ACPDAtmospheric Chemistry and Physics DiscussionsACPDAtmos. Chem. Phys. Discuss.1680-7375Copernicus GmbHGöttingen, Germany10.5194/acpd-15-20395-2015The imprint of stratospheric transport on column-averaged methaneOstlerA.andreas.ostler@kit.eduSussmannR.PatraP. K.https://orcid.org/0000-0001-5700-9389WennbergP. O.DeutscherN. M.https://orcid.org/0000-0002-2906-2577GriffithD. W. T.https://orcid.org/0000-0002-7986-1924BlumenstockT.https://orcid.org/0000-0003-4005-900XHaseF.KiviR.https://orcid.org/0000-0001-8828-2759WarnekeT.WangZ.De MazièreM.RobinsonJ.OhyamaH.https://orcid.org/0000-0003-2109-9874Karlsruhe Institute of Technology, IMK-IFU,
Garmisch-Partenkirchen, GermanyResearch Institute for Global Change, JAMSTEC, Yokohama,
236–0001, JapanCalifornia Institute of Technology, Pasadena, USAUniversity of Wollongong, New South Wales, Wollongong,
AustraliaInstitute of Environmental Physics, University of Bremen,
Bremen, GermanyKarlsruhe Institute of Technology, IMK-ASF, Karlsruhe,
GermanyFinnish Meteorological Institute, Arctic Research Center,
Sodankylä, FinlandBelgian Institute for Space Aeronomy, BIRA-IASB, Brussels,
BelgiumNational Institute of Water and Atmospheric Research, NIWA, Omakau,
New ZealandEarth Observation Research Center, EORC, Aerospace
Exploration Agency, JAXA, Tsukuba, Japannow at: Solar-Terrestrial Environment Laboratory, Nagoya
University, Nagoya, JapanA. Ostler (andreas.ostler@kit.edu)28July20151514203952044719June20154July2015This 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/preprints/15/20395/2015/acpd-15-20395-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/preprints/15/20395/2015/acpd-15-20395-2015.pdf
Model simulations of column-averaged methane mixing ratios
(XCH4) are extensively used for inverse estimates of methane
(CH4) emissions from atmospheric measurements. Our study
shows that virtually all chemical transport models (CTM) used for
this purpose are affected by stratospheric model-transport
errors. We quantify the impact of such model transport errors on the
simulation of stratospheric CH4 concentrations via an
a posteriori correction method. This approach compares measurements
of the mean age of air with modeled age and expresses the difference
in terms of a correction to modeled stratospheric CH4 mixing
ratios. We find age differences up to ∼3years yield
to a bias in simulated CH4 of up to 250 parts per billion
(ppb). Comparisons between model simulations and ground-based
XCH4 observations from the Total Carbon Column Network
(TCCON) reveal that stratospheric model-transport errors cause
biases in XCH4 of ∼20ppb in the midlatitudes
and ∼27ppb in the arctic region. Improved overall as
well as seasonal model-observation agreement in XCH4
suggests that the proposed, age-of-air-based stratospheric
correction is reasonable.
The latitudinal model bias in XCH4 is supposed to reduce the
accuracy of inverse estimates using satellite-derived XCH4
data. Therefore, we provide an estimate of the impact of
stratospheric model-transport errors in terms of CH4 flux
errors. Using a one-box approximation, we show that average model
errors in stratospheric transport correspond to an overestimation of
CH4 emissions by ∼40 % (∼7Tgyr-1) for the arctic, ∼5 % (∼7Tgyr-1) for the northern, and ∼60 % (∼7Tgyr-1) for the southern hemispheric mid-latitude
region. We conclude that an improved modeling of stratospheric
transport is highly desirable for the joint use with atmospheric
XCH4 observations in atmospheric inversions.
Introduction
The global budget of CH4 is driven by emissions from the
Earth's surface and chemical loss in the atmosphere. While the
dominant sink due to hydroxyl radicals (OH) is commonly regarded as
stable over a long time period (Montzka et al., 2011), the sources of
CH4 exhibit high variability in their spatial and temporal
distribution (Dlugokencky et al., 2009; Nisbet et al., 2014). Methane
emissions are estimated by top-down approaches on the basis of
chemical transport models (CTMs) constrained by atmospheric
measurements. Required a priori information about the distribution of
emissions and sinks is often provided by bottom-up approaches. Both,
top-down and bottom-up estimates have been used to assess and to
explain changes of the global CH4 budget in the past (Kirschke
et al., 2013). Recently, top-down inversions have increasingly
benefitted from newly available satellite remote sensing measurements
(Bergamaschi et al., 2013; Fraser et al., 2013, 2014; Monteil et al.,
2013; Houweling et al., 2014; Wecht et al., 2014; Cressot et al.,
2014; Alexe et al., 2015; Turner et al., 2015) covering large
geographical areas where surface observation networks lack in density,
such as the tropical lands. In contrast to in situ observations,
mostly performed at the surface, satellite retrievals of
column-averaged dry-air CH4 mixing ratios (XCH4)
contain information about the complete CH4 vertical
distribution. The stratospheric contribution relative to the
CH4 total column only is ∼5 % at the tropics but
increases up to ∼25 % at mid- and high latitudes. This
implies that CTMs have to account for both stratospheric and
tropospheric chemistry and transport when using satellite XCH4
retrievals for optimizing fluxes. Although the modeling of
stratospheric transport has been improved, it still remains
a challenging task for CTMs with a focus on the simulation of
tropospheric tracers such as CH4. As a result of errors in the
model transport parameterizations, stratospheric CH4
distributions simulated by various models show significant
differences, especially in the lower stratosphere (up to ∼50 % between the models) (Patra et al., 2009b).
