Recent assessments of climate sensitivity per doubling of atmospheric CO₂ concentration have combined likelihoods derived from multiple lines of evidence. These assessments were very influential in the Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) assessment of equilibrium climate sensitivity, the likely range lower limit of which was raised to 2.5 °C (from 1.5 °C previously). This study evaluates the methodology of and results from a particularly influential assessment of climate sensitivity that combined multiple lines of evidence, Sherwood et al. (Rev Geophys 58(4):e2019RG000678, 2020). That assessment used a subjective Bayesian statistical method, with an investigator-selected prior distribution. This study estimates climate sensitivity using an Objective Bayesian method with computed, mathematical priors, since subjective Bayesian methods may produce uncertainty ranges that poorly match confidence intervals. Identical model equations and, initially, identical input values to those in Sherwood et al. are used. This study corrects Sherwood et al.'s likelihood estimation, producing estimates from three methods that agree closely with each other, but differ from those that they derived. Finally, the selection of input values is revisited, where appropriate adopting values based on more recent evidence or that otherwise appear better justified. The resulting estimates of long-term climate sensitivity are much lower and better constrained (median 2.16 °C, 17–83% range 1.75–2.7 °C, 5–95% range 1.55–3.2 °C) than in Sherwood et al. and in AR6 (central value 3 °C, very likely range 2.0–5.0 °C). This sensitivity to the assumptions employed implies that climate sensitivity remains difficult to ascertain, and that values between 1.5 °C and 2 °C are quite plausible.

最近对大气中 CO ₂浓度每增加一倍的气候敏感性的评估结合了来自多行证据的可能性。这些评估对政府间气候变化专门委员会第六次评估报告 (AR6) 对平衡气候敏感性的评估非常有影响，可能范围下限提高到 2.5 °C（之前为 1.5 °C）。Sherwood 等人，这项研究评估了一项特别有影响力的气候敏感性评估的方法和结果，该评估结合了多条证据。（地球物理学修订版 58(4):e2019RG000678, 2020）。该评估使用了主观贝叶斯统计方法，以及研究者选择的先验分布。本研究使用具有计算的数学先验的客观贝叶斯方法来估计气候敏感性，因为主观贝叶斯方法可能会产生与置信区间不匹配的不确定性范围。与 Sherwood 等人相同的模型方程和最初相同的输入值。被使用。这项研究纠正了 Sherwood 等人的似然估计，从三种方法得出的估计值彼此非常吻合，但与它们得出的估计值不同。最后，重新审视输入值的选择，在适当的情况下采用基于更新证据或以其他方式看起来更合理的值。与 Sherwood 等人相比，对长期气候敏感性的最终估计要低得多，而且受到更好的约束（中位数 2.16 °C，17-83% 范围 1.75-2.7 °C，5-95% 范围 1.55-3.2 °C）。在 AR6 中（中心值 3 °C，2 °C) 比 Sherwood 等人。在 AR6 中（中心值 3 °C，2 °C) 比 Sherwood 等人。在 AR6 中（中心值 3 °C，很可能在 2.0–5.0 °C 的范围内）。这种对所用假设的敏感性意味着气候敏感性仍然难以确定，而 1.5 °C 和 2 °C 之间的值是相当合理的。