Development and Evaluation of Chemistry‐Aerosol‐Climate Model CAM5‐Chem‐MAM7‐MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol

Zaveri, Rahul A.; Easter, Richard C.; Singh, Balwinder; Wang, Hailong; Lu, Zheng; Tilmes, Simone; Emmons, L. K.; Vitt, Francis; Zhang, Rudong; Liu, Xiaohong; Ghan, Steven J.; Rasch, Philip J.

doi:10.1029/2020ms002346

Cited by 25 publications

(50 citation statements)

References 89 publications

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“…Several novel methods (e.g., “dynamic pH” and “adaptive time stepping”) are used in MOSAIC to ensure accurate yet computationally efficient solutions. The first implementation of MOSAIC in a global climate and chemistry model was in the Community Atmosphere Model version 5, the atmosphere component, with chemistry (CAM5‐chem), of the Community Earth System Model version 1 (CESM1) and coupled with the 7‐mode version of the Modal Aerosol Module (MAM7) (Liu et al., 2012) and MOZART gas‐phase chemistry (Zaveri et al., 2021). In this study, we further implement the MOSAIC module in the 4‐mode version of the Modal Aerosol Module (MAM4) (Liu et al., 2016) in the CAM version 6 with chemistry (CAM6‐chem).…”

Section: Introductionmentioning

confidence: 99%

Radiative Forcing of Nitrate Aerosols From 1975 to 2010 as Simulated by MOSAIC Module in CESM2‐MAM4

Liu

Zaveri

et al. 2021

JGR Atmospheres

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Nitrate is one of the most important atmospheric aerosols that greatly impacts global nitrogen cycles, climate, and human health (Myhre, Shindell, et al., 2013, IPCC AR5 Chapter 8;Pagalan et al., 2018). Nitrate aerosols can affect climate through scattering shortwave (SW) radiation and acting as cloud condensation nuclei (CCN) affecting cloud microphysical properties. Anthropogenic nitrate aerosols are usually formed from gas precursors such as nitrogen oxides (NO x ), which is largely related to heavy air pollution caused by economic activities. Aerosol mass spectrometer measurements over the globe show that nitrate aerosols can contribute a significant fraction to total aerosol mass, especially over metropolitan areas during winter according to the measurements (Zhang et al., 2007).Despite the importance of nitrate aerosols, only a handful climate models simulate their formation and lifecycle in the atmosphere (

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Section: Introductionmentioning

confidence: 99%

Radiative Forcing of Nitrate Aerosols From 1975 to 2010 as Simulated by MOSAIC Module in CESM2‐MAM4

Liu

Zaveri

et al. 2021

JGR Atmospheres

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“…The MOSAIC model is also widely used in regional and global chemical transport models, that is, the Weather, Research and Forecasting model with Chemistry (WRF-Chem) 50,51 and the Community Earth System Model (CESM). 52 We then applied the model to predict the particle size evolution and SOA composition upon IEPOX uptake by pure ammonium bisulfate particles from the chamber experiments reported in Riva et al 35 This work focuses on a single aerosol system, ammonium bisulfate seed aerosols under dry conditions (<5% RH).…”

Section: ■ Introductionmentioning

confidence: 99%

“…In this study, we integrated and applied new IEPOX-SOA modeling capabilities in a box model to understand kinetic processes governing evolution of particle size, chemical composition, and overall particle density due to reactive uptake of IEPOX, as measured by Riva et al The capability of a chemistry-aerosol inorganic box model, Model for Simulating Aerosols Interaction and Chemistry (MOSAIC), developed by Zaveri et al, was extended to include key processes of IEPOX-SOA formation on polydisperse aerosols. The MOSAIC model is also widely used in regional and global chemical transport models, that is, the Weather, Research and Forecasting model with Chemistry (WRF-Chem) , and the Community Earth System Model (CESM) . We then applied the model to predict the particle size evolution and SOA composition upon IEPOX uptake by pure ammonium bisulfate particles from the chamber experiments reported in Riva et al This work focuses on a single aerosol system, ammonium bisulfate seed aerosols under dry conditions (<5% RH).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Size Distribution and Chemical Composition of Secondary Organic Aerosols during the Reactive Uptake of Isoprene-Derived Epoxydiols under Low-Humidity Condition

Octaviani

Shrivastava

Zaveri

et al. 2021

ACS Earth Space Chem.

