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: 0417909
• 负责人: Alexey Kaplan
• 金额: --
• 依托单位: Columbia University
• 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=0417909&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.

了解过去的气候变率和预测未来的气候变化需要长期的观测气候场记录。然而，过去的观测通常是嘈杂的，在时间和空间上不规则地采样，具有很大的空间差距。在分析领域的不确定性的改进表示将是非常有用的气候研究。为了帮助满足这一需求，该项目将继续研究一种系统和强大的方法，从有缺口的气候数据中产生一个分析字段。要采取的方法是尝试融合两种计算效率高的方法，解决不同尺度的变化。两种方法中的第一种是在气候研究中开发的，它使用系统误差协方差矩阵的低秩近似。该方法主要重建气候场的大尺度特征，因此错过了大部分小尺度变率。第二种方法源于地质统计学中的贝叶斯估计，它将全局估计协方差的强度与非平稳局部估计相结合;它不仅表示由于观测噪声和采样缺陷引起的不确定性，而且还表示由于未知协方差关系引起的不确定性。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集合历史分析中代表不确定性还有待完成。如果成功的话，该项目的结果将是开发一种有效的数值算法，适用于非常大的、不同的、不规则间隔的气候数据集，a）在不同尺度上重建变率，B）在规则的时空网格上产生平均分析场，c）产生产生反映平均分析场中的不确定性的等可能分析场的集合（系综）的方法。然后，该方法将被应用到海洋和陆地气候的数据集，对当前的气候研究很重要。研究结果还将提供一种更好的技术，为气候模拟集合创造初始条件，从而能够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产生的气候数据集将向公众提供。