An Observationally-Based Quantification of Climate Feedbacks

地球对气候的敏感度/随着温室气体的增加而变暖的程度，可以说是气候变化预测中最重要的未知因素。模型给出了大约1.5-4.5K的范围，当二氧化碳增加一倍时，平衡全球平均气温预计会增加。最近，科学家试图使用观测和模型的组合来限制这一范围，但如果说有什么不同的话，那就是范围扩大了。主要是云反馈中的不确定性，但也有其他反馈中的不确定性，如水蒸气和冰，这是气候模型之间存在巨大差异的原因。这些气候反馈作用要么放大，要么降低气候变化机制的初始影响。水蒸气是最大的正反馈，单独行动被认为会增加大约两倍于最初温室气体扰动效果的量。主要目标：-使用观测变量评估气候系统中的四个主要反馈项。评估的反馈将是1)水汽，2)云(特别是云量、云高度和云光学厚度)，3)流失率和4)地表反照率。将结合来自许多来源的各种全球范围的观测，这些观测将被纳入离线辐射传输计算，以衡量这些反馈在改变全球能量平衡方面的作用。不确定性评估：哈德利中心的项目合作伙伴将在气候模型模拟中对拟议的方法和其他更传统的气候反馈计算方法进行评估。这些反馈计算及其模型输出将直接惠及中心，因为到目前为止，中心尚未在其模型中计算这些反馈条件。这些模型和数据比较将用于：测试和评估拟议方法中使用的假设，并量化每个反馈项的现实不确定性。-由NASA戈达德空间研究所(GISS)的项目合作伙伴进行的平行能源预算计算也将用于评估我们分析的不确定性估计。次要目标：--该项目的第二个目标采用了与主要目标类似的方法，即在较短的时间尺度和区域范围内分析反馈，还将分析不同制度的反馈。这项工作将用于设计气候模型中反馈机制的诊断测试。在这里，我们将利用哈德利中心已经进行的反馈的制度分析。-研究的第三个目标是测试气候反馈的线性模型：在这里，我们将使用两种不同的方法来评估这些反馈项的线性和非线性部分，测试非线性假设。其他成果：-我们将制作大气层顶部通量的合成数据集，我们将向更广泛的社区提供，供他们自己的模型评估练习。总而言之，该项目将试图量化我们对全球气候变化预测中一些最大的“未知数”。它还将开发用于气候模型反馈分析的诊断测试。总体而言，这将带来更好和更可信的气候模型预测，这不仅对气候模型界大有裨益，也将使需要依赖此类气候模型预测准确性的政策制定者受益。

The Earth's climate sensitivity / how much it warms as greenhouses gases increase, is arguably the most important 'unknown' in predictions of climate change. Models give a range of approximately 1.5 - 4.5 K for the increase in equilibrium global mean temperature expected when carbon dioxide is doubled. Recently scientists have attempted to use combinations of observations and models to constrain this range / but if anything the range has increased. Uncertainties, mainly in the cloud feedback but also in other feedbacks such as water vapour and ice, account for these large differences between the climate models. These climate feedbacks act to either amplify or reduce the initial effects of the climate change mechanism. Water vapour is the largest positive feedback and acting alone is believed to increase by an amount which roughly doubles the effectiveness of the initial greenhouse gas perturbation. Prime objective: - To evaluate the four main feedback terms in the climate system using observed varaibles. The feedbacks evaluated will be 1) water vapour, 2) clouds (specifically cloud amount, cloud height and cloud optical depth), 3) lapse-rate and 4) surface albedo. A variety of global-scale observations will be combined from many sources and these will be incorporated into offline radiative transfer calculations to gauge the role of these feedbacks in modifying the global energy balance. Uncertainty assessment: - Both the proposed methodology and other more conventional methodologies of calculating climate feedbacks will be assessed in climate model simulations from project partners at the Hadley Centre. These feedback calculations with their model output will be of direct benefit to the Centre who to date have not calculated these feedback terms within their model. These model and data comparisons will be used to: test and assess assumptions used in the proposed methodology, and to quantify realistic uncertainties for each of the feedback terms. - A parallel energy budget calculation by project partners at the NASA Goddard Institute for Space Studies (GISS) will also be used to gauge uncertainty estimates from our analyses. Secondary objectives: - The second aim of the project employs similar methodologies to those of the prime aim to analyse feedbacks on both shorter timescales and on regional scales, and will also analyse feedbacks for different regimes. This work will be used to design diagnostic tests of feedback mechanisms in climate models. Here we will make use of the regime analysis of feedbacks already undertaken by the Hadley Centre. - The third aim of the study is to test the linear model of climate feedbacks: here we will use two different methodologies to evaluate the linear and non linear components of these feeback terms, testing assumptions of non-linearity. Additional output: - We will produce a synthetic dataset of the top-of-atmosphere fluxes, which we will make available to the wider community for their own model evaluation exercises. In summary the project will attempt to quantify some of the largest 'unknowns' in our predictions of global climate change. It will also develop diagnostic tests for feedback analysis in climate models. Overall it will lead to better and more trustworthy climate model predictions, which would not only be of great benefit to the climate modelling community, it would also benefit policy makers who need to rely on the accuracy of such climate model predictions.