首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >Multi-dataset comparison of gridded observed temperature and precipitation extremes over China
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Multi-dataset comparison of gridded observed temperature and precipitation extremes over China

机译:栅格化观测到的中国大陆极端温度和降水极端值的多数据集比较

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摘要

Five gridded datasets containing measures of temperature and precipitation extremes over the past five decades over China are compared. Time series, spatial averages and trends in China's coldest and warmest days and nights and wettest days are estimated from these datasets that vary in their station network density, interpolation procedures and the order in which extremes are calculated. We find that country-wide trends in temperature extremes are coherent irrespective of dataset choice although actual values are generally smaller in the dataset in which indices are calculated from daily interpolated grids rather than from station points. There are also some regional differences in trends especially over regions with sparse data networks, e.g. the Tibetan Plateau. Averaged across China, trends calculated over the period 1961-2009 vary from a minimum of 0.13 degrees Cdecade(-1) (hottest day) to 0.65 degrees Cdecade(-1) (coldest night). The coldest day and night have tended to warm faster than the warmest day and night. Trends in precipitation extremes are much less coherent spatially and can be positive or negative depending on the choice of dataset. The length of station record chosen is also vital to eliminate spurious trends. The results have important implications for detection and attribution and model evaluation studies.
机译:比较了五个网格数据集,这些数据集包含了过去五年来中国极端温度和降水的度量。根据这些数据集估算的时间序列,空间平均数和中国最冷,最热的白天和黑夜以及最潮湿的日子的趋势,这些数据集的站点网络密度,内插程序和极端值的计算顺序各不相同。我们发现,全国范围内的极端温度趋势是一致的,而与数据集的选择无关,尽管在实际数据中,实际值通常较小,在该数据集中,数据是根据每日内插网格而不是根据站点测得的。趋势上还存在一些地区差异,尤其是在数据网络稀疏的地区,例如青藏高原。从1961年到2009年,中国的平均趋势是从最低0.13摄氏度(-1)(最热的一天)到0.65摄氏度(-1)(最冷的夜晚)变化的。最冷的白天和黑夜趋向于比最暖的白天和黑夜变暖。极端降水的趋势在空间上不那么连贯,取决于数据集的选择,可能为正或负。所选电台记录的长度对于消除杂散趋势也至关重要。结果对检测和归因以及模型评估研究具有重要意义。

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