Introduction
Human-driven emissions of carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O), and other greenhouse gases
(GHGs) to the Earth's atmosphere perturb the balance between net incoming
solar radiation and outgoing terrestrial radiation. These emissions,
primarily from the combustion of fossil fuels and land-use-change activities,
are the dominant cause of the warming trend in the climate system since the
1950s . Minimizing the manifold impacts of increasing
atmospheric GHGs demands a structured timetable of emission reductions.
Avoiding a 2 ∘C global temperature rise ,
for which we are already close to peak emissions, requires stringent
reductions that lead to zero or negative net emissions by 2100. At the Paris
Conference of the Parties (COP) in December 2015, 195 countries agreed to
accelerate this schedule in order to achieve net zero emissions later this
century. Achieving this objective demands accurate knowledge of national GHG
emissions and the contributions from individual sectors. The United National
Framework Convention on Climate Change (UNFCCC) requires that all countries
included in Annex 1 of that convention report their annual GHG inventory,
including CO2, CH4, and N2O. The bottom-up
approach to determining these emissions from individual sectors is on a
production, in-use, and disposal basis using source-dependent activity data
and emissions factors. A complementary top-down approach is to verify
nationwide GHG emissions using atmospheric measurements of these GHGs, but in
practice this is non-trivial and presents many scientific challenges. Here,
we describe the UK Natural Environment Research Council (NERC) Greenhouse gAs
Uk and Global Emissions (GAUGE) project. In particular, we (1) define the
scientific objectives of GAUGE; (2) describe individual measurement types and
the atmospheric transport models used to interpret these data; and
(3) outline the broader modelling approach that is adopted in order to
determine the magnitude and uncertainty of UK flux estimates of GHGs.
Throughout this paper, where relevant, we refer the reader to peer-reviewed
publications describing the analysis of individual GAUGE datasets.
The UK Climate Change Act 2008 commits the UK to reduce GHG emissions by at
least 80 % below 1990 baseline levels by 2050, with an interim target of
a 34 % reduction compared the same baseline by 2020. To establish a
realistic trajectory towards the 2020 and 2050 goals, the Climate Change Act
established five 5-year carbon budgets (2008–2032). Seven GHGs are the
subject of these staged emission reductions: CO2, CH4,
N2O, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride,
and nitrogen trifluoride.
UK government statistics report that CO2, CH4, and
N2O correspond to ≃81 %, 11 %, and 5 % of the UK's
estimated 495.7 MtCO2e (budget in 2015; ); the
remaining 3 % is due to fluorinated gases. This budget, broken down by
sector in 2015, consists of energy supply (29 %), transport (24 %), business
(17 %), residential (13 %), agriculture (10 %), waste management
(4 %), industrial processes (2 %), and other (1 %). Emissions of
CO2 are largest for energy supply, transport, business, and
residential sectors. CH4 emissions are largest for agriculture and
waste management, and N2O emissions are largest for agriculture.
These emission sources are very different in nature, ranging from point
sources (e.g. industry) to geographically large, diffuse sources (e.g.
agriculture). We take into account these differences in the GAUGE measurement
strategy, as described below.
The primary objective of GAUGE is to quantify the magnitude,
distribution, and uncertainty of the UK GHG CO2, CH4, and N2O
budgets, 2013–2015. Our rationale is that better understanding the
national GHG budget will inform the development of effective emission
reduction policies that help the UK to meet the interim targets of the
UK Climate Change Act and to achieve its commitments to the Paris
Agreement. To achieve our primary objective, we put together a 42-month
research programme, bringing together a purpose-built atmospheric
measurement network and a range of atmospheric transport models and
inverse methods to translate those measurements into UK GHG flux
estimates. More broadly, GAUGE provides an assessment of our current
ability to infer GHG fluxes from atmospheric data and strengthens the
UK capability to verify national GHG budgets beyond the lifetime of
GAUGE.
GAUGE builds on a long heritage of UK atmospheric observations that have been
used to estimate national GHG emissions. were the first
to apply an inverse model approach to infer UK CH4 and
N2O emissions, using data collected from Mace Head (MHD), Ireland,
during 1995–2000. This approach contrasted clean upwind air that arrived
from the North Atlantic with air masses that passed over mainland UK and
Europe and were influenced by continental fluxes . Although
these data provided incomplete measurement coverage of the UK, results using
this method have been part of the UK reporting to the UNFCCC. In later work,
used research aircraft observations of GHG mole fractions
from the NERC-funded AMPEP campaign (Aircraft Measurement of Chemical
Processing and Export fluxes of Pollutants over the UK) to infer fluxes of
CO2, CH4, and N2O and a range of halocarbons.
During AMPEP the research aircraft circumnavigated the UK during the summer
of 2005 and September 2006. They found that the inferred CO2 fluxes
during the campaign were close to the bottom-up emission inventory, but the
CH4 and N2O fluxes were much larger than the inventory
data, albeit with significant uncertainties. The main advantage of using an
aircraft is its ability to sample nationwide emissions over a
relatively short time period. However limited sorties during AMPEP left gaps
in sampling, which affected their ability to describe GHG emissions that
include large seasonal cycles (e.g. agriculture).
For more than a decade the UK has included a verification annex chapter to
its annual National Inventory Report to the UNFCCC
(https://www.unfccc.int, last access: 8 August 2018).
This chapter provides an annual comparison of the reported Greenhouse Gas
Inventory (GHGI) of each reported gas to those estimated using atmospheric
observations and the Bayesian inverse modelling technique InTEM (Inversion
Technique for Emission Modelling). The precursor to InTEM is described by
. InTEM uses the output from the NAME (Numerical
Atmospheric dispersion Modelling Environment) transport model
, which describes how emissions disperse and dilute in the
atmosphere, and observations from the UK DECC (Deriving Emissions related to
Climate Change) tall-tower network (described below). A recent study used
NAME and a hierarchical Bayesian approach to determined UK emissions of
CH4 and N2O using the UK DECC network from 2012 to 2014
. They found that a posteriori fluxes, consistent with the
atmospheric mole fraction data, were lower than a priori values. Using
geographical distributions of sectoral emissions,
tentatively attributed their result to an overestimation of agricultural
emissions of CH4 and a significant seasonal cycle of N2O
emissions. Recent work has incorporated the reversible-jump Markov chain
Monte Carlo (MCMC) inverse modelling method . The main
advantage of this new approach is that the algorithm chooses the number of
the unknown parameters, including the geographical size of the region, to be
solved given the data. A posteriori CH4 emissions for March 2014
inferred from the DECC network data were consistent with
. Within the GAUGE project InTEM is used together with other
inverse methods (Sect. ) to provide an ensemble of flux
estimates, which provide a broader picture of the range of estimates. Using
InTEM also provides a link between GAUGE and previous UK GHG estimates.
The measurement strategy we have adopted within GAUGE includes long-term
measurements and shorter-term, higher-resolution network measurements;
focused aircraft experiments; CO2 sondes; characterization of point
sources such as landfills; and satellite remote sensing. Our approach
accounts for the heterogeneity of UK sources, e.g. point sources for power
generation to large, diffuse and seasonal sources from agriculture. It also
addresses the need to focus attention on smaller regional and city scales.
This focus on smaller regions will progressively grow in importance with
ongoing rapid rates of urbanization across the world. GAUGE included new
in situ and remote-sensing technologies, and new measurement platforms (e.g.
unmanned aerial vehicles, UAVs) that will help to future-proof the UK GHG
measurement network. To help attribute observed variations in atmospheric
GHGs to individual sources, e.g. fossil fuel combustion, we explored the
potential of isotopologues to chemically identify source signatures and of
high-density measurements to exploit geographical distributions of individual
sector emissions.
Calibration activities are an integral component of GAUGE. They enable
different data collected within the GAUGE project to be compared and to be
analysed using atmospheric transport models. The use of common,
internationally recognized calibration scales places GAUGE data in the same
framework as other international activities, including the pan-European
Integrated Carbon Observing System (ICOS,
https://www.icos-ri.eu/, last access: 8 August 2018),
the Integrated Global Greenhouse Gas Information System (IG3IS,
https://goo.gl/4t1x6i, last access: 8 August 2018), and
the National Oceanic & Atmospheric Administration (NOAA) Global Greenhouse Gas Reference Network run by the Earth System Research Laboratory (ESRL).
