Lagrangian Particle Dispersion Model FLEXPART Research Articles

Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions

Abstract. Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian particle dispersion model (LPDM) FLEXPART (FLEXible PARTicle Dispersion Model), driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main sources of errors in LPDMs are the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gas measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens), and we conduct both high-resolution (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate spatial distribution of the emissions. Furthermore, the total emission estimates from the high-resolution inversions (351 ± 44 Mg yr−1 for HFC-134a, 101 ± 21 Mg yr−1 for HFC-125, 50 ± 8 Mg yr−1 for HFC-32, 9.0 ± 1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20 %–40 % increase) and result in total a posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10 %–20 % difference between high-resolution inversion estimates and inventory values compared to 30 %–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the abovementioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a priori emissions have the largest impact on the a posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of national GHG emission inventories and support policymakers.

High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India

Accurate estimation of the flux of the most important greenhouse gas, carbon dioxide (CO2), plays a decisive role in designing the policies to reduce the atmospheric CO2 burden to combat climate change. Here we integrate recent (2017–2020) in-situ measurements with a high-resolution particle dispersion model for optimising CO2 flux over peninsular India based on a top-down approach. Using weekly observations of atmospheric boundary layer CO2 from three distinct locations representing peninsular India, the total surface fluxes of CO2 are optimised based on the fluxes from fossil fuel, wildfire, terrestrial biosphere and ocean. Our Bayesian mode inversion using the Lagrangian Particle Dispersion Model FLEXPART suggests that the peninsular Indian region is a slightly stronger source of CO2 (3.34 TgC yr−1) than what is synthesised in the fossil fuel and ecosystem exchanges based on independent estimations. On seasonal scale, the flux corrections to the prior fluxes over peninsular India are 4.68, 6.53, −2.28 and 4.41 TgC yr−1during winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The CO2 measurements from the stations attempted in the present inversion experiments could capture the footprints of peninsular India, giving better constraints to fluxes in the inversion. However, the data gap, pristineness of the samples, and large variabilities in the observations call for sustained monitoring for a longer term to further reduce the uncertainty of the estimated flux.

Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution

Abstract. Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatiotemporal locations of the particles that move independently from a regular grid. Traditionally, these high-resolution data have been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g., using linear interpolation in space and time. However, interpolation is a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single-image super-resolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to upscale them to a higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art enhanced deep residual networks for super-resolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to upscale these data to an arbitrary spatial resolution. We show that the resulting upscaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we find that absolute horizontal transport deviations of calculated trajectories from “true” trajectories calculated with un-degraded 0.5∘ × 0.5∘ winds are reduced by at least 49.5 % (21.8 %) after 48 h relative to trajectories using linear interpolation of the wind data when training on 2∘ × 2∘ to 1∘ × 1∘ (4∘ × 4∘ to 2∘ × 2∘) resolution data.

Modelling the 2021 East Asia super dust storm using FLEXPART and FLEXDUST and its comparison with reanalyses and observations

The 2021 East Asia sandstorm began from the Eastern Gobi desert steppe in Mongolia on March 14, and later spread to northern China and the Korean Peninsula. It was the biggest sandstorm to hit China in a decade, causing severe air pollution and a significant threat to human health. Capturing and predicting such extreme events is critical for society. The Lagrangian particle dispersion model FLEXPART and the associated dust emission model FLEXDUST have been recently developed and applied to simulate global dust cycles. However, how well the model captures Asian dust storm events remains to be explored. In this study, we applied FLEXPART to simulate the recent 2021 East Asia sandstorm, and evaluated its performance comparing with observation and observation-constrained reanalysis datasets, such as the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and CAMS global atmospheric composition forecasts (CAMS-F). We found that the default setting of FLEXDUST substantially underestimates the strength of dust emission and FLEXPART modelled dust concentration in this storm compared to that in MERRA-2 and CAMS-F. An improvement of the parametrization of bare soil fraction, topographical scaling, threshold friction velocity and vertical dust flux scheme based on Kok et al. (Atmospheric Chemistry and Physics, 2014, 14, 13023–13041) in FLEXDUST can reproduce the strength and spatio-temporal pattern of the dust storm comparable to MERRA-2 and CAMS-F. However, it still underestimates the observed spike of dust concentration during the dust storm event over northern China, and requires further improvement in the future. The improved FLEXDUST and FLEXPART perform better than MERRA-2 and CAMS-F in capturing the observed particle size distribution of dust aerosols, highlighting the importance of using more dust size bins and size-dependent parameterization for dust emission, and dry and wet deposition schemes for modelling the Asian dust cycle and its climatic feedbacks.

