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Spatiotemporal analysis of temperature and precipitation extremes over Bangladesh using a novel gridded observational dataset

降水 气候学 环境科学 最高温度 气候变化 极端气候 大气科学 地理 气象学 地质学 海洋学
作者
H. M. Imran,Jatin Kala,Shahab Uddin,A. K. M. Saiful Islam,Nachiketa Acharya
出处
期刊:Weather and climate extremes [Elsevier]
卷期号:39: 100544-100544 被引量:6
标识
DOI:10.1016/j.wace.2022.100544
摘要

This study explores spatial changes in trends of 29 climate extremes indices between 1981 and 2020 in Bangladesh, a country that frequently experiences the impacts of extreme events. This is the first study to examine trends in extreme climate over Bangladesh using a high resolution (0.05° × 0.05°) gridded daily temperature and precipitation data rather than individual stations. Trends in mean daily maximum and minimum temperatures were +0.38 °C and +0.25 °C per decade, respectively, for the last 40 years. The maximum increases in both warm days and nights were 12.75 days per decade with the fastest rate of warming in the eastern part of the country, whereas the maximum decreases in both cold days and nights were 7 days per decade with the western part experiencing the greatest increase in extreme cold. Significant reductions in total precipitation (225 mm per decade) were observed in the major parts of the country. However, the number of very heavy precipitation days (R20mm) showed a statistically significant increase (2.25 days per decade) in the southern coastal areas, with the tendency of more extreme wet days (R30 mm). The maximum decrease in annual total precipitation was 225 mm per decade while the consecutive dry days increased by 12.5 days per decade. While previous studies reported a faster rise in minimum temperature than maximum temperature, this study shows a faster increase in maximum temperature instead of minimum temperature in the study area. Extreme temperature changes were statistically more significant than changes in extreme precipitation. This study also shows statistically significant and consistent spatial trends of extreme temperature and precipitation when using high-resolution gridded observational datasets instead of station data.
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