Understanding the Sources of Climate Uncertainty in Projections of Climate Impacts

Uncertainty in climate projections is driven by three components: scenario uncertainty, inter-model uncertainty, and internal variability. Although econometric climate impact studies increasingly take into account the first two components, little attention has been paid to the contribution of internal variability. Policymakers generally respond to short-term challenges on time horizons of days to decades, when internal variability is largest in projections of climate variables. Underestimating this uncertainty due to internal variability can lead to underestimating the socioeconomic costs of climate change and therefore estimates of the social cost of greenhouse gases. Using large ensembles from seven Coupled General Circulation Models with a total of 414 model runs, we partition the climate uncertainty in classic empirical dose-response models relating county-level corn yield, mortality, and per-capita GDP to temperature in the continental United States. 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 for projections farther in the future. These findings suggest that uncertainty due to internal variability must be included for accurate uncertainty quantification in projections of temperature-driven impacts including early- and mid- 21st century projections, projections in regions with high internal variability such as the Upper Midwest United States, and for impacts driven by non-linear relationships. We conclude with recommendations on how to account for differing sources of climate uncertainty when constructing projections of the socioeconomic impacts of climate change impact.