Temporal and spatial variability of Icelandic dust emissions and atmospheric transport

Zwaaftink, Christine D. Groot; Dagsson-Waldhauserová, Pavla; Eckhardt, Sabine; Prospero, Joseph M.; Stohl, Andreas

doi:10.5194/acp-17-10865-2017

Cited by 45 publications

(50 citation statements)

References 41 publications

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“…One simple method is to estimate the background from the measurements as, e.g., in Stohl et al (2009). A better approach is to use a concentration field taken from a long-term forward simulation with an Eulerian model or with FLEX-PART itself, especially if nudged to observations (Groot Zwaaftink et al, 2018), as an initial condition for the backward simulation. This field needs to be interfaced with the FLEXPART backward simulation by calculating the receptor sensitivity to the initial conditions (see Eqs.…”

Section: Sensitivity To Initial Conditionsmentioning

confidence: 99%

“…These sources are rarely included in global dust models, even though they appear important for the climate system and substantially contribute to dust in the Arctic (Bullard et al, 2016;Groot Zwaaftink et al, 2016). Icelandic deserts in particular are known to be highly active, and a high-resolution surface type map for Iceland can therefore be included in FLEXDUST simulations (Arnalds et al, 2016;Groot Zwaaftink et al, 2017). Like in FLEXPART, nested meteorological fields can be used for specific regions of interest.…”

Section: Dust Mobilization Schemementioning

confidence: 99%

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The Lagrangian particle dispersion model FLEXPART version 10.4

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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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Section: Sensitivity To Initial Conditionsmentioning

confidence: 99%

Section: Dust Mobilization Schemementioning

confidence: 99%

The Lagrangian particle dispersion model FLEXPART version 10.4

et al. 2019

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“…There are about 30 active volcanic systems and volcanic eruptions occurring every 3-5 years on average (Thordarson and Larsen, 2007), (Schmidt et al, 2014). Frequent dust events in Iceland transport dust over long distances, often exceeding 2500 km, towards High Arctic (> 80°N) and Europe (Ovadnevaite et al, 2009), (Groot Zwaaftink et al, 2017), (Moroni et al, 2018), (Dordevic et al, 2019). Located only 1000 km from mainland Europe, it makes a particularly interesting case study due to its proximity to densely populated European countries.…”

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confidence: 99%

Uptake and surface chemistry of SO2 on natural volcanic dusts

Urupina

Lasne

Romanías

et al. 2019

Atmospheric Environment

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“…However, it should be considered that the ice core observations are in regions with rather little dust deposition and simulations do not cover the same period as observations. Additional FLEXPART simulations confirmed that Icelandic dust events are well represented (Groot Zwaaftink et al, ), but dust deposition on Vatnajökull ice cap in 1 year was overestimated by a factor 3 compared to observations (Wittmann et al, ). Overall, it seems that in the Arctic the FLEXPART simulations capture atmospheric concentrations quite well, but might overestimate dust deposition.…”

Section: Methodsmentioning

confidence: 90%

Mineral Dust Instantaneous Radiative Forcing in the Arctic

Kylling

Zwaaftink

Stohl

2018

Geophysical Research Letters

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Mineral dust sources at high and low latitudes contribute to atmospheric dust loads and dust deposition in the Arctic. With dust load estimates from Groot Zwaaftink et al. (2016, https://doi.org/10.1002/2016JD025482), we quantify the mineral dust instantaneous radiative forcing (IRF) in the Arctic for the year 2012. The annual-mean top of the atmosphere IRF is 0.225 W/m 2 , with the largest contributions from dust transported from Asia south of 60 ∘ N and Africa. High-latitude (>60 ∘ N) dust sources contribute about 39% to top of the atmosphere IRF and have a larger impact (1 to 2 orders of magnitude) on IRF per emitted kilogram of dust than low-latitude sources. Mineral dust deposited on snow accounts for nearly all of the bottom of the atmosphere IRF of 0.135 W/m 2 .More than half of the bottom of the atmosphere IRF is caused by dust from high-latitude sources, indicating substantial regional climate impacts rarely accounted for in current climate models. Plain Language SummaryMineral dust sources at high and low latitudes contribute to atmospheric dust loads and dust deposition in the Arctic. We quantify the annual-mean radiative forcing (RF) in the Arctic to be 0.225 W/m 2 at the top of the atmosphere. The largest contributions are from dust transported from Asia south of 60 ∘ N and Africa. High-latitude (>60 ∘ N) dust sources contribute about 39% to top of the atmosphere RF and have a larger (1 to 2 orders of magnitude) impact on RF per emitted kilogram of dust. Mineral dust deposited on snow accounts for nearly all of the bottom of the atmosphere RF of 0.135 W/m 2 . More than half of the bottom of the atmosphere RF is caused by dust from high-latitude sources, indicating substantial regional climate impacts rarely accounted for in current climate models.

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Temporal and spatial variability of Icelandic dust emissions and atmospheric transport

Cited by 45 publications

References 41 publications

The Lagrangian particle dispersion model FLEXPART version 10.4

The Lagrangian particle dispersion model FLEXPART version 10.4

Uptake and surface chemistry of SO2 on natural volcanic dusts

Mineral Dust Instantaneous Radiative Forcing in the Arctic

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