Temporal and spatial variability of Icelandic dust emissions and atmospheric transport

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 and 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 of the same order of magnitude as observations and timing of modelled and observed dust peaks agrees well. Average annual dust emission is 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 considerable dust amounts are 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.We acknowledge funding provided by the Swiss National Science Foundation (grant 155294) and travel grants provided by the Nordic Centre of Excellence eSTICC (Nordforsk 57001). OA and PDW were supported by Icelandic Research Fund (Rannis) grant no. 152248-051 and PDW by The Recruitment Fund of the University of Iceland. The station at Stórhöfði was initially established with support from the US National Atmospheric and Oceanic Administration to JMP and later sampling and analysis with support various grants from the US National Science Foundation (AGS-0962256).Peer Reviewe

A new aerosol wet removal scheme for the Lagrangian particle model FLEXPART v10

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 MediumRange 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) Cs-137 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 aem diameter and a few hours for 10 aem 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.Peer reviewe

Impact of dust deposition on the albedo of Vatnajökull ice cap, Iceland

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 ( ∼  1210 m a.s.l.), the dispersion model produced 10 major dust deposition events and a total annual deposition of 20.5 g m−2. At the station located higher on the glacier ( ∼  1525 m a.s.l.), the model produced nine dust events, with one single event causing  ∼  5 g m−2 of dust deposition and a total deposition of  ∼  10 g m−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 1.1 m w.e. (water equivalent) of snowmelt (or 42 % of the 2.8 m w.e. total melt) compared to a hypothetical clean glacier surface at the lower station, and 0.6 m w.e. more melt (or 38 % of the 1.6 m w.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.The study described in this manuscript was supported by NordForsk as part of the two Nordic Centres of Excellence Cryosphere-Atmosphere Interactions in a Changing Arctic climate (CRAICC), and eScience Tools for Investigating Climate Change (eSTICC). Part of this work was supported by the Centre of Excellence in Atmospheric Science funded by the Finnish Academy of Sciences Excellence (project no. 272041), by the Finnish Academy of Sciences project A4 (contract 254195). Data from in situ mass balance surveys and on glacier automatic weather stations are from joint projects of the National Power Company and the Glaciology group of the Institute of Earth Science, University of Iceland. C. Groot Zwaaftink was also funded by the Swiss National Science Foundation SNF (155294), and Louise Steffensen-Schmidt, Finnur Palsson and Sverrir Gudmunds-son by the Icelandic Research Fund (project SAMAR) and the National Power Company of Iceland. Olafur Arnalds was in part funded by Icelandic Research Fund (grant no. 152248-051)Peer Reviewe

Modelling Small-Scale Drifting Snow with a Lagrangian Stochastic Model Based on Large-Eddy Simulations

Observations of drifting snow on small scales have shown that, in spite of nearly steady winds, the snow mass flux can strongly fluctuate in time and space. Most drifting snow models, however, are not able to describe drifting snow accurately over short time periods or on small spatial scales as they rely on mean flow fields and assume equilibrium saltation. In an attempt to gain understanding of the temporal and spatial variability of drifting snow on small scales, we propose to use a model combination of flow fields from large-eddy simulations (LES) and a Lagrangian stochastic model to calculate snow particle trajectories and so infer snow mass fluxes. Model results show that, if particle aerodynamic entrainment is driven by the shear stress retrieved from the LES, we can obtain a snow mass flux varying in space and time. The obtained fluctuating snow mass flux is qualitatively compared to field and wind-tunnel measurements. The comparison shows that the model results capture the intermittent behaviour of observed drifting snow mass flux yet differences between modelled turbulent structures and those likely to be found in the field complicate quantitative comparisons. Results of a model experiment show that the surface shear-stress distribution and its influence on aerodynamic entrainment appear to be key factors in explaining the intermittency of drifting snow

Modelling the 2021 East Asia super dust storm using FLEXPART and FLEXDUST and its comparison with reanalyses and observations

