Numerical Atmospheric-dispersion Modelling Environment Research Articles

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK

Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.

A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME

Abstract. Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH4 concentration time series generated by NAME, we show that our emulator achieves a mean R-squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation.

Continuous CH<sub>4</sub> and <i>δ</i><sup>13</sup>CH<sub>4</sub> measurements in London demonstrate under-reported natural gas leakage

Abstract. Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London from early 2018 onwards using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of ∼ 2, ∼ 10, and ∼ 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. Observed mole fractions were underestimated by 30 %–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20 %–28 % of the above background CH4 in the NAEI simulations and only 6 %–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation–observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than, the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation–observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling

Abstract. We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME (Numerical Atmospheric Dispersion modelling Environment), which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated GSD to the model parameters, we conducted a simple parametric study and scaling analysis. We find that the modelled aggregated GSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated particles at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the aggregated GSD on model simulations of the 2010 Eyjafjallajökull ash cloud and consider the implications for operational forecasting. Using the time-evolving aggregated GSD at the top of the eruption column to initialize dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size, the modelled area of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ∼ 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m−3) than the single grains of which they are composed and make up 75 % of the mass in the ash cloud, the extent is 1.1 times larger.

The 2019 Raikoke volcanic eruption – Part 2: Particle-phase dispersion and concurrent wildfire smoke emissions

Abstract. Between 27 June and 14 July 2019 aerosol layers were observed by the United Kingdom (UK) Raman lidar network in the upper troposphere and lower stratosphere. The arrival of these aerosol layers in late June caused some concern within the London Volcanic Ash Advisory Centre (VAAC) as according to dispersion simulations the volcanic plume from the 21 June 2019 eruption of Raikoke was not expected over the UK until early July. Using dispersion simulations from the Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME), and supporting evidence from satellite and in situ aircraft observations, we show that the early arrival of the stratospheric layers was not due to aerosols from the explosive eruption of the Raikoke volcano but due to biomass burning smoke aerosols associated with intense forest fires in Alberta, Canada, that occurred 4 d prior to the Raikoke eruption. We use the observations and model simulations to describe the dispersion of both the volcanic and forest fire aerosol clouds and estimate that the initial Raikoke ash aerosol cloud contained around 15 Tg of volcanic ash and that the forest fires produced around 0.2 Tg of biomass burning aerosol. The operational monitoring of volcanic aerosol clouds is a vital capability in terms of aviation safety and the synergy of NAME dispersion simulations, and lidar data with depolarising capabilities allowed scientists at the Met Office to interpret the various aerosol layers over the UK and attribute the material to their sources. The use of NAME allowed the identification of the observed stratospheric layers that reached the UK on 27 June as biomass burning aerosol, characterised by a particle linear depolarisation ratio of 9 %, whereas with the lidar alone the latter could have been identified as the early arrival of a volcanic ash–sulfate mixed aerosol cloud. In the case under study, given the low concentration estimates, the exact identification of the aerosol layers would have made little substantive difference to the decision-making process within the London VAAC. However, our work shows how the use of dispersion modelling together with multiple observation sources enabled us to create a more complete description of atmospheric aerosol loading.

The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Abstract. Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

Quantifying sources of Brazil's CH<sub>4</sub> emissions between 2010 and 2018 from satellite data

Abstract. Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010–2018 are 33.6±3.6Tgyr-1, which are comprised of 19.0±2.6Tgyr-1 from anthropogenic (primarily related to agriculture and waste), 13.0±1.9Tgyr-1 from wetlands and 1.7±0.3Tgyr-1 from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9±5.3Tgyr-1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015–2016 El Niño by 3.7±2.7Tgyr-1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

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.

