Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining parameters in expensive climate models by comparing model outputs with observations. Using the C3.ai Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with two weeks' worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every three hours and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that, within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods.

翻译：大气气溶胶主要通过影响云形成和散射可见辐射来影响地球气候。然而，气候模拟中与气溶胶相关的物理过程具有高度不确定性。约束这些过程有助于改进基于模型的气候预测。我们提出了一种可扩展的统计框架，通过将模型输出与观测数据进行比较，对昂贵气候模型中的参数进行约束。利用C3.ai套件这一云计算平台，我们采用UKESM1气候模型的扰动参数集合，高效训练了一个替代模型。描述了一种估计数据驱动模型偏差项的方法，并应用严格界限方法以规范方式量化参数不确定性。我们使用模拟的南大西洋和中非地区两周的气溶胶光学厚度数据（每三小时从模型输出一次，并与每日两次的MODIS卫星观测在时间上进行匹配），展示了该框架的可扩展性。当使用真实卫星观测数据约束模型时，我们利用比以往研究时间分辨率更高的气候模型输出，建立了两个模型参数组合的约束条件。这一结果表明，在不完美气候模型的限制范围内，若将该框架扩展至分析更多观测数据并延长分析时段，可能实现非常强的约束效果。