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Emissions reduction legislation relies upon 'bottom-up' accounting of industrial and biogenic greenhouse-gas (GHG) emissions at their sources. Yet, even for relatively well-constrained industrial GHGs, global emissions based on 'top-down' methods that use atmospheric measurements often agree poorly with the reported bottom-up emissions. For emissions reduction legislation to be effective, it is essential that these discrepancies be resolved. Because emissions are regulated nationally or regionally, not globally, top-down estimates must also be determined at these scales. High-frequency atmospheric GHG measurements at well-chosen station locations record 'pollution events' above the background values that result from regional emissions. By combining such measurements with inverse methods and atmospheric transport and chemistry models, it is possible to map and quantify regional emissions. Even with the sparse current network of measurement stations and current inverse-modelling techniques, it is possible to rival the accuracies of regional 'bottom-up' emission estimates for some GHGs. But meeting the verification goals of emissions reduction legislation will require major increases in the density and types of atmospheric observations, as well as expanded inverse-modelling capabilities. The cost of this effort would be minor when compared with current investments in carbon-equivalent trading, and would reduce the volatility of that market and increase investment in emissions reduction.
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April 2011 , published 18
, doi: 10.1098/rsta.2011.0006369 2011 Phil. Trans. R. Soc. A
Ray F. Weiss and Ronald G. Prinn
check for climate legislation
atmospheric measurements: a critical reality
Quantifying greenhouse-gas emissions from
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Phil. Trans. R. Soc. A (2011) 369, 1925–1942
doi:10.1098/rsta.2011.0006
Quantifying greenhouse-gas emissions from
atmospheric measurements: a critical reality
check for climate legislation
BYRAY F. WEISS1,*AND RONALD G. PRINN2
1Scripps Institution of Oceanography, University of California, San Diego,
La Jolla, CA 92093-0244, USA
2Center for Global Change Science, Massachusetts Institute of Technology,
Building 54-1312, Cambridge, MA 02139-4307, USA
Emissions reduction legislation relies upon ‘bottom-up’ accounting of industrial and
biogenic greenhouse-gas (GHG) emissions at their sources. Yet, even for relatively well-
constrained industrial GHGs, global emissions based on ‘top-down’ methods that use
atmospheric measurements often agree poorly with the reported bottom-up emissions.
For emissions reduction legislation to be effective, it is essential that these discrepancies
be resolved. Because emissions are regulated nationally or regionally, not globally, top-
down estimates must also be determined at these scales. High-frequency atmospheric
GHG measurements at well-chosen station locations record ‘pollution events’ above the
background values that result from regional emissions. By combining such measurements
with inverse methods and atmospheric transport and chemistry models, it is possible
to map and quantify regional emissions. Even with the sparse current network of
measurement stations and current inverse-modelling techniques, it is possible to rival
the accuracies of regional ‘bottom-up’ emission estimates for some GHGs. But meeting
the verification goals of emissions reduction legislation will require major increases
in the density and types of atmospheric observations, as well as expanded inverse-
modelling capabilities. The cost of this effort would be minor when compared with current
investments in carbon-equivalent trading, and would reduce the volatility of that market
and increase investment in emissions reduction.
Keywords: global warming; greenhouse-gas emissions; climate legislation
1. Introduction
Entering the second decade of the twenty-first century, legislation to stem
the adverse effects of anthropogenic climate change by requiring reductions
in anthropogenic carbon dioxide (CO2) and non-CO2long-lived greenhouse-
gas (GHG) emissions is becoming increasingly widespread. In addition to the
multi-national Kyoto Protocol, established under the United Nations Framework
*Author for correspondence (rfweiss@ucsd.edu).
One contribution of 17 to a Discussion Meeting Issue ‘Greenhouse gases in the Earth system:
setting the agenda to 2030’.
This journal is ©2011 The Royal Society1925
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1926 R. F. Weiss and R. G. Prinn
Convention on Climate Change (UNFCCC), there are many national, bilateral,
multi-lateral and regional initiatives to limit GHG emissions. Emissions of non-
CO2GHGs, many of which have global warming potentials (GWPs) that are
tens to thousands of times greater than CO2per unit mass, represent roughly
35 per cent of current GHG emissions on a carbon-equivalent basis [1], and
play a significant role in the current emissions trading market. Among the long-
lived non-CO2GHGs are a number of high-GWP anthropogenic gases that are
not regulated by the Kyoto Protocol, but which still contribute significantly
to anthropogenic radiative forcing, especially the chlorofluorocarbons (CFCs)
and other stratospheric ozone-depleting substances that are regulated by the
Montreal Protocol.
Current emissions reduction legislation is based on accounting methods that
are prescribed under the UNFCCC for calculating inventories of emissions of
industrial and biogenic GHGs at their sources, so-called ‘bottom-up’ emissions
reporting. Detailed guidelines for emissions reporting have been developed under
the auspices of the Intergovernmental Panel on Climate Change (IPCC; [2] and
subsidiary volumes). These prescribed procedures are based on activity metrics
such as economic and land-use databases, emission factors relating these activities
to GHG emissions, and time delays between GHG production and release. There is
capacity for including uncertainties in these estimates, but they are often reported
as ‘unknown’. This is a complex task involving estimates for a very wide range
of GHG emission sources; each reported individually and then aggregated. The
resulting national GHG emission inventories for UNFCCC Annex I developed
countries are reported with many digits of resolution, but usually without
uncertainties [3]. However, estimated emissions of GHGs of primarily industrial
origin and with limited types of sources are generally held to have greater accuracy
than emissions of GHGs from primarily biogenic sources that are more difficult
to quantify.
It is important to note that the Kyoto Protocol and other similar legislation
requires carbon-equivalent emissions reductions relative to a base period that are
often specified with resolutions of 5 per cent or less, with required ‘certification’
according to the IPCC or other formal bottom-up reporting procedures. But do
these procedures yield actual emissions? This is a critical question because the
anthropogenic affect on climate is ultimately driven by actual emissions of GHGs
to the atmosphere, not by reported ones.
2. Emission estimation approach
For emissions control legislation to be effective, and considering that enforcement
is likely to be practical only by bottom-up methods, it is essential that significant
discrepancies between bottom-up emissions estimates and ‘top-down’ emissions
estimates based on atmospheric measurements be resolved. But because emissions
control legislation is national or regional in nature, not global, it is also essential
that top-down emission estimates be determined at these same geographic scales.
Atmospheric GHG measurements and inverse modelling, when proceeding in
tandem, allow observations to be used to answer important scientific, as well
as regional, emission questions. Modelling can also help to define measurement
strategies (target species, site location, measurement precision and frequency)
Phil. Trans. R. Soc. A (2011)
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Greenhouse-gas emissions verification 1927
and priorities. Using the so-called minimum variance Bayesian inverse methods,
optimal estimates of fluxes by process and/or region in a chemical transport
model (CTM) can be obtained by minimizing the errors (variances) in the
estimated emissions given the errors in the observations. These inverse studies
can use measurements from a number of independent observational networks,
after conversion to a common calibration scale using results of inter-laboratory
measurement comparisons.
(a)Statistical methods
There are a number of statistical approaches to flux (source and sink)
estimations [46]. As an illustration here, we discuss specifically the discrete
Kalman filter (DKF). A desirable feature of the DKF is its capability for
objective estimation of the errors in the estimated fluxes and for inclusion of
both observational and certain CTM errors in the measurement error treatment.
