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We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of -0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period. [ABSTRACT FROM AUTHOR]

In this study, we present and analyze the first continuous timeseries of relevant aerosol precursor vapors from the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These precursor vapors include sulfuric acid (SA), methanesulfonic acid (MSA), and iodic acid (IA). We use FLEXPART simulations, inverse modeling, sulfur dioxide (SO2) mixing ratios, and chlorophyll-a (chl-a) observations to interpret the seasonal variability of the vapor concentrations and identify dominant sources. Our results show that both natural and anthropogenic sources are relevant for the concentrations of SA in the Arctic, but anthropogenic sources associated with Arctic haze are the most prevalent. MSA concentrations are an order of magnitude higher during polar day than during polar night due to seasonal changes in biological activity. Peak MSA concentrations were observed in May, which corresponds with the timing of the annual peak in chl-a concentrations north of 75°N. IA concentrations exhibit two distinct peaks during the year: a dominant peak in spring and a secondary peak in autumn, suggesting that seasonal IA concentrations depend on both solar radiation and sea ice conditions. In general, the seasonal cycles of SA, MSA, and IA in the central Arctic Ocean are related to sea ice conditions, and we expect that changes in the Arctic environment will affect the concentrations of these vapors in the future. The magnitude of these changes and the subsequent influence on aerosol processes remains uncertain, highlighting the need for continued observations of these precursor vapors in the Arctic. [ABSTRACT FROM AUTHOR]

Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatiotemporal locations of the particles that move independently from a regular grid. Traditionally, these high-resolution data have been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g., using linear interpolation in space and time. However, interpolation is a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single-image super-resolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to upscale them to a higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art enhanced deep residual networks for super-resolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to upscale these data to an arbitrary spatial resolution. We show that the resulting upscaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we find that absolute horizontal transport deviations of calculated trajectories from "true" trajectories calculated with un-degraded 0.5 ∘ × 0.5 ∘ winds are reduced by at least 49.5 % (21.8 %) after 48 h relative to trajectories using linear interpolation of the wind data when training on 2 ∘ × 2 ∘ to 1 ∘ × 1 ∘ (4 ∘ × 4 ∘ to 2 ∘ × 2 ∘) resolution data. [ABSTRACT FROM AUTHOR]

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k -means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies. [ABSTRACT FROM AUTHOR]

Using the example of sulfur hexafluoride (SF6), we investigate the use of Lagrangian particle dispersion models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global-distribution-based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods can cause large systematic errors, which lead to inversion results that are sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that show a better agreement with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 d, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods might help to better constrain emissions in regions poorly covered by the global SF6 monitoring network. We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well-placed flask sampling sites would have great value for improving global a posteriori emission fields. [ABSTRACT FROM AUTHOR]

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance, and hence the climate response, in the region; yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long dataset of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019 - 2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic Haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds with the global radiation, surface air temperature, and the timing of sea ice melting/freezing, which drives changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic Haze signal in the PNSD and BC observations, which contributed to higher accumulation mode PNC and BC mass concentration in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January - March 2020, explains the unusual timing and magnitude of Arctic Haze across the Arctic region compared to longer-term observations. In summer, the PNC of nucleation and Aitken mode aerosol is enhanced, but concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies. [ABSTRACT FROM AUTHOR]

Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatio-temporal locations of the particles that move independently from a regular grid. Traditionally, this high-resolution data has been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g. using linear interpolation in space and time. However, interpolation errors are a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single image superresolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to up-scale them to higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art Enhanced Deep Residual Networks for Superresolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to up-scale these data to arbitrary spatial resolution. We show that the resulting up-scaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we demonstrate that absolute horizontal transport deviations of calculated trajectories from 'ground-truth'' trajectories calculated with undegraded 0.5° x 0.5° winds are reduced by at least 49.5 % (21.8 %) after 48 hours relative to trajectories using linear interpolation of the wind data when training on 2° x 2° to 1° x 1° (4° x 4° to 2° x 2°) resolution data. [ABSTRACT FROM AUTHOR]

Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH 4). The strong localization of CH 4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448 and 720 ktCH4yr-1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2), respectively, make the USCB a prime research target for validating and improving CH 4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g., single facilities) to regional-scale (e.g., agglomerations) sources in the context of the CoMet 1.0 campaign in early summer 2018. A quantum cascade–interband cascade laser (QCL–ICL)-based spectrometer adapted for airborne research was deployed aboard the German Aerospace Center (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH 4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility-level estimates. Emission rates from individual sources not only are needed for unambiguous comparisons between bottom-up and top-down inventories but also become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional-scale and facility-level emission estimates. We find instantaneous coal mine emission estimates of 451/423 ± 77 / 79 ktCH4yr-1 for the morning/afternoon flight of 6 June 2018. The derived fuel-exploitation emission rates coincide (± 6 %) with annual-average inventorial data from E-PRTR 2017 although they are distinctly lower (-28 % / -32 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories. [ABSTRACT FROM AUTHOR]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [ABSTRACT FROM AUTHOR]

Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448/720 kt CH4 yr-1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g. single facilities) to regional-scale sources (e.g. agglomerations) in the context of the CoMet 1.0 campaign in early summer 2018. A Quantum Cascade/Interband Cascade Laser (QCL/ICL) based spectrometer adapted for airborne research was deployed aboard the German Aerospace Centers (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights, due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility level estimates. Emission rates from individual sources are not only needed for unambiguous comparisons between bottom-up and top-down inventories but become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional scale and facility level emission estimates. We find instantaneous emission estimates of 452/442 ± 78/75 kt CH4 yr-1 for the morning/afternoon flight of June 6th, 2018. The derived emission rates coincide (±2 %) with annual-average inventorial data from E-PRTR 2017 albeit they are distinctly lower (-37 %/-40 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories. [ABSTRACT FROM AUTHOR]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem where the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties e.g. in measurements or atmospheric transport modeling, hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information, the prior source term, also known as the first guess, and regularization parameters for shape of the source term. While the first guess is based on information independent of the measurements, such as physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term as well as regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence and in particular its effect on tracer dispersion may be measured by cameras sensitive to the absorption of ultraviolet (UV) sunlight by sulfur dioxide (SO2), a gas that can be considered a passive tracer over short transport distances. We present a method to simulate UV camera measurements of SO2 with a 3D Monte Carlo radiative transfer model which takes input from a large eddy simulation (LES) of a SO2 plume released from a point source. From the simulated images the apparent absorbance and various plume density statistics (centre-line position, meandering, absolute and relative dispersion, and skewness) were calculated. These were compared with corresponding quantities obtained directly from the LES. Mean differences of centre-line position, absolute and relative dispersions, and skewness between the simulated images and the LES were generally found to be smaller than or about the voxel resolution of the LES. Furthermore, sensitivity studies were made to quantify how changes in solar azimuth and zenith angles, aerosol loading (background and in plume), and surface albedo impact the UV camera image plume statistics. Changing the values of these parameters within realistic limits has negligible effects on the centre-line position, meandering, absolute and relative dispersions, and skewness of the SO2 plume. Thus, we demonstrate that UV camera images of SO2 plumes may be used to derive plume statistics of relevance for the study of atmospheric turbulent dispersion. [ABSTRACT FROM AUTHOR]

