Extratropical cyclones (ETCs) are responsible for the majority of cool-season extreme events in the northeastern United States (NEUS), often leading to high-impact weather conditions which can have wide-ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing the confidence in future projections used by stakeholders and policymakers. Traditionally, ETCs are studied using techniques such as case studies and synoptic typing. However, these approaches require subjective approaches and do not necessarily identify the coincident large-scale meteorological patterns (LSMPs).

Here, we apply self-organizing maps (SOMs) as an objective approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then apply the reanalysis-derived SOM as a reference in order to evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events over NEUS. Broadly, CMIP6 models tend to favor the more zonal patterns and struggle to reproduce the more amplified patterns typically associated with highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be far more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around reanalysis references when ACA is normalized by storm frequency. This methodology is also used to analyze the occurrence of extreme precipitation events (sleet, freezing rain, and snow) associated with each pattern.