The 2014–2015 Bárðarbunga‐Veiðivötn fissure eruption at Holuhraun produced about 1.5 km³ of lava, making it the largest eruption in Iceland in more than 200 years. Over the course of the eruption, daily volcanic sulfur dioxide (SO₂) emissions exceeded daily SO₂ emissions from all anthropogenic sources in Europe in 2010 by at least a factor of 3. We present surface air quality observations from across Northern Europe together with satellite remote sensing data and model simulations of volcanic SO₂ for September 2014. We show that volcanic SO₂ was transported in the lowermost troposphere over long distances and detected by air quality monitoring stations up to 2750 km away from the source. Using retrievals from the Ozone Monitoring Instrument (OMI) and the Infrared Atmospheric Sounding Interferometer (IASI), we calculate an average daily SO₂ mass burden of 99 ± 49 kilotons (kt) of SO₂ from OMI and 61 ± 18 kt of SO₂ from IASI for September 2014. This volcanic burden is at least a factor of 2 greater than the average SO₂ mass burden between 2007 and 2009 due to anthropogenic emissions from the whole of Europe. Combining the observational data with model simulations using the United Kingdom Met Office's Numerical Atmospheric‐dispersion Modelling Environment model, we are able to constrain SO₂ emission rates to up to 120 kilotons per day (kt/d) during early September 2014, followed by a decrease to 20–60 kt/d between 6 and 22 September 2014, followed by a renewed increase to 60–120 kt/d until the end of September 2014. Based on these fluxes, we estimate that the eruption emitted a total of 2.0 ± 0.6 Tg of SO₂ during September 2014, in good agreement with ground‐based remote sensing and petrological estimates. Although satellite‐derived and model‐simulated vertical column densities of SO₂ agree well, the model simulations are biased low by up to a factor of 8 when compared to surface observations of volcanic SO₂ on 6–7 September 2014 in Ireland. These biases are mainly due to relatively small horizontal and vertical positional errors in the simulations of the volcanic plume occurring over transport distances of thousands of kilometers. Although the volcanic air pollution episodes were transient and lava‐dominated volcanic eruptions are sporadic events, the observations suggest that (i) during an eruption, volcanic SO₂ measurements should be assimilated for near real‐time air quality forecasting and (ii) existing air quality monitoring networks should be retained or extended to monitor SO₂ and other volcanic pollutants.

Five different atmospheric transport and dispersion model's (ATDM) deposition and air concentration results for atmospheric releases from the Fukushima Daiichi nuclear power plant accident were evaluated over Japan using regional (137)Cs deposition measurements and (137)Cs and (131)I air concentration time series at one location about 110 km from the plant. Some of the ATDMs used the same and others different meteorological data consistent with their normal operating practices. There were four global meteorological analyses data sets available and two regional high-resolution analyses. Not all of the ATDMs were able to use all of the meteorological data combinations. The ATDMs were configured identically as much as possible with respect to the release duration, release height, concentration grid size, and averaging time. However, each ATDM retained its unique treatment of the vertical velocity field and the wet and dry deposition, one of the largest uncertainties in these calculations. There were 18 ATDM-meteorology combinations available for evaluation. The deposition results showed that even when using the same meteorological analysis, each ATDM can produce quite different deposition patterns. The better calculations in terms of both deposition and air concentration were associated with the smoother ATDM deposition patterns. The best model with respect to the deposition was not always the best model with respect to air concentrations. The use of high-resolution mesoscale analyses improved ATDM performance; however, high-resolution precipitation analyses did not improve ATDM predictions. Although some ATDMs could be identified as better performers for either deposition or air concentration calculations, overall, the ensemble mean of a subset of better performing members provided more consistent results for both types of calculations.

