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.