Strong temperature falls as a cold frontal metric in Australian station observations, reanalyses and climate models

Abstract Cold fronts are an important component of subtropical and midlatitude weather systems. While a range of objective front detection methods exist, these methods are only applicable to gridded data and generally require smoothing and threshold parameter changes with differing spatial resolution in order to obtain similar results. This precludes analyses using these methods from using point‐location data (such as station observations) or comparing datasets with different spatial resolutions. We present a method using daily maximum temperature changes as a way of identifying strong, dry cold fronts (that is, fronts without much associated rainfall) across a range of datasets. Frontal zones identified using this method are well correlated with cold‐front locations using an objective front scheme, and results do not change significantly as data resolution changes over the range from 0.25 to 2.25°. The method is applied to generate front climatologies in 41 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), showing these models overestimate the number of cold fronts in polar regions relative to the number over midlatitude land regions, and that there is a large intermodel spread in front occurrence. The method is also applied to southern Australian stations, showing that there are minimal trends in front occurrence for longer running stations, although an increase in front occurrence since 1950 is seen in the southeast of Australia. Interannual variability in strong front occurrence is connected to the state of both El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM), with El Niño and negative SAM conditions both seeing increases in the occurrence of strong cold fronts in southeastern Australia.