ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-14003-2016Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observationsSaadKatherine M.katsaad@caltech.eduhttps://orcid.org/0000-0002-2501-6223WunchDebraDeutscherNicholas M.GriffithDavid W. T.https://orcid.org/0000-0002-7986-1924HaseFrankDe MazièreMartineNotholtJustusPollardDavid F.https://orcid.org/0000-0001-9923-2984RoehlColeen M.https://orcid.org/0000-0001-5383-8462SchneiderMatthiashttps://orcid.org/0000-0001-8452-0035SussmannRalfWarnekeThorstenWennbergPaul O.https://orcid.org/0000-0002-6126-3854Environmental Science and Engineering, California Institute of Technology, Pasadena, California, USADepartment of Physics, University of Toronto, Toronto, Ontario, CanadaCenter for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, AustraliaInstitute of Environmental Physics, University of Bremen, Bremen, GermanyInstitute for Meteorology and Climate Research, Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, GermanyRoyal Belgian Institute for Space Aeronomy, Brussels, BelgiumNational Institute of Water and Atmospheric Research, Omakau, New ZealandInstitute for Meteorology and Climate Research, Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, GermanyKatherine M. Saad (katsaad@caltech.edu)11November2016162114003140247April201610May201624October201625October2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/14003/2016/acp-16-14003-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/14003/2016/acp-16-14003-2016.pdf
Global and regional methane budgets are markedly uncertain.
Conventionally, estimates of methane sources are derived by bridging
emissions inventories with atmospheric observations employing chemical
transport models. The accuracy of this approach requires correctly simulating
advection and chemical loss such that modeled methane concentrations scale
with surface fluxes. When total column measurements are assimilated into this
framework, modeled stratospheric methane introduces additional potential for
error. To evaluate the impact of such errors, we compare Total Carbon Column
Observing Network (TCCON) and GEOS-Chem total and tropospheric
column-averaged dry-air mole fractions of methane. We find that the model's
stratospheric contribution to the total column is insensitive to
perturbations to the seasonality or distribution of tropospheric
emissions or loss. In the Northern Hemisphere, we identify disagreement
between the measured and modeled stratospheric contribution, which increases
as the tropopause altitude decreases, and a temporal phase lag in the model's
tropospheric seasonality driven by transport errors. Within the context of
GEOS-Chem, we find that the errors in tropospheric advection partially
compensate for the stratospheric methane errors, masking inconsistencies
between the modeled and measured tropospheric methane. These
seasonally varying errors alias into source attributions resulting from model
inversions. In particular, we suggest that the tropospheric phase lag error
leads to large misdiagnoses of wetland emissions in the high latitudes of the
Northern Hemisphere.
Introduction
Identifying the processes that have driven changes in atmospheric methane
(CH4), a potent radiative forcing agent and major driver of
tropospheric oxidant budgets, is critical for understanding future impacts on
the climate system. Methane's growth rate, which had been decreasing through
the 1990s from about 10 to 0 ppb per year, began to increase again in 2006
and over the past decade has averaged 5 ppb per year
. Developing robust constraints on the global
CH4 budget is integral to understanding which processes produced
these decadal trends
(e.g., ; ).
One common approach to quantifying changes in the spatial distribution of
sources are atmospheric inversions, which incorporate surface fluxes
estimated by bottom-up inventories as boundary conditions for a chemical
transport model (CTM). The modeled CH4 concentrations are compared to
observations within associated grid boxes, and prior emissions are scaled to
minimize differences with measured dry-air mole fractions (DMFs), producing
posterior estimates. The accuracy of these optimized emissions depends on how
well the CTM simulates atmospheric transport and CH4 sinks, which are
generally prescribed.
Pressure-weighted total column-averaged DMFs (Xgas) provide a
relatively new constraint and have previously been shown to improve estimates
of regional and interhemispheric gradients in trace gases .
Infrared spectrometers can measure CH4 DMFs (XCH4) from
ground-based sites, such as those in the Total Carbon Column Observing
Network (TCCON) and Network for the Detection of Atmospheric Composition
Change (NDACC), and satellites, including SCanning Imaging Absorption
spectroMeter for Atmospheric CartograpHY (SCIAMACHY) ,
Greenhouse gases Observing SATellite (GOSAT) , and the
upcoming TROPOspheric Monitoring Instrument (TROPOMI) . These
observations complement surface measurements because they add information
about the vertically averaged profile and are sensitive in the free
troposphere . Additionally, they complement aircraft
observations by measuring trace gases at higher temporal frequency, although
they share the limitation of not measuring in inclement weather. Satellite
measurements add global coverage that can fill in gaps where in situ
observations are sparse. found that assimilating GOSAT
CH4 columns into the GEOS-Chem CTM with an ensemble Kalman filter
reduced posterior emissions uncertainties by 9–48 % for individual
source categories and by more than three times those of inversions that only
assimilated surface data for most regions. determined from
their analysis of observing system simulation experiments (OSSEs) that
TROPOMI's daily frequency and global coverage performs similarly to aircraft
campaigns on sub-regional scales, and could provide a constraint on
California's CH4 emissions similar to CalNex aircraft observations
.
Incorporating total columns into modeling assessments can also be used to
diagnose systematic issues with model transport. For example, comparing
carbon dioxide (CO2) from TCCON and TransCom ,
found that most models included in the comparison lack
sufficiently strong vertical exchange between the planetary boundary layer
(PBL) and the free troposphere, thereby dampening the seasonal cycle
amplitude of XCO2. The limitations of models to accurately
represent vertical transport can lead to radically different spatial
distributions of fluxes; found, for example, that the
northern terrestrial carbon land sink and tropical emissions were
overestimated by 0.9 and 1.7 PgC year-1, respectively, when comparing
models to aircraft CO2 profiles. More recent studies attribute to
model transport errors the tendency of simulated CH4 in the Southern
Hemisphere to be higher at the surface than the free troposphere, in contrast
with measurements .
TCCON sites, coordinates, altitudes, start date of measurements and
locations used in this analysis.
Tropospheric CH4 typically does not vary radically with height above
the PBL; above the tropopause, however, the vertical profile of CH4
exhibits a rapid decline with altitude as a result of its oxidation and the
lack of any source beyond advection from the troposphere. Fluctuations in
stratospheric dynamics, including the height of the tropopause, change the
contribution of the stratosphere to the total column. CH4 profiles
with similar tropospheric values can thus have significant differences in
XCH4.
Provided that simulations replicate seasonal and zonal variability of
stratospheric CH4 loss, tropopause heights, and vertical exchange
across the upper troposphere and lower stratosphere (UTLS), posterior flux
estimates from inversions incorporating XCH4 measurements would
not be sensitive to stratospheric processes. However, most models do not
accurately represent stratospheric transport, producing low age-of-air values
and zonal gradients in the subtropical lower stratosphere that are less steep
than observations . The TransCom-CH4 CTM
intercomparison assessment of transport using sulfur hexafluoride
(SF6)
showed a strong correlation between the stratosphere–troposphere exchange
(STE) rate and the model's CH4 budget, and a weaker correlation
between the CH4 growth rate and vertical gradient in the model's
equatorial lower stratosphere . These forward model
dependencies of CH4 concentrations on vertical transport, both within
the troposphere and across the tropopause, have the potential to introduce
substantial errors in atmospheric inversions. As temporal and spatial biases
in a model's vertical profile will alias into posterior emissions, inversions
that incorporate total column measurements must ensure that the stratosphere
is sufficiently well described so as to not introduce spurious seasonal,
zonal, and interhemispheric trends in CH4 concentrations and
consequently emissions.