A clear separation between systematic model errors and systematic
measurement errors is a central problem, since inversion-estimated
fluxes are obtained by minimizing model-measurement residuals (Fraser
et al., 2013; Houweling et al., 2014; Turner et al., 2015). Efforts to
determine fluxes from satellite-derived XCH4 have used
different strategies to account for ill-defined residual biases where
most of the bias corrections are based on ad hoc latitudinal
functions. For example, a recent CH4 inversion study removes
a high-latitude bias by fitting a quadratic regression to the
latitudinal distribution of model-measurement XCH4 residuals
(Turner et al., 2015). In this context, the latitudinal bias is
attributed to errors in either the satellite observations or the
modeled stratospheric CH4 without an unambiguous
assignment. This suggests, that a precise description of stratospheric
model errors is needed. Therefore, the goal of this study is (i) to
better understand the sensitivity of XCH4 to the details of
simulated stratospheric transport and (ii) to estimate the impact of
stratospheric model-transport errors in terms of emissions.
The paper has the following structure: after introducing the models
(Sect. 2) and the observations (Sect. 3), we present the stratospheric
correction in Sect. 4. Results from the comparison between model and
TCCON data are shown in Sect. 5. Section 6 shows that the impact of
stratospheric model-transport errors on stratospheric CH4 can
be converted into CH4 emissions. Section 7 contains a summary
and conclusions.
Model simulations
In order to analyse the relationship between stratospheric CH4
and stratospheric transport, we use model simulations of CH4
and of mean age – a well-known diagnostics for stratospheric
transport (Waugh and Hall, 2002).
ACTM
The main part of our analysis is based on simulations from
a state-of-the-art CTM: the Center for Climate System
Research/National Institute for Environmental Studies/Frontier
Research Center for Global Change (CCSR/NIES/FRCGC) atmospheric
general circulation model (AGCM) based CTM (hereafter, ACTM). ACTM
simulations of CH4 were provided with a horizontal resolution
of T42 spectral truncation (∼2.8∘×2.8∘)
with 67 sigma-pressure levels in the vertical (surface
-90 km). The modeled meteorology from ACTM is nudged towards
the fields of Japan Meteorological Agency Reanalysis (JRA) data
products.
Tropospheric OH concentrations are predefined and scaled to match
global methyl chloroform (CH3CCL3) decay rates
(Spivakovsky et al., 2000). Stratospheric OH distributions are used
from the full stratospheric chemistry model version of the ACTM along
with a monthly mean climatology of chlorine (Cl) (Takigawa et al.,
1999). The O (1D) concentrations are calculated in
ACTM. Comparisons of ACTM-simulated SF6 with atmospheric
measurements demonstrate that the modeled transport fairly captures
synoptic and seasonal variations in tropospheric transport as well as
interhemispheric exchange (Patra et al., 2009a). Additionally, the
modeled NH/SH OH ratio of 0.99 is in line with CH3CCL3
observations (Patra et al., 2014). As a consequence, the accurate
model description of tropospheric transport and photochemical removal
of CH4 leads to realistic vertical and interhemispheric
CH4 gradients in the troposphere (Saito et al., 2013).
Emission distributions correspond to the control scenario used by the
TransCOM CH4 intercomparison study (Patra et al., 2011). The
prior CH4 fluxes includes interannually varying anthropogenic
emissions, based on annual mean 1∘×1∘ maps
from the Emission Database for Global Atmospheric Research (EDGAR;
version 3.2/FT) (Olivier and Berdowski, 2001) and cyclostationary
natural emissions, based on the GISS inventory (Fung et al.,
1991). Emission data are extrapolated for the years after 2008. The
photochemical removal of CH4 in the troposphere and
stratosphere is simulated by ACTM via reactions involving OH, Cl, and
O (1D) along with recommended temperature-dependent reaction
and wavelength-dependent photolysis rates (Sander et al., 2006; Patra
et al., 2011).
Besides the simulation of CH4, ACTM provides distributions of
the mean age of stratospheric air to analyze transport in the
troposphere and stratosphere. Mean age is calculated as the difference
between surface and upper air concentrations normalized by the
concentration increase rate at the surface using a Green's function
method (Hall and Plumb, 1994). The latter is estimated from the
simulation of an idealized transport tracer with uniform surface
fluxes, linearly increasing trend, and no loss in the atmosphere
(Patra et al., 2009).
TransCOM models
The chemistry-transport model intercomparison experiment
(TransCom-CH4) is a comparison between CH4 simulations
from twelve CTMs with a special focus on the role of surface
emissions, transport, and chemical loss (Patra et al., 2011). For this
study we analyzed model simulations from six selected TrancCom CTMs:
ACTM (Patra et al., 2009a, b, 2011, 2014), GEOS-Chem
(Pickett-Heaps et al., 2011; Fraser et al., 2011), LMDZ (Hourdin
et al., 2006), NIES08i (Belikov et al., 2011), TM5 (Krol et al., 2005),
and TOMCAT (Chipperfield, 2006). Note that the TransCom version of
ACTM uses meteorological products from the National Centers for
Environmental Prediction (NCEP) and hence differs from the ACTM
version used in this study. We derived mean age distributions via
sulfur hexafluoride (SF6) model simulations for all CTMs. As SF6 is
a chronological tracer, stratospheric mean age was calculated as the
difference between SF6 mixing ratio at the tropical
tropopause and stratospheric SF6 mixing ratio normalized
by the tropospheric growth rate of SF6 at the tropics.
Observational datasets
In addition to model simulations, observations of mean age and
XCH4 are used to evaluate both the accuracy of stratospheric
model transport and the sensitivity of XCH4 to stratospheric
model transport.