The 2021 East Asia sandstorm began from the Eastern Gobi desert steppe in Mongolia on March 14, and later spread to northern China and the Korean Peninsula. It was the biggest sandstorm to hit China in a decade, causing severe air pollution and a significant threat to human health. Capturing and predicting such extreme events is critical for society. The Lagrangian particle dispersion model FLEXPART and the associated dust emission model FLEXDUST have been recently developed and applied to simulate global dust cycles. However, how well the model captures Asian dust storm events remains to be explored. In this study, we applied FLEXPART to simulate the recent 2021 East Asia sandstorm, and evaluated its performance comparing with observation and observation-constrained reanalysis datasets, such as the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and CAMS global atmospheric composition forecasts (CAMS-F). We found that the default setting of FLEXDUST substantially underestimates the strength of dust emission and FLEXPART modelled dust concentration in this storm compared to that in MERRA-2 and CAMS-F. An improvement of the parametrization of bare soil fraction, topographical scaling, threshold friction velocity and vertical dust flux scheme based on Kok et al. (Atmospheric Chemistry and Physics, 2014, 14, 13023-13041) in FLEXDUST can reproduce the strength and spatio-temporal pattern of the dust storm comparable to MERRA-2 and CAMS-F. However, it still underestimates the observed spike of dust concentration during the dust storm event over northern China, and requires further improvement in the future. The improved FLEXDUST and FLEXPART perform better than MERRA-2 and CAMS-F in capturing the observed particle size distribution of dust aerosols, highlighting the importance of using more dust size bins and size-dependent parameterization for dust emission, and dry and wet deposition schemes for modelling the Asian dust cycle and its climatic feedbacks.Peer reviewe

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 C UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH(4) yr (-1) (EDGAR v5.0) and 19.0 TgCH(4) yr(-1) (GAINS), consistent with the NGHGI estimates of 18.9 +/- 1.7 TgCH(4) yr(-1). The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH(4) yr(-1). Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH(4) yr(-1)) and surface network (24.4 TgCH(4) yr (-1)). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH(4) yr(-1). For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 TgN(2)Oyr(-1), respectively, agreeing with the NGHGI data (0.9 0.6 TgN(2)Oyr(-1)). Over the same period, the average of the three total TD global and regional inversions was 1.3 +/- 0.4 and 1.3 +/- 0.1 TgN(2)Oyr(-1), respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU CUK scale and at the national scale.Peer reviewe

Aerosol optical properties in the Arctic: The role of aerosol chemistry and dust composition in a closure experiment between Lidar and tethered balloon vertical profiles

A closure experiment was conducted over Svalbard by comparing Lidar measurements and optical aerosol properties calculated from aerosol vertical profiles measured using a tethered balloon. Arctic Haze was present together with Icelandic dust. Chemical analysis of filter samples, aerosol size distribution and a full set of meteorological parameters were determined at ground. Moreover, scanning electron microscopy coupled with energy-dispersive X-ray (SEM-EDS) data were at disposal showing the presence of several mineralogical phases (i.e., sheet silicates, gypsum, quartz, rutile, hematite). The closure experiment was set up by calculating the backscattering coefficients from tethered balloon data and comparing them with the corresponding lidar profiles. This was preformed in three subsequent steps aimed at determining the importance of a complete aerosol speciation: (i) a simple, columnar refractive index was obtained by the closest Aerosol Robotic Network (AERONET) station, (ii) the role of water-soluble components, elemental carbon and organic matter (EC/OM) was addressed, (iii) the dust composition was included. When considering the AERONET data, or only the ionic water-soluble components and the EC/OM fraction, results showed an underestimation of the backscattering lidar signal up to 76, 53 and 45% (355, 532 and 1064 nm). Instead, when the dust contribution was included, the underestimation disappeared and the vertically-averaged, backscattering coefficients (1.45±0.30, 0.69±0.15 and 0.34±0.08 Mm-1 sr-1, at 355, 532 and 1064 nm) were found in keeping with the lidar ones (1.60±0.22, 0.75±0.16 and 0.31±0.08 Mm-1 sr-1). Final results were characterized by low RMSE (0.36, 0.08 and 0.04 Mm-1 sr-1) and a high linear correlation (R2 of 0.992, 0.992 and 0.994) with slopes close to one (1.368, 0.931 and 0.977, respectively). This work highlighted the importance of all the aerosol components and of the synergy between single particle and bulk chemical analysis for the optical property characterization in the Arctic

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al., 2020b)