In this analysis, we identify systematic model errors in the seasonal cycle
and spatial distribution of CH4 DMFs by comparing TCCON total and
tropospheric columns to vertically integrated profiles
derived from the GEOS-Chem CTM . We assess
the impact of errors in the characterization of stratospheric processes on
the assimilation of XCH4 and resulting posterior emissions
estimates. In Sect. we describe the TCCON column measurements
and GEOS-Chem setup and characteristics. In Sect. we present
the results of the measurement–model comparison. In Sect.
we compare the base case simulation to one in which emissions do not vary
within each year and quantify the sensitivity of source attribution of the
biggest seasonal emissions sector, wetlands, to the tropospheric seasonal
delay.
MethodsTropospheric methane columns
TCCON has provided precise measurements of XCH4 and other
atmospheric trace gases for over ten years .
Developed to address open questions in carbon cycle science, the earliest
sites are located in Park Falls, Wisconsin, United States and Lauder, New
Zealand at about 45∘ north and south, respectively. Since 2004, the
ground-based network of Fourier transform spectrometers has expanded greatly.
XCH4 are processed with the current version of the TCCON software,
GGG2014, to be consistent, and thereby comparable, across sites. Total column
retrievals are generated with the GFIT nonlinear least-squares fitting
algorithm, which calculates the best spectral fit of the solar absorption
signal to an a priori vertical profile and outputs a scaling factor. The
pressure-weighted integration of the scaled a priori profile produces column
abundances, which are then divided by the dry air column, calculated using
concurrently retrieved oxygen (O2) columns
. Trace gas a priori profiles are
derived with empirical models, which are generated incorporating aircraft and
balloon in situ and satellite measurements seefor a complete
list, and for CH4 include a secular increase of 0.3 %
per year and an interhemispheric gradient in the altitude dependence of the
vertical profiles . These models are fit to daily
noontime National Centers for Environmental Protection and National Center
for Atmospheric Research (NCEP/NCAR) reanalysis pressure grids
, interpolated to the surface pressure measured real-time
on site. Because the profile of CH4 drops off rapidly in the
stratosphere, the accuracy of the a priori shape, and thus the retrieved
column, depends on correctly determining the tropopause.
Tropospheric columns have been shown to represent the magnitude and
seasonality of in situ measurements
. The tropospheric CH4
column-averaged DMFs (XCH4t) are derived by the hydrogen fluoride (HF) proxy
method described in , which uses the relationship between
CH4 and HF in the stratosphere, derived from ACE-FTS satellite
measurements , to
calculate and remove the stratospheric contribution to XCH4. The
XCH4t used in this analysis have been processed
consistently with the GGG2014 TCCON products, with air-mass dependence and
calibration factors calculated for and applied to XCH4t. Additional details about the tropospheric
CH4 measurements can be found in Appendix .
With the exception of Eureka and Sodankylä, which are highly influenced
by the stratospheric polar vortex, all TCCON sites that provide measurements
before December 2011 are included in this analysis (Fig. ).
Table lists locations and data collection start dates for
each of the sites.
Map of TCCON sites used in this analysis. Site colors are on a
spectral color scale in order of latitude, with Northern Hemisphere sites
designated by cool colors and Southern Hemisphere sites designated by warm
colors.
GEOS-Chem model
Model comparisons use the offline CH4 GEOS-Chem version 9.02 at
4∘× 5∘ horizontal resolution on a reduced vertical
grid (47L). CH4 loss is calculated on 60 min intervals and is set by
annually invariable monthly 3-D fields: hydroxyl radical (OH) concentrations
in the troposphere and parameterized CH4 loss
rates per unit volume in the stratosphere
(; ; ). Emissions are released
at 60 min time steps and are provided by the GEOS-Chem development team for
10 sectors: (i) gas and oil, (ii) coal, (iii) livestock, (iv) waste, (v) biofuel and (vi) other
anthropogenic annual emissions from EDGAR v4.2
, (vii) other natural annual emissions from
, (viii) rice agriculture and (ix) wetland
monthly emissions, which incorporate GEOS5 annual
and monthly mean soil moisture values, and (x) biomass burning daily emission
from GFED3 estimates . Loss via soil
absorption , set annually, is subtracted from the total
emissions at each time step.
Model setup
We initialized zonal CH4 distributions with GGG2014 data version a
priori profiles produced at horizontal grid centers,
which we adjusted vertically to match the zonally averaged daily mean model's
tropopause, derived from the National Aeronautics and Space Administration
Global Modeling and Assimilation Office (NASA/GMAO) Goddard Earth Observing
System Model, Version 5 (GEOS5). The model was run from December 2003, the
first month in which GEOS5 meteorological data were available, to June 2004,
the beginning of the TCCON time series; we then ran the model repeatedly over
the June 2004–May 2005 time frame, which allowed us to make comparisons with
the TCCON data at Park Falls and Lauder, until CH4 concentrations
reached equilibrium. A number of perturbation experiments were run in this
way to quantify the sensitivity of CH4 distribution and seasonality
to the offline OH fields, prescribed emissions, and tropopause levels
(Table ). These model experiments are described in greater
detail in Appendix .
Seasonality of the difference between base and aseasonal CH4
for tropospheric, total and stratospheric contribution to total columns. Site
colors are as in Fig. .
Sensitivity experiments.
Simulation nameDescriptionCH4 lifetimeFinal CH4 burden(years)(Tg)BaseDefault OH and emissions9.554825AseasonalConstant monthly emission rates9.574872Updated OHMonthly OH fields from standard chemistry + biogenic VOCs8.534828
Smoothed daily mean XCH4t and stratospheric
contribution to XCH4 at Park Falls (blue) and Lauder (red) for
(a) base equilibrium simulation and the difference between the base
and (b) aseasonal and (c) updated OH simulations.
Using CH4 fields for 1 January 2005 from the equilibrium simulation
as initial conditions, model daily mean CH4 mole fractions were
computed through 2011. These were converted to dry mole fractions, as
described in Appendix . In addition to the default
emissions scheme, an aseasonal simulation setup, in which rice, wetland, and
biomass burning emissions were disabled and aseasonal emissions scaled up
such that total annual zonal fluxes approximate those in the base simulation,
was similarly run to equilibrium and used as initial conditions for the
2005–2011 run. The model infrastructure posed difficulties for setting the
seasonally varying fluxes constant throughout each year; thus we implement
this scaling technique as an alternative to assess first-order impacts of
emission seasonality. The resulting changes to the spatial distribution of
CH4 emissions are shown in Fig. .
