Python codes that support the manuscript "Anthropogenic changes in atmospheric circulation patterns conducive to summer hot extremes in the Northern Hemisphere"

by Zeqin Huang, Xuezhi Tan*, Thian Yew Gan, Bingjun Liu*, Xiaohong Chen

We analyze thermodynamic and dynamic responses of hot extremes to anthropogenic changes in atmospheric circulation patterns. Changes in atmospheric teleconnection patterns under various forcings are interpreted by the self-organizing maps (a python package, MiniSom is applied). Detection and attribution analyses are performed through the regularized optimal fingerprinting and the Ribes' attribution method.

Organization of repository

|--HWdna_2022
| |--proc_scripts
| |--data_preproc
| |--README.md
| |--Figures
| |--Notebooks
| |--attribution_data

Subdirectory

data_preproc

Preprocessing codes for the raw datasets

|--data_preproc
| |--MIROC6_mx2t_preproc.py
| |--IPSL-CM6A-LR_hgt_preproc_500hpa_piControl.py
| |--MRI-ESM2-0_hgt_trend_1979-2014.py
| |--era5_mx2t_preproc.py
| |--IPSL-CM6A-LR_hgt_trend_1979-2014.py
| |--MRI-ESM2-0_mx2t_trend_1979-2014.py
| |--IPSL-CM6A-LR_mx2t_trend_1979-2014.py
| |--MRI-ESM2-0_hgt_preproc_500hpa.py
| |--HadGEM-GC31-LL_hgt_trend_1979-2014.py
| |--ncep_hgt_preproc_500hpa.py
| |--CanESM5_hgt_preproc_500hpa.py
| |--era5_hgt_preproc_500hpa.py
| |--HadGEM-GC31-LL_hgt_preproc_500hpa.py
| |--CanESM5_hgt_trend_1979-2014.py
| |--ncep_preproc_dealwith_2008.py
| |--ncep_tmax_preproc.py
| |--CanESM5_mx2t_trend_1979-2014.py
| |--HadGEM-GC31-LL_hgt_preproc_500hpa_piControl.py
| |--MIROC6_hgt_preproc_500hpa_piControl.py
| |--CanESM5_hgt_preproc_500hpa_piControl.py
| |--MIROC6_hgt_trend_1979-2014.py
| |--ncep_hgt_preproc_200hpa.py
| |--ACCESS-ESM1-5_hgt_preproc_500hpa.py
| |--IPSL-CM6A-LR_hgt_preproc_500hpa.py
| |--MIROC6_hgt_preproc_500hpa.py
| |--MRI-ESM2-0_hgt_preproc_500hpa_piControl.py
| |--era5_hgt_preproc_200hpa.py
| |--HadGEM-GC31-LL_mx2t_trend_1979-2014.py
| |--MIROC6_mx2t_trend_1979-2014.py

proc_scripts

Codes for analyses

|--proc_scripts
| |--calculate_hot_extreme_occur_WNA.py
| |--determine_winner_for_forcings_relative_to_reanalyses_mean_SOM.py
| |--hgt_tmax_regional_variation.py
| |--concat_hgt_of_forcings_for_SOM.py
| |--calculate_hot_extreme_occur_all_pattern_yearly_piControl.py
| |--calculate_500hpa_GPH_for_patterns_under_forcings.py
| |--calculate_hgt_yearly_for_DNA_piControl.py
| |--calculate_surface_Tmax_historical_average.py
| |--calculate_hot_extreme_occur_trend_sig_concat.py
| |--calculate_tmax_historical_seasonal_cycle_threshold.py
| |--calculate_hot_extreme_occur_all_pattern_yearly.py
| |--som_winner_pattern_historical_nativegrid.py
| |--calculate_surface_Tmax_for_patterns_under_forcings.py
| |--calculate_hgt_yearly_for_DNA.py
| |--calculate_hot_extreme_occur_EAS.py
| |--hot_extreme_per_pattern_occur_trend_concat.py
| |--determine_winner_for_historical_relative_to_reanalyses_mean_SOM.py
| |--calculate_500hpa_GPH_historical_average.py
| |--calculate_hgt_historical_seasonal_cycle.py
| |--calculate_hot_extreme_occur_reanalyses.py
| |--calculate_hot_extreme_occur_EU.py

Notebooks

Jupyter notebooks for analyses and visualization

|--Notebooks
| |--Optimal_fingerprinting_GPH.ipynb
| |--Fig1_hgt_trend_and_variation.ipynb
| |--Fig2_target_patterns_and_occurrence_under_forcings.ipynb
| |--Fig3_trends_circulation_pattern_and_hot_extreme.ipynb
| |--Fig4_detection_and_attribution_of_hot_extreme.ipynb
| |--Fig5_future_changes_in_patterns_and_hot_extremes.ipynb
| |--FigS1_best_grid_compare.ipynb
| |--FigS2_Trends_of_JJA_GPH_and_Tmax_for_different_forcings.ipynb
| |--FigS3-5_circulation_patterns_for_individual_reanalysis.ipynb
| |--FigS6-11_trends_of_patterns_and_hot_extreme_for_each_subregions.ipynb
| |--FigS12_circulation_anomalies_under_extreme_pattern.ipynb
| |--FigS13_detection_and_attribution_for_reanalyses.ipynb
| |--FigS14_partitioned_trends_in_hot_extreme.ipynb

Figures

Rendered figures

|--Figures
| |--Fig1_changes_in_GPH_and_associated_hot_extreme.pdf
| |--Fig2_distribution_patt_occur_external_forcing.pdf
| |--Fig3_changes_in_pattern_occurrence_and_associated_hot_extreme.pdf
| |--Fig4_distribution_patt_occur_external_forcing_GPH.pdf
| |--Fig5_future_changes_in_pattern_occurrence_and_associated_hot_extreme.pdf
| |--FigS1_best_grid_selected_for_SOM_analysis.pdf
| |--FigS2_trends_in_circulation_patterns_and_hot_extreme_under_all_forcings.pdf
| |--FigS3_circulation_patterns_categorization_era5.pdf
| |--FigS4_circulation_patterns_categorization_jra55.pdf
| |--FigS5_circulation_patterns_categorization_ncep2.pdf
| |--FigS6_pattern_trend_EU.pdf
| |--FigS7_hot_extreme_trend_EU.pdf
| |--FigS8_pattern_trend_EAS.pdf
| |--FigS9_hot_extreme_trend_EAS.pdf
| |--FigS10_pattern_trend_WNA.pdf
| |--FigS11_hot_extreme_trend_WNA.pdf
| |--FigS12_circulation_anomalies_under_external_forcings.pdf
| |--FigS13_detection_and_attribution_for_reanalysis_using_ROF_Ribes.pdf
| |--FigS14_partitioned_trends_in_hot_extreme.pdf

Datasets used

• Detection and Attribution Model Intercomparison (DAMIP) dataset from CMIP6
• ERA5 from European Centre for Medium-Range Weather Forecasts (ECMWF)
• NCEP2 provided by the NOAA/OAR/ESRL PSL
• JRA55 available at the National Center for Atmospheric Research