Revisiting the Observation-Model Discrepancy in Southern Hemisphere Winter Storm Track Trends

Reanalysis datasets show storminess in the Southern Hemisphere (SH) has significantly increased during wintertime since 1979. Previous work reported an observation-model discrepancy whereby CMIP6 models were unable to reproduce the trend, calling into question the ability of climate models to make accurate projections of anthropogenic climate change impacts in the SH extratropics. Here we revisit the observation-model discrepancy in SH winter storminess trends using a wider range of model simulations and reanalysis datasets than used previously and take care to ensure a like-for-like comparison in terms of the time frequency and spatial resolution of the data used. We find when a like-for-like trend calculation is performed for all available reanalysis and climate model data, the distribution of trends from prescribed SST (AMIP) models is statistically similar to the reanalysis trend distribution. However, trends from coupled models (CMIP) are not in statistical agreement with reanalysis trends even when a like-for-like calculation is performed. The difference between AMIP and CMIP suggests SST trend biases underlie the discrepancy. We test the importance of SST trend biases using Pacific and Southern Ocean pacemaker simulations, which involve constraining coupled model SSTs to match observations. The storminess trends from pacemaker simulations are found to be statistically similar to the reanalysis, confirming the importance of SST trend biases for the coupled model-observation discrepancy. Overall, the results show that observation-model trend comparisons should involve all available datasets and like-for-like trend calculations. Furthermore, regional SST trend biases can lead to non-local storminess trend discrepancies between observations and models.