We reconstruct atmospheric abundances of the potent greenhouse gas c - C4F8 (perfluorocyclobutane, perfluorocarbon PFC-318) from measurements of in situ, archived, firn, and aircraft air samples with precisions of ∼1 %–2 % reported on the SIO-14 gravimetric calibration scale. Combined with inverse methods, we found near-zero atmospheric abundances from the early 1900s to the early 1960s, after which they rose sharply, reaching 1.66 ppt (parts per trillion dry-air mole fraction) in 2017. Global c - C4F8 emissions rose from near zero in the 1960s to 1.2±0.1 (1 σ) Gg yr -1 in the late 1970s to late 1980s, then declined to 0.77±0.03 Gg yr -1 in the mid-1990s to early 2000s, followed by a rise since the early 2000s to 2.20±0.05 Gg yr -1 in 2017. These emissions are significantly larger than inventory-based emission estimates. Estimated emissions from eastern Asia rose from 0.36 Gg yr -1 in 2010 to 0.73 Gg yr -1 in 2016 and 2017, 31 % of global emissions, mostly from eastern China. We estimate emissions of 0.14 Gg yr -1 from northern and central India in 2016 and find evidence for significant emissions from Russia. In contrast, recent emissions from northwestern Europe and Australia are estimated to be small (≤1 % each). We suggest that emissions from China, India, and Russia are likely related to production of polytetrafluoroethylene (PTFE, "Teflon") and other fluoropolymers and fluorochemicals that are based on the pyrolysis of hydrochlorofluorocarbon HCFC-22 (CHClF2) in which c - C4F8 is a known by-product. The semiconductor sector, where c - C4F8 is used, is estimated to be a small source, at least in South Korea, Japan, Taiwan, and Europe. Without an obvious correlation with population density, incineration of waste-containing fluoropolymers is probably a minor source, and we find no evidence of emissions from electrolytic production of aluminum in Australia. While many possible emissive uses of c - C4F8 are known and though we cannot categorically exclude unknown sources, the start of significant emissions may well be related to the advent of commercial PTFE production in 1947. Process controls or abatement to reduce the c - C4F8 by-product were probably not in place in the early decades, explaining the increase in emissions in the 1960s and 1970s. With the advent of by-product reporting requirements to the United Nations Framework Convention on Climate Change (UNFCCC) in the 1990s, concern about climate change and product stewardship, abatement, and perhaps the collection of c - C4F8 by-product for use in the semiconductor industry where it can be easily abated, it is conceivable that emissions in developed countries were stabilized and then reduced, explaining the observed emission reduction in the 1980s and 1990s. Concurrently, production of PTFE in China began to increase rapidly. Without emission reduction requirements, it is plausible that global emissions today are dominated by China and other developing countries. We predict that c - C4F8 emissions will continue to rise and that c - C4F8 will become the second most important emitted PFC in terms of CO2 -equivalent emissions within a year or two. The 2017 radiative forcing of c - C4F8 (0.52 mW m -2) is small but emissions of c - C4F8 and other PFCs, due to their very long atmospheric lifetimes, essentially permanently alter Earth's radiative budget and should be reduced. Significant emissions inferred outside of the investigated regions clearly show that observational capabilities and reporting requirements need to be improved to understand global and country-scale emissions of PFCs and other synthetic greenhouse gases and ozone-depleting substances. [ABSTRACT FROM AUTHOR]

Turbulence is one of the unsolved problems of physics. Atmospheric turbulence and in particular its effect on tracer dispersion may be measured by cameras sensitive to the absorption of ultraviolet (UV) sun-light by sulfur dioxide (SO2), a gas that can be considered a passive tracer over short transport distances. We present a method to simulate UV camera measurements of SO2 with a 3D Monte Carlo radiative transfer model which takes input from a large eddy simulation (LES) of a SO2 plume released from a point source. From the simulated images the apparent absorbance and various plume density statistics (centerline position, meandering, absolute and relative dispersion, skewness, and fractal dimension) were calculated. These were compared with corresponding quantities obtained directly from the LES. Mean differences of centerline position, absolute and relative dispersion, and skewness between the simulated images and the LES were found to be smaller than a quarter of one camera pixel, with standard deviations between 1/2 and 3/2 camera pixel. Furthermore sensitivity studies were made to quantify how changes in solar azimuth and zenith angles, aerosol loading (background and in plume), and surface albedo impact the UV camera image plume statistics. Changing the values of these parameters within realistic limits have negligible effect on the centerline position, meandering, absolute and relative dispersions, and skewness of the SO2 plume. Thus, we demonstrate that UV camera images of SO2 plumes may be used to derive plume statistics of relevance for the study of atmospheric turbulent dispersion. [ABSTRACT FROM AUTHOR]

We reconstruct atmospheric abundances of the potent greenhouse gas c-C4F8 (perfluorocyclobutane, perfluorocarbon PFC-318) from measurements of in situ, archived, firn, and aircraft air samples with precisions of ~1-2% reported on the SIO-14 gravimetric calibration scale. Combined with inverse methods, we found near zero atmospheric abundances from the early 1900s to the early 1960s, after which they rose sharply, reaching 1.66ppt (parts per trillion dry-air mole fraction) in 2017. Global c-C4F8 emissions rose from near zero in the 1960s to ~1.2Ggyr-1 in the late 1970s to late 1980s, then declined to ~0.8Ggyr-1 in the mid-1990s to early 2000s, followed by a rise since the early 2000s to ~2.2Ggyr-1 in 2017. These emissions are significantly larger than inventory based emission estimates. Estimated emissions from eastern Asia rose from 0.36Ggyr-1 in 2010 to 0.73Ggyr-1 in 2016 and 2017, 31% of global emissions, mostly from eastern China. We estimate emissions of 0.14Ggyr-1 from Northern and Central India in 2016 and find evidence for significant emissions from Russia. In contrast, recent emissions from North Western Europe and Australia are estimated to be small (≤1% each). We conclude that emissions from China, India and Russia are likely related to production of polytetrafluoroethylene (PTFE, "Teflon") and other fluoropolymers that are based on the pyrolysis of hydrochlorofluorocarbon HCFC-22 (CHClF2) in which c-C4F8 is a known by-product. The semiconductor sector, where c-C4F8 is used, is estimated to be a small source. Without an obvious correlation with population density, incineration of waste containing fluoropolymers is probably a minor source, and we find no evidence of emissions from electrolytic production of aluminum in Australia. While many possible emissive uses of c-C4F8 are known, the start of significant emissions may well be related to the advent of commercial PTFE production in 1947. Process controls or abatement to reduce c-C4F8 by-product were probably not in place in the early decades, explaining the increase in emissions. With the advent of by-product reporting requirements to the United Nations Framework Convention on Climate Change (UNFCCC) in the 1990s, concern about climate change and product stewardship, abatement, and perhaps the collection of c-C4F8 by-product for use in the semiconductor industry where it can be easily abated, it is conceivable that emissions in developed countries were stabilized and then reduced, explaining the observed emission reduction in the 1980s and 1990s. Concurrently, production of PTFE in China began to increase rapidly. Without emission reduction requirements, it is plausible that global emissions today are dominated by China and other developing countries, in agreement with our analysis. We predict that c-C4F8 emissions will continue to rise and that c-C4F8 will become the second most important emitted PFC in terms of CO2-equivalent emissions within a year or two. The 2017 radiative forcing of c-C4F8 (0.52mWm-2) is small but emissions of c-C4F8 and other PFCs, due to their very long atmospheric lifetimes, essentially permanently alter Earth's radiative budget and should be reduced. Significant emissions outside of the investigated regions clearly show that observational capabilities and reporting requirements need to be improved to understand global and country scale emissions of PFCs and other synthetic greenhouse gases and ozone depleting substances. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a powerful greenhouse gas. Its atmospheric mixing ratios have been increasing since 2005. Therefore, quantification of CH4 sources is essential for effective climate change mitigation. Here we report observations of the CH4 mixing ratios measured at the Zeppelin Observatory (Svalbard) in the Arctic and aboard the research vessel (RV) Helmer Hanssen over the Arctic Ocean from June 2014 to December 2016, as well as the long-term CH4 trend measured at the Zeppelin Observatory from 2001 to 2017. We investigated areas over the European Arctic Ocean to identify possible hotspot regions emitting CH4 from the ocean to the atmosphere, and used state-of-the-art modelling (FLEXPART) combined with updated emission inventories to identify CH4 sources. Furthermore, we collected air samples in the region as well as samples of gas hydrates, obtained from the sea floor, which we analysed using a new technique whereby hydrate gases are sampled directly into evacuated canisters. Using this new methodology, we evaluated the suitability of ethane and isotopic signatures (δ13C in CH4) as tracers for ocean-to-atmosphere CH4 emission. We found that the average methane / light hydrocarbon (ethane and propane) ratio is an order of magnitude higher for the same sediment samples using our new methodology compared to previously reported values, 2379.95 vs. 460.06, respectively. Meanwhile, we show that the mean atmospheric CH4 mixing ratio in the Arctic increased by 5.9±0.38 parts per billion by volume (ppb) per year (yr -1) from 2001 to 2017 and ∼8 pbb yr -1 since 2008, similar to the global trend of ∼ 7–8 ppb yr -1. Most large excursions from the baseline CH4 mixing ratio over the European Arctic Ocean are due to long-range transport from land-based sources, lending confidence to the present inventories for high-latitude CH4 emissions. However, we also identify a potential hotspot region with ocean–atmosphere CH4 flux north of Svalbard (80.4 ∘ N, 12.8 ∘ E) of up to 26 nmol m -2 s -1 from a large mixing ratio increase at the location of 30 ppb. Since this flux is consistent with previous constraints (both spatially and temporally), there is no evidence that the area of interest north of Svalbard is unique in the context of the wider Arctic. Rather, because the meteorology at the time of the observation was unique in the context of the measurement time series, we obtained over the short course of the episode measurements highly sensitive to emissions over an active seep site, without sensitivity to land-based emissions. [ABSTRACT FROM AUTHOR]

