We present a new modelling approach for assessing
atmospheric emissions from a city, using an aircraft measurement sampling
strategy similar to that employed by previous mass balance studies. Unlike
conventional mass balance methods, our approach does not assume that
city-scale emissions are confined to a well-defined urban area and that
peri-urban emissions are negligible. We apply our new approach to a case
study conducted in March 2016, investigating CO,
For comparison, we also calculate fluxes using a conventional mass balance approach and compare these to the emissions inventory aggregated over the Greater London area. Using this method we derive much higher inventory scale factors for all three gases, as a direct consequence of the failure to account for emissions outside the Greater London boundary. That substantially different conclusions are drawn using the conventional mass balance method demonstrates the danger of using this technique for cities whose emissions cannot be separated from significant surrounding sources.
Over half the people in the world (54 %) live in urban areas. This proportion is projected to increase to 66 % by 2050 (United Nations, 2014). Consequently, cities are responsible for a large proportion of anthropogenic greenhouse gas (GHG) emissions. The 2015 UNFCCC Paris Agreement requires signatory states to not only report national GHG emissions, but also to establish and improve independent methods for verifying these reported emissions (UNFCCC, 2015). Top-down methods that use atmospheric measurements to determine city-scale emissions can assess the accuracy of bottom-up emission inventories and provide crucial information to help improve bottom-up accounting methods.
In the UK, spatially and sectorally disaggregated emissions calculated using
a bottom-up methodology are given in the National Atmospheric Emissions
Inventory (NAEI; Brown et al.,
2017). For Greater London, nearly all sources of anthropogenic
Natural emissions, which are not included in the NAEI, contribute to varying
extents for the three species. Wetlands are the most significant source of
natural
Aircraft mass balance techniques have previously been employed to measure trace gas fluxes from several cities (e.g. Mays et al., 2009; Turnbull et al., 2011; Cambaliza et al., 2014) including London (O'Shea et al., 2014a). Typically, horizontal transects are conducted downwind of a city at several altitudes to sample its emitted plume for various trace gas species. These vertically stacked transects define a 2-D plane of sampling downwind of the city. A background mole fraction can be determined by sampling upwind of the city or from downwind measurements outside of the plume. The mass flux of the plume through the 2-D plane of sampling is then calculated from the measured mole fraction enhancements (above background) and wind speed. This approach works well for isolated cities, but for cities such as London that are surrounded by other emission sources it is difficult to measure a background that allows direct comparison between the mass balance flux and inventory emissions aggregated over a well-defined area. The impact of this issue is one of the key focus points of this study.
Another approach to flux quantification involves the use of an atmospheric transport model to represent transport of emitted species from the source to measurement site. This enables simulated enhancements to be calculated at the measurement location based on a prescribed emissions map (e.g. from a bottom-up inventory). A range of inverse modelling techniques can then be employed to optimise the emissions map according to the measured mole fractions. This is frequently performed at a regional scale using ground-based measurements from long-term monitoring sites (e.g. Manning et al., 2011; Bergamaschi et al., 2015; Ganesan et al., 2015), but it has also been performed using aircraft data to provide the spatial sampling coverage required to estimate city-scale emissions using a few hours of measurement data (e.g. Brioude et al., 2013).
The approach taken to performing the inversion must be tailored to the measurement dataset. It is important to allow the inversion sufficient freedom such that significant differences between measured and modelled mole fractions are reflected in differences between the posterior and prior emission maps. However, allowing too much freedom can result in unrealistically large redistributions of emissions in the posterior solution driven by errors in either model transport or measurements. Striking this balance is particularly difficult when using aircraft data, which have a greatly reduced temporal coverage compared to continuous ground-based measurements. Inversions using ground-based measurements are typically performed on monthly to annual timescales; the systematic biases in model transport over these timescales are greatly reduced compared with the model transport error at the time of a given aircraft flight.
