Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"

The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (https://doi.org/10.1029/2022EF003211). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: 

- data pre-processing scripts

- code to run the self-organizing map analysis

- code to calculate the SOM and ETC statistics

- composites of 500-hPa geopotential height for each dataset as organized by the SOM

- ETC tracking script and tracking output for each dataset

- SOM output for each dataset