Introduction
The global mean atmospheric concentration of methane
(CH4) has increased from ∼ 700 parts per billion by
volume (ppb) at the start of the industrial era to ∼ 1808 ppb in 2012 and constitutes ∼ 20 % of the anthropogenic radiative forcing by greenhouse gases
. Increases in atmospheric CH4
concentrations potentially have a large impact on the global climate,
through its direct radiative forcing effect the radiative
efficiency of CH4 is about 10 times greater than that of
carbon dioxide per tonne emitted: and, indirectly,
through the formation of tropospheric ozone and aerosols . In
consequence, control of CH4 emissions is potentially an
important lever for international climate change policy and possible
(short-term) mitigation actions
e.g.. An accurate knowledge of
its contemporary sources and sinks is therefore essential.
CH4 is emitted to the atmosphere from a number of sources
: (a) biogenic sources, covering wetlands,
agriculture (livestock and rice production), landfills, forests,
oceans and termites, and (b) non-biogenic sources, comprising
fossil-fuel mining and burning, biomass burning, waste treatment and
geological sources. The major removal process for CH4 in the
atmosphere is reaction with hydroxyl (OH) radicals. Minor sinks are reactions
with chlorine (Cl) atoms in the boundary layer, reactions with OH, Cl and
excited oxygen atoms (O(1D)) in the stratosphere, and uptake by soils.
The overall atmospheric lifetime
of CH4 is estimated to be 9.1±1.9 years
.
In situ measurements of CH4 concentrations have been made from
global networks of surface atmospheric sites since the 1980s
.
The globally averaged CH4 growth rate, derived from the
surface measurements, has fallen from a high of
16 ppbyr-1 in the late 1970s/early 1980s
to almost zero between
1999 and 2006 . This period of declining or low
growth was however interspersed with years of positive growth-rate
anomalies (e.g. in 1991–1992, 1998–1999 and 2002–2003). Since
2007, renewed growth has been evident
, with the largest increases observed
to originate over polar northern latitudes and the Southern Hemisphere (SH)
in 2007 and in the tropics in 2008. There is significant concern that
this might be the restart of an ongoing upward trend in atmospheric
CH4 concentrations.
The observed interannual variability in atmospheric CH4
concentrations and the associated changes in growth rates have
variously been ascribed to changes in the different CH4
sources and sinks: (a) CH4 sources directly influenced by
human activities, such as fossil fuel production
,
(b) wetland emissions
and (c) biomass burning, especially during the intense El Niño
years in 1997 and 1998 . The most
likely causes of the CH4 anomalies observed during 2007 and
2008 were the anomalously high temperatures in the Arctic
or larger CH4 emissions from natural
wetlands in tropical South America and boreal Eurasia
.
Atmospheric column CH4 measurements with sensitivity to the
surface and lower troposphere are now available from satellite
instruments: SCIAMACHY on ENVISAT from 2003
and, since 2009, the Greenhouse Gas Observing Satellite GOSAT,
. The satellite measurements complement the observations
from the sparse network of surface sites.
concluded that the SCIAMACHY measurements could be used in inverse
modelling and were an important step in reducing the uncertainties in
the global methane budget. extended the
inverse modelling analysis to include both surface and satellite
observations. Their results indicated significantly greater
CH4 emissions in the tropics compared to either the a priori
estimates or the inversion based on the surface measurements alone.
The discrepancy was partially reduced after taking account of
spectroscopic changes to interfering water vapour absorption lines
. More recently,
have used column CH4 measurements from the
Thermal and Near-infrared Sensor for Carbon Observation (TANSO) on the
GOSAT to estimate global and
regional monthly CH4 fluxes.
The surface and satellite atmospheric measurements have been used to
constrain the total global annual source strength of CH4 (in
Tg CH4yr-1): 550 ± 50 ; 582
; 515 ± 3 (1999–2006), 536 (2007) and 533
(2008) ; 513 ± 9 (1990s) and 514 ± 14
(2000s) TRANSCOM Methane Model Intercomparison,,
510–516 (2009–2010) and 551(500–592) (1980s),
554(529–596) (1990s) and 548(526–569) (2000s) .
However, there remain considerable uncertainties in the
partitioning of sources and their spatial and temporal distribution
.
Wetlands are generally accepted as being the largest, but least well
quantified, single natural source of CH4, with global emission
estimates ranging from 100 to 231 TgCH4yr-1
. The modelling of wetlands and their associated
emissions of CH4 has become the subject of much current interest. The review by provides a summary of the current
state of knowledge on wetlands and the outcome of the WETland and Wetland
CH4 Inter-comparison of Models project (WETCHIMP).
found a large variation in the wetland areas and associated CH4
emissions from the participating models and varying responses to climate
change (as represented by increases in the driving CO2
concentrations, temperature and precipitation).
