Bayesian analysis of Earth's climate sensitivity: past, present and future

在预测未来全球变暖的幅度时，对于给定的二氧化碳排放模式，不确定性的最大原因在于地球对气候的敏感性(大气二氧化碳持续增加一倍后，地表平均温度上升)。目前使用许多不同的证据来评估地球的气候敏感性(Knutti等人，2017年)，包括古气候档案；复杂的气候模型；工业革命以来地球能源失衡和变暖的观测(例如，见Goodwin，2018年)；以及观测到的对火山活动的气候反应。然而，这些不同的证据产生了对气候敏感性的不同估计，导致了未来变暖的巨大不确定性。气候敏感度估计的不一致可能是因为气候敏感度在不同的响应时间尺度上演变(Goodwin，2018)，例如，由于海洋的大热容量推迟了对温室气体浓度变化的响应。也会出现分歧，因为气候敏感性可能取决于气候系统的背景状态(因此，气候变暖加剧本身可能会增加气候敏感性，并导致额外的变暖)。在这项研究中，学生将应用统计技术，从多条证据中得出地球气候敏感性的概率评估。这将包括但不限于以下证据：当代观测、历史重建、过去气候变化的地质档案和复杂的气候模型模拟。这项研究将使用一系列观测和模型数据来限制不同时间尺度上的气候敏感性，并评估气候敏感性对背景气候状态的可能依赖性。将使用贝叶斯统计方法从多个独立的证据线建立气候敏感性的概率估计。计算效率高的地球系统模型(Goodwin，2018)将扩展到既包括气候敏感性的背景状态相关性，也包括在世纪或更长时间尺度上改变气候敏感性的长时间尺度反馈。这一地球系统模型将用于生成具有反映特定证据线(例如古档案)的先前气候敏感性特征的大型整体模拟。然后，将对照其他证据线的限制对这些大型先前的总体进行评估。这将在古德温等人的一项试点研究的基础上，产生具有气候敏感性特征的后验气候模型集合，反映了多种证据。(2018)和古德温(2018)。

The biggest cause of uncertainty in predicting the magnitude of future global warming, for a given pattern of CO2 emissions, lies in Earth's 'climate sensitivity' (the increase in average surface temperature following a sustained doubling of atmospheric carbon dioxide). Earth's climate sensitivity is currently evaluated using many separate lines of evidence (Knutti et al., 2017), including palaeo-climate archives; complex climate models; observations of Earth's energy imbalance and warming since the industrial revolution (e.g. see Goodwin, 2018); and from the observed climate responses to volcanic activity. However, these different lines of evidence produce different estimates of climate sensitivity leading to large uncertainty in future warming. The disagreement in climate sensitivity estimates may arise because climate sensitivity evolves over different response timescales (Goodwin, 2018), for instance due to the ocean's large heat capacity delaying the response to a change in greenhouse gas concentrations. Disagreements also arise because climate sensitivity may be dependent on the background state of the climate system (such that increased warming may itself increase the climate sensitivity and lead to additional warming). For this study, the student will apply statistical techniques to produce a probabilistic assessment of Earth's climate sensitivity from multiple lines of evidence. This will include, but not be limited to evidence from: contemporary observations, historical reconstructions, geological archives of past climate change, and complex climate model simulations.This study will use a range of observational and model data to constrain climate sensitivity over different timescales, and assess possible dependency of climate sensitivity on background climate state.Bayesian statistical approaches will be employed to build a probabilistic estimate of climate sensitivity from multiple independent lines of evidence. A computationally efficient Earth System Model (Goodwin, 2018) will be extended to include both a background state-dependence of climate sensitivity, and long-timescale feedbacks that alter climate sensitivity over century timescales and longer. This Earth System Model will be used to generate large ensemble simulations with prior climate sensitivity characteristics reflecting particular lines of evidence (for example palaeo-archives). These large prior ensembles will then be assessed against constraints from other lines of evidence. This will generate posterior climate model ensembles with climate sensitivity characteristics reflecting multiple lines of evidence, building on a pilot studies by Goodwin et al. (2018) and Goodwin (2018).