In atmospheric tracer experiments, a substance is released into the turbulent atmospheric flow to study the dispersion parameters of the atmosphere. That can be done by observing the substance's concentration distribution downwind of the source. Past experiments have suffered from the fact that observations were only made at a few discrete locations and/or at low time resolution. The Comtessa project (Camera Observation and Modelling of 4-D Tracer Dispersion in the Atmosphere) is the first attempt at using ultraviolet (UV) camera observations to sample the three-dimensional (3-D) concentration distribution in the atmospheric boundary layer at high spatial and temporal resolution. For this, during a three-week campaign in Norway in July 2017, sulfur dioxide (SO2), a nearly passive tracer, was artificially released in continuous plumes and nearly instantaneous puffs from a 9 m high tower. Column-integrated SO2 concentrations were observed with six UV SO2 cameras with sampling rates of several hertz and a spatial resolution of a few centimetres. The atmospheric flow was characterised by eddy covariance measurements of heat and momentum fluxes at the release mast and two additional towers. By measuring simultaneously with six UV cameras positioned in a half circle around the release point, we could collect a data set of spatially and temporally resolved tracer column densities from six different directions, allowing a tomographic reconstruction of the 3-D concentration field. However, due to unfavourable cloudy conditions on all measurement days and their restrictive effect on the SO2 camera technique, the presented data set is limited to case studies. In this paper, we present a feasibility study demonstrating that the turbulent dispersion parameters can be retrieved from images of artificially released puffs, although the presented data set does not allow for an in-depth analysis of the obtained parameters. The 3-D trajectories of the centre of mass of the puffs were reconstructed enabling both a direct determination of the centre of mass meandering and a scaling of the image pixel dimension to the position of the puff. The latter made it possible to retrieve the temporal evolution of the puff spread projected to the image plane. The puff spread is a direct measure of the relative dispersion process. Combining meandering and relative dispersion, the absolute dispersion could be retrieved. The turbulent dispersion in the vertical is then used to estimate the effective source size, source timescale and the Lagrangian integral time. In principle, the Richardson–Obukhov constant of relative dispersion in the inertial subrange could be also obtained, but the observation time was not sufficiently long in comparison to the source timescale to allow an observation of this dispersion range. While the feasibility of the methodology to measure turbulent dispersion could be demonstrated, a larger data set with a larger number of cloud-free puff releases and longer observation times of each puff will be recorded in future studies to give a solid estimate for the turbulent dispersion under a variety of stability conditions. [ABSTRACT FROM AUTHOR]

A Lagrangian particle dispersion model, the FLEXible PARTicle dispersion chemical transport model (FLEXPART CTM), is used to simulate global three-dimensional fields of trace gas abundance. These fields are constrained with surface observation data through nudging, a data assimilation method, which relaxes model fields to observed values. Such fields are of interest to a variety of applications, such as inverse modelling, satellite retrievals, radiative forcing models and estimating global growth rates of greenhouse gases. Here, we apply this method to methane using 6 million model particles filling the global model domain. For each particle, methane mass tendencies due to emissions (based on several inventories) and loss by reaction with OH, Cl and O(1D) , as well as observation data nudging were calculated. Model particles were transported by mean, turbulent and convective transport driven by 1∘×1∘ ERA-Interim meteorology. Nudging is applied at 79 surface stations, which are mostly included in the World Data Centre for Greenhouse Gases (WDCGG) database or the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION) in Siberia. For simulations of 1 year (2013), we perform a sensitivity analysis to show how nudging settings affect modelled concentration fields. These are evaluated with a set of independent surface observations and with vertical profiles in North America from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL), and in Siberia from the Airborne Extensive Regional Observations in SIBeria (YAK-AEROSIB) and the National Institute for Environmental Studies (NIES). FLEXPART CTM results are also compared to simulations from the global Eulerian chemistry Transport Model version 5 (TM5) based on optimized fluxes. Results show that nudging strongly improves modelled methane near the surface, not only at the nudging locations but also at independent stations. Mean bias at all surface locations could be reduced from over 20 to less than 5 ppb through nudging. Near the surface, FLEXPART CTM, including nudging, appears better able to capture methane molar mixing ratios than TM5 with optimized fluxes, based on a larger bias of over 13 ppb in TM5 simulations. The vertical profiles indicate that nudging affects model methane at high altitudes, yet leads to little improvement in the model results there. Averaged from 19 aircraft profile locations in North America and Siberia, root mean square error (RMSE) changes only from 16.3 to 15.7 ppb through nudging, while the mean absolute bias increases from 5.3 to 8.2 ppb. The performance for vertical profiles is thereby similar to TM5 simulations based on TM5 optimized fluxes where we found a bias of 5 ppb and RMSE of 15.9 ppb. With this rather simple model setup, we thus provide three-dimensional methane fields suitable for use as boundary conditions in regional inverse modelling as a priori information for satellite retrievals and for more accurate estimation of mean mixing ratios and growth rates. The method is also applicable to other long-lived trace gases. [ABSTRACT FROM AUTHOR]

This paper presents the results of BC inversions at high northern latitudes (> 50° N) for the 2013-2015 period. A sensitivity analysis was performed to select the best representative species for BC and the best a priori emission dataset. The same model ensemble was used to assess the uncertainty of the a posteriori emissions of BC due to scavenging and removal and due to the use of different a priori emission inventory. A posteriori concentrations of BC simulated over Arctic regions were compared with independent observations from flight and ship campaigns showing, in all cases, smaller bias, which in turn witnesses the success of the inversion. The annual a posteriori emissions of BC at latitudes above 50° N were estimated as 560±171 kt yr-1, significantly smaller than in ECLIPSEv5 (745 kt yr-1/, which was used and the a priori information in the inversions of BC. The average relative uncertainty of the inversions was estimated to be 30 %. A posteriori emissions of BC in North America are driven by anthropogenic sources, while biomass burning appeared to be less significant as it is also confirmed by satellite products. In northern Europe, a posteriori emissions were estimated to be half compared to the a priori ones, with the highest releases to be in megacities and due to biomass burning in eastern Europe. The largest emissions of BC in Siberia were calculated along the transect between Yekaterinsburg and Chelyabinsk. The optimised emissions of BC were high close to the gas flaring regions in Russia and in western Canada (Alberta), where numerous power and oil and gas production industries operate. Flaring emissions in Nenets--Komi oblast (Russia) were estimated to be much lower than in the a priori emissions, while in Khanty-Mansiysk (Russia) they remained the same after the inversions of BC. Increased emissions at the borders between Russia and Mongolia are probably due to biomass burning in villages along the Trans-Siberian Railway. The maximum BC emissions in high northern latitudes (> 50° N) were calculated for summer months due to biomass burning and they are controlled by seasonal variations in Europe and Asia, while North America showed a much smaller variability. [ABSTRACT FROM AUTHOR]

Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol--cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify largescale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large ( *** 10 W m-2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40% of the CF differences between the full dataset and the clean-condition subset, compared to between 57% and 91% of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol--cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic. [ABSTRACT FROM AUTHOR]

