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MEMO2 : MEthane goes MObile - MEasurements and MOdelling

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Abstract and Figures

CH4 emissions are a major contributor to Europe's global warming impact and emissions are not well quantified yet, although this is indispensable knowledge to reach the targets of 2015 United Nations Climate Change Conference in Paris (COP21) and the required massive reductions of greenhouse gas emissions. There are significant discrepancies between official inventories of emissions and estimates derived from direct atmospheric measurement, and effective emission reduction can only be achieved if sources are properly quantified and mitigation efforts are verified. MEMO 2 is a H2020 MSCA European Training Network with more than 20 collaborators from 7 countries and will contribute to the targets of the EU with a focus on methane (CH4). The goal of the project is to bridge the gap between large-scale scientific estimates from in situ monitoring programs and the 'bottom-up' estimates of emissions from local sources that are used in the national reporting by the combination of I) developing and deploying new and advanced mobile methane (CH4) measurements tools and networks, II) isotopic source identification, and III) modelling at different scales. This paper will give a brief overview of the project and its results achieved during the first two years.
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MEMO2: MEthane goes MObile MEasurements and MOdelling
Sylvia Walter, Thomas Röckmann, and the MEMO2 team!
Sylvia Walter, Utrecht University, s.walter@uu.nl
Thomas Röckmann, Utrecht University, t.roeckmann@uu.nl
Semra Bakkaloglu, Royal Holloway and Bedford New College, semra.bakkaloglu@rhul.ac.uk
Hermann W. Bange, Helmholtz Centre for Ocean Research Kiel, hbange@geomar.de
Philippe Bousquet, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), philippe.bousquet@lsce.ipsl.fr
Gregoire Broquet, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), gregoire.broquet@lsce.ipsl.fr
Dominik Brunner, Swiss Federal Laboratories for Materials Science and Technology, Dominik.Brunner@empa.ch
Huilin Chen, University of Groningen, huilin.chen@rug.nl
Sara Defratyka, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), sara.defratyka@lsce.ipsl.fr
Tim de Groot, Royal Netherlands Institute for Sea Research, tim.de.groot@nioz.nl
Hugo Denier van der Gon, Netherlands Organisation for applied scientific research, hugo.deniervandergo n@tno.nl
Lukas Emm enegger, Swiss Feder al Labo ratories for Materials Scienc e and Technol ogy, Lukas.Emmenegger@empa.ch
Julianne Fernandez, Royal Holloway and Bedford New College, Julianne.Fernandez.2018@live.rhul.ac.uk
Rebecca Fisher, Royal Holloway and Bedford New College, r.fisher@es.rhul.ac.uk
Arjan Hensen, Netherlands Organisation for applied scientific researc h, arjan.hensen@tno.nl
Bill Hirst, Shell, bill.hirst@shell.com
Magdalena Hofman, Picarro, mhofmann@picarro.com
Jutta Holst, Lund University, jutta.holst@nateko.lu.se
Piotr Korbeń, Heidelberg Unive rsity, pkorben@iup.uni-heidelberg.de
Maarten Krol, Wageningen University, maarten.krol@wur.nl
Patryk Łakomiec, Lund University, patryk.lakomiec@nateko.lu.se
David Lowry, Royal Holloway and Bedford New College, d.lowry@es.rhul.ac.uk
Hossein Maazallahi, Utrecht University, h.maazallahi@uu.nl
Malika Menoud, Utrecht University, m.menoud@uu.nl
Randulph Morales, Swiss Federal Laboratories for Materials Science and Technology, randulph.morales@empa.ch
Jaroslaw Necki, AGH University of Science and Technology, necki@ agh.edu.pl
Euan Nisbet, Royal Holloway and Bedford New College, e.nisbet@es.rhul.ac.uk
Hans Oonk, OONKAY, hans@o onkay.nl
Jean-Daniel Paris, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), jean-daniel.paris@lsce.ipsl.fr
Isabelle Pison, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), isabelle.pison@lsce.ipsl.fr
Jonas Ravelid, Swiss Federal Laboratories for Materials Science and Technology, jonas.ravelid@empa.ch
Anja Raznjevic, Wageningen University, anja.raznjevic@wur .nl
Janne Rinne, Lund University, janne.rinne@nateko.lu.se