Mean age
Mean age data was inferred from vertical profiles of SF6
measured by balloon-borne cryogenic air sampler. The observed mean age
dataset used in the current study consists of 7 vertical profiles of
SF6 obtained at three different locations in the Northern
Hemisphere (NH) at altitudes between 17 and 37 km (see
Table 1). One profile was presented in Patra et al. (1997), the
remaining profiles are part of the study from Harnisch et al. (1996).
TCCON
Solar absorption measurements in the near-infrared (NIR) are performed
via ground-based Fourier Transform Spectrometers (FTS) at TCCON sites
across the globe. TCCON-type measurements are analyzed with the GGG
software package including the spectral fitting code GFIT to derive
total column abundances of several trace gases (Wunch et al.,
2011). The CH4 total column is inverted from the spectra in 3
different spectral windows centered at 5938, 6002, and
6076 cm-1. The spectral fitting method is based on
iteratively scaling a priori profiles to provide the best fit to the
measured spectrum. The general shape of the a priori profiles has been
inferred from aircraft, balloon and satellite profiles in order to
provide a realistic interhemispheric gradient. In addition, the shape
of the daily a priori profile is stretched vertically depending on
tropopause altitude and the latitude of the measurement
site. XCH4 is calculated by dividing the CH4 total
column by the simultaneously measured dry-air pressure column.
These XCH4 retrievals are a posteriori corrected for known
airmass-dependent biases and calibrated to account for
airmass-independent biases which can among other errors arise from
spectroscopic uncertainties. The airmass-independent calibration
factor is determined by comparisons with coincident airborne or
balloon-borne in situ measurements over TCCON sites (Wunch et al.,
2010; Messerschmidt et al., 2011; Geibel et al., 2012). Although the
absolute accuracy of TCCON XCH4 retrievals is calibrated to in
situ measurements on WMO scale, it can still be affected by additional
systematic uncertainties. However, the quality of the retrievals is
continuously improved by correcting the influence of systematic
instrumental changes over time. As a result of these improvements
there are different versions of the GGG software package. In this
study we use TCCON retrievals performed with version GGG2014 (for
details see https://tccon-wiki.caltech.edu/). The TCCON
measurement precision for XCH4 is ∼0.3 % for single
measurements.
TCCON data were obtained from the TCCON Data Archive, hosted by the
Carbon Dioxide Information Analysis Center (CDIAC:
http://cdiac.ornl.gov/). The individual data sets of the TCCON
sites used in this study are available at this database (Blumenstock
et al., 2014; Deutscher et al., 2014; Griffith et al., 2014a, b; Hase
et al., 2014; Kawakami et al., 2014; Kivi et al., 2014; de Mazière
et al., 2014; Sherlock et al., 2014a, b; Sussmann et al., 2014;
Warneke et al., 2014; Wennberg et al., 2014a, b).
Stratospheric correction
Inaccurate model transport in the stratosphere implies errors in the
description of stratospheric CH4 and, hence, errors in
simulated XCH4. In order to correct these model errors, we
perform a two-step correction. We first correct modeled mean age and
subsequently use the corrected mean age to adjust stratospheric
CH4.
Stratospheric model-transport error
Recently, Miyamoto et al. (2013) compared ACTM-modeled age of air with
ages inferred from SF6 observations presented in
Sect. 3.1. They obtained correction factors at each SF6
profile location and interpolated these factors to all model grid cells between the
equator and the North Pole. The mirror image was used for the SH. Applying the correction factors to the modeled age produces
distributions of corrected age. Both the original and the corrected
model distribution of mean age correspond to climatological monthly
mean distributions.
We detect stratospheric model-transport errors as the difference
between original and corrected model distributions of age. Zonal
annual means of original age (Fig. 1a) and corrected age (Fig. 1c), as
well as the corresponding age differences (Fig. 1e) provide evidence
that ACTM underestimates the age in the lower stratosphere
(50–100 hPa) by up to ∼3years, with the
magnitude of the error increasing poleward. In general, the magnitude
of the modeled age is lower compared to the corrected age and the
shape of age isopleths differs.
Adjustment of CH4 simulations
To account for the impact of model-transport error on stratospheric
CH4 we applied an a posteriori correction to the modeled
CH4 mixing ratio profiles x. The correction method is
based on expressing the stratospheric mixing ratio of long-lived
tracers in terms of the (corrected) age of air (Γ). According
to Volk et al. (1997) the following Taylor expansion can be used for
chemically active tracers in the vicinity of the tropopause (Γ=Γtp,x=xtp):
xΓ=x01-β0Γ-γ0Γ+β0γ0Γ2+2Δ2
where
β0=-1ΓtpdxdΓΓtp
is the normalized gradient of x with respect to age at the
tropopause and Δ is the width of the age spectrum. The
normalized average growth rate γ0 reflects the increase in
tropospheric CH4. Eq. () describes the decline of
stratospheric CH4 due to three contributions: (A) chemical
loss, (B) tropospheric growth, and (C) interaction of chemistry and
growth. For our correction of CH4 mixing ratios we found that
term (A) is predominant in particular within the lower
stratosphere. Terms (B) and (C) have minor contributions within the
upper stratosphere (see Fig. A1).
The chemical loss of CH4 in terms of age (i.e. β0)
was derived from the original CH4 model profiles. The linear
tropospheric trend (Γ0) was estimated to be
6 ppbyr-1 since the beginning of the year 2006, which on
average is in agreement with recent trend studies (Dlugokencky et al.,
2009; Sussmann et al., 2012). The width of the age spectrum Δ
was parameterized in terms of Γ as Δ2=1.25 yr (Γ+0.5 yr) according to an approximation obtained from GCM simulations
(Volk et al., 1997). Since modeled and the corrected ages differ only
above the tropopause, the position of the tropopause can be determined
easily. Applying the corrected age of air to Eq. () yields
the corrected stratospheric CH4 mixing ratios.