A Lagrangian particle dispersion model (FLEXPART CTM) is used to simulate global three-dimensional fields of trace gas abundance. These fields are constrained with surface observation data through nudging, a data assimilation method which relaxes model fields to observed values. Such fields are of interest to a variety of applications, such as inverse modelling, satellite retrievals and estimating global growth rates of greenhouse gases. Here, we apply this method to methane using 6 million model particles filling the global model domain. For each particle methane mass tendencies due to emissions based on several inventories, loss by reaction with OH, Cl, and O(¹D), as well as observation data nudging were calculated. Model particles were transported by mean, turbulent and convective transport driven by 1° x 1° ERA Interim meteorology. Nudging is applied at 79 surface stations, which are mostly included in the WDCGG database or JR-STATION network in Siberia. For simulations of one year (2013), we perform a sensitivity analysis to show how nudging settings affect modelled concentration fields. These are evaluated with a set of independent surface observations and with vertical profiles in North America (NOAA/ESRL) and Siberia (YAK-AEROSIB and NIES). FLEXPART CTM results are also compared to simulations from the global Eulerian model, TM5, based on optimized fluxes. Results show that nudging strongly improves modelled methane near the surface, not only at the nudging locations, but also at independent stations. Mean bias at all surface locations could be reduced from over 20 ppb to less than 5 ppb through nudging. Near the surface, FLEXPART CTM, including nudging, appears better able to capture methane molar mixing ratios than TM5 with optimized fluxes, based on a larger bias of over 13 ppb in TM5 simulations. The vertical profiles indicate that nudging affects model methane at high altitudes, yet leads to very little improvement in the model results there. Averaged from 19 aircraft profile locations in North America and Siberia, root-mean square error (RMSE) changes only from 16.3 to 15.7 ppb through nudging, while the mean absolute bias increases from 5.3 to 8.2 ppb. The performance for vertical profiles is thereby similar to TM5 simulations based on TM5 optimized fluxes where we found a bias of 5 ppb and RMSE of 15.9 ppb. With this rather simple model setup, we thus provide three-dimensional methane fields suitable for use as boundary conditions in regional inverse modelling, as prior information for satellite retrievals, and for more accurate estimation of mean mixing ratios and growth rates. The method should also be applicable to other long-lived trace gases. [ABSTRACT FROM AUTHOR]

In atmospheric tracer experiments, a substance is released into the turbulent atmospheric flow to study the dispersion parameters of the atmosphere. That can be done by observing the substance's concentration distribution downwind of the source. Past experiments have suffered from the fact that observations were only made at few discrete locations and/or at low time resolution. The Comtessa project (Camera Observation and Modelling of 4D Tracer Dispersion in the Atmosphere) is the first attempt at using ultraviolet (UV) camera observations to sample the three-dimensional (3D) concentration distribution in the atmospheric boundary layer at high spatial and temporal resolution. For this, during a three-week campaign in Norway in July 2017, sulfur dioxide (SO2), a nearly passive tracer, was artificially released in continuous plumes and nearly-instantaneous puffs from a 9m high tower. Column-integrated SO2 concentrations were observed with six UV SO2 cameras with sampling rates of several Hertz and a spatial resolution of a few centimetres. The atmospheric flow was characterised by eddy covariance measurements of heat and momentum fluxes at the release mast and two additional towers. By measuring simultaneously with six UV cameras positioned in a half circle around the release point, we could collect a data set of spatially and temporally resolved tracer column densities from six different directions, allowing a tomographic reconstruction of the 3D concentration field. However, due to unfavourable cloudy conditions on all measurement days and their restrictive effect on the SO2 camera technique, the presented data set is limited to case studies. In this paper, we present a feasibility study demonstrating that the turbulent dispersion parameters can be retrieved from images of artificially released puffs. The 3D trajectories of the centre of mass of the puffs were reconstructed enabling both a direct determination of the centre of mass meandering and a scaling of the image pixel dimension to the position of the puff. The latter made it possible to retrieve the temporal evolution of the puff spread projected to the image plane. The puff spread is a direct measure of the relative dispersion process. Combining meandering and relative dispersion, the absolute dispersion could be retrieved. The turbulent dispersion in the vertical is then used to estimate the effective source size, source time scale and the Lagrangian integral time. In principle, the Richardson-Obukhov constant of relative dispersion in the inertial subrange could be also obtained, but the observation time was not sufficiently long in comparison to the source time scale to allow an observation of this dispersion range. While the feasibility of the methodology to measure turbulent dispersion could be demonstrated, a larger data set with a larger number of cloud-free puff releases and longer observation times of each puff will be recorded in future studies to give a solid estimate for the turbulent dispersion under a variety of stability conditions. [ABSTRACT FROM AUTHOR]

Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol-cloud interactions. Here we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify regional-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (~ 25 W m−2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain much of this signal – for example, explaining up to 91 % of the CF differences between the full dataset and the clean-condition subset. After normalizing for meteorological regime, aerosol microphysical effects have small but significant regional-scale impacts on CF, CP, and precipitation frequency. These effects indicate that dominant aerosol-cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic. [ABSTRACT FROM AUTHOR]

Highly unusual open fires burned in Western Greenland between 31 July and 21 August 2017, after a period of warm, dry and sunny weather. The fires burned on peat lands that became vulnerable to fires by permafrost thawing. We used several satellite data sets to estimate that the total area burned was about 2345 hectares. Based on assumptions of typical burn depths and BC emission factors for peat fires, we estimate that the fires consumed a fuel amount of about 117 kt C and produced BC emissions of about 23.5 t. We used the Lagrangian particle dispersion model to simulate the atmospheric BC transport and deposition. We find that the smoke plumes were often pushed towards the Greenland Ice Sheet by westerly winds and thus a large fraction of the BC emissions (7 t or 30 %) was deposited on snow or ice covered surfaces. The calculated BC deposition was small compared to BC deposition from global sources, but not entirely negligible. Analysis of aerosol optical depth data from three sites in Western Greenland in August 2017 showed strong influence of forest fire plumes from Canada, but little impact of the Greenland fires. Nevertheless, CALIOP lidar data showed that our model captured very effectively the presence and structure of the plume from the Greenland fires. The albedo changes and instantaneous surface radiative forcing in Greenland due to the fire BC emissions were estimated with the SNICAR model and the uvspec model from the libRadtran radiative transfer software package. We estimate that the maximum albedo change due to the BC deposition was about 0.006, too small to be measured by satellites or other means. The average instantaneous surface radiative forcing over Greenland at noon on 31 August was 0.03 W m−2, with locally occurring maximum values of 0.63 W m−2. The average value is at least an order of magnitude smaller than the radiative forcing due to BC from other sources. Overall, the fires burning in Greenland in summer of 2017 had little impact on BC deposition on the Greenland Ice Sheet, causing almost negligible extra radiative forcing. This was due to the – in a global context – still rather small size of the fires. However, the very large fraction of the BC emissions deposited on the Greenland Ice Sheet makes these fires very efficient climate forcers on a per unit emission basis. If the expected further warming of Greenland produces much larger fires in the future, this could indeed cause substantial albedo changes and thus lead to accelerated melting of the Greenland Ice Sheet. The fires burning in 2017 may be a harbinger of such future changes [ABSTRACT FROM AUTHOR]

Short-lived climate forcers have been proven important both for the climate and human health. In particular, black carbon (BC) is an important climate forcer both as an aerosol and when deposited on snow and ice surface because of its strong light absorption. This paper presents measurements of elemental carbon (EC; a measurement-based definition of BC) in snow collected from western Siberia and northwestern European Russia during 2014, 2015 and 2016. The Russian Arctic is of great interest to the scientific community due to the large uncertainty of emission sources there. We have determined the major contributing sources of BC in snow in western Siberia and northwestern European Russia using a Lagrangian atmospheric transport model. For the first time, we use a recently developed feature that calculates deposition in backward (so-called retroplume) simulations allowing estimation of the specific locations of sources that contribute to the deposited mass. EC concentrations in snow from western Siberia and northwestern European Russia were highly variable depending on the sampling location. Modelled BC and measured EC were moderately correlated (R = 0.53-0.83) and a systematic region-specific model underestimation was found. The model underestimated observations by 42% (RMSED49 ngg-1) in 2014, 48% (RMSE=37 ngg-1) in 2015 and 27%(RMSE=43 ngg-1) in 2016. For EC sampled in northwestern European Russia the underestimation by the model was smaller (fractional bias, FB>-100 %). In this region, the major sources were transportation activities and domestic combustion in Finland. When sampling shifted to western Siberia, the model underestimation was more significant (FB<-100 %). There, the sources included emissions from gas flaring as a major contributor to snow BC. The accuracy of the model calculations was also evaluated using two independent datasets of BC measurements in snow covering the entire Arctic. The model underestimated BC concentrations in snow especially for samples collected in springtime. [ABSTRACT FROM AUTHOR]