Rod Robinson, National Physical L aboratory, rod.robinson@npl.co.uk
Alessandro Sarno, Avfall Sverige,aless andro.sarno@gotland.se
Marielle Saunois, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), marielle.saunois@lsce.ipsl.fr
Martina Schmidt, Heidelberg University, Martina.schmidt@iup.uni-Heidelberg.de
Mila Stanisavljevic, AGH University of Science and Technology, mila.stanisavljevic@gmail.com
Barbara Szenasi, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), barbar a.szenasi@lsce.ipsl.fr
Danielle van Dinther, Netherlands Organisation for applied scientific research, daniell e.vandinther@tno.nl
Chiel van Heerwaarden, Wageningen University, chiel.vanheerwaarden@wur.nl
Ilona Velzeboer, Netherlands Organisation for applied scientific research, ilona.velzeboer@tno.nl
Katarina Vinkovic, University of Groningen, k.vinkovic@rug.nl
Felix Vogel, Environment and Climate Change Canada, felix.vogel@canada.ca
Wojciech Wolkowicz, Polish Geological Institute, wwol@pgi.gov.pl
Camille Yver-Kwok, University of Versailles Saint-Quentin-en-Yvelines (UVSQ), camille.yver@lsce.ipsl.fr
!
Keywords: methane, greenhouse gas emissions, H2020 MSCA ITN-ETN!
!
Abstract
CH4 emissions are a major contributor to Europe’s global warming impact and emissions are not well
quantified yet, although this is indispensable knowledge to reach the targets of 2015 United Nations
Climate Change Conference in Paris (COP21) and the required massive reductions of greenhouse gas
emissions. There are significant discrepancies between official inventories of emissions and estimates
derived from direct atmospheric measurement, and effective emission reduction can only be achieved
if sources are properly quantified and mitigation efforts are verified.!
MEMO2 is a H2020 MSCA European Training Network with more than 20 collaborators from 7 countries
and will contribute to the targets of the EU with a focus on methane (CH4). The goal of the project is to
bridge the gap between large-scale scientific estimates from in situ monitoring programs and the
‘bottom-up’ estimates of emissions from local sources that are used in the national reporting by the
combination of I) developing and deploying new and advanced mobile methane (CH4) measurements
tools and networks, II) isotopic source identification, and III) modelling at different scales. This paper will
give a brief overview of the project and its results achieved during the first two years. !
1. Introduction
Mitigation of climate change is a key scientific and societal challenge, and of pivotal societal and public
interest. The 2015 United Nations Conference of the Parties in Paris (COP21) agreed to limit global
warming "well below" 2oC and, if possible, below 1.5oC. Reaching this target requires massive
reductions of greenhouse gas emissions, far beyond the intended Nationally Determined Contributions
(NDCs). In this context, achieving significant reduction of greenhouse gas emissions is a logical headline
target of the EU climate action [1], which envisages as one of the key targets for the year 2020 a “20 %
cut in greenhouse gas emissions from 1990 levels”. In addition, the Sustainable Development Goal
(SDG) nr. 13 of the 2030 Agenda for Sustainable Development, implemented in 2015 by the United
Nations, aims to “take urgent action to combat climate change and its impact”. In this context of urgent
required massive greenhouse gas emission reductions, CH4 is a promising target. CH4 is the second
most important greenhouse gas after CO2, its emissions are a major contributor to Europe's global
warming impact and it is one of Europe’s most important sources of energy. With a global warming
potential (cumulative forcing over 20 years) of 84, a rather short lifetime of 12.4 years [2] and several
sources such as landfills, gas leaks and manure offering possibilities of “no-regret” emission reduction,
a reduction of CH4 can make a significant contribution to climate change mitigation actions. CH4 emission
reductions are more cost-effective than most CO2 emission reduction measures and will lead to quicker
gains in reduction of greenhouse gas radiative forcing. !