The UK DECC network funded by the UK government (sites
denoted by green triangles, 2012–ongoing), the NERC GAUGE project (denoted
by red squares, 2013–2015), and other (blue circle). Sites are
described in Table and Appendix . The
enlarged geographical region over East Anglia shows the church
network. These sites are described in Table .
In Sect. we describe the measurements we collected during
GAUGE and the attributes that make them ideal for quantifying nationwide GHG
fluxes. We also discuss the calibration efforts that put these
different data on internationally recognized calibration scales, placing
GAUGE data into a wider context. In Sect. we describe the
models we use to describe atmospheric chemistry and transport, the challenges
faced, and the associated inverse methods that we use to infer GHG fluxes
from the GAUGE data. We conclude in Sect. .
Measurements
We present an overview of the measurements collected as part of GAUGE in
Tables , , ,
, and . We distinguish between in situ
measurements, mobile measurements platforms, and space-borne data. We also
include a description of how we calibrate these different data.
The name, location, and inlet heights of the UK tall-tower
network. Entries denoted by an asterisk denote an intake used by a
GC–multidetector and, if present at site, by a Medusa gas chromatograph–mass spectrometer (GC-MS).
Site name
Acronym
Location
Start/end date
Altitude
Inlet heights
(m a.s.l.)
(m a.g.l.)
Mace Head
MHD
53.327∘ N 9.904∘ W
23/01/87–
4
10*
Ridge Hill
RGL
51.998∘ N 2.540∘ W
23/02/11–
204
45 & 90*
Tacolneston
TAC
52.518∘ N 1.139∘ E
26/07/11–
56
54, 100* & 185
Angus
TTA
56.555∘ N 2.986∘ W
13/05/11–29/09/15
400
222
Bilsdale
BSD
54.359∘ N 1.150∘ W
30/01/14–
380
42, 108* & 248
Heathfield
HFD
50.977∘ N 0.231∘ E
20/11/13–
150
50 & 100*
Greenhouse gas and ozone-depleting substance species and instrumentation at each UK DECC site.
Species
MHD
TAC
RGL
TTA
BIL
HFD
CO2
Picarro 2301
Picarro 2301
Picarro 2301
Picarro 2301
Picarro 2401
Picarro 2401
CH4
GC-FID
Picarro 2301
Picarro 2301
Picarro 2301
Picarro 2401
Picarro 2401
CO
GC-RGA3
GC-PP1
-
-
Picarro 2401
Picarro 2401
N2O
GC-ECD
GC-ECD
GC-ECD
-
GC-ECD
GC-ECD
SF6
Medusa GC-MS
GC-ECD
GC-ECD
-
GC-ECD
GC-ECD
Medusa GC-MS
H2
GC-RGA3
GC-PP1
-
-
-
-
CRDS Nafion
Cryodried, no Nafion
Start-19/6/15
Start-6/6/15
11/1/14-end
Start-1/10/15
Start-17/6/15
drying period
In situ measurements
We use tall-tower measurements and the atmospheric baseline observatory at
MHD to provide a long-term in situ measurement record to underpin the main
objectives of GAUGE. Tall towers (TTs) are used to collect atmospheric GHG
measurements that are sensitive to fluxes on a horizontal scale of
10–100 s km. We also established a
geographically dense network of observations to help isolate GHG emissions
from individual sources.
Tall-tower measurement network
Figure shows the geographical locations of the TTs
that collect atmospheric measurements of GHGs (Tables and
) and provide the long-term, core measurement
capability of the UK GHG measurement network. Sampling air high above the
land surface reduces the influence of local signals that can compromise
interpretation of observed variations of GHGs .
With the exception of the MHD atmospheric research station (described below)
air is typically sampled at least 50 m above the local terrain and at
multiple heights (Table ) to assess the role of atmospheric
mixing in the planetary boundary layer.
Tables and describe the five TT
locations and the MHD site used in the GAUGE project. High-frequency
measurements of GHGs have been collected for the past 3 decades at the
MHD Northern Hemisphere background measurement station on the west coast of
Ireland. They predominately represent clean western baseline conditions for
the UK and mainland Europe. These MHD data have been previously used to infer
UK-wide GHG emissions . In 2012, the UK DECC tall-tower network was established across
mainland UK using funding from the UK Department of Energy and Climate Change
(with the responsibility now residing in the Department for Business, Energy
and Industrial Strategy, BEIS). Three sites were established (Angus, Ridge
Hill, and Tacolneston; Table ) with the purpose of improving
the spatial and temporal distribution of measurements across the UK to reduce
uncertainties of GHG emissions for the devolved administrations (i.e.
England, Wales, Scotland, and Northern Ireland). As part of the GAUGE
project, we augmented the UK DECC network with two TT sites at Bilsdale and
Heathfield (Fig. ), which started collecting data from 2013
onwards. These two new sites were chosen to help fill the measurement
coverage over mid-northern England, where there is significant industrial
activity, and to collect measurements south of London. For detailed
descriptions of each site, measurement and data logging instrumentation, and
the calibration protocols we refer the reader to Appendix ;
; and Stavert et al. (2018) – hereafter ARS18a.
As an example, Fig. shows CO2, CH4, and
N2O mole fraction data from Bilsdale, North Yorkshire.
Figure also shows the statistically determined baseline, long-term
trend, and mean diurnal cycle for each season. The statistical fitting
procedure is described in and on the associated NOAA/ESRL
website (http://www.esrl.noaa.gov/gmd/ccgg/mbl/crvfit/crvfit.html, last access: 8 August 2018). The mean Bilsdale growth rates for CO2,
CH4, and N2O are 3, 8, and 0.8 ppb yr-1,
respectively. The mean seasonal amplitudes for these gases are 18, 51, and
0.8 ppb, respectively. Table summarizes the descriptive
statistics for tall-tower data. Diurnal variations of the atmospheric mole
fractions vary seasonally, particularly CO2 and CH4 that
have large surface fluxes. Atmospheric mole fractions of CO2, for
instance, have a peak diurnal cycle of ≃10ppm during summer
months. Diurnal variations during winter months (≃3ppm),
particularly evident at lower inlet heights, provide some indication of the
role of boundary layer height. Shallow wintertime boundary layer heights that
are lower than an inlet height result in measurements of free-tropospheric
air that is disconnected from direct surface exchange. Variations of
CH4 are due not only to changes in anthropogenic emissions but also to
higher summertime OH concentrations, which represent the main loss term.
N2O has an atmospheric lifetime ≃120 years, determined by
stratospheric photolysis. Our measurements show a growth rate that is
consistent with the global value of ≃0.9 ppb yr-1.
(a)–(c) Hourly mean of CO2 (ppm), CH4
(ppb), and N2O (ppb) measurements at three inlet heights (42, 108, and
248 m) at Bilsdale, North Yorkshire, from March 2014 to July 2017
(Table ). The statistical baseline (dashed line) and
the long-term trend (solid line) are shown in the inset for each inlet
height. (d)–(f) Mean seasonal diurnal cycle for CO2,
CH4, and CO. The dotted lines denote the ±5th and 95th
percentile. Statistical fitting procedures follow ;
further details can be found in ARS18a.
Mean seasonal amplitude and mean growth rates of CO2, CH4, and
N2O at the Bilsdale (BSD), Heathfield (HFD), Ridge Hill (RGL), Tacolneston
(TAC), and Angus (TTA) tall-tower sites. The mean seasonal amplitude
(±1 standard deviation) was calculated from the annual peak-to-peak amplitudes.
The mean growth rate is the average of the first derivative of the statistical
long-term trend.
Site
Intake
Mean seasonal
Mean growth
height (m)
amplitude (ppm)
rate (ppm yr-1)
CO2
BSD
42
18±2
3
108
18±1
3
248
18±1
3
HFD
50
11±6
3
100
13±5
3
RGL
45
16±2
3
90
17±2
3
TAC
54
17±2
3
100
18±2
3
185
18±2
2
TTA
222
16±1
2
CH4
BSD
42
57±7
8
108
56±2
8
248
41±4
7
HFD
50
70±40
6
100
60±10
7
RGL
45
70±20
8
90
60±10
8
TAC
54
70±20
9
100
70±20
9
185
60±10
8
TTA
222
31±9
13
N2O
BSD
108
0.8±0.3
0.8
HFD
100
1.0±0.4
0.9
RGL
90
1.2±0.3
0.9
TAC
100
0.6±0.3
1.0
We also analysed the radiocarbon content of CO2
(Δ14CO2) at MHD and TAC as an approach to estimate the
fossil fuel contribution to observed atmospheric variations of CO2
(ffCO2). The underlying idea is that fossil fuels, by virtue of
their age, are devoid of 14C, which has a half-life of
5700±30 years . Measurements of
Δ14CO2 have been used extensively to determine
ffCO2 (e.g. ). Our sampling strategy at MHD (nominally unpolluted
site) and TAC (nominally polluted site) was designed to determine the west–east
gradient of ffCO2, reflecting the prevailing wind direction over
the UK.
