Characterising the atmospheric conditions leading to large error growth in volcanic ash cloud forecasts

Volcanic ash poses an ongoing risk to the safety of airspace worldwide. The accuracy to which we can forecast volcanic ash dispersion depends on the conditions of the atmosphere into which it is emitted. In this paper we use meteorological ensemble forecasts to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajokull eruption. From analysis of these simulations we determine why the skill of deterministic-meteorological forecasts decrease with increasing ash residence time, and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge leading to a reduction in the forecast accuracy of deterministic forecasts which do not represent variability in wind fields at the synoptic-scale. The flow separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash

Linking atmospheric rivers and warm conveyor belt airflows

Extreme precipitation associated with extratropical cyclones can lead to flooding if cyclones track over land. However, the dynamical mechanisms by which moist air is transported into cyclones is poorly understood. In this paper we analyse airflows within a climatology of cyclones in order to understand how cyclones redistribute moisture stored in the atmosphere. This analysis shows that within a cyclones' warm sector the cyclone-relative airflow is rearwards relative to the cyclone propagation direction. This low-level airflow (termed the feeder airstream) slows down when it reaches the cold front resulting in moisture flux convergence and the formation of a band of high moisture content. One branch of the feeder airstream turns towards the cyclone centre supplying moisture to the base of the warm conveyor belt where it ascends and precipitation forms. The other branch turns away from the cyclone centre exporting moisture from the cyclone. As the cyclone travels, this export results in a filament of high moisture content marking the track of the cyclone (often used to identify atmospheric rivers). We find that both cyclone precipitation and water vapour transport increase when moisture in the feeder airstream increases, thus explaining the link between atmospheric rivers and the precipitation associated with warm conveyor belt ascent. Atmospheric moisture budgets calculated as cyclones pass over fixed domains relative to the cyclone tracks, show that continuous evaporation of moisture in the pre cyclone environment moistens the feeder airstream. Evaporation behind the cold front acts to moisten the atmosphere in the wake of the cyclone passage, potentially preconditioning the environment for subsequent cyclone development

Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

The Eyjafjallajökull volcano in Iceland emitted a cloud of ash into the atmosphere during April and May 2010. Over the UK the ash cloud was observed by the FAAM BAe-146 Atmospheric Research Aircraft which was equipped with in-situ probes measuring the concentration of volcanic ash carried by particles of varying sizes. The UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) has been used to simulate the evolution of the ash cloud emitted by the Eyjafjallajökull volcano during the period 4–18 May 2010. In the NAME simulations the processes controlling the evolution of the concentration and particle size distribution include sedimentation and deposition of particles, horizontal dispersion and vertical wind shear. For travel times between 24 and 72 h, a 1/t relationship describes the evolution of the concentration at the centre of the ash cloud and the particle size distribution remains fairly constant. Although NAME does not represent the effects of microphysical processes, it can capture the observed decrease in concentration with travel time in this period. This suggests that, for this eruption, microphysical processes play a small role in determining the evolution of the distal ash cloud. Quantitative comparison with observations shows that NAME can simulate the observed column-integrated mass if around 4% of the total emitted mass is assumed to be transported as far as the UK by small particles (< 30 μm diameter). NAME can also simulate the observed particle size distribution if a distal particle size distribution that contains a large fraction of < 10 μm diameter particles is used, consistent with the idea that phraetomagmatic volcanoes, such as Eyjafjallajökull, emit very fine particles

Horizontal and vertical structure of the Eyjafjallajökull ash cloud over the UK: a comparison of airborne lidar observations and simulations

During April and May 2010 the ash cloud from the eruption of the Icelandic volcano Eyjafjallajökull caused widespread disruption to aviation over northern Europe. The location and impact of the eruption led to a wealth of observations of the ash cloud were being obtained which can be used to assess modelling of the long range transport of ash in the troposphere. The UK FAAM (Facility for Airborne Atmospheric Measurements) BAe-146-301 research aircraft overflew the ash cloud on a number of days during May. The aircraft carries a downward looking lidar which detected the ash layer through the backscatter of the laser light. In this study ash concentrations derived from the lidar are compared with simulations of the ash cloud made with NAME (Numerical Atmospheric-dispersion Modelling Environment), a general purpose atmospheric transport and dispersion model. The simulated ash clouds are compared to the lidar data to determine how well NAME simulates the horizontal and vertical structure of the ash clouds. Comparison between the ash concentrations derived from the lidar and those from NAME is used to define the fraction of ash emitted in the eruption that is transported over long distances compared to the total emission of tephra. In making these comparisons possible position errors in the simulated ash clouds are identified and accounted for. The ash layers seen by the lidar considered in this study were thin, with typical depths of 550–750 m. The vertical structure of the ash cloud simulated by NAME was generally consistent with the observed ash layers, although the layers in the simulated ash clouds that are identified with observed ash layers are about twice the depth of the observed layers. The structure of the simulated ash clouds were sensitive to the profile of ash emissions that was assumed. In terms of horizontal and vertical structure the best results were obtained by assuming that the emission occurred at the top of the eruption plume, consistent with the observed structure of eruption plumes. However, early in the period when the intensity of the eruption was low, assuming that the emission of ash was uniform with height gives better guidance on the horizontal and vertical structure of the ash cloud. Comparison of the lidar concentrations with those from NAME show that 2–5% of the total mass erupted by the volcano remained in the ash cloud over the United Kingdom

