AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4843-2016Evaluation of column-averaged methane in models and TCCON with a focus on
the stratosphereOstlerAndreasSussmannRalfralf.sussmann@kit.eduPatraPrabir K.https://orcid.org/0000-0001-5700-9389HouwelingSanderhttps://orcid.org/0000-0002-6189-1009De BruineMarkohttps://orcid.org/0000-0002-9892-8442StillerGabriele P.https://orcid.org/0000-0003-2883-6873HaenelFlorian J.PlieningerJohanneshttps://orcid.org/0000-0001-9096-5962BousquetPhilippeYinYihttps://orcid.org/0000-0003-4750-4997SaunoisMarielleWalkerKaley A.https://orcid.org/0000-0003-3420-9454DeutscherNicholas M.https://orcid.org/0000-0002-2906-2577GriffithDavid W. T.https://orcid.org/0000-0002-7986-1924BlumenstockThomasHaseFrankWarnekeThorstenWangZhitingKiviRigelhttps://orcid.org/0000-0001-8828-2759RobinsonJohnKarlsruhe Institute of Technology, IMK-IFU, 82467 Garmisch-Partenkirchen, GermanyResearch Institute for Global Change, JAMSTEC, Yokohama, 236-0001, JapanInstitute for Marine and Atmospheric Research Utrecht, Utrecht University, 3584 CC Utrecht, the NetherlandsSRON Netherlands Institute for Space Research, 3584 CA Utrecht, the NetherlandsKarlsruhe Institute of Technology, IMK-ASF, 76021 Karlsruhe, GermanyLaboratoire des Sciences du Climat et de l'Environnement, IPSL-LSCE, CEA-CNRS-UVSQ, UMR8212, 91191 Gif-sur-Yvette, FranceUniversité de Versailles Saint Quentin en Yvelines, 78000 Versaille, FranceDepartment of Physics, University of Toronto, Toronto, Ontario M5S 1A7, CanadaSchool of Chemistry, University of Wollongong, Wollongong, NSW 2522, AustraliaInstitute of Environmental Physics, University of Bremen, 28334 Bremen, GermanyFinnish Meteorological Institute, Arctic Research Center, 99600 Sodankylä, FinlandDepartment of Atmospheric Research, National Institute of Water and Atmospheric Research
(NIWA) Ltd, Wellington 6021, New ZealandRalf Sussmann (ralf.sussmann@kit.edu)28September2016994843485914March201611May201612September201615September2016This 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://amt.copernicus.org/articles/9/4843/2016/amt-9-4843-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4843/2016/amt-9-4843-2016.pdf
The distribution of methane (CH4) in the stratosphere can be a major
driver of spatial variability in the dry-air column-averaged CH4 mixing
ratio (XCH4), which is being measured increasingly for the assessment of
CH4 surface emissions. Chemistry-transport models (CTMs) therefore need
to simulate the tropospheric and stratospheric fractional columns of
XCH4 accurately for estimating surface emissions from XCH4.
Simulations from three CTMs are tested against XCH4 observations from
the Total Carbon Column Network (TCCON). We analyze how the model–TCCON
agreement in XCH4 depends on the model representation of stratospheric
CH4 distributions. Model equivalents of TCCON XCH4 are computed
with stratospheric CH4 fields from both the model simulations and from
satellite-based CH4 distributions from MIPAS (Michelson Interferometer
for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to
ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer)
observations. Using MIPAS-based stratospheric CH4 fields in place of
model simulations improves the model–TCCON XCH4 agreement for all
models. For the Atmospheric Chemistry Transport Model (ACTM) the average
XCH4 bias is significantly reduced from 38.1 to 13.7 ppb, whereas
small improvements are found for the models TM5 (Transport Model, version 5;
from 8.7 to 4.3 ppb) and LMDz (Laboratoire de Météorologie
Dynamique model with zooming capability; from 6.8 to 4.3 ppb). Replacing
model simulations with MIPAS stratospheric CH4 fields adjusted to
ACE-FTS reduces the average XCH4 bias for ACTM (3.3 ppb), but increases
the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These
findings imply that model errors in simulating stratospheric CH4
contribute to model biases. Current satellite instruments cannot definitively
measure stratospheric CH4 to sufficient accuracy to eliminate these
biases. Applying transport diagnostics to the models indicates that
model-to-model differences in the simulation of stratospheric transport,
notably the age of stratospheric air, can largely explain the inter-model
spread in stratospheric CH4 and, hence, its contribution to XCH4.