However, effective emission reduction can only be achieved if sources are properly quantified, and
mitigation efforts are verified. Europe's CH4 emissions are yet not well quantified. There are significant
discrepancies between official inventories of emissions and estimates derived from direct atmospheric
measurement. New advanced combinations of measurement and modelling are needed to achieve
reliable emission quantification. The H2020 European Training Network (ETN) MEMO2 (MEthane goes
MObile MEasurements and MOdelling, https://h2020-memo2.eu) aims to bridge the gap between
large-scale scientific estimates from in situ monitoring programs and the 'bottom-up' estimates of
emissions from local sources that are used in the national reporting. As an ETN, MEMO2 aims not only
on scientific excellence but also on the combination with training of early stage researchers (ESRs). So
MEMO2 has two goals:!
The main scientific goal of MEMO2 is to develop and apply innovative experimental and modelling tools,
based on recently developed mobile analysers, on state-of-the-art isotope techniques, and on a
hierarchy of models, including newly developed high-resolution dispersion models, to identify and
quantify CH4 emissions from local sources in Europe and use these updated emissions to improve
estimates at the European scale. These tools will enable improved and objective verification of CH4
emission reduction strategies for specific source sectors. !
The second goal is based on the complexity and interdisciplinary character of detecting and quantifying
CH4 emissions, and the evaluation of climate mitigation measures. It requires skilled scientists with high-
level of theoretical and practical competences that are able to cooperate in networks. So MEMO2
developed and implemented a dedicated research training program, which follows a holistic approach
to stimulate key competences and knowledge exchange, aiming at the education of a generation of
“cross–thinking” scientists. The training includes activities on local, individual, network-wide and
international level. As a training network, MEMO2 fosters the education of qualified scientists in the use
and implementation of interdisciplinary knowledge and techniques that are essential to meet and verify
emission reduction goals.
By this, MEMO2 also contributes to associated targets of SDG 13, which focus e.g. on the improvement
of education and awareness-raising (https://sustainabledevelopment.un.org/sdg13).
!
2. MEMO2
2.1 General set-up
MEMO2 is an international and interdisciplinary project, with 9 academic and 16 non-academic partners,
and makes synergistic use of highly specialized competencies and facilities of these partners from the
fields of atmospheric physics and chemistry, environmental sciences, meteorology, and metrology. This
includes atmospheric and isotopic measurement facilities, mobile measurement equipment, UAV and
AirCores and several modelling facilities to increase the overall scientific quality and societal impact.
The research program comprises of three scientific work packages (WPs), which are strongly
interconnected (Fig. 1).!
WP1 is dedicated to mobile measurements across
Europe. In WP2 state-of-the-art isotope techniques
are used to attribute observed CH4 elevations to
individual sources. The translation of these CH4
elevations into emissions and to integrate local
measurements from WP1 and WP2 to the European
scale is the task within WP3. The scientific WPs
share a common objective and complement each
other by detecting (WP1), attributing (WP2) and
quantifying (WP3) CH4 emissions in Europe using
measurements on mobile platforms. !
The geographic locations of the partners provide
excellent opportunities to characterize important CH4
source categories around Europe, such as e.g. agriculture and gas industry in the Netherlands, landfills
in UK, city emissions in France and Germany or coal mining in Poland.
2.2 General scientific and methodological approach
CH4 measurements within MEMO2 span the full range from high-precision flask samples for isotope
analysis, to continuous time series using laser spectroscopy, and airborne measurements by vehicles,
airplanes and drones, which will allow in-situ CH4 monitoring in all three dimensions. The modelling
activities allow the development of new modelling concepts, covering European, regional and local
scales by such diverse techniques as inversion of Lagrangian Particle Dispersion Models and Large
Eddy Simulations. Another benefit of the network is the opportunity to perform joint field campaigns and
intercomparison campaigns. !
