Python code and Jupyter notebooks support the manuscript "Relative contributions of large-scale atmospheric circulation dynamics and anthropogenic warming to the unprecedented 2022 Yangtze River Basin Heatwave" by Zeqin Huang et al.
All the analysis and visualization code are performed using Jupyter notebook in a python environment. The analysis notebooks include the ensemble constructed circulation analogue (CCA) analysis for the 2022 YRB heatwave specifically and for the whole research period (during 1979~2022).
Both geopotential height at 500 hPa pressure level (Z500) and sea level pressure (SLP) are utilized for the ensemble CCA analysis.
Figs. 1~7 an S1~S10 are generated using matplotlib and proplot
- Fig1_unprecedented_YRB_HW.ipynb
- Fig2_wget_fire_weather_index.ipynb
- Fig3_YRB_HW_nonstationary_fitting.ipynb
- Fig4_Large-scale_conditions_2022.ipynb
- Fig5_CCA_plot_for_2022_YRB_HW.ipynb
- Fig6_CCA_plot_for_historical.ipynb
- Fig7_CCA_plot_for_2022_YRB_HW_with_different_analogue_numbers.ipynb
- FigS2_unprecedented_YRB_HW_characteristics.ipynb
- FigS3_SAT_HW_characteristics_spatial_trends.ipynb
- FigS5_wget_fire_weather_index_anomalies.ipynb
- FigS6_YRB_HW_nonstationary_fitting.ipynb
- FigS7_compare_HRLT_ERA5.ipynb
- FigS8_CCA_plot_for_historical_variance.ipynb
- FigS9_CCA_plot_for_2022_YRB_HW_SLP.ipynb
- FigS10_CCA_plot_for_historical_SLP.ipynb
The script for nonstationary generalized extreme value (GEV) fitting is adapted from https://github.com/clairbarnes/wwa.
ALL the supporting figures for the manuscript.
- ERA5: The ERA5 data is available from the European Centre for Medium-range Weather Forecasts (ECMWF, https://www.ecmwf.int).
- JRA55: The JRA55 data is available from the Japanese 55-year Reanalysis (https://climatedataguide.ucar.edu/climate-data/jra-55).
- HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation (HRLT) across China (https://doi.pangaea.de/10.1594/PANGAEA.941329)
- FWI dataset: The fire weather index data is available from the Global Fire WEather Database (https://data.giss.nasa.gov/impacts/gfwed/).