CMG Collaborative Research: Statistical Evaluation of Model-Based Uncertainties Leading to Improved Climate Change Projections at Regional to Local Scales

CMG 合作研究：基于模型的不确定性的统计评估可改善区域到地方尺度的气候变化预测

• 批准号: 0724522
• 负责人: Xiao-Li Meng
• 金额: $ 16.72万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0724522&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Statistical; Evaluation

This research project brings together an interdisciplinary team of atmospheric scientists and statisticians to attack an outstanding issue in the field of climate change research: namely, how to obtain statistically robust projections of future climate change at regional to local scales. It is well known that global change is modified by local and regional features in ways that even regional models are challenged to capture, producing unique patterns in each individual region. Quantifying these patterns of change is essential to identifying appropriate adaptation and mitigation strategies to cope with the likely impacts of climate change on both human and natural systems. Driven by both the persistent limitations in present-day modeling capacity, as well as the potential global-scale impacts of climate change, the investigators propose to develop a set of scientifically- and statistically-advanced techniques to reduce the uncertainties inherent in use of global and regional climate model output fields to generate local-scale climate projections. Utilizing available observations, reanalysis data, and historical global and regional climate model simulations, the investigators will first develop a set of statistical techniques that will reduce the dimensionality of both global and regional model differences relative to observations. Statistical techniques to quantify model-observational differences and capture the range of future climate projections will include proven methods for spatial interpolation of observations, as well as new spectral and wavelet analyses, and development of an advanced quantile regression approach with Bayesian empirical likelihoods. Building on the investigators? previous research analyzing the ability of both global and regional climate models to simulate key atmospheric dynamical features, we will then assess the physical features of the models that are likely contributing to these differences. Both physical and statistical characterizations of model limitations will then be applied reduce uncertainty in a range of IPCC AR4 global model simulations of future climate change, based on multiple realizations of future emissions scenarios and available regional climate model simulations. The final project goal is to synthesize the above methods into a generalized framework that combines physical and statistical analyses to assess historical global and regional model performance, and then use these characterizations of model performance to reduce the uncertainty in future projections of key surface climate variables at regional to local scales. The work proposed addresses an on-going and crucial need in climate change research to characterize and account for model limitations in order to reduce uncertainties at the regional to local scale where the societal, economic, and environmental impacts of climate change occur. This project is unique from both a scientific and statistical perspective, combining a well-established research program on global and regional climate model analysis with innovative statistical approaches. Advanced statistical methods will be used to merge all available information including observations, data assimilations, global and regional climate model simulations, and other depictions of the internal variability of the climate system to characterize model differences relative to observations, and to produce improved high-resolution projections of future changes in surface climate. This project will involve the extensive use of high-performance computing capabilities The capabilities that will be developed are designed to reduce uncertainties in the likely range of future climate change, enabling more effective analyses of the potential impacts of climate change at regional to local scales. At the same time, the project will challenge the state-of-the-art in terms of the techniques and statistical tools developed, and their application to the field of regional climate projections. The proposed collaborative research will also provide interdisciplinary training to students and postdoctoral fellows at several institutions, with the cross-disciplinary fertilization of ideas fostered through the close interactions on this project providing invaluable insights into both the research and the educational processes.

该研究项目汇集了一个由大气科学家和统计学家组成的跨学科小组，以解决气候变化研究领域的一个突出问题：即如何在区域到地方尺度上获得对未来气候变化的可靠的统计预测。众所周知，全球变化受到地方和区域特征的影响，甚至区域模型也难以捕捉，从而在每个区域产生独特的模式。量化这些变化模式对于确定适当的适应和缓解战略以科普气候变化对人类和自然系统可能产生的影响至关重要。由于当前建模能力的持续局限性以及气候变化的潜在全球规模影响，研究人员建议开发一套科学和技术先进的技术，以减少使用全球和区域气候模型输出字段生成局部规模气候预测时固有的不确定性。利用现有的观测、再分析数据以及历史上的全球和区域气候模式模拟，研究人员将首先开发一套统计技术，以减少全球和区域模式相对于观测差异的维度。量化模型-观测差异和获取未来气候预测范围的统计技术将包括经验证的观测空间插值方法，以及新的光谱和小波分析，并开发具有贝叶斯经验可能性的先进分位数回归方法。在调查员的基础上？在分析全球和区域气候模式模拟关键大气动力特征能力的基础上，我们将评估可能导致这些差异的模式的物理特征。然后，将根据对未来排放情景的多种认识和现有区域气候模型模拟，对模型局限性进行物理和统计表征，以减少气专委第四次评估报告对未来气候变化的一系列全球模型模拟的不确定性。项目的最后目标是将上述方法综合成一个综合框架，将物理和统计分析结合起来，评估全球和区域模式的历史性能，然后利用这些模式性能的特征来减少区域到地方尺度关键地表气候变量未来预测的不确定性。拟议的工作解决了气候变化研究中的一个持续和关键的需求，以描述和说明模型的局限性，以减少气候变化对社会、经济和环境影响发生的区域到地方尺度的不确定性。该项目从科学和统计的角度来看都是独一无二的，将全球和区域气候模型分析的成熟研究计划与创新的统计方法相结合。先进的统计方法将用于合并所有现有信息，包括观测、数据同化、全球和区域气候模型模拟以及气候系统内部变异性的其他解释，以说明模型相对于观测的差异，并对未来地面气候变化作出更好的高分辨率预测。将开发的能力旨在减少未来气候变化可能范围的不确定性，从而能够更有效地分析气候变化在区域到地方范围内的潜在影响。与此同时，该项目将在所开发的技术和统计工具及其在区域气候预测领域的应用方面挑战最新技术水平。拟议的合作研究还将为几个机构的学生和博士后研究员提供跨学科培训，通过该项目的密切互动促进跨学科思想的丰富，为研究和教育过程提供宝贵的见解。