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  • Author or Editor: Bruce C. Macdonald x
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Armin Sorooshian, Bruce Anderson, Susanne E. Bauer, Rachel A. Braun, Brian Cairns, Ewan Crosbie, Hossein Dadashazar, Glenn Diskin, Richard Ferrare, Richard C. Flagan, Johnathan Hair, Chris Hostetler, Haflidi H. Jonsson, Mary M. Kleb, Hongyu Liu, Alexander B. MacDonald, Allison McComiskey, Richard Moore, David Painemal, Lynn M. Russell, John H. Seinfeld, Michael Shook, William L. Smith Jr, Kenneth Thornhill, George Tselioudis, Hailong Wang, Xubin Zeng, Bo Zhang, Luke Ziemba, and Paquita Zuidema

Abstract

We report on a multiyear set of airborne field campaigns (2005–16) off the California coast to examine aerosols, clouds, and meteorology, and how lessons learned tie into the upcoming NASA Earth Venture Suborbital (EVS-3) campaign: Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE; 2019–23). The largest uncertainty in estimating global anthropogenic radiative forcing is associated with the interactions of aerosol particles with clouds, which stems from the variability of cloud systems and the multiple feedbacks that affect and hamper efforts to ascribe changes in cloud properties to aerosol perturbations. While past campaigns have been limited in flight hours and the ability to fly in and around clouds, efforts sponsored by the Office of Naval Research have resulted in 113 single aircraft flights (>500 flight hours) in a fixed region with warm marine boundary layer clouds. All flights used nearly the same payload of instruments on a Twin Otter to fly below, in, and above clouds, producing an unprecedented dataset. We provide here i) an overview of statistics of aerosol, cloud, and meteorological conditions encountered in those campaigns and ii) quantification of model-relevant metrics associated with aerosol–cloud interactions leveraging the high data volume and statistics. Based on lessons learned from those flights, we describe the pragmatic innovation in sampling strategy (dual-aircraft approach with combined in situ and remote sensing) that will be used in ACTIVATE to generate a dataset that can advance scientific understanding and improve physical parameterizations for Earth system and weather forecasting models, and for assessing next-generation remote sensing retrieval algorithms.

Open access