Does Higher Vertical Resolution in Meteorological Data Guarantee the Identification of More Tropopause Folds Using the 3-D Labeling Algorithm?

Abstract Stratosphere-to-troposphere transport (STT), a major source of tropospheric ozone, is largely driven by tropopause folds. The accurate identification of these folds is therefore crucial for atmospheric chemistry and air quality studies. In this study, we compared tropopause folds identified from ERA5 reanalysis data with high (37 levels) and low (19 levels) vertical resolution over China and its surroundings (2014–2022), to systematically investigate the impact of vertical resolution on fold identifications. We found that, from a long-term, domain-averaged perspective, high-resolution data identifies 1.5 times as many total folds as its low-resolution counterpart. However, many exceptions exist where folds are detected in low-resolution data but missed in high-resolution data. By examining representative examples, we identified two primary sources for the discrepancies in fold identifications: the inclusion of additional vertical layers and differences in data values at the same pressure levels. We also quantified the contribution of each cause to the identification of different fold types. The results show that the inclusion of more layers is the primary driver for the increase in total folds in the high-resolution data, whereas deviations in low-altitude potential vorticity (PV) values are the dominant factor for the increased identification of medium and deep folds. Our findings indicate that higher resolution does not always identify more tropopause folds and underscore that for the medium and deep folds, the accuracy of low-altitude PV in the reanalysis data is of critical importance.