The Role of Atmospheric Transport for El Niño‐Southern Oscillation Teleconnections

AbstractThe El Niño‐Southern Oscillation (ENSO) is one of Earth's main modes of climate variability, having huge impacts on weather, agriculture, and people worldwide. Although these impacts and teleconnections have been studied for decades, the role of atmospheric transport is not completely understood. We analyze the atmospheric transport outgoing from the Equatorial Pacific with the Lagrangian particle dispersion model FLEXPART driven by reanalysis data. Our results demonstrate the interocean‐basin exchange via the atmosphere: anomalously energetic air from the ENSO region mainly remains within the Tropics and Subtropics, while more air is transported toward the east during El Niño. Transport of anomalous moist air can directly be linked to several observed teleconnections, for example, droughts in the Amazon Basin and precipitation in Southeastern U.S. during El Niño. These results show that atmospheric transport plays a role in several ENSO teleconnections.

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

Abstract. Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources of anthropogenic methane (CH4) emissions in Europe. Here, we report on CH4 emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pairwise upwind–downwind observations of the column-average dry-air mole fractions of CH4 (XCH4) by a network of four portable, ground-based, sun-viewing Fourier transform spectrometers of the type EM27/SUN operated during the CoMet campaign in May–June 2018. The EM27/SUN instruments were deployed in the four cardinal directions around the USCB approximately 50 km from the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips–Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the emissions averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the emissions averaging kernels is essential to detect correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for the southeastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 % and 36 %, dominated by the error contribution from uncertain wind fields.

Moisture sources for the weather pattern classified extreme precipitation in the first rainy season over South China

AbstractMoisture transport, associated with moisture sources and synoptic‐scale weather conditions, is a key dynamic process of precipitation events. Using the K‐means clustering method and the FLEXPART Lagrangian particle dispersion model, this paper investigates moisture contributions from different source regions to extreme precipitation in the first rainy season (hereafter FRS) over South China. In average, land regions contribute more to the FRS extreme precipitation over South China than the ocean regions. The main source regions are Southeast Asia (22.7%), South China (17.2%), the South China Sea (14.3%), and the Bay of Bengal (8.3%). Extreme precipitation events are classified into three types by the K‐means clustering based on 850 hPa geopotential height, which are all characterized by an anomalous low‐pressure system over South China with varying intensity and locations. The distribution of geopotential height anomaly for Type I (30.3%) is characterized the low trough extending from Japan to South China, while Type II (42.5%) and Type III (27.2%) are characterized by “west negative–east positive” and “north positive–south negative” patterns over East Asia with anomalous cyclone over South China, respectively. The much larger contribution of land sources than ocean regions are mainly concentrated in Type I and Type III, of which the contribution from each source region is similar. Ocean sources play a more important role in Type II and are mainly from the Indian Ocean (16.2%) associated with the onset of South China Sea summer monsoon.

Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

“Military Parade Blue Skies” in Beijing: Decisive Influence of Meteorological Factors on Transport Channel and Atmospheric Pollutant Concentration Level

The severity of high atmospheric pollution has been a major social problem in northern China. To improve the air quality in the Beijing–Tianjin–Hebei (BTH) region and guarantee a suitable environment during the military parade and other celebrating activities for the 70th anniversary of the victory for anti-Fascist Warcraft in the year 2015, a series of strict air quality control policies were carried out. To analyze the reduction extents of PM2.5 and organic matter components during the control period and to examine the meteorological conditions in this region and their decisive influence on the air quality, PM2.5 samples were collected and the Lagrangian particle dispersion model FLEXPART was performed to calculate potential source locations within the BTH region. PM2.5, organic carbon (OC), elementary carbon (EC), and three species types were specifically analyzed. Although the results showed that PM2.5, OC, and EC reduced by 64.55%, 48.74%, and 60.75% during the control period, the air mass transport patterns showed great difference at certain periods, which altered the dominant transport direction of air mass and the potential source region of pollutants and organic matters. This alteration completely changed major atmospheric pollutants sources contribution and caused huge concentration changes. Parallel cases also showed that meteorological conditions could avoid massive atmospheric transported from a major emission source region to a receptor site. The meteorological conditions changed the main contribution source region in control and non-control periods and proved the air quality control measures were less necessary in some southern Hebei cities during special events periods.

Metals in coarse ambient aerosol as markers for source apportionment and their health risk assessment over an eastern coastal urban atmosphere in India.

Ambient PM10 (particulate matter with aerodynamic diameter ≤ 10µm) samples were collected and characterized from July 2012 to August 2013 with the objective to evaluate the variation in elemental concentration and use the same as markers for source apportionment and health risk assessment for the first time over Bhubaneswar, India. The yearly average mass of PM10 was 82.28µg/m3, which was ~ 37% higher than the national ambient air quality (NAAQ) standards. Maximum PM10 concentration was observed during winter season followed by post-monsoon, pre-monsoon, and monsoon months. Acid soluble components in the PM10 samples were analyzed using ICP-OES (inductive coupled plasma optical emission spectroscopy), and 19 different elements including heavy metals were determined. Enrichment factor analysis attributed the source to either crustal or non-crustal origin. Principal component analysis (PCA) revealed that crustal sources, industrial activities, and vehicular emissions were significant contributors to PM mass. The contribution of total average elemental concentration showed a seasonal variation with the lowest (11.96µg/m3) and highest (17.77µg/m3) during monsoon and winter, respectively, which is relatively less significant than the variation in total PM10 mass that ranged between 48.43µg/m3 in monsoon and 138.24µg/m3 during the winter season. This observation evidences the predominant contribution of local/regional emission sources to the metallic components in coarse PM10 mass, which is corroborated by the wind pattern studies carried out using polar plots and a Lagrangian Particle Dispersion Model (LPDM) FLEXPART. Further, carcinogenic and non-carcinogenic health risk assessments of the measured elements that find their way into the human body through different exposure pathways have been calculated using United State Environmental Protection Agency (USEPA) standards. The carcinogenic risk of most of the elements was insignificant. The potential risk assessment study revealed that regular exposure to heavy metals through the ingestion pathway caused detrimental health effects. These effects were observed to be more severe in children in comparison to adults.

Simulations of anthropogenic bromoform indicate high emissions at the coast of East Asia