In this study we have developed a new approach for assessing bottom-up inventory fluxes, using the same aircraft sampling technique typically employed by mass balance studies. This method uses the UK Met Office's Lagrangian dispersion model, NAME (Numerical Atmospheric dispersion Modelling Environment), to simulate the transport of inventory fluxes to the location of the measurements. We perform a simple inversion by comparing the average measured and simulated fluxes at the aircraft sample locations and rescaling the inventory according to their ratio. This approach has two main advantages over traditional inversion techniques. Firstly, it effectively controls the inversion behaviour by removing the freedom to spatially redistribute emissions, thus allowing a solution to be derived whose magnitude is completely independent from the magnitude of emissions in the prior. Secondly, by comparing fluxes rather than mole fractions at the measurement locations, the sensitivity of the results to biases in the modelled wind speed is reduced. On the other hand, relative to the mass balance method, we are able to account for the presence of sources outside the Greater London boundary such that they do not bias our conclusions.
We demonstrate the implementation of this method by applying it to a single-flight case study downwind of London, conducted in March 2016. In addition to applying our new method, we also apply the conventional mass balance technique to the same data and compare the top-down fluxes derived to inventory emissions aggregated over the Greater London administrative area. We discuss the differences between the results from both methods and reflect on the appropriate context in which each can be applied.
We recorded measurements on board the UK's Facility for Airborne Atmospheric Measurement (FAAM) BAe-146 atmospheric research aircraft (henceforth referred to as the FAAM aircraft). For full details of the aircraft payload see Palmer et al. (2018). Here we describe only those measurements that are relevant to this case study.
Mole fractions of
We measured a target cylinder containing intermediate mole fraction values
approximately half way between these hourly calibrations to quantify any
instrument non-linearity or drift. This flight formed part of the wider
GAUGE (Greenhouse gAs Uk and Global Emissions) campaign, during which we
derived average target cylinder measurement offsets of 0.036 ppm for
Another source of measurement uncertainty was the impact of water vapour in
the sampled air on the retrieved
We measured CO mole fractions using vacuum ultraviolet florescence
spectroscopy (AL5002, Aerolaser GmbH, Germany). The principle of this system
is described by Gerbig et al. (1999), who also evaluate its
performance on board an aircraft. Calibration was performed using in-flight
measurements of a single gas standard and the background signal at zero CO
mole fraction. Gerbig et al. (1999) derive a 1 Hz
repeatability of 1.5 ppb (at 100 ppb) and an accuracy of 1.3 ppb
Details of the meteorological instrumentation on board the FAAM aircraft are
provided by Petersen and Renfrew (2009). In
summary, we measured temperature with a Rosemount 102AL sensor, with an
overall measurement uncertainty of 0.3 K at 95 % confidence; we took
static pressure measurements from the air data computer, based on
measurements from pitot tubes around the fuselage, with an estimated
absolute accuracy of 0.5 hPa; we made 3-D wind measurements using the five-hole probe system described by Brown et al. (1983),
with an estimated uncertainty in horizontal wind measurements of < 0.5 m s
On 4 March 2016 we conducted a targeted case study flight to measure
Figure 1 shows the flight track from an aerial perspective; between points A and B we flew repeated horizontal transects at various altitudes through a plume of enhanced mole fractions downwind of London emission sources. At the southernmost end of these transects, the constraints of UK airspace forced us to deviate from our desired course perpendicular to the prevailing wind. However, as we sampled the overwhelming majority of the London plume north of this imposed turning point, such that measurements during the deviation to point B represented background (out-of-plume) sampling, we do not expect this deviation to impact on the derived fluxes.
Aircraft flight track on 4 March 2016, coloured by altitude. Wind barbs are used to represent wind speed and direction, averaged over 5 min, using the convention where each full wind barb represents a wind speed of 10 kn. The border of the Greater London administrative region is shown in grey for reference.
