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

该项目的动机是，气溶胶一直被气专委评估为工业时期气候辐射强迫的最大不确定性。这意味着，我们理解工业时期温度变化的能力受到模型中非常不受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--所谓的气溶胶间接效应，人为气溶胶通过这种间接效应使云更具反射性。在IPCC的评估中，对气溶胶间接强迫的预测范围在-0.4至-1.8Wm-2之间，远远大于与二氧化碳强迫有关的范围(1.6-1.9Wm-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效应中的不确定性。间接效应中的控制因素是大气中云凝结核(CCN)的浓度。CCN是大气中气溶胶粒子的一个子集，通常直径大于50 nm，具有足够的水溶性，可以形成云滴。直到最近，才开发出能够显式模拟CCN浓度的全球模型。这打开了通过利用多年来对CCN进行的广泛测量来减少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全面综合。总体目标是在现有观测系统和模型的基础上，通过尽可能限制现代气溶胶，减少间接影响中的不确定性。我们将通过建立一个具有明确定义的不确定性的CCN全球模型来减少不确定性，这些不确定性受到全球观测的约束。然后，我们将使用“校准的”气溶胶模型来量化间接辐射强迫及其不确定性。我们还将使用新的更好的模型来了解不同环境中CCN的来源，从而了解推动未来集中度变化的因素。作为项目的副产品，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有关全球模型的新的不确定性信息。在大多数类似的研究中，该模型只可能运行几次。然而，在现实中，由于模型中存在大量的不确定过程，模型具有很大的不确定性范围。在这个项目中，我们将使用新的信息，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”，以前从未对复杂的全球模型进行过尝试。通过这种方法，我们可以确保该模型尽可能接近目前所能实现的CCN的观测结果。

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.