发表机构
Learning the Earth with Artificial Intelligence and Physics (LEAP) National Science Foundation (NSF) Science and Technology Center, Columbia University; Department of Earth and Environmental Engineering, Columbia University; NSF National Center for Atmospheric Research; NASA Goddard Institute for Space Studies; Department of Applied Physics and Applied Mathematics, Columbia University（哥伦比亚大学学习人工智能与物理（LEAP）国家科学基金会（NSF）科学与技术中心; 哥伦比亚大学地球与环境工程系; 美国国家科学基金会国家大气研究中心; 美国宇航局戈达德空间研究所; 哥伦比亚大学应用物理与应用数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于扰动参数集合的结构误差感知参数估计方法，通过分解高维问题、构建简单仿真器并顺序排除不一致变量，实现自动校准，减少集合离散度并提升模拟与观测的匹配。该方法在CAM6的100成员集合上验证，优于默认配置，并强调可解释性对诊断结构误差的重要性。
AI 中文摘要
我们提出了一种全自动、结构误差感知、可解释的气候模型参数估计方法，该方法利用扰动参数集合（PPEs）。它基于历史匹配，并与日益常用的迭代模拟-仿真-校准方法相一致。该方法的提出源于结构误差以及仿真器和观测不确定性对气候模型参数估计工作的负面影响，以及稀疏采样PPE所带来的问题。为应对这些挑战，该方法明确构建更简单的仿真器以避免过拟合，检测结构误差，避免通过膨胀的不匹配容差来补偿结构误差，并顺序排除结构不一致的变量以进行参数估计。该方法将高维校准问题分解为相互关联的低维子问题，并整合其约束以重建全参数空间的联合可行区域。该方法应用于一个包含34个扰动参数的100成员PPE，该PPE由带有基于机器学习的暖雨微物理参数化的CAM6版本生成。通过迭代应用，该方法大幅减少了集合离散度，并改善了模拟与观测的纬向气候学之间的匹配。该方法还找到了在多个诊断指标上均方根误差优于默认CAM6配置的集合成员。对照实验表明，过度保守的仿真器不确定性可能导致对信息性观测的忽视，而在此方法背景下，对结构误差的容忍会使估计参数偏向于补偿结构误差。我们的工作还强调了可解释性在诊断结构误差和为基于PPE的校准提供参数估计信息方面的价值。
英文摘要
We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the negative impacts of structural error and emulator and observational uncertainties on climate model parameter estimation efforts, as well as the problems associated with sparsely-sampled PPEs. To address these challenges, the method explicitly builds simpler emulators that avoid overfitting, detect structural error, avoids compensating for structural error through inflated mismatch tolerances, and sequentially excludes structurally inconsistent variables for parameter estimation. The method decomposes the high-dimensional calibration problem into linked low-dimensional subproblems, and integrates their constraints to reconstruct the jointly plausible region of the full parameter space. The method is applied to a 100-member PPE with 34 perturbed parameters generated by a version of CAM6 with machine learning-based warm rain microphysics parameterization. Through iterative application, the method greatly reduces the ensemble spread and improves the matching between simulated and observed zonal climatologies. The method also finds ensemble members that outperform the default CAM6 configuration in root mean square error across multiple diagnostics. Controlled experiments demonstrate that overly-conservative emulator uncertainty could lead to neglect of informative observations, and tolerance of the structural error, in the context of this method, biases the estimated parameters toward compensating for structural error. Our work also emphasizes the value of interpretability for diagnosing structural error and informing parameter estimation in PPE-based calibration.