During an initial transect at 1550 m altitude we measured typical uniform
free-tropospheric background mole fractions for all three gases (
To determine the air history corresponding to the continuous aircraft sampling we ran the NAME dispersion model in backwards mode, releasing 100 tracer particles at each 1 Hz aircraft measurement location and tracking their motion back in time. NAME was driven by meteorological data from the UK Met Office's UKV model (Tang et al., 2013), which provides hourly data on 70 vertical levels at 1.5 km horizontal resolution over the British Isles. NAME determines particle motion based on the mean wind field (which is determined by interpolating the met data spatially and temporally to the particle location for each time step) and a parameterisation of unresolved turbulent and mesoscale motions (for details see Jones et al., 2007, and references therein). In this study we used a NAME model time step of 1 min. By way of guidance, it is worth noting that although this NAME setup is more computationally intensive than is typically employed, because the release duration was less than 3 h and the maximum particle travel time before leaving the domain was 37 h (and less than 24 h for the majority of particles) the run completion time remained on the order of hours rather than days using the JASMIN scientific computing facility.
To quantify the sensitivity of the sampling to surface fluxes, we used NAME
to calculate an air history matrix for each minute of the flight (henceforth
referred to as a release period). Each tracer particle was assigned a
nominal mass and molar mass, enabling NAME to calculate the volumetric
mixing ratio of tracer within the lowest 100 m above ground level on a 1 km
This air history matrix represents the mole fraction enhancement at the
sample locations due to a unit flux in each grid box. By combining this
information with the NAEI inventory emissions (
Altitude–latitude projections of measured mole fraction
In this section we present two approaches to assess the accuracy of the NAEI inventory emissions relative to the measured mole fractions during this case study. The first is a new approach, referred to hereafter as the flux-dispersion method, using the simulated mole fraction enhancements from Sect. 2.3 to derive simulated fluxes through the downwind sampling plane based on inventory emissions, thus enabling comparison with corresponding measured fluxes. The results from this method represent our best assessment of inventory fluxes for this case study.
In Sect. 3.2 we then employ a conventional mass balance method to derive top-down fluxes which are compared to an aggregated NAEI value. We discuss the outcomes of both approaches in Sect 3.3 and explain how the conventional mass balance approach can lead to spurious conclusions in cases such as this.
It is important to note that the NAEI contains only annually averaged
emissions and so does not capture the potentially large temporal variability
on diurnal, weekly and seasonal timescales. Clearly this represents a likely
source of difference between the top-down results derived from our single-flight case study (which represent a snapshot in time) and the inventory.
The most recent gridded emissions available in the NAEI at the time of
writing were for the year 2015; therefore we have used these 2015 emissions
to represent the 2016 values in both approaches. The UK totals (not
spatially disaggregated) for 2016 have been released, allowing us to compare
these to the 2015 totals. For CO there was a 9.4 % reduction in total
reported emissions between 2015 and 2016, while for
To make a comparison between the measured and simulated datasets described
in Sect. 2 it is first necessary to calculate a background mole fraction for
both, so that the mole fraction enhancement due to the London plume can be
determined. To determine periods of sampling that were not significantly
influenced by the London plume, and therefore can be considered to represent
background mole fractions, we again utilised the air history information
given by the NAME dispersion modelling. From the gridded air histories
described in Sect. 2.3, we calculated the fraction of
Altitude–latitude projection showing the influence of London on
the downwind sampling, as determined from the NAME air histories. The colour
scale represents the fraction of aggregated NAME air history
In practice there is no sharp distinction between in-plume and background
sampling, so any criteria used to separate sampling into these two
categories inherently involves some level of human judgement. The key
consideration when defining the background for use with this method is to
use a threshold that optimises the sensitivity of the results to the region
of interest, in this case Greater London. This is illustrated by Fig. 4,
which shows the air history (
NAME air histories aggregated over
The comparison between measured and simulated flux discussed later in this section is a comparison between the flux enhancement from the areas sampled in Fig. 4b relative to the flux enhancement from the air histories sampled in Fig. 4a. Clearly this comparison is not entirely selective of emissions from Greater London, with additional influence from emissions within a wider area (largely upwind and downwind of Greater London). However, given the sampling strategy employed it is not possible to isolate Greater London emissions from other upwind and downwind sources using any technique, and the 0.05 % threshold employed represents the best choice to isolate sampling periods with significant Greater London influence. The relative advantages of different sampling strategies for background determination are discussed further in the context of the mass balance method in Sect. 3.2.1.