Improved representation of particle size and solubility in model simulations of atmospheric dispersion and wet-deposition from Fukushima

Radionuclides released into the atmosphere following the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident were detected by ground-based monitoring stations worldwide. The inter-continental dispersion of radionuclides provides a unique opportunity to evaluate the ability of atmospheric dispersion models to represent the processes controlling their transport and deposition in the atmosphere. Co-located measurements of radioxenon (133Xe) and caesium (137Cs) concentrations enable individual physical processes (dispersion, dry and wet deposition) to be isolated. In this paper we focus on errors in the prediction of 137Cs attributed to the representation of particle size and solubility, in the process of modelling wet deposition. Simulations of 133Xe and 137Cs concentrations using the UK Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) model are compared with CTBTO (Comprehensive Nuclear-Test-Ban Treaty Organisation) surface station measurements. NAME predictions of 137Cs using a bulk wet deposition parameterisation (which does not account for particle size dependent scavenging or solubility) significantly underestimate observed 137Cs. When a binned wet deposition parameterisation is implemented (which accounts for particle size dependent scavenging) the correlations between modelled and observed air concentrations improve at all 9 of the Northern Hemisphere sites studied and the respective RMSLE (root-mean-square-log-error) decreases by a factor of 7 due to a decrease in the wet-deposition of Aitken and Accumulation mode particles. Finally, NAME simulations were performed in which insoluble submicron particles are represented. Representing insoluble particles in the NAME simulations improves the RMSLE at all sites further by a factor of 7. Thus NAME is able to predict 137Cs with good accuracy (within a factor of 10 of observed 137Cs values) at distances greater than 10,000 km from FDNPP only if insoluble submicron particles are considered in the description of the source. This result provides further evidence of the presence of insoluble Cs-rich microparticles in the release following the accident at FDNPP and suggests that these small particles travelled across the Pacific Ocean to the US and further across the North Atlantic Ocean towards Europe.

Haze in Singapore – source attribution of biomass burning PM<sub>10</sub> from Southeast Asia

Abstract. This paper presents a study of haze in Singapore caused by biomass burning in Southeast Asia over the 6-year period from 2010 to 2015, using the Numerical Atmospheric-dispersion Modelling Environment (NAME), which is a Lagrangian dispersion model. The major contributing source regions to the haze are identified using forwards and backwards model simulations of particulate matter. The coincidence of relatively strong southeast monsoonal winds with increased biomass burning activities in the Maritime Continent create the main Singapore haze season from August to October (ASO), which brings particulate matter from varying source regions to Singapore. Five regions are identified as the dominating sources of pollution during recent haze seasons: Riau, Peninsular Malaysia, South Sumatra, and Central and West Kalimantan. In contrast, off-season haze episodes in Singapore are characterised by unusual weather conditions, ideal for biomass burning, and contributions dominated by a single source region (different for each event). The two most recent off-season haze events in mid-2013 and early 2014 have different source regions, which differ from the major contributing source regions for the haze season. These results challenge the current popular assumption that haze in Singapore is dominated by emissions/burning from only Indonesia. For example, it is shown that Peninsular Malaysia is a large source for the Maritime Continent off-season biomass burning impact on Singapore. The results demonstrate that haze in Singapore varies across year, season, and location and is influenced by local and regional weather, climate, and regional burning. Differences in haze concentrations and variation in the relative contributions from the various source regions are seen between monitoring stations across Singapore, on a seasonal as well as on an inter-annual timescale. This study shows that even across small scales, such as in Singapore, variation in local meteorology can impact concentrations of particulate matter significantly, and it emphasises the importance of the scale of modelling both spatially and temporally.

Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network

Abstract. We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO2. Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8±79 Tg CO2 yr−1 (DALEC prior) and 64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO2 than the prior estimates, which show much more pronounced uptake in summer months.

Saharan dust and biomass burning aerosols during ex-hurricane Ophelia: observations from the new UK lidar and sun-photometer network

