These scripts reproduce the main results of the paper:
Simona Meiler(1,2), Chahan M. Kropf(1,2), Jamie W. McCaughey (1,2), Chia-Ying Lee (3), Suzana J. Camargo (3), Adam H. Sobel (3,4), Nadia Bloemendaal (5,6), Kerry Emanuel(7), and David N. Bresch(1,2): Navigating uncertainty and sensitivity analysis of future tropical cyclone risk estimates
Publication status: under review
(1) Institute for Environmental Decisions, ETH Zurich, Switzerland
(2) Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland
(3) Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
(4) Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
(5) Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
(6) Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
(7) Lorenz Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Contact: Simona Meiler
CSV and Excel files with GDP growth factors from the SSP public database. iamc_db.csv is needed for SSP_GDP_scenarios_preprocessing.py
and yields ssps_gdp_annual.csv, which is used in the UA_SA* Python scripts.
HDF5 files containing the output of the uncertainty and sensitivity analyses described below. All files required
to reproduce the figures of the publication are provided.
Script folders, each containing Python scripts to run the analysis of this study and visualize the results.
figures-tables contains Python scripts to generate all figures and tables in the main text and supplementary material.
risk-model-various contains Python scripts necessary for various components of the risk modelling chain.
unsequa contains Python scripts to execute the uncertainty and sensitivity analysis central to this study.
Python scripts to reproduce Supplementary Figures 8 and 9.
Python scripts to reproduce Figure 1 and Supplementary Figure 1.
Python scripts to reproduce Supplementary Figures 4 and 5 and Supplementary Table 3.
Python scripts to reproduce Figure 3 and Supplementary Figure 3 and Supplementary Table 2.
Python scripts to reproduce Supplementary Figures 6 and 7.
Python scripts to reproduce Figure 2, Supplementary Figures 2 and Supplementary Table 1.
Python script to generate the centroids files.
Python script to generate the baseline Exposure files for different exponent combinations of the LitPop method.
Python script which converts GDP growth factors downloaded from the SSP public database and stored in iamc_db.csv
into annual growth factors for each SSP scenario and country (ssps_gdp_annual.csv). Needed for the UA_SA* Python scripts.
Naming: {TC-model} = CHAZ, IBTrACS, MIT, STORM
Various Python scripts to load TC track sets from the different TC models (for present and the two future periods, various GCMs and emission scenarios) and calculate the 2D windfields using two wind models. Some TC model output was processed in chunks to optimize computational efficiency. Some outputs need frequency bias corrections and other steps. The documentation of the single Python scripts contains all relevant information.
The output hdf5 files are the hazard sets, which are further used for the uncertainty and sensitivity analysis UA_SA*.
Note that this step requires a computer cluster and that the output files are large (multiple GB per file).
Naming: {TC-model} = CHAZ, IBTrACS, MIT, STORM
Python scripts to run the publication's central uncertainty and sensitivity analyses. Files are named after their primary
analysis focus: abs refers to UA/SA for absolute TC risk estimate in the future, cc and soc are to assess climate
change and socio-economic development independently, main yields UA/SA results for the total TC risk increase.
Note that this step requires a computer cluster.
Requires:
- Python 3.9+ environment (best to use conda for CLIMADA repository)
- CLIMADA repository version 4.1.1+: https://wcr.ethz.ch/research/climada.html https://github.com/CLIMADA-project/climada_python
- branch: develop
Computationally demanding calculations were run on the Euler cluster of ETH Zurich.
Publication: submitted to Science Advances
Documentation for CLIMADA is available on Read the Docs:
If script fails, revert CLIMADA version to release v4.1.1:
- from GitHub
Created on 26 February 2024