For both the measured and simulated datasets the mole fraction enhancement
due to the London plume is calculated by subtracting the background mole
fraction. For each constant-altitude aircraft transect we calculated average
background mole fractions to the north and south of the plume separately,
for both measured and simulated datasets. We then calculated the mole
fraction enhancement,
Background mole fractions for each species to the north and the south of the London plume, calculated using the flux-dispersion method.
The time series of measured and simulated mole fraction enhancements calculated using Eq. (3) are directly comparable quantities. However, the simulated mole fraction enhancements are strongly dependent on the model wind speeds (which directly impact the time-integrated tracer mixing ratios in Eq. 1). Any bias in the model wind speeds relative to the measured wind speeds consequently produces a bias in the simulated mole fraction enhancements. Figure 5 shows a comparison of modelled and measured wind speeds throughout the course of the flight. It can be seen that the model tends to overestimate wind speed within the boundary layer, particularly at lower altitudes.
Comparison between wind speeds measured by the aircraft and the corresponding wind speeds at the aircraft location from the UKV model. It can be seen that the model generally overestimates wind speed within the boundary layer.
In order to account for the low-biased simulated enhancements resulting from
the high-biased model wind speeds, we convert both measured and simulated
mole fraction enhancements into fluxes per unit area in the mean wind
direction (i.e. through the downwind sampling plane) before making a
comparison between them. To define a representative wind direction, we took
the average of the mean UKV model wind direction and the mean measured wind
direction during the sampling period. A time series of flux per unit area in
this average wind direction, hereafter referred to as the flux density, was
then calculated for both measured and simulated datasets using Eq. (4):
Figure 6 shows a comparison between these measured and simulated flux
densities as a function of latitude for each plume transect. The lowest
transect from Figs. 2 and 3 (
Measured and simulated flux densities for
We note that the flux density enhancements for the two transects above the model boundary layer are underestimated by the simulation. A possible cause for this would be suppressed vertical mixing in the model as a result of the simplified turbulence parameterisation above this height. A full investigation into the impact of turbulence parameterisation on the vertical mixing within the NAME model would require a separate study, but we note that if the vertical mixing in the model is suppressed this could represent a potential source of bias, leading to larger simulated flux densities within the boundary layer than would in reality be produced by the inventory emissions.
A notable feature of the transects shown in Fig. 6 is that the centre of the simulated plume is consistently further north than the centre of the measured plume. This could suggest the spatial distribution of emissions within the inventory is incorrectly weighted towards sources in the north of London. Alternatively, any inaccuracy in the model wind field could lead to the simulated plume being advected to a more northerly position on the sampling plane than the measured plume. The fact that all three species exhibit the same northerly offset of the simulated plume points to the latter explanation, as each species has a different source mix, making it unlikely that they would all exhibit the same spatial bias.