Weekly glass flask sample pairs were collected at MHD and TAC. A
commercial sampling package is used at MHD (Sherpa 60, High Precision
Devices Inc., USA)
as part of the NOAA
Global Greenhouse Gas Reference Network global flask sampling programme run by the Earth
System Research Laboratory (ESRL). A similar system, custom-built by the
University of Bristol, was used at TAC. Flask pairs have been filled at MHD
for NOAA since 1991, but they have not been previously analysed for
14CO2. We collected and additional flask from June 2014.
Weekly sampling commenced in June 2014 and concluded in February 2016. To
determine the radiocarbon CO2 content of our measurements, the
samples are graphitized by the Institute of Arctic and Alpine Research (INSTAAR) and then sent for analysis to the
accelerator mass spectrometer at the University of California at Irvine.
Results are reported in Δ14C against the NBS oxalic acid I
standard with an uncertainty of 1.8 ‰–2.5 ‰. Over the course of the
GAUGE project a total of around 250 samples were analysed for
14CO2. From this analysis we also received information about the
stable isotopes 13CO2, CO18O, and 13CH4,
which we do not report here. As part of the deployment of the Atmospheric
Research Aircraft (ARA, described below) we collected glass flasks for the
14CO2 and Tedlar bags for analysis of 13CH4 by
Royal Holloway, University of London. Using the aircraft allowed us to
improve our knowledge of the spatial gradient of these gases. Samples were
taken using an oxygen radical absorbance capacity (ORAC) metal bellows pump, fitted with a pressure relief valve.
For the glass flask sampling an adapter containing a downstream pressure relief
valve was used to prevent the accidental over-pressurizing of the glass
flasks during flight sampling.
A preliminary study of 14CO2 at Tacolneston during the GAUGE
project has highlighted the benefits and difficulties associated with
determining the fossil fuel content of CO2 in the UK. The key
outcome from the measurement programme has suggested that the amount of
CO2 originating from fossil fuel burning is not significantly
different from model simulations using Emission Database for Global Atmospheric Research (EDGAR) emissions. However, there were a
number of difficulties associated with making these measurements. First, we
used a number of assumptions and data corrections to account for terrestrial
biosphere fluxes and nuclear emissions. For nuclear emissions, we expect that
the applied correction can be significantly improved by provision of
higher-frequency emissions data from the nuclear industry. Second, the location of
the sampling site, and timing and frequency of measurements are paramount in
determining a strong enough 14CO2 signal from fossil fuels to
distinguish it from the background uncertainty. Many lessons were learnt in
the GAUGE project that will allow for an improved and more robust sampling
strategy to be applied to future measurements (Wenger et al.,
2018).
East Anglian church network
A key objective of GAUGE was to improve understanding of how to attribute
observed variations of GHGs to particular sectors. To help address that
objective, we established a regional network of five sensors over East Anglia
(Fig. , Table ), where there is a high
density of crop agriculture, a sector with large seasonal emissions of
CH4 and N2O attributed to fertilizer application
(Sect. ). Developing this regional network supports the
inference of higher-resolution emission estimates . We
used data from this network to determine how well we can distinguish between
sources of CH4 that range from spatially diffuse agricultural
sources to point sources such as landfills.
Details of the measurements made in the GAUGE East Anglian network.
Site
Lat [∘ N], long [∘ E]
Site elevation
Inlet height
Start
End
Measurements
Compounds
Institute lead
[m]
[m]
Haddenham (HAD)
52.359, 0.148
40
25
07/2012
Ongoing
GC-FID
CH4
UCAM
Weybourne (WEY)
52.950, 1.122
15
15
02/2013
Ongoing
GC-FID
CH4, N2O
UCAM/UEA
Tilney (TIL)
52.737, 0.321
6
25
06/2013
Ongoing
GC-FID
CH4
UCAM
Glatton (GLA)
52.461,-0.304
28
20
10/2014
04/2016
In situ FTIR
CH4, CO2, N2O, CO
ULeic
Earls Hall (ELH)
51.813, 1.118
17
50
11/2014
12/2015
CRDS/QCL
CH4, CO2, N2O
UCAM
We purposely distributed the network across East Anglia (Fig. ),
comprising one atmospheric observatory (Weybourne) and three churches (Holy
Trinity, Haddenham; All Saints, Tilney; and St Nicholas, Glatton), and one
wind turbine (Earl's Hall). East Anglia is one of several dense regions of UK
agriculture. It was chosen for two reasons: (1) there is little variation in
terrain height, simplifying boundary layer transport and mixing, and (2) all
sites are within an hour of Cambridge, simplifying logistics associated with
maintaining long-term sites. Additional criteria for site selection included
sufficient sampling height (15–50 m for the East Anglia network,
Table ), remoteness from very local sources of
CH4, easy accessibility for maintenance, and low running costs.
Figure shows that the CH4 mole fraction
data collected from the three churches exhibit similar variations on diurnal,
daily, and monthly timescales, suggesting that the surrounding
villages have similar sources and/or at least some of the observed variation
reflect larger-scale variations. Observed sub-annual variations of
CH4 at the Weybourne Atmospheric Observatory (WAO), for different years, are comparable to those at inland
sites on seasonal timescales but are muted on faster timescales because it
mainly observes clean upwind air. The shape of the diurnal cycle at the
church sites suggests that the boundary layer height likely plays the
dominant role. Seasonal variations reflect changes in regional sources,
boundary layer variations, and the OH sink.
Observed variations of CH4 mole fraction data collected at
one atmospheric observatory (Weybourne, WAO, 13 February 2013–6 May 2014) and
three church steeples at Haddenham (HAD, 3 July 2012–23 September 2015), Tilney
(TIL, 7 June 2013–31 August 2015), and Glatton (GLA, 22 October 2014–5 April 2016). The
coloured envelope denotes the 95 % confidence interval of the hourly,
daily, and monthly mean.
Using the NAME-InTEM inverse model framework , we used the
East Anglian network to infer county-level CH4 fluxes for
Cambridgeshire, Norfolk, and Suffolk. Our a posteriori fluxes were
consistent with those from the UK National Atmospheric Emissions Inventory
(Connors et al., 2018). For this work it was difficult to accurately estimate associated
uncertainties because of difficulties associated with defining the
“background” CH4 entering into the small, regional domain chosen.
This difficulty will be avoided when these data are included in larger,
regional-scale inversions. We find that regional networks, embedded within a
nationwide network, show great potential for revealing additional spatial and
temporal details of emissions such as point source emissions from landfills
. Such a regional network would best serve a
national-scale network over regions where a priori emission uncertainties are
largest.
Mobile GHG measurement platforms
We use mobile platforms to help integrate measurements that are
sensitive to different spatial scales. The two principal platforms we
use are the Rosyth–Zeebrugge North Sea ferry and the British Aerospace 146
(BAe-146) Atmospheric Research Aircraft. We also describe the deployment of
balloon-borne sensors and a fixed-wing UAV,
as examples of GAUGE fostering new atmospheric GHG measurement
technology. In the conventional sense, a mobile measurement platform
is one that is fixed in one place for some length of time but is
sufficiently mobile that it can be moved elsewhere to continue
measurements. The ferry platform can be considered a
continually moving mobile platform.
North Sea ferry
We installed an 8 ft. air-conditioned sea container on the Rosyth
(56.02262∘ N, 3.43913∘ W)-to-Zeebrugge
(51.35454∘ N, 3.175863∘ E) ferry operated by DFDS Seaways.
