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

全球气溶胶合成与科学项目（GASSP）旨在减少气溶胶辐射强迫的不确定性

• 批准号: NE/J023515/1
• 负责人: Hugh Coe
• 金额: $ 20.45万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ023515%2F1
• 关键词: Global; Aerosol; Synthesis; Science; Project

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.

该项目的动机是，IPCC持续评估了气溶胶，这是工业时期辐射强迫辐射强迫的最大不确定性。这意味着我们在工业期间了解温度变化的能力受到模型中的气溶胶过程的限制受到阻碍。主要不确定性是由于气溶胶对云的影响 - 人为气溶胶使云更具反射性的所谓气溶胶间接效应。在IPCC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，远大于与CO2强迫相关的范围（1.6-1.9 WM-2）。因此，为了提高我们对气候变化的理解，我们需要减少气溶胶间接效应的不确定性。间接效应的控制因子是“云凝结核”（CCN）大气中的浓度。 CCN是大气中气溶胶颗粒的子集，通常大于50 nm的直径，并且足够可溶的水以形成云滴。直到最近，才开发出能够明确模拟CCN浓度的全球模型。通过利用多年来对CCN的广泛测量，这为减少模型不确定性的可能性打开了可能性。我们建议在英国气溶胶 - 化学气候模型中进行全球CCN及相关气溶胶观测的首次全面合成。总体目的是通过根据当前观察系统和模型来限制现代气溶胶，以减少间接效应的不确定性。我们将通过产生具有明确定义的不确定性的全球CCN模型来减少不确定性，这些模型受到全球观察的限制。然后，我们将使用“校准”气溶胶模型来量化间接辐射强迫及其不确定性。我们还将使用新的和更好的模型来了解不同环境中CCN的来源，从而推动集中注意力的未来变化的因素。作为项目的衍生产品，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。该项目的重要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，只能运行几次模型。但是，实际上，由于模型中大量的不确定过程，该模型的不确定性范围很大。在这个项目中，我们将使用新信息，告诉我们模型在不确定性的所有可能假设下的行为。从这个模型运行的集合中，我们将能够确定世界各地的最佳模型。此过程称为“校准”，并且以前尚未尝试使用复杂的全局模型。使用这种方法，我们可以确保模型与目前可以实现的CCN的观察一样接近。

项目成果

Surprising similarities in model and observational aerosol radiative forcing estimates

模型和观测气溶胶辐射强迫估计惊人的相似

• DOI: 10.5194/acp-2019-533
• 发表时间: 2019
• 作者: Gryspeerdt E

The DACCIWA project: Dynamics-aerosol-chemistry-cloud interactions in West Africa

• DOI: 10.1175/bams-d-14-00108.1
• 发表时间: 2014-05
• 期刊: Bulletin of the American Meteorological Society
• 影响因子: 8
• 作者: P. Knippertz;H. Coe;J. C. Chiu;M. Evans;A. Fink;N. Kalthoff;C. Liousse;C. Mari;R. Allan;B. Brooks;S. Danour;C. Flamant;O. O. Jegede-O.;F. Lohou;J. Marsham

Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing

气候模型中不确定气溶胶过程和排放的稳健观测约束及其对气溶胶辐射强迫的影响

• DOI: 10.5194/acp-2019-834
• 发表时间: 2019
• 作者: Johnson J

Properties and evolution of biomass burning organic aerosol from Canadian boreal forest fires

• DOI: 10.5194/acp-15-3077-2015
• 发表时间: 2014-10
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Matthew D. Jolleys;H. Coe;G. Mcfiggans;Jonathan W. Taylor;S. O'Shea;M. Breton;S. Bauguitte;S. Molle

Submicron particle mass concentrations and sources in the Amazonian wet season (AMAZE-08)

• DOI: 10.5194/acp-15-3687-2015
• 发表时间: 2015-04
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Qi Chen;D. Farmer;L. Rizzo;T. Pauliquevis;M. Kuwata;T. Karl;A. Guenther;J. Allan;H. Coe;M. Andreae;U. Pöschl;J. Jimenez;P. Artaxo;S. Martin