Wetland emissions are particularly sensitive to climate change
. concluded that the
wetlands model used in the Joint UK Land Earth Simulator (JULES, the
UK community land surface model) would lead to a doubling of
CH4 emissions from wetlands by 2100 for the IPCC IS92a
scenario considered. As a major emission source of CH4 which
responds strongly to climate change, it is vital that the description
of wetlands and the associated emissions of CH4 used in land
surface and climate models reflects current understanding and the
implications of emerging data sets.
In this paper, we use atmospheric
observations of CH4 (surface concentrations and total columns
derived from the SCIAMACHY instrument) to evaluate simulations of the
Hadley Centre's Global Environmental Model
HadGEM2, and hence to assess the wetland
methane emission parameterization used in JULES. The paper is structured as follows.
Sect. provides a brief description of the models, the
experimental set-up and the key data sets used in the model runs and
subsequent analysis. Sect. compares the modelled
CH4 concentrations with atmospheric methane measurements and includes
discussion of the results. Finally, conclusions can be found in Sect. .
Results and discussion
Four HadGEM2 runs were undertaken for the period 1999–2007, which
differed only in the wetland emission inventory used (FUNG, TRANSCOM-FUNG,
JULES and JULES-GIEMS). Figure shows the spatial
distribution of the global annual methane emissions for the year 2000 for the
four runs. The model runs all used the same previously derived
initial conditions, which represented a spun-up atmosphere for the
early 2000s.
Comparison of the latitudinal distribution of the surface
atmospheric methane mixing ratio (in ppb) as observed (black) and
from the HadGEM2 runs using the following wetland emission
inventories, (1) FUNG (red), (2) TRANSCOM-FUNG (magenta), (3) JULES (blue), and
(4) JULES-GIEMS (green) between the years 2000 and
2010 for the months January, April, October and December. The index
of agreement (IOA) is shown for each run (see Sect. 3 of the Supplement for the definition of the IOA).
Comparison with surface measurements
We use the surface measurements of atmospheric CH4 dry air mole
fractions made at sites in the National Oceanic & Atmospheric
Administration's Earth System Research Laboratory (NOAA ESRL) Carbon Cycle
Cooperative Global Air Sampling Network . Section 2.1
in the Supplement includes a map of the monitoring sites and has time series
of the observed and modelled atmospheric CH4 concentrations between
the years 2000 and 2010 at 16 of the 64 sites, covering both Northern Hemisphere (NH) and SH locations, for the different model runs.
Figure shows a comparison of the latitudinal
distribution of the observed monthly surface atmospheric methane mixing
ratios from all the sites for the months of January, April, July and October
(as a mean of the available measurements between 2000 and 2010) with the
corresponding values derived from the four HadGEM2 runs. All four model runs
reproduce the increase in methane mixing ratio between the SH and NH. The model runs also capture the variability
(or lack thereof) in the NH (in the SH).
The runs also reproduce the annual cycles observed at many of the SH sites.
Comparison of the annual cycle in the surface atmospheric
methane mixing ratio (in ppb) at selected sites between the years 2000 and 2010, as observed (black)
and from the HadGEM2 runs using the following wetland emission inventories,
(1) FUNG (red), (2) TRANSCOM-FUNG (magenta), (3) JULES (blue), and
(4) JULES-GIEMS (green).
The IOA is shown for each run.
There are differences in the modelled annual cycles at the NH sites for the four runs, which is more clearly seen in
Fig. . The model run using the FUNG wetland
emissions gives very high surface CH4 concentrations and an incorrect
seasonality at all the high- and mid-latitude NH sites (illustrated here by
the Barrow, Pallas-Sammaltunturi and Mace Head sites). This has been seen by
other authors e.g. and is also seen to a lesser extent
in the run using the TRANSCOM-FUNG wetland inventory. The runs using
the JULES wetland emission inventories are generally better in terms
of amplitude and seasonality for these sites. We subsequently evaluate the
model outputs using various metrics (see below). There is further evidence of
the different spatial and temporal patterns between the wetland emission
inventories at other mid-latitude NH sites (Hegyhatsal, Hungary; Ulaan Uul,
Mongolia; Southern Great Plains, USA; and Plateau Assy, Kazakhstan). The
modelled concentrations at the Arembepe site in Brazil provide evidence of
the overprediction of the CH4 emissions from the JULES wetland
inventories. At many of the sites (e.g. Ulaan Uul, Mongolia; Southern Great
Plains, USA; Tae-ahn Peninsula, Korea; Mount Waliguan, China; Mahe Island,
Seychelles), the concentrations in the winter months are significantly
overestimated, suggesting that the annual pattern of the non-wetland methane
emissions may not be correct. The remote SH sites (illustrated here by the
Tierra del Fuego and South Pole sites) are located a long distance from the
large CH4 sources (which are mainly in the NH) and are representative
of the remote and well-mixed SH, although there is evidence
of the higher SH wetland emissions in the JULES and JULES-GIEMS
runs.
