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The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO , MODIS AOD, and OMI tropospheric NO2 , we find that future field campaign(s) and more in situ observations in the East African region (5 ∘ S–5 ∘ N, 30–45 ∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources. [ABSTRACT FROM AUTHOR]

The representation of aerosols in climate–chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model's chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model's performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate–chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models. [ABSTRACT FROM AUTHOR]

The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (~28 km x 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model-satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in an East African region (30°E - 45°E, 5°S - 5°N) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model-in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources. [ABSTRACT FROM AUTHOR]

While meteorology and aerosols are identified as key drivers of snow cover (SC) variability in High Mountain Asia, complex non‐linear interactions between them are not adequately quantified. Here, we attempt to unravel these interactions through a simple relative importance (RI) analysis of meteorological and aerosol variables from ERA5/CAMS‐EAC4 reanalysis against satellite‐derived SC from Moderate Resolution Imaging Spectroradiometer across 2003–2018. Our results show a statistically significant 7% rise in the RI of aerosol‐meteorology interactions (AMI) in modulating SC during late snowmelt season (June and July), notably over low snow‐covered (LSC) regions. Sensitivity tests further reveal that the importance of meteorological interactions with individual aerosol species are more prominent than total aerosols over LSC regions. We find that the RI of AMI for LSC regions is clearly dominated by carbonaceous aerosols, on top of the expected importance of dynamic meteorology. These findings clearly highlight the need to consider AMI in hydrometeorological monitoring, modeling, and reanalyses. Plain Language Summary: Understanding the changes in snow cover (SC) over glaciers in High Mountain Asia (HMA) is important yet challenging. Despite its impact on water resources, physical processes that drive these changes are complex. In particular, large‐scale weather patterns, together with aerosol pollution hotspots in the vicinity, and its steep elevation strongly interact with each other. We use a statistical approach to assess the relevance of these interactions using geophysical data from present day reanalysis and observed SC extent from satellite products for two decades. We find that during the late snowmelt period from June to July, interactions between aerosols and meteorology are significant, specifically in low SC regions. Interactions of individual aerosol species, especially carbonaceous aerosols like black carbon are more important than total aerosol concentration. This approach in quantifying the interactions of these processes can help improve the monitoring and modeling of snow hydrology. Representing these relevant interactions in current models and reanalysis of hydrometeorology can lead to more accurate predictions of the state of snow for critical regions like HMA. Key Points: Interactions between aerosols and meteorology are significant during late snowmelt (June and July) over low snow‐covered regions in High Mountain AsiaSpecies related interactions drive the seasonal variability of the overall relative importanceCarbonaceous aerosols are more relevant than mineral dust during late snowmelt [ABSTRACT FROM AUTHOR]

We analyze the effects of the diurnal cycle of fire emissions (DCFE) and plume rise on U.S. air quality using the MUSICAv0 (Multi‐Scale Infrastructure for Chemistry and Aerosols Version 0) model during the FIREX‐AQ (Fire Influence on Regional to Global Environments and Air Quality) and WE‐CAN (Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen) field campaigns. To include DCFE in the model, we employ two approaches: a DCFE climatology and DCFE derived from a satellite fire radiative power product. We also implemented two sets of plume‐rise climatologies, and two plume‐rise parameterizations. We evaluate the model performance with airborne measurements, U.S. EPA Air Quality System surface measurements, and satellite products. Overall, including plume rise improves model agreement with observations such as aircraft observations of CO and NOx for FIREX‐AQ and WE‐CAN. Applying DCFE also improves model performance, such as for surface PM2.5 in fire‐impacted regions. The impact of plume rise is larger than the impact of DCFE. Plume rise can greatly enhance modeled long‐range transport of fire‐emitted pollutants. The simulations with plume‐rise parameterizations generally perform better than the simulations with plume‐rise climatologies during FIREX‐AQ, but not for WE‐CAN. The 2019 Williams Flats Fire case study demonstrates that DCFE and plume rise change fire impacts because fire emissions are subject to different meteorology and chemistry when emitted at different times of a day and altitudes. Moreover, DCFE and plume rise also impact local‐to‐regional meteorology and chemical reaction rates. DCFE and plume rise will be included in future MUSICA versions. Plain Language Summary: Fires have significant impacts on U.S. air quality. The characteristics of fires such as diurnal variation and plume rise can largely alter these impacts. We study the impact of fire diurnal variation and plume rise on U.S. air quality using a newly developed modeling system—Multi‐Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0). Specifically, we employ two approaches to include the diurnal cycle of fire emissions (DCFE) and four approaches to include fire plume rise in the model. The model experiments were conducted for two campaign periods, FIREX‐AQ (Fire Influence on Regional to Global Environments and Air Quality) and WE‐CAN (Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen). We compare the model results with airborne measurements, surface measurements, and satellite products over the continental U.S. during the two campaign periods. Overall, including plume rise and/or DCFE improves model agreement with observations of the studied air‐quality‐related chemical species, and the impact of fire plume rise is larger than the impact of the DCFE. A case study also demonstrates that fire plume rise and the DCFE also impact local‐to‐regional meteorology and chemical reaction rates. Key Points: Including plume rise and diurnal cycle of fire emissions (DCFE) overall improves MUSICAv0 model agreement with observationsSimulations with plume rise parameterizations generally perform better than the simulations with plume rise climatologies during FIREX‐AQPlume rise and DCFE impact local‐to‐regional meteorology and chemical reaction rates [ABSTRACT FROM AUTHOR]