A detailed introduction to the DKF in vector/matrix form for application to
estimating sources and sinks for atmospheric trace gases can be found in Prinn
[6]. To illustrate some of the key aspects of the use of the DKF for emissions
estimation, we can look at the highly simplified but informative one-dimensional
case of estimating sequential emissions from a single region using a sequence
of observations at a single site where vectors and matrices are now replaced
by scalars.
In this case, the filter is effectively minimizing the square of the error (pk=s2
xk )
in the estimated emissions (xk) at discrete time k. We define yo
kas the ‘observation’
(mole fraction) at time k,rkas the square of the error in the observation at time
k(rk=(so
yk )2), xf
kas the ‘forecast’ value for xk(value before using observation k),
xa
kas the ‘analysis’ value for xk(corrected value after using observation k),
hk=dyk/dxk(sensitivity of model mole fraction to model emissions) and yk=hkxf
k
as the model estimate for observation k. If we also define pf
kand pa
kas the
forecast and analysis values, respectively, for pk(values before and after using
observation k) and mas a scalar that multiplies the analysis values from a prior
time step to serve as a forecast for the current time step, then the recursive filter
equations to estimate emissions and their errors are simply
xf
k=mk1xa
k1, (2.1)
xa
k=xf
k+kk(yo
khkxf
k), (2.2)
kk=pf
khk
h2
kpf
k+rk
=1
hk+rk/(hkpf
k)=Kalman gain (scalar) at time k, (2.3)
pf
k=m2
k1pa
k1(2.4)
and pa
k=(1 kkhk)pf
k=11
1+[rk/(h2
kpf
k)]pf
k. (2.5)
Note that because pa
kpf
k, then the square of the error in the estimated emissions
after use of the observation is less than its forecast value. In addition, for
the desired large pkreduction (or large reductions in emission errors from
their forecasts), we want rkh2
kpf
k. For the desired large emission correction
Phil. Trans. R. Soc. A (2011)
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1928 R. F. Weiss and R. G. Prinn
atmospheric greenhouse-gas measurements
analysed observed winds
surface-emissions models (uncertain parameters)
atmospheric chemistry models
predictions of concentrations,
emission parameters,
sensitivities to emission parameters
corrected
emission
parameters
optimal estimation method
for emission parameters
global three-
dimensional
atmospheric
transport
models
alternative structures
Figure 1. Schematic illustrating the general inverse method for estimating emission fluxes or
parameters. The Kalman filter is an example of an optimal estimation method. The concentrations,
emission parameters and sensitivities to emission parameters refer to y,xand dy/dxin the simple
example discussed in the text. The Model of Atmospheric Transport and Chemistry (MATCH)
model, containing an atmospheric chemistry sub-model where appropriate, and driven by analysed
observed winds (National Centers for Environmental Prediction (NCEP), the European Centre
for Medium Range Weather Forecasts (ECMWF)) is an example of a suitable three-dimensional
model. Surface-emission models range from substantial codes that simulate the surface source and
sink processes down to simple specifications of time- and space-varying emissions based on in situ
flux measurements and information about anthropogenic sources. Alternative structures refer to
different choices for the emission models.
(xa
kxf
k), for a given large difference (yo
khkxf
k) between observation and model,
we want the Kalman gain kk1/hk(its maximum value), which occurs when
rkh2
kpf
k. That is, for both desirable outcomes, we want (so
yk )2(dyk/dxk)2(sf
xk )2
or equivalently so
yk <(dyk/dxk)sf
xk (i.e. the error in the observation yo
kneeds to be
less than the error in the model value yk=hkxf
kfor the observation). A schematic
of the general approach is given in figure 1.
It is important to ensure that model and observation imperfections are
accounted for properly (e.g. [4,6]). The processes and parameters to be estimated
are chosen so that they have effects on the emission patterns in space and
time that are sufficiently distinct from each other to ensure uniqueness and
stability (e.g. [7,8]). The estimations are also formulated so that small fractional
changes in concentrations do not yield unacceptably large fractional changes in
deduced emissions. Observational uncertainties that are associated with absolute
calibration, instrument precision and inadequate sampling in space and time are
incorporated into the observation error whenever possible. It is also important
to recognize when observation errors are correlated, thus possibly violating a
condition of optimal filters like the DKF or complicating the definition of the
observational error treatment. Various methods exist to address this (see [4,6]
for reviews). Weak nonlinearities in the chemical models (e.g. the lowering of the
hydroxyl radical (OH) when methane (CH4) emissions increase) are routinely
handled by recalculating the time-dependent sensitivities (hkin the above
simplified equations) after each run through all the data, and repeating the inverse
method to ensure convergence. Structural errors and random and systematic
transport errors (i.e. errors in hkabove) are handled through utilization of
multiple model versions and Monte Carlo methods (e.g. [9]) and/or by increasing
the measurement error to include the error owing to the model (e.g. [6,10]).
Phil. Trans. R. Soc. A (2011)
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Greenhouse-gas emissions verification 1929
observed CH
4
(ppb)
modelled CH
4
(ppb)
modelled CH
4
(ppb)
observed CH
4
(ppb)
1640
1680
1720
1640
1680
1720
1760
1760
1640
1680
1720
1760
(a)
(b)
observations 1640
1680
1720
1760
Feb Jan Mar AprNov Dec May
1998/1999
model
model
model
mirror plot
mirror plot
observations
observations
observations
Samoa
Samoa
La Niña winds
NAO (+) winds
NAO (–) winds
El Niño winds
model
Feb Jan Mar AprNov Dec May
1997/1998
150°–150°180°
150° –150°180°
–30°
1996
1997
1998
1999
2000
2001
4
SOI
NAO index
–4 0
–30°
observed CH
4
(ppb) observed CH
4
(ppb)
modelled CH
4
(ppb) modelled CH
4
(ppb)
Jan Feb Mar Apr May
1995
1500
1700
1900
2100 1500
1700
1900
2100
1500
1700
1900
2100 1500
1700
1900
2100
60°
60°
–30°0°
–30°0°
4–4 0
1992
1993
1995
1994
1996
1997
1998
1999
Jan Feb Mar Apr May
1996
mirror plot
mirror plot
Figure 2. MATCH simulates the very significant effects of temporal and inter-annual variability of
circulation patterns on atmospheric methane (CH4) mole fractions. Advanced Global Atmospheric
Gases Experiment (AGAGE) CH4observations (black) versus MATCH simulations (red) are shown
at (a) Samoa and (b) Mace Head, Ireland [7,8]. The El Niño Southern Oscillation Index (SOI) and
the North Atlantic Oscillation (NAO) index are shown adjacent, respectively. The comparison
in (a) is for the same months during the 1998 El Niño (top) and 1999 La Niña (bottom) with
the January–May average surface wind fields for the 2 years, and the comparison in (b)isfor
the same months during the 1995 positive NAO (top) and 1996 negative NAO (bottom) with the
January–May average surface winds also shown. Note that observations and model estimates are
plotted on scales of opposite direction so that an exact mirror image of the two datasets implies
perfect agreement. The CH4measurement station location is shown by a cross on each surface
wind field map.
Phil. Trans. R. Soc. A (2011)
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1930 R. F. Weiss and R. G. Prinn
(b)Models
A basic requirement for inverse modelling is an accurate and realistic multi-
dimensional atmospheric CTM. Even apparently small transport errors can lead
to significant errors in estimated sources or sinks [11,12]. Three-dimensional
models are essential to resolve pollution events and solve for regional sources and
sinks. In addition, three-dimensional models must possess realistic atmospheric
circulations. These models may compute quantities at fixed grid points (Eulerian)
or compute them following the trajectories of the air parcels (Lagrangian).
