Introduction
The atmospheric CH4 abundance has more than doubled over the industrial
era. The resulting radiative forcing is second after CO2 in terms of
anthropogenic forcing from greenhouse gases (Myhre et al., 2013). High
uncertainty remains regarding the contributions from specific source sectors
and regions to the CH4 emissions (Neef et al., 2010; Kirschke et al.,
2013; Houweling et al., 2014; Melton et al., 2013; Bruhwiler et al., 2014;
Schwietzke et al., 2014; Bridgham et al., 2013; Pison et al., 2009; Ciais et
al., 2013), the underlying factors contributing to observed trends
(Dlugokencky et al., 2009, 2003; Wang et al., 2004; Kai et al., 2011; Aydin
et al., 2011; Simpson et al., 2012; Bousquet et al., 2006, 2011; Pison et
al., 2013; Bergamaschi et al., 2013; Monteil et al., 2011; Ghosh et al.,
2015; Nisbet et al., 2014; Fiore et al., 2006; Levin et al., 2012), and in
feedbacks from the biosphere and permafrost (Bridgham et al., 2013; Melton et
al., 2013; Isaksen et al., 2011; O'Connor et al., 2010). The uncertainties in
our understanding of current budgets, recent trends, and feedbacks limit
confidence in accurately projecting the future evolution of CH4.
Increasing atmospheric CH4 would accelerate near-term warming, due to
its strong climate impact on a 20-year time frame (Myhre et al., 2013).
Enhanced CH4 levels would also increase the ozone levels in surface air
(Fiore et al., 2008, 2012; West and Fiore, 2005; Isaksen et al., 2014), and
thereby worsen air pollution impacts on vegetation, crops, and human health.
This study seeks to increase our understanding of CH4 by providing a
detailed analysis on global and regional CH4 evolution over the last
40 years. We investigate essential natural and anthropogenic drivers
controlling the atmospheric CH4 budget over the period, with a
particular focus on the last 15 years. We perform a balanced analysis of both
sources and sinks. The sinks depend on the atmospheric oxidation capacity,
which is determined by complex chemical and meteorological interactions. This
study tries to reveal the key chemical components and meteorological factors
affecting recent changes in the oxidation capacity. We compare model studies
and observations to understand causes for both long-term trends and
short-term variations (year-to-year). We also address reasons for differences
between observed and modelled CH4 trends. The methods used are described
in Sect. 2. Section 3 presents the results from our main analysis and discuss
them in a broader context related to findings from other studies. Additional
sensitivity studies are presented in the Supplement. In Sect. 4 we summarize
our findings.
Emissions used in the model simulations. The grey shaded area is
the total CH4 emissions (left y axis). The total emissions in the
alternative extrapolation accounting for the financial crisis are shown from
2006 and onwards as the grey line with markers. The other coloured lines are
the CH4 emissions from the main emission sectors (right y axis).
The methane evolution and decisive factors over the period
1970–2012
Global methane budget
Figure 2 shows the evolution of the CH4 budget over the period
1970–2012 for the main simulation. It presents total burden and loss
calculated by the forward CTM run and the emissions applied in this
simulation. The total burden shown in black is balanced by the emissions
(blue) and the loss (red). There is a steady growth in atmospheric CH4
burden from 1970 to the beginning of the 1990s, then a short period of
decline after the Mount Pinatubo volcanic eruption in 1991. After 1994 there
is a slight increase in CH4 burden towards the millennium. Then the
CH4 burden is stable for 5–6 years. After 2006 there is a rapid growth
in CH4 burden.
The evolution of emissions and the modelled CH4 burden share many common
features (Fig. 2). However, the growth in emissions is about 35 % from
1970 to 2012, while the growth in atmospheric burden is about 15 %
(additional burden increase after 2012 due to the long response time of
CH4, is not accounted for in this number). The CH4 burden increased
less than expected solely from the increase in CH4 emissions since a
growth in the atmospheric CH4 loss occurred over the period. The growth
in instantaneous atmospheric CH4 loss is almost 25 %. In the period
2001–2006 when emissions were quite stable increasing CH4 loss likely
contributed to the stagnation of the CH4 growth. Interestingly, for
2010–2012, the loss deviates from its steady increase over the previous
decades. A stabilization of the CH4 loss probably contributed to the
continuing increase (2009–2012) in CH4 burden after the high emission
years 2007 and 2008. Due to the long response time of CH4 this change in
the loss pattern might also contribute to future growth in CH4. However,
there are additional uncertainties in the model burden and loss after 2009
due to the extrapolation of emissions after this year.
Global mean surface CH4 mixing ratio in the main model
simulation compared to global mean surface CH4 mixing ratio calculated
from the global networks AGAGE
(http://agage.eas.gatech.edu/data_archive/global_mean/global_mean_md.txt), NOAA ESRL
(http://www.esrl.noaa.gov/gmd/ccgg/mbl/data.php), and WDCGG (http://ds.data.jma.go.jp/gmd/wdcgg/pub/global/globalmean.html).
Especially after 1997 and the introduction of variation in meteorology, we
see that the loss follows a different path than the burden. Comparing the
main model simulation with the one with fixed meteorology (Fig. 3) for the
period 1997–2012 it becomes evident that inclusion of varying meteorological
factors is important to take into account to understand the development of
the CH4 budget. This was also shown in other studies (Johnson et al.,
2002; Fiore et al., 2006; Warwick et al., 2002; Holmes et al., 2013). If
there had been no variation in meteorology and only changes in emissions, the
CH4 loss would have been significantly different and there would have
been a stronger increase in CH4 burden after 2006. Meteorological
variability explains to a large degree much of the stabilization of CH4
loss after 2010, and might thereby explain part of the large CH4 burden
increase in 2011 and 2012. Around the millennium we see a stabilization of
the loss in the simulation with fixed meteorology, but increased loss in the
main run. This implies that meteorological variations contribute to a
prolonged period (2003–2006) of stabilization in CH4 burden (Fig. 3).
From the comparison in Fig. 3 it can also be seen that it is meteorological
factors and not emissions that cause the large enhancements of CH4 loss
in 1998 (El Niño event) and 2010 (warm year on global scale). Such
episodes do not show up as immediate perturbations of the CH4 burden
(Figs. 2 and 3) due to the long response time of atmospheric CH4.
Meteorology and other drivers for the modelled evolution of methane loss are
discussed in detail in Sects. 3.5–3.6.
Evolution of global mean surface methane
Figure 4 compares the global mean surface CH4 in the main model
simulation, to global mean surface CH4 calculated from networks of
surface stations. The main picture is discussed in this section while more
detailed evaluations of CH4 development on continental scale, trends,
and inter-annual variations are made in the following sections. The time
evolution of global mean surface CH4 is very similar for the three
observational networks shown in Fig. 4 but there are some differences for the
absolute methane level. The AGAGE (mountain and coastal sites) and NOAA ESRL
(sites in the marine boundary layer) stations are distant from large
pollution sources. WDCGG uses curve fitting and data extension methods very
similar to those developed by NOAA and many of the same stations (Tsutsumi et
al., 2009), but in addition to marine boundary layer sites, WDCGG includes
many continental locations strongly influenced by local sources and sinks
(http://www.esrl.noaa.gov/gmd/ccgg/mbl/mbl.html). The methane emission
estimates from Bousquet et al. (2011) are optimized against atmospheric
observations. Since we only use their natural and biomass-burning emission
inventories, we use different anthropogenic emissions (from EDGAR), and the
OH field in their inverse model is substantially different from our modelled
OH, there is no guarantee that our model will match observations.