The HadGEM2 configuration used for these runs does not provide “tagged” or
“coloured” outputs (i.e. the contribution of the different methane source
sectors cannot be derived). Instead, we estimate the contribution from the
various source sectors (anthropogenic, rice paddy fields, shipping, wetlands,
biomass burning, termites and oceanic/hydrates) using the sector emissions
local to that region. In Table 4 of the Supplement, we present the relative
contribution of the emissions sectors for a 20∘×20∘
box centred on the Barrow and Plateau Assy sites. At Barrow, the emissions in
the TRANSCOM-FUNG run are mainly from wetlands (>62 %), whereas
the wetland emissions are smaller in the JULES and JULES-GIEMS
runs and the emissions from anthropogenic sources make the largest
contribution. A similar pattern is also observed at the Pallas-Sammaltunturi
site. At the Plateau Assy site, anthropogenic emissions are the largest
contributing sector with wetlands at 25–29 % (TRANSCOM-FUNG),
0.3–6.0 % (JULES) and 11–13 % (JULES-GIEMS).
Taylor plot derived from the observed surface atmospheric methane
mixing ratio (in ppb) and the HadGEM2 runs using the following
wetland emission inventories, FUNG (red), TRANSCOM FUNG (magenta), JULES
(blue) and JULES-GIEMS (green), for all valid data pairs
from all sites.
A wide variety of methods have been developed within the atmospheric
composition and air pollution community to assess model performance
e.g.. For each of the HadGEM2 runs, we
derived these different metrics (linear regression, bias, normalized mean
bias, IOA, hit rate – see Sect. 3 in the Supplement)
for each site where there were at least 20 pairs of monthly observed and
modelled concentrations. The valid data from all sites for a given run were
then aggregated and the same set of metrics derived for this “global”
data set. Table provides the output of this
analysis. There are some remarkably good fits with slopes close to unity and
high correlation coefficients (R2=0.82 for the JULES-GIEMS
inventory). That said, there are specific sites where the performance appears
superficially good but is less robust on closer inspection (see Table 6 in
Sect. 2.1 of the Supplement). This can also be seen in
Fig. , which shows a Taylor plot
for the four runs (FUNG, TRANSCOM-FUNG,
JULES and JULES-GIEMS). The JULES-based inventories represent
an improvement over the FUNG and, to a lesser extent, the
TRANSCOM-FUNG wetland inventories, where a negative correlation
between the observed and modelled concentrations at high-latitude NH sites is
evident for the latter. The index of agreement (and, to a lesser extent, the
hit rate) did show some discrimination between the model runs. The IOA varies
between 0.76 (FUNG) and 0.94 (JULES-GIEMS), the run in which
the JULES-modelled wetland fraction is replaced with the EO-derived value.
The run using the JULES-modelled wetland fraction gave an IOA of 0.91,
showing that the JULES-based emission inventories are, in general, a
considerable improvement over the run using the FUNG inventory (but not
the run using TRANSCOM-FUNG inventory, for which an IOA of 0.91 is
derived).
Statistical outputs from the “global” analysis of the observed and
modelled surface methane concentrations for the HadGEM2 runs (FUNG,
TRANSCOM-FUNG, JULES and JULES-GIEMS) using valid
co-located data from all monitoring sites.
Statistic/Metric
FUNG
TRANSCOM-FUNG
JULES
JULES-GIEMS
Number of valid data pairs
5591
5591
5591
5591
Linear regression – slope
1.33
1.09
0.79
0.99
Linear regression – intercept
-563.3
-130.8
391.6
30.8
Coefficient of determination (R2)
0.58
0.81
0.71
0.82
Mean of observations (in ppb)
1816.4
1816.4
1816.4
1816.4
Mean of modelled conc. (in ppb)
1849.8
1839.1
1820.9
1828.9
Mean normalized bias
0.02
0.01
0.003
0.01
Number of modelled results within a factor of 2
1.0
1.0
1.0
1.0
Index of agreement
0.76
0.91
0.91
0.94
Hit rate
0.93
0.97
0.99
0.98
Root mean square error (RMSE, in ppb)
78.4
38.7
33.0
30.8
Coefficient of variation in RMSE
0.04
0.02
0.02
0.02
Comparison of the growth rates in the surface atmospheric
methane mixing ratio (in ppb) as observed (black) and from the
HadGEM2 runs using the following wetland emission inventories,
FUNG (red), TRANSCOM FUNG (magenta), JULES (blue) and JULES-GIEMS
(green) at selected sites between the years 1998 and
2010.
Of more relevance is whether the model can reproduce the observed growth
rates and hence explain the origin of the positive anomalies. Following
and references therein, the average trend and
seasonal cycle in the modelled or observed concentrations were approximated
by a second-order polynomial and four harmonics. A low-pass filter was then
applied to the residuals of the fit to remove variations occurring on
timescales less than ∼ 1 year. The smoothed residuals were added
to the quadratic portion to give a deseasonalized trend. The growth rate was
derived as the derivative of the monthly concentrations of this deseasonalized
trend. Figure shows the growth rates derived from
the observed and calculated surface concentrations at six sites (Alert,
Niwot Ridge, Mauna Loa, Ascension Island, Bukit Kototabang and South Pole)
for all the runs. The modelled growth rates are similar to each other and
generally larger than those observed, reflecting the generally larger
modelled annual cycles (see figures in Sect. 2.1 of the Supplement). It is
less clear that the JULES-based inventories are generally better. The
correspondence at many sites is variable and there is some indication that
the modelled changes are more rapid than those observed.