It is important to note that we did not explicitly account for
interannual variability in age since we used monthly mean
distributions, which can be regarded as climatological
distributions. Therefore, our stratospheric correction of CH4
concentrations could be affected by uncertainties due to interannual
variability. However, these errors cancel out in the column's ratio of
corrected and modeled age. This ratio is implicitly contained in the
predominant chemical loss term β0Γ. Minor seasonal
errors in upper stratospheric mixing ratios might result from
tropospheric growth (term B) and mixed growth-chemistry (term C), but
cause a small impact on XCH4 given the relative contribution
of the upper stratospheric CH4 to the total column. Trends in
stratospheric age are not considered as they are still under debate
(Engel et al., 2009; Mahieu et al., 2014).
Applying the stratospheric correction to original ACTM simulations of
CH4 yields “age-corrected” model CH4 distributions
(ACTMac). Figure 1f shows differences between zonal CH4
distributions of ACTM (Fig. 1b) and ACTMac (Fig. 1d). These
CH4 differences between original and corrected stratospheric
CH4 mixing ratios are minimal at the tropics and increase
towards high latitudes with maxima in the lower stratosphere (up to
∼250ppb). Furthermore, the model errors in
stratospheric CH4 depend on season; i.e., the correction of
stratospheric CH4 has a seasonal component with maximum in
winter-time and minimum in summer (see Fig. A2).
Results from validation of the model correction
In the following, we compare original model simulations (ACTM) with
model simulations where we have corrected for errors in the simulated
stratospheric CH4 (ACTMac). As we are interested in the impact
of the stratospheric model-transport error on XCH4, both model
datasets are evaluated by TCCON XCH4 retrievals from selected
sites (see Table 1). For this reason, vertical CH4 model
distributions were extracted for each TCCON site, interpolated to the
time of measurement, and converted to XCH4. The comparison
with TCCON XCH4 retrievals (GGG2014 release) is performed by
accounting for the TCCON retrieval a priori and vertical
sensitivity. The evaluation of the stratospheric model-transport error
is based on the statistical analysis of model-measurement
differences. Mean differences (bias) and residual standard deviations
(RSD) were calculated from XCH4 residual time series for each
TCCON site. We distinguish between mean and seasonal bias
components. The overall bias is derived from the deseasonalized
XCH4 time series, whereas the seasonal bias is obtained from
the detrended XCH4 time series.
Global effects in XCH4
Figure 2a shows that the overall bias between ACTM and TCCON in the NH
increases with latitude. At subtropical sites (Izaña, Lamont), the
bias is 10.2 ppb; at mid-latitude sites (Karlsruhe, Garmisch,
Park Falls, Orléans, Białystok), the bias is ∼25ppb. The bias increases up to ∼36.7ppb at
high latitudes (Sodankylä). In contrast, at tropical sites
(Darwin, Reunion), we find a negative bias (-11.5,
-6.6 ppb). In the Southern Hemisphere (SH), the bias at the
subtropical site Wollongong (-2.0 ppb) and the mid-latitude
site Lauder (-8.1 ppb) is smaller compared to the NH. The
biases at all mid-latitude sites as well as at the high-latitude site
are significant on 2σ confidence level. Regarding the high
values of the bias in the NH (the region with the majority of
anthropogenic emissions), one might conjecture that this
latitude-dependent bias is caused by emissions not correctly
considered in the a priori flux distribution of the model. However,
such biases for ACTM simulation and in situ measurements are not
observed in the troposphere (Saito et al., 2013). Instead, the bias is
much smaller between ACTMac and TCCON XCH4 (typically <10ppb) meaning that most of the error is due to poor
description of stratospheric CH4 in ACTM. With ACTMac, there
is no significant bias at mid-latitude and arctic TCCON sites (the
accuracy of TCCON is estimated to be ∼4ppb; Wunch
et al., 2010). Only in the SH at the tropical site Reunion
(-10.3 ppb) and the subtropical site Wollongong
(-11.5 ppb) we still find significant negative biases.
The overall impact of the stratospheric model-transport error on
XCH4 is illustrated by the difference of the biases derived
from ACTM and ACTMac, respectively (Fig. 2c). The bias difference
increases from ∼1ppb at the tropical site Darwin up to
∼27ppb at the arctic site Sodankylä. It is obvious
that the latitude-dependent error originates from the
latitude-dependent age differences, which created latitude-dependent
differences between the modeled CH4 distributions, i.e., ACTM
– ACTMac (Fig. 1).
Seasonal effects in XCH4
We quantify seasonal effects in terms of RSD as a proxy for seasonal
bias. In contrast to the mean bias, the seasonal bias does not show
a strong latitude-dependence (Fig. 2b). E.g. sites of similar
geographical latitude (Garmisch, Karlsruhe, Orléans, and Park
Falls) differ in the corresponding RSD values. However, the seasonal
biases at sites in the NH are somewhat larger (∼6-8 ppb) than in the SH (∼4-6 ppb). ACTM has
been shown to provide a realistic description of the NH/SH OH ratio
(Patra et al., 2014), therefore this NH-SH difference may be caused
by higher NH emissions (and prior emission errors).
We find that the agreement between model and TCCON XCH4 is
generally improved after correcting the stratospheric model transport,
see Fig. 2b. Values of RSD (1σ) are reduced and range from
∼3 to ∼5ppb, except for the sites Darwin and
Saga. At Darwin, an above-average RSD is not surprising since there is
high interannual variability due to monsoon periods. The impact of
stratospheric transport on the seasonal bias is shown in the
difference of RSD values obtained from ACTM and ACTMac,
respectively. As in the case of the annual-mean effects, for the
seasonalities we also see a latitude-dependent difference with
increasing magnitude at high latitudes (Fig. 2d). The improved
seasonal agreement between model and TCCON XCH4 is a result of
seasonal differences between modeled and corrected age, which produce
seasonal differences in the corresponding stratospheric CH4
distributions (Fig. 1).