Therefore, it would be worthwhile to analyze how individual model components
(e.g., physical parameterization, meteorological data sets, model
horizontal/vertical resolution) impact the simulation of stratospheric
CH4 and XCH4.
Introduction
The column-averaged dry-air mixing ratio of methane (CH4), denoted as
XCH4, is an integrated measure of CH4 with contributions from the
troposphere and the stratosphere. Observations of XCH4 contain
source/sink information on a global to regional scale. They are provided by
the ground-based networks NDACC (Network for the Detection of Atmospheric
Composition Change, http://www.ndacc.org/; Kurylo, 1991; for XCH4
retrievals see, e.g., Sussmann et al., 2011, 2012, 2013) and TCCON (Total
Carbon Column Observing Network, http://www.tccon.caltech.edu/; Wunch
et al., 2011), and also by satellite-based observation platforms like
SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric
Cartography; Burrows et al., 1995; Frankenberg et al., 2011) and GOSAT
(Greenhouse Gases Observing Satellite; Kuze et al., 2009; Yokota et al.,
2009). Satellite-inferred XCH4 observations are increasingly used in
atmospheric inverse modeling because of their beneficial spatiotemporal data
coverage (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; Locatelli et al., 2015). Given the
high accuracy of ground-based XCH4 TCCON retrievals, these observations
are typically used for the evaluation of both chemistry-transport model (CTM)
simulations (Saito et al., 2012; Belikov et al., 2013; Monteil et al., 2013;
Fraser et al., 2014; Alexe et al., 2015; Turner et al., 2015) and
satellite-retrieved XCH4 (Parker et al., 2011, 2015; Schepers et al.,
2012; Dils et al., 2014; Houweling et al., 2014; Parker et al., 2015; Kulawik
et al., 2016; Pandey et al., 2016; Inoue et al., 2016).
Because of the various influences on XCH4, however, the interpretation
of residual XCH4 differences with TCCON may be difficult. For example,
a good agreement between XCH4 simulations and observations may suggest
that a CTM is able to represent atmospheric conditions in a realistic way.
However, it could also be the case that systematic model and satellite data errors in
the troposphere and the stratosphere compensate each other. For this reason,
it is necessary to extend model validations with additional atmospheric
CH4 observations that are complementary to XCH4 observations, like
surface or airborne in situ measurements, or balloon-based vertical profiles
(Karion et al., 2010). In the context of a refined model comparison, it is
also possible to separate ground-based XCH4 observations into
tropospheric and stratospheric partial columns (Washenfelder et al., 2003;
Sepúlveda et al., 2012, 2014; Wang et al., 2014; Saad et al., 2014).
Model–measurement XCH4 residuals are minimized by atmospheric inversions
in order to constrain CH4 emission fluxes. Inversion models are also
able to make use of in situ measurements and XCH4 observations at the
same time in order to adjust prior emission fluxes. Nevertheless, such
inverse models still have to deal with ill-defined XCH4 biases, which,
in contrast to well-quantified biases, can only be attributed to errors in
the model or the observations with an ambiguous assignment (Houweling et al.,
2014). Currently, there are various approaches to optimize bias correction
functions within the inverse model or to construct bias corrections as ad hoc
functions of latitude or air mass. Ad hoc bias corrections, like removing a
latitudinal background pattern in XCH4 model–observation differences,
are common, even though they bear the risk of obscuring real signals from
emissions on the Earth's surface. Given the fact that the stratospheric
contribution relative to the CH4 total column increases from
∼ 5 % at the tropics up to ∼ 25 % at midlatitudes and high
latitudes, model errors in the representation of stratospheric CH4
mixing ratios are expected to give rise to a latitudinal varying bias (Turner
et al., 2015). Although it is known that CTMs differ by up to
∼ 50 % in the simulation of lower stratospheric CH4
distributions (Patra et al., 2011), an atmospheric region with a steep
methane gradient of ∼-50 ppb km-1, the impact of model errors in
stratospheric CH4 on XCH4 has not been rigorously quantified up to
now. In this context, the goal of this study is to better understand the
sensitivity of XCH4 model–observation differences to the model
representation of stratospheric CH4.
Overview of CTMs used for model–TCCON comparison.