For comparisons with column measurements, model vertical profiles were
smoothed with corresponding TCCON CH4 averaging kernels, interpolated
for the daily mean solar zenith angles, and prior profiles, scaled with daily
median scaling factors, following the methodology in
and . Averaging kernels and prior
profiles were interpolated to the model's pressure grid, and all terms in the
smoothing equation were interpolated to daily mean surface pressures measured
at each site. Tropospheric columns were integrated in the same manner as the
total columns up to the grid level completely below the daily mean
tropopause, consistent with how GEOS-Chem partitions the atmosphere in the
offline CH4 simulation. To test the dependence of our results on the
chosen vertical integration level, tropospheric columns were also calculated
assuming the tropopause was one and two grid cells above this level. While
XCH4t changed slightly, by a median of about 1 and 5 ppb
for a one and two-level increase respectively, shifting the tropopause did
not alter the findings discussed in this paper. A description of the model
smoothing methodology and assumptions is provided in
Appendix . The stratospheric contribution to the total
column, which is calculated as the residual between the
XCH4t and XCH4, is the amount by which the
stratosphere attenuates XCH4 via stratospheric loss and transport
(see Appendix for the derivation).
Daily median TCCON and smoothed daily mean GEOS-Chem base (top) and
aseasonal (bottom) DMFs for (a)XCH4t,
(b)XCH4, and (c) stratospheric contribution.
Site colors are as in Fig. . Northern Hemisphere least squares
regression equations are in the top left, and Southern Hemisphere least
squares regression equations are in the bottom right of each plot. Dashed
lines mark the one-to-one lines.
Model features
The seasonal amplitude of the differences between base and aseasonal
simulations are small – within ±4 ppb – for all vertical levels in the
Southern Hemisphere (Fig. ). In the Northern
Hemisphere, however, the difference is much larger and primarily impacts the
troposphere, where it varies between -10 and +13 ppb. The insensitivity
of the stratosphere to the seasonality of emissions is due to the common
source of stratospheric air in the tropics and the loss
of seasonal information as the age of air increases .
Due to the relatively short photochemical lifetime of CH4 in the
stratosphere, about 22 months in the base simulation, stratospheric
CH4 concentrations stabilize much more quickly than in the
troposphere (Fig. a). This rapid response time of the
stratosphere occurs regardless of perturbations to the troposphere, such as
the seasonality of emissions (Fig. b) or tropospheric OH
fields (Fig. c). In both hemispheres the differences between
the base and experimental simulations asymptotically approach steady state
with seasonal variability over a decade in the troposphere, but oscillate
seasonally around a constant mean in the stratosphere. Stratospheric
differences between simulations are considerably smaller than the seasonal
amplitude of the base run: within 6 and 1 ppb, respectively, vs. a
seasonal range of 30 ppb at Park Falls. By contrast,
XCH4t have differences within 30 and 10 ppb,
respectively, vs. a seasonal range of 20 ppb at Park Falls. The stratosphere
at Lauder is even less sensitive to tropospheric perturbations.
Measurement–model comparison
The TCCON daily median and GEOS-Chem daily mean CH4 column-averaged
DMFs demonstrate a strong interhemispheric difference for
XCH4t and XCH4 in both the base and aseasonal
simulations (Fig. ). The Northern Hemisphere
XCH4t slope deviates from the one-to-one line more than
the XCH4 slope (0.60± 0.02 vs. 0.86± 0.03), and the
correlation coefficients are equivalent (R2= 0.41), which indicates that
the poorer agreement between measurements and models in the troposphere
drives the scatter in the total column.
The stratospheric contribution comparison between TCCON and the base
simulation for the Northern Hemisphere sites has an equivalent slope
(0.60± 0.1) and higher correlation coefficient (R2= 0.68) compared
to XCH4t (Fig. c). GEOS-Chem's
larger stratospheric contribution to the total column, coupled with lower
tropospheric values, depresses XCH4. Because this effect on
XCH4 occurs more at higher latitudes, zonal errors in the model's
stratosphere balances those in the troposphere. The result is better
measurement–model agreement in the total columns.
The aseasonal simulation produces lower slopes and correlation coefficients
for, XCH4t (slope = 0.42 ± 0.02, R2= 0.32),
XCH4 (slope =0.60±0.03, R2= 0.26), and the stratospheric
contribution (slope =0.52± 0.01, R2= 0.66) in the Northern Hemisphere.
Removing the seasonality of emissions increases both measurement–model
differences and scatter, as we would expect given the seasonality of Northern
Hemisphere emissions noted in bottom-up studies . The
aseasonal simulation also reduces the offset between TCCON and GEOS-Chem,
whereby modeled XCH4t and XCH4 are
systematically low. TransCom-CH4 showed that GEOS-Chem CH4
concentrations tend to be lower than the model median, and much lower than
the range of other models when using the same OH fields .
The aseasonal emissions used in this analysis likely reduce this documented
imbalance with the model's tropospheric OH fields.
Zonally averaged ACE minus GEOS-Chem climatological CH4 mole
fractions for boreal spring and fall. Black line represents the mean zonal
tropopause level. Site colors of squares on the x axis are as in
Fig. .
The XCH4 and XCH4t regression equations across
Southern Hemisphere sites are nearly equivalent, which suggests that the
Southern Hemisphere is not as impacted by the STE errors as the Northern
Hemisphere. This consistency between XCH4 and
XCH4t could also be a function of the zonal dependence of
the stratospheric error: whereas more than half of the Northern Hemisphere
sites are north of 45∘ N, the most poleward site in the Southern
Hemisphere is located at 45∘ S. The increased scatter associated with
the slightly lower XCH4tR2 value of 0.63, compared to
the XCH4R2 value of 0.88, does indicate that the Southern
Hemisphere is not exempt from model errors associated with emissions, the OH
distribution, or transport. The lower XCH4t slope of the
aseasonal simulation (1.1 vs. 1.3) illustrates the influence of emissions:
removing their seasonality leads to better measurement–model agreement,
evidenced by a slope closer to both the one-to-one line and the
zero-intercept. We hypothesize that either the seasonality of Southern
Hemispheric emissions is too strong or, more likely, errors in the Northern
Hemispheric seasonality of emissions drive measurement–model mismatch in the
Southern Hemisphere via interhemispheric transport. If this effect was solely
due to a changed emissions distribution, we would expect the XCH4
slope to also change for the Southern Hemisphere sites, if only slightly;
instead the slope is equivalent to the base simulation
XCH4t and XCH4 slopes, and R2=0.87, only
marginally less than the base simulation XCH4 correlation
coefficient.
The stratospheric contribution regression equations differ only slightly
between the base and aseasonal simulations: (0.64± 0.02)x+ 14, R2= 0.68,
vs. (0.62± 0.02)x+ 15, R2=0.67. The insensitivity of both the
stratospheric contribution and the total columns in the Southern Hemisphere
to perturbations in the seasonality of tropospheric emissions could be driven
by the smaller vertical gradient across the UTLS that results from the
influence of Northern Hemispheric air both in the free troposphere
and the stratosphere . This effect
would also support the interpretation of Northern Hemispheric emission errors
driving disagreement between observations and the model in the Southern
Hemisphere.