The container includes a Picarro 1301 cavity ring-down spectrometer (CRDS) to measure mole fractions of
CH4, CO2, and H2O. This ship of opportunity
completes three return journeys per week, traversing the North Sea at
different times of day, thereby minimizing temporal measurement bias, which can
sometimes complicate the analysis of data from mobile platforms. The
prevailing winds over the North Sea are westerly and southwesterly, so that
measurements frequently sample the outflow from the UK, and also allow us to
distinguish between UK and mainland European emissions.
Figure shows the view from the mobile laboratory, with
sample inlets located at the bow away from local sources on the ferry
(chimney stacks towards the stern). The initial installation was on 25 February 2014 on DFDS Seaways Longstone (now the Finnmerchant) and ran until
15 April 2014.
A weather station (Vaisala WXT
520) located on the top deck provides basic meteorological data (air
temperature, pressure, wind speed and direction); geolocation
information (latitude, longitude, ship speed, course) is obtained from
a Garmin GPS unit fixed to the roof of the sea container.
Photos of the North Sea ferry mobile GHG laboratory on the
DFDS Seaways Longstone (now the Finnmerchant). View of the (a) weather station mounted
on the top deck and (b) from the air inlet mounted on top of the
mobile laboratory located on the weather deck.
Figure shows example CH4 data for sailings in
March, April, July, and September 2014, which shows a dynamic range that
reflects geographical variations in sources. Differences between individual
sailings reflect changes in seasonal emissions and prevailing meteorology.
Figure shows instances when observed values are
influenced by emissions from the UK and the North Atlantic background during
spring and summer (Fig. a, b), and when observed values
are influenced by high emissions from Germany and central Europe
(Fig. c) and by lower emissions from Scandinavia
(Fig. d). To avoid contamination from GHG emissions
on board the ship (e.g. engine emissions, venting of the below-deck cargo
area), individual data points were removed when the ship was in port or when
the wind blew from the direction of the chimney stacks. A more detailed
description of the instruments and the data interpretation can be found in
Helfter et al. (2018).
Observed temporal and spatial variations in CH4 mole
fractions along the route of the DFDS freight ferry in March, April, July,
and August 2014. Arrows denote local wind direction.
BAe-146 Atmospheric Research Aircraft
We use the NERC/Met Office Atmospheric Research Aircraft (ARA), operated by
AirTask Group Ltd, to provide vertical profile distributions of atmospheric
GHGs over and around the British Isles. The specific objectives of deploying
the ARA include (1) collecting a snapshot of precise and traceable GHG
concentration distributions over and around the UK; (2) integrating atmospheric
GHG information collected by tall towers, ferry transects, and space-borne
instruments; (3) defining and executing sampling experiments to enable
measurement-led quantification of GHG fluxes at the regional scale
(O(100 km)); and (4) defining and executing sampling experiments to
challenge Earth system models and inversion models in terms of better
understanding model atmospheric transport error and surface emission
distribution.
The ARA is a BAe-146-301 aircraft that has been converted to a mobile
laboratory, including a variety of forward- and backward-facing external
inlets so that air can be sampled by instruments within the main cabin. It
also includes a number of ports that can host remote-sensing instruments.
Table describes the instruments that we deployed during
GAUGE, including in particular instruments that measure CO2,
CH4, and N2O, and a small complementary suite of other
trace gases and thermodynamic parameters. We made continuous measurements of
CO2 and CH4 at a frequency of 1 Hz using a Fast
Greenhouse Gas Analyzer (FGGA, Los Gatos, USA). For a detailed description of
the FGGA – including its operating principles, data processing, and
calibration – we refer the reader to . We also collect 1 Hz
measurements of N2O and CH4 from a quantum cascade laser
absorption spectrometer (Aerodyne Research Inc., USA). Further details of the
instrument are described by . We use the Met Office Airborne
Research Interferometer Evaluation System (ARIES), a Fourier transform
infrared spectrometer (FTIR), to retrieve partial columns of CH4 and
CO2 and vertical profiles of H2O and temperature. Further
details about ARIES can be found in . Other instruments
listed in Table are core ARA science instruments, which are
described in and references therein.
Key instrumentation on board the FAAM aircraft for GAUGE-specific
flights, including measurement principles and references to instrument
characteristics (where available). VUV denotes vacuum ultraviolet (light);
HFCs, PFCs, and VOCs denote hydrofluorocarbon, perfluorocarbons, and volatile
organic compounds, respectively; and PRT denotes platinum resistance thermometer.
Parameter
Technique
Manufacturer/model
Reference
CO
VUV fluorescence
Aerolaser, AL5002
O3
UV absorption
Thermo Electron Corporation, 49C
CH4, CO2
Off axis-integrated cavity
Los Gatos, FGGA 907-0010
Output spectroscopy
N2O, CH4
Tunable infrared laser
Aerodyne Research, QC-TILDAS-CS
Differential absorption spectroscopy
NOx
Chemiluminescence
Air Quality Design
HFCs, PFCs, SF6, C2–C7 VOCs
Whole-air sampling
Thames Restek
Δ14CO2
Glass flask sampling
NORMAG
δ13CH4
Tedlar bag sampling
SKC
CO2, CH4, O3, H2O, CO
FTIR total column remote sensing
UK Met Office, ARIES
Humidity
Chilled mirror
General Eastern, GE 1011B
Temperature
PRT
Rosemount Aerospace, 102 AL
Wind vector
5-hole probe
BAE Systems & UK Met Office
During GAUGE we conducted a total of 16 individual flight sorties over/around
mainland UK and Ireland between May 2014 and March 2016, comprising over
65 h of atmospheric sampling. These flights are summarized in
Table and Fig. . A typical flight sortie
coordinated upwind and downwind sampling of a target flux region (e.g. the
London metropolitan area), based on the prevailing boundary layer wind
direction, to attempt sampling of air masses that have been impacted by
regions with GHG emissions and uptake. We also designed flights to sample
outflow from mainland UK and continental Europe, and outflow from the Irish
and North seas on days with strong westerly flow regimes (e.g. Pitt et
al., 2018).
To capture regional emissions during GAUGE, we collected measurements that
were mostly in the boundary layer, as defined by in-flight thermodynamic
profiling, which was typically below 2 km altitude. Occasionally, to
characterize long-range transport of pollutants into our study region, we
collected measurements during deeper vertical profiles into the free and
upper troposphere.
Other flight profiles included surveys around
Britain and Ireland and flying around tall towers, as described below.
Figure shows a summary plot of the CO2 and
CH4 data collected during GAUGE. In particular, it illustrates the
horizontal and vertical spatial coverage of the aircraft sampling and the
dynamic range of mole fractions sampled. These observed variations are due to
differences in flight altitude and the time of year of the superimposed
flights (Table ), differences in air mass history, and the
spatial and temporal variability of local and regional fluxes across seasons
and sources.
Balloon CO2 sondes
Balloons offer an alternative platform for the collection of vertical
profiles of GHGs, building on the approaches used widely by the
meteorological and stratospheric communities. Here, we describe some of the
first balloon launches of small-scale CO2 sensor technology that
have been adapted for atmospheric sciences as part of a collaboration between
the University of Cambridge, SenseAir (Sweden,
https://senseair.com/, last access: 8 August 2018), and
Vaisala (Finland). The instrument consists of a small, sensitive
nondispersive infrared (NDIR) CO2 sensor developed by SenseAir. The
instrument sampling is 1 Hz with data transmitted to the Vaisala MW41 ground
station via a Vaisala RS41 radiosonde. The corresponding vertical resolution
of the collected data is 4–5 m. The dimensions and weight of the instrument
package are approximately 150×150×300 mm and 1 kg,
respectively. Heavy-duty cable ties are used to seal the enclosure and secure
the radiosonde to the outside. A 1200 g balloon (TOTEX, Japan) is used for
lifting the payload up to a ceiling of ≃35km. A typical
flight is 3–4 h, including rapid descent of 20–30 mins. The system used
during GAUGE is expendable but could be easily recycled with the
installation of an onboard GPS sensor.
Flight tracks for all FAAM flights during GAUGE from 15
May 2014 to 4 April 2016 (Table ). Colours
denote (a) altitude, (b) CO2 mole fraction, and
(c) CH4 mole fraction.
