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

NSF 融合加速器轨道 J：预测极端气候对粮食系统的影响，以提高全球和地方粮食安全的抵御能力

• 批准号: 2236021
• 负责人: Kathy Baylis
• 金额: $ 74.87万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236021&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Predicting

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.

随着我们进入由La Niña事件造成的创纪录极端天气的第三年，美国和全球面临饥饿的人数惊人。气候冲击、价格上涨和冲突等致命因素将5000多万人推向饥荒边缘，凸显了全球粮食系统的脆弱性。对粮食系统的冲击不是孤立的，可能是级联的。例如，气候冲击往往是相互关联的，同时打击世界各地的多个生产区，导致粮食价格上涨，可能导致一些国家实施出口禁令，从而推高全球价格。幸运的是，相关的气候冲击越来越容易预测。在过去的二十年里，我们预测极端高温、干旱和暴雨的能力在海面温度的驱动下急剧提高。该项目利用这种预测能力与粮食系统中的利益相关者合作，开发适合其需求和决策点的可操作模型。了解并预测全球粮食系统在可预测的气候冲击面前的脆弱性至关重要。它将允许政府机构和援助组织减轻粮食危机，并帮助美国和国外的社区建立恢复能力。这种收敛性研究有两个主要目标。首先，它将创建预测模型，解释粮食安全驱动因素之间的相互联系。虽然存在作物生产、气象预报、价格、贸易和家庭粮食安全的模型，但它们目前缺乏互操作性，这意味着这些模型不容易允许反馈、相互作用或不确定性措施在整个系统中持续存在。开发一个综合模型需要多学科的融合科学方法，将气候学家、水文学家、社会学家、农业经济学家、统计学家和政策专家聚集在一起，适当地模拟相关冲击及其在粮食系统中的联系。其次，为了产生可操作的输出，这些模型需要从一开始就与利益相关者共同开发。利益相关者将指导模型输入、目标、场景并帮助设计其输出。研究人员将与决策者合作，共同建立模型，量化气候冲击对当地和全球粮食生产、贸易和价格的影响，并列举家庭和地区对这些冲击的脆弱性。该项目将加强我们对综合模型中每个组成部分驱动因素的理解，例如加强我们对海洋表面温度与气候、全球食品贸易系统关键节点以及特定食品安全驱动因素组合影响之间联系的理论理解。随着改进每个单独的模型组件，该项目将促进它们之间的相互作用，提高我们识别天气与国际贸易和价格之间相关性的能力，同时更仔细地考虑不确定性。为了支持在其他领域采用这种方法进行模型开发，该项目将为决策者共同生产模型开发协议。通过将学者与从事国际和国内粮食安全工作的现实世界重要决策者聚集在一起，该项目将有助于确定在发展中国家和发达国家不同背景下相关的饥饿驱动因素。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tailored Forecasts Can Predict Extreme Climate Informing Proactive Interventions in East Africa

• DOI: 10.1029/2023ef003524
• 发表时间: 2023-07
• 期刊: Earth's Future
• 作者: C. Funk;L. Harrison;Z. Segele;T. Rosenstock;P. Steward;C. L. Anderson;Erin Coughlan de Perez;Daniel Maxwell;H. S. Endris;Eunice Koch;G. Artan;Fetene Teshome;S. Aura;G. Galu;D. Korecha;Weston Anderson;A. Hoell;Kerstin Damerau;E. Williams;Aniruddha Ghosh;J. Ramirez-Villegas;David Hughes