Since 1994, the In-service Aircraft for a Global Observing System (IAGOS) program has produced in situ measurements of the atmospheric composition during more than 51 000 commercial flights. In order to help analyze these observations and understand the processes driving the observed concentration distribution and variability, we developed the SOFT-IO tool to quantify source-receptor links for all measured data. Based on the FLEXPART particle dispersion model (Stohl et al., 2005), SOFT-IO simulates the contributions of anthropogenic and biomass burning emissions from the ECCAD emission inventory database for all locations and times corresponding to the measured carbon monoxide mixing ratios along each IAGOS flight. Contributions are simulated from emissions occurring during the last 20 days before an observation, separating individual contributions from the different source regions. The main goal is to supply added-value products to the IAGOS database by evincing the geographical origin and emission sources driving the CO enhancements observed in the troposphere and lower stratosphere. This requires a good match between observed and modeled CO enhancements. Indeed, SOFT-IO detects more than 95% of the observed CO anomalies over most of the regions sampled by IAGOS in the troposphere. In the majority of cases, SOFT-IO simulates CO pollution plumes with biases lower than 10-15 ppbv. Differences between the model and observations are larger for very low or very high observed CO values. The added-value products will help in the understanding of the trace-gas distribution and seasonal variability. They are available in the IAGOS database via http://www.iagos.org. The SOFT-IO tool could also be applied to similar data sets of CO observations (e.g., ground-based measurements, satellite observations). SOFT-IO could also be used for statistical validation as well as for intercomparisons of emission inventories using large amounts of data. [ABSTRACT FROM AUTHOR]

Existing Lagrangian particle dispersion models are capable of establishing source-receptor relationships by running either forward or backward in time. For receptororiented studies such as interpretation of "point" measurement data, backward simulations can be computationally more efficient by several orders of magnitude. However, to date, the backward modelling capabilities have been limited to atmospheric concentrations or mixing ratios. In this paper, we extend the backward modelling technique to substances deposited at the Earth's surface by wet scavenging and dry deposition. This facilitates efficient calculation of emission sensitivities for deposition quantities at individual sites, which opens new application fields such as the comprehensive analysis of measured deposition quantities, or of deposition recorded in snow samples or ice cores. This could also include inverse modelling of emission sources based on such measurements. We have tested the new scheme as implemented in the Lagrangian particle dispersion model FLEXPART v10.2 by comparing results from forward and backward calculations. We also present an example application for black carbon concentrations recorded in Arctic snow. [ABSTRACT FROM AUTHOR]

In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model. [ABSTRACT FROM AUTHOR]

Ice-nucleating particles (INPs) active at modest supercooling (e.g. -8 °C; INP-8/ can transform clouds from liquid to mixed phase, even at very small number concentrations (<10m-3/. Over the course of 15 months, we found very similar patterns in weekly concentrations of INP-8 in PM10 (median D1.7m-3, maximum D10.1m-3/ and weekly amounts of rainfall (median D28 mm, maximum D153 mm) at Birkenes, southern Norway. Most INP-8 were probably aerosolised locally by the impact of raindrops on plant, litter and soil surfaces. Major snowfall and heavy rain onto snow-covered ground were not mirrored by enhanced numbers of INP-8. Further, transport model calculations for large (>4m-3/ and small (<4m-3/ numbers of INP-8 revealed that potential source regions likely to provide precipitation to southern Norway were associated with large numbers of INP-8. The proportion of land cover and land use type in potential source regions was similar for large and small numbers of INP-8. In PM2:5 we found consistently about half as many INP-8 as in PM10. From mid-May to mid-September, INP-8 correlated positively with the fungal spore markers arabitol and mannitol, suggesting that some fraction of INP-8 during that period may consist of fungal spores. In the future, warmer winters with more rain instead of snow may enhance airborne concentrations of INP-8 during the cold season in southern Norway and in other regions with a similar climate. [ABSTRACT FROM AUTHOR]

This paper describes the results of an inverse modeling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modeling tools were further developed, inverse modeling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high-quality measurements, which are essential for inverse modeling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations but also deposition measurements from the most recent public data set. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to provide available measurements to anyone interested. In regards to our inverse modeling results, emissions of 134Cs were estimated to be 80 PBq or 30-50% higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, on the same order as previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10% less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km) than previously assumed (≈2.2 km) in order to better match both concentration and deposition observations over Europe. The results of the present inversion were confirmed using an independent Eulerian model, for which deposition patterns were also improved when using the estimated posterior releases. Although the independent model tends to underestimate deposition in countries that are not in the main direction of the plume, it reproduces country levels of deposition very efficiently. The results were also tested for robustness against different setups of the inversion through sensitivity runs. The source term data from this study are publicly available. [ABSTRACT FROM AUTHOR]

Aerosol indirect effects have potentially large impacts on the Arctic Ocean surface energy budget, but model estimates of regional-scale aerosol indirect effects are highly uncertain and poorly validated by observations. Here we demonstrate a new way to quantitatively estimate aerosol indirect effects on a regional scale from remote sensing observations. In this study, we focus on nighttime, optically thin, predominantly liquid clouds. The method is based on differences in cloud physical and microphysical characteristics in carefully selected clean, average, and aerosol-impacted conditions. The cloud subset of focus covers just ~5% of cloudy Arctic Ocean regions, warming the Arctic Ocean surface by ~1-1.4W m-2 regionally during polar night. However, within this cloud subset, aerosol and cloud conditions can be determined with high confidence using CALIPSO and CloudSat data and model output. This cloud subset is generally susceptible to aerosols, with a polar nighttime estimated maximum regionally integrated indirect cooling effect of ~-0.11W m-2 at the Arctic sea ice surface (~8% of the clean background cloud effect), excluding cloud fraction changes. Aerosol presence is related to reduced precipitation, cloud thickness, and radar reflectivity, and in some cases, an increased likelihood of cloud presence in the liquid phase. These observations are inconsistent with a glaciation indirect effect and are consistent with either a deactivation effect or less-efficient secondary ice formation related to smaller liquid cloud droplets. However, this cloud subset shows large differences in surface and meteorological forcing in shallow and higher-altitude clouds and between sea ice and open-ocean regions. For example, optically thin, predominantly liquid clouds are much more likely to overlay another cloud over the open ocean, which may reduce aerosol indirect effects on the surface. Also, shallow clouds over open ocean do not appear to respond to aerosols as strongly as clouds over stratified sea ice environments, indicating a larger influence of meteorological forcing over aerosol microphysics in these types of clouds over the rapidly changing Arctic Ocean. [ABSTRACT FROM AUTHOR]

Short–lived climate forcers have been proven important both for the climate and human health. In particular, black carbon (BC) is an important climate forcer both as an aerosol and when deposited on snow and ice surface, because of its strong light absorption. This paper presents measurements of elemental carbon (EC; a measurement-based definition of BC) in snow collected from Western Siberia and northwestern European Russia during 2014, 2015 and 2016. The Russian Arctic is of great interest to the scientific community due to the large uncertainty of emission sources there. We have determined the major contributing sources of BC in snow in Western Siberia and northwestern European Russia using a Lagrangian atmospheric transport model. For the first time, we use a recently developed feature that calculates deposition in backwards (so-called retroplume) simulations allowing estimation of the specific locations of sources that contribute to the deposited mass. EC was found in higher levels compared to previously reported concentrations and it was highly variable depending on the sampling location. Modelled BC was in good agreement (R = 0.53–0.83) with measured EC. However, a systematic region–specific model underestimation was found. For EC sampled in northwestern European Russia the underestimation by the model was smaller (> &munus;100 %). In this region, the major sources were transportation activities and domestic combustion in Finland. When sampling shifted to Western Siberia, the model underestimation was more significant (< -100 %). There, the sources included emissions from gas flaring as a major contributor to snow BC. The accuracy of the model calculations was also evaluated using two independent datasets of BC measurements in snow covering the entire Arctic. The model reproduced snow BC concentrations quite accurately, although small discrepancies occurred mainly for samples collected in springtime. Nevertheless, EC concentrations in snow presented here are about 20 % lower than previously reported ones in Western Siberia and northwestern European Russia. [ABSTRACT FROM AUTHOR]

Icelandic dust sources are known to be highly active, yet there exist few model simulations of Icelandic dust that could be used to assess its impacts on the environment. We here present estimates of dust emission and transport in Iceland over 27 years (1990-2016) based on FLEXDUST & FLEXPART simulations and meteorological re-analysis data. Simulations for the year 2012 based on high-resolution operational meteorological analyses are used for model evaluation based on PM2.5 and PM10 observations in Iceland. For stations in Reykjavik, we find that the spring period is well predicted by the model, while dust events in late fall and early winter are overpredicted. Six years of dust concentrations observed at Stórhöfði (Heimaey) show that the model predicts concentrations in the same order of magnitude as observations and timing of modelled and observed dust peaks agrees well. Annual dust emission amounts to 4.3±0.8 Tg during the 27 years of simulation. Fifty percent of all dust from Iceland is on average emitted in just 25 days of the year, demonstrating the importance of a few strong events for annual total dust emissions. Annual dust emission as well as transport patterns correlate only weakly to the North Atlantic Oscillation. Deposition amounts in remote regions (Svalbard and Greenland) vary from year to year. Only limited dust amounts reach the upper Greenland Ice Sheet, but much dust is deposited on Icelandic glaciers and can impact melt rates there. Approximately 34 % of the annual dust emission is deposited in Iceland itself. Most dust (58 %) however, is deposited in the ocean and may strongly influence marine ecosystems. [ABSTRACT FROM AUTHOR]

