Citlalli Almaguer-Gómez

Major stratospheric sudden warmings are key processes in the coupling between the stratosphere and the troposphere, exerting a direct influence on mid-latitude climate variability. This study evaluates projected changes in the frequency of these phenomena during the 2006–2100 period using six high-top general circulation models from the CMIP5 project under the Representative Concentration Pathway scenarios 2.6, 4.5, and 8.5. The analysis combines the full future period with a moving-window approach of 27 and 48 years, compared against both the satellite-era (1979–2005) and extended historical (1958–2005) periods. This methodology reveals that model responses are highly heterogeneous, with alternating periods of significant increases and decreases in event frequency, partially modulated by internal variability. The magnitude and statistical significance of the projected changes strongly depend on the chosen historical reference period, and most models tend to reproduce displacement-type polar vortex events preferentially over split-type events. These results indicate that assessments based solely on multi-model means or long aggregated periods may mask subperiods with robust signals, although some of these may arise by chance given the 5% significance threshold. This underscores the need for temporally resolved analyses to improve the understanding of stratospheric variability and its potential impact on climate predictability.

This study assesses the ability of fourteen CMIP5 climate models to reproduce the main characteristics of major stratospheric sudden warmings (SSWp) in historical simulations. Model performance is evaluated through comparisons with three reanalysis datasets, considering two periods: the satellite era (1979–2005) and an extended period including pre-satellite years (1958–2005). Results show that model consistency with reanalyses is significantly higher during the satellite period, with up to seven models showing no statistically significant differences (p > 0.05) in at least eight out of ten key diagnostics. In contrast, performance decreases in the extended period, likely due to greater observational uncertainty. A systematic overrepresentation of displacement-type events (SSW_D) was found in most models, compared to the split-type dominance in reanalysis data. These findings highlight the importance of the validation period and event classification in model evaluation and establish a robust framework for future comparisons using CMIP6 models and newer reanalysis products.