Collaborative Research: CMG: Gridded Analyses of Large Multi-Scale Climate Data Sets with Ensemble Representation of Uncertainty

合作研究：CMG：使用不确定性集合表示的大型多尺度气候数据集的网格分析

• 批准号: 0417971
• 负责人: Stephan Sain
• 金额: --
• 依托单位: University of Colorado at Denver-Downtown Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0417971&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Gridded; Analyses

Understanding past climate variability and predicting future climate changes requires long records of observed climate fields. However, observations from the past are typically noisy and irregularly sampled in time and space, with large spatial gaps. Improved representations of the uncertainty in analyzed fields would be extremely useful in climatological research. To help meet this need, this project will pursue research into a systematic and robust method of producing an analyzed field from gappy climate data. The approach to be taken is to attempt to fuse two computationally efficient approaches that address variability at different scales. The first of the two methods was developed in climate research and uses a low-rank approximation to the system error covariance matrix. The method reconstructs mainly large-scale features of a climate field and hence misses most of small-scale variability. The second approach stems from Bayesian estimation in geostatistics that combines the strength of a globally estimated covariance with nonstationary local estimates; it represents the uncertainty not only due to the observational noise and sampling deficiencies, but also uncertainty due to unknown covariance relationships. The methods have been developed independently and their coupling for a practical climatological application or representing uncertainty in a historical analysis by ensemble has yet to be accomplished. To make this approach work for realistic climate problems requires the theoretical research proposedIf successful, the result of the project will the development of an efficient numerical algorithm suitable for very large, disparate, irregularly spaced climatological data sets that a) reconstructs variability on different scales, b) produces an average analysis field over a regular spatio-temporal grid and, c) yields a method to produce a collection of equally likely analysis fields (an ensemble) which reflects the uncertainty in the average analysis field. The method will then be applied to data sets of marine and land climate important to current climatological research. The results will also provide a better technique for creating the initial conditions for climate simulation ensembles, permitting better estimation of uncertainty in predictions of climate variation. Software implementing the new approach and the climate data sets derived from it will be made publicly available.

了解过去的气候变化和预测未来的气候变化需要长期记录观测到的气候场。然而，过去的观测结果通常是嘈杂的，在时间和空间上采样不规律，具有很大的空间差距。改进对分析领域中不确定性的表示将在气候学研究中非常有用。为了帮助满足这一需求，该项目将继续研究一种系统和可靠的方法，从令人惊讶的气候数据中产生分析场。要采取的方法是尝试融合两种计算效率高的方法，以解决不同尺度上的可变性问题。这两种方法中的第一种是在气候研究中开发的，并使用系统误差协方差矩阵的低阶近似。该方法主要重建气候场的大尺度特征，因此忽略了大部分小尺度变异性。第二种方法源于地质统计学中的贝叶斯估计，它结合了全局估计协方差的强度和非平稳局部估计的强度；它不仅表示由于观测噪声和采样缺陷而产生的不确定性，而且还表示由于未知协方差关系而产生的不确定性。这些方法是独立开发的，它们在实际气候学应用中的耦合或在由系综进行的历史分析中表示不确定性的工作尚未完成。为了使这一办法适用于现实的气候问题，需要拟议的理论研究，如果成功，该项目的结果将开发出一种适用于非常大的、不同的、不规则间隔的气候数据集的有效数值算法，即a)重建不同尺度上的可变性，b)在规则的时空网格上产生平均分析场，以及c)产生一种方法来产生一组同等可能的分析场(集合)，以反映平均分析场中的不确定性。然后，该方法将被应用于对当前气候学研究至关重要的海洋和陆地气候的数据集。这些结果还将为气候模拟组合创造初始条件提供更好的技术，允许更好地估计气候变化预测中的不确定性。实施新方法的软件和由此得出的气候数据集将公开提供。