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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]

Models assume that mixed‐phase clouds consist of uniformly mixed ice crystals and liquid cloud droplets when observations have shown that they consist of clusters, or "pockets," of ice crystals and liquid cloud droplets. We characterize the spatial distribution of cloud phase over the Arctic and the Southern Ocean using active satellite observations and determine the relative importance of collocated meteorological parameters and aerosols from reanalysis to predict how uniformly mixed mixed‐phase clouds are for the first time. We performed a multi‐linear regression fit to the data set to predict the spatial distribution of the ice and liquid pockets. Contrary to what models suggest, mixed‐phase clouds are rarely perfectly homogeneous. Our results suggest that high temperatures are associated with homogeneously mixed ice and liquid pockets. We also find that a high mixing ratio of black carbon is associated with heterogeneously mixed ice and liquid pockets. Plain Language Summary: The representation of clouds in numerical models remains one of the largest uncertainties in predicting our future climate. Clouds can consist solely of liquid droplets, ice crystals, or the coexistence of both hydrometeor types. The latter cloud type is referred to as mixed phase. Climate models assume that liquid droplets and ice crystals are uniformly mixed in space in mixed‐phase clouds, but observations show that mixed‐phase clouds are organized in separate pockets of clustered liquid droplets and ice crystals. This difference in representation has a large impact on the lifetime of clouds and on their role in climate change. Using satellite observations over the Arctic and the Southern Ocean, we quantify the spatial distribution of ice and liquid in clouds. We used a statistical method to determine the relationship between meteorology and aerosols and the spatial distribution of ice and liquid. Our results suggest that high temperatures are associated with homogeneously mixed mixed‐phase clouds and high concentrations of soot are associated with heterogeneously mixed mixed‐phase clouds. Furthermore, pockets of liquid within ice clouds are larger than pockets of ice within liquid clouds. These results will improve the representation of mixed‐phase clouds in large‐scale models. Key Points: Space‐based observations and reanalysis are considered to determine the factors that control how mixed mixed‐phase clouds areLiquid dominated clouds contain small and isolated ice pockets whereas ice dominated clouds contain large and isolated liquid pocketsTemperature and black carbon play an important role in controlling the cloud phase spatial distribution and increasing phase heterogeneity [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]

Black Carbon (BC) aerosol is a major climate forcer in the Arctic. Here, we present 15 years (2001–2015) of surface observations of the aerosol absorption coefficient babs (corresponding to Equivalent BC), obtained at the Zeppelin Observatory, Ny Ålesund, Svalbard, coupled with backward transport modeling with Flexpart in order to calculate the Potential Source Contribution Function (PSCF) for BC. The observed long‐term variability superimposed on a strong annual cycle is studied as a function of large‐scale circulation patterns represented by monthly index values for the North Atlantic Oscillation (NAO) and the Scandinavian pattern (SCAN). We find a 35% increase of babs values at Zeppelin during the SCAN− phase in the winter half‐year compared to the SCAN+ phase but no significant difference in babs values between the NAO index phases. Both NAO and SCAN induce significant regional variability on the areas of origin of babs, mainly Siberia, Europe, and North America. Plain Language Summary: Here, we investigate the effect of the most important large circulation patterns for the European Arctic, namely the North Atlantic Oscillation (NAO) and Scandinavian (SCAN) patterns on the light absorbing carbon aerosol reaching the highly important area experiencing the effects of climate change as found before. We report important first time findings for an extended observation record of 15 years of aerosol light absorbing carbon, which was never been employed before as a continuous long record, and we performed the first rigorous assessment of the impact of large atmospheric circulation patterns like NAO and SCAN on the transport of black carbon aerosol expressed as the climate relevant aerosol absorption coefficient (babs) for the first time. We find a significant 35% increase in babs at Zeppelin during one of the phases of the SCAN. However, a significant regional variability induced by both NAO and SCAN phases is revealed. These findings allow a better quantified estimate of the fate of emissions over the years in the Northern Hemisphere and a better assessment of their influence in the observed BC metrics in the Artic. Key Points: An extended 15 years' observation record of Arctic aerosol light absorbing carbon is employedLarge‐scale circulation patterns appear to induce a long‐term modulation in aerosol absorption coefficient (babs) with regional featuresA 35% increase is found in babs at Zeppelin during the negative Scandinavian pattern (SCAN) phase in the 6‐month cold period compared to the positive SCAN phase [ABSTRACT FROM AUTHOR]

