Inverse transport modeling of volcanic sulfur dioxide emissions  using large-scale simulations

Heng, Yi; Hoffmann, Lars; Grießbach, Sabine; Rößler, Thomas; Stein, Olaf

doi:10.5194/gmd-9-1627-2016

Cited by 31 publications

(60 citation statements)

References 50 publications

(80 reference statements)

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“…For instance, the Puyehue-Cordón Caulle emissions reached the lower stratosphere and were rapidly transported eastward by the jet stream (Klüser et al, 2013;Hoffmann et al, 2016), while the Nabro emissions were entrained first by the ASM circulation in the UTLS region before being transported to the tropics (Fairlie et al, 2014;Heng et al, 2016). In this study, the transport of the equatorward dispersion of Sarychev aerosol is driven by the ASM anticyclone.…”

Section: Discussionmentioning

confidence: 89%

“…More details on the SI can be found in Hoffmann et al (2014Hoffmann et al ( , 2016 and Heng et al (2016). AIRS was able to detect the SO 2 cloud from the beginning of the eruption of Sarychev on 12 June 2009 up to 5 weeks later.…”

Section: Airsmentioning

confidence: 96%

“…Extratropical volcanic eruptions are expected to have less impact on the global climate because the downward flow of the Brewer-Dobson circulation (BDC) in the stratospheric extratropics prevents sulfate aerosol from rising up (Seviour et al, 2012), and sulfate aerosol in the extratropical stratosphere easily subsides back to the troposphere within months (Holton et al, 1995;Kremser et al, 2016). Although some studies show that extratropical volcanic eruptions can also have a significant impact on climate (Highwood and Stevenson, 2003;Oman et al, 2005Oman et al, , 2006Kravitz and Robock, 2011;Pausata et al, 2015a, b), the impact is usually found to be limited to the specific hemisphere in which the eruption occurred (Graf and Timmreck, 2001;Kravitz and Robock, 2011;Pausata et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

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Equatorward dispersion of a high-latitude volcanic plume and its relation to the Asian summer monsoon: a case study of the Sarychev eruption in 2009

Grießbach²,

Hoffmann³

2017

Atmos. Chem. Phys.

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Abstract. Tropical volcanic eruptions have been widely studied for their significant contribution to stratospheric aerosol loading and global climate impacts, but the impact of high-latitude volcanic eruptions on the stratospheric aerosol layer is not clear and the pathway of transporting aerosol from high latitudes to the tropical stratosphere is not well understood. In this work, we focus on the high-latitude volcano Sarychev (48.1 • N, 153.2 • E), which erupted in June 2009, and the influence of the Asian summer monsoon (ASM) on the equatorward dispersion of the volcanic plume. First, the sulfur dioxide (SO 2 ) emission time series and plume height of the Sarychev eruption are estimated with SO 2 observations of the Atmospheric Infrared Sounder (AIRS) and a backward trajectory approach using the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC). Then, the transport and dispersion of the plume are simulated using the derived SO 2 emission time series. The transport simulations are compared with SO 2 observations from AIRS and validated with aerosol observations from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The MPTRAC simulations show that about 4 % of the sulfur emissions were transported to the tropical stratosphere within 50 days after the beginning of the eruption, and the plume dispersed towards the tropical tropopause layer (TTL) through isentropic transport above the subtropical jet. The MPTRAC simulations and MIPAS aerosol data both show that between the potential temperature levels of 360 and 400 K, the equatorward transport was primarily driven by anticyclonic Rossby wave breaking enhanced by the ASM in boreal summer. The volcanic plume was entrained along the anticyclone flows and reached the TTL as it was transported southwestwards into the deep tropics downstream of the anticyclone. Further, the ASM anticyclone influenced the pathway of aerosols by isolating an "aerosol hole" inside of the ASM, which was surrounded by aerosol-rich air outside. This transport barrier was best indicated using the potential vorticity gradient approach. Long-term MIPAS aerosol detections show that after entering the TTL, aerosol from the Sarychev eruption remained in the tropical stratosphere for about 10 months and ascended slowly. The ascent speed agreed well with the ascent speed of the water vapor tape recorder. Furthermore, a hypothetical MPTRAC simulation for a wintertime eruption was carried out. It is shown that under winter atmospheric circulations, the equatorward transport of the plume would be suppressed by the strong subtropical jet and weak wave breaking events. In this hypothetical scenario, a high-latitude volcanic eruption would not be able to contribute to the tropical stratospheric aerosol layer.