In the troposphere, CH4 increases from south to north; the
stratospheric contribution of CH4, however, increases from the
Equator to the poles due to the zonal gradient in tropopause height. In the
Northern Hemisphere total column, the zonal gradient largely disappears: at
high latitudes, the larger tropospheric emissions balances the larger
stratospheric contribution. By contrast, zonal gradients in the Southern
Hemisphere troposphere and stratosphere are additive, and greater south to
north differences are apparent in the total column.
Figure illustrates how the model differs from ACE-FTS
CH4 measurements in the stratosphere over boreal spring
(March–April–May) and fall (September–October–November). Except above the
tropical tropopause, CH4 is considerably lower in the ACE-FTS
climatology v. 2.2, compared to GEOS-Chem. The
difference varies both with altitude and latitude, especially in the Northern
spring poleward of 40∘ N. The vertical gradient is the least pronounced
in Lauder, where the stratospheric contributions of TCCON and GEOS-Chem fall
most closely to the one-to-one line (Fig. ). The low
CH4 in the tropical mid and upper stratosphere in GEOS-Chem could be
a result of too-weak vertical ascent to the stratosphere; however, the
ACE-FTS data gaps in the tropical troposphere make this hypothesis difficult
to test.
TCCON minus GEOS-Chem CH4 column-averaged DMFs as a function
of the effective GEOS-Chem tropopause height, shown for Northern Hemisphere
sites. Site colors are as in Fig. .
Dependence on tropopause height
In the Northern Hemisphere, the measurement–model mismatch of the
stratospheric contribution increases as the tropopause altitude shifts
downward (Fig. ). As the model's stratospheric portion of
the pressure-weighted total column increases, the error in stratospheric
CH4 is amplified, causing a larger disagreement with measurements.
Because the tropopause height decreases with latitude, and this gradient
increases during winter and spring, this introduces both zonal and seasonal
biases. The disagreement exhibits a large spread for relatively few
tropopause pressure heights because the model's effective tropopause, that
is, the pressure level at which the model divides the troposphere from the
stratosphere in GEOS-Chem, is defined at discrete grid level pressure
boundaries.
The tropospheric mismatch (ΔXCH4t), by contrast,
decreases with tropopause height for the majority of days and exhibits a much
weaker correlation to tropopause height, 0.099 vs. 0.22 for the
stratospheric contribution. Thus, as expected, the tropopause height explains
less of the variance in the measurement–model mismatch in
XCH4t: the upper troposphere is generally well-mixed, and
chemical loss does not vary with altitude as much as in the lower
stratosphere. This weaker relationship also demonstrates that the choice of
tropopause used in the tropospheric profile integration does not strongly
impact ΔXCH4t.
The relationship between ΔXCH4t and tropopause
height has a clear zonal component that indicates that the correlation is
instead a result of another parameter that varies with latitude. The
tropospheric slope is dominated by high-latitude sites; the subtropical sites
exhibit a much weaker correlation. At Izaña, which is in the sub-tropics at
an altitude of 2.4 km, the correlation between
ΔXCH4t and tropopause position is weak: the slope
of -0.035± 0.03 is nearly flat within error, and R2 is 0.025. By
contrast, the stratospheric relationship at Izaña corresponds more closely
with the other Northern Hemisphere sites: the slope is -0.088± 0.02, and
R2= 0.36.
Seasonal agreement
The tropospheric difference between TCCON and GEOS-Chem,
ΔXCH4t, has a periodic trend indicating that the
model error has a strong seasonal component in the troposphere. To isolate
stable seasonal patterns from the cumulative influence of emissions, we
calculate the detrended seasonal mean column-averaged DMFs for each site. In
the Southern Hemisphere, the measurements and model agree well. Across the
Northern Hemisphere sites, however, the seasonality differs
(Fig. ). The seasonal amplitude of GEOS-Chem
XCH4t is about equal to that of TCCON, but the TCCON
XCH4t seasonal minimum is in June/July while the GEOS-Chem
seasonal minimum is in September/October. Additionally, while TCCON
XCH4t begins to decrease in January, GEOS-Chem shows some
persistence into the spring.
Detrended seasonality of TCCON (black diamonds), GEOS-Chem base (red
circles), and GEOS-Chem aseasonal (blue squares) CH4 column-averaged
DMFs, averaged across Northern Hemisphere sites, excluding Saga, which has
less than one year of measurements prior to 2012. Error bars denote the
1σ standard deviation across sites.
The seasonal delay also appears in comparisons of GEOS-Chem surface
CH4 with National Oceanic and Atmospheric Administration (NOAA)
surface flask measurements at the LEF site in Park Falls
(Fig. ). The seasonality of GEOS-Chem's surface is
regulated more by emissions than transport: CH4 peaks in the summer,
when wetland emissions are highest (Fig. ). This
contrasts with the flask measurements, which reach a minimum in the summer
(Fig. ). The seasonality covaries remarkably closely
with respect to other features: the late winter decrease, spring persistence,
and local minimum in October. The spring plateau lasts twice as long as seen
in observations, however, and matches XCH4t, indicating
that feature is not the result of vertical transport between the PBL and free
troposphere.
Not surprisingly, a time lag does not occur in the stratosphere; the TCCON
stratospheric seasonal amplitude is less than half but in phase with that of
GEOS-Chem (Fig. ). The vertical inconsistency
of the seasonality produces unusual features in the model total column. From
January through April, the TCCON and GEOS-Chem XCH4 are consistent
because the model's bias in the troposphere is balanced by the larger
stratospheric contribution. Starting in May, however, the model diverges from
the measurements as the higher tropopause limits the stratosphere's
influence, and the phase lag in the troposphere dominates. This balancing
effect is also demonstrated by the greater variance across sites in the model
XCH4t and stratospheric contribution compared to
measurements, but about the same variance in XCH4.
NOAA surface flask (black) and GEOS-Chem surface level (red)
seasonality of CH4 DMFs over 2005–2011 at Park Falls, WI, USA and
Baring Head, NZ. Lower and upper bounds denote the 25th and 75th percentiles,
respectively, of detrended data for each month.
For the aseasonal simulation, the tropospheric seasonal cycle amplitude and
variance across sites increase (Fig. ). The
greatest model differences, from August through October, are a result of
dampening the large wetland fluxes in the base simulation that balance higher
OH concentrations. The seasonal amplitude does not increase as drastically in
the sub-tropics, where the total emissions are not as impacted by
seasonally varying sources, leading to the greater variance across sites. The
second largest difference between simulation amplitudes occurs in the spring,
and OH loss could potentially be contributing to the discrepancy in these months also. The aseasonal
simulation spreads the wetland fluxes so as to introduce emissions in the
winter and spring, when the OH concentrations are lowest. Another possibility
is that the model could be subject to errors that are in phase with the base
simulation seasonal emissions, which would then have an ameliorating effect
that produces the reasonable seasonal cycle amplitude. The stratospheric
contribution does not change, however, further demonstrating that the
stratosphere is insensitive to perturbations to Northern Hemisphere
emissions.
