term trend analysis of daily temperature gridded data shows that the daily average temperature and daily minimum temperature are trends of warming, especially the daily minimum temperature has the largest warming amplitude. On the contrary, the daily maximum temperature shows a cooling trend in mountainous areas, which is different from the results of previous dynamic models (Lin and Cheng 2022). To further explore the reasons for this cooling situation in mountainous areas, the trends of cloud cover, cloud water content, and cloud optical depth data from the International Satellite Cloud Climatology Project (ISCCP) show that the decreasing trend of daily maximum temperature in Taiwan's mountainous areas is related to the increase of daytime cloud cover. In addition, the ERA5 data of the European Centre for Medium-Range Weather Forecasts (ECMWF) are also analyzed, including average temperature, maximum and minimum temperature, solar shortwave radiation, rainfall, water vapor divergence field, and 500mb gravitational potential height field. The data show that some analysis results of the ERA5 data support the trend of cooling in the mountainous area with the daily maximum temperature grid data. Although the number of stations in Taiwan's mountainous areas is insufficient, which leads to the grid data of rainfall and temperature having great uncertainty, the cloud data of ISCCP and the results of ERA5 point out that under a warming climate, the increase of moisture caused by the enhanced convergence may intensify the hydrological cycle and thereby increasing the cloud amount. This would lead to a reduction of insolation and in-turn affects the downward trend of daytime maximum temperature in Taiwan's mountainous areas. In other words, Taiwan may have a negative feedback mechanism of the hydrological cycle, resulting in a long-term trend of the maximum temperature decline in mountainous areas under global warming.

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