On global and continental scales, the scientific community assesses atmospheric CH4 by in situ
monitoring programs, e.g., the ICOS ESFRI infrastructure in Europe and the UN's Global Atmosphere
Watch [3, 4]. This provides “top-down” quantification of emissions on a large scale (e.g. Germany,
France, UK), but is by design not sensitive to local emissions from individual sources [5, 6, 7]. In contrast,
emission reductions happen at the local scale where emission estimates usually rely on “bottom-up”
assessments (e.g. cattle statistics, estimating leaks from landfills), which are aggregated to yield
national emission inventories [8]. Often large discrepancies occur between bottom-up and top-down
estimates of emissions [9, 10]. Mitigation legislation drives reductions in reported emissions of CH4, e.g.
emissions from landfills. However, such reductions are mostly reported by bottom-up assessment, but
not independently confirmed by top-down measurements and models. The concept “trust but verify” can
only be applied if adequate verification tools are available, which is not the case yet for most greenhouse
gases and ozone–depleting gases. !
Current approaches to estimating CH4 sources at the EU-level use both bottom-up and top-down
methods [7, 9]. Bottom-up estimates rely on emission reporting, in which various sources are integrated
into emission totals per country based on emission factors and activity magnitudes. These estimates
are uncertain, partly because of a lack of observations to constrain the emission factors. The top-down
approach usually starts with the bottom-up emission inventories as a prior estimate, and optimally
adjusts the sources to make the emissions consistent with CH4 observations. This approach requires a
transport model to translate emissions into atmospheric concentration fields that can be compared to
observations. Top-down approaches are limited by the density of atmospheric observations, by the
quality of the transport model, but also by the quality of the prior estimate of emissions [4]. Here, scale
issues become important. Local measurements close to sources are hard to reproduce by coarse-scale
(> 10 km) models. Therefore, top-down approaches normally employ only “background” measurements
that are considered representative for larger geographical domains. On the other hand, the model-
Fig. 1: Interconnection scheme of the 3 scientific Work
Packages of MEMO2
calculated concentrations cannot be attributed to individual sources at the (local) scale of the emissions.
As a result, the information exchange is partial and mainly one-way: from the (uncertain) inventories to
the atmospheric concentrations. Feedbacks from the larger-scale model calculations to the emission
inventories, and integration of local scale emission factors into inventories remain both limited. !
The approach of the MEMO2 research program is to use innovative measurements and modelling of
CH4 using mobile platforms as principle tool to bridge the current scale gaps between local
measurements, emission inventories, and European scale modelling.!
2.3 Mobile measurements
Mobile measurements of CH4 emissions are available since a few years [11]. The interpretation of such
campaign results is challenging due to several factors which could impact the measurements, e.g. the
spatial distribution of sources, measurement conditions (e.g. distance to the source, speed of the
vehicles), changing emission rates and emissions-weighted distributions, or plume diluting atmospheric
conditions. !
The key measurement components are fast and accurate analysers on mobile platforms. Analysers
used are various CRDS models and OA-ICOS to measure CH4, CO2, 13CH4, 13CO2, C2H6, H2O, C2H2 or
CO. The main experimental platform are cars, but also unmanned aerial vehicle (UAV) platforms and
light aircrafts are used to investigate focus source types such as wetlands, landfills, city emissions,
lakes, gas leaks, agricultural emissions, and mining emissions. By this MEMO2 maps the small-scale
distribution of CH4 across Europe, and identify and quantify CH4 emissions at the local scale [11] and
provide emission factors for further modelling activities. The CH4 source mix is different per country, and
- based on the inventories - MEMO2 targets the largest uncertainties in the individual countries. A key
advantage of the network is that due to close cooperation the regional/national scale but also the
European scale is covered.!