Figure shows preliminary data from two ChemSonde launches
from WAO on 14 April 2016 to test the viability of the system. Met Office
surface analysis charts (not shown) indicate that the UK was under the
influence of a low-pressure anticyclone in the North Atlantic, transporting
moist air over the southern half of the UK, during the period of
measurements. A low-level stratus cloud deck, with drizzle, and low SW winds
predominated over WAO during the morning of 14 April, with light winds
and steady rain during the afternoon. The first instrument was launched at
10:39 UTC, and the second at 14:30 UTC. For brevity, we only show data to
10 km. The sharp decrease in CO2 from near-surface altitudes to
≃1km during the morning launch and the increase in
boundary layer CO2 concentrations from morning to afternoon
launches suggest some local influence. We also noticed that some small-scale
increases in CO2 (1.8 and 7.5 km from the morning launch and
2.5 km from the afternoon launch) correspond to increased relativity
humidity, indicating possible cloud layers. NOAA HYSPLIT 48 h back
trajectories initialized at these lower and mid-troposphere
altitudes (not shown) indicate that we are sampling background maritime air
over the North Atlantic that has been lofted prior to interaction with land
surfaces. Differences in relative humidity close to 6 km suggest that the
morning cloud structure has been dissipated by the stronger afternoon winds.
We attribute the 4–5 ppm difference between CO2 instruments above
6.5 km to problems with the zero baseline drift and to a faulty span
measurement during the afternoon pre-launch preparation. Further studies with
ChemSonde are planned, with emphasis on improving design, operation, and
post-processing of data.
Preliminary balloon-borne
CO2 data launched on 14 April 2016 from Weybourne Atmospheric
Observatory, UK (Fig. ). Correlative measurements of
(b) relative humidity, (c) wind speed, and (d) wind direction are also
shown. Data are averaged every 10 s. Red ticks denote the
morning launch, and black ticks denote the afternoon launch.
Unmanned aerial vehicles for hotspot measurement campaign
UAVs represent a new atmospheric measurement platform for studying
atmospheric GHGs. They can be deployed rapidly to provide vertical
information across a horizonal dimension O(100 m). Within GAUGE,
researchers used a variety of measurement technologies, including fixed-wing
and rotary UAVs, to develop and refine new methods to use atmospheric
measurements to quantify CH4 and CO2 emission from a
landfill site . This
represents one of the first demonstrations of using UAVs to sample GHG
emissions. The reader is referred to and for further
details of the underlying technology.
We conducted a 2-week measurement campaign at a landfill site near
Ipswich, England (operated by Viridor Ltd), in August 2014. This
campaign brought together researchers from the University of Bristol,
University of Cambridge, Denmark Technical University, University of Edinburgh, University of Leicester,
University of Manchester, Royal Holloway University of London, University of Southampton, and
Ground Gas Solutions (GGS) Ltd. The landfill includes historic,
capped and active, and open landfill cells; a leachate plant; a gas collection
network; and a gas-burning energy generation facility.
We equipped the site with a 20 m eddy covariance flux tower, three Los Gatos
Research Ultraportable Greenhouse Gas (CO2 and CH4)
Analyzers (triangulated across the capped and open cell areas), a closed-path
FTIR, and five 3-D sonic anemometers to characterize flow over the site.
Conventional walkover flux surveys were conducted by GGS, and dynamic
automated flux chambers were operated on the flanks of the capped landfill
area to investigate seeps under the capped area where this met an active
cell. Tracer releases of perfluorocarbon and acetylene were also conducted
from various key points across the site to allow proxy flux calculations from
mobile (public road) plume sampling downwind. Specific experiments and
instrument siting were designed on each day of the intensive period in
response to weather (especially wind) conditions to characterize inflow and
outflow from different areas of the site. We deployed a fixed-wing UAV
equipped with a CO2 NDIR sensor around the site (Edinburgh
Instruments Gascard NG). We also launched a tethered rotary UAV, which
sampled air up to 120 m above the local terrain. This air was analysed using
a ground-based instrument (Los Gatos Research Ultraportable Greenhouse Gas
Analyzer) via a 150 m length of Teflon tube. This configuration allowed us
to sample vertical profiles of CH4 and CO2 over the
landfill site.
We also established a fixed-site monitoring station measuring CO2
and CH4 mole fractions to put the campaign into a longer temporal
context, to help test plume inversion techniques, and to test the efficacy of
continuous in situ monitoring to generate flux climatologies
. demonstrate how to
combine a computational fluid dynamics model (which accounts for topographical
data from a 3-D lidar survey data) with continuous in situ FTIR
measurements to infer and apportion fluxes across the surface area of the
landfill site. They showed in particular the ability of this approach to
distinguish between individual emission regions within a landfill site,
allowing better source apportionment compared with other methods that derive
bulk emissions.
Our UAV deployment during this experiment has since led to further
refinements to the method and platform, and to our use of similar technology
to infer fluxes from other UK landfills . A recent
validation of a new mass balancing algorithm based on tethered UAV sampling
of a known CH4 release rate demonstrated that a 20 min flight on a
single rotary UAV flight can reproduce the known release rate with an mean
accuracy of 14 % and an (1σ) uncertainty of <40 %
. Collectively, these measurements allowed us to test and
compare a wide range of established and novel sampling technologies and flux
quantification approaches. It also allowed us to examine how to optimize
different combinations of data to determine net bulk (whole-site) GHG fluxes.
Space-borne observations of GHGs
Satellites provide global, near-continuous, and multi-year measurements of
GHGs that are used to infer GHG fluxes on sub-continental scales and to
provide boundary conditions for regional atmospheric transport models. Within
GAUGE, we explore the potential of short-wave infrared (SWIR) column measurements
of CO2 and CH4 from the Japanese Greenhouse Gases
Observing SATellite (GOSAT) and thermal IR column measurements of
CH4 from the European Infrared Atmospheric Sounding Interferometer
(IASI). For the sake of brevity, we describe here only the pertinent details
of GOSAT and IASI and refer the reader to other studies dedicated to these
satellite instruments (e.g. ).
GOSAT is the first space-borne mission dedicated to measuring GHGs. It was
launched in a sun-synchronous orbit with a local overpass time of 13:00 by
the Japanese Space Agency (JAXA) in January 2009 . We use the
Thermal And Near-infrared Sensor for carbon Observation (TANSO) Fourier transform spectrometer (FTS), which
observes atmospheric spectra, and the Cloud and Aerosol Imager (CAI), which
provides multi-spectral imagery and coincident cloud and aerosol information
. TANSO-FTS has a ground footprint of approximately
10.5 km2 and returns to the same point every 3 days. For
illustration, we show GOSAT SWIR dry-air column-averaged CH4 mole
fractions that are inferred from version 7.0 of the proxy retrieval developed
by the University of Leicester (Sect. ). These data are
sensitive to changes in atmospheric CH4 in the lower troposphere.
The proxy retrieval method simultaneously fits CH4 and
CO2 spectral features in nearby wavelengths. The underlying idea is
that taking the ratio of the CH4 and CO2 fitted in nearby
wavelength regions effectively removes spectral artefacts common to both
CH4 and CO2 (e.g. scattering). The conventional method
of using these data is to multiply the ratio by model CO2, assuming
that CO2 varies in space and time less than CH4. The
resulting proxy XCH4 data have been evaluated extensively using data
from the Total Carbon Observing Network .
Diary of FAAM survey flights for GAUGE between May 2015 and March 2016, including take-off and landing times,
sampling locations, and a brief description of mission profiles.
Flight No.