How accurate are volcanic ash simulations of the 2010 Eyjafjallajökull eruption?

In the event of a volcanic eruption the decision to close airspace is based on forecast ash maps, produced using volcanic ash transport and dispersion models. In this paper we quantitatively evaluate the spatial skill of volcanic ash simulations using satellite retrievals of ash from the Eyja allajökull eruption during the period from 7 to 16 May 2010. We find that at the start of this period, 7–10 May, the model (FLEXible PARTicle) has excellent skill and can predict the spatial distribution of the satellite-retrieved ash to within 0.5∘ × 0.5∘ latitude/longitude. However, on 10 May there is a decrease in the spatial accuracy of the model to 2.5∘× 2.5∘ latitude/longitude, and between 11 and 12 May the simulated ash location errors grow rapidly. On 11 May ash is located close to a bifurcation point in the atmosphere, resulting in a rapid divergence in the modeled and satellite ash locations. In general, the model skill reduces as the residence time of ash increases. However, the error growth is not always steady. Rapid increases in error growth are linked to key points in the ash trajectories. Ensemble modeling using perturbed meteorological data would help to represent this uncertainty, and assimilation of satellite ash data would help to reduce uncertainty in volcanic ash forecasts

Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe

Winter 2013/14 was the stormiest on record for the UK and was characterized by recurrent clustering of extratropical cyclones. This clustering was associated with a strong, straight and persistent North Atlantic jet and was also associated with Rossby wave breaking (RWB) on both flanks, pinning the jet in place. The occurrence of RWB and cyclone clustering is further studied in 36 years of the ERA-Interim Reanalysis. Clustering at 55°N is associated with an extended and anomalously strong eddy-driven jet flanked on both sides by RWB. However, clustering at 65(45)°N has a dominance of RWB to the south (north) of the jet, deflecting the jet northward (southward). A positive correlation was found between clustering and RWB occurrence to the north and south of the jet. However, there is considerable spread in these relationships

How have surface NO2 concentrations changed as a result of the UK's COVID-19 travel restrictions?

Restrictions as a result of the COVID-19 pandemic have led to fewer vehicles on UK roads. Since fuel combustion is responsible for a large fraction of UK emissions it is expected that surface NO2 concentrations would reduce as a result. However, over parts of the UK, surface NO2 concentrations have increased following the implementation of travel restrictions. NO2 measurements from 142 Automatic Urban and Rural Network sites are combined with meteorological data from the Met Office high-resolution weather prediction model to build site specific models. These models predict NO2 concentrations given no change in emissions. It is found that both meteorological and emission changes contribute to the observed changes in NO2 concentrations. Given no change in emissions, changes in meteorology between pre- and post-lockdown periods would have led to a mean increase in NO2 concentrations of +6%. Conversely, changes in emissions would have led to a mean reduction in NO2 concentrations of -18%, resulting in the observed total change in NO2 concentrations of -12%. However at some sites the reduction in emissions is smaller than the increase in NO2 concentrations due to meteorology. The largest increases associated with changes in the meteorology are seen at rural sites (+20%) where NO2 measurements are representative of large areas and thus dominated by the regional advection of secondary NO2 from Europe. Conversely, the largest decreases associated with reduced emissions are found at urban traffic and urban background sites (-27% and -14% respectively) where NO2 concentrations are representative of local areas and thus dominated by local reduction in emissions from vehicles. As lockdown measures are relaxed, NO2 concentrations are likely to return to pre-COVID levels, but these results demonstrate that changes in our behaviour can result in positive impacts on air quality and illustrate the effectiveness of travel-reducing strategies in urban area

How do atmospheric rivers form?