2.4 Isotopic measurements
Different sources emit CH4 with slightly different isotopic composition [12]. So, measuring the isotopic
composition of CH4 helps to identify the sources responsible for observed elevations of CH4 in the
atmosphere and improves the understanding of the temporal and spatial variability of isotopic signatures
of CH4 emissions. Its helps to verify emission inventories and to distinguish CH4 sources in complex
environments with many overlapping sources, such as cities. The link to the development of UAV
sampling methods and modelling allows identifying possible vertical as well as horizontal variability in
isotopic signature in emission plumes. The information can be used to provide novel EU-wide “isotopic
source signature maps” of the most important CH4 sources, and give important input for the use of
isotope information in atmospheric models.!
2.5 Modelling approaches
The quantification of emissions from concentration measurements and isotopic compositions requires
complementary modelling tools applicable on various scales, from the local scale of an intensive
measurements campaign up to the EU and global scale [13]. At the local scale, Large Eddy Simulations
(LES) are employed to predict and analyse detailed dispersion patterns from local sources. Virtual
vehicles and UAVs are used to sample simulated 3D dispersion fields from CH4 sources. On the regional
scale, flow patterns integrating mixed sources from e.g. a city are analysed using regional high-
resolution modelling. Combined, these approaches allow optimal interpretation and usage of the
measurement results. At the European scale, forward simulations of CH4 concentrations and top-down
emission estimates are derived, using detailed and updated bottom-up emissions maps. This joint
bottom-up and top-down activity links the modelling and the measurements, and specifically includes
the improvement of European CH4 inventories. Modelling activities also assist in the design of
measurement strategies. The goal is to determine areas where measurements will have the largest
benefit concerning both uncertainty of emissions and possibilities of mitigation measures.
3. Results
3.1 Mobile measurements of CH4
Measurement campaigns are an essential component of
projects such as MEMO2. Within the first two years of the
project more than 150 days of measurement campaigns
were performed. Fig. 2 gives an overview of (joint) sampling
locations.
As one example, in May 2017 and June 2018, we
participated in the CoMet (Carbon dioxide and Methane
mission) campaigns in Upper Silesia, which were
(co)organized by the German Aerospace Center (DLR) and
the University of Science and Technology (AGH). CoMet
aimed at industrial emission of CH4 with a priority on mining
activities over Silesia as one of the European anthropogenic
CH4 hotspots. In this region 33 mines are active, but also
additional methane sources are present: landfills, cities gas
networks, cow farms, wetlands and agriculture. Several
teams deployed in-situ and remote sensing instruments on
aircraft as well as on ground, performed measurements
using mobile platforms (CRDS analyser in cars, planes and
an active AirCore system on a drone [14]) and applied FTIR
technique with stationary and mobile platform. Preliminary
results show, that CH4 in general mainly originates from
thermogenic sources, but with significant differences
between the mine shafts, not only regarding mole fractions
but also the isotopic composition of CH4. (Fig. 3, Fig. 4). This
might indicate either different origins of methane gas,
different levels of coal excavation inside the mines or different types of ventilation.
Fig. 4: dD and d13C of shafts in the Silesian region
!
Fig. 2: Overview of (joint) measurement
campaigns, the colour codes indicate the type of
samples
Fig. 3: CH4 concentration measurements in
Upper Silesian coal mining region.
The exhaust shafts of the mines are delivering air from different levels of the mines to the atmosphere,
where CH4 can have different origins. According to the geological history of the coal beds and the layers
above them, the coal also has less or more methane accumulated. Thus, the same exhaust shaft can
provide different amounts of CH4 with different isotopic signatures of it during our observations. The
amount of CH4 released depends on the distance from the ridge (crack of rock bed) and the isotopic
composition strongly depends on the depth of the coal bed excavated.