Our model generally reproduces the different periods of growth and stagnation
and the overall observed increase in concentration from 1984 to 2012 of
almost 180 ppb is replicated. This gives us confidence when evaluating the
decisive drivers explaining the variable evolution over time. However, the
model fails to reproduce the strength of the growth rate during some eras,
for instance the growth since 2006 is overestimated. Over the whole period
the model also underestimate the observed CH4 level. Even though there
are also large uncertainties in total CH4 emission levels (Kirschke et
al., 2013; Ciais et al., 2013), we find it more likely that our model
overestimates the atmospheric CH4 sink. In a recent model
inter-comparison, the multi-model global mean CH4 lifetime was
underestimated by 5–13 % (Naik et al., 2013) compared to observational
estimates. Our study shows a similar underestimation of CH4 lifetime.
Though the multi-model lifetime is within the uncertainty range of
observations, it is likely that models tend to overestimate OH abundances in
the Northern Hemisphere (Naik et al., 2013; Strode et al., 2015; Patra et
al., 2014).
Location of the 18 surface stations used in comparison between
measurements and model in this section. Blue: stations in the Southern
Hemisphere; orange: stations in or near North America; green: stations in or
near Europe; red: stations in or near Asia.
Methane evolution and emission drivers in different regions
In the Supplement, we explain how the CH4 mole fraction can be split
into two components: a quite uniform background component and an
inhomogeneous recently emitted component. The latter is advected and mixed,
and when achieving a good mixing (after 1–2 months) it is converted into the
background component. We show how the use of a 1-month e-folding fictitious
tracer (total tracer) is valid as a proxy for the inhomogeneous component.
The CH4 surface emissions act as the sources for the tracer. In the
Supplement we use the continuity equation for the CH4 mole fraction
(CH4 model) as starting point and further arguments to derive the
following approximation:
<CH4model>-[<CH4model>]=B×(<total tracer>-[<total tracer>])+residual,
where [ ] denotes longitudinal mean along a whole terrestrial parallel and <> denotes annual running mean. We are interested in the
inter-annual variation of CH4, so we have carried out annual running
means to remove the strong seasonal cycle. The subtraction of longitudinal
means on each side of Eq. (1) removes the influence of differences in
lifetimes (the mean lifetime of CH4 is around 9 years, whereas the mean
lifetime of the total tracer is 1 month). B and “residual” are constants (or
almost constant) if the prerequisites discussed in the Supplement (Sect. S3, last
paragraph) are met. We expect B to be near or equal to 1 and residual to
be small. If B and residual were exactly constant, the Pearson linear
correlation coefficient between <CH4model>-[<CH4model>] and <total tracer>-[<total tracer>] would be exactly equal to 1. The tracer approach then gives
valuable information concerning the contribution to CH4 variation from recent
regional–local emission or transport changes. We therefore use the
correlation coefficient (indeed, its square, R2: the coefficient of
determination obtained when performing a linear least-square fit between both
magnitudes in Eq. 1 to determine B and residual) as one criterion when
selecting interesting stations for methane trend studies. Only stations where
R2 is higher than 0.5 is used. This criterion excludes only a small
number of the available stations. In addition, we use the general station
selection criteria discussed earlier in the manuscript (sufficient coverage
in the different world regions, long time series etc., see Sect. 2.3).
Figure 5 shows the locations of stations used in Figs. 6–10 for detailed
trend analysis and evaluation of model performance.
Coefficient of determination (R2) between <CH4model>-[<CH4model>] and <total tracer>-[<total tracer>] for stations
shown in Figs. 5–10. Parameters for Eq. (1) and RMSE for a linear fit
between <CH4model>-[<CH4model>] and <total tracer>-[<total tracer>].
Station
Figure
R2 between <CH4model>
residual
B
RMSE
-[<CH4model>] and
<total tracer>-[<total tracer>]
Ascension Island
6a
0.80
-3.01
1.21
0.74
Tutuila
6b
0.87
5.08
1.49
0.82
Cape Grim
6c
0.98
-0.15
0.97
0.05
Ushuaia
6d
0.83
-0.27
0.94
0.09
Alert
7a
0.69
-2.16
1.66
0.85
Wendover
7b
0.54
-5.74
0.78
1.07
Key Biscayne
7c
0.95
6.10
1.38
1.40
Mauna Loa
7d
0.87
18.41
1.80
1.27
Zeppelinfjellet
8a
0.91
-1.67
1.13
0.59
Pallas–Sammaltun
8b
0.95
-3.38
1.18
0.75
Mace Head
8c
0.97
-3.28
1.16
0.56
Hegyhatsal
8d
1.00
-2.46
1.15
0.96
Sede Boker
9a
0.83
5.41
1.23
0.97
Ulaan Uul
9b
0.95
1.15
1.10
0.65
Sary Taukum
9c
0.97
-8.27
1.11
0.96
Tae-ahn Peninsula
9d
0.97
0.77
1.07
1.15
Cape Rama
10a
0.92
-9.60
1.24
1.02
Mahe Island
10b
0.85
6.68
1.42
1.22
Evolution of CH4 and tracers at stations (a: Ascension
Island, b: Tutuila, c: Cape Grim, d: Ushuaia) in the Southern Hemisphere.
Upper panel in each figure: comparison of monthly mean surface CH4 in
model and observations. The model results are scaled to the observed mean
CH4 level over the periods of measurements. Mid panels: variables from
Eq. (1). <> denotes annual running mean, [ ] denotes
longitudinal mean. Left y axis: <CH4model> and
[<CH4model>] are scaled down to be initialized
to zero in the first year. Right y axis: B×(<total tracer>-[<total tracer>]) and residual.
Lower panels: Evolution of various emission tracers, see Table S1 in the
Supplement for detailed information.
Table 2 shows R2, the constants B and residual, and RMSE from a linear
fit of the variables in Eq. (1). All stations except one (reason for
exception at the Wendover station is discussed in the Supplement) have
R2 above 0.8. Such high coefficients support that the approximation in
Eq. (1) is useful for these stations. As expected, B is usually larger than
1. The fictitious tracer will underestimate somewhat the inhomogeneous
recently emitted CH4, in particular at remote stations, because part of
it is removed by the e-folding sink before being smoothed to the
characteristic variation length of the background. Mauna Loa is probably the
most remote station and located at high altitude. It has the largest B and
residual. Alert, Tutuila, Mahe Island, and Key Biscayne are also remote
stations that have a high B. As explained below the tracers play a small
role in explaining CH4 at Cape Grim and Ushuaia, where B is below
1.
In the upper panels of Figs. 6–10, the model results are scaled to the
observed mean CH4 level over the periods of measurements to better
discern differences in trends between observations and model. The scaling
procedure is explained in the Supplement. In general, the model reproduces
the seasonal and year-to-year variations very well with high coefficients of
determination, R2, for most stations (the median is 0.76, and R2
is above 0.65 for 15 of 18 stations). The model performance is lower at
highly polluted sites due to large gradients in concentrations and
non-linearity of oxidant chemistry not fully captured by a global model with
coarse resolution (approximately 2.8∘ × 2.8∘). The
model also captures the long-term evolution of CH4 seen in the
observations but overestimates the increase after 2005 at most stations.
The stations in the Southern Hemisphere (Fig. 6) are located far from the
dominating emissions sources, and the CH4 concentration is to a large
degree determined by transport and chemical loss. The high coefficients of
determination ranging from 0.92 to 0.95 and reproduction of the seasonality
and trends indicate that our model is performing excellent with respect to
transport and seasonal variation in the chemical loss.