Comparison with SCIAMACHY measurements
Initial comparison
We convert the modelled 4-D methane mass mixing ratio fields
(longitude, latitude, altitude, time) into 3-D fields (longitude,
latitude, time) of the mean dry-air atmospheric column methane mixing
ratio, using the SCIAMACHY averaging kernels .
We then derive contour maps of the mean atmospheric mixing ratios of
methane from the HadGEM2 model runs and the regridded version of the
SCIAMACHY product (v2.3, Sect. ) for the period 2003
to 2007. The model outputs are only sampled at the valid space and
time points present in the SCIAMACHY product and a land–sea mask is
applied to remove all data over the oceans as the SCIAMACHY data set
only includes measurements over the oceans for the period between 2003
and 2005. As shown in Fig. 19 in the Supplement, there is a clear
underprediction in the modelled atmospheric column methane mixing
ratios by ∼ 50 ppb (i.e. ∼ 3 % of a typically observed mean column mixing ratio).
Comparison of the HadGEM2 modelled vertical concentration
profile of CH4 with the corresponding profiles from the
TES (red) and the HALOE-assimilated
TOMCAT model for the grid point (10∘ N, 1∘ E) in
July 2005. The red and green lines show the results from replacing
the HadGEM2 modelled concentrations above 200 hPa with TES
and the HALOE-assimilated TOMCAT
output, respectively.
We attribute the underprediction to a faster fall-off in modelled methane
concentrations with altitude than that observed. To test this, we initially
replaced the HadGEM2 model outputs above 400 hPa with methane mixing
ratios derived from the thermal infrared (TIR) channel of the Tropospheric
Emission Spectrometer TES, AURA, 2004–2011:, because of
its availability and ease of use. As discussed by , the
CH4 in the upper troposphere is biased high relative to the lower
troposphere by 4 % on average. Given this and the poor temporal overlap
with the SCIAMACHY data set, we subsequently constrained the HadGEM2 output
above 300 hPa with data from HALOE/ACE-assimilated TOMCAT output (see
Sect. ), which covered the entire period of the HadGEM2 runs
(2000–2007) and the SCIAMACHY measurements.
Figure shows a typical comparison of the
HadGEM2 modelled vertical concentration profile of CH4 with the
corresponding profiles from TES and the HALOE/ACE-assimilated TOMCAT model
for the grid square centred on the location (10∘ N, 1∘ E)
in July 2005. The figure also shows the revised profiles derived by replacing
the HadGEM2 modelled concentrations with interpolated TES measurements (above
400 hPa) and the HALOE-assimilated TOMCAT output (above
300 hPa). The derived mean atmospheric methane column mixing ratios
(in ppb) were: 1725.9 (HadGEM2, original), 1780.2 (HadGEM2+TES) and 1766.4
(HadGEM2+HALOE-TOMCAT), compared to the SCIAMACHY measurement of
1760.9 ppb.
introduce an explicit loss term in the
standard tropospheric chemistry scheme to compensate for the lack of
CH4 oxidation in the stratosphere. However, the faster fall-off with
height cannot be attributed to this additional explicit loss term (see Sect.
). In the model runs carried out here, although the global annual
loss rate of stratospheric CH4 is higher than previous estimates
(53 ± 4 Tg CH4 yr-1 compared to 40 Tg
CH4 yr-1 from ), similar behaviour has been
seen in the stratospheric configuration of UKCA .
Given the different treatment of stratospheric CH4 removal in the two
UKCA configurations and that stratospheric chemical removal rates are much
slower than transport timescales , it is likely that the
faster fall-off of modelled stratospheric CH4 with height than that
observed is an indication that stratospheric transport timescales are too
long.
Constraining the modelled CH4 concentrations at model
levels above 300 hPa improved the agreement with the SCIAMACHY
SWIR CH4 product (Fig. 19 in the Supplement). All subsequent
comparisons with the SCIAMACHY product are based on the merged HadGEM2
and HALOE/ACE-assimilated TOMCAT outputs. As our emphasis is on
testing different wetland CH4 emission configurations,
this extra constraint being applied to HadGEM2 output is appropriate.
Contour maps of the average atmospheric column methane mixing
ratio between the years 2003 and 2007, as derived from monthly regridded SCIAMACHY data
(a) and from the HadGEM2 runs using the FUNG (b), TRANSCOM-FUNG (c),
JULES (d) and JULES-GIEMS (e) wetland emission inventories
and the EDGAR v3.2 (E3.2) anthropogenic methane emission time series, sampled at co-located space
and time points.