Date
Take-off (UTC)
Landing (UTC)
Description
B848
15/05/14
12:07:07
16:46:25
North Sea Gas Rigs (+instrument test flight)
B849
16/05/14
09:33:16
12:45:28
Bristol Channel (+instrument test flight)
B850
21/05/14
07:59:54
15:22:59
Around Britain – UK outflow
B851
17/06/14
09:56:43
14:43:25
Southwest approaches – UK inflow
B852
18/06/14
08:25:01
16:29:35
Around Britain – DECC Tower survey
B861
09/07/14
08:55:32
13:20:52
Around London – mass balancing
B862
15/07/14
10:59:32
15:17:35
Around London – mass balancing
B864
01/09/14
08:09:57
10:49:27
Irish Sea – transit to Prestwick
B865
01/09/14
13:03:45
15:51:41
Around Scotland – mass balancing
B866
02/09/14
08:08:16
12:01:38
Around Ireland – mass balancing
B867
02/09/14
13:24:29
17:11:09
Around Ireland – area survey
B868
04/09/14
11:57:58
16:40:22
Northwest England – sources of 14C
B905
12/05/15
07:59:00
11:34:02
Irish Sea SW Approaches – upwind of UK
B906
12/05/15
13:09:14
17:03:19
North Sea – UK outflow
B911
28/05/15
07:55:04
10:19:26
Around Britain – aborted (instrument fault)
B948
04/03/16
08:55:20
14:10:19
Around London – mass balancing
IASI is one of a series of FTS instruments
on the polar-orbiting meteorological MetOp platforms (Hilton et al., 2012)
designed primarily for operational meteorology. There are two IASI
instruments currently operating: MetOp-A was launched on 19 October 2006, and
MetOp-B was launched on 17 September 2012. IASI has an across-track
measurement swath of 2200 km, resulting in near-global coverage twice a day
with a local solar overpass time of 09:30 and 21:30. It measures three
spectral bands that span a range of thermal IR wavelengths from 4 to
15.5 µm , which are most sensitive to CH4
in the mid-troposphere. Vertical profile retrievals of column-averaged volume
mixing ratios of atmospheric CH4 have been inferred using optimal
estimation from IASI spectra by the Rutherford Appleton Laboratory
. The retrieval produces two pieces of information in the
mid- and upper troposphere each with a single retrieval precision of 20–40 ppbv.
Differences between IASI and GOSAT CH4 are within 10 ppbv except
over southern mid-latitudes, where IASI is lower than GOSAT by 20–40 ppbv
.
The spatial coverage of satellite SWIR observations of CO2 and
CH4 over the UK is limited mainly by cloud-free scenes that are
themselves determined by the spatial resolution of the instruments and the
repeat frequency of the orbits. Currently, there are insufficient cloud-free
data to overtake the information provided by the
in situ measurements. However, we will soon have daily CH4
measurements from TROPOMI aboard Sentinel-5P, launched 16 October 2017. Data
from future and planned missions represent at least an order of magnitude
more satellite data than we have now. Until then, these GOSAT data represent
constraints on larger-scale sub-continental CO2 and CH4
flux estimates (e.g. ).
Calibration activities
Linking measurements in the GAUGE network to a common calibration scale
ensures comparability of these measurements, and simultaneously linking them
to a common set of traceable gas standards ensures they are also compatible
with ongoing international GHG measurement activities. Prominent examples of
such activities include the NOAA/ESRL GHG reference network, ICOS, and
IG3IS (https://goo.gl/4t1x6i). This approach also minimizes any
associated systematic errors for flux estimation using Bayesian inference
methods.
The GAUGE project encompassed a large number of data streams collected using
a range of instrumental techniques and at a variety of temporal resolutions,
increasing the risk of compatibility and comparability errors. Inversion
methods used in GAUGE to infer GHG fluxes from atmospheric mole fraction
measurements are particularly sensitive to site biases and offsets
. Consequently, ensuring comparability and assessing
compatibility were key to the success of GAUGE.
As far as possible we ensured measurement comparability by linking all
observations directly to common World Meteorological Organization (WMO) calibration scales, but due to the
historical nature of some data records this was not uniformly possible. All
CO2 measurements collected within the project were linked to the
WMO x2007 scale. All CH4 measurements other than MHD
gas chromatography–flame ionization detector (GC-FID; Table ), which uses the Tohoku scale, were calibrated
to the WMO x2004A scale. In contrast, N2O measurements used either
the Scripps Institute of Oceanography 1998 (SIO-98) scale (MHD and the rural tall-tower sites BSD, HFD, RGL, TAC, and
TTA) or the WMO x2006A scale (all other locations).
Numerical models of atmospheric GHGs
Figure shows the modelling strategy we employed
to quantify the magnitude, distribution, and uncertainty of UK emissions of
GHGs. We use models of atmospheric chemistry and transport, using prescribed
a priori flux estimates, to describe the relationship between sector
emissions of GHGs and atmospheric variations observed by the fixed and mobile
GHG measurement platforms used during GAUGE (Fig. ). These
models, which account for instrument-specific sampling, constitute the
forward model. Inverse models infer the magnitude and uncertainty of regional
flux estimates by fitting the forward model to observations, accounting for
their respective uncertainties.
Schematic of the generalized GAUGE modelling strategy. The
diagram neglects the non-linear inverse modelling approaches.
Because of the complex physical and chemical relationships between the
surface fluxes and the atmospheric observations, and because of the
assumptions embedded within individual models, we use a range of atmospheric
transport models and inverse methods to quantify the role of model transport
error on a posteriori fluxes.
Atmospheric chemistry transport models
Table summarizes the three different chemical transport
models (CTMs) and one atmospheric dispersion model that we use to interpret
the GAUGE data. All models are well established and have been used to
interpret a wide range of atmospheric GHG measurements.
Brief description of individual models
We use the following models: (1) the Goddard Earth Observing System
atmospheric Chemistry transport model (GEOS-Chem)
; (2) the Model for OZone and
Related chemical Tracers (MOZART) ; (3) the TOMCAT model
; and (4) NAME . These models vary
in their basic methodologies for representing atmospheric transport,
parameterizations of physical atmospheric processes, and horizontal
and vertical resolutions. GEOS-Chem, MOZART, and TOMCAT are global Eulerian
models, and NAME is a Lagrangian dispersion model that is applied on a
regional basis. We also use GEOS-Chem in a nested model that involves running
it at a higher resolution over a limited geographical domain with boundary
conditions determined by a coarser global simulation with consistent flux
inventories. The boundary conditions for NAME are solved as part of the
inverse problem. Model differences therefore provide us an opportunity to
quantify the impact of model error on describing observations and
consequently on inferred GHG flux estimates. For further details about an
individual model, the reader is encouraged to consult the model-specific
literature as provided above.
For the purpose of this overview of GAUGE and as part of our model assessment
within GAUGE, we ran global 3-D experiments to describe observed variations
of CO2, CH4, and N2O from 2004 to 2016,
including the main GAUGE measurement period of 2014–2015, inclusively. The
CTMs used common flux estimates and chemical loss fields as described below.
Preparation of these estimates, collected from different sources, were
regridded to the different model resolutions (Table ),
ensuring that the total emitted mass was conserved. The CTMs also used common
atmospheric mole fraction initial conditions for 2003.
To describe anthropogenic emissions of CO2 from 2003 to 2009, we
use the Carbon Dioxide Information Analysis Center (CDIAC) inventory
(available online at
http://cdiac.ornl.gov/trends/emis/overview.html, last access: 8 August 2018). In later years, we repeat values from 2009. We use the
NASA-CASA biosphere model to describe terrestrial
biospheric fluxes during 2003–2015, including biomass burning emissions.
Climatological ocean fluxes of CO2 are taken from
, covering the period 2003–2011. We acknowledge that
there are errors associated with using climatological flux estimates.
However, the purpose of this model intercomparison was to assess the model
spread associated with simulating atmospheric CO2, CH4,
and N2O.
The formulation of our CH4 simulations generally follows
and . We use updated anthropogenic CH4
emissions from the EDGAR
v4.2FT inventory , covering the period 2000–2010.
We repeat 2010 emissions for years beyond 2010. Biomass burning emissions
were taken from the Global Fire Emissions Database (GFED) v3.1 inventory
. Wetland and rice emissions were taken from
. Other natural emissions, including the soil sink (treated
as a negative flux), were taken from the TransCom CH4 model
intercomparison . We use monthly 3-D mean OH fields taken
from to describe the main atmospheric sink of
CH4. Reaction rates are taken from .
Stratospheric loss of CH4 due to reaction with O(1D) and
Cl radicals are based on loss rates taken from the Cambridge 2-D model
. The resulting atmospheric lifetime of
CH4 is ≃10 years, which is determined mainly by the
tropospheric OH sink.
Fluxes for our N2O simulations are taken from four broadly defined
source categories: natural soils , agricultural and other
anthropogenic emissions , ocean fluxes
, and biomass burning . We
parameterized an offline stratospheric loss of N2O in each model
using photolysis and O(1D) climatologies . We
did not consider this sink for NAME because of the short duration of model
runs compared to the atmospheric lifetime of N2O
(≃120 years). The relatively long atmospheric lifetime of
N2O, determined by stratospheric sinks, means that interpreting
observed tropospheric variations of N2O presents different
challenges to interpreting observed variations of CH4.
Model descriptions used in the GAUGE intercomparison. Forward model types include Eulerian (E) and
Lagrangian (L).