As seen in the mid panels, Ascension Island (Fig. 6a) and Tutuila (Fig. 6b)
have negative <total tracer>-[<total tracer>]. Since
these are rather remote stations, their tracer levels are below the
longitudinal mean. The modelled CH4 evolution from 1990 to 2005 is well
correlated with the development of the natural tracers. However, changes in
natural emissions do not seem to explain the periods with large growth before
1990 and for the period 2005–2012. While the model underestimates the growth
before 1990 it overestimates the growth in the recent years. The small steady
increases in contributions from all anthropogenic sectors only has a minor
contribution to the modelled CH4 increase for these periods. However,
since these source tracers have an e-folding lifetime of 1 month their
evolution is only representative for changes in contribution from regional
sources. Inter-hemispheric transport occurs on longer timescales; hence,
changes in large anthropogenic sources in the Northern Hemisphere most likely
also had a significant contribution as discussed below. At Ascension Island,
extra strong influences of regional sources (<CH4model>-[<CH4model>] change different from
zero) are mainly associated with El Niño episodes (1987, 1997–1998, and
2004–2005). In the 1997–1998 period, there are peaks both for the natural tracer and <total tracer>-[<total tracer>] indicating a rise in nearby
natural emissions and/or transport from such a source. For 1987 a regional
drop in natural emissions has a smaller impact at Ascension compared to the
whole latitude band. At Tutuila <total tracer>-[<total tracer>] decreases over time due to a relatively larger increase in the
latitudinal mean anthropogenic tracers (not shown), especially enteric
fermentation. This explains why the CH4 growth at the site (<CH4model>) is slightly less than the mean latitudinal
([<CH4model>]) growth.
Ushuaia (Fig. 6c) and Cape Grim (Fig. 6d) are the southernmost stations. In
the mid panels it can be seen that both terms on the right side in Eq. (1)
are small (B×(<total tracer>-[<total tracer>] and residuals) resulting in small (<CH4model>-[<CH4model>]). This
indicates that the contribution to CH4 from regional emissions are small
and that long-range transport from other latitudes is decisive. Distant
latitudinal transport is not seen by the tracer term if it takes more than
around 2 months. Such transport would also result in very similar
<CH4model> and [<CH4model>] since
atmospheric species with lifetime of that timescale or longer are quite
homogenously distributed over latitudinal bands. Since both the emissions and
their trends are small at high southern latitudes, the distant transport
likely originates from low latitudes in the Southern Hemisphere or the
Northern Hemisphere.
Evolution of CH4 and tracers at stations (a: Alert, b:
Wendover, c: Key Biscayne, d: Mauna Loa) in or near North America. See Fig. 6 caption for further description.
Evolution of CH4 and tracers at stations (a: Zeppelinfjellet,
b: Pallas–Sammaltun, c: Mace Head, d: Hegyhatsal) in or near Europe. See
Fig. 6 caption for further description.
Evolution of CH4 and tracers at stations (a: Sede Boker, b:
Ulaan Uul, c: Sary Taukum, d: Tae-ahn Peninsula) near Asian emission
sources. See Fig. 6 caption for further description.
Evolution of CH4 and tracers at stations (a: Cape Rama, b:
Mahe Island) in background/outflowing air in or near Asia. See Fig. 6
caption for further description.
At stations in or near North America (Fig. 7) the model reproduces the
observed trends with increases in the 1980s, less change in the period
1990–2005 and increase from 2006. For the latest period, the increase in the
model is larger than that observed. The seasonal and year-to-year variations
are well represented by the model at all stations (coefficients of
determination from 0.73 to 0.82). Key Biscayne (Fig. 7c) and Mauna Loa
(Fig. 7d) have relatively large negative <total tracer>-[<total tracer>] which shows that these are background stations and
that important emission sources exist at their latitude. The tracer
difference is quite small and negative at Alert (Fig. 7b) and since the
residual is quite close to zero, this may indicate small sources at the
station latitude. The contribution from natural emissions is decisive for
year-to-year variations at all four stations in Fig. 7, and the influence of
emission from the gas sector increases gradually. Key Biscayne situated in
the boundary layer (Fig. 7c) is mostly influenced by emissions from the
American continent, and the rest of the anthropogenic sectors have moderately
declining impact after 1990. However, this decline occurs only initially for
the solid fuel (mainly coal) sector as its contribution increases from 2003
and onwards. The same occurs for this sector at Alert (Fig. 7a). It
corresponds with the start of an increase in US fugitive solid fuel emissions
in the applied EDGAR v4.2 inventory. The increase in US coal emissions from
2003 to 2008 is almost 12 % in EDGAR v4.2. An increase of 28 % is
found from 2005 to 2010 in the EPA inventory (EPA, 2012). At the high altitude
sites Mauna Loa and Wendover (Fig. 7b and d) there are small or large
increases in the contribution from all anthropogenic sectors from the year 2000
and onwards. These stations are subject to efficient transport from Asia at
high altitudes. There are large emission increases after 2000 in eastern Asia
in the EDGAR v4.2 inventory (Bergamaschi et al., 2013). Especially coal
related emissions in China show a strong increase with a doubling from 2000
to 2008.
At Wendover, Mauna Loa and Key Biscayne <total tracer>-[<total tracer>] decrease over the 3 decades studied (Fig. 7, mid
panels). Several emission sectors contribute. The implication is a lower
growth rate for <CH4model> than for [<CH4model>] (Fig. 7, mid panels); i.e. other locations
(for Asian stations, see discussion below) at the same latitudes have a larger
trend in CH4. There are large fluctuations of tracer transport to Mauna
Loa in 1997–1998 and 2010–2011 that strongly impacts <CH4model>. The observations also show changes in growth
and seasonal pattern during these years.
At the Arctic site Zeppelin (Fig. 8a), located on the coast of western Svalbard,
there is a small CH4 increase both in model and observations up to 2004.
A large part of the CH4 variability in the period 1997–1999 (Morimoto
et al., 2006) was due to fluctuations in wetland and biomass-burning
emissions. Our modelled variation in the natural source tracer conforms to
the fluctuations deduced from the isotopic measurements of Morimoto et
al. (2006). Seasonal tracer analysis (not shown) is in agreement with the
conclusion of Fisher et al. (2011), who found that wetlands and gas are the main
contributors in summer and winter, respectively. A CH4 concentration drop from
2004 to 2006 seems to mainly be explained by natural source contribution in
the model falling from a period maximum in 2004 to low values in 2005–2006.
This is also the case for the sub-Arctic site Pallas (Fig. 8b) located in a
region characterized by forest and wetlands. Gas, enteric fermentation and
various other small regional anthropogenic sources seems to contribute to the
CH4 increase at Zeppelin after 2006. The contribution from natural
emissions and recent regional coal mining peaked in 2007. A quite strong
CH4 enhancement occurs for 2009–2010 in both the model and
observations. The longitudinal mean tracers for individual sectors are almost
stable to declining (not shown) while contribution from the <gas> and some other tracers show a small maximum (lower panel Fig. 8a
and b). Pallas has a similar pattern. The runs with fixed meteorology suggest
enhanced transport from Russia passing major gas fields and Pallas.
Mace Head (Fig. 8c) is a rural background coastal site in Europe. The result of <total tracer>-[<total tracer>] is quite large and
negative, suggesting important emission sources along the station's latitude. In the
beginning of the 1990s, there is a mismatch between declining model
concentrations and the increase found from the observations. Some of the
decrease in the model is due to decreasing contributions from solid fuel
(mainly coal), enteric fermentation and other regional anthropogenic sources.