Comparisons in space and time
Figure compares the mean atmospheric
column measurements of methane derived from the regridded SCIAMACHY
product for the period 2003–2007 and the HadGEM2 runs using the
FUNG, TRANSCOM-FUNG, JULES and JULES-GIEMS methane
wetland emission inventories, constrained as described in the previous
section. We note that (i) the model reproduces the latitudinal
gradient in the atmospheric methane column, with higher methane
columns in the NH; (ii) the model captures the high
emission areas over south and south-east Asia, although the modelled
concentrations are much higher than those observed; (iii) the
different spatial patterns of the wetland methane emissions used are
evident in the maps. We see enhanced atmospheric columns over the
boreal Eurasia region in the run using the FUNG wetland
inventory and over the Amazon in the run using the JULES
wetland inventory.
We compare the latitudinal distributions in
Fig. . The run using the TRANSCOM-FUNG wetland
inventory gives a remarkably good description. The larger emissions present at
temperate and higher NH latitudes in the FUNG wetland
inventory result in higher zonal averages at these
latitudes compared to both TRANSCOM-FUNG and the JULES-based inventories.
The JULES-based inventories give better agreement in the tropics and
SH compared to the FUNG inventory but
underestimate the atmospheric column at boreal and higher northern latitudes.
The high modelled mixing ratios over the Ganges
Valley in India are evident in the peaks in the modelled profiles
between 20 and 30∘ N in all four runs.
Comparisons of the latitudinal distribution of the average
atmospheric column methane mixing ratio between the years 2003 and 2007,
as derived from monthly regridded SCIAMACHY data
and from the HadGEM2 runs using the FUNG (a),
TRANSCOM-FUNG (b),
JULES (c) and JULES-GIEMS (d) wetland emission inventories
and the EDGAR v3.2 (E3.2) anthropogenic methane emission time series, sampled at co-located space
and time points. Note that the SCIAMACHY data between 60 and 90∘ S have been
removed because of their sparsity and quality.
Time series of the area-weighted average atmospheric column
methane mixing ratio from January 2003 to December 2007, as derived
from monthly regridded SCIAMACHY data (v2.3) and from the
HadGEM2 runs using (1) the FUNG (red), (2) the TRANSCOM FUNG (magenta),
(3) the JULES (blue), and (4) the JULES-GIEMS (green)
wetland emission inventories and the EDGAR v3.2 (E3.2) anthropogenic methane emission time series,
sampled at co-located space and time points for all land surface points and for the 11 terrestrial
TRANSCOM regions (a). (b) shows
the corresponding annual cycles. The IOA is shown for each run
(see Sect.3 of the Supplement for the definition of the IOA).
Figure shows time series and annual cycles of the
area-weighted mean atmospheric column methane mixing ratios between
January 2003 and December 2007 from the SCIAMACHY data and the four HadGEM2
runs for all land surface points and for the 11 terrestrial TRANSCOM regions
(see map at
http://transcom.project.asu.edu/transcom03_protocol_basisMap.php). In
Fig. 20 in the Supplement, we include similar time series and annual cycle
plots using the unconstrained HadGEM2 model outputs. We know that the
FUNG wetland emission inventory used here gives too much emission at
boreal and higher latitudes. This is apparent from the very strong annual
cycles with summer maxima (30–50 ppb enhancements) for Europe and
the two boreal zones in North America and Eurasia. The run using
TRANSCOM-FUNG wetland inventory also has annual cycles with summer
maxima for Europe and the two boreal zones in North America and Eurasia. The
JULES-based inventories, on the other hand, show summer minima, similar to
the behaviour seen in the surface measurement sites (see
Fig. ). It is also evident that the monthly emission
profiles of some source sectors appear incorrect. In the Tropical Asia
region, the annual cycle shows a minimum in July for all four runs whereas
the SCIAMACHY data show a maximum in the late summer/early autumn. Also
included in each panel of Fig. are the IOAs derived for the four HadGEM2 runs. As presented, the values
generally show that the model run using the FUNG wetland emission
inventory performed the best when all land surface points are considered
together (IOA = 0.86) and for some of the TRANSCOM regions in the Northern
Hemisphere. However, the JULES-based inventories were better in the SH (e.g. IOA for JULES-GIEMS = 0.59 for South American
Temperate, Southern Africa and Australia). The high modelled mixing ratios
over the Ganges Valley in India, evident in Figs. and in all four
runs, occur in the winter months. This suggests that the stronger summer
emissions in the FUNG wetland emission inventory compensate for the
lack of or opposite seasonality in the emissions from other source sectors
(see Figs. 4–7 in the Supplement).
Comparison of the deseasonalized emission fluxes between 1997
and 2009 from the HadGEM2 runs (using the wetland emission
inventories: FUNG – red, JULES – blue and
JULES-GIEMS – green) and the two inverse flux estimates of
(black and purple). (a) and (b) show
the anomalies in the global methane emissions and in the wetland
emissions, respectively.
