Global Aerosol Synthesis and Science Project (GASSP) - to reduce the uncertainty in aerosol radiative forcing

该项目的动机是，IPCC一直将气溶胶评估为工业时期气候辐射强迫中最大的不确定性因素。这意味着我们理解工业时期温度变化的能力受到了模式中非常缺乏约束的气溶胶过程的阻碍。主要的不确定性是由于气溶胶对云的影响，即所谓的气溶胶间接效应，即人为气溶胶使云更具反射性。在IPCC的评估中，气溶胶间接强迫的预测范围在-0.4至-1.8 Wm-2之间，远远大于与二氧化碳强迫相关的预测范围（1.6-1.9 Wm-2）。因此，为了提高我们对气候变化的认识，我们需要减少气溶胶间接影响的不确定性。间接效应的控制因素是大气中“云凝结核”（CCN）的浓度。CCN是大气中气溶胶颗粒的一个子集，通常直径大于50纳米，并且足以溶于水形成云滴。直到最近，才开发出能够明确模拟CCN浓度的全球模式。这开辟了通过利用多年来对CCN进行的广泛测量来降低模型不确定性的可能性。我们建议在英国气溶胶-化学-气候模式中进行全球CCN和相关气溶胶观测的首次综合。总体目标是在现有观测系统和模式的基础上，通过尽可能约束现代气溶胶来减少间接影响的不确定性。我们将通过制作一个全球CCN模式来减少不确定性，该模式具有明确定义的不确定性，受全球观测的限制。然后，我们将使用“校准的”气溶胶模式来量化间接辐射强迫及其不确定性。我们还将使用新的和更好的模型来了解不同环境中CCN的来源，从而了解驱动未来浓度变化的因素。作为该项目的副产品，我们还将能够使用模型和数据来确定新的测量将对进一步减少不确定性产生最大影响的区域或环境。该项目的一个重要的新方面将是使用关于全球模式的新的不确定性信息。在大多数类似的研究中，模型只能运行几次。然而，在现实中，由于模型中存在大量的不确定过程，模型的不确定范围很广。在这个项目中，我们将使用新的信息来告诉我们模型在所有可能的不确定性假设下的行为。从这些模型运行的集合中，我们将能够确定世界各地可能的最佳模型。这一过程被称为“校准”，在此之前还没有对复杂的全局模型进行过尝试。通过这种方法，我们可以确定该模型与目前所能实现的CCN观测结果尽可能接近。

The motivation for this project is that aerosols have persistently been assessed by the IPCC as the largest uncertainty in the radiative forcing of climate over the industrial period. This means that our ability to understand temperature changes over the industrial period is hampered by very poorly constrained aerosol processes in models. The main uncertainty is due to the effect that aerosols have on clouds - the so-called aerosol indirect effect by which anthropogenic aerosols make clouds more reflective. In the IPCC assessment, the range of predictions of the aerosol indirect forcing lies between -0.4 to -1.8 Wm-2, a far larger range than associated with CO2 forcing (1.6-1.9 Wm-2). Thus, to improve our understanding of climate change, we need to reduce the uncertainty in the aerosol indirect effect.The controlling factor in the indirect effect is the concentration in the atmosphere of "cloud condensation nuclei" (CCN). CCN are a subset of the aerosol particles in the atmosphere, typically larger than 50 nm diameter and sufficiently water soluble to form cloud drops. Only recently, global models have been developed that are able to explicitly simulate CCN concentrations. This opens up the possibility of reducing model uncertainty by exploiting extensive measurements of CCN that have been made over many years. We propose to undertake the first ever comprehensive synthesis of global CCN and related aerosol observations within the UK aerosol-chemistry-climate model. The overall aim is to reduce uncertainty in the indirect effect by constraining modern aerosol as much as possible based on present observing systems and models. We will reduce the uncertainty by producing a global model of CCN with well defined uncertainties that are constrained by worldwide observations. We will then use the "calibrated" aerosol model to quantify the indirect radiative forcing and its uncertainty. We will also use the new and better model to understand the sources of CCN in different environments, and thereby the factors that will drive future changes in the concentration. As a spin-off of the project we will also be able to use the model and data to identify the regions or environments in which new measurements would have the greatest impact on reducing the uncertainty further.An important new aspect of the project will be the use of new uncertainty information about the global model. In most similar studies it has been possible to run the model only a few times. However, in reality the model has a wide uncertainty range due to the very large number of uncertain processes in the model. In this project we will use new information that tells us how the model behaves under all possible assumptions of uncertainty. From this collection of model runs we will be able to identify the best possible model in all parts of the world. This procedure is known as "calibration", and it has not been attempted before for a complex global model. With this approach we can be sure the model is as close to observations of CCN as can presently be achieved.