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

摘要：合作研究：未来气候模式预测中参数不确定性的随机表示知识价值：本研究将开发新的有效方法来估计由多个非线性相关参数的综合影响引起的气候模式预测不确定性，并解释气候模式对预估大气CO2浓度增加的响应存在的巨大差异。量化参数不确定性的初始尝试将使用基于多次快速模拟退火（Multiple VFSA）的贝叶斯随机反演，这是一种随机重要采样技术，已开发并成功应用于地球物理数据的解释。与此同时，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，以推进该理论对目前可能由于计算成本（例如气候模型）或数据集规模（例如地球物理数据的解释）而受到可行的样本数量限制的问题的适用性。具体目标包括：1)估算最新版本CAM2 （NCAR开发的气候模式）中与云、对流和辐射相关的一组参数的多维概率分布，这些参数是气候模式预测不确定性的重要来源；2)评估多个VFSA中的抽样偏差，并从MCMC的角度原型新的高效和健壮的统计推断方法；3)用一个耦合的大气-混合层海洋模式计算双重CO2实验集，该模式代表了这些参数的综合不确定性；4)评估解释模式对CO2强迫变化响应差异的气候过程。更广泛的影响：量化气候模式不确定性的主要来源是减少由相同输入所造成的不同模式之间差异的第一步。仪器记录和古气候档案中都有尚未充分利用的潜在有用数据。拟议的研究为使用这些数据源来实现这些目标提供了方法学基础。本文的研究结果将首次证明全面、系统地探索大气GCM参数空间的可行性。有了当前便宜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，参数选择可以更容易地针对模型性能进行优化。拟议的研究活动也将有助于培训和支持两名研究生。

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.