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首页> 外文期刊>Theoretical and applied climatology >Geo-spatial analysis of temporal trends of temperature and its extremes over India using daily gridded (1A degrees x1A degrees) temperature data of 1969-2005
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Geo-spatial analysis of temporal trends of temperature and its extremes over India using daily gridded (1A degrees x1A degrees) temperature data of 1969-2005

机译：使用1969-2005年每日网格（1A度x1A度）温度数据对印度温度及其极端温度的时空趋势进行地理空间分析

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摘要

Daily gridded (1A degrees x1A degrees) temperature data (1969-2005) were used to detect spatial patterns of temporal trends of maximum and minimum temperature (monthly and seasonal), growing degree days (GDDs) over the crop-growing season (kharif, rabi, and zaid) and annual frequencies of temperature extremes over India. The direction and magnitude of trends, at each grid level, were estimated using the Mann-Kendall statistics (alpha = 0.05) and further assessed at the homogeneous temperature regions using a field significance test (alpha=0.05). General warming trends were observed over India with considerable variations in direction and magnitude over space and time. The spatial extent and the magnitude of the increasing trends of minimum temperature (0.02-0.04 A degrees C year(-1)) were found to be higher than that of maximum temperature (0.01-0.02 A degrees C year(-1)) during winter and pre-monsoon seasons. Significant negative trends of minimum temperature were found over eastern India during the monsoon months. Such trends were also observed for the maximum temperature over northern and eastern parts, particularly in the winter month of January. The general warming patterns also changed the thermal environment of the crop-growing season causing significant increase in GDDs during kharif and rabi seasons across India. The warming climate has also caused significant increase in occurrences of hot extremes such as hot days and hot nights, and significant decrease in cold extremes such as cold days and cold nights.
机译：使用每日网格化（1A度x1A度）温度数据（1969-2005）来检测作物生长期（kharif，狂犬病和zaid）以及印度各地极端温度的年度频率。使用Mann-Kendall统计量（alpha = 0.05）估算每个网格级别的趋势方向和强度，并使用现场显着性检验（alpha = 0.05）进一步评估均匀温度区域的趋势。在印度各地观察到总体变暖趋势，方向和强度随时空变化很大。发现最低温度（0.02-0.04 A摄氏度年（-1））的空间范围和上升趋势的幅度高于最高温度（0.01-0.02 A摄氏度年（-1））。冬季和季风前的季节。在季风月份，印度东部地区发现最低气温有明显的负趋势。对于北部和东部的最高温度，尤其是在一月份的冬季，也观察到了这种趋势。总体变暖的方式也改变了农作物生长季节的热环境，导致印度海夫和狂犬病季节的GDD显着增加。气候变暖还导致极端天气（例如炎热的白天和夜晚）的发生率显着增加，而极端寒冷的天气（例如寒冷的白天和夜晚）的发生率显着下降。

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