Aerosols have important impacts on air quality and climate, but the processes affecting their removal from the atmosphere are not fully understood and are poorly constrained by observations. This makes modelled aerosol lifetimes uncertain. In this study, we make use of an observational constraint on aerosol lifetimes provided by radionuclide measurements and investigate the causes of differences within a set of global models. During the Fukushima Dai-Ichi nuclear power plant accident of March 2011, the radioactive isotopes cesium-137 (¹³⁷Cs) and xenon-133 (¹³³Xe) were released in large quantities. Cesium attached to particles in the ambient air, approximately according to their available aerosol surface area. ¹³⁷Cs size distribution measurements taken close to the power plant suggested that accumulation-mode (AM) sulfate aerosols were the main carriers of cesium. Hence, ¹³⁷Cs can be used as a proxy tracer for the AM sulfate aerosol's fate in the atmosphere. In contrast, the noble gas ¹³³Xe behaves almost like a passive transport tracer. Global surface measurements of the two radioactive isotopes taken over several months after the release allow the derivation of a lifetime of the carrier aerosol. We compare this to the lifetimes simulated by 19 different atmospheric transport models initialized with identical emissions of ¹³⁷Cs that were assigned to an aerosol tracer with each model's default properties of AM sulfate, and ¹³³Xe emissions that were assigned to a passive tracer. We investigate to what extent the modelled sulfate tracer can reproduce the measurements, especially with respect to the observed loss of aerosol mass with time. Modelled ¹³⁷Cs and ¹³³Xe concentrations sampled at the same location and times as station measurements allow a direct comparison between measured and modelled aerosol lifetime. The e-folding lifetime τ_e, calculated from station measurement data taken between 2 and 9 weeks after the start of the emissions, is 14.3 days (95 % confidence interval 13.1–15.7 days). The equivalent modelled τ_e lifetimes have a large spread, varying between 4.8 and 26.7 days with a model median of 9.4 ± 2.3 days, indicating too fast a removal in most models. Because sufficient measurement data were only available from about 2 weeks after the release, the estimated lifetimes apply to aerosols that have undergone long-range transport, i.e. not for freshly emitted aerosol. However, modelled instantaneous lifetimes show that the initial removal in the first 2 weeks was quicker (lifetimes between 1 and 5 days) due to the emissions occurring at low altitudes and co-location of the fresh plume with strong precipitation. Deviations between measured and modelled aerosol lifetimes are largest for the northernmost stations and at later time periods, suggesting that models do not transport enough of the aerosol towards the Arctic. The models underestimate passive tracer (¹³³Xe) concentrations in the Arctic as well but to a smaller extent than for the aerosol (¹³⁷Cs) tracer. This indicates that in addition to too fast an aerosol removal in the models, errors in simulated atmospheric transport towards the Arctic in most models also contribute to the underestimation of the Arctic aerosol concentrations.

During the 2010 eruption of Eyjafjallajökull, improvements were made to the modeling procedure at the Met Office, UK, enabling peak ash concentrations within the volcanic cloud to be estimated. In this paper we describe the ash concentration forecasting method, its rationale and how it evolved over time in response to new information and user requirements. The change from solely forecasting regions of ash to also estimating peak ash concentrations required consideration of volcanic ash emission rates, the fraction of ash surviving near‐source fall‐out, and the relationship between predicted mean and local peak ash concentrations unresolved by the model. To validate the modeling procedure, predicted peak ash concentrations are compared against observations obtained by ground‐based and research aircraft instrumentation. This comparison between modeled and observed peak concentrations highlights the many sources of error and the uncertainties involved. Despite the challenges of predicting ash concentrations, the ash forecasting method employed here is found to give useful guidance on likely ash concentrations. Predicted peak ash concentrations lie within about one and a half orders of magnitude of the observed peak concentrations. A significant improvement in the agreement between modeled and observed values is seen if a buffer zone, accounting for positional errors in the predicted ash cloud, is used. Sensitivity of the predicted ash concentrations to the source properties (e.g., the plume height and the vertical distribution of ash at the source) is assessed and in some cases, seemingly minor uncertainties in the source specification have a large effect on predicted ash concentrations.

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

The total heat gained by the North Atlantic Ocean over the past 50 years is equivalent to a basinwide increase in the flux of heat across the ocean surface of 0.4 ± 0.05 watts per square meter. We show, however, that this basin has not warmed uniformly: Although the tropics and subtropics have warmed, the subpolar ocean has cooled. These regional differences require local surface heat flux changes (±4 watts per square meter) much larger than the basinwide average. Model investigations show that these regional differences can be explained by large-scale, decadal variability in wind and buoyancy forcing as measured by the North Atlantic Oscillation index. Whether the overall heat gain is due to anthropogenic warming is difficult to confirm because strong natural variability in this ocean basin is potentially masking such input at the present time.