Model
Institute
Forward
Horizontal
Vertical resolution
Meteorology
Inverse
Key references
model type
(nested) resolution
method
GEOS-Chem
U. Edinburgh
E
2∘×2.5∘
47 levels
NASA GEOS-5
EnKF
(0.25∘×0.3125∘)
(surface to 0.01 hPa)
MOZART
U. Bristol
E
2.5∘×1.9∘
56 levels
NASA GEOS-5
4D-Var
(surface to 1.65 hPa)
TOMCAT
U. Leeds
E
1.125∘×1.125∘
60 levels
ECMWF ERA
4D-Var
;
(surface to 0.1 hPa)
Interim
NAME
Met Office/
L
1.5 km
60 levels
Met. Office
Bayesian
U. Bristol
over UK domain
(surface to 29 km)
inference
Assessment of model performance using large-scale independent data
To assess the global-scale GAUGE models, we use data that are representative
of large spatial and temporal scales. In particular, we use surface mole
fraction data from NOAA/ESRL and column data from the GOSAT and IASI
satellite instruments (Sect. ). We use these data to evaluate
the three CTMs, described above, by sampling each model at the time and
location of each observation.
Figure shows that the models reproduce the broad-scale
zonal-mean distribution of CO2 and CH4. Given the common
set of source and sink terms, model divergence will mostly reflect
differences in atmospheric transport. The latitudinal distribution has been
normalized to the South Pole value for each model to account for the drift
(incorrect sources/sinks) associated with the 8-year simulation.
Generally, the largest model biases for CO2 are at mid- and high
northern latitudes, where the emissions are largest, but will also be reflected in
interhemispheric transport times. Model divergence
is highest at these latitudes during northern winter months, with GEOS-Chem
having the largest model bias during these months. Model performance
generally improves in the northern summer months, with model differences
typically within a few ppm and much closer to the observations. The outlier
(≃23 ppm) at 44∘ N is the Black Sea site in Constanţa, Romania,
which we believe is influenced by local emissions that are not included in
our models. The model spread supports our strategy of using different models
to infer GHG fluxes. For CH4, the models have a similar level of
skill. None of the models reproduce the observed interhemispheric gradients,
likely due to errors in the a priori distribution of emissions used by the
inventories. The model spread is largest in January with a value of 45 ppb.
Model performance for N2O is the most variable, although this
partly reflects that N2O has the smallest observed
interhemispheric gradients of the three gases. The maximum model range is
1.4 and 1.7 ppb in January and July, respectively. The GEOS-Chem and MOZART
models have gradients similarly small in the Southern Hemisphere and tropics,
while TOMCAT is much larger. We find this model spread plays only a small
role in our UK-centric inversion because of the higher density of data
available over that region.
Simulated and observed surface zonal-mean latitudinal
gradient of (a) CO2 (ppm), (b) CH4 (ppb), and (c) N2O (ppb)
in January (solid lines and circles) and July (dashed lines and
triangles) 2011. Observations are made as part of the NOAA/ESRL
measurement campaign. For each model, its South Pole value is
subtracted for all latitudes. Observations are treated similarly.
Figure shows an example comparison between the
GEOS-Chem, TOMCAT, and NAME models and the observed atmospheric CO2
mole fraction at the Bilsdale tall-tower site (Fig. ).
GEOS-Chem and TOMCAT models use CO2 fluxes that have been
pre-fitted to global-scale NOAA/ESRL data, while the NAME model uses
atmospheric mole fraction boundary conditions taken from the MOZART model
that have been adjusted downwards by 20 ppm to match NOAA data. The seasonal
cycle represents the largest observed mode of variability, which the models
capture with Pearson correlations r2>0.7 (range: 0.7–0.8). The annual mean model minus observation
difference ranges from -0.3 to 1.7 ppm. These differences are greatly
reduced after the models have been fitted to GAUGE tall-tower data (not
shown).
Figure shows that MOZART and GEOS-Chem have similar
vertical distributions of CH4 during January, displaying a stronger
vertical gradient from the surface to 400 hPa than the TOMCAT model. This
corresponds to higher northern hemispheric mole fraction values. During July,
the three models all display different rates of vertical transport throughout
the Northern Hemisphere troposphere. TOMCAT has a slight gradient between the
surface and 600 hPa, and a much steeper gradient above; MOZART displays the
opposite behaviour; and GEOS-Chem lies between those extremes. Differences in
atmospheric transport are important and for some gases can represent a
substantial fraction of the signal. Our use of multiple models and combining
the resulting analysis improves our ability to quantify the uncertainty of
our results.
We also evaluate the models using the GOSAT proxy XCH4 V7.0 data
product developed by the University of Leicester
(http://www.esa-ghg-cci.org/, last access: 8 August 2018) and the IASI MetOp-A thermal IR V1.0 XCH4 data products
developed by the Rutherford Appleton Laboratory
(http://dx.doi.org/10.5285/B6A84C73-89F3-48EC-AEE3-592FEF634E9B, last access: 8 August 2018).
Observed and model atmospheric CO2 mole fraction values at
the Bilsdale tall tower during 2014 (Fig. and Table ).
All models are sampled at the latitude and longitude and
the 250 m inlet altitude of the Bilsdale site.
Figure shows the spatial coverage provided by both
instruments during June–August 2014. The sparser coverage of GOSAT
observations reflects its sensitivity to clouds and aerosols. Measurements
over the ocean used a glint observing model that takes advantage of specular
reflection and its associated high signal-to-noise ratio. Despite GOSAT and
IASI observing different parts of the atmosphere, there are many common
features associated with fossil fuel extraction/combustion (North America,
China, and parts of Saudi Arabia), wetlands (South America, Africa, and part
of India and China), and rice paddies (mostly India and China). Both
GEOS-Chem and TOMCAT models reproduce the broad spatial distributions of GOSAT
and IASI CH4 observations (not shown), with negative global mean
model biases that are approximately 10 ppb for GOSAT and between 1 ppb
(GEOS-Chem) and 10 ppb (TOMCAT) for IASI.
Inverse methods
The ultimate objective of GAUGE is to characterize the magnitude,
distribution, and uncertainty of UK GHG emissions. Relating
a priori GHG flux estimates to the atmosphere sampled at the time
and location of observations is called the forward problem
(Fig. ). The corresponding inverse problem refers
to the process of relating observed atmospheric measurements to the
underlying geographical distribution of GHG fluxes. Each of the atmospheric
transport models listed above employs its own inverse method, as described
below. Individual inverse methods employed in GAUGE have generally used all
data described in Sect. , either as constraints for flux
estimates or as independent data for model evaluation of a posteriori fluxes.
Different assumptions employed by these inverse methods, e.g. description of
atmospheric model transport error and specification of error covariances,
will also contribute to the spread of a posteriori flux estimates.
Inferring CO2, CH4, and N2O fluxes directly
from atmospheric observations is generally an ill-posed inverse problem, with
a wide range of scenarios that could fit these data. A priori information is
used to regularize the problem (Fig. ).
The results of inverse modelling are typically dependent on the distribution
of the observations used. For example, the sparsity of data at low latitudes
places a limit on our ability to infer GHG fluxes over geographical regions
that are not well sampled, e.g. tropical ecosystems. The spatial and temporal
density of GHG measurements collected during GAUGE allows us to constrain
a posteriori emission estimates on a devolved UK administration scale
and on sub-annual timescales.
Zonal-mean distribution of CH4 (ppb) for January
(a) and July (b) 2011 in each of the GAUGE CTMs. For
each model the concentration of CH4 at the surface South Pole
concentration is subtracted from the global distribution.
Although Bayes' theorem provides the basis for each of the inverse modelling
techniques used in GAUGE, each approach employs a slightly different
methodology to infer optimized surface fluxes. As we have already seen, there
can be relatively large differences in atmospheric transport models. Indeed,
the errors associated with atmospheric transport models are amongst the
largest source of errors associated with estimating GHG fluxes (e.g.
).
In the interest of brevity, we only briefly introduce the inverse methods
employed within GAUGE and refer the reader to dedicated papers on the techniques.