Abstract. Bromoform is the major by-product from chlorination of cooling water in coastal power plants. The number of power plants in East and Southeast Asian economies has increased rapidly, exceeding mean global growth. Bottom-up estimates of bromoform emissions based on few measurements appear to under-represent the industrial sources of bromoform from East Asia. Using oceanic Lagrangian analyses, we assess the amount of bromoform produced from power plant cooling-water treatment in East and Southeast Asia. The spread of bromoform is simulated as passive particles that are advected using the three-dimensional velocity fields over the years 2005/2006 from the high-resolution NEMO-ORCA0083 ocean general circulation model. Simulations are run for three scenarios with varying initial bromoform concentrations based on the range of bromoform measurements in cooling-water discharge. Comparing the modelled anthropogenic bromoform to in situ observations in the surface ocean and atmosphere, the two lower scenarios show the best agreement, suggesting initial bromoform concentrations in cooling water to be around 20–60 µg L−1. Based on these two scenarios, the model produces elevated bromoform in coastal waters of East Asia with average concentrations of 23 and 68 pmol L−1 and maximum values in the Yellow Sea, Sea of Japan and East China Sea. The industrially produced bromoform is quickly emitted into the atmosphere with average air–sea flux of 3.1 and 9.1 nmolm-2h-1, respectively. Atmospheric abundances of anthropogenic bromoform are derived from simulations with the Lagrangian particle dispersion model FLEXPART based on ERA-Interim wind fields in 2016. In the marine boundary layer of East Asia, the FLEXPART simulations show mean anthropogenic bromoform mixing ratios of 0.4–1.3 ppt, which are 2–6 times larger compared to the climatological bromoform estimate. During boreal winter, the simulations show that some part of the anthropogenic bromoform is transported by the northeasterly winter monsoon towards the tropical regions, whereas during boreal summer anthropogenic bromoform is confined to the Northern Hemisphere subtropics. Convective events in the tropics entrain an additional 0.04–0.05 ppt of anthropogenic bromoform into the stratosphere, averaged over tropical Southeast Asia. In our simulations, only about 10 % of anthropogenic bromoform is outgassed from power plants located in the tropics south of 20∘ N, so that only a small fraction of the anthropogenic bromoform reaches the stratosphere. We conclude that bromoform from cooling-water treatment in East Asia is a significant source of atmospheric bromine and might be responsible for annual emissions of 100–300 Mmol of Br in this region. These anthropogenic bromoform sources from industrial water treatment might be a missing factor in global flux estimates of organic bromine. While the current emissions of industrial bromoform provide a significant contribution to regional tropospheric budgets, they provide only a minor contribution to the stratospheric bromine budget of 0.24–0.30 ppt of Br.

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART

Abstract. Flex_extract is an open-source software package to efficiently retrieve and prepare meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) as input for the widely used Lagrangian particle dispersion model FLEXPART and the related trajectory model FLEXTRA. ECMWF provides a variety of data sets which differ in a number of parameters (available fields, spatial and temporal resolution, forecast start times, level types etc.). Therefore, the selection of the right data for a specific application and the settings needed to obtain them are not trivial. Consequently, the data sets which can be retrieved through flex_extract by both member-state users and public users as well as their properties are explained. Flex_extract 7.1.2 is a substantially revised version with completely restructured code, mainly written in Python 3, which is introduced with all its input and output files and an explanation of the four application modes. Software dependencies and the methods for calculating the native vertical velocity η˙, the handling of flux data and the preparation of the final FLEXPART input files are documented. Considerations for applications give guidance with respect to the selection of data sets, caveats related to the land–sea mask and orography, etc. Formal software quality-assurance methods have been applied to flex_extract. A set of unit and regression tests as well as code metric data are also supplied. A short description of the installation and usage of flex_extract is provided in the Appendix. The paper points also to an online documentation which will be kept up to date with respect to future versions.

Influence of the Madden-Julian Oscillation on moisture transport by the Caribbean Low Level Jet during the Midsummer Drought in Mexico

This study aims to improve the understanding of the role of the Madden-Julian Oscillation (MJO) in modulating the midsummer drought (MSD) in Mexico. First, the main moisture sources for the MSD region in Mexico during the summer were identified using the Lagrangian particle dispersion model FLEXPART for the period 1979–2017. From this analysis, the Caribbean Sea was identified as one of the main moisture sources, particularly the area where the core of the Caribbean low-level jet (CLLJ) is located. Second, the water vapour flux transported by the CLLJ to Mexico was then analysed. It was found that the summer seasonal cycle of the CLLJ is associated with the bimodal precipitation pattern in Mexico through the seasonal variability of the moisture contribution from the CLLJ to precipitation over the MSD region, thereby confirming the CLLJ as a key element in the occurrence of the MSD in the Americas. Third, the influence of the MJO on this transport was examined. This analysis showed that the locally dry phases of the MJO decrease the contribution of moisture from the CLLJ core towards the MSD region, while the locally wet phases increase it. Moreover, phases 1 and 2 of the MJO were found to influence the first precipitation peak that occurs in the southwestern region of Mexico by increasing the contribution of moisture from the northeastern tropical Pacific. The study ends by proposing, for the first time, a mechanism by which the MJO modulates the MSD in Mexico.