In itself, the mismatched position of the measured and simulated plumes does
not bias the results. This is one of the key advantages of comparing average
flux densities for each transect, rather than using differences between the
measured and simulated time series to optimise an emission map (e.g. using a
cost function). However, if the plume position mismatch reflects an error in
the transport model this does have the potential to impact the results, as
it suggests the air histories for in-plume and background periods simulated
by NAME may differ slightly from the actual air histories of the
measurements. This is one possible reason why the simulated background CO
and
The high bias of the simulated wind speeds relative to the measurements (shown in Fig. 5) is a further indication of transport model error. While we have accounted for the most obvious impact of this issue by comparing flux density rather than mole fraction, it could also result in simulated air histories which underestimate the cross-wind spread in the sample footprint. This could result in the simulation overestimating the sensitivity of in-plume sampling to emissions from Greater London, producing a low bias in the inventory scale factors. A robust quantification of the uncertainty associated with model wind field inaccuracy (incorporating both effects discussed above) would require an ensemble of NAME runs to be performed, driven by met data with perturbed wind fields. Such quantification is beyond the scope of this study, but we note that this is a potentially significant source of uncertainty in the context of the uncertainty ranges calculated below.
Having calculated time series of flux density for the measured and simulated
datasets, we then calculated average flux densities for each transect
altitude. We also calculated flux densities as an overall average using data
from all three transects. These values are given in Table 2 for
Mean flux densities calculated using the flux-dispersion method, given for each transect and taken over all three transects. The ratios between measured and simulated flux densities are all given.
While there are small uncertainties associated with the measured mole
fractions (as discussed in Sect. 2.1), the uncertainty in these overall
inventory scale factors is expected to be dominated by NAME transport
uncertainty. As discussed above, quantification of the uncertainties
associated with the dispersion modelling would require a more involved
modelling study using an ensemble of NAME runs. Here we take the range of
scale factors across the different transects of 0.92–1.16 for CO, 0.66–0.79
for
Our results suggest that, to obtain simulated enhancements consistent with
our measurements, the NAEI would require downscaling for
Temporal variability in emissions (not included in the NAEI) is an obvious
source of difference between our results for
The fact that our study focussed on London and its surrounding areas, while the verification report presents national-scale results, represents another key difference between them. It is possible that, although NAEI emission totals agree with long-term observations, the spatial distribution of these emissions is not well represented in the inventory, such that the proportion of emissions ascribed to urban areas is too large. The impact of temporal variability makes it impossible to draw such a conclusion from a single case study; however, repeated flights at different times of day, week and year would enable this hypothesis to be tested.
For
Prior quantification of the biospheric impact on the derived scale factor
would require the use of an ecosystem model and is beyond the scope of this
study. However, some inferences can be made by considering the different
scale factors derived for CO and
Detailed descriptions of the mass balance technique in the context of
measuring urban GHG emissions are provided by many sources. In general, in
the context of bulk area flux measurement, these sources can be categorised
into two basic approaches: either the emissions are assumed to be well mixed
up to a given height at which they are capped by a temperature inversion
(Turnbull
et al., 2011; Karion et al., 2013; Smith et al., 2015), or the vertically
varying shape of the plume is derived by interpolation between transects
flown at multiple altitudes
(Mays
et al., 2009; Cambaliza et al., 2014; O'Shea et al., 2014a), often using a
kriging approach. Figure 2a, b and c clearly show that the assumptions of
the first of these approaches (i.e. well mixed plumes up to a capping
height) are not met in this case. We therefore adopt the latter of these
approaches and use kriging to represent the full structure of the plume.
This approach necessarily assumes temporal invariance of the plume over the
period of sampling: in this case
Following the work of
Mays
et al. (2009), Cambaliza et al. (2014) and O'Shea et al. (2014a) we derive fluxes
using Eq. (5):
Kriging is an interpolation method based on a stochastic Gaussian model and is described in detail by Kitanidis (1997). It converts samples with sparse spatial coverage into a 2-D grid of estimated values, with an associated grid of standard errors for these values. Here we use a modified version of the EasyKrig software (©Dezhang Chu and Woods Hole Ocean Institution) to perform the kriging; again more detail regarding the application of this software with regards to aircraft mass balance flux calculations is given by Mays et al. (2009). More detail regarding the kriging parameters used is included in the supplementary material.