Ice nucleating particles active at warm temperatures (e.g. -8 °C; INP-8) can transform clouds from liquid to mixed-phase, even at very small number concentrations (< 10 m-3). Over the course of 15 months, we found very similar patterns in weekly concentrations of INP-8 in PM10 (median = 1.7 m-3, maximum = 10.1 m-3) and weekly amounts of rainfall (median = 28 mm, maximum = 153 mm) at Birkenes, southern Norway. Probably, INP-8 were aerosolised locally by the impact of raindrops on plant, litter and soil surfaces. Major snowfall or heavy rain onto snow-covered ground were not mirrored by enhanced numbers of INP-8. Further, source receptor sensitivity fields obtained from transport model calculations for large and small numbers of INP-8 differed more in their likelihood to provide precipitation to southern Norway, than in the proportion of land cover or landuse type. In PM2.5 we found consistently about half as many INP-8 as in PM10. From mid-May to mid-September, INP-8 correlated positively with the fungal markers arabitol and mannitol, suggesting that INP-8 may consist largely of fungal spores. In the future, warmer winters with more rain instead of snow may enhance airborne concentrations of INP-8 during the cold season in southern Norway and in other regions with a similar climate. [ABSTRACT FROM AUTHOR]

A new, more physically based wet removal scheme for aerosols has been implemented in the Lagrangian particle dispersion model FLEXPART. It uses three-dimensional cloud water fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) to determine cloud extent and distinguishes between in-cloud and below-cloud scavenging. The new in-cloud nucleation scavenging depends on cloud water phase (liquid, ice or mixed-phase), based on the aerosol's prescribed efficiency to serve as ice crystal nuclei and liquid water nuclei, respectively. The impaction scavenging scheme now parameterizes below-cloud removal as a function of aerosol particle size and precipitation type (snow or rain) and intensity. Sensitivity tests with the new scavenging scheme and comparisons with observational data were conducted for three distinct types of primary aerosols, which pose different challenges for modeling wet scavenging due to their differences in solubility, volatility and size distribution: (1) 137Cs released during the Fukushima nuclear accident attached mainly to highly soluble sulphate aerosol particles, (2) black carbon (BC) aerosol particles, and (3) mineral dust. Calculated e-folding lifetimes of accumulation mode aerosols for these three aerosol types were 11.7, 16.0, and 31.6 days respectively, when well mixed in the atmosphere. These are longer lifetimes than those obtained by the previous removal schem, and, for mineral dust in particular, primarily result from very slow in-cloud removal, which globally is the primary removal mechanism for these accumulation mode particles. Calculated e-folding lifetimes in FLEXPART also have a strong size dependence, with the longest lifetimes found for the accumulation-mode aerosols. For example, for dust particles emitted at the surface the lifetimes were 13.8 days for particles with 1 µm diameter and a few hours for 10 µm particles. A strong size dependence in below-cloud scavenging, combined with increased dry removal, is the primary reason for the shorter lifetimes of the larger particles. The most frequent removal is in-cloud scavenging (85% of all scavenging events) but it occurs primarily in the free troposphere, while below-cloud removal is more frequent below 1000m (52%of all events) and can be important for the initial fate of species emitted at the surface, such as those examined here. For assumed realistic in-cloud removal efficiencies, both BC and sulphate have a slight overestimation of observed atmospheric concentrations (a factor of 1.6 and 1.2 respectively). However, this overestimation is largest close to the sources and thus appears more related to overestimated emissions rather than underestimated removal. The new aerosol wet removal scheme of FLEXPART incorporates more realistic information about clouds and aerosol properties and it compares better with both observed lifetimes and concentration than the old scheme. [ABSTRACT FROM AUTHOR]

Deposition of small amounts of airborne dust on glaciers causes positive radiative forcing and enhanced melting due to the reduction of surface albedo. To study the effects of dust deposition on the mass balance of Brúarjökull, an outlet glacier of the largest ice cap in Iceland, Vatnajökull, a study of dust deposition events in the year 2012 was carried out. The dust-mobilisation module FLEXDUST was used to calculate spatio-temporally resolved dust emissions from Iceland and the dispersion model FLEXPART was used to simulate atmospheric dust dispersion and deposition. We used albedo measurements at two automatic weather stations on Brúarjökull to evaluate the dust impacts. Both stations are situated in the accumulation area of the glacier, but the lower station is close to the equilibrium line. For this site (~1210ma.s.l.), the dispersion model produced 10 major dust deposition events and a total annual deposition of 20.5 gm-2. At the station located higher on the glacier (~1525ma.s.l.), the model produced nine dust events, with one single event causing ~5 gm-2 of dust deposition and a total deposition of ~10 gm-2 yr-1. The main dust source was found to be the Dyngjusandur floodplain north of Vatnajökull; northerly winds prevailed 80% of the time at the lower station when dust events occurred. In all of the simulated dust events, a corresponding albedo drop was observed at the weather stations. The influence of the dust on the albedo was estimated using the regional climate model HIRHAM5 to simulate the albedo of a clean glacier surface without dust. By comparing the measured albedo to the modelled albedo, we determine the influence of dust events on the snow albedo and the surface energy balance. We estimate that the dust deposition caused an additional 1.1mw.e. (water equivalent) of snowmelt (or 42% of the 2.8mw.e. total melt) compared to a hypothetical clean glacier surface at the lower station, and 0.6mw.e. more melt (or 38% of the 1.6mw.e. melt in total) at the station located further upglacier. Our findings show that dust has a strong influence on the mass balance of glaciers in Iceland. [ABSTRACT FROM AUTHOR]

We present methane (CH4) flux estimates for 2005 to 2013 from a Bayesian inversion focusing on the high northern latitudes (north of 50° N). Our inversion is based on atmospheric transport modelled by the Lagrangian particle dispersion model FLEXPART and CH4 observations from 17 in situ and five discrete flask-sampling sites distributed over northern North America and Eurasia. CH4 fluxes are determined at monthly temporal resolution and on a variable grid with maximum resolution of 1° x 1°. Our inversion finds a CH4 source from the high northern latitudes of 82 to 84 Tg yr-1, constituting ~15% of the global total, compared to 64 to 68 Tg yr-1 (~12 %) in the prior estimates. For northern North America, we estimate a mean source of 16.6 to 17.9 Tg yr-1, which is dominated by fluxes in the Hudson Bay Lowlands (HBL) and western Canada, specifically the province of Alberta. Our estimate for the HBL, of 2.7 to 3.4 Tg yr-1, is close to the prior estimate (which includes wetland fluxes from the land surface model, LPX-Bern) and to other independent inversion estimates. However, our estimate for Alberta, of 5.0 to 5.8 Tg yr-1, is significantly higher than the prior (which also includes anthropogenic sources from the EDGAR-4.2FT2010 inventory). Since the fluxes from this region persist throughout the winter, this may signify that the anthropogenic emissions are underestimated. For northern Eurasia, we find a mean source of 52.2 to 55.5 Tg yr-1, with a strong contribution from fluxes in the Western Siberian Lowlands (WSL) for which we estimate a source of 19.3 to 19.9 Tg yr-1. Over the 9-year inversion period, we find significant year-to-year variations in the fluxes, which in North America, and specifically in the HBL, appear to be driven at least in part by soil temperature, while in the WSL, the variability is more dependent on soil moisture. Moreover, we find significant positive trends in the CH4 fluxes in North America of 0.38 to 0.57 Tg yr-2, and northern Eurasia of 0.76 to 1.09 Tg yr-2. In North America, this could be due to an increase in soil temperature, while in North Eurasia, specifically Russia, the trend is likely due, at least in part, to an increase in anthropogenic sources. [ABSTRACT FROM AUTHOR]