As successful measurement campaigns depend on excellent and suitable equipment, MEMO2 also
develops new instrumentation such as a lightweight high-precision mid-IR methane laser spectrometer
for unmanned aerial vehicles (UAV) (Fig. 5). The spectrometer is based on a single-mode quantum
cascade laser (DFB-QCL) and a circular, segmented multi-pass cell with an optical path length of 10 m
[15]. This novel cell design has a compact footprint, and it achieves low optical noise and high stability
against mechanical distortion. The overall instrument weighs 1.6 kg (excluding battery) and has an
average power consumption of 15 W which is achieved by optimized laser driving and a system-on-chip
FPGA data acquisition module [16]. The spectrometer is equipped with additional sensors for pressure,
temperature, and relative humidity, as well as a GPS receiver and an optional module for real-time data
transmission. Therefore, it is possible to use the device aboard any drone, regardless of its specific
communication protocol.
The spectrometer reaches a precision of few ppb at 1 s time resolution and significantly below 1 ppb
after 10 – 1000 s integration. It has been regularly flown on a commercial drone (DJI Matrice 600). The
open-path design allows very fast sampling, and absorption spectra are measured at > 10 kHz. This
gives a wide flexibility in terms of the required precision and time resolution. Ongoing field experiments
explore the potential of this unique instrument for the identification, characterization and quantification
of natural and anthropogenic methane sources.
Fig. 5: Photography of the high-precision methane sensor (left side) and its mounting on a UAV (right side)
3.2 Source identification by isotopic characterization
Isotopic characterization and mapping of CH4 sources requires that all laboratories measuring the
isotopes of CH4 be on the same scale across the range of values commonly encountered in emissions
from European sources. The use of newly-developed cavity ring-down laser spectroscopy (CRDS)
techniques for measurements of the 13C/12C ratio of methane (d13C) allows field measurements of
isotopes to a much lower precision than by IRMS, but gives near instantaneous measurements rather
than later laboratory analyses to discriminate CH4 produced by biogenic (e.g. cows), thermogenic (e.g.
natural gas) and pyrogenic (combustion) sources. This can be measured on emissions directly from
source where the CH4 % is very high, such as a gas supply (95 %) or a landfill gas (50 %), but for the
MEMO2 studies many of the sources are unknown and sometimes sampled at many hundreds of meters
downwind of the point of emission and measured CH4 varies from the ambient global background
(around 1.9 ppm CH4) to about 10 ppm or even more CH4. International isotopic standards are not
available for CH4 in air at these concentrations, however. Given that the CRDS instruments have
inherently poor precision at the 2 ppm ambient background levels of CH4, but with an improvement of
an order of magnitude at 10 ppm CH4 (from ±4 ‰ to ±0.4 ‰), we first prepared an inter-calibration with
the aim of bringing these instruments onto the common isotopic scale. Results were in the range of
analytical error so that the results obtained within the MEMO2 project can be confidently interpreted, and
directly compared with other results obtained globally.
Next to the intercomparison, long-term monitoring experiments were intensified and new ones started.
Fig. 6 shows a 6-months’ time series at Cabauw, the Netherlands [17], similar long-term monitoring
experiments were executed in Krakow from October 2018 till March 2019. First results show, that the
CH4 in Krakow mainly originates from thermogenic sources, whereas CH4 at the Dutch stations is mainly
biogenic even at Lutjewad, which is near Europe´s largest gas reservoirs in the North Sea and close to
Groningen. The long-term monitoring allows the identification of specific events with elevated
contributions from more enriched sources such as natural gas and landfills. The results are used to
compare models such as the global TM5 model and the mesoscale model FLEXPART-COSMO.
Fig. 6: 6-month time series of dD, d13C, and the CH4 mole fraction at Cabauw [16] (left), mean results of long-term campaigns
in Krakow (PL), Lutjewad and Cabouw (N L) (right)
Next to the atmospheric measurements, MEMO2 made an excursion to the marine environment. During
a campaign to the North Sea in 2018, the origin of CH4 above an active cold seep at the Doggerbank
was investigated [18]. Fig. 7 shows depth profiles of d13C and dD taken during a 3-days’ time series at
the Doggerbank. The isotopic signature from the left-over methane that is not directly oxidized by
methanotrophs during transport trough the water column indicates that at lower concentrations of
methane a shift occurs in dD as well as d13C. Both isotopic signals indicate that methane was from
biogenic methanogenesis. Furthermore, the increase in heavy isotopes at lower concentrations is an
indication that microbial methane oxidation occurs.