Intercomparison of FLEXPART and CALPUFF dispersion models. An application over a small tropical island

A typical practice in air quality modeling assessment is the intercomparison between different dispersion models results and air quality measurements at different atmospheric conditions. In this study, a comparison between the results of two Lagrangian dispersion models, the Lagrangian Particle Dispersion Model FLEXPART and the Lagrangian Puff Model CALPUFF (regulatory model), coupled to the same meteorological fields produced by the Weather Research and Forecasting (WRF) model, was done. As a case study, atmospheric dispersion of anthropogenic nitrogen oxides (NOx) emissions (considered as a passive tracer) was considered, during a typical case of severe pollution over the densely populated area of the Guadeloupe archipelago (West French Indies), including complex terrain and, of course, coastal influence. Even though Lagrangian models usually provide better results of plume dispersion under strong winds, in this case study weak trade winds are dominant, in order to check both models under non-ideal atmospheric conditions. As a result, compared to NOx ground level concentration (glc) observations, FLEXPART shows better agreement than CALPUFF. However, as a regulatory model, CALPUFF overestimates both glc observations and FLEXPART maximum NOx glc results, with higher values when a higher horizontal resolution is applied. Also, differences between models results arise in the spatial distribution of NOx over a 1 × 1 km2 horizontal resolution grid domain, showing quite homogenous isopleths with smooth contours for CALPUFF vs. fragmented isopleths with irregular contours for FLEXPART.

The Lagrangian particle dispersion model FLEXPART version 10.4

Abstract. The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

Designing surface CO2 monitoring network to constrain the Indian land fluxes

Optimal network design, for ground-based monitoring of atmospheric mole fractions of carbon dioxide (CO2) over India to better constrain the Indian terrestrial surface fluxes, is proposed here using a Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm are used to calculate prior and posterior cost functions. A total of 73 grid cells are identified over the Indian region in 2°x2° latitude by longitude resolution assuming each cell as a potential site, ‘a-priori’. Further, the effectiveness of CO2 observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified using an incremental optimization method. Based on the above objective, we have (a) ranked the existing nine stations, (b) devised a methodology to design an extended network by adding a few more potential stations to the existing stations, and (c) identified a completely new set of optimal stations for measuring atmospheric CO2 over India.The study shows that the base network of existing stations scattered over India could reduce the uncertainty of Indian terrestrial surface flux estimation in an inversion framework, at this resolution, by more than 30% during post-monsoon and winter season, and by more than 15% during other parts of the year. Addition of five new stations to the base network could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. In the ‘new network’, twelve stations are able to achieve an equivalent flux uncertainty reduction of fourteen stations achieved by the ‘extended network’ during each season. Locations of stations are more important than the magnitude of reduction since it depends on the values of the prior and observational uncertainties. The most important and essential ‘new’ stations in the extended network are over the northeastern region and Indo-Gangetic plain, during most of the year. Only during the post-monsoon season, additional stations are required in the western peninsular region. Finally, a consolidated set for all seasons comprising seventeen members is proposed. The study highlights a major zone of CO2 ‘observational void’ that exists in potential locations near east and northeast parts of India. Immediate requirement of CO2 monitoring initiative in these areas is highly recommended.