To illustrate the impact of stratospheric transport on mean seasonal
variations of the CH4 budget, we calculated climatological
(period: 2008–2013) mean seasonal cycles from model and TCCON
XCH4 time series, respectively. The mean seasonal XCH4
cycle from ACTM and ACTMac are different at mid-latitude and polar
sites (Figs. 3a, b and B1). At subtropical sites, the modeled mean
seasonal cycles are very similar to each other, but ACTMac shows
better agreement with TCCON (Fig. 3c). Almost no differences between
ACTMac and ACTM are found at the tropics (Darwin, Reunion). Remaining
differences between ACTMac and TCCON are likely to be caused by
erroneous model prior emissions. Apart from that, the improved
model-observation agreement as a result of the stratospheric
correction is also confirmed by a climatological comparison of
stratospheric CH4 derived from model simulations and
stratospheric satellite observations (see Appendix C).
Finally, the latitude-dependent impact of model errors in
stratospheric transport on XCH4 is twofold: it is determined
by the magnitude of the model bias in stratospheric CH4 as
well as by the magnitude of the stratospheric airmass (tropopause
height). As both contributions depend on latitude, the impact of
stratospheric model-transport errors on XCH4 is
increasing poleward. Moreover, the stratospheric model-transport
errors depend on season with maximum in winter-time and minimum in
summer.
Impact of stratospheric model-transport errors on global
CH4 budget
Stratospheric model-transport errors are not unique to ACTM: we also
analyzed stratospheric transport for five other well-established CTMs
(Sect. 2.2) and found that these CTMs are affected by a similar
latitude-dependent transport error (Figs. D1 and D2). After validating
the correction of stratospheric model transport, we now estimate the
impact of the poor description of stratospheric CH4 in a CTM
on inverting CH4 fluxes for the assumption that no ad hoc
latitudinal bias correction would be applied to account for the
latitudinal XCH4 bias between model-observation residuals. The
model CH4 distribution is converted into a global CH4
burden [CH4]. The stratospheric correction yields a burden
difference [ΔCH4] between global burdens from the
original and the corrected CTM. A change in the global burden
corresponds to a change, ΔE, in the source strength assuming
a constant sink. Consequently we quantify the impact of the
stratospheric error in terms of emissions, ΔE, using a one-box
model for the whole atmosphere (Dlugokencky et al., 1998). The annual
CH4 source strength (E) was derived according to
E=d[CH4]/dt+[CH4]/τ,
where d[CH4]/dt is the increase of [CH4]
and τ is the mean atmospheric CH4 lifetime. At
steady-state, the emission E balances the sink [CH4]/τ,
since there is no change in the global burden
(d[CH4]/dt=0). On the other hand, a perturbation
of the global budget corresponds to a change, ΔE, in the
source strength assuming a constant sink. We calculated ΔE
using a perturbation of the global burden that is equal to
[ΔCH4]. In other words, we changed the global burden by
the amount of the stratospheric correction
(d[CH4]/dt=
[ΔCH4] yr-1). According to this procedure,
we derived flux adjustments for ACTM and for five additional CTMs.
Figure 4 shows zonal contributions of ΔE aggregated for
30∘ latitude bands and averaged over two years. Assuming
realistic prior emissions, stratospheric model-transport errors yield
to an overestimation of emissions by 3.2–11.1 Tgyr-1 for
the NH arctic region (60–90∘ N),
5.2–11.7 Tgyr-1 for the NH midlatitudes
(30–60∘ N), and 3.4–13.1 Tgyr-1 for the SH
midlatitudes (30–60∘ S), relative to the tropics. Mean flux
errors are ∼7Tgyr-1 for each of these three
latitudinal bands, and accumulate to ∼21Tgyr-1 on
total global scale.
We use two inversion-estimated distributions of emission adjustments
(Alexe et al., 2015: S1-NOAA and S1-GOSAT-SRON-PX) to evaluate the
significance of these flux errors, see grey and black bars in
Fig. 4. One of these inversions uses surface measurements from the
Cooperative Air Sampling Network of the National Oceanic and
Atmospheric Administration Earth System Research Laboratory (NOAA
ESRL) only. The second inversion uses both surface measurements from
NOAA ESRL and satellite total column observations from the Thermal And
Near infrared Sensor for carbon Observations–Fourier Transform
Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases
Observing SATellite (GOSAT). In addition to inversion-estimated flux
adjustments we also compared inversion-estimated total emissions with
flux errors in order to assess the level of significance for the flux
errors: these total emission are small for the arctic region (S1-NOAA:
17.6 Tgyr-1) and for the SH midlatitudes (S1-NOAA:
11.6 Tgyr-1) compared to NH mid-latitude total emissions
(S1-NOAA: 156.2 Tgyr-1).
For the NH arctic region, stratospheric model-transport errors are
significant, as the average flux error of ∼7Tgyr-1
is higher than the inversion-based emission adjustment
(2.3 Tgyr-1) and accounts for ∼40 % of the
inverted total emissions in this region (17.6 Tgyr-1). In
the SH midlatitudes as well, the average flux error of ∼7Tgyr-1 is larger than the inversion-based emission
adjustment (2.7 Tgyr-1) and corresponds to ∼60 % of the inverted total emissions
(11.6 Tgyr-1). The significance of stratospheric
model-transport errors in the NH midlatitudes is somewhat reduced,
since inverted emission adjustments reach up to
27.6 Tgyr-1 and flux errors (∼7Tgyr-1
on average) only account for ∼5 % of the inverted total
emissions (156.2 Tgyr-1).