The global and nested GEOS-Chem model is linked with an ensemble Kalman
filter . This approach does not require
that we linearize the model but assumes approximate Gaussian statistics. The
ensemble Kalman filter approach allows us to include easily estimates of
model atmospheric transport error. Flux estimates are resolved in
geographical regions informed by the ability of the data to independently
estimate fluxes on those spatial scales. Over the UK, fluxes are estimated on
pre-defined aggregated county levels and on a weekly scale. Weekly values
are subsequently aggregated to longer timescales to minimize autocorrelation
between successive flux estimates.
The inverse version of the TOMCAT model, INVICAT , uses a
variational inversion method based on 4D-Var. This approaches uses the
adjoint version of the forward model to minimize the
a posteriori fit between the model and data. This is an iterative
method that can sometimes require a large number of iterations before
convergence. Consequently, we resolve a posteriori emissions using TOMCAT at
a spatial resolution of 2.8∘.
Two inverse frameworks use the regional NAME dispersion information:
(1) InTEM, a Bayesian inverse method building on , and
(2) a hierarchical Bayesian method in which the basis function, decomposition
of the flux space, and the model and a priori uncertainties are explored
using reversible-jump MCMC . Both these models
estimate emissions across a northwest European domain at horizontal
resolutions from 25 to 100 km, depending on the frequency of sampling
different regions. Boundary conditions are solved within each NAME inversion,
following for InTEM and for the MCMC
approach. Monthly UK emission estimates of CH4 and N2O
were estimated for the period 2013–2016 and compared to the reported
inventory. For the MOZART model we used a hierarchical Bayesian method based
on .
Our GAUGE inverse model studies generally include a series of factorial
experiments that allowed us to explore the relative importance of individual
and collective data to estimate UK CO2 and CH4 flux
estimates. Based on these experiments, we define a control experiment. We test
the robustness of our results by comparing results from using half or double
the assumed measurement uncertainties. UK
a posteriori flux estimates for CO2 and CH4 are currently
being prepared for publication: Lunt et al. (2018) and Palmer et
al. (2018).
Broadly speaking, we have estimated net CO2 fluxes using regional
and global scales but have been unable to attribute those fluxes to specific
sectors; for CH4, using the continental-scale data and the regional
network data, we have begun to improve our understanding of sector emissions,
and for N2O, which has the small atmospheric gradients due to its
long atmospheric lifetime, we have not begun to analyse the data collected
within GAUGE.
Seasonal mean dry-air column-averaged mole fractions of
CH4 (XCH4) from (a) GOSAT and (b) IASI for June–August
2014, described on a regular 5∘×5∘ grid. The
bottom rows show a global mean time series of XCH4 for 2010–2015. The
GEOS-Chem and TOMCAT models have been sampled at the time and
location of individual measurements and convolved with
scene-dependent averaging kernels prior to calculating the mean
value.
Concluding Remarks
The main objective of the Greenhouse gAs Uk and Global Emissions
(GAUGE) project was to estimate the magnitude, distribution, and
uncertainty of UK emissions of three atmospheric greenhouse gases
(GHGs): carbon dioxide (CO2), methane (CH4), and nitrous oxide
(N2O). To achieve that objective, we established an interlinked
measurement and data analysis programme of activities from 2013 to
2015. These activities substantially expanded on existing measurements
and data analysis. Some measurements that were established as part of
GAUGE have continued beyond 2015. The primary motivation for GAUGE was
to develop a measurement-led system to verify UK GHG emissions in
accordance with the UK Climate Change Act 2008. GAUGE also lays the
foundations for estimating nationally determined contributions as part
of the Paris Agreement.
Emissions of CO2, CH4, and N2O represented
97 % of UK GHG emissions during 2015 (the latest budget estimates available
from the UK government). These emissions originate from a variety of sectors,
including energy supply, transport, business, residential, agriculture, waste
management, and other. These emissions are very different in nature, ranging
from point sources to large-scale, diffuse sources. We considered this
heterogeneity of course when we designed the GAUGE measurement programme.
The backbone of GAUGE is a network of measurements that are collected at
height from telecommunication masts, tall towers, distributed across the UK.
These measurements are typically collected at multiple inlet heights
(100–300 m) above the local terrain (and sources) so they have a reasonable
fetch suitable for quantifying sub-national-scale GHG fluxes. GAUGE added two
tall-tower sites to the UK Deriving Emissions linked to Climate Change (DECC)
tall-tower network. The DECC network was established in 2012 to estimate GHG
emissions from the UK's devolved administrations. The GAUGE sites included a
site on the North Yorkshire Moors, with sensitivity to the greater
Manchester–Leeds–Liverpool–Sheffield region, and in East Sussex, which has
sensitivity to emissions from London.
We collected data on a commercial ferry that travelled regularly
between Rosyth, Scotland, and Zeebrugge, Belgium. This mobile
measurement platform provided information on UK and mainland European
outflow of GHGs, which complemented the tall-tower data. Using a
regional tower network over East Anglia, comprising mostly
measurements collected on church steeples, we found additional spatial
and temporal flux distributions over the region could be achieved. We
chose East Anglia because it is where there is a high density of
agriculture and where the local terrain is relatively flat, so that
church steeples often represent the highest local landmarks. As part
of GAUGE we deployed the UK Atmospheric Research Aircraft for a
limited number of flights around and across the UK. These data have
been used to study the transport of atmospheric GHGs on local to
regional spatial scales.
To explore how the UK GHG measurement network could develop in the
future, we incorporated new technologies and new measurement platforms
into the GAUGE programme. We deployed small sensors that were launched
on a small number of sonde launches, which offer a potentially new way
to obtain vertical distributions of GHGs. We also used unmanned aerial
vehicles as part of a larger measurement campaign to characterize GHG
emissions from a landfill, helping to pave the way for using this
technology more generally within larger-scale GHG emission
experiments. We also explored how we can use satellites effectively to
estimate UK GHG fluxes. The spatial and temporal coverage of clear-sky
measurements over the UK from current SWIR instruments, which are
sensitive to changes in CO2 and CH4, is too sparse to provide
competitive constraints on CO2 fluxes. We anticipate this situation
will slowly change with new instruments (e.g. TROPOMI) and proposed
mission concepts (e.g. Copernicus CO2 service) that will result in
higher spatial resolution and consequently more cloud-free scenes.
We used a range of global and regional atmospheric transport models linked
with inverse methods to interpret the atmospheric GHG observations. We showed
that these models have skill in reproducing observed atmospheric
CO2 and CH4 variations on hemispheric scales but
disagree with N2O observations due to much smaller gradients that
reflect its longer atmospheric lifetime. This multi-model approach was
adopted to help study the model spread in a posteriori GHG fluxes and to
study the relative importance of individual data to estimate UK GHG fluxes.
For this work, we refer the reader to the dedicated papers.
We approached source attribution in two ways. First, we used the
regional-scale network to improve the distribution of CH4 fluxes
due to agriculture, taking advantage of reasonable spatial
disaggregation of this source over East Anglia. We also established an
isotope measurement programme, including concurrent measurements
collected at Mace Head, Ireland, and Tacolneston, East Anglia. Data
from these two sites provided a crude meridional gradient over the
UK. Our sampling approach was designed, using the prevailing wind
direction over the UK, to determine the gradient due to fossil fuel
CO2. Despite our best efforts, neither approach to source
attribution was definitive. For example, our analysis of radiocarbon
was compromised by the influence of the nuclear power sector. We
anticipate the development of a more optimal sampling approach is
possible by working more closely with this sector to avoid instances
when sampled air masses are dominated by upwind nuclear sources.
GAUGE represents a first concerted attempt by the UK science community
to quantify nationwide GHG fluxes. We have laid the foundations of
measurement infrastructure that moves forward with a better
understanding of the advantages and disadvantages of individual GHG
data. The post-GAUGE tall-tower network has continued. For instance,
the UK DECC network has adopted the North Yorkshire site, which provides
valuable flux information about northern England and to a lesser
extent southern Scotland, and the National Physical Laboratory now
runs the tall tower at Heathfield. We also anticipate a growing role
for satellite observations, which are free at the point of delivery,
as new instruments provide better spatial coverage and
probabilistically a higher number of cloud-free scenes. Data analysis
will continue as improved models and inverse methods progressively
better describe the physical and chemical processes that determined
atmospheric GHGs. The UK is a geographically small country and plays a
proportional role in the Paris Agreement, but we expect the design of
GAUGE can be scaled upwards to larger geographical regions, taking
advantage of specific technologies relevant to the sectors that
dominate continental GHG budgets.