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Section: Discussionmentioning

confidence: 89%

Section: Airsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Equatorward dispersion of a high-latitude volcanic plume and its relation to the Asian summer monsoon: a case study of the Sarychev eruption in 2009

Grießbach²,

Hoffmann³

2017

Atmos. Chem. Phys.

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“…An appropriate selection of the numerical scheme and the size of the time step is mandatory to maximize computational efficiency. This is particularly important for large-scale simulations, like Lagrangian transport simulations aiming at emission estimation by means of inverse modeling (e.g., Stohl et al, 2011;Heng et al, 2016) or long-term simulations coupled to chemistry climate models (e.g., Hoppe et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Trajectory errors of different numerical integration schemes diagnosed with the MPTRAC advection module driven by ECMWF operational analyses

Rößler

Stein²,

Heng

et al. 2018

Geosci. Model Dev.

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Abstract. The accuracy of trajectory calculations performed by Lagrangian particle dispersion models (LPDMs) depends on various factors. The optimization of numerical integration schemes used to solve the trajectory equation helps to maximize the computational efficiency of large-scale LPDM simulations. We analyzed global truncation errors of six explicit integration schemes of the Runge-Kutta family, which we implemented in the Massive-Parallel Trajectory Calculations (MPTRAC) advection module. The simulations were driven by wind fields from operational analysis and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) at T1279L137 spatial resolution and 3 h temporal sampling. We defined separate test cases for 15 distinct regions of the atmosphere, covering the polar regions, the midlatitudes, and the tropics in the free troposphere, in the upper troposphere and lower stratosphere (UT/LS) region, and in the middle stratosphere. In total, more than 5000 different transport simulations were performed, covering the months of January, April, July, and October for the years 2014 and 2015. We quantified the accuracy of the trajectories by calculating transport deviations with respect to reference simulations using a fourth-order Runge-Kutta integration scheme with a sufficiently fine time step. Transport deviations were assessed with respect to error limits based on turbulent diffusion. Independent of the numerical scheme, the global truncation errors vary significantly between the different regions. Horizontal transport deviations in the stratosphere are typically an order of magnitude smaller compared with the free troposphere. We found that the truncation errors of the six numerical schemes fall into three distinct groups, which mostly depend on the numerical order of the scheme. Schemes of the same order differ little in accuracy, but some methods need less computational time, which gives them an advantage in efficiency. The selection of the integration scheme and the appropriate time step should possibly take into account the typical altitude ranges as well as the total length of the simulations to achieve the most efficient simulations. However, trying to summarize, we recommend the third-order Runge-Kutta method with a time step of 170 s or the midpoint scheme with a time step of 100 s for efficient simulations of up to 10 days of simulation time for the specific ECMWF high-resolution data set considered in this study. Purely stratospheric simulations can use significantly larger time steps of 800 and 1100 s for the midpoint scheme and the third-order Runge-Kutta method, respectively.

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“…This capability can be useful for other applications beyond releases from nuclear power plants, including compliance monitoring of the nuclear test ban treaty (Issartel and Baverel, 2003) and inverse modeling of emissions from large-scale industrial accidents and volcanic plumes (e.g., Heng et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

et al. 2017

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Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 10 3 ), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200 m. The start time is determined to within 5 min out of 2 h, and the duration to within 50 min out of 4 h. Over a range of release amounts of 10 to 1000 kg, the estimated amount exceeds the actual amount of 146 kg by only 32 kg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.

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Inverse transport modeling of volcanic sulfur dioxide emissions using large-scale simulations

Cited by 31 publications

References 50 publications

Equatorward dispersion of a high-latitude volcanic plume and its relation to the Asian summer monsoon: a case study of the Sarychev eruption in 2009

Equatorward dispersion of a high-latitude volcanic plume and its relation to the Asian summer monsoon: a case study of the Sarychev eruption in 2009

Trajectory errors of different numerical integration schemes diagnosed with the MPTRAC advection module driven by ECMWF operational analyses

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

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