Collaborative Research: CMG: Stochastic Representation of Parameter Uncertainty within Model Predictions of Future Climate

合作研究：CMG：未来气候模型预测中参数不确定性的随机表示

• 批准号: 0415738
• 负责人: Charles Jackson
• 金额: $ 49.01万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0415738&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Stochastic; Representation

Abstract: 0415738 & 0415251CMG COLLABORATIVE RESEARCH: Stochastic Representation of Parameter Uncertainty Within Model Predictions of Future ClimateIntellectual Merit: This study will develop new efficient methods to estimate climate model prediction uncertainties stemming from the combined influence of multiple, non-linearly related parameters and explain the large disparity that exists among climate models in their response to projected increases in atmospheric CO2 concentration. Initial attempts to quantify parameter uncertainties will use Bayesian Stochastic Inversion based on Multiple Very Fast Simulated Annealing (Multiple VFSA), a stochastic importance sampling technique developed and successfully applied to the interpretation of geophysical data. In parallel, stochastic sampling strategies will be reviewed from a Monte-Carlo Markov Chain (MCMC) perspective to advance this theory's applicability to problems that are currently limited by the number of samples that may be feasible to consider either because of computational cost (e.g. climate models) or the size of data sets (e.g. interpretations of geophysical data). Specific objectives include 1) estimating a multidimensional probability distribution for a set of parameters related to clouds, convection, and radiation within the latest version of CAM2 (the climate model developed at NCAR) that are significant sources of climate model prediction uncertainty, 2) evaluate sampling biases within Multiple VFSA and prototype new efficient and robust methods of statistical inference from a MCMC perspective, 3) the calculation of an ensemble of doubled CO2 experiments with a coupled atmosphere-mixed layer ocean model that is representative of the combined uncertainty in these parameters, and 4) evaluate the climate processes that explain differences in model response to changes in CO2 forcing. Broader Impacts: Quantifying the main sources of climate model uncertainty is the first step toward reducing differences between different models forced by the same inputs. There is potentially useful data from both the instrumental record as well as paleoclimate archives that have not been fully exploited. The proposed research provides a methodological foundation for using these data sources to address these objectives. The results from the proposed research will demonstrate for the first time the feasibility of exploring atmospheric GCM parameter space in a comprehensive and systematic manner. With current inexpensive but powerful distributed computing architectures and the ability to automatically navigate parameter space, parameter choices can be more easily optimized for model performance. The proposed research activity will also contribute to the training and support of two graduate students.

翻译后摘要：0415738& 0415251 CMG合作研究：未来气候模型预测内的参数不确定性的随机表示智力优点：这项研究将开发新的有效方法来估计气候模型预测的不确定性，所产生的多个，非线性相关的参数的综合影响，并解释气候模型之间存在的巨大差异，在他们的响应大气CO2浓度的预计增加。量化参数不确定性的初步尝试将使用基于多重快速模拟退火（多重VFSA）的贝叶斯随机反演，这是一种开发并成功应用于地球物理数据解释的随机重要性采样技术。 同时，随机抽样策略将从蒙特-卡罗马尔可夫链（MCMC）的角度进行审查，以提高这一理论对目前受样本数量限制的问题的适用性，由于计算成本的原因，这些样本数量可能是可行的（例如气候模型）或数据集的大小（例如地球物理数据的解释）。 具体目标包括：1）在最新版本的CAM 2中估计与云、对流和辐射相关的一组参数的多维概率分布（NCAR开发的气候模型）是气候模型预测不确定性的重要来源，2）评估多重VFSA中的抽样偏差，并从MCMC的角度建立新的有效和稳健的统计推断方法的原型，3）用一个耦合的大气-混合层海洋模式计算一组加倍的CO2实验，该模式代表这些参数的综合不确定性; 4）评估气候过程，以解释模式对CO2强迫变化的响应差异。 更广泛的影响：量化气候模型不确定性的主要来源是减少相同输入所导致的不同模型之间差异的第一步。来自仪器记录和古气候档案的潜在有用数据尚未得到充分利用。拟议的研究提供了一个方法论基础，使用这些数据源，以解决这些目标。拟议研究的结果将首次证明以全面和系统的方式探索大气GCM参数空间的可行性。利用当前廉价但功能强大的分布式计算架构和自动导航参数空间的能力，可以更容易地针对模型性能优化参数选择。 拟议的研究活动还将有助于培训和支助两名研究生。