Stressed economies respond more strongly to climate extremes - Data and Code Supplement

This repository provides data and code to reproduce the results of the publication "R. Middelanis, S. N. Willner, K. Kuhla, L. Quante, C. Otto, and A. Levermann (2023). Stressed economies respond more strongly to climate extremes. Environmental Research Letters."

dependencies:

a working environment is provided in environment.yml

the Acclimate post-processing package can be downloaded from the respective GitHub repostory with [e-mail]:acclimate/post-processing.git. Switch to the develop branch with git checkout develop and install the package with conda develop . from within the repository

data:

See ./data/README.md for the required data and sources for those data that are not included in this repository.

Steps to reproduce the results:

1. Generate Acclimate input data

Direct loss time series are obtained from "Kuhla et al. (2021). Ripple resonance amplifies economic welfare loss from weather extremes. Environmental Reserach Letters".

Acclimate input data (cf. ./data/README.md) are generated with ./code/forcing.py

The input data used in the pubilcation are available at ./data/acclimate_input

2. Run Acclimate

the Acclimate model can be downloaded from the respective GitHub repository at https://github.com/acclimate/acclimate

3. Run the analyses

Acclimate output files of the calibration runs and the scenario runs are aggregated with functions aggregate_calibration_ensemble and  aggregate_ensembles in ./code/utils.py, respectively.

Aggregated ready-to-use output data is located in ./data/acclimate_output

All figures can be reproduced with ./code/plotting.py