The impact of a static stratosphere and changing troposphere is to make the
seasonality of the aseasonal simulation XCH4 bimodal: the October
local minimum in the base simulation becomes a fall absolute minimum. The
aseasonal XCH4 agrees with TCCON in late winter, masking the
greater disagreement in the troposphere. Notably, the main tropospheric
features of the base simulation, the seasonal phase lag and spring
persistence, are still apparent. Thus, the seasonality of emissions
prescribed in the forward model is not the driver of the discrepancies
between measurement and model XCH4t seasonalities. OH is
not likely the driver of these features, as the Northern Hemisphere phase
shift also occurs in simulations performed with large changes in OH
(Fig. , in Appendix ). Transport is thus
the most likely driver of these tropospheric trends in the model.
Discussion
The stratospheric insensitivity to changes in emissions and tropospheric loss
has significant implications for flux inversions. Model inversions use the
sensitivity of trace gas concentrations at a given location to perturbations
of different emission sources to adjust those emissions so as to match
observations at that location. The response of modeled CH4 DMFs to
changing emissions depends on the model's transport and chemical loss, as
well as assumptions about the seasonal and spatial distribution of emissions
relative to each other. Thus the model sensitivity kernel, the linear
operator that maps emissions to CH4 concentrations, implicitly
includes uncertainties in these terms. The model's stratospheric response to
emission perturbations differs from that of the troposphere and is subject to
different transport and loss errors. Because the tropospheric transport
errors covary with emissions, they alias into the resulting source
attribution.
Comparing measurement and model stratospheric CH4 as a fraction of
the total column provides a normalized comparison that isolates differences
in the vertical structure from those caused by initial conditions and
unbalanced sources and sinks. Figure illustrates the error
associated with the normalized stratospheric column and the associated
stratospheric contribution to XCH4 at Park Falls. Although the
stratosphere accounts for less than 30 % of XCH4, a relatively
small error can produce significant seasonal differences; the springtime
error of 4.5× 1017 molecules cm-2 (23 ppb) is more than
twice the seasonal cycle amplitude. Winter and spring are also when
XCH4t is least sensitive to seasonal emissions; by
contrast, the error is about 15 ppb in the summer, when seasonal emissions
have the greatest influence (Fig. , top panel). The
seasonality of the stratospheric error will therefore distort the inversion
mechanism and thus posterior emissions estimates.
Top: Seasonally averaged fraction of model emissions from
seasonally varying sources, north of 40∘ N. Bottom:
Seasonally averaged normalized model stratospheric column error (teal) and
the difference between base and aseasonal simulation tropospheric columns
(orange) at Park Falls.
Additional bias is introduced by differences in the seasonal patterns of
ΔXCH4t and ΔXCH4. Wetlands are
the largest seasonal source of CH4 in models and the largest natural
source in flux inventories, and their emissions are very uncertain: estimates
range between 142 and 284 TgC year-1 for the 2000–2009 time period
. A priori GEOS-Chem CH4 emissions from northern
high-latitude wetlands are extremely variable, with large fluxes in June,
July and August, moderate fluxes in May and September, and almost no fluxes
the remainder of the year (Fig. a). Surface CH4
concentrations in models depend on the assumed seasonally varying emissions.
found that correlations between the seasonal cycles of the
forward model averages and in situ observations of CH4 DMFs at the
surface varied for a given site by up to 0.78± 0.4 depending on wetland
and biomass burning fields used. Model inversions that scale emissions in a
given grid box based on the incorrect seasonality will invariably change the
posterior attribution of seasonal emissions. found that
optimized wetland emissions from inversions that assimilate surface data only
are smaller than the priors, while those from inversions that assimilate
GOSAT total columns are larger, even if surface measurements are also
assimilated. From this we infer that the transport errors in the model's free
troposphere lead to an “optimization” of the prior fluxes of opposite sign
to that of the emission errors that the inversion attempts to correct.
(a) GEOS-Chem monthly zonal mean wetland emissions, in Gg.
(b) The Northern Hemisphere sensitivity of GEOS-Chem wetland
emission attribution caused by a 3-month lag for each 1 ppb increase of
CH4 in the tropospheric column, in Gg.
A two- to three-month shift in the phase of the XCH4t
seasonality will produce a strong under- or over-estimation of posterior
wetland fluxes in late spring through early fall. In an inversion, prior
emissions are adjusted in proportion to the deviation of the model's
CH4 DMFs from observed values. Attribution of these posterior
emissions to different sectors depends on a priori information and
assumptions about how they vary in time and location relative to one another.
Thus, an increase in posterior emissions relative to the prior in the
northern mid- and high latitudes during winter will not change emissions from
wetlands. For example, Fig. b illustrates the
sensitivity of posterior wetland emissions to a three-month lag in the
Northern Hemisphere. The change in posterior emissions is derived by
calculating the total emissions required to produce an increase of 1 ppb of
CH4 in each tropospheric column and scaling those emissions according
to the a priori contribution of wetlands, estimated as the fractional
contribution of wetlands to the total monthly mean emissions. The difference
between this change in wetland emissions and the value in the same location
three months prior produces the sensitivity of wetland emissions to the
tropospheric phase lag. This approach provides an alternative to the
computationally expensive calculation of the gain matrix over the entire time
series but does not include information about model transport.
The tropics and subtropics are less sensitive to a phase shift, but polewards
of 40∘ N, both the magnitude and seasonality of the difference are
significant. Large differences between measured and modeled
XCH4t are concurrent with low emissions from seasonal
sources. The adjustments to prior emissions produced by larger
measurement–model disagreement that occur when seasonal sources are a small
fraction of total emissions will overestimate posterior emissions from
aseasonal sources. Thus these seasonal errors will bias source apportionment
toward emissions that do not vary on timescales shorter than annually.
Conclusions
Assimilation of total column measurements into CTMs can improve constraints
on the global CH4 budget; however, the model's treatment of
stratospheric chemistry and dynamics must be carefully considered. This work
has compared TCCON and GEOS-Chem pressure-weighted total and tropospheric
column-averaged CH4 DMFs, XCH4, and
XCH4t respectively, parsing out the seasonality of the
troposphere and stratosphere and the resulting impacts on XCH4
(Fig. a). The Southern Hemisphere measurement–model
agreement is robust to changes in emissions or tropospheric OH. In the
Northern Hemisphere the model's stratospheric contribution is larger than
that of the measurements, and the mismatch increases as the tropopause
altitude decreases. The result is greater model error at high-latitude sites,
with the magnitude of this error varying seasonally. Moreover, in the
Northern Hemisphere the GEOS-Chem XCH4t exhibits a 2–3 month phase lag. The combined tropospheric and stratospheric errors smooth
the model XCH4 such that they may agree with total column
measurements despite having an incorrect vertical distribution.
