Post-processed data and scripts for "Ocean eddies lower the global-mean, maximum intensity of tropical cyclones in a one-year global, coupled simulation"

This dataset contains the post-processed data and the python scripts used to obtain the post-processed data and generate the plots for "Ocean eddies lower the global-mean, maximum intensity of tropical cyclones in a one-year global, coupled simulation (submitted for publication)". The dataset is subdivided into five folders: Modules, TC_tracking, Eddy_identification, Postprocessed_data and Plots. Modules contains .py files with various functions that are imported in the other python scripts. TC_tracking contains three scripts: search_for_candidate_points.py, stitching_tracks.ipynb and Along_track_diagnostics.py. The first two are used to obtain the tropical cyclone tracks. The latter is required to compute additional diagnostics along the TC tracks, including sea-surface cooling and surface latent heat flux. Eddy_identification contains two files: calc_Okubo_Weiss_param.ipynb and Eddy_algorithm.py. The former derives the Okubo-Weiss parameter from raw ICON output and is required for the latter script, which identifies ocean eddies at a given snapshot in time. Postprocessed_data contains four subfolders: 1h_candidate_TCpoints, 1h_eddy_data, 1h_tracks and 1h_track diagnostics. As their names suggest, the candidate tropical cyclone points for the tracking algorithm are stored in h_candidate_TCpoints as csv files. The resulting 56 tracks are stored in 1h_tracks and the additional diagnostics, such as sea-surface cooling, are located in 1h_track_diagnostics as csv files. In 1h_eddy_data, there is a .npy file with the eddy data for each of the 56 tropical cyclones. Finally, Plots contains three scripts. TC_statistics.ipynb generates the global map of tropical cyclone tracks and the monthly TC-frequency plot in Figure 1. The plot for Figure 2 is generated using the script, eddy_TC_statistics.ipynb. Figure S2 in the supplementary materials section is created with TC_eddy_example_plot.ipynb.