A study of different frequencies of major stratospheric sudden warmings in CMIP5 historical simulations. Issue 10 (19th May 2017) - Taguchi, M.
Abstract: Climatological frequency of major stratospheric sudden warmings (MSSWs) during northern winter has been repeatedly investigated using model simulations, but its model‐to‐model differences are relatively unexplained. Using an archive of the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations with 30 models, this study investigates the MSSW frequency of the simulations by building a framework in which the frequency is decomposed into a few factors. Results first demonstrate that the multimodel differences in the MSSW frequency are closely related to the mean and variability of the zonal mean zonal wind in the extratropical stratosphere in each simulation. An important point is that for each simulation, the zonal wind variability is further represented by a combination, or product, of average strength of extreme transient planetary wave forcings from the troposphere and average deceleration response of the polar vortex to an extreme wave forcing of fixed strength. This product can be interpreted and used as a proxy of large‐scale dynamical variability of the polar vortex arising from the planetary wave‐mean flow interaction. It turns out that a large portion of the CMIP5 model simulations underestimate one or both of the factors, leading to underestimations in dynamical variability. This contributes to the general underestimation of MSSW frequency in the simulations. This study proposes that this framework can be used in general to better understand MSSW frequency in different models and climate conditions (e.g., future climate change). Key Points: MSSW frequency in CMIP5 historical simulations is well related to the mean and variability of the zonal mean zonal wind in each simulation The zonal wind variability is further represented by average strength of extreme wave forcings and average vortex response The framework will be useful in general for different models and climate conditions (e.g., future climate change)