DOI: 10.5281/zenodo.10888194
title: Supporting datasets for the article Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia

description: Supporting datasets for the article Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia, submitted to Environmental Research: Climate

 

The dataset contains all intermediate data used for the article. The raw outputs of the model may be available upon reasonable request to clement.lpr@gmail.com

The climate model Plasim can be downloaded, together with its documentation, from the webpage of  the « Theoretische Meteorologie » group at the University of Hamburg: https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html

The three jupyter notebooks Figures_article_archive.ipynb, Zg500_maps_article_archive.ipynb, Correlation_maps_3days_ERA5_Plasim_archive.ipynb contain the analysis and code used to produce the figures in the article.

pyscripts.tar contains 4 python utilities, data_proceeding_module.py, plot_routines.py, subseasonal_stats_utilities.py, utilities_REA_analysis.py that are imported by the notebooks.
description: Algorithm_script.tar

This archive contains the script of the algorithm:

GKTL_PakiIndia_April-June_k=0.1_step=5days_batch0005: Main shell script to run one algorithm experiment. It calls the other script.

create_initial_conditions: Shell script. Copies 200 restart files of the model saved at the end of March into a directory of initial conditions. NB: The restart files are not provided in this repository and could not be used on a different computer than the one they were produced on.

organize_output: Shell script that organizes the output of the model in different folders at the end of each block.

extract_observable: Shell script. Extract a netcdf file for the desired variable from the .srv file produced by the model.

Resampling.py: Python script. Performs the resampling step of the algorithm and save all useful informations (weights of trajectories, number of clones, …) in a .npz file.

plasim_t42_gparea_Rome.nc: Netcdf file. Grid point area for the model configuration. Used by resampling.py to compute the spatial average of the observable

plasim_t42_lsmask_Rome.nc: Netcdf file. Land sea mask for the model configuration. Not used.
description: Algorithm_outputs.tar 

Contains all the dictionary saved as .npz files by resampling.py at the end of each resampling step of the algorithm. The resampling is performed every 5 days.

 

 

The folder GKTL_PakiIndia_April-July_k=0.1_step=5days contains 24 files created during a 4 month long experiment. It is used as batch n°1 of the April-June experiment by considering only the first 18 files. It is also used for fig. 12 b) of the article.

The folders GKTL_PakiIndia_April-June_k=0.1_step=5days_batch000* each contain 18 files. 

These folders are the ones used for all analysis in the article.

 

The folders GKTL_PakiIndia_March20-June_k=0.1_step=5days and GKTL_PakiIndia_March25-June_k=0.1_step=5days each contain 6 subfolders corresponding to 6 run of the experiment starting on March 20th and March 25th respectively. They are used solely for fig. 12 of the article.

 

Content of the dictionaries :¶



k [float]: selection strength of the algorithm

ntrajs [int]: number of trajectories (fixed)

nc [np.array]: number of copy from each trajectory

Nc = sum(nc)

dN = Nc-ntrajs

obsts [np.array]: time series of the observable for each trajectory

obsmean [np.array]: time average of the observable for each trajectory

W [np.array] : array of weights before normalization

R [float] : normalization factor (R=sum(W)/ntrajs)

w=W/R [np.array] : array of normalized weights (sum(w)=ntrajs)

mask [np.array]: mask of the target region of the experiment

killedlabel [np.array]: 0 if the trajectory has survived, 1 if it was killed and replaced by a succesful trajecory

udkilledlabel [np.array]: should be zero everywhere. Initially equal to killedlabel and updated during the resampling.

initlabel [np.array]: label of the trajectories whose restart will be used to continue the evolution
description: EVT_fit&CI.tar

Contains the R script used to perform an EVT fit and compute its confidence interval

EVT_fit_AMJ_Medium.r   is the R script used to perform the EVT fit using the Peak Over Threshold method and compute the confidence interval with the Profile Likelihood Method.

AMJ_Medium_1200yrs.RData is the time series used for the fit converted in RData.

POT_fit_AMJ_1200yrs.RData is the result of the fit 

return_levels_ci_proflike.Rdata contains the 95% CI on the the return levels using the profile Likelihood method.

POT_95CI_non-param_bootstrap.npy contains the 95% CI on the return levels using a non parametric bootstrap and was created from the notebook Figures_article_archive.ipynb
description: data.tar

This folder contains processed data output from the model.

The numbers 03, 04, 05, 06 at the end of the file names indicate the months of March, April, May and June respectively.

 

Content of the folders:

ERA5: 3-day correlation maps between temperature over the study region and the Geopotential, quantiles from the bootstrap test. Detrended temperature anomaly time series over the target region for the months of MAMJ, averaged by block of 3 days + std of the same time series.

misc_climatology: climatology for several variables, including the 2m air temperature, computed from the 8000-year long control run.

misc_composite_CTRL_Long: composite for several variables, including the 2m air temperature, conditioned on 100-year heatwave seasons computed from the 8000-year long control run

plasim_PakiIndia_weighted_mask_Occigen.nc : mask of the target region with grid points weighted by their cell area. Used to compute some spatial averages.

regional_quantiles&std: quantiles and std of several variables, including the 2m air temperature, averaged of the target region.

tas_control_run_8000yrs: contain a single file EastAndPakiIndia_MonthlyAv_tas.npy. This is nested dictionary with the temperature averaged over a region and the months of March, April, May and June separately. Note that there are two regions, EastIndia and PakiIndia and only the second one is studied in the article. These temperature were computed from the 8000-year long run.

tas_control_run_2000yrs : same as above but temperatures are computed from an independent 2000-year long control run

zg_climatology: Climatological average of zg on 10 pressure levels. Computed from the 8000-year long control run.

zg_composite_CTRL_Long: Composites of zg conditioned on 100-year or 1000-year heatwave season over the target region.  The folders contain daily values of zg for the months of March, April, May, June (files zg_composite_100yrs_HW_PakiIndia_03.nc, etc) for 100-year heatwave seasons and AMJ averages for both 100-year and 1000-year heatwaves seasons. The standard deviations of the set of maps used to build the composites are also available. They are used for computing the significance of the maps.

zg_composite_CTRL_Medium: Same as for zg_composite_CTRL_Long but for the second independent run of 2000 years. The composites are computed using the full dataset (yrlim=2000) or only 1500 years (yrlim=1500) for comparison with the algorithm.

zg_composite_CTRL_GKTL: 100-year and 1000-year heatwave seasons composite maps for each algorithm realizations.

zg_correlation_maps: Correlation maps for the months of March, April, May and June computed using1000 years of the long control run.