Model transport errors coupled with spatial and seasonal measurement sparsity
can limit the accuracy of the location and timing of emissions scaling. The
differences in the seasonality mismatch across vertical levels amplify the
error uncertainty because the timing of optimized fluxes will be especially
susceptible to limitations in model transport. The stronger influence of the
stratosphere at higher latitudes due to lower tropopause heights, together
with the higher temporal variability of the stratospheric fraction of the
total column due to the stronger seasonal cycle of the tropopause, also
impacts the seasonality of the meridional gradient of XCH4.
The influence of stratospheric variability on emissions is not unique to the
model chosen for this analysis. ran TM5-4DVAR
inversions using SCIAMACHY column and NOAA surface measurements and found
that the mean biases between the optimized CH4 profiles and aircraft
measurements differ between the PBL, free troposphere, and UTLS. Seasonal
emissions from wetlands and biomass burning vary by ±10 and ±7 TgCH4, respectively, from year to year, and the zonal partitioning of
posterior emissions is sensitive to the wetland priors chosen. Moreover, the
larger changes to emissions and sensitivity to assumptions in the Northern
Hemisphere indicate that TM5 is also subject to the strong hemispheric
differences found in GEOS-Chem. The TransCom-CH4 model comparison
found that the interhemispheric exchange time in GEOS-Chem was near the model
median over the 1996–2007 time series , which suggests that
GEOS-Chem's interhemispheric transport, and thus associated errors, is not
particularly distinct. found that atmospheric CTM (ACTM) and other CTMs used
in TransCom-CH4 are subject to transport errors that impact emissions
optimization. Furthermore, ACTM profiles show a similar over-estimation of
stratospheric CH4, zonally varying measurement–model mismatch
dependent on tropopause height.
In this analysis we have used TCCON XCH4t derived with the
HF-proxy method; however, XCH4t calculated using other
stratospheric tracers such as nitrous oxide (N2O)
would provide an additional constraint on models' representations of the
stratosphere, as N2O is not subject to the spectral interference with
water vapor that impacts HF. Information about the vertical tropospheric
CH4 profile directly retrieved from NDACC spectra
can also be used to assess whether transport errors
differ at different levels of the free troposphere. Ideally, information from
these tropospheric products could be integrated to overcome the limitations
of each: the sensitivity of XCH4t to prior assumptions of
STE and the sensitivity of profile retrievals
to UTLS variability .
A limitation of the aseasonal simulation was that the distribution of
emissions was not identical to that of the base simulation due to the scaling
approach we employed. Ideally, the aseasonal emissions for each sector would
have been fluxes calculated for each grid box from the base simulation annual
emissions. The robustness of the model's tropospheric phase shift, which was
apparent regardless of the emissions used, demonstrates that this feature is
not a product of the chosen emissions fields. However, more nuanced analysis
on smaller spatial scales would benefit from simulations that prescribe the
annual mean for each of the seasonal sources. The most recent version of
GEOS-Chem has a much more flexible emissions scheme
that allows these more nuanced experiments to be performed and analyzed.
The insensitivity of model stratospheres to tropospheric change allows for a
straightforward solution: prescribed stratospheric CH4 fields based
on satellite observations from ACE-FTS, MIPAS , or a
compilation of remote sensing instruments . As the
representation of tropical convection and exchange across the UTLS advances
in models and reduces stratospheric isolation, chemical loss and transport
mechanisms would need to be improved. The output from more accurate
stratospheric models over the time period of interest could be used to set
the stratospheric component in the offline CH4 simulation. For
instance, the Universal tropospheric–stratospheric Chemistry eXtension (UCX)
mechanism, which has been added to more recent versions of GEOS-Chem, updates
the stratospheric component of the standard full chemistry simulation such
that CH4 has more sophisticated upwelling, advection, and chemical
reaction schemes . Models that account for interannual
variability in both stratospheric and tropospheric dynamics can then
assimilate total column measurements to develop more accurate global
CH4 budgets.
Data availability
The citations for the TCCON measurements in Table 1 are for the data files
themselves and have individually assigned DOIs. They can be found at
http://tccon.ornl.gov/.
Updates to tropospheric methane data
The TCCON XCH4t data used in this analysis were developed
as in with several adjustments to both the parameters used
and the methodology.
The HF-proxy method for determining XCH4t incorporates the
relationship between CH4 and HF in the stratosphere, which is
calculated using ACE-FTS data. These CH4-HF slopes now use updated
ACE-FTS version 3.5 measurements with v.1.1 flags
. The data quality flags are provided for profile
data on a 1 km vertical grid, which uses a piecewise quadratic method to
interpolate from the retrievals . Additionally, the
CH4 and HF measurement errors are now considered in the
pressure-weighted linear regression that determines the slopes. All other
data processing to produce the CH4-HF slopes followed methods
described in . Figure shows the
updated annual zonal values used to calculate X^CH4t
with and MkIV (retrieved from
http://mark4sun.jpl.nasa.gov/m4data.html) values included for reference
cf.their Fig. 2. These updates altered
X^CH4t for the sites and time period covered in this
paper by less than 2 ppb.
The derivation of the tropospheric column in ,
, and implicitly assumed that the
CH4 profile is continuous across the tropopause; however, the
boundary condition for stratospheric CH4 is rather set by
tropospheric air transported through the tropical tropopause
. showed that the
concentration of CO2 directly above the tropopause can be
approximated by introducing a two-month phase lag to the average
concentration at northern and southern tropical surface sites: Mauna Loa,
Hawaii (MLO) and Tutuila, American Samoa (SMO), respectively. As the
CH4 entering the stratosphere originates in both hemispheres
, stratospheric CH4 exhibits a smaller
interhemispheric gradient than in the troposphere: about 20 ppb, as calculated
from ACE-FTS measurements, vs. about 50 ppb, taken as the difference at MLO
and SMO. To calculate the stratospheric boundary condition for CH4 we
remove the seasonal component of the mean of CH4 DMFs at MLO and SMO,
which are made available through 2014 by the NOAA Earth System Research
Laboratory (ESRL) Global Monitoring Division . To
capture the interhemispheric gradient observed in ACE stratospheric
CH4 measurements, we add and subtract 10 ppb, in the northern and
southern extratropics respectively, the limits of which we choose as the
Tropic of Cancer (23∘ N) and the Tropic of Capricorn (23∘ S). A
constant value is chosen in each hemisphere to reflect the rapid mixing time
of air from the extra-tropics in the region directly above the tropopause,
which found to be less than one month. Within the
tropics, we interpolate the boundary condition as a linear function of
altitude such that xCH4(Pt)=x‾CH4s+1023λ, where xCH4(Pt) is the boundary
condition at the tropopause, x‾CH4s is the mean DMF of
CH4 at the surface, and λ is the latitude of the site.
Long-term CH4–HF slopes from ,
MkIV, and updated ACE-FTS measurements. Inset: Time series of zonal
pressure-weighted ACE-FTS slopes (β) used to calculate
X^CH4t, with error bars denoting the 2σ
standard error. Zonal slopes are offset each year for visual clarity.
