Climate nonlinearities: selection, uncertainty, projections, and damages
Climate nonlinearities: selection, uncertainty, projections, and damages
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DOI：
10.1088/1748-9326/ac8238
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发表时间：
2022-08-01
影响因子：
6.7
通讯作者：
Goodwin, P.
中科院分区：
文献类型：
--
作者：
Cael, B. B.;Britten, G. L.;Goodwin, P.
气候预测是不确定的;这种不确定性代价高昂，阻碍了气候政策的进展。这种不确定性主要是参数性的（我们在方程中插入什么数字？），结构（我们首先使用什么方程？），以及由于内部变化（气候系统固有的自然变化）。原则上，前者和后者的特征很简单，但对于复杂的气候模型来说可能需要大量计算。第二个是更具有挑战性的，因此往往被忽视。我们开发了一种贝叶斯方法来量化气候预测中的结构不确定性，使用气候物理学的理想化能量平衡模型表示，这些模型支持许多经济学家的综合评估模型（IAM）（因此也支持他们的政策建议）。我们定义了一个模型选择参数，它打开一套拟议的气候非线性和多年代际气候反馈。我们发现，与温度相关的气候反馈模型是最一致的全球平均地表温度观测，但温度依赖性的迹象是相反的地球系统模型建议。这种符号的差异可能是由于假设最近模式效应可以表示为温度依赖性。此外，除了最可能的模型之外，其他模型包含了大部分的后验概率，这表明结构不确定性对气候预测很重要。事实上，在使用类似于当前减排目标的共享社会经济路径的预测中，结构不确定性使温度的参数不确定性相形见绌。因此，结构的不确定性占主导地位的整体非社会经济的不确定性在经济预测的气候变化的损害，估计从一个简单的温度损害计算。这些结果表明，考虑结构的不确定性是至关重要的，特别是对IAM，并在一般的气候预测。
Climate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists' integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general.