The station experiences unusual meteorological conditions in the ENSO year
1997, as there are abrupt shifts in concentrations of CH4 and several of
the anthropogenic tracers having small year-to-year variations in emissions.
Similarly, there seems to be transport of less polluted air masses to the
station in 2004 compared to earlier years resulting in lower CH4
concentration in measurements and model in 2004 and 2005. Several regional
sources seem to have small contributions to the modelled and observed
CH4 increases from 2006 to 2009. After 2009 we extrapolate emission
trends due to lack of emission inventories and this may be the reason why the
model doesn't reproduce the observed levelling off in growth in 2010 and
2011.
The model has larger discrepancies at Hegyhatsal, a semi-polluted site in
central Europe (Fig. 8d). Despite seasonal issues the model performance is
reasonable for the long-term CH4 changes. In years with high
contributions from natural sources, the seasonal maxima tend to be too high
in the model. It could be that the coarse model resolution results in too
much transport from nearby wetlands or that the emission inventory has overly large natural emissions in surrounding regions. <total tracer>-[<total tracer>] is very large and
positive meaning that the station is very sensitive to emissions close
upwind. The evolution of <CH4model> therefore deviates
strongly from the longitudinal mean [<CH4model>]. The deviation starts in 1996 when a sharp increase in natural
emission occurs. From 2003 to 2008 there is a period with stable to declining
modelled CH4 concentrations. This is caused by decreasing central
European emissions particularly from enteric fermentation and the category
“other anthropogenic sectors” together with decreasing or fluctuating
natural sources.
In general, the model reproduces the features in the observations over and
near Asia quite well (Figs. 9 and 10) with coefficient of determination in
the range of 0.24–0.84. For the trends, the overestimation after 2006
is higher here than modelled in other world regions (Figs. 6–8). Gas is the
major cause of increases in CH4 in Israel (Sede Boker, Fig. 9a). The
increase of the <gas> tracer is much larger than for the
longitudinal mean [<gas>], suggesting important emission increases
from nearby gas fields. Small changes in regional natural emissions and in the
category “other anthropogenic sources” (lower panel) are correlated with the
modelled year-to-year variations (upper panel). The station in Kazakhstan
(Fig. 9c) is downwind of large sources (<total tracer>-[<total tracer>] large and positive), and the modelled CH4
increase after 2005 is much larger than for the longitudinal mean. Also at
this station, the CH4 trend is heavily influenced by gas, although not
to the same extent as in Israel. Other regional anthropogenic emission
changes also contribute somewhat to the modelled CH4 increase over recent years. High natural emissions in 2008–2009 also had an impact. Since we
use repetitive year-2009 natural emissions for the latter years, it could be
that the contribution from this source is too large after 2009.
Unfortunately, the modelled CH4 increase cannot be confirmed by
measurements since data at the station is missing after 2008.
Regional solid fuel emissions (mainly coal) is the main cause of last-decade-modelled CH4 increase in eastern continental Asia (Ulaan Uul and Tae-ahn
Peninsula, Fig. 9b and d), but gas and other reginal anthropogenic sectors
also contribute. There is large growth in <CH4model> for Ulaan Uul in 2006–2007 and 2010 mainly due to peaks in the
contribution from solid fuel sources, but also other anthropogenic sectors
have a role in this. Similar pattern appears for Tae-ahn Peninsula in 2009.
The first peak at Ulaan Uul is also partly seen in the observations, but the
existence of the latest episode and the event at Tae-ahn Peninsula is less
clear from the measurements. Our tracer analysis for Minamitori-shima (not
shown), a background station affected by outflow from the Asian continent
indicates less continental outflow in 2007. For these polluted continental
sites the correlation coefficients are lower than for the other stations. The
coarse resolution of the model has problems resolving large gradients in
concentrations and non-linearity of oxidant chemistry. At Tae-ahn Peninsula
<CH4model> starts increasing in 2005, while the increase
at Ulaan Uul first starts in 2006. At Ulaan Uul decreasing regional natural
emissions over the period 2000–2005 seems to compensate for the large
increase of solid fuel emissions from around 2000.
CH4 year-to-year variation (ppb) in surface CH4 in
model (a) compared to the levels of surface CH4 estimated from
observations (b) in various latitudinal bands based on the NOAA ESRL
network of surface stations (Ciais et al., 2013, and data set provided by
Edward J. Dlugokencky, personal communication, 2015).
For Cape Rama in India (Fig. 10a, the observations show signatures of both
Northern Hemispheric and Southern Hemispheric (NH and SH) air masses (Bhattacharya et
al., 2009). Mixed with regional fluxes and varying chemical loss, this results
in large seasonal variation. During the summer monsoon, the station is
located south of the inter-tropical convergence zone. Air arriving during
this period (June to September) represent tropical or SH oceanic air masses
and the station is upwind of Mahe Island (Fig. 10b). During the winter
monsoon the situation is opposite. There is outflow from the continent
affecting both Cape Rama and Mahe Island. The ENSO event in 1997 seems to
have opposite effects on modelled and observed CH4 variability at Cape
Rama. Despite that, the model does a reasonable job in reproducing the
measurements. Most regional tracers show stable to upward levels over the
period of comparison and likely contribute to a small fraction of the
modelled CH4 trend. At Mahe Island in the SH (Fig. 10b), the CH4
concentration peaks sharply during NH winter when the station is influenced
by outflow from continental Asia. The station is therefore an indicator of
inflow to the SH. This feature is well captured by the model. Over the last
decade, there is a small and continuous rise in the levels of all
anthropogenic tracers at the station. This coincides with large emission
increases in Asia, suggesting that the recent development in Asia has some
influence on the SH.
(Upper panel) Mean year-to-year growth (ppb yr-1) in surface
CH4 in Oslo CTM3 over the period 1998–2000. The 32 circles show the
observed growth rates over the same period. The stations picked for
comparison are based on the criteria described in Sect. 2.3, and only observation sites that have
measurements available for all months within the given time is included.
(a–f) Mean year-to-year growth ppb yr-1) of emission tracers in the
same period. (a) Natural (wetlands + other natural + biomass burning),
(b) enteric, (c) agricultural soils, (d) gas, (e) solid fuel, (f) the sum of all other
anthropogenic tracers.
