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Long-term measurements of the mass concentration of black carbon (BC) in the atmosphere (MBC) with well-constrained accuracy are indispensable to quantify its emission, transport, and deposition. The aerosol light absorption coefficient (babs), usually measured by a filter-based absorption photometer, including an Aethalometer (AE), is often used to estimate MBC. The measured babs is converted to MBC by assuming a value for the mass absorption cross section (MAC). Previously, we derived the MAC for AE (MAC (AE)) from measured babs and independently measured MBC values at two sites in the Arctic. MBC was measured with a filter-based absorption photometer with a heated inlet (COSMOS). The accuracy of the COSMOS-derived MBC (MBC (COSMOS)) was within about 15%. Here, we obtained additional MAC (AE) measurements to improve understanding of its variability and uncertainty. We measured babs (AE) and MBC (COSMOS) at Alert (2018–2020), Barrow (2012–2022), Ny-Ålesund (2012–2019), and Pallas (2019–2022). At Pallas, we also obtained four-wavelength photoacoustic aerosol absorption spectrometer (PAAS-4λ) measurements of babs. babs (AE) and MBC (COSMOS) were tightly correlated; the average MAC (AE) at the four sites was 11.4 ± 1.2 m2 g−1 (mean ± 1σ) at 590 nm and 7.76 ± 0.73 m2 g−1 at 880 nm. The spatial variability of MAC (AE) was about 11% (1σ), and its year-to-year variability was about 18%. We compared MAC (AE) in the Arctic with values at mid-latitudes, measured by previous studies, and with values obtained by using other types of filter-based absorption photometer, and PAAS-4λ. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

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 (RBC, 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 RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). 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 χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC 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. Plain Language Summary: Black carbon (BC) mixing state is an important property for assessing impacts of BC on human health and climate. It describes how BC is distributed among the particle population and within individual particles. Due to computational costs, global climate models (GCMs) consider aerosols as either external mixtures (e.g., bulk models) or fully internal mixtures with uniform composition within a certain size range (e.g., an aerosol bin in sectional models or an aerosol mode in modal models). To improve BC mixing state representation, we coupled a machine learning (ML) model learned from particle‐resolved simulations to the online simulations of the Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4‐ML). We used the mass ratio of coating to BC (RBC) to determine the mass fraction of BC‐free particles and BC‐containing particles, and we adjusted RBC to make the modeled BC mixing state match ML model. The MAM4‐ML model reduces the degree of internal mixture of BC substantially. In addition, MAM4‐ML RBC is reduced by 52%, leading to a 20% reduction in BC activation fraction and a 4% increase in BC burden. This study proposes a method for the application of the ML to improve GCMs. Key Points: Machine learning model trained on particle‐resolved simulations has been coupled to CAM6 MAM4 to improve BC mixing state representationThe BC mixing state index is reduced by 19%, and the mass ratio of coating to BC is reduced by 52%, in better agreement with observationsDue to the drop in hygroscopicity of BC‐containing aerosol (9%), BC activation fraction decreased by 20%, and BC burden increased by 4% [ABSTRACT FROM AUTHOR]

Here we apply new analysis methods and approaches to existing long-term measurement series that provide additional insights into the atmospheric processes that control black carbon (BC) in the Arctic. Based on clustering size distribution data from Zeppelin Observatory for the years 2002–2010, observations classified as 'Polluted' were further investigated based on BC properties. The data were split into two subgroups, and while the microphysical and chemical fingerprints of the two subgroups are very similar, they show larger differences in BC concentration and correlation with the particle size distribution. Therefore, a source–receptor analysis was performed with HYSPLIT 10-days backward trajectories for both subsets. We demonstrate that within this 'Polluted' category, the airmasses that contributed to the largest BC signal at the Zeppelin station are not necessarily associated with traditional transport pathways from Eurasia. Instead, the strongest signal is from a region east of the Ural Mountains across the continent to the Kamchatka Peninsula. [ABSTRACT FROM AUTHOR]

Long-term measurements of atmospheric mass concentrations of black carbon (BC) are needed to investigate changes in its emission, transport, and deposition. However, depending on instrumentation, parameters related to BC such as aerosol absorption coefficient (babs) have been measured instead. Most ground-based measurements of babs in the Arctic have been made by filter-based absorption photometers, including particle soot absorption photometers (PSAPs), continuous light absorption photometers (CLAPs), Aethalometers, and multi-angle absorption photometers (MAAPs). The measured babs can be converted to mass concentrations of BC (MBC) by assuming the value of the mass absorption cross section (MAC; MBC= babs/ MAC). However, the accuracy of conversion of babs to MBC has not been adequately assessed. Here, we introduce a systematic method for deriving MAC values from babs measured by these instruments and independently measured MBC. In this method, MBC was measured with a filter-based absorption photometer with a heated inlet (COSMOS). COSMOS-derived MBC (MBC (COSMOS)) is traceable to a rigorously calibrated single particle soot photometer (SP2), and the absolute accuracy of MBC (COSMOS) has been demonstrated previously to be about 15 % in Asia and the Arctic. The necessary conditions for application of this method are a high correlation of the measured babs with independently measured MBC and long-term stability of the regression slope, which is denoted as MAC cor (MAC derived from the correlation). In general, babs – MBC (COSMOS) correlations were high (r2= 0.76–0.95 for hourly data) at Alert in Canada, Ny-Ålesund in Svalbard, Barrow (NOAA Barrow Observatory) in Alaska, Pallastunturi in Finland, and Fukue in Japan and stable for up to 10 years. We successfully estimated MAC cor values (10.8–15.1 m 2 g -1 at a wavelength of 550 nm for hourly data) for these instruments, and these MAC cor values can be used to obtain error-constrained estimates of MBC from babs measured at these sites even in the past, when COSMOS measurements were not made. Because the absolute values of MBC at these Arctic sites estimated by this method are consistent with each other, they are applicable to the study of spatial and temporal variation in MBC in the Arctic and to evaluation of the performance of numerical model calculations. [ABSTRACT FROM AUTHOR]