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Reactive uptake of isoprene epoxydiols (IEPOX), which are isoprene oxidation products, onto acidic sulfate aerosols is recognized to be an important mechanism for the formation of isoprene-derived secondary organic aerosol (SOA). While a mechanistic understanding of IEPOX-SOA formation exists, several processes affecting their formation remain uncertain. Evaluating mechanistic IEPOX-SOA models with controlled laboratory experiments under longer atmospherically relevant time scales is critical. Here, we implement our latest understanding of IEPOX-SOA formation within a box model to simulate the measured reactive uptake of IEPOX on polydisperse ammonium bisulfate seed aerosols within an environmental Teflon chamber. The model is evaluated with single-particle measurements of size distribution, volume, density, and composition of aerosols due to IEPOX-SOA formation at time scales of hours. We find that the model can simulate the growth of particles due to IEPOX multiphase chemistry, as reflected in increases of the mean particle size and volume concentrations, and a shift of the number size distribution to larger sizes. The model also predicts the observed evolution of particle number mean diameter and total volume concentrations at the end of the experiment. We show that in addition to the self-limiting effects of IEPOX-SOA coatings, the mass accommodation coefficient of IEPOX and accounting for the molar balance between inorganic and organic sulfate are important parameters governing the modeling of the IEPOX-SOA formation. Thus, models which do not account for the molar sulfate balance and/or diffusion limitations within IEPOX-SOA coatings are likely to predict IEPOX-SOA formation too high.

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“…Within the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), the Scenario Model Intercomparison Project (ScenarioMIP) provides future projections of many climate variables based on state-of-the-art climate models with various emission scenarios and land use changes under the Shared Socioeconomic Pathways (SSPs). However, many of the CMIP6 models do not provide variables of aerosol components and chemical processes since they focus on future changes in climate rather than air quality. , Moreover, different aerosol components, such as nitrate, ammonium, and organic aerosols, are not fully represented in the majority of climate models, partly due to the consideration of the computational efficiency. , With available data, Turnock et al reported that PM 2.5 was underrepresented in the CMIP6 models over Asia, Europe, and North America. Some studies directly used emissions in the future scenarios as input to global chemical transport models, driven by present-day meteorological data, to project future aerosol changes. , This method does not take into account any impacts of climate change on aerosols through chemical, transport, and scavenging processes.…”

Section: Introductionmentioning

confidence: 99%

“…18,19 Moreover, different aerosol components, such as nitrate, ammonium, and organic aerosols, are not fully represented in the majority of climate models, partly due to the consideration of the computational efficiency. 20,21 With available data, Turnock et al 20 reported that PM 2.5 was underrepresented in the CMIP6 models over Asia, Europe, and North America. Some studies directly used emissions in the future scenarios as input to global chemical transport models, driven by present-day meteorological data, to project future aerosol changes.…”

Section: Introductionmentioning

confidence: 99%

Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

Yang

Wang

et al. 2022

Environ. Sci. Technol.

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Projection of future aerosols and understanding the driver of the aerosol changes are of great importance in improving the atmospheric environment and climate change mitigation. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) provides various climate projections but limited aerosol output. In this study, future near-surface aerosol concentrations from 2015 to 2100 are predicted based on a machine learning method. The machine learning model is trained with global atmospheric chemistry model results and projects aerosols with CMIP6 multi-model simulations, creatively estimating future aerosols with all important species considered. PM2.5 (particulate matter less than 2.5 μm in diameter) concentrations in 2095 (2091–2100 mean) are projected to decrease by 40% in East Asia, 20–35% in South Asia, and 15–25% in Europe and North America, compared to those in 2020 (2015–2024 mean), under low-emission scenarios (SSP1-2.6 and SSP2-4.5), which are mainly due to the presumed emission reductions. Driven by the climate change alone, PM2.5 concentrations would increase by 10–25% in northern China and western U.S. and decrease by 0–25% in southern China, South Asia, and Europe under the high forcing scenario (SSP5-8.5). A warmer climate exerts a stronger modulation on global aerosols. Climate-driven global future aerosol changes are found to be comparable to those contributed by changes in anthropogenic emissions over many regions of the world in high forcing scenarios, highlighting the importance of climate change in regulating future air quality.

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Development and Evaluation of Chemistry‐Aerosol‐Climate Model CAM5‐Chem‐MAM7‐MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol

Cited by 25 publications

References 89 publications

Radiative Forcing of Nitrate Aerosols From 1975 to 2010 as Simulated by MOSAIC Module in CESM2‐MAM4

Radiative Forcing of Nitrate Aerosols From 1975 to 2010 as Simulated by MOSAIC Module in CESM2‐MAM4

Modeling the Size Distribution and Chemical Composition of Secondary Organic Aerosols during the Reactive Uptake of Isoprene-Derived Epoxydiols under Low-Humidity Condition

Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

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