Matthew C. Hort

Operational prediction of ash concentrations in the distal volcanic cloud from the 2010 Eyjafjallajökull eruption

[1] During the 2010 eruption of Eyjafjallajokull, 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.

Satellite detection, long-range transport, and air quality impacts of volcanic sulfur dioxide from the 2014-2015 flood lava eruption at Bárðarbunga (Iceland)

The 2014–2015 Barðarbunga-Veiðivotn 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 Offices 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.

Ash mists and brown snow: Remobilization of volcanic ash from recent Icelandic eruptions

Recent eruptions in Iceland and Chile have demonstrated that volcanic ash problems persist long after an eruption. For this reason, ash dispersion models are being extended to include ash remobilization. Critical to these models is knowledge of the ash source and the particle sizes that can be mobilized under different wind and moisture conditions. Here we characterize the physical and chemical characteristics of ash deposited on new snow in Reykjavik, Iceland, following a blizzard on 6 March 2013. Morphological, textural, and compositional analyses indicate resuspension from multiple eruptive deposits, including both Grimsvotn (2011) and Eyjafjallajokull (2010) eruptions. Grain size measurements show a mode of 32–63 µm, with particles as large as 177 µm; there is little mass in the very fine fraction, ≤10 µm (PM10). We compare our observations to predictions using the Lagrangian particle dispersion model, NAME (UK Met Office). The model output is consistent with observations in that it forecasts resuspension from both Eyjafjallajokull and Grimsvotn source regions, and shows ash deposition coincident with the timing of observed deposition in Reykjavik. The modeled deposit in Reykjavik predicts, however, a substantially lower proportion of Grimsvotn ash than observed. This discrepancy has highlighted the need to reassess the assumptions used in the simulations, particularly regarding the source area and precipitation thresholds. Furthermore, we suggest that modification of ash deposits in the form of erosion, redeposition, compaction, or cementation may influence the dynamics of resuspension over time, thus influencing the ability of model simulations to accurately forecast remobilization events.

Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long-range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajokull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajokull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.

Assessing hazards to aviation from sulfur dioxide emitted by explosive Icelandic eruptions

Volcanic eruptions take place in Iceland about once every 3 to 5 years. Ash emissions from these eruptions can cause significant disruption to air traffic over Europe and the North Atlantic as is evident from the 2010 eruption of Eyjafjallajokull. Sulfur dioxide (SO2) is also emitted by volcanoes, but there are no criteria to define when airspace is considered hazardous or nonhazardous. However, SO2 is a well-known ground-level pollutant that can have detrimental effects on human health. We have used the United Kingdom Met Offices NAME (Numerical Atmospheric-dispersion Modelling Environment) model to simulate SO2 mass concentrations that could occur in European and North Atlantic airspace for a range of hypothetical explosive eruptions in Iceland with a probability to occur about once every 3 to 5 years. Model performance was evaluated for the 2010 Eyjafjallajokull summit eruption against SO2 vertical column density retrievals from the Ozone Monitoring Instrument and in situ measurements from the United Kingdom Facility for Airborne Atmospheric Measurements research aircraft. We show that at no time during the 2010 Eyjafjallajokull eruption did SO2 mass concentrations at flight altitudes violate European air quality standards. In contrast, during a hypothetical short-duration explosive eruption similar to Hekla in 2000 (emitting 0.2 Tg of SO2 within 2 h, or an average SO2 release rate 250 times that of Eyjafjallajokull 2010), simulated SO2 concentrations are greater than 1063 µg/m3 for about 48 h in a small area of European and North Atlantic airspace. By calculating the occurrence of aircraft encounters with the volcanic plume of a short-duration eruption, we show that a 15 min or longer exposure of aircraft and passengers to concentrations ≥500 µg/m3 has a probability of about 0.1%. Although exposure of humans to such concentrations may lead to irritations to the eyes, nose and, throat and cause increased airway resistance even in healthy individuals, the risk is very low. However, the fact that volcanic ash and sulfur species are not always collocated and that passenger comfort could be compromised might be incentives to provide real-time information on the presence or absence of volcanic SO2. Such information could aid aviation risk management during and after volcanic eruptions.

Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

Abstract Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite‐based active‐fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up‐to‐date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real‐time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

Quantifying airborne dispersal routes of pathogens over continents to safeguard global wheat supply

Infectious crop diseases spreading over large agricultural areas pose a threat to food security. Aggressive strains of the obligate pathogenic fungus Puccinia graminis f.sp. tritici (Pgt), causing the crop disease wheat stem rust, have been detected in East Africa and the Middle East, where they lead to substantial economic losses and threaten livelihoods of farmers. The majority of commercially grown wheat cultivars worldwide are susceptible to these emerging strains, which pose a risk to global wheat production, because the fungal spores transmitting the disease can be wind-dispersed over regions and even continents1–11. Targeted surveillance and control requires knowledge about airborne dispersal of pathogens, but the complex nature of long-distance dispersal poses significant challenges for quantitative research12–14. We combine international field surveys, global meteorological data, a Lagrangian dispersion model and high-performance computational resources to simulate a set of disease outbreak scenarios, tracing billions of stochastic trajectories of fungal spores over dynamically changing host and environmental landscapes for more than a decade. This provides the first quantitative assessment of spore transmission frequencies and amounts amongst all wheat producing countries in Southern/East Africa, the Middle East and Central/South Asia. We identify zones of high air-borne connectivity that geographically correspond with previously postulated wheat rust epidemiological zones (characterized by endemic disease and free movement of inoculum)10,15, and regions with genetic similarities in related pathogen populations16,17. We quantify the circumstances (routes, timing, outbreak sizes) under which virulent pathogen strains such as ‘Ug99’5,6 pose a threat from long-distance dispersal out of East Africa to the large wheat producing areas in Pakistan and India. Long-term mean spore dispersal trends (predominant direction, frequencies, amounts) are summarized for all countries in the domain (Supplementary Data). Our mechanistic modelling framework can be applied to other geographic areas, adapted for other pathogens and used to provide risk assessments in real-time3.Recent outbreaks of wheat stem rust pose a threat to global wheat production. A powerful computational modelling approach is now used to trace and predict the spread of these new pathogen stains, and can potentially be applied to other wind-dispersed spores.

Haze in Singapore n Source Attribution of Biomass Burning from Southeast Asia

This paper presents a study of haze in Singapore caused by biomass burning in Southeast Asia over the six year period from 2010 to 2015, using the Lagrangian dispersion model, NAME. The major contributing source regions are shown to be Riau, Peninsular Malaysia, South Sumatra, and Central and West Kalimantan. However, we see differences in haze concentrations and variation in the relative contributions from the various source regions between monitoring stations across Singapore, as well as on an inter-annual timescale. These results challenge 5 the current popular assumption that haze in Singapore is dominated by emissions/burning from only Indonesia. It is shown that Peninsular Malaysia is a large source for the Maritime Continent off-season biomass burning impact on Singapore. As should be expected, the relatively stronger Southeast monsoonal winds that coincide with increased biomass burning activities in the Maritime Continent create the main haze season from August to October (ASO), which brings particulate matter from varying source regions to Singapore. Five regions dominate as the source of pollution during recent haze seasons. 10 In contrast, off-season haze episodes in Singapore are characterised by unusual weather conditions, ideal for biomass burning, and emissions dominated by a single source region (for each event). The two most recent off-season haze events in mid-2013 and early-2014 have different source regions, which differ to the major contributing source regions for the haze season. Haze in Singapore varies across year, season, and location; it is influenced by local and regional weather, climate, and regional burning. The study shows that even across small scales, such as in Singapore, variation in local meteorology can 15 impact concentrations of particulate matter significantly, and emphasises the importance of the scale of modelling both spatially and temporally.

Communicating geographical risks in crisis management : the need for research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.