Explaining the spread in climate model predictions using a moist energy balance model
Key Points & Overview
- Reducing uncertainty in tropical cloud behavior will improve warming projections over a wide range of latitudes
- Improved understanding of polar feedbacks will primarily reduce warming uncertainty in polar regions
Climate projection uncertainty can arise from three distinct sources: internal climate variability, emissions scenario, and the climate response. Internal climate variability refers to the natural fluctuations of the climate system that can often mask warming trends. Uncertainty in emissions scenario arises primarily from our inability to predict future anthropogenic greenhouse-gas emissions and land use changes. Finally, comprehensive general circulation models (GCMs) have different representations of how the climate system behaves, producing divergent predictions of climate change under the same greenhouse-gas forcing.
Previous studies have shown that the global-mean warming value differs across GCMs because of uncertainty in climate feedbacks, which act to amplify or dampen the greenhouse-gas forcing. However, while the global-mean value is an important quantity for intermodel comparison, its policy relevance is limited, given that it is a single number. Arguably, knowledge of the spatial pattern of climate change is of greater consequence for society, as the effects of climate change are predominantly experienced at the regional level. For example, to what extent does tropical cloud feedback uncertainty affect uncertainty in polar warming? In a recent study, we deconstructed uncertainty in the spatial pattern of climate change by using an idealized climate model that emulates the behavior of the GCMs. This idealized climate model links regional physical processes to warming responses across latitudes by representing changes in poleward atmospheric heat transport, which allowed us to disaggregate the pattern of warming predicted by each GCM. By fixing climate feedback uncertainty to certain geographic regions, for instance, we found that uncertainty in tropical cloud feedbacks creates an even layer of uncertainty across the pattern of warming. Conversely, uncertainty in polar climate feedbacks resulted in warming uncertainty that was confined to the poles. This result suggests that predicting future polar warming is difficult as it is a combination of tropical and polar feedback uncertainty. It also suggests that improved knowledge of tropical cloud feedbacks will improve warming projections everywhere.
Considerable work remains to reduce uncertainty in projections of climate change. But, the first step towards reducing uncertainty in future climate change is to characterize the relative importance of each source. Though this study is idealized and does not allow us to directly reduce such uncertainty, it does allow us to disaggregate the contributing sources of uncertainty.