Coexchangeable Process Modeling for Uncertainty Quantification in Joint Climate Reconstruction

Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. These boundary conditions are typically fixed using available reconstructions in climate modeling studies; however, in reality they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangeable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgments over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration boundary conditions at the last glacial maximum (23–19 kya) and use it to force an ensemble of ice-sheet simulations using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate that existing boundary conditions typically used in these experiments are implausible given our uncertainties and demonstrate the impact of using more plausible boundary conditions on ice-sheet simulation. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

任何气候模型实验均依赖于规模可能较大的时空边界条件集。此类边界条件既可表征系统的初始状态，也可代表驱动整个实验期间模型输出的强迫项。气候建模研究中，此类边界条件通常借助已有重建数据予以固定；但实际上它们存在高度不确定性，且该不确定性尚未被量化，其对实验输出的影响可能相当显著。我们提出了一种高效的不确定性量化方法，该方法整合了多模型与观测的相关数据。我们从协同可交换性模型（coexchangeability model）出发，构建了协同可交换过程模型，以捕捉变量间多组相关的时空场。我们证明，对该表征框架内的参数施加进一步的可交换性约束，可得到层级模型的贝叶斯线性类比形式。我们利用该框架对末次冰盛期（23~19千年前）的海表温度与海冰浓度边界条件开展联合重建，并借助FAMOUS-Ice耦合大气-冰盖模型，将其作为强迫场驱动冰盖模拟集合实验。我们证明，结合我们的不确定性分析结果，当前此类实验中常用的边界条件并不合理；同时还展示了采用更合理的边界条件对冰盖模拟结果的影响。本文的补充材料可在线获取，其中包含可用于复现研究工作的标准化材料说明。