Resolution Model nameInstitutionHorizontalaVerticalbOutput CH4Mean age derived fromReferenceACTMJAMSTEC∼ 2.8 × 2.8∘67σ1-hourly,idealized transportPatra et al. (2016)monthlytracer simulationsTM5SRON∼ 6 × 4∘25ηdailySF6 simulationsPandey et al. (2016)LMDzLSCE∼ 3.75 × 1.875∘39ηmonthlySF6 simulationsLocatelli et al. (2015)
a Longitude × latitude. b Vertical
coordinates in sigma-pressure σ (pressure divided by surface
pressure) and hybrid sigma-pressure η.
Our XCH4 model–observation analysis is based on optimized model
simulations from three well-established CTMs on the one side and accurate
XCH4 observations from TCCON on the other. The impact of model
stratospheric CH4 distributions on XCH4 is estimated by replacing
modeled stratospheric CH4 fields with monthly mean CH4
distributions observed by MIPAS (Michelson Interferometer for Passive
Atmospheric Sounding) and by ACE-FTS (Atmospheric Chemistry Experiment
Fourier Transform Spectrometer). In addition to this, we briefly evaluate the
model characteristics of stratospheric transport in order to understand
differences between simulated and observed CH4 distributions. The paper
has the following structure: After introducing the models (Sect. 2) and the
observations (Sect. 3), we present both a direct model–TCCON comparison and a
comparison with refined model data using satellite data products of
stratospheric CH4 in Sect. 4. The transport characteristics of the
models are discussed in Sect. 5, followed by a summary and conclusions in
Sect. 6.
Model simulations
The focus of this study is the assessment of the impact of stratospheric CH4 on
XCH4. Therefore, we try to ensure that model simulations represent
tropospheric CH4 mixing ratios as well as possible. For this purpose, we
use optimized CH4 model simulations that have been constrained by
surface observations. Our model analysis comprises simulations from three
well-established CTMs that have already been part of the chemistry-transport
model intercomparison experiment TransCom-CH4 (Patra et al., 2011) and
used in inverse modeling of CH4 emissions. Furthermore, we use model
simulations of stratospheric mean age for an evaluation of model transport
characteristics in Sect. 5. Basic model features are given in Table 1.
ACTM
The Atmospheric Chemistry Transport Model (ACTM) model (Patra et al., 2009a)
is an atmospheric general circulation model (AGCM)-based CTM from the Center
for Climate System Research/National Institute for Environmental
Studies/Frontier Research Center for Global Change (CCSR/NIES/FRCGC). Here,
we use optimized ACTM simulations presented in Patra et al. (2016) as
inversion case 2 (CH4ags). The ACTM horizontal resolution is
∼ 2.8∘× 2.8∘ (T42 spectral truncations) with
67 sigma-pressure vertical levels. The meteorological fields of ACTM are
nudged with reanalysis data from the Japan Meteorological Agency, version
JRA-25 (Onogi et al., 2007). ACTM uses an optimized OH field (Patra et al.,
2014) based on a scaled version of the seasonally varying OH field from
Spivakovsky et al. (2000). The concentration fields that are relevant for
stratospheric CH4 loss – OH, O(1D), and chlorine (Cl) radicals –
are based on simulations by the ACTM's stratospheric model run (Takigawa et
al., 1999). ACTM mean age is derived from the simulation of an idealized
transport tracer with uniform surface fluxes, linearly increasing trend, and
no loss in the atmosphere (Patra et al., 2009b). The ACTM simulates the
observed CH4 interhemispheric gradient in the troposphere and individual
in situ measurements generally within 10 ppb (Patra et al.,
2016).
TM5
The global chemistry Tracer Model, version 5 (TM5) has been described in Krol
et al. (2005) and used as an atmospheric inversion model for CH4
emissions (Bergamaschi et al., 2005; Meirink et al., 2008; Houweling et al.,
2014). Here, we use TM5 simulations of CH4 optimized with surface
measurements only (Pandey et al., 2016). TM5 is run with a horizontal
resolution of 6∘× 4∘ and a vertical grid of 25
layers. TM5 meteorology is driven by the reanalysis data set ERA-Interim (Dee
et al., 2011) from the European Centre for Medium Range Weather Forecasts
(ECMWF). The simulation of the chemical CH4 sink uses OH fields from
Spivakovsky et al. (2000), which have been scaled to match methyl chloroform
measurements. In addition to that, stratospheric CH4 loss via Cl and
O(1D) radicals is simulated using their concentration fields based on
the 2-D photochemical Max Planck Institute (MPI) model (Brühl and
Crutzen, 1993). Known deficiencies in the TM5 simulation of interhemispheric
mixing have been corrected by extending the model with a horizontal diffusion
parameterization that is adjusted to match SF6 simulations with SF6
measurements (Monteil et al., 2013).
