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Daniel J. Cziczo. Garimella, Sarvesh Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences. Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences 2017-02-22T19:02:58Z 2017-02-22T19:02:58Z 2016 2016 http://hdl.handle.net/1721.1/107087 971248952 Thesis: Ph. D. in Climate Physics and Chemistry, Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 125-136). The role anthropogenic aerosol particles play in the formation and persistence of ice clouds remains one of the most uncertain aspects of understanding past, present, and future climate. Studying how these particles influence ice cloud formation requires careful measurement of their ice nucleating ability as well as robust uncertainty quantification of experimental results. These measurements and their corresponding uncertainties form the basis for parameterizations used in climate models to probe how anthropogenic particle emissions affect climate through ice cloud formation. This type of investigation can help to inform policy decisions about controls on anthropogenic particle emissions. This study aims to clarify the human role in the climate system by 1) developing instrumentation to perform ice nucleation measurements, 2) quantifying the uncertainty associated with these measurements using machine learning algorithms, 3) incorporating measurements and uncertainty quantification in climate model simulations, and 4) using the modeled climate response to help inform policy decisions for anthropogenic particle emissions. by Sarvesh Garimella. Ph.D. in Climate Physics and Chemistry 136 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Earth, Atmospheric, and Planetary Sciences. A vertically-integrated approach to climate science : from measurements and machine learning to models and policy Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7b9b3191-c85f-4908-ac40-66413106ad16"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A vertically-integrated approach to climate science : from measurements and machine learning to models and policy</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Garimella, Sarvesh</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Earth, Atmospheric, and Planetary Sciences.</Keyword> <Abstract>The role anthropogenic aerosol particles play in the formation and persistence of ice clouds remains one of the most uncertain aspects of understanding past, present, and future climate. Studying how these particles influence ice cloud formation requires careful measurement of their ice nucleating ability as well as robust uncertainty quantification of experimental results. These measurements and their corresponding uncertainties form the basis for parameterizations used in climate models to probe how anthropogenic particle emissions affect climate through ice cloud formation. This type of investigation can help to inform policy decisions about controls on anthropogenic particle emissions. This study aims to clarify the human role in the climate system by 1) developing instrumentation to perform ice nucleation measurements, 2) quantifying the uncertainty associated with these measurements using machine learning algorithms, 3) incorporating measurements and uncertainty quantification in climate model simulations, and 4) using the modeled climate response to help inform policy decisions for anthropogenic particle emissions.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>