Identifying the source of atmospheric rivers: Are they rivers of moisture exported from the subtropics or footprints left behind by poleward travelling storms? The term atmospheric river is used to describe corridors of strong water vapor transport in the troposphere. Filaments of enhanced water vapor, commonly observed in satellite imagery extending from the subtropics to the extratropics, are routinely used as a proxy for identifying these regions of strong water vapor transport. The precipitation associated with these filaments of enhanced water vapor can lead to high impact flooding events. However, there remains some debate as to how these filaments form. In this paper we analyse the transport of water vapor within a climatology of wintertime North Atlantic extratropical cyclones. Results show that atmospheric rivers are formed by the cold front which sweeps up water vapor in the warm sector as it catches up with the warm front. This causes a narrow band of high water vapor content to form ahead of the cold front at the base of the warm conveyor belt airflow. Thus, water vapor in the cyclone's warm sector, and not long-distance transport of water vapor from the subtropics, is responsible for the generation of filaments of high water vapor content. A continuous cycle of evaporation and moisture convergence within the cyclone replenishes water vapor lost via precipitation. Thus, rather than representing a direct and continuous feed of moist air from the subtropics into the centre of a cyclone (as suggested by the term atmospheric river), these filaments are, in-fact, the result of water vapor exported from the cyclone and thus they represent the footprints left behind as cyclones travel polewards from subtropics

The impact of ensemble meteorology on inverse modeling estimates of volcano emissions and ash dispersion forecasts: Grímsvötn 2011

Volcanic ash can interact with the earth system on many temporal and spatial scales and is a significant hazard to aircraft. In the event of a volcanic eruption, fast and robust decisions need to be made by aviation authorities about which routes are safe to operate. Such decisions take into account forecasts of ash location issued by Volcanic Ash Advisory Centers (VAACs) which are informed by simulations from Volcanic Ash Transport and Dispersion (VATD) models. The estimation of the time-evolving vertical distribution of ash emissions for use in VATD simulations in real time is difficult which can lead to large uncertainty in these forecasts. This study presents a method for constraining the ash emission estimates by combining an inversion modeling technique with an ensemble of meteorological forecasts, resulting in an ensemble of ash emission estimates. These estimates of ash emissions can be used to produce a robust ash forecast consistent with observations. This new ensemble approach is applied to the 2011 eruption of the Icelandic volcano Grímsvötn. The resulting emission profiles each have a similar temporal evolution but there are differences in the magnitude of ash emitted at different heights. For this eruption, the impact of precipitation uncertainty (and the associated wet deposition of ash) on the estimate of the total amount of ash emitted is larger than the impact of the uncertainty in the wind fields. Despite the differences that are dominated by wet deposition uncertainty, the ensemble inversion provides confidence that the reduction of the unconstrained emissions (a priori), particularly above 4 km, is robust across all members. In this case, the use of posterior emission profiles greatly reduces the magnitude and extent of the forecast ash cloud. The ensemble of posterior emission profiles gives a range of ash column loadings much closer in agreement with a set of independent satellite retrievals in comparison to the a priori emissions. Furthermore, airspace containing volcanic ash concentrations deemed to be associated with the highest risk (likelihood of exceeding a high concentration threshold) to aviation are reduced by over 85%. Such improvements could have large implications in emergency response situations. Future research will focus on quantifying the impact of uncertainty in precipitation forecasts on wet deposition in other eruptions and developing an inversion system that makes use of the state-of-the-art meteorological ensembles which has the potential to be used in an operational setting

A case study analysis of the impact of a new free tropospheric turbulence scheme on the dispersion of an atmospheric tracer

Most Lagrangian dispersion models represent free tropospheric turbulence as a homogeneous steady-state process. However, intermittent turbulent mixing in the free troposphere may be a significant source of mixing. We test anew parametrization scheme that represents spatial- and temporal-varying turbulence in the free troposphere in the Met Office’s Numerical Atmospheric-dispersion Modelling Environment. We use semi-idealized emissions of radon-222 (222Rn) from rocks and soil in the United Kingdom to evaluate the impact of using a variable free tropospheric turbulence parameterization on the dispersion of 222Rn. We performed two experiments, the first using the existing steady-state scheme and the second using the newly implemented spatio-temporal-varying scheme, for two case periods July 2018 and April 2021. We find that the turbulence in the varying scheme (represented by the vertical velocity variance) can range by two to three orders of magnitude (10−4to 10−1 m2 s−2) when compared with the steady-state scheme(10−2 m2 s−2). In particular, low-altitude turbulence is enhanced when synoptic conditions are conducive to forming low-level jets. This leads to a greater dispersion in the free troposphere, reducing the mean monthly 222Rn concentration above the boundary layer by 20–40% relative to the steady-state scheme. We conclude that without a space–time-varying free tropospheric turbulence scheme atmospheric dispersion may be significantly underestimated under synoptic conditions that are favourable for low-level jet formation. This underestimation of dispersion may potentially result in inaccurate estimations of local emissions in top-down greenhouse gas inventory studies