NSF Convergence Accelerator Track J: Predicting the effect of climate extremes on the food system to improve resilience of global and local food security

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

As we enter a third year of record extreme weather driven by a La Niña event, a staggering number of people are facing hunger in the United States and globally. A deadly combination of climate shocks, price increases and conflict have pushed 50+ million people to the edge of famine, highlighting the vulnerability of our global food system. Shocks to the food system are not isolated and can cascade. For example, climate shocks are often correlated, hitting multiple production areas around the world at the same time, resulting in food price hikes that can lead some countries to impose export bans, driving global prices even higher. Fortunately, correlated climate shocks are increasingly predictable. Our ability to forecast extreme heat, drought and heavy rains, driven by sea surface temperatures, has increased dramatically over the past two decades. This project leverages that predictive ability to collaborate with stakeholders along the food system to develop actionable models tailored to their needs and decision-points. Understanding, and anticipating, the vulnerability of the global food system to predictable climate shocks is critical. It will allow government agencies and aid groups to mitigate food crises and help communities build resiliency both in the United States and abroad. This convergence research has two primary objectives. First, it will create predictive models that account for interlinkages across food security drivers. While models for crop production, meteorological forecasts, price, trade, and household food security exist, their current lack of interoperability means that the models do not readily allow for feedbacks, interactions or measures of uncertainty to be perpetuated throughout the system. Developing an integrated model requires a multidisciplinary convergence science approach, bringing together climatologists, hydrologists, sociologists, agricultural economists, statisticians and policy experts to appropriately model correlated shocks and their connections through the food system. Second, to produce actionable output, these models need to be co-developed with stakeholders from the beginning. Stakeholders will guide model inputs, objectives, scenarios and help design their output. The researchers will work with decision-makers to co-produce models that quantify the effects of climate shocks on local and global food production, trade and prices, and enumerate the vulnerability of the households and regions to these shocks. This project will enhance our understanding of the drivers within each component of the integrated model, such as bolstering our theoretical understanding of the linkages between sea surface temperatures and climate, key nodes in the global food trade system, and the effect of combinations of specific food security drivers. Along with improving each separate model component, this project will facilitate their interaction, improving our ability to identify the correlation of weather to international trade and prices while more carefully accounting for uncertainty. To support the adoption of this approach to model development in other fields, the project will develop protocols for decision-maker coproduction of models. By bringing together academics with consequential real-world decision-makers working on both international and domestic food security, this project will help identify drivers of hunger that are relevant in different settings within developing and developed countries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.