In the fall of 2011, iodine-131 (I-131) was detected at several radionuclide monitoring stations in Central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that I-131 was released from the Institute of Isotopes Ltd in Budapest, Hungary. It was reported that a total activity of 342 GBq of I-131 was emitted between September 8 and November 16, 2011. In this study, we use the ambient concentration measurements of I-131 to determine the location of the release as well as its magnitude and temporal variation. Although the location of the release became eventually known, its temporal variation is still uncertain and only partial information is available. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available I-131 measurements. Subsequently, we use the information about the source term for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor-sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, Flexpart and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) weather forecast model. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the I-131 emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform forward calculation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA, while our estimate for the total released activity (490 GBq) is higher than the reported one (342 GBq). Nevertheless, even using our larger source term, dose amounts were very low and never exceeded regulatory limits. [ABSTRACT FROM AUTHOR]

Aerosol indirect effects have uncertain, but potentially large, impacts on the Arctic energy budget. Here, we have reduced uncertainty in current-day Arctic net aerosol indirect effects on the surface by better constraining various physical and microphysical characteristics of optically thin, liquid-containing clouds in clean, average and aerosol-impacted conditions using a combination of CALIPSO and CloudSat data and model output. This work provides a foundation for how future observational studies can evaluate previous model estimates of the aerosol indirect effect. Clouds over sea ice and open ocean show large differences in surface and meteorological forcing, including a near doubling of multi-layer cloud presence over the open ocean compared to sea ice. The optically thin cloud subset is susceptible to aerosols, and over sea ice we estimate a regional scale maximum net indirect effect on these clouds during polar night equivalent to ~ 0.6-0.8 W m-2 at the surface. Aerosol presence is related to reduced precipitation, cloud thickness, and radar reflectivity, and may be associated with an increased likelihood of cloud presence in the liquid phase. The observations are consistent with a thermodynamic indirect effect hypothesis and are inconsistent with a glaciation indirect effect. [ABSTRACT FROM AUTHOR]

Increasing wildfire activities in the mountainous western US may present a challenge for the region to attain a recently revised ozone air quality standard in summer. Using current Eulerian chemical transport models to examine the wildfire ozone influences is difficult due to uncertainties in fire emissions, inadequate model chemistry, and resolution. Here we quantify the wildfire influence on the ozone variability, trends, and number of high MDA8 (daily maximum 8 h average) ozone days over this region in summers (June, July, and August) 1989-2010 using a new approach. We define a fire index using retroplumes (plumes of back-trajectory particles) computed by a Lagrangian dispersion model (FLEXPART) and develop statistical models based on the fire index and meteorological parameters to interpret MDA8 ozone concentrations measured at 13 Intermountain West surface sites. We show that the statistical models are able to capture the ozone enhancements by wildfires and give results with some features different from the GEOS-Chem Eulerian chemical transport model. Wildfires enhance the Intermountain West regional summer mean MDA8 ozone by 0.3-1.5 ppbv (daily episodic enhancements reach 10-20 ppbv at individual sites) with large interannual variability, which are strongly correlated with the total MDA8 ozone. We find large fire impacts on the number of exceedance days; for the 13 CASTNet sites, 31 % of the summer days with MDA8 ozone exceeding 70 ppbv would not occur in the absence of wildfires. [ABSTRACT FROM AUTHOR]

The offline FLEXible PARTicle (FLEXPART) stochastic dispersion model is currently a community model used by many scientists. Here, an alternative FLEXPART model version has been developed and tailored to use with the meteorological output data generated by the CMIP5- version of the Norwegian Earth System Model (NorESM1- M). The atmospheric component of the NorESM1-M is based on the Community Atmosphere Model (CAM4); hence, this FLEXPART version could be widely applicable and it provides a new advanced tool to directly analyse and diagnose atmospheric transport properties of the state-of-theart climate model NorESM in a reliable way. The adaptation of FLEXPART to NorESM required new routines to read meteorological fields, new post-processing routines to obtain the vertical velocity in the FLEXPART coordinate system, and other changes. These are described in detail in this paper. To validate the model, several tests were performed that offered the possibility to investigate some aspects of offline global dispersion modelling. First, a comprehensive comparison was made between the tracer transport from several point sources around the globe calculated online by the transport scheme embedded in CAM4 and the FLEXPART model applied offline on output data. The comparison allowed investigating several aspects of the transport schemes including the approximation introduced by using an offline dispersion model with the need to transform the vertical coordinate system, the influence on the model results of the sub-grid-scale parameterisations of convection and boundary layer height and the possible advantage entailed in using a numerically non-diffusive Lagrangian particle solver. Subsequently, a comparison between the reference FLEXPART model and the FLEXPART-NorESM/CAM version was performed to compare the well-mixed state of the atmosphere in a 1-year global simulation. The two model versions use different methods to obtain the vertical velocity but no significant difference in the results was found. However, for both model versions there was some degradation in the well-mixed state after 1 year of simulation with the buildup of a bias and an increased scatter. Finally, the capability of the new combined modelling system in producing realistic, backward-in-time transport statistics was evaluated calculating the average footprint over a 5-year period for several measurement locations and by comparing the results with those obtained with the reference FLEXPART model driven by re-analysis fields. This comparison confirmed the effectiveness of the combined modelling system FLEXPART with NorESM in producing realistic transport statistics. [ABSTRACT FROM AUTHOR]

A new, more physically based wet removal scheme for aerosols has been implemented in the Lagrangian particle dispersion model FLEXPART. It uses three-dimensional cloud water fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) to determine cloud extent and distinguishes between in-cloud and below-cloud scavenging. The in-cloud nucleation scavenging differentiates between cloud water phases (liquid, ice or mixed-phase) to allow for aerosol and cloud type specific removal. The impaction scavenging scheme parameterizes below-cloud removal as a function of aerosol size and precipitation type (snow or rain) and intensity. Sensitivity tests with the new scavenging scheme and comparisons with observational data were conducted for three distinct types of primary aerosols, which pose different challenges for modelling wet scavenging due to their differences in solubility, volatility and size distribution: (1) 137Cs released during the Fukushima nuclear accident attached mainly to highly soluble sulphate aerosol, (2) black carbon (BC) aerosol, and (3) mineral dust. Calculated e-folding lifetimes of accumulation mode aerosols for these three aerosol types were, 11.7, 16.0, and 31.6 days respectively, when well mixed in the atmosphere. The long lifetimes of mineral dust in particular are primarily a result of slow in-cloud removal which is the primary removal mechanism. Calculated e-folding lifetimes in FLEXPART also have a strong size dependence, with the longest lifetimes found for the accumulation-mode aerosols. For example, for dust particles emitted at the surface the lifetimes were 13.8 days for particles with 1 &956;m diameter and a few hours for 10 &956;m particles. A strong size dependence in below cloud scavenging, combined with increased dry deposition and gravitational settling, is the primary reason for the shorter lifetimes of the larger particles. The most frequent removal is in-cloud scavenging (85 % of all scavenging events) but it occurs primarily in the free troposphere, while below-cloud removal is more frequent below 1000 m (52 % of all events) and can be important for the initial fate of species emitted at the surface, such as those examined here. For assumed realistic in-cloud removal efficiencies, both BC and sulphate have a slight overestimation of observed atmospheric concentrations (a factor of 1.6 and 1.2 respectively). However, this overestimation is largest close to the sources and thus appears more related to overestimated emissions rather than underestimated removal. [ABSTRACT FROM AUTHOR]

