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P2C2: Leveraging Bayesian Approaches to Link Reconstructed, Observed, and Projected Meteorological Drought while Accounting for Inherent Data Biases

P2C2：利用贝叶斯方法将重建、观测和预测的气象干旱联系起来，同时考虑固有的数据偏差

基本信息

批准号：
2002539
负责人：
James Stagge
金额：
$ 49.92万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002539&HistoricalAwards=false
关键词：
P2C2 Leveraging Bayesian Approaches Link

项目摘要

This project aims to investigate past, present and future meteorological droughts. Specifically, this project will address the issue of incompatible data biases e.g. (temporal resolution) from these three data sources by testing two fundamental questions: (1) do errors in proxy-based drought reconstructions significantly differ from those in Global Climate Model (GCM)-derived climate projections, particularly for higher order statistics, and (2) can a unified statistical modeling framework explicitly account for inherent data source differences to estimate drought severity from the pre-industrial past, through the instrumental period, and into the future. The researcher will apply novel statistical methods (Hierarchical Bayesian modelling) to merge drought reconstructions, observations, and projections, and generate a gridded drought time-series beginning 2000 years ago and extending to the end of this century (2100). The potential Broader Impacts include developing a visualization website that will allow the public to view 2100 years of drought index time series for any gridded cell, in North America which will also include model and climate uncertainty. A conference-based workshop with hands-on code demonstrations will be organized to train scientists on the statistical modelling framework developed in the project.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.

该项目旨在调查过去、现在和未来的气象干旱。具体而言，该项目将解决不兼容数据偏差的问题，例如：（时间分辨率）通过测试两个基本问题从这三个数据源中获取：（1）基于代理的干旱重建中的误差是否与全球气候模式（GCM）衍生的气候预测中的误差显著不同，特别是对于高阶统计量，以及（2）统一的统计建模框架是否能够明确地解释固有的数据源差异，以估计从工业化前的过去到仪器时期以及未来的干旱严重程度。研究人员将应用新的统计方法（分层贝叶斯模型）来合并干旱重建，观测和预测，并生成一个网格化的干旱时间序列，从2000年前开始，一直延伸到本世纪末（2100年）。潜在的更广泛影响包括开发一个可视化网站，使公众能够查看北美任何网格单元的2100年干旱指数时间序列，其中还包括模型和气候不确定性。该奖项反映了NSF的法定使命，并被认为值得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来支持。