标题： 从未来观测中学习气候敏感性，快速和慢速 显示英文标题

标题： Learning Climate Sensitivity from Future Observations, Fast and Slow

摘要： 自45年前Charney报告发表以来，气候敏感性一直顽固地不确定。 未来气候预测中的两个因素可能会改变这一困境：(i) 由于CO$_2$的持续积累和气溶胶排放的减少，CO$_2$强迫与气溶胶冷却的比率增加，以及(ii) 变暖的世界，其中CO$_2$引起的变暖相对于气候变率更加明显。 在这里，我们开发了一种新的建模方法，以探索对平衡气候敏感性（ECS）和瞬态气候响应（TCR）的学习速率，并确定支撑这些学习速率的物理驱动因素。 我们的方法相较于以往的工作具有优势，因为它考虑了参数不确定性和协方差的完整范围，同时考虑到序列相关的内部气候变率。 此外，我们提供了关于气候敏感性学习速度的物理解释。 我们发现，尽管无论真实值如何，我们都能限制未来的TCR，但限制ECS更为困难，低ECS值比高ECS值更容易确定。 这种不对称性可以解释为本世纪大部分变暖归因于快速气候模式，该模式对于限制TCR比限制ECS更有用。 我们进一步表明，我们无法限制深海洋响应，这限制了我们学习高ECS值的能力。 显示英文摘要

摘要： Climate sensitivity has remained stubbornly uncertain since the Charney Report was published some 45 years ago. Two factors in future climate projections could alter this dilemma: (i) an increased ratio of CO$_2$ forcing relative to aerosol cooling, owing to both continued accumulation of CO$_2$ and declining aerosol emissions, and (ii) a warming world, whereby CO$_2$-induced warming becomes more pronounced relative to climate variability. Here, we develop a novel modeling approach to explore the rates of learning about equilibrium climate sensitivity and the transient climate response (TCR) and identify the physical drivers underpinning these learning rates. Our approach has the advantage over past work by accounting for the full spectrum of parameter uncertainties and covariances, while also taking into account serially correlated internal climate variability. Moreover, we provide a physical explanation of how quickly we may hope to learn about climate sensitivity. We find that, although we are able to constrain future TCR regardless of the true underlying value, constraining ECS is more difficult, with low values of ECS being more easily ascertained than high values. This asymmetry can be explained by most of the warming this century being attributable to the fast climate mode, which is more useful for constraining TCR than it is for ECS. We further show that our inability to constrain the deep ocean response is what limits our ability to learn high values of ECS.