How might climate change affect the acidification of the world’s oceans or air quality in China and india in the coming decades, and what climate policies could be effective in minimizing such impacts? To answer such questions, decision-makers routinely rely on science-based projections of physical and economic impacts of climate change on selected regions and economic sectors. But the projections they obtain may not be as reliable or useful as they appear: today’s gold standard for climate impact assessments—model intercomparison projects (MIPs)—fall short in many ways.

MIPs, which use detailed climate and impact models to assess environmental and economic effects of different climate-change scenarios, require international coordination among multiple research groups, and use a rigid modeling structure with a fixed set of climate-change scenarios. This highly dispersed, inflexible modeling approach makes it difficult to produce consistent and timely climate impact assessments under changing economic and environmental policies. In addition, MIPs focus on a single economic sector at a time and do not represent feedbacks among sectors, thus degrading their ability to produce accurate projections of climate impacts and meaningful comparisons of those impacts across multiple sectors.

To overcome these drawbacks, researchers at the MIT Joint Program on the Science and Policy of Global Change propose an alternative method that only a handful of other groups are now pursuing: a self-consistent modeling framework to assess climate impacts across multiple regions and sectors. They describe the Joint Program’s implementation of this method and provide illustrative examples in a new study published in Nature Communications.