Fig. 7: d13C (left) and dD (right) during a 3-days’ time series at the Doggerbank, North Sea
3.3 Modelling: A multi-scale interpretation framework for CH4 observations
As the modelling part within MEMO2 aims on e.g. the interpretation of mobile observations as well as
on the estimation of emission fluxes at different scales, a variety of models are used, compared, and
improved.
A modelling tool to assist in planning of mobile measurements and campaigns is MicroHH [19], a model
which is more advanced than a simple Gaussian plume model to get information about source variability.
As the interpretation of measurements as done within MEMO2 requires some more flexibility, the
MicroHH has been improved by adding a point and a line source in form of a Gaussian “ball” or “pipe”
that spans over multiple grid points and is limited by four standard deviations in order to avoid unwanted
numerical behaviour of the simulation which would happen if all the mass was injected at a single grid
point. The Gaussian function is normalized in a way that preserved the prescribed source strength. It is
now possible to simulate multiple sources at arbitrary locations in the domain (Fig. 8). After solving the
circular boundary conditions, which are desirable for the flow field, but not for CH4, the model is ready
to interpret observations.
Fig. 8: DNS simulation of a plume from a line source in stationary homogeneous turb ulence (above left); ensemb le avera ge of
CH4 mixing ratios at one point (above right); MicroHH simulated dispersion from a point source (arbitrary scales) (below left);
MicroHH simulated dispersion from a line source (arbitrary scales) (below right).
On a meso-scale or European scale new simulations of CH4 mixing ratios have been performed with
the CHIMERE chemistry transport model using the EDGAR version 4.3.2 and TNO-MACC_III emission
inventories from the year 2011. Multi-year simulations have been carried out from 2011 to 2015 with a
horizontal resolution of 0.5°x0.5° (~50x50 km). Also, a large number of sensitivity experiments were
performed. The comparison and the sensitivity tests aim at a better understanding of the differences
between modelled and measured CH4 concentrations and thus help reveal which part can be attributed
to errors in inventories and serve the goal of estimating top-down CH4 emissions on the European scale.
An example for the site Lutjewad in the Netherlands shows the correlation between measurements and
simulated values of the grid cell corresponding to the station location and its eight neighbouring cells
was checked (Fig. 9), and the values of those model grid cells with the highest correlation coefficients
can be used for the comparison against the measurements.
One of the sensitivity tests consisted of running the model with boundary conditions obtained from the
CAMS-MACC reanalysis product [20] and in contrast to this using the pre-optimized boundary conditions
derived from the LMDz model. The comparison showed, that simulations using the MACC boundary
conditions compared clearly better to the measurements. To investigate the impact of the use of natural
CH4 emissions in addition to the anthropogenic emissions, we carried out a sensitivity run for which
emissions from wetlands were included [21]. The inclusion of wetland emissions increased the mixing
ratio especially over the wetland areas and coasts by up to about 36 ppb. Compared to the
measurements, the addition of wetlands makes a slight positive difference to the simulation results,
which seems advantageous as the measurements are mostly underestimated by the model.
Fig. 9: Comparison of the simulated concentrations in the model grid cell corresponding to the measurement site’s location
and in its eight neighbouring cells. It is an example of mix ing ratios simulated using EDG ARv4.3.2 for the site Lutjewad. The
analysis is base d on ho urly aft ernoon values from 2015.
