Constraining marine boundary layer cloud properties in climate models: (CLOSURE)

限制气候模型中的海洋边界层云特性：（关闭）

• 批准号: NE/W001713/1
• 负责人: Daniel Partridge
• 金额: $ 82.8万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FW001713%2F1
• 关键词: Constraining; marine; boundary; layer; cloud

Concentrations of both greenhouse gases (GHG) and aerosols (tiny particles suspended in the atmosphere) have increased considerably since pre-industrial time. Whilst anthropogenic emissions of GHG warm the planet, aerosol emissions exert a significant, yet poorly quantified cooling that acts to offset a significant fraction of global warming from GHG. Despite decades of research, the Intergovernmental Panel on Climate Change Assessment Report continues to highlight the climate sensitivity and aerosol-cloud-interactions (ACI) as the two key uncertainties limiting our understanding of climate change. Improving model estimates of climate change sensitivity (global temperature change per unit climate forcing) to greenhouse gas emissions is primarily driven by inter-model differences how climate models represent the impacts of feedbacks between low-level clouds and the climate system as temperature increases. Reducing these inter-model differences is severely hampered by the accuracy by which low level marine boundary layer (MBL) clouds, key modulators of the net radiation budget, are represented in the Earth System Models (ESMs) we use to provide estimates of future climate scenarios. Due to computational limitations these ESMs cannot explicitly represent small-scale atmospheric processes key for the formation of MBL at the scale at which they occur in nature (down to the size of aerosols). Instead, atmospheric physical processes related to cloud formation have to be parameterised (a simplified form of the complex process). Creating simplified representations of complex cloud processes that occur over a wide range of temporal/spatial scales is a challenging undertaking for climate scientists. Uncertainties in these parameterisations propagates through to our ability to accurately represent MBL in ESMs. The focus of this project will be to improve understanding of small-scale MBL processes by addressing current deficiencies in ESM parameterisations of cloud droplet formation, the direct microphysical link between aerosols and clouds. This will be achieved by using new modelling frameworks to capitalise on detailed flight measurements of MBL clouds from the NASA Earth Venture Suborbital mission called ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment). ACTIVATE represents a novel measurement campaign of unprecedented scope for understanding MBL clouds as it will involve the deployment of two aircraft with well-matched groundspeeds. This strategy will allow for co-location of radiative properties of clouds from an aircraft flying above the MBL with an aircraft performing in-situ aerosol and cloud measurements within the MBL. This will provide a unique dataset with which we can constrain both process-scale cloud models, and large-scale ESMs to improve current small-scale ACI parameterisations, and subsequently the accuracy by which MBL clouds are represented in ESMs. To reach these goals the CLOSURE will use a new modelling framework in which a computationally fast cloud model known as a cloud parcel model (CPM). has been embedded in an ESM for the first time. These types of cloud models can accurately simulate the growth of a population of aerosol particles into cloud droplets in an ascending parcel of air. This embedded CPM framework will crucially allow for a detailed investigation of ACI in ESMs against measurements from ACTIVATE by providing additional model information for evaluation, e.g. droplet spectra. Furthermore, it will provide an efficient and seamless integration of process knowledge gained at the process scale from offline simulation to the large-scale when embedded in the ESM. This will be used to provide better understanding on the role of key small-scale processes involved in ACI for the representation of MBL clouds. The resulting improved theoretical descriptions of MBL cloud processes will reduce current uncertainties in future climate scenarios estimates.

自工业化前时期以来，温室气体 (GHG) 和气溶胶（悬浮在大气中的微小颗粒）的浓度已大幅增加。虽然人为排放的温室气体使地球变暖，但气溶胶排放却产生了显着但难以量化的降温作用，可以抵消温室气体造成的全球变暖的很大一部分。尽管经过数十年的研究，政府间气候变化专门委员会评估报告仍然强调气候敏感性和气溶胶-云相互作用（ACI）是限制我们对气候变化理解的两个关键不确定因素。改善气候变化对温室气体排放的敏感性（每单位气候强迫的全球温度变化）的模型估计主要是由模型间差异驱动的，即气候模型如何表示随着温度升高而低层云和气候系统之间的反馈的影响。减少这些模型之间的差异受到低层海洋边界层（MBL）云（净辐射预算的关键调节器）的准确性的严重阻碍，我们用地球系统模型（ESM）来提供未来气候的估计场景。由于计算限制，这些 ESM 无法明确表示小尺度大气过程，而小尺度大气过程对于在自然界中发生的 MBL 形成至关重要（小至气溶胶的大小）。相反，与云形成相关的大气物理过程必须参数化（复杂过程的简化形式）。对于气候科学家来说，创建在广泛的时间/空间尺度上发生的复杂云过程的简化表示是一项具有挑战性的任务。这些参数化的不确定性会影响我们在 ESM 中准确表示 MBL 的能力。该项目的重点是通过解决云滴形成（气溶胶和云之间的直接微物理联系）ESM 参数化的当前缺陷，提高对小规模 MBL 过程的理解。这将通过使用新的建模框架来实现，该框架利用 NASA Earth Venture 亚轨道任务的 ACTIVATE（西大西洋实验上的气溶胶云气象学相互作用）对 MBL 云的详细飞行测量结果来实现。 ACTIVATE 代表了一种新颖的测量活动，其范围前所未有，旨在了解 MBL 云，因为它将涉及部署两架具有良好匹配地速的飞机。该策略将允许在 MBL 上方飞行的飞机与在 MBL 内进行原位气溶胶和云测量的飞机共同定位云的辐射特性。这将提供一个独特的数据集，我们可以用它来约束过程规模云模型和大规模 ESM，以改进当前的小规模 ACI 参数化，以及随后在 ESM 中表示 MBL 云的准确性。为了实现这些目标，CLOSURE 将使用新的建模框架，其中计算速度快的云模型称为云地块模型 (CPM)。首次嵌入 ESM 中。这些类型的云模型可以准确地模拟上升空气团中气溶胶颗粒群成长为云滴的过程。这个嵌入式 CPM 框架将至关重要，通过提供用于评估的附加模型信息，例如，根据 ACTIVATE 的测量对 ESM 中的 ACI 进行详细调查。液滴光谱。此外，当嵌入到 ESM 中时，它将提供从离线模拟到大规模过程规模所获得的过程知识的高效、无缝集成。这将用于更好地理解 ACI 中涉及的关键小规模流程在 MBL 云表示中的作用。由此产生的改进的 MBL 云过程理论描述将减少未来气候情景估计中当前的不确定性。

项目成果

Process-evaluation of forest aerosol-cloud-climate feedback shows clear evidence from observations and large uncertainty in models.

森林气溶胶-云-气候反馈的过程评估显示了来自观测的明确证据和模型中的巨大不确定性。

• DOI: http://dx.10.1038/s41467-024-45001-y
• 发表时间: 2024
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Blichner SM

Cloud response to co-condensation of water and organic vapors over the boreal forest

北方森林上空水和有机蒸气共凝结的云响应

• DOI: 10.5194/acp-24-5117-2024
• 发表时间: 2024-04-30
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: L. Heikkinen;D. Partridge;S. Blichner;Wei Huang;Rahul Ranjan;Paul Bowen;E. Tovazzi;T. Petäjä;Claudia Mohr;I. Riipinen
• 通讯作者: I. Riipinen