Philip Stier
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Machine learning approaches to constrain and understand the role of clouds in climate change (ML4CLOUDS)
Open Date: 2022-05-31
Close Date: 2025-05-30
Grant: Close
Constraining aerosol-low cloud interactions with multi-target machine learning
Open Date: 2020-01-01
Close Date:
Articles (10)
Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement‐oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global‐scale satellite and surface‐based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi‐variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co‐variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface‐based) program aimed at systematically quantifying the cloud‐scale microphysics, cloud optical properties, and cloud‐related vertical velocities associated with aerosol‐cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2022
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2022
Collaborators (5)
William Jones
University of Oxford
Roy Grainger
University of Oxford
Adam Povey
University of Oxford
Daniel Grosvenor
University of Leeds
Susan C. van den Heever
Colorado State University
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