Climate uncertainty in climate impact studies
These data allow the full reproduction of Schwarzwald and Lenssen, "The Importance of Internal Climate Variability in Climate Impact Projections," currently accepted at PNAS. All code used in this study is available at https://github.com/ks905383/iv_impacts. All data used in this study are either available from this repository, or can be generated from the code itself. Uncertainty in climate projections is driven by three components: scenario uncertainty, inter-model uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, though underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles from seven Coupled General Circulation Models with a total of 414 model runs, we partition the climate uncertainty in classic dose-response models relating county-level corn yield, mortality, and per-capita GDP to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, though its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early- and mid-century projections, projections in regions with high internal variability such as the Upper Midwest United States, and for impacts driven by non-linear relationships.
Steps to reproduce
See https://github.com/ks905383/iv_impacts for the most recent code and definitions. This dataset contains: - climate_raw: All raw climate data used in this study. Data has been downloaded from the ESGF or the Large Ensemble Archive, and preprocessed by stitching together historical and future climate runs into single files. The path to this directory should be used for both obs and mod in dir_list.csv. - geo_data: The raw and processed US county shapefiles used in this study. UScounties_proc.shp has been pre-generated; otherwise it can be regenerated through the raw county shapefile in this document and the files in pop_data using the create_data_counties.ipynb notebook in the code respository. The path to this directory should be used for geo in dir_list.csv. - pop_data: The raw mortality, GDP, and corn yield databases used in this study. See main text for data citations. These are used to generate UScounties_proc.shp (which is also included). The path to this directory should be used for pop in dir_list.csv. - code: The code used to generate the analysis and figures in Schwarzwald and Lenssen (2022). See the file README.md for details.