Summary and conclusions
Based on a parameterization of stratospheric CH4 in terms of
mean age of air, this study investigates the sensitivity of
XCH4 to stratospheric transport. After constraining
stratospheric model transport with SF6-inferred mean age
observations, we account for the impact of stratospheric
model-transport errors on stratospheric CH4. Our analysis
shows that inaccurate modeling of stratospheric transport leads to
a poor description of stratospheric CH4 with a systematic
overestimation of CH4 mixing ratios by up to ∼250ppb. ACTM model errors in stratospheric transport and
CH4 are increasing towards high latitudes and exhibit
a seasonal component. Consequently, the impact of such errors on
simulating XCH4 results in biases that are increasing from the
tropics (∼1ppb) towards the mid- and high-latitude
region (>20ppb). Our correction method produces both an
improved overall and seasonal agreement in XCH4 between ACTM
and TCCON.
As satellite XCH4 observations combined with CTM simulations
are used for inverting CH4 emission fluxes, we provided an
estimate for the impact of stratospheric model-transport errors in
terms of emissions. Based on a set of CTMs used for inverse studies,
where we found transport errors similar to ACTM, model errors in
stratospheric CH4 were converted into flux errors. Global flux
errors are ∼21Tgyr-1 on average and correspond to
an overestimation of CH4 emissions by 40 % (∼7Tgyr-1) in the NH arctic region, by ∼5 %
(∼7Tgyr-1) in the NH midlatitudes, and by ∼60 % (∼7Tgyr-1) in the SH
midlatitudes. Assessing these flux errors does not imply that inverted
emission estimates are affected by such errors, since inversion
studies try to remove systematic error sources, like latitudinal
XCH4 biases between model and satellite data, before inverting
CH4 fluxes.
Overall, the comparison between ACTM simulations and atmospheric
CH4 observations suggests that our stratospheric correction is
reasonable, although it does not explicitely account for interannual variability
in stratospheric transport and only is based on a small observational
dataset of mean age (7 SF6 profiles at three
locations). However, as long as modeled and corrected mean age
datasets are consistent in temporal resolution, errors from missing
interannual variations in stratospheric transport should cancel each
other. We expect, that extending the observational dataset of mean age
with additional measurements will contribute to a refined
stratospheric correction and, thus, is favorable for future
applications. E.g., satellite-inferred age of air data are available
for long-term analysis of stratospheric model transport including
interannual and seasonal variations (Stiller et al., 2012).
In summary, our results imply three important shortcomings in
atmospheric inversions using satellite-derived XCH4. First,
satellite-based emission estimates at mid- and high latitudes can be
affected by systematic flux errors of ∼7Tgyr-1 on
average, if latitudinal bias corrections are not included. These flux
errors are in the same order of magnitude as flux differences between
satellite-inferred and surface-inferred inversions (Monteil et al.,
2013; Alexe et al., 2015). Second, our results indicate that
inaccurate stratospheric model transport causes unrealistic emission
adjustments by an inversion which tries to consistently interpret
constraints on satellite-derived column-averaged mixing ratios and in
situ measurements of surface mixing ratios at the same time (Monteil
et al., 2013). Third, residuals between model and satellite
XCH4 contain latitude-dependent contributions due to errors in
stratospheric model transport superimposed on residuals caused by real
atmospheric signals. As long as the stratospheric model bias has not
been quantified, latitudinal ad hoc bias corrections can obscure real
signals inferred from satellite observations.
We expect that these shortcomings in inversions can be alleviated by
the correction of stratospheric model transport. In addition, the
potential of mean age observations as diagnostic and modality for
stratospheric model errors is not limited to CH4, but can also
be exploited for other long-lived greenhouse gases like carbon dioxide
(CO2) or nitrous oxide (N2O). In this context, we
conclude that an improved description of the stratospheric column will
help to clarify discrepancies between CO2 flux estimates
inferred from surface and satellite observations (Basu et al., 2013;
Deng et al., 2014; Chevallier et al., 2014). It also is likely, that
improved stratospheric modeling can give new insights into an ongoing
debate about the inversion-estimated European uptake of CO2
(Reuter et al., 2014; Feng et al., 2015).
Our findings suggest that an accurate representation of the
stratosphere is essential in order to determine source/sink
information from the inversion of satellite total column
observations. An alternative to separate tropospheric and
stratospheric contributions from XCH4 has been presented for
ground-based observations (Washenfelder et al., 2003), but is not
applicable for satellite observations at the moment without additional
use of a CTM. It is obvious, that the best solution to the model bias
in stratospheric methane is to improve the representation of the
stratosphere. This is a reasonable goal, as a realistic simulation of
stratospheric transport has already been achieved by models focused on
stratospheric tracers (Strahan et al., 2011). However, for atmospheric
inversions, the age correction proposed here can act as a solution at
an intermediate stage between currently used ad hoc bias corrections
and accurate modeling of stratospheric CH4 in the future.
Apart from that, the stratospheric correction via mean age can be
beneficial for satellite or ground-based retrieval methods;
i.e. improved stratospheric a priori information from CTMs will result
in enhanced retrieval accuracy. Finally, this study again reveals the
synergistic potential of combining CTMs with high-precision
measurements.
Details of the stratospheric correction
As explained in Sect. 4.2, the correction of stratospheric CH4
mixing ratios has three contributions: chemical loss, tropospheric
growth, and interaction of chemistry and growth. These individual
contributions are illustrated in Fig. A1, using original and corrected
ACTM model profiles of CH4 mixing ratios extracted for the
TCCON site Garmisch. It is obvious that the predominant part of the
correction for a chemically-active trace gas such as CH4
originates from the chemical loss term with CH4 adjustments up
to ∼150ppb in the lower stratosphere. The contribution
caused by tropospheric growth is typically lower than 20 ppb
throughout the whole stratosphere. In contrast to the contributions
from chemical loss and tropospheric growth, the amount of the mixed
chemistry-growth term is monotonically increasing from ∼20ppb in the lower stratosphere up to ∼90ppb
in the upper stratosphere.