Methane evolution and emission drivers over distinct time
periods
Figure 11 compares the latitudinal distribution of surface CH4 in the
model and observations. Generally, the model and the observational approach
reveal the same pattern and characteristics both in time and space, although
some clear differences are evident. From 1985 to the early 1990s, there is
a homogeneous growth in the observations (Fig. 11b). The model (Fig. 11a)
also has growth over the same period but a distinct period (1987–1988) with
no growth, corresponding to smaller emissions from wetlands and biomass
burning (Fig. 1). 1987–1988 were El Niño years, and there is a tendency
of low wetland emissions for those years, e.g. an anti-correlation between
wetland emissions and ENSO index (Hodson et al., 2011). One possibility is that our
applied emission inventory for natural CH4 sources (Bousquet et al.,
2011) has overly large variability in wetland emissions in the 1980s and
overly strong reductions in wetland emissions in 1987–1988. Bousquet et al. (2006)
state that bias in OH inferred from methyl chloroform (CH3CCl3)
observations (Bousquet et al., 2005) could account for some of the
variability that they attributed to wetland emissions. Later findings
(Montzka et al., 2011) support this. If OH changes are set to zero instead of
the large variability in the 1980s, suggested by early CH3CCl3
studies (Bousquet et al., 2005), the fluctuations in wetland emissions are
dampened by 50 %. On the other hand, the model simulation has no
year-to-year variation in meteorology before 1997, and the meteorology used
corresponds to the year 2001, which has a weak ENSO index. Therefore, during
the 1987–1988 El Niño, the meteorology used is less representative than
for other years with weaker ENSO. In the two periods of CH4 growth
before and after 1987–1988, the CH4 increase is strong in the model
(Fig. 11a) in the Northern Hemisphere and might be overestimated. However, it
might be that the model is able to better capture latitudinal gradients, as
only a few measurement sites are available to make latitudinal averages for
the 1980s. In 1992 and 1993 there is a pause in the CH4 growth in the
measurements (Fig. 11b) at all latitudes. This pause has been explained as a
consequence of the Mount Pinatubo volcanic eruption in 1991 (Dlugokencky et
al., 1996; Bekki and Law, 1997; Bânda et al., 2013). The eruption
results in an initial increase in the CH4 growth rate (less OH) lasting
for half a year. This is due to backscattering by volcanic stratospheric
aerosols, which reduces the UV radiation to the troposphere. After that, the
growth rate due to Pinatubo becomes negative (more OH plus less natural
methane emissions are the dominating effects) reaching a minimum after
2 years (1993), before levelling off towards zero after 5 years. The main
cause of the OH increase is reduction in stratospheric ozone allowing more UV
radiation to the troposphere. In contrast to the measurements the model shows
a stronger decrease in CH4 after the eruption, and the pause in CH4
growth is longer. This might be due the fact that the model does not fully
include all factors affecting CH4 related to the Mount Pinatubo
eruption. Reduced emissions are implicitly included in the natural CH4
emission inventories, but changes in meteorology (temperature, water vapour,
etc.) and volcanic SO2 and sulphate aerosols in the stratosphere, are
not accounted for in the simulations. In the period 1994–1997 the model
struggles to reproduce the latitudinal distribution of growth (Fig. 11). The
model seems to have overly large growth in the Tropics probably due to a small
but significant growth in wetland and biomass-burning emissions in the period
(Fig. 1).
(Upper panel) Mean year-to-year growth (ppb yr-1) in surface
CH4 in Oslo CTM3 over the period 2001–2006. The 25 circles show the
observed growth rates over the same period. The stations picked for
comparison is based on the criteria described in Sect. 2.3, and only observation sites that have
measurements available for all months within the given time is included.
(a–f) Mean year-to-year growth (ppb yr-1) of emission tracers in the
same period. (a) Natural (wetlands + other natural + biomass burning),
(b) enteric, (c) agricultural soils, (d) gas, (e) solid fuel, (f) the sum of all other
anthropogenic tracers.
In the next paragraphs, we study whether the model is able to reproduce
CH4 measurements when we split the time frame into shorter epochs that
measured distinct different growth rates. The splits are made within the
period 1998–2009 when our simulations have both inter-annual variation in
meteorology and complete emission data (no extrapolations made). We have only
included observation sites that have measurements available for all months
within the given time period, see Sect. 2.3 for details about data selection.
Figure 12 shows the modelled CH4 growth in the CTM in the period
1998–2000, compared to the observed changes at various sites. The model
seems to slightly underestimate increases at several stations. The largest
underestimation occur in eastern Asia. In parts of eastern Asia and some
other regions in the Northern Hemisphere there are declines in modelled
CH4 concentrations caused by decreased contribution from several
anthropogenic sectors. Increased emissions from gas fields in Russia, the Middle East, and in several anthropogenic tracers over India explain why these
are the regions in the Northern Hemisphere with largest modelled CH4
increase.
Earlier studies find that a low CH4 growth rate in the 1990s is
mostly caused by lower fugitive fossil fuel emissions from oil and gas
industries, mainly due to the collapse of the Soviet Union (Bousquet et al.,
2006; Simpson et al., 2012; Dlugokencky et al., 2003; Aydin et al., 2011).
Another important factor is decreased emissions from rice paddies. Lower
emissions from agricultural soils last until around the year 2000 in the EDGAR
v4.2 inventory (Fig. 1) and are also evident in Fig. 12c. Kai et al. (2011)
exclude fossil fuel emissions as the primary cause of the slowdown of
CH4 growth. According to their isotopic studies, it is more likely
long-term reductions in agricultural emissions from rice crops in Asia, or
alternatively another microbial source in the Northern Hemisphere that is the
major factor. Another isotope study (Levin et al., 2012) disagrees and finds
that both fossil and microbial emissions were quite stable.
Wetland and biomass burning sources seem to play the key role for the
variations in the model from 1997 to 2000 (Fig. 12a). They were particularly
large in 1998 due to the 1997–1998 El Niño (Chen and Prinn, 2006;
Simpson et al., 2002; Dlugokencky et al., 2001; Bousquet et al., 2006; Pison
et al., 2013; Spahni et al., 2011; Hodson et al., 2011). Simpson et
al. (2002) also conclude that the increase in observed surface CH4
between 1996 and 2000 was driven primarily by a large growth in 1998. Both
model and measurements have the strongest growth (Fig. 12) in the Southern
Hemisphere, which had large wetland emissions in 1998 (Bousquet et al., 2006;
Dlugokencky et al., 2001). In the model, slowly rising anthropogenic
emissions in the Southern Hemisphere also seems to contribute (Fig. 12b–f).
Natural emissions (Fig. 12a) are also important for the irregular pattern
seen at mid-to-high northern latitudes. This is expected due to the 1997–1998
ENSO-event, showing a dip in high northern wetland emissions in 1997 followed
by unusual large emissions in 1998 (Bousquet et al., 2006; Dlugokencky et
al., 2001). During the ENSO event, the zonal pattern in the model and
measurements (Fig. 11) is very similar for the Southern Hemisphere but there
are larger differences for the Northern Hemisphere.
(Upper panel) Mean year-to-year growth (ppb yr-1) in surface
CH4 in Oslo CTM3 over the period 2007–2009. The 36 circles show the
observed growth rates over the same period. The stations picked for
comparison are based on the criteria described in Sect. 2.3, and only observation sites that have
measurements available for all months within the given time is included.
(a–f) Mean year-to-year growth (ppb yr-1) in mole fraction of emission
tracers in the same period. (a) Natural (wetlands + other natural + biomass
burning), (b) enteric, (c) agricultural soils, (d) gas, (e) solid fuel, (f) the
sum of all other anthropogenic tracers.
During 2000–2006 the CH4 growth levelled off and there was a period
with stagnation in global mean growth rate (Fig. 13). The agreement between
the zonal averages from the model and the measurement approach is excellent,
both with regards to timing and strength of the growth (Figs. 11 and 13). The
2002–2003 anomaly in the Northern Hemisphere is captured by the model
(Fig. 11) and explained by enhanced emissions from biomass burning in
Indonesia and boreal Asia (Bergamaschi et al., 2013; Simpson et al., 2006;
van der Werf et al., 2010).
The EDGAR v4.2 inventory applied here and in other studies (e.g. Bergamaschi
et al., 2013) show that global anthropogenic emissions rise substantially,
especially in Asia after the year 2000. This increase in the anthropogenic
emissions is compensated by a drop in northern tropical wetland emissions
associated with years of dry conditions (Bousquet et al., 2006, 2011).
