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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]

The aim of this study is to assess the probability of black carbon (BC) transfer from high-intensity model forest fires in the taiga zone, determine the fields of concentrations and deposition of BC on the ice–snow surface, and assess the BC contribution to climate change in the Arctic. Model regions with the highest probability of large forest fires (the Komi Republic, Krasnoyarsk krai, and the Republic of Sakha (Yakutia)) are selected based on an analysis of remote monitoring data. The probability of a BC cloud from model fires transferring to the snowy-ice surface of the Arctic is analyzed using the HYSPLIT trajectory model. The probability of a particle emitted from the boreal taiga zone to traverse the sea ice-covered part of the Arctic Ocean is found to be between 1–10%. Effects of BC (the balance of shortwave radiation) on the Arctic climate are estimated on the basis of global climate model of the Institute of Numerical Mathematics Russian Academy of Sciences (INM RAS). According to the results of model experiments, the effect of BC on the balance of shortwave radiation from all model fires in the Northern Hemisphere is 0.11 W m–2 under cloudy conditions and 0.21 W m–2 in a clear sky. The estimated effect on the shortwave radiation balance is a 1.5–2% larger income when compared to the scenario without emission sources. The simulation results showed that the effect of BC emissions from forest fires on the ice-covered Arctic region is negligible. [ABSTRACT FROM AUTHOR]

As explained in the latest Arctic Monitoring and Assessment Programme (AMAP) report released in early 2021, the Arctic has warmed 3 times more quickly than the planet as a whole, as well as faster than previously thought. The Siberian Arctic is of great interest mainly because observations are sparse or largely lacking. A research aerosol station has been developed on Bely Island (Kara Sea) in western Siberia. Measurements of equivalent black carbon (EBC) concentrations were carried out at the "Island Bely" station continuously from August 2019 to November 2020. The source origin of the measured EBC and the main contributing sources were assessed using atmospheric transport modeling coupled with the most updated emission inventories for anthropogenic and biomass burning sources of BC. The obtained climatology for BC during the period of measurements showed an apparent seasonal variation with the highest concentrations between December and April (60 ± 92 ng m -3) and the lowest between June and September (18 ± 72 ng m -3), typical of the Arctic haze seasonality reported elsewhere. When air masses arrived at the station through the biggest oil and gas extraction regions of Kazakhstan, Volga-Ural, Komi, Nenets and western Siberia, BC contribution from gas flaring dominated over domestic, industrial and traffic sectors, ranging from 47 % to 68 %, with a maximum contribution in January. When air was transported from Europe during the cold season, emissions from transportation were more important. Accordingly, shipping emissions increased due to the touristic cruise activities and the ice retreat in summertime. Biomass burning (BB) played the biggest role between April and October, contributing 81 % at maximum in July. Long-range transport of BB aerosols appeared to induce large variability to the absorption Ångström exponent (AAE) with values > 1.0 (excluding outliers). As regards the continental contribution to surface BC at the Island Bely station, Russian emissions dominated during the whole year, while European and Asian ones contributed up to 20 % in the cold period. Quantification of several pollution episodes showed an increasing trend in surface concentrations and frequency during the cold period as the station is directly in the Siberian gateway of the highest anthropogenic pollution sources to the Russian Arctic. [ABSTRACT FROM AUTHOR]