Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or “deep” uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min−max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost−benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

全球气候系统的数值模拟为综合评估建模提供输入，以估计温室气体缓解和其他应对全球气候变化的政策的影响。虽然为此目的必不可少的工具，但计算气候模型存在相当大的不确定性，包括模型间“结构”不确定性。结构不确定性分析强调对多模型集合的输出进行简单或加权平均，有时对模型之间的概率进行主观贝叶斯分配。然而，选择合适的权重是有问题的。为了在综合评估中使用气候模拟，我们建议将气候模型的不确定性构建为部分识别或“深度”不确定性的问题。该术语指的是潜在机制的情况，动力学或控制系统的规律并不完全清楚，即使在统计意义上没有数据限制的情况下，也无法可靠地明确建模。我们提出了最小-最大遗憾 (MMR) 决策标准，以在没有加权气候模型预测的情况下解释综合评估中的深层气候不确定性。我们开发了一个基于 MMR 的气候政策成本效益分析的理论框架，并通过简单的综合评估模型在计算上应用它。我们建议进一步研究的途径。我们提出了最小-最大遗憾 (MMR) 决策标准，以在没有加权气候模型预测的情况下解释综合评估中的深层气候不确定性。我们开发了一个基于 MMR 的气候政策成本效益分析的理论框架，并通过简单的综合评估模型在计算上应用它。我们建议进一步研究的途径。我们提出了最小-最大遗憾 (MMR) 决策标准，以在没有加权气候模型预测的情况下解释综合评估中的深层气候不确定性。我们开发了一个基于 MMR 的气候政策成本效益分析的理论框架，并通过简单的综合评估模型在计算上应用它。我们建议进一步研究的途径。