Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Shen, Wenxiang; Wang, Minghuai; Riemer, Nicole; Zheng, Zhonghua; Liu, Yawen; Dong, Xinyi

doi:10.1029/2023MS003889

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Journal Article · Tue Jan 23 00:00:00 EST 2024 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1029/2023MS003889· OSTI ID:2282676

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School of Atmospheric Sciences Nanjing University Nanjing China, Joint International Research Laboratory of Atmospheric and Earth System Sciences and Institute for Climate and Global Change Research Nanjing University Nanjing China
Department of Atmospheric Sciences University of Illinois Urbana‐Champaign Urbana IL USA
Department of Earth and Environmental Sciences The University of Manchester Manchester UK
School of Atmospheric Sciences Nanjing University Nanjing China, Joint International Research Laboratory of Atmospheric and Earth System Sciences and Institute for Climate and Global Change Research Nanjing University Nanjing China, Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China

Abstract

Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC ( R _BC , the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use R _BC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust R _BC to make the modeled BC mixing state index ( χ _mode ) match the one predicted by the ML model ( χ _ML ). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χ _mode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χ _ML (66%). The R _BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

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Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0022130

OSTI ID:: 2282676

Alternate ID(s):: OSTI ID: 2311025
OSTI ID: 2577981

Journal Information:: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 1 Vol. 16; ISSN 1942-2466

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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