Black Carbon (BC) aerosol is a major climate forcer in the Arctic. Here, we present 15 years (2001–2015) of surface observations of the aerosol absorption coefficient babs (corresponding to Equivalent BC), obtained at the Zeppelin Observatory, Ny Ålesund, Svalbard, coupled with backward transport modeling with Flexpart in order to calculate the Potential Source Contribution Function (PSCF) for BC. The observed long‐term variability superimposed on a strong annual cycle is studied as a function of large‐scale circulation patterns represented by monthly index values for the North Atlantic Oscillation (NAO) and the Scandinavian pattern (SCAN). We find a 35% increase of babs values at Zeppelin during the SCAN− phase in the winter half‐year compared to the SCAN+ phase but no significant difference in babs values between the NAO index phases. Both NAO and SCAN induce significant regional variability on the areas of origin of babs, mainly Siberia, Europe, and North America. Plain Language Summary: Here, we investigate the effect of the most important large circulation patterns for the European Arctic, namely the North Atlantic Oscillation (NAO) and Scandinavian (SCAN) patterns on the light absorbing carbon aerosol reaching the highly important area experiencing the effects of climate change as found before. We report important first time findings for an extended observation record of 15 years of aerosol light absorbing carbon, which was never been employed before as a continuous long record, and we performed the first rigorous assessment of the impact of large atmospheric circulation patterns like NAO and SCAN on the transport of black carbon aerosol expressed as the climate relevant aerosol absorption coefficient (babs) for the first time. We find a significant 35% increase in babs at Zeppelin during one of the phases of the SCAN. However, a significant regional variability induced by both NAO and SCAN phases is revealed. These findings allow a better quantified estimate of the fate of emissions over the years in the Northern Hemisphere and a better assessment of their influence in the observed BC metrics in the Artic. Key Points: An extended 15 years' observation record of Arctic aerosol light absorbing carbon is employedLarge‐scale circulation patterns appear to induce a long‐term modulation in aerosol absorption coefficient (babs) with regional featuresA 35% increase is found in babs at Zeppelin during the negative Scandinavian pattern (SCAN) phase in the 6‐month cold period compared to the positive SCAN phase [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]

The circum-Arctic region experienced serious fires in 2019 and 2020. Biomass burning is considered a primary source of black carbon (BC) aerosols. BC contributes to Arctic warming and impacts snow/ice melting. However, the impacts of biomass burning on BC in the Arctic during these recent serious fires have not been quantified in detail. In this study, based on numerical simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), we calculated the contribution ratios of biomass burning to Arctic BC and revealed its transport pathways. Affected by biomass burning emissions, the near-surface BC concentrations over the terrestrial areas within the Arctic Circle were highest in summer, and declined in spring and autumn. Spatially, high-concentration levels of BC were distributed in the Russian central and eastern areas. Biomass burning accounted for 63.72% and 45.18% of the summer BC in the Arctic near-surface and middle troposphere, respectively. In the near-surface, the contributions from local Arctic sources were comparable to those from sources outside the Arctic Circle in summer. In the middle troposphere, contributions from sources outside the Arctic Circle were dominant. In summer, BC originating from biomass burning in Siberia was transported a short distance by southwesterly winds to the central Arctic near-surface, while the enhanced southwesterly winds in the middle troposphere transported BC from Siberia, Alaska and northern Canada to the central Arctic and Greenland. In spring and autumn, most BC originating from biomass burning in the near-surface of Eurasia was transported eastward by westerly winds and then transported northward over the North Pacific Ocean, and the long distance may have resulted in fewer effects on the Arctic. These results highlight the important role of biomass burning in the Arctic environment under a warming climate. [Display omitted] • Affected by biomass burning, the near-surface BC concentrations over the terrestrial areas within the Arctic Circle were highest in summer. • The biomass burning contributions from local sources to Arctic BC in the near-surface in summer were obvious. • BC from biomass burning in the near-surface of Siberia were transported by southwesterly winds to the central Arctic. • The enhanced southwesterly winds in the middle troposphere transported BC from Siberia, Alaska, and northern Canada to the central Arctic and Greenland. [ABSTRACT FROM AUTHOR]

The outbreak of SARS-CoV-2 and subsequent spread of the disease COVID-19 became classified as a pandemic in March of 2020, leading to global safety measures introduced to limit the impact of the virus. This combination of safety measures has become commonly referred to as "lockdown". The associated industry and lifestyle changes led to reductions in the anthropogenic emission of atmospheric pollutants such as black carbon (BC), which is transported from the mid-latitudes into the Arctic during the winter and spring. Measurements of BC and other anthropogenic pollutants are of increasing importance in the Arctic due to the rapid warming observed there in the past few decades. It is believed that BC has a significant role in this warming, and so understanding the Arctic's response to reduced BC emissions at lower latitudes will provide insight into how future changes might mitigate further warming. Reductions in BC have been reported worldwide, and so in this study, the impact of these reductions on BC concentrations at the High Arctic site Villum Research Station was investigated. The effect was examined from March 2020, around when global lockdowns began, to June 2020, when the Arctic haze period ended and BC levels were once again low. Firstly, the Danish Eulerian Hemispheric Model (DEHM) was used to assess this impact on BC concentrations by adjusting global anthropogenic pollution emission inventories to simulate those observed during the lockdown period and comparing the results to a similar model run with standard emission inventories. Secondly, equivalent BC data from an aethalometer at Villum Research Station were analysed, comparing the concentrations during the lockdown period to both aethalometer data from previous years and DEHM results from the lockdown period. It was found that when adjusted DEHM emission inventories were introduced from the 1st of March, the model predicted a reduction in BC concentrations beginning on the 10th of March and reached a 10% reduction by the 1st of April. This reduction fluctuated around 10% until the end of the Arctic haze period. Aethalometer data did not show any significant change from previous years, and no concentration reduction could be concluded from its comparison with DEHM results. This is likely because the predicted reduction of 10% is smaller than both the inter-annual and intra-annual variability of measured BC concentrations at Villum. • DEHM model and aethalometer used to assess effect of lockdown on Villum BC levels. • DEHM model simulated a 10% reduction in BC at Villum during April–May 2020. • Similar reductions are expected throughout the Arctic region (between 7% and 11%). • The aethalometer showed no response to the lockdown due to inherent BC variability. [ABSTRACT FROM AUTHOR]