Abstract. On 15–16 October 2017, ex-hurricane Ophelia passed to the west of the British Isles, bringing dust from the Sahara and smoke from Portuguese forest fires that was observable to the naked eye and reported in the UK's national press. We report here detailed observations of this event using the UK operational lidar and sun-photometer network, established for the early detection of aviation hazards, including volcanic ash. We also use ECMWF ERA5 wind field data and MODIS imagery to examine the aerosol transport. The observations, taken continuously over a period of 30 h, show a complex picture, dominated by several different aerosol layers at different times and clearly correlated with the passage of different air masses associated with the intense cyclonic system. A similar evolution was observed at several sites, with a time delay between them explained by their different location with respect to the storm and associated meteorological features. The event commenced with a shallow dust layer at 1–2 km in altitude and culminated in a deep and complex structure that lasted ∼12 h at each site over the UK, correlated with the storm's warm sector. For most of the time, the aerosol detected was dominated by mineral dust mixtures, as highlighted by depolarisation measurements, but an intense biomass burning aerosol (BBA) layer was observed towards the end of the event, lasting around 3 h at each site. The aerosol optical depth at 355 nm (AOD355) during the whole event ranged from 0.2 to 2.9, with the larger AOD correlated to the intense BBA layer. Such a large AOD is unprecedented in the UK according to AERONET records for the last 20 years. The Raman lidars permitted the measurement of the aerosol extinction coefficient at 355 nm, the particle linear depolarisation ratio (PLDR), and the lidar ratio (LR) and made the separation of the dust (depolarising) aerosol from other aerosol types possible. A specific extinction has also been computed to provide an estimate of the atmospheric concentration of both aerosol types separately, which peaked at 420±200 µg m−3 for the dust and 558±232 µg m−3 for the biomass burning aerosols. Back trajectories computed using the Numerical Atmospheric-dispersion Modelling Environment (NAME) were used to identify the sources and strengthen the conclusions drawn from the observations. The UK network represents a significant expansion of the observing capability in northern Europe, with instruments evenly distributed across Great Britain, from Camborne in Cornwall to Lerwick in the Shetland Islands, and this study represents the first attempt to demonstrate its capability and validate the methods in use. Its ultimate purpose will be the detection and quantification of volcanic plumes, but the present study clearly demonstrates the advanced capabilities of the network.

Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters

Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajökull in 2010 there has been a move towards producing quantitative predictions of volcanic ash concentration using volcanic ash transport and dispersion simulators. However, there is no formal framework for determining the uncertainties of these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayesian linear emulation approach is applied to the Numerical Atmospheric-dispersion Modelling Environment (NAME) to better understand the influence of source and internal model parameters on the simulator output. Emulation is a statistical method for predicting the output of a computer simulator at new parameter choices without actually running the simulator. A multi-level emulation approach is applied using two configurations of NAME with different numbers of model particles. Information from many evaluations of the computationally faster configuration is combined with results from relatively few evaluations of the slower, more accurate, configuration. This approach is effective when it is not possible to run the accurate simulator many times and when there is also little prior knowledge about the influence of parameters. The approach is applied to the mean ash column loading in 75 geographical regions on 14 May 2010. Through this analysis it has been found that the parameters that contribute the most to the output uncertainty are initial plume rise height, mass eruption rate, free tropospheric turbulence levels and precipitation threshold for wet deposition. This information can be used to inform future model development and observational campaigns and routine monitoring. The analysis presented here suggests the need for further observational and theoretical research into parameterisation of atmospheric turbulence. Furthermore it can also be used to inform the most important parameter perturbations for a small operational ensemble of simulations. The use of an emulator also identifies the input and internal parameters that do not contribute significantly to simulator uncertainty. Finally, the analysis highlights that the faster, less accurate, configuration of NAME can, on its own, provide useful information for the problem of predicting average column load over large areas.

Source apportionment and health risk assessment among specific age groups during haze and non-haze episodes in Kuala Lumpur, Malaysia

