On Objective Identification of Atmospheric Fronts and Frontal Precipitation in Reanalysis Datasets

Abstract

Abstract Reanalysis datasets are frequently used in the study of atmospheric variability owing to their length of record and gridded global coverage. In the midlatitudes, 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 midlatitudes, 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 (1980–2001), 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 multireanalysis range in the global mean frontal frequency and the proportion of precipitation attributed to atmospheric fronts are 12% and 69%, respectively. A percentage reduction of 77% and 81%, respectively, in these absolute multireanalysis ranges occurs, however, upon regridding all datasets to the same coarser grid. Therefore, these findings have important implications for any study on precipitation variability and not just those that consider atmospheric fronts.