Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets
Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets
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DOI：
10.5194/esd-12-709-2021
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发表时间：
2021-06-17
影响因子：
7.3
通讯作者：
Cael, B. B.
中科院分区：
文献类型：
--
作者：
Goodwin, Philip;Cael, B. B.
未来的气候变化预测、影响和缓解目标直接受到地球全球平均表面温度对人为强迫的敏感程度的影响，通过气候敏感性（S）和瞬态气候响应（TCR）来表示。然而，S 和 TCR 的约束很差，部分原因是历史观测和未来气候预测考虑了不同响应时间尺度下的气候系统，可能具有不同的气候反馈强度。在这里，我们通过使用自 19 世纪中叶以来的地表变暖历史观测值和自 20 世纪中叶以来的海洋吸热历史观测值来评估 S 和 TCR，以约束具有在多个响应时间尺度上起作用的独立气候反馈分量的模型。采用贝叶斯方法，我们的先验使用瞬时普朗克反馈的约束分布，结合快速反馈（作用几天）和数十年反馈的强度的广泛均匀分布。我们通过应用从观测数据集的不同组合导出的似然函数来提取后验分布。当使用历史数据集的两个首选组合时，所得的 TCR 分布均发现 TCR 为 1.5（5-95% 范围内的 1.3 至 1.8）摄氏度。我们发现我们首选数据集组合的 S 后验概率分布从 20 年响应时间尺度上的 2.0（1.6 至 2.5）摄氏度的 S 演变为在 20 年响应时间尺度上的 2.3（1.4 至 6.4）摄氏度的 S。由于数十年反馈的影响，响应时间尺度为 140 年。我们的结果表明，多年代际反馈如何使 S 的上限显着高于历史观测值的上限。
Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5-95 % range) degrees C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) degrees C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) degrees C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.