Our body of research contains work along several different themes, but all our projects have one thing in common: They aim to reduce uncertainty in future projections of climate. With less uncertainty, society can make better-informed decisions about curbing and adapting to climate change.
Goal: Better overall projections of global climate
The basic tools that underpin our work are global climate models, powerful supercomputing programs that simulate the global climate system. A major source of uncertainty is that different global climate models give different answers about future climate. The range of answers across the different models—what climate scientists refer to as the spread—can be very large.
One theme of our research is to analyze global climate models to understand the sources of spread, and to identify ways it can be reduced. Our group has pioneered the use of emergent constraints, an approach that uses observations of the current climate to constrain, or narrow the range of, global climate model answers about the future. As part of this work, we are improving our understanding of certain climate feedbacks, processes within the climate system that can either worsen or lessen climate change.
Goal: Better projections of future climate at the scales where policy decisions are made
Another source of uncertainty is that it’s not always clear how global climate change will play out in particular places. Global climate models are meant to analyze changes on global or continental scales; their output is too coarse in spatial resolution to accurately represent climate in places with a complex topography.
A major focus of our work is to improve projections of regional climate change. We do this by developing and refining downscaling techniques that produce high-spatial-resolution climate projections using global climate model information. We then use these fine-scale projections to better understand climate impacts on human and natural systems.
Goal: Better projections of changes in extremes
For many climate variables, changes in the mean—such as average temperature or precipitation—are relatively easy to project with confidence. But when it comes to changes in extremes—such as droughts and heavy precipitation events—it’s harder to make credible projections, particularly on regional scales. Taking on this challenge is an increasingly important focus of our work.