Pristine oceans control the uncertainty in aerosol–cloud interactions

IF 5.2 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Atmospheric Chemistry and Physics Pub Date : 2024-07-01 DOI:10.5194/egusphere-2024-1863
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, Matthias Tesche
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Abstract

Abstract. Quantifying global cloud condensation nuclei (CCN) concentrations is crucial for reducing uncertainties in radiative forcing resulting from aerosol-cloud interactions. This study analyzes two novel, independent, open-source global CCN datasets derived from spaceborne Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis and examines the spatio-temporal variability of CCN concentrations pertinent to liquid clouds. The results reveal consistent large-scale patterns in both CALIOP and CAMS datasets, although CALIOP values are approximately 79 % higher than those from CAMS. Comparisons with existing literature demonstrate that these datasets effectively bound the regionally observed CCN concentrations, with CALIOP typically representing the upper bound and CAMS the lower bound. Monthly and annual variations in CCN concentrations obtained from the two datasets largely agree over the Northern Hemisphere and align with previously reported variations. However, inconsistencies emerge over pristine oceans, particularly in the Southern Hemisphere, where the datasets show not only opposing seasonal changes but also contrasting annual trends. A closure study of trends in CCN and cloud droplet concentrations suggests that dust-influenced and pristine-maritime environments primarily limit our current understanding of CCN-cloud-droplet relationships. Long-term CCN observations in these regions are crucial for improving global datasets and advancing our understanding of aerosol-cloud interactions.
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原始海洋控制着气溶胶-云相互作用的不确定性
摘要。量化全球云凝结核(CCN)浓度对于减少气溶胶-云相互作用产生的辐射强迫的不确定性至关重要。本研究分析了两个新颖、独立、开源的全球 CCN 数据集,这两个数据集分别来自带有正交偏振的空间云气溶胶激光雷达(CALIOP)测量数据和哥白尼大气监测服务(CAMS)再分析数据,并研究了与液态云相关的 CCN 浓度的时空变异性。结果显示,CALIOP 和 CAMS 数据集的大尺度模式一致,但 CALIOP 的数值比 CAMS 的数值高出约 79%。与现有文献的比较表明,这些数据集有效地约束了区域观测到的 CCN 浓度,其中 CALIOP 通常代表上限,而 CAMS 代表下限。从这两个数据集获得的北半球 CCN 浓度的月度和年度变化基本一致,并与之前报告的变化相吻合。但是,在原始海洋,特别是南半球,出现了不一致的情况,在南半球,这两个数据集不仅显示了相反的季节变化,还显示了截然不同的年度趋势。对 CCN 和云滴浓度趋势的闭合研究表明,受尘埃影响和多棱柱海洋环境主要限制了我们目前对 CCN 与云滴关系的了解。在这些地区进行长期的 CCN 观测对于改进全球数据集和促进我们对气溶胶-云相互作用的理解至关重要。
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来源期刊
Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics 地学-气象与大气科学
CiteScore
10.70
自引率
20.60%
发文量
702
审稿时长
6 months
期刊介绍: Atmospheric Chemistry and Physics (ACP) is a not-for-profit international scientific journal dedicated to the publication and public discussion of high-quality studies investigating the Earth''s atmosphere and the underlying chemical and physical processes. It covers the altitude range from the land and ocean surface up to the turbopause, including the troposphere, stratosphere, and mesosphere. The main subject areas comprise atmospheric modelling, field measurements, remote sensing, and laboratory studies of gases, aerosols, clouds and precipitation, isotopes, radiation, dynamics, biosphere interactions, and hydrosphere interactions. The journal scope is focused on studies with general implications for atmospheric science rather than investigations that are primarily of local or technical interest.
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