The results from the kriging were output on a
Altitude–latitude projections of
The background mole fraction
The background mole fractions used were 147.3 ppb for CO, 1941.6 ppb for
Bulk fluxes calculated using a conventional mass balance technique and corresponding NAEI emissions, aggregated over the Greater London administrative region. The ratio of mass balance flux to NAEI emission is also given. Uncertainties on spatially disaggregated emission maps are not reported in the NAEI.
The fluxes calculated using Eq. (5) are given in Table 3, along with
1
In Sect. 3.1 and 3.2 two different methods were applied to the same dataset to derive scale factors for the NAEI inventory such that it agrees with aircraft observations. However, the scale factors derived using the flux-dispersion method are significantly lower than those derived using the conventional mass balance method. This is because one of the key elements of the mass balance method, the assumption that a city acts as an isolated emission source surrounded by areas with negligible emissions, is clearly violated in this case. In order to deal with these extraneous emissions one either needs to account for them in the background mole fraction (such that all downwind enhancements are solely a product of Greater London emissions) or include them in the aggregated inventory emission total against which the top-down flux is compared. Here we consider the issues associated with both approaches.
From Fig. 4 it is evident that measurements within the London plume are strongly influenced by sources upwind, downwind, to the north and to the south of Greater London. The mole fraction that would be observed at a given location in the presence of these sources, but the absence of emissions within the Greater London boundary, is clearly not a measurable quantity. Our calculated background, which we derive using measurements on either side of the plume, is subject to greater influence from emission sources to the north and to the south of Greater London relative to the in-plume measurements, while it fails to adequately capture emissions upwind and downwind of Greater London. There is no prior reason to assume that these two effects cancel each other out.
An alternative approach to background calculation utilises measurements upwind of the city. O'Shea et al. (2014a) use this method to calculate fluxes for Greater London, making the following implicit assumptions: (1) emissions upwind of the background measurements are well-mixed throughout the boundary layer, (2) the air history of the upwind sampling does not differ significantly from the air history of the downwind sampling, and (3) entrainment of air into the boundary layer from above does not significantly impact the downwind mole fractions relative to the upwind measurements. All three of these assumptions appear dubious for the case study presented here; in particular it seems likely that there was significant entrainment of air into the boundary layer as it increased in depth throughout the morning. We also have insufficient sampling upwind of London to determine the extent to which the boundary layer can be considered well-mixed. These factors motivated our decision to use a downwind background.
Even in cases where the above assumptions are satisfied, using an upwind background does not solve the fundamental issue of extraneous emission sources. All emissions between the upwind and downwind transects, including those outside Greater London, contribute to the measured downwind enhancements to some extent. Therefore it is not possible to isolate the mole fraction enhancement due solely to Greater London emissions using either background calculation method. The influence of these surrounding emission sources could explain the large inventory upscaling factors derived by O'Shea et al. (2014a) when comparing their calculated mass balance fluxes to the NAEI totals for Greater London.
Given that it is not possible (even in principle) to isolate enhancements due to Greater London emissions, it is logical to consider over what area emissions can be considered to contribute to the calculated mass balance flux (for a given choice of background). Dispersion model air histories are frequently used to define the flux footprint when using Lagrangian mass balance techniques (e.g. O'Shea et al., 2014b) and integrative mass boundary layer techniques (e.g. Font et al., 2015). These techniques balance the change in species concentration within a column of air against the fluxes through the top, bottom and sides of the column.
An analogous approach to footprint calculation here would be to attribute the derived mass balance flux to the area given by the NAME air history for in-plume sampling. However, such an approach would be invalid because emissions from within this area also contribute to background sampling. This is evident from the overlap between the aggregate air histories for in-plume sampling (Fig. 4b) and background sampling (Fig. 4a); assuming all of the emissions from the area covered by the in-plume air history contributed directly to an enhancement above the background would yield a huge aggregate bottom-up flux that would not be representative of the calculated mass balance flux. Fundamentally, because emissions from many source areas contribute to some extent to both the in-plume and background measurements, making it unclear whether or not to include these in the aggregated inventory total, any choice of inventory aggregation area is inherently arbitrary.
