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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]

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]

There is ample evidence that Black Carbon (BC) is harmful to the Arctic. BC can darken the otherwise highly reflective surfaces of snow and ice and increase atmospheric and ice surface temperatures. Because of the importance of BC to the Arctic, this work was designed to resolve the most significant source regions of Arctic BC as measured by monitoring stations in the Arctic. Using a bottom‐up approach, it is shown for the first time that there is one particular BC transport pathway from lower latitudes into the Arctic that registers at all but one of the Arctic surface monitoring stations included in this study. Through this pathway, pollutants are transported from the Indo‐Gangetic plane over Central Asia into the high Arctic in as little as 7 days. The measurement sites and BC pathways in this study are shown to be well representative of the Arctic as a whole. Plain Language Summary: Snow and sea ice are two of the most reflective surfaces occurring naturally on planet Earth. Light absorbing aerosols, such as wind blown black carbon, that stick to these pristine surfaces can make them less reflective, thus converting more sunlight into heat. The added heat leads to increased surface temperatures and is detrimental to the Arctic climate. There are few sources of light absorbing aerosols in the Arctic compared to the vast amounts of black carbon aerosols that are emitted closer to the equator. This research highlights an important pathway for light absorbing aerosols to enter the Arctic. Light absorbing aerosols that enter the Arctic through this pathway can then subsequently spread out throughout the Arctic. This pathway is shown to transport light absorbing airborne pollutants from South Asia, over Central Asia, and into the high Arctic in quantities that well exceed the background levels, thus highlighting the importance of this newly found pathway. Key Points: Black Carbon aerosols can travel from South Asia over Central Asia and to the Arctic in 7 daysBlack Carbon transported via this pathway measures well above the station average at almost all Arctic stationsThe identified pathway is significant for Black Carbon concentrations throughout the Arctic [ABSTRACT FROM AUTHOR]

The representation of aerosol activation into cloud droplets in climate models is important for accurate understanding of aerosol radiative impacts on the Arctic climate, but it remains highly uncertain. Here we show that the uncertainty range of subgrid vertical velocity (SVV) and maximum supersaturation (SSmax) in aerosol activation produces fourfold to fivefold differences in the direct radiative effect of black carbon (BC) in the Arctic (0.091–0.40 W m−2) because SVV and SSmax determine the activated fraction and wet removal efficiency of aerosols. Aerosols are particularly sensitive to SVV in remote regions but not near their sources because many aerosols near sources are not yet influenced by wet removal processes. Our results demonstrate that SVV treatment is a major source of uncertainty in Arctic aerosol simulations and may be key for reducing the large discrepancies among global models in estimates of BC and its radiative effects in the Arctic. Plain Language Summary: Black carbon aerosol, emitted mainly in midlatitude regions by combustion of fossil fuel and biomass, is transported to the Arctic and deposited on snow and ice surfaces, where it contributes to Arctic heating. However, estimates of its importance in Arctic warming have large uncertainties. Because global climate models usually use a coarse horizontal grid spacing, they rely on many assumptions to represent the small‐scale atmospheric processes within clouds. This study uses a global climate model to investigate the importance of one of these assumptions, the treatment of "subgrid vertical velocity," to aerosol simulations. We show that varying the subgrid vertical velocity within its uncertainty range changes the calculated heating effect of black carbon in the Arctic by as much as five times. Our results underscore the importance of treating subgrid vertical velocity treatment accurately in estimating how much black carbon from midlatitudes is warming the Arctic. Key Points: The importance of subgrid vertical velocity in activation to aerosol burden and radiative effects was investigated by using a global modelThe current uncertainty in subgrid vertical velocity produces fivefold differences in the radiative effect of black carbon in the ArcticThe subgrid treatment of updraft is important in estimating the long‐range transport of black carbon and its impacts on Arctic climate [ABSTRACT FROM AUTHOR]