The importance of internal climate variability in climate impact projections

Abstract

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel 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, although 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 gross domestic product 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, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships.

Assessing the Impact of Stratospheric Aerosol Injection on US Convective Weather Environments

“…The analyses in Figure 8 also begin to illustrate the potential role of unforced, internal climate variability in projected future changes in convective weather environments under both SSP2‐4.5 and SAI. It is well documented that internal climate variability has the potential to significantly modulate forced changes in climate (e.g., Deser et al., 2012, 2020; Schwarzwald & Lenssen, 2022). This is explored further in the next section.…”

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

“…Model uncertainty is quantified as the variance across the mean outcome (i.e., extreme wind speeds) over multiple runs. Internal variability is determined as the mean over the variance of the outcomes across the runs (Schwarzwald & Lenssen, 2022). Figure S13 in Supporting Information S1 shows the spatial distribution of model uncertainty and internal variability in extreme wind speed simulation during the historical period.…”

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

“…Although the ratio of new record high temperatures compared to record lows continues to widen (Meehl et al., 2022), the overall detectability of CONUS temperature signals continues to remain challenging, partially due to the anomalous warmth observed in the Dust Bowl era (Donat et al., 2016; Hansen et al., 2001; Peterson et al., 2013). Given the broad range of consequences associated with future projected warming over the CONUS (Program, 2018; Wuebbles et al., 2014), it remains urgent to better characterize the ToE of summertime temperatures in order to aid in future decision‐making on regional health hazards and other impacts that could fall outside of historical climate variability (Bevacqua et al., 2023; Deser, 2020; Mankin et al., 2020; Schwarzwald & Lenssen, 2022).…”