On Objective Identification of Atmospheric Fronts and Frontal Precipitation in Reanalysis Datasets
On Objective Identification of Atmospheric Fronts and Frontal Precipitation in Reanalysis Datasets
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DOI：
10.1175/jcli-d-21-0596.1
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发表时间：
2022-04
影响因子：
4.9
通讯作者：
Frederick M. Soster;R. Parfitt
中科院分区：
文献类型：
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作者：
Frederick M. Soster;R. Parfitt
再分析数据集由于其记录长度和网格化的全球覆盖范围，经常用于大气变率的研究。在中纬度地区，大部分的日常大气变率与大气锋有关。这些锋面也造成了中纬度地区的大部分降水，并经常与极端天气、洪水和野火活动有关。因此，重要的是在研究之间对锋面及其相关降雨的识别尽可能保持一致。然而，通常情况下，只使用一个再分析数据集和一个客观诊断来检测大气锋。通过对8个再分析数据集（1980 ~ 2001年）的两种不同锋面识别方法的应用，发现锋面和锋面降水的个体识别受到识别方法和数据集选择的显著影响。结果表明，这随后会影响全球锋面频率和锋面降水的气候学，并存在显著的区域差异。例如，对于一项诊断，全球平均锋面频率（归因于大气锋面的降水比例）的绝对多次再分析范围为12%（69%）。然而，在这个绝对的多次再分析范围内，将所有数据集重新划分到相同的粗网格后，百分比减少了77%（81%）。因此，这些发现对任何有关降水变率的研究都具有重要意义，而不仅仅是那些考虑大气锋的研究。
Reanalysis datasets are frequently used in the study of atmospheric variability owing to their length of record and gridded global coverage. In the mid-latitudes, much of the day-to-day atmospheric variability is associated with atmospheric fronts. These fronts are also responsible for the majority of precipitation in the mid-latitudes, and are often associated with extreme weather, flooding, and wildfire activity. As such, it is important that identification of fronts and their associated rainfall remains as consistent as possible between studies. Nevertheless, it is often the case that only one reanalysis dataset and only one objective diagnostic for the detection of atmospheric fronts is used. By applying two different frontal identification methods across the shared time period of eight reanalysis datasets (1980 to 2001), it is found that the individual identification of fronts and frontal precipitation is significantly affected by both the choice of identification method and dataset. This is shown to subsequently impact the climatologies of both frontal frequency and frontal precipitation globally with significant regional differences as well. For example, for one diagnostic, the absolute multi-reanalysis range in the global mean frontal frequency (the proportion of precipitation attributed to atmospheric fronts) is 12% (69%). A percentage reduction of 77% (81%) in this absolute multi-reanalysis range occurs however upon regridding all datasets to the same coarser grid. Therefore, these findings have important implications for any study regarding precipitation variability, not just those considering atmospheric fronts.