Processes leading to extreme seasons – research at the weather-climate interface based on reanalyses and large ensemble climate simulations

Research on extreme weather typically investigated the physical and dynamical processes involved in the formation of specific meteorological events that occur on time scales of hours to a several days (e.g., heavy precipitation events, windstorms, heat waves). Such events can be extremely hazardous, but for certain socioeconomic sectors the seasonal aggregation of weather is particularly harmful. These sectors include, for instance, agriculture, forestry, energy, and reinsurance. This presentation introduces the concept of “extreme seasons” as an important and not yet thoroughly investigated research field at the interface of weather and climate science. Extreme seasons are defined as seasons during which a particular meteorological or impact-related parameter (or a combination thereof) strongly deviates from climatology. An important conclusion of the presentation will be that large ensemble climate simulations (here using an extended CESM1-LENS data set with 6-hourly output of 3D fields), with about 1000 simulated years per climate period, are an essential resource enabling novel quantitative insight into the processes leading to and characteristics of extreme seasons. The presentation provides examples for the identification of extreme seasons and emphasizes the importance of studying their substructure, including the occurrence of specific weather systems. A first approach to systematically study extreme seasons is to consider the top 10 seasons (for a given metric) in the large ensemble at every grid point, e.g., the 10 wettest winters or hottest summers, or the 10 summers with the largest vapour pressure deficit (as an example for a more impact-related metric). Alternatively, one can look at anomalies in a multi-dimensional parameter phase space, identifying extreme seasons that result from a highly unusual combination of, e.g., surface temperature, precipitation, and surface energy balance. Or, using a pragmatic method based on fitting a statistical model to seasonal mean values at each grid point, spatially coherent extreme season objects can be identified that exceed a local return period threshold of, e.g., 40 years. The same statistical approach can be applied to ERA5 reanalyses to compare characteristics of extreme season objects (e.g., their size and intensity) in climate models with observation-based data. With this approach we can meaningfully estimate how often, e.g., an observed extreme winter like the cold North American 2013/14 winter is expected anywhere in midlatitude regions. The last part of the presentation addresses the substructure and weather system characteristics of extreme seasons. Illustrative results are shown that address the questions: (i) Where are extremely hot summers the result of the warmest days being anomalously hot vs. the coldest days being anomalously mild? (ii) Where are wettest seasons the result of more frequent wet days vs. more intense precipitation on wet days? and (iii) How does the frequency of weather systems and their precipitation efficiency change during the wettest seasons? The answers to these questions reveal interesting and large regional differences.