Assuming hydrostatic equilibrium, the tropospheric column of CH4,
cCH4t, can be calculated as the integral of the vertical
profile, xCH4≡xCH4(P), from the surface, Ps, to
the tropopause, Pt:
cCH4t=∫PtPsxCH4dPgm=XCH4tPs-Ptg∗tm,
where P is the pressure height, g is the gravitational
acceleration, g∗t is the pressure-weighted tropospheric value of
g, and m is the mean molecular mass of CH4. The profile of CH4 in the stratosphere can
be expressed as a linear function of pressure altitude, xCH4(P)=xCH4(Pt)+δ⋅P, where
δ=dxCH4dP is the stratospheric loss of
CH4. This stratospheric loss term is estimated by the HF-proxy method
to produce the retrieved tropospheric column-averaged DMF,
X^CH4t, such that
X^CH4tPsg∗m=c^CH4t=∫0PsxCH4dPgm-∫0Ptδ⋅PdPgm,
where g∗ is the pressure-weighted column average of g. The
stratospheric boundary condition can thus be related to the retrieved
tropospheric column as
∫0PtxCH4dPgm=∫0PtxCH4PtdPgm-c^CH4t+∫0PsxCH4dPgm.
Given that the total column integration is the sum of the tropospheric and
stratospheric partial columns, and substituting Eq. (),
∫PtPsxCH4dPgm=∫0PsxCH4dPgm-∫0PtxCH4dPgm=∫0PsxCH4dPgm-∫0PtxCH4(Pt)dPgm+c^CH4t,-∫0PsxCH4dPgm,=c^CH4t-∫0PtxCH4PtdPgm,XCH4tPs-Ptg∗tm=X^CH4tPsg∗m-xCH4PtPtg∗0m,
where g∗0 is the pressure-weighted average of g from the
tropopause to the top of the atmosphere. While the molecular mass of air
changes as a function of water vapor and thus altitude and gravity changes as
a function of both altitude and latitude, assuming constant values of g and
m changes XCH4t by less than 2 ppb. Thus, to good
approximation these variables can be canceled out:
XCH4tPs-Pt=X^CH4t⋅Ps-xCH4(Pt)⋅Pt,XCH4t=X^CH4t⋅Ps-xCH4Pt⋅PtPs-Pt.
The surface pressure is measured at each site, and the tropopause
pressure is calculated from the TCCON prior temperature profiles. The
uncertainties associated with the interpolated value of the tropopause height
are determined by calculating XCH4t for ±30 % of
Pt and adding these confidence intervals in quadrature to the
precision error of X^CH4t. The aforementioned
deseasonalization of xCH4(Pt) is an approximation that
adds another uncertainty. The signal of the tropospheric seasonal cycle of a
trace gas entering the stratosphere is apparent directly above the tropopause
and both dampens in amplitude and shifts in time with increasing altitude
. Thus, the stratospheric boundary condition is not truly
constant throughout the column, but rather the pressure-weighted sum of these
attenuated signals. Calculating xCH4(Pt) without removing
the seasonality, which provides the maximum impact of this uncertainty,
decreases XCH4t by an average of 1 and 4 ppb in the
Northern and Southern Hemispheres, respectively, and does not alter the
seasonal cycle of XCH4t. Moreover, as described below, the
mismatch between the calibrated TCCON XCH4t and the in
situ aircraft XCH4t does not correlate with season
(R2= 0.017). Thus, we retain the simpler computation of deseasonalized
xCH4(Pt) in Eq. ().
Calibration curve of TCCON XCH4tcf.their Fig. 8. Site colors are as in
Fig. . Aircraft campaigns are described in Table 6 of
.
Air-mass-dependent artifacts were derived for updated values consistently with
the total column CH4. Removing these artifacts, the
XCH4t was then calibrated with in situ aircraft profiles
using the same methodology described in and including the
updates delineated in to produce a calibration correction
factor of 0.9700 (Fig. ). The covariance between the
difference between the calibrated TCCON and aircraft XCH4t
and several parameters were assessed to ensure biases were not introduced
into the measurements. These differences had an uncertainty-weighted
correlation coefficient of 0.1 for solar zenith angle and
uncertainty-weighted correlation coefficients of less that 0.02 for
tropopause and surface pressures, year, and season. Measurement precisions
and errors were determined as in , with the additional
uncertainties mentioned in this section included. Individual TCCON sites have
median XCH4t precisions in the range of 0.1–0.8 %, and
mean and median precisions are 0.3 and 0.2 %, respectively, for all sites
through May 2016.
All equilibrium runs for a given simulation have identical meteorology,
emissions, and OH fields over June 2004–May 2005. Initial conditions for each
year are set by the restart files of the previous run. To calculate columns
at each site, GEOS-Chem monthly mean mole fractions are adjusted for the
monthly medians of the site's daily mean surface pressures and smoothed with
the monthly median scaled prior profiles and averaging kernels, interpolated
using the monthly medians of the daily mean solar zenith angles. Because Park Falls
and Lauder are the only TCCON sites that had started taking measurements over
this time period, they are the only sites used to generate smoothed columns
for the comparisons to the experimental simulations.
Emissions in the aseasonal simulation were derived by running a
two-dimensional regression on the annual emissions to determine the scale
factors that would produce the smallest residual of total emissions and the
interhemispheric gradient. Figure illustrates the
difference in total emissions between the base and aseasonal simulations for
each zonal band.
The updated OH simulation used OH output from a 2012 GEOS-Chem standard
chemistry simulation with extensive updates to the photochemical oxidation
mechanisms of biogenic volatile organic compounds (VOCs), described in
and references therein. These were converted to 3-D monthly
mean OH concentrations to conform to the infrastructure of the GEOS-Chem
offline CH4 tropospheric loss mechanism. The OH was then scaled by
90 % to keep the lifetime above 8 years, and emissions were scaled by
112 % to maintain the same balance between sources and sinks in the base
simulation. Figure provides zonal averages of the difference
between the base and updated OH columns.
The full list of simulations run is provided in Table ,
with descriptions and the CH4 emissions, tropospheric OH, and total
chemical loss lifetimes. Figure shows each simulation's
seasonality of XCH4t at Park Falls, with TCCON seasonality
plotted for reference, as well as the seasonality of the difference between
the base and each simulation.
Derivation of dry gas values
Versions of GEOS-Chem prior to v.10 have inconsistencies in wet vs. dry
definitions of pressure, temperature, and air mass, which propagate into
model diagnostics and conversions calculated using these terms. As a
consequence, CH4 concentrations are output assuming air masses that
include water vapor but calculated with the molar mass of dry air. For all
comparisons in this analysis CH4 DMFs are calculated taking into
account the GEOS-5 specific humidity, qs (in units of
gH2O kgair-1), such that
xCH4,dry=xCH41-qs×10-3
where xCH4 is the model profile in mole fractions. Dry
air profiles were derived by subtracting the water vapor mole fraction, also
calculated from the GEOS-5 specific humidity, from the total air mass at each
pressure level, as in and .
Monthly averages of the difference in total CH4 emissions
between the base and aseasonal GEOS-Chem simulations, summed over each zonal
band, in Tg mo-1.
Zonal averages of the difference in total column OH
(molecules cm-2) between the base and updated monthly OH fields.
List of sensitivity experiments.