Monteil et al. (2011) find that moderate increases in anthropogenic
emissions and decreased wetland emissions together with moderate increasing
OH can explain the stagnation in CH4 growth from 2000. Bergamaschi et
al. (2013), assuming constant OH, also finds a decrease in wetland emissions
but that a large increase in anthropogenic emissions first occurs from 2006
and beyond. Uncertainty in wetland emissions in the period is well
illustrated by Pison et al. (2013). Using different methods to estimate
global wetland emissions from 2000 to 2006, Pison et al. (2013) finds either a
decrease or an increase. On the other hand, increase in both wetland and anthropogenic
emission would not conform to the observed stable global mean CH4 levels
in this period. Spahni et al. (2011) found a small decrease in wetland
emissions from 1999–2004 followed by an increase from 2004 to 2008. Our
model results from simulations with declining natural emissions and
increasing anthropogenic emissions (Fig. 1) reproduce the measurements in
most regions (Fig. 13). Eastern Asian stations are exceptions. Gas and solid
fuels (coal) (Fig. 13d, e) are causing much of the modelled increases over
southern and eastern Asia. Since the observation at the eastern Asian
stations close to large anthropogenic sources show smaller changes it is
plausible that the emission growth is overly strong in the applied EDGAR v4.2
inventory, for this region. However, it is difficult to be conclusive since
the few observation sites available are situated in zones with sharp
gradients in modelled concentration changes. The EDGAR v4.2 emissions from
the region increase gradually between 2000 and 2008, with a larger growth
rate after 2002. Findings from Bergamaschi et al. (2013) question this as
they suggest a large increase mostly since 2006.
The period 2007 to 2009 is characterized by strong growth in observed global
mean growth rate and even stronger growth in the model (Figs. 11 and 14). The
model overestimation seems to occur almost everywhere. Due to the long
lifetime of CH4, strong increase in regional emissions has a global
impact. Increases in anthropogenic sources in Asia (e.g. Figs. 9,
14b–f), in particular, natural gas in the Middle East and solid fuel
(coal) in eastern Asia have large contributions. The influence from emission
increases in these regions can be seen at downwind stations over and near
northern America and in the Southern Hemisphere (Seychelles) (see Figs. 6
and 7). For the Southern Hemisphere a small steady increase in several
regional anthropogenic emissions also contributes. For the Arctic stations
the responsible sectors for the recent increase and their geographical origin
varies but high wetland emissions in 2007–2008, gas in Russia, and coal and
other anthropogenic emissions in Asia seem to play a central roles (Figs. 7,
8 and 14). For North America anthropogenic emissions increase in the central and
eastern US and decrease in the eastern parts (Fig. 14). A similar west–east
gradient is seen over the continent for natural sources but this is likely
temporary due to special conditions in 2007–2008. These factors, together
with the distant contributions from rising emissions in eastern Asia explain
the modelled CH4 trends. In central Europe there is a decline in
modelled CH4 due to a combination of declining emissions from enteric
fermentation, solid fuels (coal), and several other anthropogenic sectors
(Fig. 14b, d, f), and fluctuations in natural emissions (Fig. 14a). A
decrease over a small region of South America is mainly explained by
variations in natural emissions (Fig. 14a).
Other studies (Kirschke et al., 2013; Rigby et al., 2008; Bergamaschi et al.,
2013; Bousquet et al., 2011; Dlugokencky et al., 2009; Crevoisier et al.,
2013; Bruhwiler et al., 2014) attribute the resumed strong growth of observed
(Dlugokencky et al., 2009; Rigby et al., 2008; Frankenberg et al., 2011;
Sussmann et al., 2012; Crevoisier et al., 2013) global CH4 levels after
2006 to increases in both natural and anthropogenic emissions. However, the
share of natural vs. anthropogenic contribution varies in the different
studies. The studies agree that abnormally high temperatures at high northern
latitudes in 2007 and increased tropical rainfall in 2007 and 2008 resulted
in large wetland emissions these years. There is also a likely contribution
from forest fires in the autumn of 2006 due to drought in Indonesia
(Bergamaschi et al., 2013; Worden et al., 2013). Top-down (Bergamaschi et
al., 2013; Bousquet et al., 2006, 2011; Kirschke et al., 2013; Bruhwiler et
al., 2014) and bottom-up studies (EC-JRC/PBL, 2011; Schwietzke et al., 2014;
Höglund-Isaksson, 2012; EPA, 2012) suggest steady moderate to substantial
increases in anthropogenic emissions in the period 2007–2009. Much of this
is due to intensification of oil and shale gas extraction in the United
States and coal exploitation in China.
Evolution of yearly global average atmospheric instantaneous
CH4 lifetime in the main and fixed methane simulations (left y axis).
Evolution of yearly global average atmospheric OH concentration in the main
simulation (right y axis) using the reaction rate with CH4 as
averaging kernel.
Using the EDGAR v4.0 inventory as input to a CTM and observations of CH4
and its isotopic composition Monteil et al. (2011) led to the conclusion that a reduction
of biomass burning and/or of the growth rate of fossil fuel emissions is
needed to explain the observed growth after 2005. The differences between the
EDGAR v4.0 and EDGAR v4.2 used in this study are moderate. Other bottom-up
inventories (EPA, 2012; Höglund-Isaksson, 2012; Schwietzke et al., 2014)
report lower increases in anthropogenic emissions, see also comparison with
ECLIPSE emission in the Supplement. Using the mean of the EPA and EDGAR v4.2
inventory for anthropogenic emissions, Kirschke et al. (2013) find that
either is the increase in fossil fuel emissions overestimated by inventories,
or the sensitivity of wetland emissions to temperature and precipitation is
too large in wetland emission models. Schwietzke et al. (2014) and the
top-down studies by Bergamaschi et al. (2013) and Bruhwiler et al. (2014)
conclude that the EDGAR v4.2 emission inventory overestimates the recent
emission growth in Asia. This is especially the case for coal mining in
China. From our results above, it is plausible that overly high growth of fossil
fuel emissions, in particular in Asia, is the reason why the recent CH4
growth is higher in our model than for the observations. However, in 2007 and
2008 much of the increase in the model in the Northern Hemisphere is driven
by high natural wetland emissions. Our natural emissions are from Bousquet et
al. (2011) who attributes much of the 2007–2008 increase in total emissions
to wetlands. According to Bergamaschi et al. (2013) a substantial fraction of
the total increase is attributed to anthropogenic emissions. There is
therefore a possibility that we could combine two emission inventories
(anthropogenic from EDGAR v4.2 and natural from Bousquet et al.,
2011) that both have overly large growth in the
period 2006–2008.
Extrapolating anthropogenic emissions that likely have overly strong growth
probably explain why the model also overestimates the CH4 growth from
2009 to 2012. Mismatch between the spatial distributions of the model and
measurements (Fig. 11) on regional scales from 2009 to 2012 are expected due
to the extrapolation of anthropogenic emissions and use of constant 2009
natural and biomass-burning emissions. Of these, especially wetland emissions
have large spatial and temporal variation from year to year.