To simulate the dispersion of CH4 emitted from individual sources, the GRAL (Graz Lagrangian Model)
dispersion model as implemented. An example simulation applied to a tracer release experiment
conducted during the first MEMO2 winter school in February 2018 is presented in Fig. 10. To improve
the efficiency and applicability, the model has been improved by I) replacing the GRAL Graphical User
Interface by a python module preparing all input data for a simulation (land cover, 3D obstacles,
topography, etc.) and launching the computation jobs, (II) implementing the option to run dynamic (rather
than static) simulations allowing to account for rapidly changing winds and turbulence, and (III)
developing a python package for post-processing and visualization of the output. Furthermore, a simple
Gaussian plume model has been implemented to compare the results obtained with GRAL.
Fig. 10: Left: GRAL simulat ed CH4 concentration (5-minute average) during a tracer release experiment in February 2018. The
red arrows denote the paths of the mobile measurement platforms crossing the plume multiple times at two distances from the
source. Right: Simulated (solid lines) and m easured (dotted lines with s ymbols) CH4 mole fractions along different transects
sampled by the car of RHUL. Matching the areas below the curves allows estimating the strength of the source.
4. Summary and discussion
Effective emission reduction of any atmospheric relevant gas can only be achieved if sources are
properly quantified, and mitigation efforts are verified. MEMO2 as a European H2020 project focus on
CH4, and aims to bridge the gap between large-scale scientific estimates from in situ monitoring
programs. The main benefit of MEMO2 is the interdisciplinary character, the inclusion of many different
technological and scientific advances covering the development and application of mobile platforms,
isotope studies and modelling. The combination of measurement and modelling approaches as done
by MEMO2 are highly needed to achieve better and more reliable emission quantification and 'bottom-
up' estimates of emissions from local sources that are used in the national reporting.
MEMO2 provides a unique opportunity of data sets, especially including isotopic data which will go
beyond individual measurements or forward and backward modelling alone to get realistic emission
estimates. By focusing on the combination of interdisciplinary expertise and skills, MEMO2 ensures that
the interdisciplinary expertise will be capitalized such that MEMO2 offers added value to the scientific
community and creates a high societal impact.
Acknowledgements
The authors would like to acknowledge the support of all colleagues during the campaigns,
measurements, and data evaluation. This project has received funding from the European Union’s
Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement
No 722479.
References
[1] EU climate action: http://ec.europa.eu/clima/policies/strategies/2020/indexen.htm
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Figure captions
Fig. 1: Interconnection scheme of the 3 scientific Work Packages of MEMO2
Fig. 2: Overview of (joint) measurement campaigns, the colour codes indicate the type of samples
Fig. 3: CH4 concentration measurements in Upper Silesian coal mining region.
Fig. 4: dD and d13C of shafts in the Silesian region
Fig. 5: Photography of the high-precision methane sensor (left side) and its mounting on a UAV (right
side)
Fig. 6: 6-month time series of dD, d13C, and the CH4 mole fraction at Cabauw [16] (left), mean results of
long-term campaigns in Krakow (PL), Lutjewad and Cabouw (NL) (right)
Fig. 7: d13C (left) and dD (right) during a 3-days’ time series at the Doggerbank, North Sea
Fig. 8: DNS simulation of a plume from a line source in stationary homogeneous turbulence (above left);
nsemble average of CH4 mixing ratios at one point (above right); MicroHH simulated dispersion from a
point source (arbitrary scales) (below left); MicroHH simulated dispersion from a line source (arbitrary
scales) (below right).
Fig. 9: Comparison of the simulated concentrations in the model grid cell corresponding to the
measurement site’s location and in its eight neighbouring cells. It is an example of mixing ratios
simulated using EDGARv4.3.2 for the site Lutjewad. The analysis is based on hourly afternoon values
from 2015.
Fig. 10: Left: GRAL simulated CH4 concentration (5-minute average) during a tracer release experiment
in February 2018. The red arrows denote the paths of the mobile measurement platforms crossing the
plume multiple times at two distances from the source. Right: Simulated (solid lines) and measured
(dotted lines with symbols) CH4 mole fractions along different transects sampled by the car of RHUL.
Matching the areas below the curves allows estimating the strength of the source.
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