We present methane (CH4) flux estimates for 2005 to 2013 from a Bayesian inversion focusing on the high northern latitudes (north of 50° N). Our inversion is based on atmospheric transport modelled by the Lagrangian particle dispersion model, FLEXPART, and CH4 observations from 17 in-situ and 5 discrete flask-sampling sites distributed over northern North America and Eurasia. CH4 fluxes are determined at monthly temporal resolution and on a variable grid with maximum resolution of 1° × 1°. Our inversion finds a CH4 source from the high northern latitudes of 82 to 84 Tg y-1, constituting ~15% of the global total, compared to 64 to 68 Tg y-1 (~12%) in the prior estimates. For northern North America, we estimate a mean source of 16.6 to 17.9 Tg y-1, which is dominated by fluxes in the Hudson Bay Lowlands (HBL) and western Canada, specifically, the province of Alberta. Our estimate for the HBL, of 2.7 to 3.4 Tg y-1, is close to the prior estimate (which includes wetland fluxes from the land surface model, LPX-Bern) and to other independent inversion estimates. However, our estimate for Alberta, of 5.0 to 5.8 Tg y-1 is significantly higher than the prior (which also includes anthropogenic sources from the EDGAR-4.2FT2010 inventory). Since the fluxes from this region persist throughout the winter, this may signify that the anthropogenic emissions are underestimated. For North Eurasia, we find a mean source of 52.2 to 55.5 Tg y-1, with a strong contribution from fluxes in the Western Siberian Lowlands (WSL) for which we estimate a source of 19.3 to 19.9 Tg y-1. Over the 9-year inversion period, we find significant year-to-year variations in the fluxes, which in North America and, specifically, in the HBL appear to be driven at least in part by soil temperature, while in the WSL, the variability is more dependent on soil moisture. Moreover, we find significant positive trends in the CH4 fluxes in North America of 0.38 to 0.57 Tg y-1 per year, and North Eurasia of 0.76 to 1.09 Tg y-1 per year. In North America, this could be due to an increase in soil temperature, while in North Eurasia, specifically, Russia, the trend is likely due, at least in part, to an increase in anthropogenic sources. [ABSTRACT FROM AUTHOR]

In recent decades much attention has been given to the Arctic environment, where climate change is happening rapidly. Black carbon (BC) has been shown to be a major component of Arctic pollution that also affects the radiative balance. In the present study, we focused on how vegetation fires that occurred in northern Eurasia during the period of 2002-2013 influenced the budget of BC in the Arctic. For simulating the transport of fire emissions from northern Eurasia to the Arctic, we adopted BC fire emission estimates developed independently by GFED3 (Global Fire Emissions Database) and FEI-NE (Fire Emission Inventory - northern Eurasia). Both datasets were based on fire locations and burned areas detected by MODIS (Moderate resolution Imaging Spectroradiometer) instruments on NASA's (National Aeronautics and Space Administration) Terra and Aqua satellites. Anthropogenic sources of BC were adopted from the MACCity (Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment) emission inventory. During the 12-year period, an average area of 250 000 km² yr-1 was burned in northern Eurasia (FEINE) and the global emissions of BC ranged between 8.0 and 9.5 Tg yr-1 (FEI-NE+MACCity). For the BC emitted in the Northern Hemisphere (based on FEI-NECMACCity), about 70% originated from anthropogenic sources and the rest from biomass burning (BB). Using the FEI-NE+CMACCity inventory, we found that 102-29 kt yr-1 BC was deposited in the Arctic (defined here as the area north of 67° N) during the 12 years simulated, which was twice as much as when using the MACCity inventory (56±8 kt yr-1/. The annual mass of BC deposited in the Arctic from all sources (FEI-NE in northern Eurasia, MACCity elsewhere) is significantly higher by about 37% in 2009 (78 vs. 57 kt yr-1/ to 181% in 2012 (153 vs. 54 kt yr-1/, compared to the BC deposited using just the MACCity emission inventory. Deposition of BC in the Arctic from BB sources in the Northern Hemisphere thus represents 68% of the BC deposited from all BC sources (the remaining being due to anthropogenic sources). Northern Eurasian vegetation fires (FEI-NE) contributed 85% (79-91%) to the BC deposited over the Arctic from all BB sources in the Northern Hemisphere. We estimate that about 46% of the BC deposited over the Arctic from vegetation fires in northern Eurasia originated from Siberia, 6% from Kazakhstan, 5% from Europe, and about 1% from Mongolia. The remaining 42% originated from other areas in northern Eurasia. About 42%of the BC released from northern Eurasian vegetation fires was deposited over the Arctic (annual average: 17%) during spring and summer. [ABSTRACT FROM AUTHOR]

The off-line FLEXible PARTicle stochastic dispersion model (FLEXPART) is nowadays a community model used by many scientists. Here, an alternative FLEXPART model version has been developed, tailored to use with the meteorological output data generated by the CMIP5-version of the Norwegian Earth System Model (NorESM1-M). The atmospheric component of NorESM1-M is based on the Community Atmosphere Model (CAM4), hence this FLEXPART version could be widely applicable and it provides a new advanced tool to directly analyse and diagnose atmospheric transport properties of the state-of-the-art climate model NorESM in a reliable way. The adaptation of FLEXPART to NorESM required new routines to read meteorological fields, new post-processing routines to obtain the vertical velocity in the FLEXPART coordinate system and other changes. These are described in detail here. To validate the model, several tests were performed that offered the possibility to investigate some aspects of off-line global dispersion modelling. First, a comprehensive comparison was made between the tracer-transport from several point sources around the globe calculated on-line by the transport scheme embedded in CAM4 and the FLEXPART model applied off-line on output data. The comparison allowed investigating several aspects of the transport schemes including: the approximation introduced by using an off-line dispersion model with the need to transform the vertical coordinate system, the influence on the model results of the sub-grid scale parameterizations of convection and boundary layer height and the possible advantage entailed in using a numerically non-diffusive Lagrangian particle solver. Subsequently, a comparison between the reference FLEXPART model and the FLEXPART-NorESM/CAM version was performed to compare the well-mixed state of the atmosphere in a one-year global simulation. The two model versions use different methods to obtain the vertical velocity but no significant difference in the results was found. However, for both model versions there was some degradation in the well-mixed state after one-year of simulation. Finally, the capability of the new combined modelling system in producing realistic backward in time transport statistics was evaluated calculating the average footprint over a five-year period for several measurement locations and by comparing the results with those obtained with the reference FLEXPART model driven by re-analysis fields. This comparison confirmed the effectiveness of the combined modelling system FLEXPART with NorESM in producing realistic transport statistics. [ABSTRACT FROM AUTHOR]

The properties of low-level liquid clouds in the Arctic can be altered by long-range pollution transport to the region. Satellite, tracer transport model, and meteorological data sets are used here to determine a net aerosol-cloud interaction (ACInet) parameter that expresses the ratio of relative changes in cloud microphysical properties to relative variations in pollution concentrations while accounting for dry or wet scavenging of aerosols en route to the Arctic. For a period between 2008 and 2010, ACInet is calculated as a function of the cloud liquid water path, temperature, altitude, specific humidity, and lower tropospheric stability. For all data, ACInet averages 0:12±0:02 for cloud-droplet effective radius and 0:16±0:02 for cloud optical depth. It increases with specific humidity and lower tropospheric stability and is highest when pollution concentrations are low. Carefully controlling for meteorological conditions we find that the liquid water path of arctic clouds does not respond strongly to aerosols within pollution plumes. Or, not stratifying the data according to meteorological state can lead to artificially exaggerated calculations of the magnitude of the impacts of pollution on arctic clouds. [ABSTRACT FROM AUTHOR]

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g. concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation allows to estimate also all tuning parameters from the measurements. The estimation procedure is based on the Variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to estimate also all tuning parameters from the observations. The proposed algorithm is tested and compared with the state-of-the-art method on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download. [ABSTRACT FROM AUTHOR]

The properties of clouds in the Arctic can be altered by long-range aerosol transport to the region. The goal of this study is to use satellite, tracer transport model, and meteorological data sets to determine the effects of pollution on cloud microphysics due only to pollution itself and not to the meteorological state. Here, A-Train, POLDER-3 and MODIS satellite instruments are used to retrieve low-level liquid cloud microphysical properties over the Arctic between 2008 and 2010. Cloud retrievals are co-located with simulated pollution represented by carbon-monoxide concentrations from the FLEXPART tracer transport model. The sensitivity of clouds to pollution plumes - including aerosols - is constrained for cloud liquid water path, temperature, altitude, specific humidity, and lower tropospheric stability (LTS). We define an Indirect Effect (IE) parameter from the ratio of relative changes in cloud microphysical properties to relative variations in pollution concentrations. Retrievals indicate that, depending on the meteorological regime, IE parameters range between 0 and 0.34 for the cloud droplet effective radius, and between ..0.10 and 0.35 for the optical depth, with average values of 0.12±0.02 and 0.15±0.02 respectively. The IE parameter increases with increasing specific humidity and LTS. Further, the results suggest that for a given set of meteorological conditions, the liquid water path of arctic clouds does not respond strongly to pollution. Or, not constraining sufficiently for meteorology may lead to artifacts that exaggerate the magnitude of the aerosol indirect effect. The converse is that the response of arctic clouds to pollution does depend on the meteorologic state. Finally, we find that IE values are highest when pollution concentrations are low, and that they depend on the source of pollution. [ABSTRACT FROM AUTHOR]