Changes in methane lifetime
The modelled evolution of CH4 is not only decided by changes in sources
but also changes in the atmospheric CH4 loss and soil uptake. The
CH4 lifetime is an indicator of the CH4 loss. The lifetime is
dependent on the efficiency of soil uptake (Curry, 2009) as well as on concentrations
of atmospheric chemical components reacting with CH4, including the
kinetic rates of the corresponding reactions. It also depends on how
efficiently the emitted CH4 is transported between regions with
differences in loss rate. Our prescribed fields for soil uptake (Bousquet et
al., 2011) are responsible for about 5 % of the loss and the difference
between the year with smallest and largest soil uptake is only 2 %. The
main reactant removing CH4 chemically in the atmosphere is OH, but there
is also a small loss due to reactions with excited atomic oxygen (O1D)
and chlorine (Lelieveld et al., 1998; Crutzen, 1991). Due to the limited
influence of soil uptake, chlorine, and O1D we will hereafter focus on
the role of changes in OH and the kinetic loss rate for this reaction. A
number of components (CO, NOx, NMVOCs, CH4, SO2, aerosols,
meteorological factors, solar radiation) control the atmospheric OH level and
the kinetic loss rate (Dalsøren and Isaksen, 2006; Lelieveld et al., 2004;
Holmes et al., 2013; Levy, 1971). Due to the extremely high reactivity of OH,
measurements on large scale are impossible (Heard and Pilling, 2003). Forward
models have been employed to calculate the OH evolution over time on global
scale. Another alternative is inverse models in combination with observations
of 14CO , CH3CCl3 or other long-lived species reacting with
OH. This section discusses the modelled evolution of CH4 lifetime in
this study and compares it to findings from other relevant studies on
CH4 lifetime and OH change. In the section thereafter we try to identify
the key drivers behind the modelled changes in CH4 lifetime.
The overall picture from the main simulation (blue lines Fig. 15) is that
there is a clear decrease in the CH4 lifetime over the last 4 decades, more than 8 % from 1970 to 2012 and a similar increase in OH
concentration. Of particular importance are large increases in OH over
Southeast Asia, mainly due to strong growth in NOx emissions. From
2000–2010 the modelled tropospheric OH column increase by 10–20 % over
China and India (not shown). In Fig. 15, the reaction rate with methane is
used as an averaging kernel to examine the OH change relevant for changes in
methane lifetime. There is a very strong anti-correlation between the
evolution of OH and methane lifetime suggesting causality. This is especially
the case for the period 1970–1997 run without inter-annual variation in
meteorology resulting in a static CH4 + OH reaction rate (k) for
these years. The lifetimes in the fixed CH4 run (red line) and the main
CH4 run (blue line) are highly correlated. This is another way of
illustrating that OH (k × OH), and not the CH4 burden itself,
is driving the long-term evolution and year-to-year variations of CH4
lifetime. However, some influence from CH4 fluctuations is evident in a
few of the years studied (mainly in the 1980s), with large variations in CH4
emissions (Fig. 1). CH4 itself is important for its own lifetime length
(blue line well above red line), due to the decrease in the OH concentration
produced by the reaction with the CH4.
Development in atmospheric CH4 lifetime and key parameters
known to influence CH4 lifetime. All variables values are relative to
1970. (To make it apparent in the figure, temperature variations are relative
to the Celsius scale).
Other forward models also suggest a similar decrease in CH4 lifetime due
to an increase in global OH concentrations the recent decades (Karlsdóttir
and Isaksen, 2000; Dentener et al., 2003; Wang et al., 2004; Dalsøren and
Isaksen, 2006; Fiore et al., 2006; John et al., 2012; Holmes et al., 2013;
Naik et al., 2013). However, some of these studies focus on the effect of
certain factors (emissions or meteorology) and do not cover changes in all
central physical and chemical parameters affecting CH4 lifetime. Using
observations of CH4 and its isotopic composition, Monteil et al. (2011)
find that moderate (< 5 % per decade) increases in global OH over the
period 1980–2006 are needed to explain the observed slowdown in the growth
rate of atmospheric CH4 at the end of that period. In contrast, large
increases in OH in the 1980s and a large negative trend for the 1990s were
inferred from CH3CCl3 observations (Prinn et al., 2005, 2001; Krol
and Lelieveld, 2003; Bousquet et al., 2005; Montzka et al., 2000). These
studies also found large inter-annual variability of OH. However, the studies
were debated (Krol and Lelieveld, 2003; Lelieveld et al., 2006; Bousquet et
al., 2005; Wang et al., 2008) and it was shown that largely reduced
variations and trends are possible within the uncertainties bonds of the
CH3CCl3 emission inventory. In a more recent analysis of
CH3CCl3, measurements for the period 1998–2007 Montzka et
al. (2011) find small inter-annual OH variability and trends and attribute
previously estimated large year-to-year OH variations before 1998 to
uncertainties in CH3CCl3 emissions and representation issues due to the sparse observation network. Kai et al. (2011) find that relatively stable
dD-CH4 suggested small changes in the OH sink between 1998 and 2005.
Rigby et al. (2008) finds declining OH from 2004 to 2007. Bousquet et
al. (2011) also finds a decline in 2007 and 2008, compared to 2006. However
the decline is much less than that found by Rigby et al. (2008). Holmes et al. (2013)
concludes that better understanding of systematic differences between
different CH3CCl3 observation networks is required before using
them as constraints on inter-annual variability of CH4 lifetime and OH.
Using 14CO Manning et al. (2005) finds no significant long-term trend in
OH in the Southern Hemisphere but short-term large variations persisting for
a few months. Like CH3CCl3 there are uncertainties related to
inferring OH from 14CO (Krol et al., 2008). Ghosh et al. (2015) does not
consider trends in OH but anyway they find a decrease in CH4 lifetime
over the last century and attribute it to temperature increase (larger
reaction rate) and the increase of stratospheric chlorine (larger loss
through reaction with Cl).
CH4 lifetime evolution 1970–1996. Comparison of the main model
simulation (blue line) with CH4 lifetime from the simple model (red line)
obtained from multiple linear regression.
It is evident from the above discussion that there are uncertainties related
to all methods (models, CH3CCl3, and 14CO) and missing
consensus on OH trends. To increase understanding and facilitate discussion,
it is important not to stop by a derived number for change in OH or methane
lifetime, but investigate the major drivers for the changes. The next
section address drivers in this model study.
Major drivers for changes in the methane lifetime
Figure 16 shows the evolution of main factors known to determine atmospheric
CH4 lifetime. The factors chosen are based on the study by Dalsøren
and Isaksen (2006) and Holmes et al. (2013).
Using the NOx / CO emission ratio and linear regression analysis
(Dalsøren and Isaksen, 2006) found a simple equation describing the
evolution of OH resulting from emission changes in the period 1990–2001. In
general, CO emission increases lead to an overall reduction in current global
averaged OH levels. An increase in NOx emissions increases global OH as
long as it takes place outside highly polluted regions. In this study the
general picture is that the NOx / CO emission ratio increases over the
1970–2012 period (Fig. 16). Despite the general increase, periods of
declining ratio can be seen both after the oil crisis in 1973 and the energy
crisis in 1979. This occurs since NOx emissions are more affected than CO
emissions. After 1997 when we include year-to-year variation in emissions
from vegetation fires the NOx / CO emission ratio is more variable.
Large drops in ratio can be seen in years with high incidences of fires
resulting in large CO emissions. This is typical for ENSO episodes
(1997–1998) and warm years (2010). Agreement with observed CO trends (see
comparison in Supplement Sect. S5) indicates that the modelled changes of CO
and OH, and applied CO emissions are internally consistent.
Holmes et al. (2013) found formulas for predicting CH4 lifetime due to
changes in meteorology using some of the factors shown in Fig. 16. It is only
from 1997 that our simulations include inter-annual variation in meteorology.