Discussion
Comparison against measurements
The comparison of the model outputs against the in situ surface
atmospheric and atmospheric column measurements of methane has
indicated varying levels of agreement. The run using the
JULES-GIEMS wetland emission inventory gives the best
description of the surface observations and the derived growth rates.
The observed growth rates clearly show the positive anomalies in
1997/1998 and 2002/2003 and the increase in methane after 2007 (see
Fig. ). The model captures these events
with varying degrees of success. There is also evidence from the high-latitude SH sites that the modelled atmospheric
burden is increasing too quickly.
We expect the in situ surface atmospheric measurements to be more sensitive
to the methane emissions, whereas the atmospheric column measurements
integrate the effects of emissions, chemistry and atmospheric transport. The
large amplitudes seen in the annual cycles of the in situ surface atmospheric
observations (Fig. ), especially at the high NH
latitude sites, are less apparent in the modelled atmospheric columns,
possibly because of the limited number of SCIAMACHY observations at these
latitudes, and the model outputs were only sampled if there was a valid
SCIAMACHY measurement. Figure and Fig. 20 in the
Supplement show comparisons of the observed SCIAMACHY and modelled time
series and annual cycles for the constrained and unconstrained HadGEM2 model
outputs, respectively. The amplitudes of the annual cycles appear larger in
the unconstrained model outputs, especially the FUNG and
TRANSCOM-FUNG runs, as these effectively have larger contributions
from the model levels close to the surface and these levels are more affected
by the surface emissions. Generally, we see similar trends and patterns
between the constrained and unconstrained model outputs, suggesting that the
different emission distributions largely account for the differences in the
modelled atmospheric concentrations and columns between the model runs.
Mean annual methane emissions for the period 2000–2009 from
the JULES (blue) and JULES-GIEMS (red) used in
this work and the bottom–up (green) and top–down (grey)
estimates of . The “all wetlands” components
have been offset by 80 TgCH4yr-1 for greater
clarity. The error bars give the range of
values.
Compared to the SCIAMACHY observations, the constrained model run using the
Fung-derived inventory appears better in terms of the annual cycle
(Fig. ), although its annual cycle in the boreal
zone is larger. The JULES-based inventories on the other hand exhibit a
smaller seasonal cycle (for the JULES inventory, this is because the
wetland emissions are dominated by those from the Amazon and these are
modelled to have little seasonality). The high concentrations modelled over
the Ganges in India in all four runs indicate that the magnitude of the
non-wetland emissions in this region and their monthly variability may be too
large (see Fig. ) or that the boundary layer
mixing in this region, close to the Himalayan mountains, is not well
represented. There is evidence in the comparison with the inverse emission
estimates that part of the explanation is that the emissions are overstated
in this region (and these are largely CH4 emissions from non-wetland
sources). Further support for this interpretation is provided by
, who found that the methane emissions from India were lower
by 13 TgCH4yr-1 in their optimized emission scenario.
Annual methane emissions for 2000 from all sources (left-hand
panels) and wetlands (right-hand panels). The upper panels
(a, d) show the emission maps from the inverse modelling of
using the data set of for the prior wetland
emissions. Panels (b) and (e) show difference maps
between the emission estimates shown in Panels (a) and
(d) and the corresponding inventories using the
JULES-GIEMS wetland emission inventory. Panels (c)
and (f) are the same as Panels (b) and (e)
but replacing the prior wetland emissions with those of Kaplan
as described in.
Comparison of global and regional estimates of methane emissions from wetlands.
Domain
Model/Observation-based
JULES
JULES-GIEMS
FUNG
TRANSCOM/
estimate (Ref.)
(1997–2009)
(1997–2009)
(as used here)
FUNG
Global
(TD: 2000s)
175 (142–208) (1)
(BU: 2000s)
181
181
181
149
217 (177–284) (1)
(178–184)
(167–194)
Global – WETCHIMP
190 (141–264) (2)
Boreal (> 30∘ N)
37.7–157.3 (3)
12.6
35.1
109
58.5
(12.2–13.2)
(32.8–37.4)
Hudson Bay Lowlands
2.3 ± 1.3 (4)
0.4
2.2
10.2
3.5
(0.3–0.6)
(1.8–2.6)
West Siberian Lowlands
2.93 ± 0.97 (5)
0.5
1.6
19.1
8.0
(0.4–0.6)
(1.3–2.2)
Tropics (23∘ S–23∘ N)
111.1 (6)
159
123
57.3
69.4
(157–162)
(112–134)
Amazon
26.6 (6)
89
53
17
25
(85–91)
(46–59)
Amazon (Nov 2008)
3.3 (1.5–4.8) (7)
5.7
2.2
1.2
1.4
Amazon (May 2009)
3.3 (1.3–5.5) (7)
6.5
3.9
0.6
1.4
Notes: For the JULES and JULES-GIEMS wetland inventories, we show
the mean (minimum–maximum) annual emission of the years 1999–2007. The JULES-GIEMS
wetland inventory was corrected for the area of rice paddy fields, as described in Sect. .