This study aims to determine PM2.5 concentrations and their composition during haze and non-haze episodes in Kuala Lumpur. In order to investigate the origin of the measured air masses, the Numerical Atmospheric-dispersion Modelling Environment (NAME) and Global Fire Assimilation System (GFAS) were applied. Source apportionment of PM2.5 was determined using Positive Matrix Factorization (PMF). The carcinogenic and non-carcinogenic health risks were estimated using the United State Environmental Protection Agency (USEPA) method. PM2.5 samples were collected from the centre of the city using a high-volume air sampler (HVS). The results showed that the mean PM2.5 concentrations collected during pre-haze, haze and post-haze periods were 24.5±12.0μgm−3, 72.3±38.0μgm−3 and 14.3±3.58μgm−3, respectively. The highest concentration of PM2.5 during haze episode was five times higher than World Health Organisation (WHO) guidelines. Inorganic compositions of PM2.5, including trace elements and water soluble ions were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC), respectively. The major trace elements identified were K, Al, Ca, Mg and Fe which accounted for approximately 93%, 91% and 92% of the overall metals' portions recorded during pre-haze, haze and post-haze periods, respectively. For water-soluble ions, secondary inorganic aerosols (SO42−, NO3− and NH4+) contributed around 12%, 43% and 16% of the overall PM2.5 mass during pre-haze, haze and post-haze periods, respectively. During haze periods, the predominant source identified using PMF was secondary inorganic aerosol (SIA) and biomass burning where the NAME simulations indicate the importance of fires in Sumatra, Indonesia. The main source during pre-haze and post-haze were mix SIA and road dust as well as mineral dust, respectively. The highest non-carcinogenic health risk during haze episode was estimated among the infant group (HI=1.06) while the highest carcinogenic health risk was estimated among the adult group (2.27×10−5).

Quantifying the mass loading of particles in an ash cloud remobilized from tephra deposits on Iceland

Abstract. On 16–17 September 2013 strong surface winds over tephra deposits in southern Iceland led to the resuspension and subsequent advection of significant quantities of volcanic ash. The resulting resuspended ash cloud was transported to the south-east over the North Atlantic Ocean and, due to clear skies at the time, was exceptionally well observed in satellite imagery. We use satellite-based measurements in combination with radiative transfer and dispersion modelling to quantify the total mass of ash resuspended during this event. Typically ash clouds from explosive eruptions are identified in satellite measurements from a negative brightness temperature difference (BTD) signal; however this technique assumes that the ash resides at high levels in the atmosphere. Due to a temperature inversion in the troposphere over southern Iceland during 16 September 2013, the resuspended ash cloud was constrained to altitudes of < 2 km a.s.l. We show that a positive BTD signal can instead be used to identify ash-containing pixels from satellite measurements. The timing and location of the ash cloud identified using this technique from measurements made by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite agree well with model predictions using the dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment). Total column mass loadings are determined from the VIIRS data using an optimal estimation technique which accounts for the low altitude of the resuspended ash cloud and are used to calibrate the emission rate in the resuspended ash scheme in NAME. Considering the tephra deposits from the recent eruptions of Eyjafjallajökull and Grímsvötn as the potential source area for resuspension for this event, we estimate that ∼ 0.2 Tg of ash was remobilized during 16–17 September 2013.

Atmospheric CO<sub>2</sub> observations and models suggest strong carbon uptake by forests in New Zealand

Abstract. A regional atmospheric inversion method has been developed to determine the spatial and temporal distribution of CO2 sinks and sources across New Zealand for 2011–2013. This approach infers net air–sea and air–land CO2 fluxes from measurement records, using back-trajectory simulations from the Numerical Atmospheric dispersion Modelling Environment (NAME) Lagrangian dispersion model, driven by meteorology from the New Zealand Limited Area Model (NZLAM) weather prediction model. The inversion uses in situ measurements from two fixed sites, Baring Head on the southern tip of New Zealand's North Island (41.408° S, 174.871° E) and Lauder from the central South Island (45.038° S, 169.684° E), and ship board data from monthly cruises between Japan, New Zealand, and Australia. A range of scenarios is used to assess the sensitivity of the inversion method to underlying assumptions and to ensure robustness of the results. The results indicate a strong seasonal cycle in terrestrial land fluxes from the South Island of New Zealand, especially in western regions covered by indigenous forest, suggesting higher photosynthetic and respiratory activity than is evident in the current a priori land process model. On the annual scale, the terrestrial biosphere in New Zealand is estimated to be a net CO2 sink, removing 98 (±37) Tg CO2 yr−1 from the atmosphere on average during 2011–2013. This sink is much larger than the reported 27 Tg CO2 yr−1 from the national inventory for the same time period. The difference can be partially reconciled when factors related to forest and agricultural management and exports, fossil fuel emission estimates, hydrologic fluxes, and soil carbon change are considered, but some differences are likely to remain. Baseline uncertainty, model transport uncertainty, and limited sensitivity to the northern half of the North Island are the main contributors to flux uncertainty.

