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. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, 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 coexchangable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgements 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 (19-23 ka) 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.

翻译：任何气候模型实验都依赖于可能大规模的空时边界条件集。这些条件既可表示系统的初始状态，也可表示整个实验过程中驱动模型输出的强迫因子。尽管在气候模拟研究中，这些边界条件通常采用现有重建数据加以固定，但它们具有高度不确定性，且这种不确定性尚未量化，对实验输出的影响可能相当显著。我们开发了高效量化这些不确定性的方法，结合来自多模型与观测的相关数据。从协交换性模型出发，我们构建了协交换过程模型，以捕捉多个变量间相关的空时场。研究表明，对该表征中的参数施加进一步的交换性判断，可得到分层模型的贝叶斯线性类比。我们利用该框架提供了末次盛冰期（19-23 ka）海面温度与海冰浓度边界条件的联合重建，并以此驱动使用FAMOUS-Ice海气耦合冰盖模型进行的冰盖模拟集合实验。结果表明，现有实验中惯用的边界条件在我们所考量的不确定性下不可行，并证明了采用更合理的边界条件对冰盖模拟的影响。