Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Journal Article · 20 August 2023 · Earth's Future

DOI: https://doi.org/10.1029/2022EF003211 · OSTI ID:1996262

Gore, Michelle J.  ^[1]; Zarzycki, Colin M.  ^[1]; Gervais, Melissa M.  ^[2]

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1. Department of Meteorology and Atmospheric Science The Pennsylvania State University PA University Park USA
2. Department of Meteorology and Atmospheric Science The Pennsylvania State University PA University Park USA, The Institute for Computational and Data Sciences The Pennsylvania State University PA University Park USA

Abstract 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 that 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 confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning 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 evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

• Research Organization: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Biological and Environmental Research (BER), Regional and Global Model Analysis Program
• Sponsoring Organization: USDOE
• OSTI ID: 1996262
• Journal Information: Earth's Future, Journal Name: Earth's Future Journal Issue: 8 Vol. 11; ISSN 2328-4277
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English