As an example of a three-dimensional Eulerian model, a large number of
inverse studies have used the Model of Atmospheric Transport and Chemistry
(MATCH) [10,13,14]. This is an offline global three-dimensional transport
model that uses meteorological fields derived from forecast-centre analyses.
MATCH has been successfully driven by meteorological data from the National
Centers for Environmental Prediction (NCEP), the European Centre for
Medium Range Weather Forecasts (ECMWF) and the Goddard Space Flight
Center/National Aeronautics and Space Administration (GSFC/NASA) Data
Assimilation Office (DAO) analysis [14]. Sub-grid mixing processes, which include
dry convective mixing, moist convective mixing and large-scale precipitation
processes, are computed in the model. MATCH can be used at a horizontal
resolution as fine as T62 (1.8×1.8), with either 42 or 28 levels in the vertical.
MATCH inversions have been used for many GHGs, including chlorofluorocarbon-
11 (CF3Cl) [10], CO2[1518], CH4[7,8], nitrous oxide (N2O) [19] and carbon
tetrachloride (CCl4)[20]. The ability of MATCH to accurately simulate the
effects of transport on long-lived trace gases is well illustrated by the methane
simulations in figure 2.
Another common modelling approach is based on back-trajectories computed
from meteorological data using a Lagrangian model (e.g. [12]). By dividing
the trace gas observations into ‘background air events’ and ‘pollution events’
and computing air mass back-trajectories, and thus air mass transit times
over predefined emission regions, the time/space average emissions from these
predefined regions can be determined. The method obviously requires accurate
definition of both back-trajectories and eddy diffusive fluxes. The Hybrid Single-
Particle Lagrangian Integrated Trajectory (HYSPLIT) model [21,22]ofthe
National Oceanic and Atmospheric Administration (NOAA) has been used for
this purpose [23]. The UK Met Office Lagrangian particle model (Numerical
Atmospheric dispersion Modelling Environment; NAME) has also been applied
extensively to determine European source and sink strengths for a variety of
species (e.g. [2429]). Similarly, Australian regional emissions of various species
have been determined using inverse studies and regional transport models [30,31].
The FLEXPART Lagrangian particle dispersion model has also been used to
determine emissions of halocarbon GHGs regionally and globally (e.g. [32]).
While two-dimensional models are not suitable for regional emission
estimation, they have their uses (e.g. solving for global or hemispheric emissions
or for model error analysis). Three-dimensional models, being computationally
expensive, do not always lend themselves well to doing very long time integrations,
and multiple runs are required to address uncertainties (e.g. thousands of runs
for Monte Carlo treatments of model transport, rate constant and absolute
calibration errors). A two-dimensional model is better suited to a full uncertainty
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Greenhouse-gas emissions verification 1931
analysis because its transport is ‘tunable’ to simulate observed latitudinal
gradients (e.g. [9,33]). For example, measurements of N2O have been used
in conjunction with three-dimensional atmospheric CTMs transport models
to attribute source and sink strengths [19,34], and use of a complementary
two-dimensional model to assess the effects of model error using a Monte
Carlo approach indicates that the errors emanating from the three-dimensional
inversions alone need to be augmented significantly to account for model
errors [19].
3. Global and regional emissions
There are large uncertainties associated with emissions of the biogenic
components of some of the most important anthropogenic GHGs such as CO2,
CH4and N2O—emissions associated with land-use changes, agriculture and waste
processing. For these gases, there is also a need to separate anthropogenic
emissions from natural emissions. As a result, the accuracies of bottom-up
emissions inventories are most easily assessed for global emissions of purely
industrial long-lived anthropogenic GHGs that are emitted from defined sources
and are easily quantified from atmospheric measurements. Furthermore, emissions
of industrial non-CO2GHGs, with their very high GWPs, are particularly
important to quantify because they represent a disproportionately large share
of the global carbon-equivalent trading market, even though their contributions
to global warming are much less than that of CO2.
Atmospheric abundances of a wide range of GHGs, including all of the
non-CO2GHGs currently regulated by the Kyoto and Montreal Protocols,
are routinely measured at a few select locations around the world by several
independent research programmes including our Advanced Global Atmospheric
Gases Experiment (AGAGE) programme [35,36]. These measurements, made
in real time at remote stations, in flask samples and in archived air samples,
yield accurate trends for the background atmospheric composition that extend
back three decades for most of these gases. They can also be extended well
before their industrial production when combined with measurements of air
trapped in polar firn or ice cores (e.g. [37]). Using such data and the estimation
approaches discussed above, it is possible to calculate top-down global emission
rates from the measured trends, especially for long-lived gases with simple
chemistry, and then to compare these values with the reported bottom-up
emission rates.
The current knowledge of methane emissions exemplifies the issues surrounding
gases with large biogenic, as well as anthropogenic, sources. After nearly a decade
of little net change, the mole fractions of this gas began to rise in both hemispheres
in 2006–2008 [38]. The inverse modelling by Rigby et al.[38] implied that the
increase in growth rate was due either to increasing tropical and high-latitude
emissions or to a smaller high-latitude emissions increase along with a few percent
tropical OH decrease (or to some combination of the two). A later study of
spatial gradients and Arctic isotopic signals by Dlugokencky et al.[39] suggested
that the rise was primarily due to increased wetland emissions in both the high
latitudes and tropics, with little influence from OH variations. Bousquet et al.[40]
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1932 R. F. Weiss and R. G. Prinn
earlier suggested that the stable period for CH4preceding 2006 was caused by
a decreasing wetland source countering an increasing fossil-fuel-related source,
with the sink of CH4owing to OH potentially playing a role in the observed
atmospheric variability. For comparison, Chen & Prinn [8] attributed the CH4
variations over 1996–2001 when compared with the literature values for the years
before that to decreased fossil-fuel-related emissions and increased rice-paddy
emissions, with the 1998 positive anomaly owing to increased global wetland and
wildfire emissions. While the suggestion of very large methane emissions from
vascular plants has been challenged [1], Chen & Prinn [8] have noted that their
computed increased rice emissions (about 25 Tg methane yr1) could also be due
to neighbouring non-rice wetland emissions.
The estimation of emissions for the essentially purely anthropogenic
(industrial) greenhouse gases is more straightforward than for those gases
whose budgets involve both biogenic and anthropogenic components. However,
challenges still remain for accurately inferring regional emission estimates for
these industrial gases. Examples for four high-GWP anthropogenic GHGs are
presented here.
Carbon tetrafluoride (CF4) is the longest-lived GHG regulated by the Kyoto
Protocol, with an atmospheric lifetime of 50 000 years and a GWP of 7390 on a
100 yr time horizon [1]. It is emitted principally as a by-product of aluminium
production, with lesser but significant emissions from the electronics industry.
The trend of CF4concentration in the atmosphere is being measured in real time
by AGAGE, and with the aid of stored samples, its trend in both hemispheres has
been extended back to the 1970s [41]. There is also a small natural source of CF4
from the continental lithosphere [42], which, because of its very long atmospheric
lifetime, accounts for about half of the approximately 78 ppt (parts per trillion,
dry air mole fraction) in the current atmosphere. But this flux is negligible when
compared with the anthropogenic flux that drives the current trend. Modelling
the AGAGE atmospheric CF4trends using a 12box inverse model [41] yields a
top-down global anthropogenic CF4emission flux that peaked in 1980 at about
18 Gg yr1and tapered to about 16 Ggyr1in 1990 and about 11 Gg yr1in 2006,
with a modelled uncertainty of about 5 per cent. This modelled top-down global
emission history is shown in figure 3, together with various reported bottom-up
CF4emission estimates.
