Revealing the statistics of extreme sudden stratospheric warming events hidden in short weather forecast data
Date:
To round out my sabbatical, an invited talk at the 2023 International Union of Geodosy and Geophysics General Assembly. Fitting that the meeting is in Berlin! As in Vienna, I’m taking it as an opportunity to show Justin Finkel’s methods to extract climatological statistics from S2S data to an audience of atmospheric scientists. This is joint work with Dorian S. Abbot and Jonathon Weare.
Abstract
Climate change will be felt primarily through changes in extreme weather: intense storms, precipitation, and temperature anomalies. Extreme events in the stratosphere, Sudden Stratospheric Warmings (SSWs), impact surface weather, driving an equatorward shift of the storm tracks and jet streams. Efforts to quantify changes in SSWs in response to anthropogenic forcing, however, have been hampered by the large uncertainty in the observational record. The problem becomes acute for more extreme SSWs, which are known to have a stronger surface impact. A once-in-a-century event takes, on average, 100 years to appear just once, far beyond the typical integration length of weather models which provide the best representation of stratosphere-troposphere coupling. The task is thus left to cheaper, but less accurate, lower-resolution models. One reduces the sampling error (aleatoric uncertainty) at the expense of increased model error (epistemic uncertainty).
We propose methods to extract climatological information from subseasonal forecast ensembles. Despite being short in duration, forecast ensembles are produced multiple times a week, collectively, adding up to centuries of data. Using hindcasts produced by the European Center for Medium-range Weather Forecasting archived in the subseasonal-to-seasonal (S2S) database, we compute multi-centennial return times of extreme SSW events. Consistent results are found between alternative methods, including counting strategies and Markov state modeling. We obtain estimates of frequencies and seasonal distributions for events of unprecedented severity. These methods hold potential for assessing extreme events throughout the climate system, and could be adopted in climate change integrations to quantify the impact of anthropogenic forcing on extreme weather.