Source apportionment of speciated PM10 in the United Kingdom in 2008: Episodes and annual averages

The Lagrangian atmospheric dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment), has been used to simulate the formation and transport of PM10 over North-West Europe in 2008. The model has been evaluated against UK measurement data and been shown to adequately represent the observed PM10 at rural and urban sites on a daily basis. The Lagrangian nature of the model allows information on the origin of pollutants (and hence their secondary products) to be retained to allow attribution of pollutants at receptor sites back to their sources. This source apportionment technique has been employed to determine whether the different components of the modelled PM10 have originated from UK, shipping, European (excluding the UK) or background sources. For the first time this has been done to evaluate the composition during periods of elevated PM10 as well as the annual average composition. The episode data were determined by selecting the model data for each hour when the corresponding measurement data was >50 μg/m3. All the modelled sites show an increase in European pollution contribution and a decrease in the background contribution in the episode case compared to the annual average. The European contribution is greatest in southern and eastern parts of the UK and decreases moving northwards and westwards. Analysis of the speciated attribution data over the selected sites reveals that for 2008, as an annual average, the top three contributors to total PM10 are UK primary PM10 (17–25%), UK origin nitrate aerosol (18–21%) and background PM10 (11–16%). Under episode conditions the top three contributors to modelled PM10 are UK origin nitrate aerosol (12–33%), European origin nitrate aerosol (11–19%) and UK primary PM10 (12–18%).

Volcanic ash layer depth: Processes and mechanisms

AbstractThe long duration of the 2010 Eyjafjallajökull eruption provided a unique opportunity to measure a widely dispersed volcanic ash cloud. Layers of volcanic ash were observed by the European Aerosol Research Lidar Network with a mean depth of 1.2 km and standard deviation of 0.9 km. In this paper we evaluate the ability of the Met Office's Numerical Atmospheric‐dispersion Modelling Environment (NAME) to simulate the observed ash layers and examine the processes controlling their depth. NAME simulates distal ash layer depths exceptionally well with a mean depth of 1.2 km and standard deviation of 0.7 km. The dominant process determining the depth of ash layers over Europe is the balance between the vertical wind shear (which acts to reduce the depth of the ash layers) and vertical turbulent mixing (which acts to deepen the layers). Interestingly, differential sedimentation of ash particles and the volcano vertical emission profile play relatively minor roles.

USA emissions estimates of CH3CHF2, CH2FCF3, CH3CF3 and CH2F2 based on in situ observations at Mace Head

We investigate the low level, surface-to-surface transport of five hydrofluorocarbons (HFC-152a, -134a, -143a, -125 and -32) from North America to the Advanced Global Atmospheric Gases Experiment (AGAGE) observing station at Mace Head, Ireland during 2005–2012. Using the UK Meteorological Office Numerical Atmospheric dispersion Modelling Environment (NAME) we select air masses, which are dominated by direct transport from the North American continent to Mace Head, thereby eliminating any significant influence from local or other regional sources. To estimate emissions of individual HFCs we use the interspecies correlation method with HFC-125 as the reference compound. We then compare these derived North American emissions with those reported to UNFCCC. Our results indicate an under-reporting of HFC-152a emissions by the USA to the UNFCCC, with an annual average difference of 24 Gg (9–36 Gg). For HFC-134a our estimated emissions indicate an over-reporting of USA reported emissions by 24 Gg (21–28 Gg). For HFC-143a USA reported emissions are lower than our estimates by 1.8 Gg (1–3.5 Gg); whereas for HFC-32 there is close agreement, within the uncertainties, between the USA emissions reported to UNFCCC and our emissions estimates. Combining our USA emissions estimates for HFC-152a, HFC-134a, HFC-143a and HFC-32 with the UNFCCC estimate for HFC-125 could contribute potentially 201 ± 36 Tg-CO2 eq yr−1 to atmospheric radiative forcing in 2011–2012. This compares with UNFCCC of 146 Tg-CO2 eq yr−1 for the same five HFCs.