Simulation nameDescriptionCH4 lifetime (years) with respect to Final CH4EmissionsTropospheric OHTotal LossBurden (Tg)BaseDefault OH and emissions9.610.79.74825AseasonalConstant monthly emission rates9.610.79.74872Updated OHMonthly OH fields from standard chemistry + biogenic VOCs, scaled down by 10 %8.59.48.64828Unscaled updated OHMonthly OH fields from standard chemistry + biogenic VOCs7.78.47.8491790 % OHDefault OH scaled down by 10 %10.511.910.75296110 % OHDefault OH scaled up by 10 %8.89.78.84425Scaled rice emissionsRice emissions increased by 20 %9.610.79.64780No wetlandsWetland emissions turned off10.710.69.53768Scaled livestock EmissionsScale livestock emissions by 50 %9.610.79.64359MERRAMERRA meteorology fields9.610.79.64849Tropopause levelSet top of troposphere 2 vertical levels higher9.610.69.64855
Seasonality of tropospheric methane (XCH4t) at
Park Falls for TCCON (black solid line), GEOS-Chem (red solid line), and the
difference from the base simulation (dotted red line) for each of the
sensitivity experiments, in ppb.
Model smoothing for measurement comparisons
Base and aseasonal daily runs were initialized using CH4 fields from
their respective 34th equilibrium cycles. Daily CH4 mole fractions
averaged over both 24 h and 10:00–14:00 local time were output to test whether
TCCON's daytime-only observations would introduce a bias in the comparisons.
Measurement–model differences were not sensitive to averaging times.
Comparison of measurements to model columns produced using the 24 h and
10:00–14:00 LT averages produce equivalent slopes and only slightly
different intercepts and correlation coefficients. The seasonality of
10:00–14:00 LT column-averaged DMFs does not differ, except for the fall
seasonal maximum of the adjusted troposphere and stratospheric contribution
at Park Falls in October, one month later than the 24 h column-averaged DMF
seasonality.
CH4 dry vertical profiles for each grid box associated with a TCCON
site, xCH4m, were smoothed with corresponding FTS column
averaging kernels, aCH4, and scaled priors for each day
and vertically integrated using pressure-weighted levels:
XCH4s=γCH4⋅XCH4a+aCH4§xCH4m-γCH4xCH4a
where XCH4s is the smoothed GEOS-Chem column-averaged
DMF, γCH4 is the TCCON daily median retrieved profile
scaling factor, and xCH4a and
XCH4a are respectively the a priori profile and
column-integrated CH4 DMFs . The pressure
weighting function, h, was applied such that X=hTx.
TCCON priors were interpolated to the GEOS-Chem pressure grid, and GEOS-Chem
pressure and corresponding gas profiles were adjusted using daily mean
surface pressures local to each site . The
averaging kernels were interpolated for the local daily mean solar zenith
angle and the GEOS-Chem pressure grid so that it could be applied to the
difference between the GEOS-Chem and TCCON profiles as a§x=∑i=1Naihixi from the surface to the highest level, N, at
i pressure levels . Figure
shows how the smoothed column compares to the column that only uses the dry
gas correction.
GEOS-Chem smoothed vs. dry integrated CH4 DMFs for base
simulation tropospheric columns, total columns, and stratospheric
contribution. Site colors are as in Fig. . Dashed lines mark
the one-to-one lines.
Derivation of stratospheric contribution
Considering the CH4 profile integration as in
Eq. (), and substituting the profile of CH4 in the
stratosphere, xCH4(P)=xCH4(Pt)+δ⋅P, described in Appendix , the total column is calculated
as:
∫0PsxCH4dPgm=∫PtPsxCH4dPgm+∫0PtxCH4Pt+δ⋅PdPgm,XCH4⋅Ps=XCH4tPs-Pt+xCH4Pt⋅Pt+cCH4δ,
where cCH4δ is the pressure-weighted column
average of CH4 loss in the stratosphere. Rearranging terms,
Eq. () becomes:
XCH4-XCH4tPs=xCH4Pt-XCH4tPt+cCH4δ,XCH4t-XCH4=XCH4t-xCH4PtPtPs-cCH4δPs,
such that the difference between the tropospheric and total column-averaged
DMFs is a function of the two terms governing the stratospheric contribution
to the total column: the gradient across the tropopause,
xCH4(Pt)-XCH4t, and stratospheric
CH4 loss, cCH4δ. The stratospheric contribution is
thus a proxy for the impact of stratospheric variability on the total column
of CH4: given a constant tropospheric column, as the stratospheric
contribution becomes larger the total column-averaged DMF becomes smaller.
Acknowledgements
This work was supported by NASA Headquarters under the NASA Earth and Space
Science Fellowship Program grant NNX14AL30H and NASA's Carbon Cycle Science
program. Park Falls, Lamont, and JPL are funded by NASA grants NNX14AI60G,
NNX11AG01G, NAG5-12247, NNG05-GD07G, and NASA Orbiting Carbon Observatory
Program; we are grateful to the DOE ARM program and Jeff Ayers for their
technical support in Lamont and Park Falls, respectively. Darwin and
Wollongong are funded by NASA grants NAG5-12247 and NNG05-GD07G and the
Australian Research Council grants DP140101552, DP110103118, DP0879468, and
LP0562346, and Nicholas Deutscher is supported by an Australian Research
Council Fellowship, DE140100178; we are grateful to the DOE ARM program for
technical support in Darwin. Bremen, Bialystok, and Orleans are funded by the
EU projects InGOS and ICOS-INWIRE and by the Senate of Bremen. Réunion
Island is funded by the EU FP7 project ICOS-INWIRE, the national Belgian
support to ICOS and the AGACC-II project (Science for Sustainable Development
Program), the Université de la Réunion, and the French regional and
national organizations (INSU, CNRS). From 2004 to 2011 the Lauder TCCON
program was funded by the New Zealand Foundation of Research Science and
Technology contracts CO1X0204, CO1X0703, and CO1X0406. We thank Shuji Kawakami
for his technical support in Saga. We thank Peter Bernath, Kaley Walker, and
Chris Boone for their guidance using the ACE-FTS data, which were obtained
through the Atmospheric Chemistry Experiment (ACE) mission, primarily funded
by the Canadian Space Agency. We are grateful to Geoff Toon for his
continuous efforts developing the GGG software, for providing the MkIV data,
and his input on the manuscript. We thank Arlyn Andrews for providing the LEF
surface flask data, which were generated by NOAA-ESRL, Carbon Cycle
Greenhouse Gases Group. Baring Head NIWA surface data were provided courtesy
of Gordon Brailsford, Dave Lowe, and Ross Martin. We also acknowledge the
contributions of in situ vertical profiles from the AirCore, HIPPO, IMECC,
INTEX, Learjet, and START08 campaigns. We are grateful to Kelvin Bates for
providing monthly OH fields for the GEOS-Chem Updated OH sensitivity
experiments. Lastly, we thank the three anonymous reviewers who provided
feedback and suggestions.
Edited by: P. Jöckel
Reviewed by: three anonymous referees
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