References: (1) top–down (TD) and bottom–up (BU) wetland emission
estimates for the 2000s taken from ; (2) taken
from the WETCHIMP wetland model intercomparison of ;
(3) range of emission estimates from using the
PEATLAND-VU wetland CH4 emission and PCR-GLOBWB hydrological
models, driven with different wetland data sets; (4) , domain taken to be 96–75∘ W and
50–60∘ N; (5) version (Bc8) of the “standard model” in
, domain taken to be 65–85∘ E and
54–70∘ N; (6) , the wetland emissions from
the Amazon are 24 % of the total wetland emissions from the
tropics; (7) mean (range of) emission estimates for Amazon Lowlands for November 2008 and May 2009 from .
Comparison with other wetland estimates
Wetlands are generally accepted as being the largest, but least well
quantified, single natural source of CH4
. In this work, the mean annual global
emission between 1999 and 2007 was effectively fixed at
181 TgCH4yr-1, the value used by
in earlier HadGEM2 model runs. The total is however consistent with
other recent estimates. derived a value of 165
TgCH4yr-1 from their inverse modelling study.
reported an ensemble mean of the annual global
emissions of 190 TgCH4yr-1 with a spread of
± 40 % from the wetland models participating in the
WETCHIMP wetland model intercomparison. obtained
wetland emissions between 184 and 195 TgCH4yr-1 from
inversions of surface and/or GOSAT measurements between 2009 and 2010.
In a synthesis paper, estimated methane emissions
from natural wetlands for the period 2000–2009 to be in the
range from 142 to 208 TgCH4yr-1 with a mean value of
175 TgCH4yr-1 using inverse modelling methods and in
the range from 177 to 284 TgCH4yr-1 with a mean value
of 217 TgCH4yr-1 from process-based approaches (see
Table ).
As the long-term mean annual emissions were fixed, the emphasis here has been
on the spatial patterns and intra-annual and interannual variability. As shown in
Fig. 2 in the Supplement, the JULES wetland emissions are concentrated in the
tropics and especially the Amazon. The JULES-GIEMS still has more emissions
in the tropics but these are located more in India and SE Asia (and a smaller
increase in the Boreal emissions). In Table , we
compare wetland emission estimates from JULES and JULES-GIEMS
with other recent global and regional literature estimates.
found a wide variation in methane emission fluxes
from wetlands and floodplains above 30∘ N for the years 2001 to 2006
for different estimates of wetland extents (37.7 to
157.3 TgCH4yr-1). The corresponding JULES-GIEMS
estimate for the same period is 35.1 TgCH4yr-1, although we
believe that this is an underestimate from the comparison against the
atmospheric measurements. For the West Siberian Lowlands,
, using more measurement sites, revised the mapping-based
estimate given by to
2.93 ± 0.97 TgCH4yr-1. The corresponding JULES
estimates are lower, which we believe arises from the absence of peatland
soils in JULES. There is better agreement for the JULES-GIEMS
inventory with the estimate of for the Hudson Bay
Lowlands. report a 7 % rise in global wetland
CH4 emissions over 2003–2007, due to warming of mid-latitude and
Arctic wetland regions. Following the introduction of a time decay of the
substrate carbon to account for the observed seasonal lag between CH4
concentrations and the peak in the equivalent water height, used as a proxy
for a wetland, derive revised global CH4 emissions
for 2003–2009. Tropical emissions amount to 111.1 TgCH4yr-1,
of which 24 % is emitted from Amazon wetlands. As expected, the emissions
in the tropics for 1999–2007 from the JULES and JULES-GIEMS
inventories are higher, at 159 TgCH4yr-1 (for the tropics
with the Amazon accounting for 89 TgCH4yr-1) and
123 TgCH4yr-1 (with the Amazon contributing
53 TgCH4yr-1), respectively. We see that the
JULES-GIEMS inventory is in reasonable agreement with these regional
estimates. The JULES–GIEMS inventory is also in good agreement with
the emission estimates obtained by for the Amazon Lowlands
for November 2008 and May 2009. The JULES inventory again
overestimates the emissions.
In Fig. , we compare the regional
emission totals given by the two JULES-based inventories with the
corresponding information given in from their top–down
and bottom–up approaches for the period 2000–2009. The comparison
again indicates that the wetland emissions are too high in the Amazon for the
JULES emission inventory and too low at boreal and higher latitudes.
The JULES-GIEMS emission estimates are an improvement in that respect.