The correction of ACTM stratospheric CH4 is not constant with
time, but shows a seasonal component due to seasonal varying
differences between simulated and corrected age. Latitude-height
distributions of age and CH4 reveal that the correction effect
has a maximum in winter-time and a minimum in summer (Fig. A2).
Supplement figures
Impact of stratospheric model-transport errors on mean seasonal variations
of XCH4 (see Fig. B1).
Comparison of stratospheric CH4 between ACTM and
satellite data
For the validation of the stratospheric model correction we also used
two stratospheric CH4 climatologies which have been inferred
from observations of the Halogen Occultation Experiment (HALOE) on
board the Upper Atmosphere Research Satellite (UARS) and of the
Atmospheric Chemistry Experiment–Fourier Transform Spectrometer
(ACE-FTS) aboard the Canadian Science Satellite (SciSat). Both
satellite instruments utilize solar occultation geometry to obtain
vertical profiles of several trace gases including CH4. The
HALOE climatology is derived from measurements covering a period of
11 years (1991–2002), the ACE-FTS climatology is based on
observations performed between 2004 and 2009. Both climatologies are
provided as zonal monthly mean distributions (Jones et al., 2012;
Grooß and Russell, 2005). Comparisons between ACE-FTS and
correlative HALOE profiles have revealed that ACE-FTS profiles are
biased positive (∼5 % up to 35 km; ∼15 %
up to 60 km) with respect to HALOE (de Mazière et al.,
2008). A comparison between a vertical CH4 profile from
ACT-FTS and from balloon-borne measurements showed biases that are
smaller than ±10 % between 15 and 24 km (de
Mazière et al., 2008). The agreement of HALOE profiles with
correlative measurements was found to be better than 15 % (Park
et al., 1996).
For comparison with satellite-inferred climatologies we used
zonally-averaged monthly mean distributions of ACTM, and ACTMac,
respectively. Modeled CH4 distributions from the years 2008
and 2009 were averaged for each month and subsequently compared with
the climatological monthly mean distributions from HALOE and
ACE-FTS. As the HALOE climatology is representative for the
1990 s, we have to account for the tropospheric
growth. I.e. the modeled CH4 distributions are adjusted by an
offset corresponding to the increase in global CH4 between the
ACTM time period (2008, 2009) and the HALOE climatology period. As
the period between 1999 and 2006 does not show significant growth, we
account for 6 years of tropospheric growth with a rate of
7 ppbyr-1 (42 ppb in total; see ref. 40 for
tropospheric growth rate). Due to the small time lag between model and
ACE-FTS climatology, the impact of tropospheric growth on
model-satellite comparison can be neglected.
Zonal differences between model and satellite climatology were
calculated as a function of altitude and month. Subsequently, these
zonal differences were used to derive the mean difference (bias) and
the residual standard deviation (RSD). Furthermore, we calculated the
standard deviation of the individual CH4 distributions (ACTM,
ACTMac, and satellite) in order to use it as a proxy for modeled and
observed atmospheric variability. We find that the average agreement
between model and satellite zonal CH4 distributions is
improved throughout the entire stratosphere at all latitudes for
ACTMac compared to ACTM. E.g., the global bias of the model relative
to ACE-FTS is reduced from ∼10 % to ∼5 % in the
lower stratosphere (50–200 hPa) when using ACTMac instead of
ACTM (Fig. C1). Similar results were found for the model bias with
respect to HALOE (Fig. C2). Furthermore, the variability of corrected
model distributions is similar to the variability of satellite
climatologies (Figs. C1e, C1f, C2e, and C2f), indicating that the
corrected model provides a better description of stratospheric
CH4 variability.
Age distributions from TransCom CTMs were compared with corrected age
distributions from ACTM in order to detect errors in stratospheric
model transport. Figure D1 shows age differences as latitude-height
distributions averaged for all months of the years 2006 and 2007. It
is obvious that all CTMs are affected by a similar latitude-dependent
transport error given the similar patterns of age errors. The modeled
CH4 distributions of the TransCom CTMs were corrected with
respect to age in order to account for the stratospheric
model-transport error. Figure D2 shows differences between original
and corrected CH4 distributions.
Acknowledgements
We thank H. P. Schmid (IMK-IFU) for his continual interest in this
work. Our work has been performed as part of the ESA GHG-cci project
via subcontract with the University of Bremen. In addition we
acknowledge funding by the EC within the INGOS project. We thank the
Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of
the Karlsruhe Institute of Technology for support. A part of work at
JAXA was supported by the Environment Research and Technology
Development Fund (A-1102) of the Ministry of the Environment,
Japan. 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. Since 2011 the program has been
funded by NIWA's Atmosphere Research Program 3 (2011/13 Statement of
Corporate Intent).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
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Overview of balloon flights where SF6 measurements were
obtained to infer mean age of air data.
Balloon flight locationLatitudeTimeAltitudeHyderabad (India)17∘ N25 Mar 19876–34 kmHyderabad (India)17∘ N16 Apr 19948–37 kmAire sur l`Adour (France)44∘ N30 Sep 19936–34 kmEsrange (Sweden)68∘ N18 Jan 19926–34 kmEsrange (Sweden)68∘ N06 Feb 19926–34 kmEsrange (Sweden)68∘ N20 Mar 19926–34 kmEsrange (Sweden)68∘ N07 Mar 19956–34 km
Overview of TCCON measurement sites used for evaluation of chemical
transport model ACTM. Information on geographical location, the time
period used for model-observation comparison, and the number of
measurement days within the corresponding time period are provided.
