In this work, we analyze 126 publicly available IAM climate scenarios modeled by six leading teams in climate science. We define a simple numerical metric that measures the decarbonization speed implied by each IAM scenario. With this metric, the narrative based, high-dimensional time series scenario datasets can be ranked and compared in a transparent way. We find that the ranking of IAM scenarios according to the decarbonization speed is consistent with their representative concentration pathway assumptions, showing that the decarbonization metric is a useful summary of a scenario's mitigation policy. We further construct an empirical distribution and a fitted parametric distribution of the decarbonization speed estimates. Key statistics such as mean, median and their confidence intervals by the bootstrap resample technique are also reported.

翻译：本文分析了由气候科学领域六个顶级团队建立的126个公开可用的IAM气候情景。我们定义了一个简单数值指标，用以衡量每个IAM情景所隐含的脱碳速度。借助该指标，基于叙事的高维时间序列情景数据集能够以透明方式实现排序与比较。研究发现，按脱碳速度对IAM情景进行的排序与其代表性浓度路径假设具有一致性，表明脱碳指标可有效概括情景的减缓政策。我们进一步构建了脱碳速度估计值的经验分布及拟合参数分布，并通过自助重采样技术报告了均值、中位数等关键统计量及其置信区间。