Comparison with bottom-up estimates reported to the UNFCCC by the
Annex I industrialized countries [3] shows that in 1990, when reporting began,
reported emissions accounted for only about 60 per cent of the measured
atmospheric increase, and that by 2006, this fraction had decreased to about 40
per cent. In other words, while the rate of CF4accumulation in the atmosphere
decreased by about 30 per cent during this period, the Annex I reported
CF4emissions decreased by about 50 per cent. Although aluminium-producing
countries like China, India and Brazil are not included in Annex I reporting,
and production by these countries certainly increased significantly over this time
period, it is difficult to reconcile that by 2006, non-Annex I countries accounted
for about 60 per cent of global CF4emissions. Reinforcing this concern are the
bottom-up results of the International Aluminium Institute (IAI) report series
(e.g. [43]), which include worldwide CF4emissions from Annex I as well as non-
Annex I aluminium production, but do not include emissions from the electronics
industry. When the IAI estimates are added to the much smaller electronics
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Greenhouse-gas emissions verification 1933
1975
0
5
CF4 emissions (Gg yr–1)
10
15
20
1980
UNFCCC
Annex I countries
global
aluminium (IAI)
global electronics (EDGAR v. 4)
global aluminium
(IAI) plus global
electronics
(EDGAR v. 4)
global atmospheric
measurements (AGAGE)
1985 1900 1995
y
ear
2000 2005 2010
Figure 3. Top-down global emissions of carbon tetrafluoride (CF4) modelled from global
atmospheric measurements in the AGAGE programme [41], with shaded combined modelling and
measurement uncertainties (±1 standard deviation), compared with the bottom-up global emissions
estimates for the aluminium industry (e.g. [43]), for the electronics industry [44] and for these two
sources combined. Also plotted are bottom-up emissions reported to the UNFCCC for the Annex I
developed countries [3]. Adapted from Mühle et al.[41].
19751970
0
SF6 emissions (Gg yr–1)
2
6
4
8
1980
UNFCCC
Annex I countries
EDGAR v. 4
global
EDGAR v. 4
Annex I countries
global atmospheric
measurements
(AGAGE)
1985 1900
year
1995 2000 2005 2010
Figure 4. Top-down global emissions of sulphur hexafluoride (SF6) modelled from AGAGE
programme global atmospheric measurements [48], with shaded combined modelling and
measurement uncertainties (±1 standard deviation) compared with bottom-up global emissions
reported to the UNFCCC for the Annex I developed countries [3] after correction of pre-1995
reported Japanese emissions [46]. Also plotted are the EDGAR global-estimated emissions [44]
that take into account bottom-up estimates as well as atmospheric measurements, and EDGAR
estimates for SF6emissions only from Annex I countries that are roughly double those reported to
the UNFCCC. Adapted from Rigby et al.[48].
Phil. Trans. R. Soc. A (2011)
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1934 R. F. Weiss and R. G. Prinn
industry global CF4emissions estimates compiled by v. 4.0 of the Emission
Database for Global Atmospheric Research (EDGAR) [44], the results account
for only about 75 per cent of the AGAGE measured increases in 1990, and by
2006, this fraction decreases to about 45 per cent. In other words, the combined
IAI and EDGAR electronics estimates are in reasonable agreement with emissions
reported to the UNFCCC, even though the former values include non-Annex I
countries and the latter ones do not. For the most recent part of the record, neither
of these sets of reported CF4emissions account for even half of the measured
increase in the global atmosphere.
Sulphur hexafluoride (SF6) is the most potent GHG regulated by the Kyoto
Protocol, with a GWP on a 100 yr time horizon of 22 800 and an atmospheric
lifetime of 3200 years [1]. It is used principally as a dielectric to prevent arcing
in high-voltage equipment, and also in industrial applications where a heavy
and chemically inert gas is required. SF6is also emitted naturally from the
continental lithosphere, but in such small quantities relative to its atmospheric
lifetime that its natural pre-industrial background concentration was less than
0.006 ppt [45]. Its present atmospheric abundance of about 6.8 ppt is therefore
effectively entirely anthropogenic. Recent trends in global atmospheric SF6
distributions have been measured and modelled extensively and independently
by several research programmes, including the University of Heidelberg [46,47],
AGAGE [48] and the NOAA Earth System Research Laboratory [49,50]. The SF6
measurements of each of these three programmes are generally in good agreement
and lead to essentially the same conclusion, namely that global SF6emissions are
greatly underestimated by bottom-up emissions reported to the UNFCCC by
Annex I countries.
The modelled SF6global emissions history based on AGAGE global
atmospheric measurements [48] is compared with various bottom-up emissions
estimates in figure 4. After correction of pre-1995 reported Japanese
emissions [46], all the datasets show that throughout the mid-1990s, only about
40 per cent of total emissions were reported by Annex I countries, and that by
2006, this fraction had reduced to about 20–25%. If the Annex I countries have
indeed reported correctly, then the non-Annex I countries emitted about 1.5 times
as much SF6as the Annex I countries in the mid-1990s, and by 2006, they were
emitting four or five times as much. A more likely explanation that is supported
by the v. 4.0 EDGAR [44] interpretation of this mismatch is that Annex I SF6
emissions are severely under-reported and that they actually represented about
80 per cent of the total in the mid-1990s and about 60 per cent of the total in
2006. In other words, the Annex I countries collectively have likely under-reported
SF6emissions by more than a factor of 2.
With respect to the use of the EDGAR [44] database for comparisons of
top-down and bottom-up emission fluxes, it is important to note that because
EDGAR’s objective is to provide the best emission estimates, both bottom-
up and top-down methods are used to arrive at its recommended values of
such parameters as emissions factors and emissions delays. Discrepancies such
as the ones discussed above, in which measured global atmospheric trends have
disagreed significantly with EDGAR bottom-up assessments, have resulted in
major revisions of EDGAR emission values in subsequent release versions. For
CF4and SF6, the current (v. 4.0) EDGAR global total and Annex I emission
estimates are neither bottom-up nor top-down, but rather are in effect a synthesis
Phil. Trans. R. Soc. A (2011)
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Greenhouse-gas emissions verification 1935
of both, which improves upon the purely bottom-up approach by adjusting
emission factors. In the case of SF6, these relationships are discussed by Maiss &
Brenninkmeijer [51] and Rigby et al.[48]
Nitrogen trifluoride (NF3) is not regulated by the Kyoto Protocol, and its
emissions from Annex I countries are therefore not required to be reported to
the UNFCCC, but its long lifetime of about 550 years and high GWP of about
16 800 on a 100 yr time horizon [52], and its use as a replacement in electronics
manufacturing for perfluorocarbons that are currently regulated, make it a strong
candidate for future emissions regulation. The first measurements of the trend of
NF3in the global atmosphere were made by the AGAGE programme [53]. They
showed that its present global abundance is about 0.5 ppt and that its emissions
in 2006 were roughly four times greater than the only published bottom-up global
NF3emission estimate for that year. When recently unpublished industry-wide
estimates of global NF3usage provided by Air Products Corporation, a major
producer, are compared with the modelled global atmospheric NF3emission
results, they suggest that about 9 per cent of current global NF3usage is emitted
to the atmosphere, 4.5 times greater than the 2 per cent emission factor that has
been used widely in bottom-up estimates [54].
