Learning about the Tail of the Probability Density Function for Equilibrium Climate Sensitivity

了解平衡气候敏感性的概率密度函数的尾部

• 批准号: 0806155
• 负责人: Michael Schlesinger
• 金额: $ 50.92万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0806155&HistoricalAwards=false
• 关键词: Learning; about; Tail; Probability; Density

The importance of human-induced climate change depends critically on the equilibrium climate sensitivity (referred to as climate sensitivity hereafter, i.e., the change in global surface air temperature resulting from a doubling of the pre industrial atmospheric carbon dioxide concentration). The Intergovernmental Panel on Climate Change (IPCC) historically assessed the climate sensitivity at a range from 1.5 to 4.5 degrees Celsius. The PIs document that there is a significant likelihood that the climate sensitivity lies outside this range, with probability density functions (pdfs) that have a thick upper tail. The most recent IPCC report presents a number of pdfs for the sensitivity, all of which have a thick upper tail. This research focuses on the thick upper tail. Testing whether or not the real climate system is located in this thick upper tail is not just a tantalizing scientific puzzle, but can also provide large economic value, as it can inform the design of improved climate policies. A recent economic analysis shows that the thick upper tail of current climate sensitivity estimates can dominate the ranking of proposed climate-change strategies in terms of their benefit/cost ratio. The intuition behind this is that a decision maker may give considerable weight to the very costly outcomes of different climate change strategies that are associated with the cases where the thick upper tail of current climate sensitivity estimates is revealed to be the true state of the system.The PIs will use their simple climate model and updated observational data to test five hypotheses: (1) The inclusion of the additional near-surface air temperature observations since 1998 will reduce the 90% confidence interval of climate sensitivity; (2) The inclusion of non-sulfate aerosol radiative forcing will increase the 90% confidence interval of climate sensitivity, this because the resulting negative radiative forcing will be greater than that for sulfate aerosol alone; (3) The inclusion of oceanic heat-content data will reduce the 90% confidence interval of climate sensitivity; (4) The combined effect of these three datasets will be a reduction of the 90% confidence interval of climate sensitivity; (5) The uncertainty in climate sensitivity due to climatic noise can be reduced by learning about climate sensitivity overtime. In addition, the PIs will examine how climate variability and paleoclimatic information influence the estimation of the pdf of climate sensitivity. The Bayesian averaging method that they will use will allow them to learn about different radiative forcing models and incorporate their associated hindcast abilities into the estimation of the distribution of climate sensitivity. The intellectual merit of the research is that it will produce improved estimates of climate sensitivity from both the Frequency and Bayesian points of view using additional observations: updated surface air temperatures, ocean heat content, paleoclimatic data, and radiative forcing from non-sulfate aerosols. This knowledge will inform decision-makers about the magnitude and rate of reductions in the anthropogenic emission of greenhouse gases that are needed to "...prevent dangerous anthropogenic interference with the climate system", as required by Article 2 of the UN Framework Convention on Climate Change. This is also the most important broader impact of the research. The second broader impact will be the use of the results by other scientists to study the impacts and policy implications of human-induced climatic change. The third broader impact will be public outreach and communication. The PI will continue this via his lectures to the public, business leaders and policymakers.

人为引起的气候变化的重要性主要取决于平衡气候敏感性（下文称为气候敏感性，即由于工业化前大气二氧化碳浓度加倍而导致的全球地表气温变化）。政府间气候变化专门委员会（IPCC）对气候敏感性的历史评估范围为1.5至4.5摄氏度。pi文件表明，气候敏感性极有可能超出这一范围，概率密度函数（pdf）具有较厚的上尾。IPCC最近的报告提供了许多pdf格式的敏感性，所有这些都有一个厚的上尾。本研究的重点是厚上尾。测试真正的气候系统是否位于这条厚厚的上尾，不仅是一个诱人的科学难题，而且还可以提供巨大的经济价值，因为它可以为改进气候政策的设计提供信息。最近的一项经济分析表明，当前气候敏感性估计的厚上尾可以主导拟议的气候变化战略在其效益/成本比方面的排名。这背后的直觉是，决策者可能会相当重视与当前气候敏感性估计的厚上尾是系统真实状态的情况有关的不同气候变化战略的非常昂贵的结果。pi将使用他们的简单气候模式和更新的观测数据来检验五个假设：(1)纳入1998年以来额外的近地表气温观测将降低90%的气候敏感性置信区间；(2)纳入非硫酸盐气溶胶辐射强迫将增加气候敏感性的90%置信区间，这是因为由此产生的负辐射强迫将大于单独的硫酸盐气溶胶辐射强迫；(3)海洋热含量数据的加入将使气候敏感性的置信区间降低90%；(4)这三个数据集的综合效应将使气候敏感性的置信区间降低90%；(5)气候噪声对气候敏感性的不确定性可以通过对气候敏感性的持续学习来降低。此外，pi将研究气候变率和古气候信息如何影响气候敏感性pdf的估计。他们将使用的贝叶斯平均方法将使他们能够了解不同的辐射强迫模式，并将其相关的后预报能力纳入气候敏感性分布的估计中。这项研究的知识价值在于，它将利用额外的观测数据，从频率和贝叶斯的角度对气候敏感性进行改进的估计：最新的地表气温、海洋热含量、古气候数据和非硫酸盐气溶胶的辐射强迫。这方面的知识将使决策者了解人为温室气体排放减少的幅度和速度，以“……防止对气候系统的危险的人为干扰”，这是《联合国气候变化框架公约》第二条所要求的。这也是该研究最重要的广泛影响。第二个更广泛的影响将是其他科学家利用这些结果来研究人类引起的气候变化的影响和政策含义。第三个更广泛的影响将是公共宣传和沟通。PI将通过他对公众、商业领袖和政策制定者的演讲继续这一点。