Impact of stratospheric transport on zonal mean distributions
of CH4. Zonal vertical distributions of mean age in years
(yr) and of CH4 volume mixing ratios (vmr) in parts per
billion (ppb), respectively. Original model distributions (ACTM;
a, b) were corrected to account for errors in
stratospheric transport (ACTMac; c, d). The
differences are shown in the lower trace (e, f). Age
distributions and CH4 distributions are annual means for the
year 2009.
Impact of stratospheric transport on the overall and seasonal
agreement between modeled and observed CH4 at TCCON
sites. The overall bias (a) is calculated from
deseasonalized model-observation differences of column-averaged
dry-air CH4 mixing ratios (XCH4) for selected TCCON
sites indexed by geographical latitude. Error bars are 2σ
standard deviations. The residual standard deviation (RSD)
(b) is calculated from detrended model-observation
XCH4 differences. The difference between bias related to
ACTM, and to ACTMac is shown as bias difference (c),
respectively. The difference between residual standard deviations
related to ACTM and to ACTMac is shown in the lower trace
(d); XCH4 biases, residual standard deviations, and
corresponding differences in parts per billion (ppb).
Contribution of stratospheric transport on mean seasonal
anomalies of CH4. Mean seasonal cycles are calculated from
climatological monthly means of detrended XCH4 time series
for ACTM (blue), ACTMac (red), and TCCON (black),
respectively. Error bars are 2σ standard errors; XCH4
in parts per billion (ppb). Mean seasonal cycles are provided for
arctic region (a), for midlatitudes (b), and for
subtropics (c). Mean seasonal cycles from remaining TCCON
sites are shown in Fig. B1.
Impact of errors in stratospheric model transport on inverted
CH4 fluxes. Zonal CH4 emission adjustments caused by
stratospheric model-transport error for six CTMs used in the
TransCom-CH4 comparison (TM5, TOMCAT, NIES-08i, ACTM, LMDZ,
GEOS-Chem), and for ACTM used in this study. In addition, two
distributions of inversion-estimated emission adjustments
(INV_NOAA, INV_NOAA+GOSAT; see Table 5 of Alexe et al., 2015)
are provided for reference. INV_NOAA uses surface measurements
only, whereas INV_NOAA+GOSAT is based on both surface and
satellite observations. CH4 emission adjustments are
two-year averages (TransCom CTMs: 2006 and 2007; ACTM: 2008 and
2009; INV_NOAA and INV_NOAA+GOSAT: 2010 and 2011). The inset to
Fig. 4 shows the correlation of flux errors and mean age errors
relative to the tropics aggregated for 10∘ latitude bands
(i.e. one point corresponds to one latitude band and one CTM). The
age error is integrated with respect to sigma-pressure and scaled by
the relative fraction of the zonal region to the total Earth
surface. Note that the flux adjustments in the main figure are
aggregated for 30∘ latitude bands because of clarity.
The impact of the individual contributions of the
stratospheric transport correction on the mean CH4 profile
at Garmisch. The stratospheric correction of ACTM model simulations
via mean age leads to the decline of stratospheric CH4 due
to three contributions: chemical loss, tropospheric growth, and
interaction of chemistry and growth.
Seasonal impact of stratospheric transport correction on
zonal mean distributions of CH4. As Fig. 1, but only
difference distributions of mean age in years (yr), and of
CH4 volume mixing ratios (vmr) in parts per billion (ppb),
respectively. Age distributions and CH4 distributions are
monthly means for the year 2009. Zonal distributions corresponding
to different months (February: a, b; June:
c, d; October: e, f) reflect the
seasonal variation of the stratospheric model-transport error.
Contribution of stratospheric transport on mean seasonal
anomalies of CH4. Mean seasonal cycles are calculated from
climatological monthly means of detrended XCH4 time series
for ACTM (blue), ACTMac (red), and TCCON (black),
respectively. Error bars are 2σ standard errors;
XCH4 in parts per billion (ppb).
Statistical evaluation of latitude-height agreement between
multi-annual stratospheric CH4 model distribution and
ACE-FTS climatology. Bias (a, b) and residual standard
deviations (c, d) are derived from monthly differences
between model (two-year average: 2008, 2009) and ACE-FTS CH4
vmr as a function of altitude (pressure) for both
hemispheres. Individual standard deviations (e, f) from
CH4 vertical distributions reflect atmospheric variability.
Statistical evaluation of latitude-height agreement between
multi-annual stratospheric CH4 model distribution and HALOE
climatology. Bias (a, b) and residual standard deviations
(c, d) are derived from monthly differences between model
(two-year average: 2008, 2009) and HALOE CH4 vmr as
a function of altitude (pressure) for both hemispheres. Individual
standard deviations (e, f) from CH4 vertical
distributions reflect atmospheric variability.
Stratospheric model-transport error. The difference between
observed mean age (ACTMac) and simulated mean age is illustrated by
zonally-averaged annual mean distributions of mean age in years
(yr). The age differences indicate regions affected by model errors
in stratospheric transport. Age distributions of TransCom models
(a–f) were averaged for
two years (2006, 2007).
Impact of stratospheric model-transport error on
stratospheric CH4. The difference between simulated and
corrected CH4 is illustrated by zonally-averaged annual mean
distributions of CH4 mixing ratios in parts per billion
(ppb). The CH4 differences indicate regions affected by
model errors in stratospheric transport. CH4 distributions
of TransCom models (a–f) were averaged for two years (2006, 2007). An offset is
added to the concentrations in each panel (given after the model
name in ppb) that adjusts the model fields to a common average value
of 1770 ppb between 950 and 500 mb.