Upper tropospheric CH<sub>4</sub> and CO affected by the South Asian summer monsoon during the Oxidation Mechanism Observations mission

Abstract. The Asian monsoon anticyclone (AMA) is annual phenomenon in the northern hemispheric upper troposphere and lower stratosphere. It is part of the South Asian summer monsoon system, and it has a clearly observable signature due to the vertical transport of polluted air masses from the surface to the upper troposphere by monsoon convection. We performed in situ measurements of carbon monoxide (CO) and methane (CH4) in the region of monsoon outflow and in background air in the upper troposphere (Mediterranean, Arabian Peninsula, and Arabian Sea) using optical absorption spectroscopy on board the High Altitude and LOng range (HALO) research aircraft during the OMO (Oxidation Mechanism Observations) mission in summer 2015. We identified the transport pathways and the origin of the trace gases with back trajectories, which were calculated using the Lagrangian particle dispersion model FLEXPART, and we compared the in situ data with simulations of the atmospheric chemistry general circulation model EMAC. CH4 and CO mixing ratios were found to be enhanced within the AMA, the in situ data increased by 72.1 and 20.1 ppbv on average, respectively, and originated in the South Asian region (Indo-Gangetic Plain, northeastern India, Bangladesh, and the Bay of Bengal). It appears that CH4 is an ideal monsoon tracer in the upper troposphere due to its extended lifetime and the strong South Asian emissions. Furthermore, we used the measurements and model results to study the dynamics of the AMA over several weeks during the monsoon season, with an emphasis on the southern and western areas in the upper troposphere. We distinguished four AMA modes based on different meteorological conditions. On one occasion we observed that under the influence of dwindling flow the transport barrier between the anticyclone and its surroundings weakened, expelling air masses from the AMA. The trace gases exhibited a distinct AMA fingerprint; we also found that CH4 accumulated over the course of the OMO campaign.

Simulation of atmospheric dispersion of airborne effluent releases at a tropical coastal site under sea-breeze circulation and internal boundary layer development

In this study the impact of land–sea breeze circulation and internal boundary layer (IBL) on the atmospheric dispersion of airborne effluent releases at the tropical coastal site Kalpakkam is simulated using mesoscale model WRF and Lagrangian particle dispersion model FLEXPART. Simulations with WRF are conducted for typical days in different seasons, i.e., 3 May 2011 (summer), 20 Sep 2010 and 20 Jun 2011 (southwest monsoon). Two K-based non-local turbulence closures (YSU, ACM) and two TKE-based local closures (MYNN, MYJ) in WRF are tested for simulating the sea breeze and IBL. Observations from a 50-m meteorological tower, GPS Sonde, and a Doppler SODAR are used for validation. Simulations indicate that the synoptic flow largely influences the characteristics of the sea breeze at the site. Results show that the sea breeze is more predominant with early onset and it is characterized by deep inland penetration, strong horizontal and vertical winds and is associated with formation of a deep IBL relative to monsoon with opposing large-scale flow. The YSU non-local diffusion scheme and the higher order local TKE closure (MYNN) simulated the sea breeze and IBL characteristics in good agreement with observations. Dispersion simulations with a hypothetical tracer release using FLEXPART showed relatively high ground-level concentrations during IBL formation in the daytime and during stable off-shore flow condition in the morning time. The coupled mesoscale dispersion model could simulate the plume trajectory and buildup of concentrations during sea-breeze time by realistically simulating the winds and mixed-layer characteristics at the coastal site.

Atmospheric transport of 131I and 137Cs from Fukushima by the East Asian northeast monsoon

The Lagrangian particle dispersion model FLEXPART was used to simulate atmospheric dispersion of radionuclides from the Fukushima nuclear power plant (FNPP) towards the Tropical Western Pacific (TWP) and Southeast Asia (SEA). The simulation model distinguished between hemispherical transport via the jet stream and regional transport within the marine boundary layer by the East Asian northeast monsoon. This regional transport was driven by anticyclonic circulation over southern Japan and the western Pacific resulting from a recurrent eastward extension of the Siberian High to the Pacific Ocean. Activity concentrations of 131I and 137Cs measured at ten monitoring stations in TWP and SEA were used to validate the particle dispersion model. Good agreement between the FLEXPART model and observations yields confidence regarding its application to assess radiation impacts and support emergency planning in response to a possible future nuclear accident in the region.