We quantified the combined effects of mineral dust nonsphericity and size on snow albedo reduction using the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package and SAMDS (Spectral Albedo Model for Dirty Snow) with the consideration of dust from Sahara, Greenland, San Juan Mountains, and Tibetan Plateau. Results indicate that the dust‐induced albedo reduction decreases by up to 30% as the effective radii of dust particles increase in 1–5 μm. Nonspherical dust enhances snow albedo reduction relative to spherical dust by up to 20%. Stronger enhancements are obtained for higher dust concentration and larger dust size. Furthermore, the dust nonsphericity‐induced enhancement of snow albedo reduction is more pronounced for more‐absorptive dust. Finally, we develop a new parameterization for quantifying the dependence of snow albedo reduction on dust nonsphericity and size, and provide a convenient way for assessing the climate impacts of dust in snow. Plain Language Summary: Mineral dust particles are a major type of light‐absorbing aerosols in the atmosphere, with the absorptivity closely dependent on their shape and size. Previous studies have confirmed that dust particles deposited on snow surface varied largely in size and shape, and caused the reduction of snow albedo worldwide. In this study, we quantified the effects of dust size and nonsphericity on the induced snow albedo reduction. Results show that small and nonspherical dust particles induce large snow albedo reduction, in comparison with large and spherical dust particles. We further develop a parameterization for the dust size and nonsphericity effects for application in climate models. Key Points: Mineral dust‐induced snow albedo reductions rapidly decrease as dust size increasesNonspherical dust causes larger snow albedo reduction than spherical dust by up to 20%A new parameterization is developed to improve the estimate of snow albedo reduction with dust size and nonsphericity [ABSTRACT FROM AUTHOR]

A set of parameterizations is developed for spectral single-scattering properties of clean and black carbon (BC)-contaminated snow based on geometric-optics surface wave (GOS) computations, which explicitly resolves BC-snow internal mixing and various snow grain shapes. GOS calculations show that, compared with nonspherical grains, volume-equivalent snow spheres show up to 20% larger asymmetry factors and hence stronger forward scattering, particularly at wavelengths <1 μm. In contrast, snow grain sizes have a rather small impact on the asymmetry factor at wavelengths <1 μm, whereas size effects are important at longer wavelengths. The snow asymmetry factor is parameterized as a function of effective size, aspect ratio, and shape factor and shows excellent agreement with GOS calculations. According to GOS calculations, the single-scattering coalbedo of pure snow is predominantly affected by grain sizes, rather than grain shapes, with higher values for larger grains. The snow single-scattering coalbedo is parameterized in terms of the effective size that combines shape and size effects, with an accuracy of >99%. Based on GOS calculations, BC-snow internal mixing enhances the snow single-scattering coalbedo at wavelengths <1 μm, but it does not alter the snow asymmetry factor. The BC-induced enhancement ratio of snow single-scattering coalbedo, independent of snow grain size and shape, is parameterized as a function of BC concentration with an accuracy of >99%. Overall, in addition to snow grain size, both BC-snow internal mixing and snow grain shape play critical roles in quantifying BC effects on snow optical properties. The present parameterizations can be conveniently applied to snow, land surface, and climate models including snowpack radiative transfer processes. [ABSTRACT FROM AUTHOR]