Stressed economies respond more strongly to climate extremes - Data and Code Supplement (Q6699778)

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 withgit@github.com:acclimate/post-processing.git. Switch to the develop branch withgit checkout developand install the package withconda develop .from within the repository data: See./data/README.mdfor 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 athttps://github.com/acclimate/acclimate 3. Run the analyses Acclimate output files of the calibration runs and the scenario runs are aggregated with functionsaggregate_calibration_ensembleand aggregate_ensemblesin./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