Data-driven emulation of Modal Aerosol Microphysics via Neural Operator-Based Modeling

Data-driven emulation of Modal Aerosol Microphysics via Neural Operator-Based Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Data-driven emulation of Modal Aerosol Microphysics via Neural Operator-Based Modeling Zhe Bai, Damian Rouson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7139301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $95.7%$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON's efficiency and potential in robust predictive modeling for large-scale Earth system computations. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor invited by journal 21 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7139301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486286663,"identity":"1a919a9b-3880-4339-b592-7048030bf7f7","order_by":0,"name":"Zhe Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCQY2BgYDGxkDdhK1pPEYMJOmheEwCVrkZzcfe/Cj4DyPOTMD84uPbURoYZxzLN2wx+A2j2UzA5vlTGK0MEvkmEnwALUYHGZgM+Y5Q4QWNon8b5J/DM6RoIVHIodNmsfgAEgL82OeCiK0SEikmRvLGCQD/cLYxjiDGC3yM5KfPXzzx07OnL358IcPBkRoQQKMbRKkaQAC5g8kaxkFo2AUjIIRAQCblyx9ij2FPgAAAABJRU5ErkJggg==","orcid":"","institution":"Lawrence Berkeley National Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Bai","suffix":""},{"id":486286664,"identity":"1f03629e-c03d-463f-96a5-5c203f2d41a8","order_by":1,"name":"Damian Rouson","email":"","orcid":"","institution":"Lawrence Berkeley National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Damian","middleName":"","lastName":"Rouson","suffix":""}],"badges":[],"createdAt":"2025-07-16 11:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7139301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7139301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33209-x","type":"published","date":"2025-12-26T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172327,"identity":"94a467da-7df5-4ec3-8d4a-15b1d3052176","added_by":"auto","created_at":"2025-12-29 16:07:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2474807,"visible":true,"origin":"","legend":"","description":"","filename":"submit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7139301/v1_covered_a78ec682-9d11-44c7-a4de-9c45075c5713.pdf"},{"id":86932315,"identity":"57555eaa-b12d-4dc4-ad98-5feb3e3eb7eb","added_by":"auto","created_at":"2025-07-17 09:58:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14576770,"visible":true,"origin":"","legend":"","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7139301/v1/c867aa6331f487be17af9a71.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-driven emulation of Modal Aerosol Microphysics via Neural Operator-Based Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7139301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7139301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. 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