Although each of the above examples finds significantly greater global emissions
than are estimated by bottom-up accounting methods, this is not uniformly the
case. Sulphuryl fluoride (SO2F2) is a fumigant that is used increasingly to kill
termites and other pests, often as a replacement for methyl bromide, which is
restricted by the Montreal Protocol because of its role in stratospheric ozone
depletion. SO2F2is not regulated by the Kyoto Protocol, and its emissions
from Annex I countries are therefore not reported to the UNFCCC. The
first measurements of SO2F2in the global atmosphere [55], coupled with
laboratory studies of its optical properties and reactivity [56], showed that its
GWP is about 4800 on a 100 yr time horizon and its atmospheric lifetime
is about 36 years. The observed global trend of SO2F2, presently at about
1.6 ppt and rising at about 5 per cent per year, is best explained by the
emission to the atmosphere of only about two-thirds of its estimated global
usage [55]. Either the usage data are overestimated, or unidentified mechanisms
destroy about one-third of the gas before it is emitted to the atmosphere so
that the flux to the atmosphere is reduced without changing the modelled
atmospheric lifetime.
The picture that emerges from these four examples of global top-down and
bottom-up comparisons for these industrial gases is that the discrepancies can
be quite large, and that more often than not, the measured accumulations
of industrially produced GHGs in the atmosphere are substantially greater
than can be explained by the emissions that have been reported. Certainly,
the discrepancies are large enough to call into serious question the reliability
of the emission factors that are used in bottom-up emissions accounting, the
many significant digits with which these emissions are typically reported, and
the viability of GHG emissions reduction legislation that depends solely on
bottom-up reporting procedures. Also called into question are the viability of
emissions trading that depends upon bottom-up emissions accounting, and the
feasibility of implementing legislation that requires reductions in emissions that
are small when compared with the ability of prescribed bottom-up methods to
resolve them.
Phil. Trans. R. Soc. A (2011)
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1936 R. F. Weiss and R. G. Prinn
4. Future prospects for improvements
The uncertainties of current regional emission estimates either by top-down
or bottom-up approaches are commonly greater than 10–20%—sometimes very
much greater—and thus are grossly inadequate for verifying claims of emission
reductions by nations (e.g. mandated reductions under the Kyoto Protocol are
generally only a few to 10%).
Looking to the future, it is clear that the spatial density of precise high-
frequency atmospheric trace-gas measurements, whether using in situ or remotely
sensed methods, needs to be increased by an order of magnitude or more.
Equally important, the knowledge (theory, observation) embraced in models of
industrial or ecosystem fluxes should be incorporated into the model system
to enable estimation of uncertain parameters in these flux models as opposed
to simply the fluxes themselves. In essence, this approach combines the best
features of the bottom-up and top-down methods in flux estimation. Specifically,
the use of an adjoint of MATCH or other similar model, coupled to models
of the surface fluxes, would enable the estimation of uncertain parameters
or controls in the flux model with a more powerful statistical approach. An
adjoint of a model code is a complementary code that relates anomalies in
model outputs (e.g. mole fractions) to changes in model inputs (e.g. emissions)
or model parameters. Using the mathematical algorithms (but not the exact
goal) of control theory (e.g. [57]), one can formulate the state estimation
problem using a cost function J(pkin the simplified discussion earlier) that is
augmented with a demand for model consistency using the so-called Lagrange
multipliers. Any variable that can be affected by changes in any of the
control variables is called active, while variables that remain unaffected are
called passive. Thus, the computations consist of an active part in which the
coupling of the surface fluxes or atmospheric destruction rates is considered
and that would be improved as part of the estimation problem to minimize
J, and a passive part in which all the other elements of the system are
considered that do not change throughout the optimization (e.g. the MATCH
atmospheric circulation).
Through variation of the controls and initial conditions of the system, a
solution of the state vector (xkin the simplified discussion earlier) is sought that
minimizes J. The general structure of Jconsists of four sums measuring: (i) the
departure of the initial state from a first guess, (ii) the difference between the
observations and the model projections of them, (iii) the deviation of the controls
from a prior, and (iv) the demand that the state vector satisfies the various
model equations through the introduction of Lagrange multipliers. Besides its
key role in the J-minimization process, and thus in the state vector and model
parameter estimations, the three-dimensional model and flux model adjoints
could also be used to analyse the origins of observed mole-fraction anomalies
in terms of specific flux model parameters and initial conditions. This linking of
effects to causes enables observation-based corrections to the industry and natural
emission models.
Improvements to the driving circulations for CTMs are also needed.
The widely used meteorological circulation re-analyses (NCEP, ECMWF,
etc.) show significant differences, particularly in regions with very sparse
meteorological observations to correct the underlying weather forecasting models.
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Greenhouse-gas emissions verification 1937
The modelling of convection and other sub-grid scale phenomena in both
chemical transport and weather forecasting models is another area needing
significant improvement.
Besides inclusion of all available surface and aircraft measurements of trace-gas
mole fractions, future inversions should also include column abundances obtained
from satellite and ground-based remote sensing. The above control system
approach also enables use of direct measurements of industrial and ecosystem
fluxes in the estimation algorithms. Besides accurate calibration comparisons
among laboratories, a key issue in combining multiple types of measurements
is an accurate estimation of the uncertainties in each type as they will be the
basis for the weighting contained in the inverses of the various measurement
uncertainty matrices involved in the inversions.
Finally, for GHGs that have natural, anthropogenic, industrial and biogenic
emissions, such as CO2,CH
4and N2O, measurements of atmospheric abundances
alone are inadequate to differentiate precisely among these different sources.
High-frequency in situ measurements of not just the total mole fractions of
these gases, but also their isotopic compositions are a new frontier in global
monitoring and hold the promise of revolutionizing understanding of the natural
cycles of these gases and verifying claims of emission reductions. At present,
stable isotopic measurements for CO2and CH4are carried out routinely only
by collecting air samples weekly to monthly at network stations for analysis
in a central laboratory by conventional gas-source magnetic-sector isotope
ratio mass spectrometry, but this sampling frequency is far too limited to
be used to accurately constrain estimates of sources and sinks by process
and by region. Also, deployment of these instruments at remote stations is
inhibited by their high costs, maintenance needs and power requirements,
which hinder reliable automation. Measurements of N2O isotopic composition
in the troposphere are even scarcer. Bulk nitrogen-15 (15N) data are available
(e.g. [58]), and some intra-molecular 15N measurements have also been made
(e.g. [59]).
However, high-frequency in situ isotope measurements are now becoming
feasible using optical (laser) detection. Recent improvements in mid-infrared
quantum cascade lasers (QCL) enable continuous wave (CW) operation near
room temperature (RT) with higher power, narrower line widths and higher
spectral mode purity than previously possible. The application of CWRT-QCLs
has greatly extended detection limits for atmospheric trace-gas measurements
without cryogenic cooling of the laser. CWRT-QCLs have been applied to
detection of the isotopes of CO2,CH
4and N2O (e.g. [60,61]). In addition to
the QCL work, there has been recent progress with isotopic monitoring using
other laser sources (e.g. [62,63]). For CH4and N2O, automated cryogenic pre-
concentration will probably be necessary to measure their isotopic compositions
with the precisions needed to differentiate their various surface fluxes (biogenic,
anthropogenic) and photochemical sinks.
