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

摘要：0415738和0415251 CMG合作研究：未来气候模型预测中参数不确定性的随机表示智能优点：这项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组合影响产生的气候模型预测不确定性，并解释气候模型在响应大气二氧化碳浓度预计增加方面存在的巨大差异。量化参数不确定性的初步尝试将使用基于多个非常快速模拟退火法的贝叶斯随机反演法，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抽样技术。同时，将从蒙特卡洛马尔科夫链(MCMC)的角度审查随机抽样战略，以推动该理论适用于目前因计算成本(例如气候模型)或数据集的大小(例如地球物理数据的解释)而可能可行地考虑的样本数量有限的问题。具体目标包括：1)估计最新版本CAM2(NCAR开发的气候模式)内与云、对流和辐射有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内的采样偏差，并从MCMC的角度建立新的高效和稳健的统计推断方法；3)计算代表这些参数组合不确定性的耦合大气-混合层海洋模式的二氧化碳倍增试验集合；以及4)评估解释模型对二氧化碳强迫变化响应的差异的气候过程。更广泛的影响：量化气候模型不确定性的主要来源是减少不同模型之间因相同投入而产生的差异的第一步。仪器记录和古气候档案中有一些潜在有用的数据尚未得到充分利用。拟议的研究为利用这些数据来源实现这些目标提供了方法论基础。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体系结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。拟议的研究活动还将有助于培训和支助两名研究生。

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.