Uncertainties and sensitivities in the quantification of future tropical cyclone risk

These scripts reproduce the main results of the paper:

Simona Meiler(1,2), Alessio Ciullo(1,2), Chahan M. Kropf(1,2), Kerry Emanuel(3), and David N. Bresch(1,2): Uncertainties and sensitivities in the quantification of future tropical cyclone risk

Publication status: published.

(1) Institute for Environmental Decisions, ETH Zurich, Switzerland

(2) Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland

(3) Lorenz Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Contact: Simona Meiler

Content:

Centroids.py

Python script to generate the centroids files.

Exposure_vary_baseline.py

Python script to generate the baseline Exposure files for different exponent combinations of the LitPop method.

Hazard_windfield_calc.py

Python scripts to load TC track sets (for present and the two future periods, various GCMs and emission scenarios) and calculate the 2D windfields using two wind models. 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).

SSP_GDP_scenarios_preprocessing.py

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.

UA_SA*.py

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.

data

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 above. All files required to reproduce the figures of the publication are provided.

Fig*.py

Python scripts named according to their Figure number in the publication; can be used to reproduce the figures. Figure numbers starting with S produce outputs and results for the Supplementary Material and contain code analogous to their main text counterparts.

SI_TabS*.py

Python script named according to their Table number in the publication's Supplementary Information to produce the respective values.

Requirements

Requires:

• Python 3.8+ environment (best to use conda for CLIMADA repository)
• CLIMADA repository version 3.3.1+: https://wcr.ethz.ch/research/climada.html https://github.com/CLIMADA-project/climada_python
• branch: feature/unsequa_delta_climate

ETH cluster

Computationally demanding calculations were run on the Euler cluster of ETH Zurich.

Documentation:

Publication: published in Communications Earth & Environment

Documentation for CLIMADA is available on Read the Docs:

• online (recommended)
• PDF file

If script fails, revert CLIMADA version to release v3.3.1:

• from GitHub

History

Created on 22 June 2023 Updated on 26 February 2024

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