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| CMG Collaborative Research: Statistical Evaluation of Model-Based Uncertainties Leading to Improved Climate Change Projections at Regional to Local Scales | |
| 项目编号 | 0724377 |
| Katharine Hayhoe | |
| 项目主持机构 | Texas Tech University |
| 开始日期 | 2007-09-01 |
| 结束日期 | 2011-08-31 |
| 英文摘要 | 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. |
| 学科分类 | 01 - 数学 |
| 资助机构 | US-NSF |
| 项目经费 | 182072 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/72621 |
| 推荐引用方式 GB/T 7714 | Katharine Hayhoe.CMG Collaborative Research: Statistical Evaluation of Model-Based Uncertainties Leading to Improved Climate Change Projections at Regional to Local Scales.2007. |
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