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.

One of the primary sources of predictability for seasonal hydroclimate forecasts are sea surface temperatures (SSTs) in the tropical Pacific, including the El Niño Southern Oscillation. Multi-year La Niña events in particular may be both predictable at long lead times and favor drought in the bimodal rainfall regions of East Africa. However, SST patterns in the tropical Pacific and adjacent ocean basins often differ substantially between first- and second-year La Niñas, which can change how these events affect regional climate. Here, we demonstrate that multi-year La Niña events favor drought in the Horn of Africa in three consecutive seasons (OND-MAM-OND). But they do not tend to increase the probability of a fourth season of drought owing to the sea surface temperatures and associated atmospheric teleconnections in the MAM long rains season following second-year La Niña events. First-year La Niñas tend to have both greater subsidence over the Horn of Africa, associated with warmer waters in the West Pacific that enhance the Walker Circulation, and greater cross-continental moisture transport, associated with a warm Tropical Atlantic, as compared to second-year La Niñas. Both the increased subsidence and enhanced cross-continental moisture transport favors drought in the Horn of Africa. Our results provide physical understanding of the sources and limitations of predictability for using multi-year La Niña forecasts to predict drought in the Horn of Africa.

Changes in precipitation variability can have large societal consequences, whether at the short timescales of flash floods or the longer timescales of multi-year droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. This work evaluates changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The “extra” broadening is negatively correlated with changes in mean precipitation: in strongly “wetting” areas, distributions broaden less than expected from a simple multiplicative mean change; in “drying” areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on timescales from days to decades, i.e. show little frequency dependence. This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.

The societies of the Greater Horn of Africa (GHA) are vulnerable to variability in two climatologically distinct rainy seasons, the March-May ‘long‘ rains and the October-December ‘short‘ rains. Recent trends in both rainy seasons, possibly related to patterns of low-frequency variability, have increased interest in future climate projections from General Circulation Models (GCMs). However, previous generations of GCMs historically have a poor record in simulating the regional hydroclimate. This study conducts a process-based evaluation of simulations of the GHA long and short rains in CMIP6, the latest generation of GCMs. Key biases in CMIP5 models remain or are worsened, including long rains that are too short and weak and short rains that are too long and strong. Model biases are driven by a complex set of related oceanic and atmospheric factors. A too strong climatological zonal sea surface temperature gradient in the Indian Ocean and convection over the GHA that is too deep in particular are connected with erroneously powerful short rains in models. Model mean state biases in the timing of the western Indian Ocean sea surface temperature seasonal cycle are associated with certain GHA rainfall timing biases; this connection is however not replicated in interannual variability within models, suggesting there may be a common driver of both biases. Ocean biases cannot explain rainfall biases on their own; simulations driven by historical SSTs (AMIP runs) often have larger biases than fully coupled runs. A path towards using biases to better understand uncertainty in projections of GHA rainfall is suggested.