In summary, it is not possible to determine a background such that all calculated enhancements result purely from Greater London emissions. Neither, for a given choice of background, can we unambiguously determine what area inventory emissions should be aggregated over. This demonstrates the difficulty in employing this type of mass balance technique to estimate emissions from a non-isolated source. Instead, the flux-dispersion method provides a good alternative in these cases because it explicitly accounts for the relative influence of all emissions on the in-plume and background sampling.
Aircraft mass balance techniques are an effective way of determining emissions from isolated sources, but they require surrounding areas to be negligible emission sources in order to yield robust results. This is a well-known assumption associated with these methods. However, in the absence of alternative techniques using the same sample dataset against which the mass balance results can be compared, one is forced either to simply state this assumption as a caveat or to abandon the effort entirely.
In this study we have developed an alternative technique using a Lagrangian
dispersion model to quantify the transport of inventory emissions to the
aircraft sample locations, so that a direct comparison of flux per unit area
can be made at the measurement locations. In contrast to the conventional
mass balance technique, this method does not require cities to be isolated
from surrounding emission sources, rendering it more appropriate in many
cases. We have demonstrated this new technique by applying it to a
single-flight case study measuring London emissions, which yielded inventory
scale factors of 1.03 (0.92–1.16) for CO, 0.71 (0.66–0.79) for
It is important to emphasise that the inventory scale factors derived here represent the results from a single case study and therefore are not necessarily representative of the annual timescale of the NAEI emissions. In order to better validate the inventory on this timescale, repeated flights following a similar sampling strategy are required. The limited spatial selectivity of the flux-dispersion technique represents another caveat on the results from a single flight, as the derived flux ratios are not only sensitive to emissions from the London conurbation but also to emissions from a fairly wide area surrounding it. Repeated flights should therefore be designed to incorporate sampling under different prevailing wind directions, so that the systematic impact of extraneous sources on the overall results is minimised. Using the flux-dispersion method developed here in combination with representative aircraft sampling on an annual timescale could provide a robust assessment of inventory fluxes at the city scale in the case of non-isolated sources for which the mass balance technique is not appropriate.
The aircraft data used in this study are archived with the Centre for Environmental Data Analysis (CEDA) and are freely available from
The supplement related to this article is available online at:
JRP led the paper writing with contributions from GA, PIP and WD. The study was designed by JRP, GA and JDL. The aircraft measurements were taken by SJBB, JDL and JRP, and the data curation was performed by JRP and SJBB. The methodology was developed by JRP, GA and AJM, with input from JDL, WD and BN. Funding was acquired by GA, MWG, JDL, AJM and PIP.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Greenhouse gAs Uk and Global Emissions (GAUGE) project (ACP/AMT inter-journal SI)”. It is not associated with a conference.
The authors would like to thank everyone at FAAM, Airtask and Avalon Aero who helped with the collection and processing of the aircraft data used here. We acknowledge use of the NAME atmospheric dispersion model and associated NWP meteorological datasets made available to us by the Met Office. We acknowledge the significant storage resources and analysis facilities made available to us on JASMIN by STFC CEDA along with the corresponding support teams. Joseph R. Pitt received a NERC CASE studentship in partnership with FAAM, grant number NE/L501/591/1, supervised by Grant Allen. This work was supported by the GAUGE (Greenhouse gAs Uk and Global Emissions) NERC project, grant numbers NE/K002449/1 and NE/K00221X/1.
This research has been supported by the Natural Environment Research Council (grant nos. NE/L501/591/1, NE/K002449/1 and NE/K00221X/1).
This paper was edited by Dominik Brunner and reviewed by Jocelyn Turnbull and one anonymous referee.