The prospects for remote sensing from satellite, aircraft and surface platforms
of some of the above isotopomers and isotopologues are less clear, but
should also be pursued. These isotopically resolved trace-gas measurements of
ambient air would need to be accompanied by accurate field and laboratory
measurements of the isotopic signatures of relevant industrial, biological and
chemical processes.
Phil. Trans. R. Soc. A (2011)
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1938 R. F. Weiss and R. G. Prinn
Finally, measurements of radiocarbon (14 C) are particularly valuable in
distinguishing fossil sources of CO2and CH4, but unfortunately the techniques
described above do not yet hold promise for measuring 14C in these gases in situ
with the required high frequency, sensitivity and precision. For the foreseeable
future, the highest temporal resolution 14C measurements will probably continue
to be made by accelerator mass spectrometry (e.g. [64]), which does not lend
itself to in situ operation.
5. Conclusions
The examples cited here show that the discrepancies between reported bottom-up
GHG emissions and measured top-down accumulations of these emissions in the
atmosphere can be substantial. There are many possible explanations for these
large discrepancies. Statistical uncertainties in emission factors used in bottom-up
protocols are always possible, but such errors ought to be mostly random, and
thus do not explain the tendency for the actual emissions to exceed the reported
ones, more often than not. When emissions from industrial processes are measured
at their sources to establish emission factors, the equipment may be adjusted to
minimize emissions, so that the measured values may be lower than they are under
typical day-to-day operating conditions, and this would lead to under-reporting.
Furthermore, the possible existence of unaccounted or unidentified sources, such
as fugitive emissions during industrial production or transportation, would also
lead to under-reporting. In addition, the negative impact of GHG emissions on
climate, and the financial value of emissions reductions in carbon-equivalent
trading markets, both create incentives to under-report actual emissions, whether
consciously or subconsciously.
Because effective emissions control legislation ultimately must depend upon
enforcement by reliable bottom-up methods, it is essential that these discrepancies
be resolved. But since the legislation is national or regional in scale, not global,
top-down emission estimates must be determined at these same scales. In addition
to recording background GHG trends driven by global and hemispheric emissions,
high-frequency atmospheric measurements at well-chosen ground-based station
locations also record GHGs that have been elevated above their background
values because of regional emissions. By analysing these and other atmospheric
measurements with three-dimensional atmospheric transport and mixing models
using inverse numerical methods, it is possible to map and quantify regional
emissions over time. To reconcile discrepancies between top-down assessments
and those obtained by bottom-up protocols, we propose that optimal estimation
and control-theory methods be used to identify and correct the most likely causes
of the observed discrepancies.
Even with the relatively sparse existing network of ground-based measurement
stations, using current modelling techniques, it is possible to rival the accuracies
of bottom-up emissions estimates for some GHGs in some regions. Awareness by
policy-makers of the large discrepancies which have been found between reported
bottom-up emissions and emissions determined from atmospheric measurements
has, so far, been limited, but there is an example for European CH4emissions
in which German CH4emissions for 2001 reported to the UNFCCC have been
revised upward substantially, and thus were brought into far better agreement
with the modelled atmospheric observations [65].
Phil. Trans. R. Soc. A (2011)
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Meeting the goals of effective top-down GHG emissions verification and
refining bottom-up protocols to bring about convergence is a research problem
that will require substantial increases in the density and scope of atmospheric
measurements, as well as improvements in inverse modelling capabilities, but
many of the basic components exist already and need only to be increased in scale.
Such an initiative would be large when compared with most current atmospheric
research programmes, but could easily be supported by an annual investment of
less than 1 per cent of the $144 billion US$ currently invested in global carbon-
equivalent trading markets [66], with the added benefit of reducing the volatility
of these markets and thereby increasing investment in emissions reductions.
We thank the convenors of the Discussion Meeting ‘Greenhouse gases in the Earth system: setting
the Agenda to 2030’ for the opportunity to present our views on this timely issue. We also thank
our colleagues in the AGAGE community for their many contributions to the work discussed here,
and NASA’s Upper Atmosphere Research Program for its continuing support of AGAGE.
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on March 12, 2014rsta.royalsocietypublishing.orgDownloaded from
... Inverse modelling is a well-known top-down emission estimation method, which utilizes numerical models to inversely estimate emissions by linking observed and simulated concentrations of atmospheric compounds. Inverse emission modelling has a recognized potential for a more general usage in real-world validation of emission inventories (bottom-up) (Nisbet and Weiss 2010;Weiss and Prinn 2011;Leip et al. 2018), which, although based on internationally recognized formalism, leave room for semi-objective choices of emission factors. It is widely applied by the scientific community for different greenhouse gases from the global to facility scale (Bousquet et al. 2006;Stohl et al. 2009Stohl et al. , 2010Brunner et al. 2012; Thomson and Wilson 2012;Pétron et al. 2014;Bergamaschi et al. 2015;Fang and Michalak 2015;Henne et al. 2016;Rust et al. 2022). ...
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... Moreover, the word 'uncertainties' appeared 27 times. The presence of this term may be related to critiques regarding the uncertainties involved in inventories since the current methods are not capable of accounting for all emissions, mainly due to data limitations and the fact that cities are open systems with impacts that far surpass their administrative boundaries (Weiss and Prinn, 2011;Jonas et al., 2019;Cai et al., 2019). It is, therefore, not surprising that using different methods could lead to different estimates. ...
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... Understanding the impact and effectiveness of policies on the atmospheric abundance of these gases is vitally important to policy makers to demonstrate the effectiveness of their policies. "Top-down" approaches to estimate emissions have been demonstrated for many different gases (Nisbet and Weiss, 2010;Weiss and Prinn, 2011;Lunt et al., 2015;Bergamaschi et al., 2015;Say et al., 2021) using high-quality, highfrequency atmospheric measurement data and inverse mod-elling to provide an alternative and complementary method to the traditional bottom-up method. This type of independent emissions verification is considered good practice by the Intergovernmental Panel on Climate Change (IPCC, 2006(IPCC, , 2019, as it assists inventory compilers by identifying inconsistencies between the two approaches (Say et al., 2016) and thereby has the potential to improve the accuracy and reduce the uncertainty of the nationally reported inventories. ...
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... To derive a more robust confirmation of emissions, efforts to narrow down the top-down modelling methods to subcontinental, regional or country scale have been made (e.g. Weiss et al., 2011;Brunner et al., 2017;Bergamaschi et al., 2018). However, an important limitation in the development of top-down methods for subregional scale is 100 the sparsity of regional observations. ...
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... The first was filled on 7 June, at 9.2 km altitude, over Czechia, and second on 12 June, at 7.6 km, during the downward profile over the Po Valley. The potential source of these two observations might be worth investigating, especially in light of the constant atmospheric increase of the SF 6 , despite substantial efforts to curb emissions of this potent greenhouse gas (Weiss and Prinn, 2011). Some attention was also given to molecular hydrogen (H 2 ) due to its potential feedbacks to the atmosphere oxidative capacity and stratospheric ozone levels (see Batenburg et al., 2012, and references therein). ...
