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一组潜在的海量时空边界条件。这些条件既可以代表系统的初始状态，也可代表贯穿整个实验驱动模型输出的强迫因素。尽管在气候建模研究中通常利用现有重建结果固定这些边界条件，但高度不确定性的存在、该不确定性缺乏量化以及其对实验输出的显著影响均不容忽视。我们开发了一种高效量化此类不确定性的方法，该方法整合了来自多模型和观测数据的相关信息。基于共可交换性模型，我们构建了共可交换过程模型来捕捉变量的多个相关时空场。研究表明，对该表示中的参数进一步施加可交换性判断，可推导出层次模型的贝叶斯线性类比。我们利用该框架对末次盛冰期（19-23 ka）的海表温度和海冰浓度边界条件进行联合重建，并以此驱动基于FAMOUS-Ice大气-冰盖耦合模型的冰盖模拟集合。结果表明，现有实验中通常使用的边界条件在我们的不确定性条件下缺乏合理性，同时验证了使用更合理边界条件对冰盖模拟的影响。