CAREER: Synthesizing Structural Uncertainty of Sea-level Rise Projections to Improve Application in Decision Making

职业：综合海平面上升预测的结构不确定性以改进决策中的应用

• 批准号: 2234866
• 负责人: Andra Garner
• 金额: $ 48.74万
• 依托单位: Rowan University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234866&HistoricalAwards=false
• 关键词: CAREER; Synthesizing; Structural; Uncertainty; Sea

Coastal populations and economic assets have increased steadily in recent decades and are likely to continue to do so. At the same time, coastal communities are facing increasing threats from climate related sea level rise (SLR). Thus, there is a rapidly growing need for both global and local SLR projections with uncertainties that reflect our scientific knowledge. This CAREER award will meet the challenge by using the wealth of information contained in four decades of SLR science to improve the scientific community’s understanding of sources of uncertainty within SLR projections. The project will analyze previous SLR science to improve the scientific community’s understanding of sources of uncertainty within SLR projections at global and local scales. The project will support development of a publicly accessible interactive website (a “SLR Dashboard”) that will serve as a central hub for comprehensive SLR projection databases, allowing users immediate access to key SLR projection data, visualizations, and statistical tools. By improving our understanding of the uncertainty that currently exists within projections of future SLR, this work will enhance our ability to develop adaptation and resiliency strategies in coastal communities around the globe. Undergraduate students at Rowan University will be engaged in development and analysis of the databases and will share their work during events with public audiences. The project includes plans to develop children’s book with literary and educational experts that will teach broad audiences about rising sea levels in an engaging and accessible format.Four decades and ~10 cm of global mean SLR since the first global sea level projections were made, projections of future SLR remain deeply uncertain. Although a major source of uncertainty relates to the amount and timing of melt from the Antarctic Ice Sheet, additional uncertainty arises from problem formulation—choices in methodology used to project future SLR, and emissions scenarios considered. While the science behind SLR projections is constantly improving, differences amongst the large numbers of available SLR projections can make it difficult for decision makers to interpret the science and decide which projections they should use. By analyzing existing projections to better understand sources of structural uncertainty, we can reduce the impact of problem formulation upon SLR projection uncertainty over various time scales, improving the practical application of SLR projections in a wide variety of settings. The scientific goals of this project are to 1) further develop publicly-available databases that describe both local and global SLR projections, 2) use machine learning and statistical analyses to quantify how uncertainty manifests and evolves in SLR projections at various spatial and temporal scales, and 3) evaluate the sensitivity of various SLR projection methodologies to climate drivers to improve our understanding of the uncertainty within current projections of SLR. The project will also support the training and education of undergraduates in data analysis and sea level rise policy issues. The project will support development of a children's book about SLR.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.

近几十年来，沿海人口和经济资产稳步增长，并且可能会继续如此。与此同时，沿海社区正面临着与气候相关的海平面上升 (SLR) 带来的日益严重的威胁。 因此，对全球和当地 SLR 预测的需求迅速增长，其不确定性反映了我们的科学知识。该职业奖将通过利用四十年 SLR 科学中包含的丰富信息来应对挑战，以提高科学界对 SLR 预测中不确定性来源的理解。该项目将分析以前的 SLR 科学，以提高科学界对全球和地方尺度 SLR 预测中不确定性来源的理解。该项目将支持开发一个可公开访问的交互式网站（“SLR 仪表板”），该网站将作为综合 SLR 投影数据库的中心枢纽，使用户能够立即访问关键的 SLR 投影数据、可视化和统计工具。通过提高我们对未来 SLR 预测中当前存在的不确定性的理解，这项工作将增强我们在全球沿海社区制定适应和恢复战略的能力。罗文大学的本科生将参与数据库的开发和分析，并将在活动中与公众分享他们的工作。该项目包括与文学和教育专家合作开发儿童读物的计划，以一种引人入胜且易于理解的形式向广大读者讲述海平面上升的情况。自首次进行全球海平面预测以来，四十年来，全球平均 SLR 约为 10 厘米，但对未来 SLR 的预测仍然非常不确定。尽管不确定性的一个主要来源与南极冰盖融化的数量和时间有关，但问题的表述（用于预测未来 SLR 的方法选择以及所考虑的排放情景）也产生了额外的不确定性。虽然 SLR 预测背后的科学不断改进，但大量可用的 SLR 预测之间的差异可能使决策者难以解释科学并决定他们应该使用哪些预测。通过分析现有的预测以更好地了解结构不确定性的来源，我们可以减少问题表述对不同时间尺度的 SLR 预测不确定性的影响，从而提高 SLR 预测在各种环境中的实际应用。该项目的科学目标是 1) 进一步开发描述本地和全球 SLR 预测的公开数据库，2) 使用机器学习和统计分析来量化 SLR 预测在不同空间和时间尺度上的不确定性如何表现和演变，3) 评估各种 SLR 预测方法对气候驱动因素的敏感性，以提高我们对当前 SLR 预测中不确定性的理解。该项目还将支持本科生在数据分析和海平面上升政策问题方面的培训和教育。该项目将支持一本有关 SLR 的儿童读物的开发。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。