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Halocarbons contribute to global warming and stratospheric ozone depletion. They are emitted to the atmosphere by various anthropogenic activities. To determine Swiss national halocarbon emissions, we applied top-down methods, which rely on atmospheric concentration observations sensitive to the targeted emissions. We present 12 months (September 2019 to August 2020) of continuous atmospheric observations of 28 halocarbons from a measurement campaign at the Beromünster tall tower in Switzerland. The site is sensitive to the Swiss Plateau, which is the most densely populated area of Switzerland. Therefore, the measurements are well suited to derive Swiss halocarbon emissions. Emissions were calculated by two different top-down methods, i.e. a tracer ratio method (TRM), with carbon monoxide (CO) as the independent tracer, and a Bayesian inversion (BI), based on atmospheric transport simulations using FLEXPART–COSMO. The results were compared to previously reported top-down emission estimates, based on measurements at the high-Alpine site of Jungfraujoch, and to the bottom-up Swiss national greenhouse gas (GHG) inventory, as annually reported to the United Nations Framework Convention on Climate Change (UNFCCC). We observed moderately elevated concentrations of chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), both banned from production and consumption in Europe. The corresponding emissions are likely related to the ongoing outgassing from older foams and refrigerators and confirm the widespread historical use of these substances. For the major hydrofluorocarbons (HFCs), HFC-125 (CHF2CF3) and HFC-32 (CH2F2), our calculated emissions of 100 ± 34 and 45 ± 14 Mg yr−1 are in good agreement with the numbers reported in the Swiss inventory, whereas, for HFC-134a (CH2FCF3), our result of 280 ± 89 Mg yr−1 is more than 30 % lower than the Swiss inventory. For HFC-152a (CH3CHF2), our top-down result of 21 ± 5 Mg yr−1 is significantly higher than the number reported in the Swiss inventory. For the other investigated HFCs, perfluorocarbons (PFCs), SF6 and NF3, Swiss emissions were small and in agreement with the inventory. Finally, we present the first country-based emission estimates for three recently phased-in, unregulated hydrofluoroolefins (HFOs), HFO-1234yf (CF3CF=CH2), HFO-1234ze(E) ((E)-CF3CH=CHF), and HCFO-1233zd(E) ((E)-CF3CH=CHCl). For these three HFOs, we calculated Swiss emissions of 15 ± 4, 34 ± 14, and 7 ± 1 Mg yr−1, respectively.
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Full-text available
National Greenhouse Gas Inventories (GHGI) are submitted annually to the United Nations Framework Convention on Climate Change (UNFCCC). They are estimated in compliance with Intergovernmental Panel on Climate Change (IPCC) methodological guidance using activity data, emission factors and facility-level measurements. For some sources, the outputs from these calculations are very uncertain. Inverse modelling techniques that use high-quality, long-term measurements of atmospheric gases have been developed to provide independent verification of national GHGI. This is considered good practice by the IPCC as it helps national inventory compilers to verify reported emissions and to reduce emission uncertainty. Emission estimates from the InTEM (Inversion Technique for Emissions Modelling) model are presented for the UK for the hydrofluorocarbons (HFCs) reported to the UNFCCC (HFC-125, HFC-134a, HFC-143a, HFC-152a, HFC-23, HFC-32, HFC-227ea, HFC-245fa, HFC-43-10mee and HFC-365mfc). These HFCs have high Global Warming Potentials (GWPs) and the global background mole fractions of all but two are increasing, thus highlighting their relevance to the climate and a need for increasing the accuracy of emission estimation for regulatory purposes. This study presents evidence that the long-term annual increase in growth of HFC-134a has stopped and is now decreasing. For HFC-32 there is an early indication its rapid global growth period has ended, and there is evidence that the annual increase in global growth for HFC-125 has slowed from 2018. The inverse modelling results indicate that the UK implementation of European Union regulation of HFC emissions has been successful in initiating a decline in UK emissions in the since 2018. Comparison of the total InTEM UK HFC emissions in 2020 with the average from 2009–2012 shows a drop of 35 %, indicating progress toward the target of a 79 % decrease in sales by 2030. The total InTEM HFC emission estimates (2008–2018) are on average 73 (62–83) % of, or 4.3 (2.7–5.9) Tg CO2-eq yr−1 lower than, the total HFC emission estimates from the UK GHGI inventory. There are also significant discrepancies between the two estimates for the individual HFCs.
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Nitrogen trifluoride (NF3) can be called the missing greenhouse gas: It is a synthetic chemical produced in industrial quantities; it is not included in the Kyoto basket of greenhouse gases or in national reporting under the United Nations Framework Convention on Climate Change (UNFCCC); and there are no observations documenting its atmospheric abundance. Current publications report a long lifetime of 740 yr and a global warming potential (GWP), which in the Kyoto basket is second only to SF6. We re-examine the atmospheric chemistry of NF3 and calculate a shorter lifetime of 550 yr, but still far beyond any societal time frames. With 2008 production equivalent to 67 million metric tons of CO2, NF3 has a potential greenhouse impact larger than that of the industrialized nations' emissions of PFCs or SF6, or even that of the world's largest coal-fired power plants. If released, annual production would increase the lower atmospheric abundance by 0.4 ppt, and it is urgent to document NF3 emissions through atmospheric observations.
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Nitrous oxide (N2O) is an important ozone-depleting gas and greenhouse gas with multiple uncertain emission processes. Global nitrous oxide observations, the Model of Atmospheric Transport and Chemistry (MATCH) and an inverse method were used to optimally estimate N2O emissions from twelve source regions around the globe. MATCH was used with forecast center reanalysis winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from 1 July 1996 to 30 June 2006. The average concentrations of N2O in the lowest four layers of the model were then compared with the monthly mean observations from four national/international networks measuring at 65 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal cycles. The inverse method was then used to solve for the time-averaged regional emissions of N2O for two time periods (1 January 1997 to 31 December 2001 and 1 January 2002 to 31 December 2005). The best estimate inversions assume that the model stratospheric destruction rates, which lead to a global N2O lifetime of 125 years, are correct. It also assumes normalized emission spatial distributions within each region from Bouwman et al. (1995). We conclude that global N2O emissions with 66% probability errors are 16.3-1.2 +1.5 and 15.4-1.3 +1.7 TgN (N2O) a-1, for 1997-2001 and 2001-2005 respectively. Emissions from the equator to 30°N increased significantly from the initial Bouwman et al. (1995) estimates while emissions from southern oceans (30°S-90°S) decreased significantly. The quoted uncertainties include both the measurement errors and modeling uncertainties estimated using a separate flexible 12-box model. We also found that 23 +/- 4% of the N2O global total emissions come from the ocean, which is slightly smaller than the Bouwman et al. (1995) estimate. For the estimation of emissions from the twelve model regions, we conclude that, relative to Bouwman et al. (1995), land emissions from South America, Africa, and China/Japan/South East Asia are larger, while land emissions from Australia/New Zealand are smaller. Our study also shows a shift of the oceanic sources from the extratropical to the tropical oceans relative to Bouwman et al. (1995). Between the periods 1997-2001 and 2002-2005, emissions increased in China/Japan/South East Asia, 0°-30°N oceans, and North West Asia and decreased in Australia/New Zealand, 30°S-90°S oceans, 30°N-90°N oceans, and Africa. The lower tropical ocean emissions in 1997-2001 relative to 2002-2005 could result from the effects of the 1997-1998 El Nino in the earlier period.
