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IPCC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到-1.8 Wm-2之间，远大于与CO2强迫相关的范围（1.6-1.9 Wm-2）。因此，为了提高对气候变化的认识，我们需要减少气溶胶间接效应的不确定性，而间接效应的控制因素是大气中的“云凝结核”（CCN）浓度。CCN是大气中气溶胶颗粒的一个子集，通常直径大于50 nm，并且具有足够的水溶性以形成云滴。直到最近，才开发出能够明确模拟云凝结核浓度的全球模型。这开辟了通过利用多年来对云凝结核的广泛测量来降低模型不确定性的可能性。我们建议进行有史以来第一次全面的综合全球CCN和相关的气溶胶观测在英国气溶胶化学气候模式。总体目标是根据目前的观测系统和模型，尽可能限制现代气溶胶，以减少间接影响的不确定性。我们将通过产生一个全球性的CCN模型来减少不确定性，该模型具有明确的不确定性，这些不确定性受到全球观测的约束。然后，我们将使用“校准”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，从而了解将推动浓度未来变化的因素。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测量将对进一步降低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，因此模型具有很宽的不确定性范围。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的最佳模型。这一过程被称为“校准”，以前没有人尝试过对复杂的全球模型进行校准。通过这种方法，我们可以确定该模型与目前所能实现的CCN观测结果一样接近。

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.