Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

Eckhardt, Sabine; Cassiani, Massimo; Evangeliou, Nikolaos; Sollum, Espen; Pisso, Ignacio; Stohl, Andreas

doi:10.5194/gmd-10-4605-2017

Cited by 46 publications

(55 citation statements)

References 26 publications

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“…Bond et al (2004) estimate the uncertainty in BC emission inventories to be a factor of about 2. Flanner et al (2007) conclude that for the climate forcing by BC in snow, the emissions introduce a bigger uncertainty than the scavenging by snowmelt water and snow aging. However, the quality of emission inventories is difficult to assess with models because of the dependence on the model (Vignati et al, 2010).…”

Section: Introductionmentioning

confidence: 90%

“…The aerosol transport and radiation simulations in this study consider the reduction of snow albedo due to deposited BC. The BC-in-snow albedo effect is parameterized in terms of a lookup table based on a single-layer version of the Snow, Ice and Aerosol Radiation (SNICAR) model from Flanner et al (2007). The scheme was first implemented in the earthsystem model version of ECHAM6 by Engels (2016) and has become available recently in ECHAM6.3-HAM2.3 (Gilgen et al, 2018).…”

Section: Calculation Of Direct Aerosol Radiative Effects Of Bcmentioning

confidence: 99%

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The importance of the representation of air pollution emissions for the modeled distribution and radiative effects of black carbon in the Arctic

et al. 2019

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Aerosol particles can contribute to the Arctic amplification (AA) by direct and indirect radiative effects. Specifically, black carbon (BC) in the atmosphere, and when deposited on snow and sea ice, has a positive warming effect on the top-of-atmosphere (TOA) radiation balance during the polar day. Current climate models, however, are still struggling to reproduce Arctic aerosol conditions. We present an evaluation study with the global aerosol-climate model ECHAM6.3-HAM2.3 to examine emission-related uncertainties in the BC distribution and the direct radiative effect of BC. The model results are comprehensively compared against the latest ground and airborne aerosol observations for the period 2005-2017, with a focus on BC. Four different setups of air pollution emissions are tested. The simulations in general match well with the observed amount and temporal variability in near-surface BC in the Arctic. Using actual daily instead of fixed biomass burning emissions is crucial for reproducing individual pollution events but has only a small influence on the seasonal cycle of BC. Compared with commonly used fixed anthropogenic emissions for the year 2000, an up-to-date inventory with transient air pollution emissions results in up to a 30 % higher annual BC burden locally. This causes a higher annual mean all-sky net direct radiative effect of BC of over 0.1 W m −2 at the top of the atmosphere over the Arctic region (60-90 • N), being locally more than 0.2 W m −2 over the eastern Arctic Ocean. We estimate BC in the Arctic as leading to an annual net gain of 0.5 W m −2 averaged over the Arctic region but to a local gain of up to 0.8 W m −2 by the direct radiative effect of atmospheric BC plus the effect by the BC-in-snow albedo reduction. Long-range transport is identified as one of the main sources of uncertainties for ECHAM6.3-HAM2.3, leading to an overestimation of BC in atmospheric layers above 500 hPa, especially in summer. This is related to a misrepresentation in wet removal in one identified case at least, which was observed during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) summer aircraft campaign. Overall, the current model version has significantly improved since previous intercomparison studies and now performs better than the multi-model average in the Aerosol Comparisons between Observation and Models (AEROCOM) initiative in terms of the spatial and temporal distribution of Arctic BC.

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Section: Introductionmentioning

confidence: 90%

Section: Calculation Of Direct Aerosol Radiative Effects Of Bcmentioning

confidence: 99%

The importance of the representation of air pollution emissions for the modeled distribution and radiative effects of black carbon in the Arctic

et al. 2019

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“…However, there are also measurements of deposition on the ground, e.g., in precipitation samples or ice cores, and for this type of measurement no backward simulations were possible until recently. Therefore, Eckhardt et al (2017) introduced the option to calculate SRR values in backward mode also for wet and dry deposition, and a first application to ice core data was presented by McConnell et al (2018). It is anticipated that quantitative interpretation of ice core data will be a major application of the new backward mode, which is efficient enough to allow for the calculation of, for example, 100 years of seasonally resolved deposition data in less than 24 h of computation time.…”

Section: Source-receptor Matrix Calculation Of Deposited Mass Backwarmentioning

confidence: 99%

“…For forward runs, additional files grid_pptv_date_nnn can be created (setting IOUT to values of 2 or 3), which contain data such as volume mixing ratios (requires molar weight in SPECIES_nnn file). Source-receptor relationships (i.e., emission sensitivities) in backward mode for atmospheric receptors are written out in grid_time_date_nnn files; those for deposited mass are recorded in files grid_wetdep_date_nnn and grid_drydep_date_nnn (see Seibert and Frank (2004), Eckhardt et al (2017), Sect. 2.5, and Table 12 for output units).…”

Section: Namementioning

confidence: 99%

The Lagrangian particle dispersion model FLEXPART version 10.4

et al. 2019

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The Lagrangian particle dispersion model FLEX-PART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Cen-ters of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEX-PART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposi-Published by Copernicus Publications on behalf of the European Geosciences Union. 4956 I. Pisso et al.: FLEXPART version 10.4tion scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEX-PART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with ...

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“…All released particles represent a unity deposition amount, which was converted immediately (i.e. upon release of a particle) to atmospheric concentrations using the deposition intensity as characterized by either dry deposition velocity or scavenging rate (for further details, see Eckhardt et al, 2017). The concentrations were subsequently treated as in normal "concentration mode" backward tracking (Seibert and Frank, 2004) to establish source-receptor relationships between the emissions and deposition amounts.…”

Section: Emissions and Modelling Of Black Carbonmentioning

confidence: 99%

Origin of elemental carbon in snow from western Siberia and northwestern European Russia during winter–spring 2014, 2015 and 2016

et al. 2018

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Abstract. 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 % (RMSE = 49 ng g−1) in 2014, 48 % (RMSE = 37 ng g−1) in 2015 and 27 % (RMSE = 43 ng g−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.

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Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

Cited by 46 publications

References 26 publications

The importance of the representation of air pollution emissions for the modeled distribution and radiative effects of black carbon in the Arctic

The importance of the representation of air pollution emissions for the modeled distribution and radiative effects of black carbon in the Arctic

The Lagrangian particle dispersion model FLEXPART version 10.4

Origin of elemental carbon in snow from western Siberia and northwestern European Russia during winter–spring 2014, 2015 and 2016

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