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An initial application of a new inverse method for the estimation of flux strengths of long-lived atmospheric trace gases is presented. CFCl3 is studied using the Australian National University's Chemical Transport Model. Unit-pulse responses are derived from the model, and used to identify a time-varying state-space model of tropospheric CFCl3. This in turn is used in a Kalman filter to perform two input-estimation studies. The first uses model generated measurements to estimate known flux strengths. This demonstrates the robustness of the method, although it is found that instantaneous stratospheric loss rates are not well-estimated using only surface concentration measurements. Emissions however, are robustly estimated. A time-averaged tropospheric lifetime can be estimated however, with an accuracy of ±5 years. Thirteen years of Atmospheric Lifetime Experiment/ Global Atmospheric Gases Experiment CFCl3 measurements are used in the second flux estimation experiment. The estimated fluxes though are outside known physical limits for CFCl3, and it is concluded that either a more accurate/appropriate transport model, or more measurement locations, are needed to obtain useful information regarding regional tropospheric CFCl3 fluxes.
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The international concern following the discovery of Antarctic stratospheric ozone depletion has prompted unprecedented international action by governments to control the production, sales and usage of a range of ozone-depleting chemicals. These international treaty obligations include the Montreal Protocol and its London and Copenhagen Amendments. They address, amongst many halocarbon species, the chlorofluorocarbons: CFC-11, -12 and -113 and the chlorocarbons: carbon tetrachloride and methyl chloroform. These chemicals have been routinely monitored at the remote, baseline monitoring station at Mace Head on the Atlantic Ocean coast of Ireland as part of the GAGE/AGAGE programme. The available monitoring data for the period 1987-1996 are presented here with a view to confirming the extent of compliance with the above Protocols on a global and European basis. Daily wind direction sectors provided by EMEP are used to sort the halocarbon data into northern hemisphere baseline air and European polluted air masses and trends have been determined for each wind direction sector. Evidence of the European phase-out of halocarbon usage is clearly apparent in the sorted halocarbon concentrations. A simple climatological long-range transport and a sophisticated Lagrangian air parcel dispersion model have been used to interpret the Mace Head halocarbon measurements and to derive estimates of European emission source strengths for each year. These emission source strengths confirm that the phase-out of halocarbon manufacture and sales is being followed in Europe.
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A wide range of scientific questions regarding the biogeochemical cycles involve determination of the sources and sinks of chemical species at regional to global scales. A powerful method, applicable to determination of either surface or internal sources or sinks, involves solution of an inverse problem in which the observables are Lagrangian line integrals and the unknowns are the integrands. The inverse problem of interest consists of determining an "optimal" estimate in the Bayesian sense of the unknowns from imperfect concentration measurements over space and time. The unknowns are arrayed in a "state" vector xt and the measurement errors are arrayed in a "noise" vector. Approximating the line integral by a summation leads to the observed concentrations being expressed as the noise vector plus a matrix of "partial derivatives" (H) multiplied by xt. H expresses the sensitivity of the chemical transport model concentrations to changes in the state vector elements. Given the discrete time series nature of many tracer measurements it is convenient (but not essential) to solve for xt using a discrete recursive optimal linear filter such as the discrete Kaiman filter (DKF). The DKF has the specific useful property that it provides an objective assessment of the uncertainty in estimates of xt as each measurement is used and thus of the usefulness of each measurement. Application of the DKF requires a chemical transport model to compute H. While our derivation of the measurement equation uses Lagrangian concepts, H can be equally well derived using an Eulerian chemical transport model. Several intuitive concepts exist regarding the important effects of observational errors and chemical transport model errors on the value of the observations in improving and lowering the errors in the state vector estimates. These concepts are illustrated through estimation of the lifetime of the trace gas trichloroethane in the atmosphere using twenty years of observations. Application of optimal linear filtering requires careful attention to both the physics of the problem expressed in the "measurement" and "system" (or "state-space") equations or models, and the sources and nature of the errors in the observations and chemical transport model.
Article
Our understanding of biogeochemical cycles is directly related to the research and analyses we bring to them, and to our critical sense of the methods we use. Of primary importance here are the theories behind, and our use of, inverse methods and data assimilation techniques across a multidisciplinary context that includes atmosphere-biosphere and atmosphere-ocean interactions, atmospheric chemistry, physical oceanography, and ocean biogeochemistry. Certainly, researchers in many earth system science disciplines, whether novice or expert, will benefit from the breadth and depth we have brought to the topics discussed in this monograph. At the same time, we have provided readers with a unique opportunity to enhance their research acumen and their understanding of ways and means. Toward this end, this monograph consists of a tutorial section with problem sets for use by readers who wish to test and enhance their knowledge, and a research section that showcases some of the latest scientific results from use of the aforementioned methods in a variety of earth science disciplines.
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Thirteen years of Atmospheric Lifetime Experiment/Global Atmospheric Gases Experiment CCl{sub 3}F and CCl{sub 2}F{sub 2} measurements at five remote, surface, globally distributed sites are analyzed. Comparisons are made against shipboard measurements by the Scripps Institution of Oceanography group and archived air samples collected at Cape Grim, Tasmania, since 1978. CCl{sub 3}F in the lower troposphere was increasing at an average rate of 9.2 ppt/yr over the period July 1978 to June 1988. CCl{sub 2}F{sub 2} was increasing at an average 17.3 ppt/yr in the lower troposphere over the same period. However, between July 1988 and June 1991 the increases of CCl{sub 3}F and CCl{sub 2}F{sub 2} in this region have averaged just 7.0 ppt/yr and 15.7 ppt/yr, respectively. The rate of increase has been decreasing 2.4 ppt/yr{sup 2} and 2.9 ppt/yr{sup 2} over this 3-year period. Based on a recent scenario of the global releases of these compounds and using the new calibration scale SIO 1993, the equilibrium lifetimes are estimated to be 44{sub -10}{sup +17} and 180{sub -81}{sup +820} years for CCl{sub 3}F and CCl{sub 2}F{sub 2}, respectively. Using these lifetime estimates and a two-dimensional model, it is estimated that global releases of these two chlorofluorocarbons in 1990 were 249 {+-} 28 x 10{sup 6} kg for CCl{sub 3}F and 366 {+-} 30 x 10{sup 6} kg for CCl{sub 2}F{sub 2}. It is also estimated that combined releases of these chlorofluorocarbons in 1990 were 21 {+-} 5% less than those in 1986. 34 refs., 12 figs., 9 tabs.
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Development of ¹C analysis with precision better than 2{per_thousand} has the potential to expand the utility of ¹CO measurements for carbon cycle investigations as atmospheric gradients currently approach traditional measurement precision of 2-5{per_thousand}. The AMS facility at the Center for Accelerator Mass Spectrometry, Lawrence Livermore National Laboratory, produces high and stable beam currents that enable efficient acquisition times for large numbers of ¹C counts. One million ¹C atoms can be detected in approximately 25 minutes, suggesting that near 1{per_thousand} counting precision is economically feasible at LLNL. The overall uncertainty in measured values is ultimately determined by the variation between measured ratios in several sputtering periods of the same sample and by the reproducibility of replicate samples. Experiments on the collection of one million counts on replicate samples of CO extracted from a whole air cylinder show a standard deviation of 1.7{per_thousand} in 36 samples measured over several wheels. This precision may be limited by the reproducibility of Oxalic Acid I standard samples, which is considerably poorer. We outline the procedures for high-precision sample handling and analysis that have enabled reproducibility in the cylinder extraction samples at the
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This book describes mathematical techniques for interpreting measurements of greenhouse gases in order to learn about their sources and sinks. The majority of the book gives general descriptions of techniques, but the last third covers the applications to carbon dioxide, methane, chlorofluorocarbons and other gases implicated in global change.