We find that variations in global averaged specific humidity and temperature
are highly correlated with each other and a 6-month delayed ENSO index. This
is reasonable as this is a typical response time for physical and chemical
signals to propagate from one hemisphere to the other. High temperature and
specific humidity, meaning high water vapour content, is for instance found in
the ENSO year 1998 and warm year 2010 (Fig. 16). Variations in these
parameters are important for the CH4 lifetime since the reaction rate
(k) between OH and CH4 is highly temperature dependent and water vapour
is a precursor of OH (Levy, 1971). The production of OH is also dependent on
UV radiation and thereby the atmospheric ozone column absorbing such
radiation (Rohrer and Berresheim, 2006). The highest UV radiation is found at
low latitudes and the ozone burden between 40∘ S and 40∘ N
is regarded as a useful indicator (Holmes et al., 2013). The emissions of
NOx from lightning are dependent on a number of meteorological factors
and thereby quite variable from year to year (Fig. 16).
In this section we investigate whether simplified expressions for the
evolution of CH4 lifetime can be found based on the parameters in
Fig. 16. Such equations could be very useful for fast prediction of future
development of CH4 lifetime and CH4 burden. Since we study
different time periods than Dalsøren and Isaksen (2006) and Holmes et
al. (2013), and both emissions and meteorology are perturbed in our
simulations, it is not obvious that simplified equations would be
statistically valid.
CH4 lifetime evolution 1997–2012. Comparison of main model
simulation (blue line) with CH4 lifetime from simple model (red line)
obtained from multiple linear regression.
Figure 17 shows the results of multiple linear regression analysis performed
to describe the CH4 lifetime over the period 1970 to 1996. For this
period, fixed year-to-year meteorology was used in the main model simulation.
This means that parameters like lightning NOx, temperature, and specific
humidity (Fig. 16) can be kept out of the regression analysis. The equation
best reproducing (R2=0.99) the lifetime evolution from the main run
(Fig. 17) and having statistical significant linear relations between its
parameters and CH4 lifetime is the following:
CH4lifetime(yr)=11.9-21.4×(NOx/CO)emissions.
This confirms the analysis from previous sections suggesting that CH4
itself has small influence on the variation in CH4 lifetime during this
period. The same seems to be the case for variations in ozone column. A
similar simple equation was found by Dalsøren and Isaksen (2006). This
suggests that near-future variation of CH4 lifetime due to changes in
emissions can be predicted solely by looking at the ratio of NOx to CO
emissions. However, it should be noted that the region of emission change is
important (Berntsen et al., 2006). This is especially the case for NOx
emissions due to the short atmospheric NOx lifetime. For instance,
changes in NOx emissions at low latitudes with moderate pollution levels
(OH response is non-linear) would have profound impacts on CH4 lifetime
due to the temperature dependency of the reaction between CH4 and OH.
The blue line in Fig. 18 shows the lifetime over the period 1997–2012 as
predicted by the main model run. The red line shows the best fit from a
simple parametric model. Because the main CTM run for this period include
year-to-year variation in meteorology, the simple regression model need more
parameters to reproduce the evolution. Still, a simplified equation
(R2=0.99) is statistically valid, predicting the CH4 lifetime by a
linear combination of the parameters specific humidity (q),
NOx / CO emission ratio (NOx / CO)e, lightning
NOx emissions (LNOx)e, and O3 column:
CH4lifetime(yr)=0.07×O3column-4.80×(NOx/CO)e-0.04×q-1.21×(LNOx)e.
It should be noted that specific humidity and temperature have almost
identical year-to-year variation, and it is therefore not given which of these
parameters should be used.
Summary and conclusions
Uncertainties in physical and chemical processes in models, input data on
emissions and meteorology, and limited spatial and temporal coverage of
measurement data, have made it hard for both bottom-up and top-down studies
to settle the global CH4 budget, untangle the causes for recent trends,
and predict future evolution (Ciais et al., 2013; Kirschke et al., 2013;
Nisbet et al., 2014). As the quality and detail level of models, input data,
and measurements progress, the chances of understanding more pieces in the
big puzzle increase. This study is an effort in such a perspective.
In our bottom-up approach, a global chemical transport model (CTM) was used
to study the evolution of atmospheric CH4 over the period 1970–2012.
The study includes a thorough comparison with CH4 measurements from
surface stations covering all regions of the globe. The seasonal variations
are reproduced at most stations. The model also reproduces much the observed
evolution of CH4 on both inter-annual and decadal time scales.
Variations in wetland emissions are the major drivers for year-to-year
variation of CH4. Regarding trends, the causes are much debated, as
discussed in the previous sections. Consensus is neither reached on the relative
contribution from individual emission sectors, nor on the share of
natural vs. anthropogenic sources. The fact that our simulations capture
much of the observed regional changes indicates that our transport and
chemistry schemes perform well and that applied emission inventories are
reasonable with regard to temporal, spatial, sectoral, and natural vs.
anthropogenic distribution of emissions. However, there are some larger
discrepancies in model performance questioning the accuracy of the CH4
emission data in certain regions and periods. Potential flaws in emission
data are pinpointed for recent years when our model simulations are more
complete with regard to input data (e.g. emissions, variable meteorology,
etc.) and there are more measurements available for comparison. After a
period of stable CH4 levels from 2000 to 2006, observations show
increasing levels from 2006 in both hemispheres. From 2006, the model
overestimates the growth in all regions, in particular in Asia. Large
emission growth in Asia influences the CH4 trends in most world regions.
Our findings support other studies, suggesting that the recent growth in Asian
anthropogenic emissions is too high in the EDGAR v4.2 inventory. Based on our
model results and the comparison between ECLIPSE and EDGAR v4.2 emissions in
the Supplement (Sect. S2) we also question the Asian emission trends in the
1990s and beginning of the 2000s in the EDGAR v4.2 inventory, although the
limited number of measurement sites in Asia makes it difficult to validate
this.
The modelled evolution of CH4 is also dependent on changes in the
atmospheric CH4 loss. The CH4 lifetime is an indicator of the
CH4 loss. In our simulations, the CH4 lifetime decreases by more
than 8 % from 1970 to 2012. The reason for the large change is increased
atmospheric oxidation capacity. Such changes are in theory driven by complex
interactions between a number of chemical components and meteorological
factors. However, our analysis reveals that key factors for the development
are changes in specific humidity, NOx / CO emission ratio, lightning
NOx emissions, and total ozone column. It is statistically valid to
predict the CH4 lifetime by a combination of these parameters in a
simple equation. The calculated change in CH4 lifetime is within the
range reported by most other bottom-up model studies. However, findings from
these studies do not fully agree with top-down approaches using observations
of CH3CCl3 or 14CO.
Without the calculated increase in oxidation capacity, the CH4 growth
over the last decades would have been much higher. Increasing CH4 loss
also likely contributed to the stagnation of CH4 growth in the period
2001–2006. Interestingly, over the last few years, the loss deviates from
its steady increase over the previous decades. Much of this deviation seems
to be caused by variation in meteorology. Our simulations reveal that
accounting for variation in meteorology has a strong effect on the
atmospheric CH4 loss. This in turn affects both inter-annual and long-term changes in CH4 burden. A stabilization of the CH4 loss, mainly
due to meteorological variability, likely contributed to a continuing
increase (2009–2012) in CH4 burden after high emission years in 2007
and 2008. Due to the long response time of CH4 this could also
contribute to future CH4 growth. However, there are extra uncertainties
in the model results after 2009 due to lack of comprehensive emission
inventories. A new inventory or update of existing ones with sector–vice
separation of emission for recent years (2009–2015) would be a very valuable
piece for model studies trying to close the gaps in the CH4 puzzle. It
will also provide important fundament for more accurate predictions of future
CH4 levels and various mitigation strategies.