Comparison with inverse emission estimates
In Fig. we compare the anomalies in
the deseasonalized global and wetland methane emissions used in the
HadGEM2 runs and from two inverse flux estimates derived by
from surface atmospheric methane measurements,
specifically, using prior wetland emission estimates based on
and Kaplan as described
in. The FUNG data set as used here shows no
change in the anomaly of the wetland emissions as a single annual
data set is used for all years; this is also the case for other methane
sources, apart from biomass burning. Any anomalies in the emissions
therefore largely result from biomass burning. The variability shown
in the JULES model run is largely from the biomass burning –
the wetlands show a steady increase. On the other hand, there is more
interannual variability in the model run using the JULES-GIEMS
wetland emission inventory. The inventories used here confirm other
studies that link the 1997/1998 and the 2002/2003 positive growth
anomalies in surface atmospheric methane concentrations to biomass
burning see Introduction, .
There is some suggestion from the JULES-GIEMS runs that wetland
emissions contributed to the 2002/2003 anomaly (see
Fig. ).
The JULES inventory shows an upward trend over time while there is more
interannual variability in the JULES emission data set driven with the EO
inundation product (see Fig. ). We compare the
annual methane emission totals derived from the JULES-based estimates used
here with two optimized inverse estimates of .
The mean (minimum–maximum) annual emissions between
1999 and 2007 are: JULES, 181 (178–184) TgCH4yr-1;
JULES-GIEMS, 181 (165–192) TgCH4yr-1; Bousquet–Fung,
161 (143–180) TgCH4yr-1; and Bousquet–Kaplan,
174 (156–198) TgCH4yr-1. There is some agreement between the
JULES-GIEMS and the inverse Bousquet–Kaplan emission inventories but
also differences in the annual emission trends.
Figure shows maps of the global annual emissions
for the year 2000 for the inverse emission inventory estimates derived by
using the wetland emission prior based on Fung
for all methane sources and for wetlands. The figure also includes
difference maps between the JULES-GIEMS emission estimates and the
inverse emission inventory estimates derived by
using emission priors based on the Fung (panels b and e) and Kaplan
(panels c and f) wetland data sets. There is some agreement, which is
not surprising as similar data sets were used, but that there are also
differences, most noticeably in the wetlands. The JULES-GIEMS
inventory has some similarities with the inverse inventory using the
Kaplan wetland data set (see material and figures in Sect. 1.3 of the
Supplement). The monthly GIEMS data set of has been
used in this work as it provides a long-term global data set derived
using a consistent methodology. As part of the wetland model
intercomparison, noted that there were significant
differences between this data set and the wetland maps derived by
Kaplan as described in. The inundation
product showed more wetlands in Europe and the Canadian Arctic but
fewer in the Hudson Bay Lowlands. identified
a number of reasons for these differences: (i) classification of water
bodies and wetlands; (ii) distinguishing agricultural (i.e. man-made)
and natural wetlands; (iii) the ability of the inundation product to
resolve saturated areas with high water tables close to the
surface. Many of these differences can be seen in the difference maps.
Conclusions
In this paper, we have evaluated wetland emission estimates derived
using the UK community land surface model (JULES) against atmospheric observations of methane,
including, for the first time, total methane columns derived from the
SCIAMACHY instrument on board the ENVISAT satellite. The modelled
atmospheric methane columns were biased low (by 50 ppb)
compared to those derived from the SCIAMACHY instrument, a consequence
of the faster fall-off in the modelled methane concentrations with
altitude than that observed. Constraining the modelled concentrations
above 300 hPa with vertically resolved methane data from the
HALOE-ACE assimilated TOMCAT output resulted in a significantly better
agreement with the SCIAMACHY observations. The model performed
significantly better against measurements of surface atmospheric
methane concentrations.
The wetland emission totals used in this work were consistent with
other recently published estimates, although considerable differences remain between wetlands models, as highlighted in the
recent WETCHIMP model intercomparison study . While
progress has been made, the JULES methane emission parameterization
overestimates the methane emissions in the tropics and underestimates
them at mid-NH and higher-NH latitudes. The use of the GIEMS product
to constrain JULES-derived wetland fraction improved the representation
of the wetland emissions in JULES and gave a good description of the
seasonality observed at surface sites influenced by wetlands,
especially at high latitudes. We found that the annual cycles
observed in the SCIAMACHY measurements and at many of the surface
sites influenced by non-wetland sources could not be reproduced in
these HadGEM2 runs. This suggests that the emissions over certain
regions (e.g. India and China) are possibly too high and/or the
monthly emission patterns for specific sectors are incorrect.
The comparisons presented in this paper have identified areas for
improvements in aspects of two components in the HadGEM family of models
– the land surface and atmospheric chemistry modules. Current
and future work will look to improve (a) the description of wetlands
and the associated emissions of methane in JULES through the inclusion
of an organic soil type related more closely to peatlands, and (b)
understanding and addressing the cause(s) of the faster fall-off,
with potentially a particular emphasis on the model's stratospheric transport timescale.
The inclusion of whole-domain methane chemistry in UKCA by
implementing a combined troposphere–stratosphere chemistry scheme
(Telford et al., 2014) may help in this regard. The EO data sets used here
(and to be extended in the future) are essential for the future
evaluations of JULES, UKCA and the HadGEM family of models.