TM5 simulations of sulfur hexafluoride (SF6) were used to derive
stratospheric mean age data. SF6 mixing ratios are monotonically
increasing with time, showing higher mixing ratios in the troposphere than in
the stratosphere, given the transport time from SF6 surface sources to
higher altitudes. This implies that tropospheric and stratospheric SF6
mixing ratios of equal size are separated from each other by a time lag,
which is commonly defined as mean age of air. In order to derive mean age
from SF6 model simulations, the same tropospheric SF6 reference
time series was used as for the derivation of MIPAS mean age data (see
Stiller et al., 2012)
LMDz
The LMDz (Laboratoire de Météorologie Dynamique model with zooming
capability) is a general circulation model (Hourdin et al., 2006), which has
been used to investigate the impact of transport model errors on inverted
CH4 emissions (Locatelli et al., 2013). Here, we use optimized LMDz
simulations of CH4, recently presented as LMDz-SP constrained by surface
measurements from background sites (Locatelli et al., 2015). These model
simulations are nudged with the ERA-Interim reanalysis data set for
horizontal winds (u, v). LMDz has a horizontal resolution of
3.75∘× 1.875∘, and 39 hybrid sigma-pressure layers.
The chemical destruction of CH4 by OH and O(1D) is based on
prescribed concentration fields simulated by the chemistry–climate model
LMDz-INCA (Szopa et al., 2013). No Cl-based CH4 destruction is
prescribed in this version of the model. Besides CH4, LMDz simulations
of SF6 were used to derive mean age data similarly to the method used
for TM5.
Intercomparison strategy and observationsIntercomparison strategy
We want to quantify the dependence of the XCH4 model–observation
agreement on the model representation of stratospheric CH4 mixing
ratios. For this purpose, we apply original CH4 model fields and two
corrected CH4 model fields, where we have replaced the modeled
stratospheric CH4 by satellite data sets of stratospheric CH4
mixing ratios. The first satellite data set consists of MIPAS CH4
observations, whereas the second satellite data set contains MIPAS CH4
observations that are adjusted to ACE-FTS-observed CH4 levels. This
allows us to represent an uncertainty range for the satellite-based model
correction. Finally, our XCH4 model–observation comparison deals with a
triplet of model CH4 fields for each CTM.
Using TCCON XCH4 observations as validation reference, we evaluate the
impact of correcting the modeled stratospheric CH4 on XCH4.
Consequently, modeled vertical profiles of CH4 were extracted for each
TCCON site and subsequently converted to XCH4 by accounting for the
TCCON retrieval a priori and vertical sensitivity. This means that model
CH4 profiles are adjusted to the actual surface pressure measured at the
time of a single TCCON observation. In addition to that, model profiles are
convolved with the daily TCCON retrieval a priori profiles of CH4, which
have been converted from wet air into dry air units by subtracting a daily
water vapor profile provided by NCEP (National Centers for Environmental
Prediction) and the averaging kernel depending on the actual solar zenith
angle. Thereby, monthly mean CH4 profiles from LMDz also receive a daily
component depending on the surface pressure, the TCCON a priori profiles and
averaging kernels. The statistical analysis of XCH4 model–TCCON
differences is then based on the daily mean time series for the year 2010.
TCCON observations of column-averaged methane
Solar absorption measurements in the near-infrared are performed via
ground-based Fourier transform spectrometers (FTSs) 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 three 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 (ACE-FTS profiles measured in the
30–40∘ N latitude range from 2003 to 2007). In addition, the shape
of the daily a priori profile is vertically squeezed/stretched depending on
tropopause altitude and the latitude of the measurement site. This means
that the tropopause altitude is used as a proxy for stratospheric
ascent/descent to represent the origin of the air mass in the a priori
profile. XCH4 is calculated by dividing the CH4 number density by
the simultaneously measured O2 number density (a proxy for the dry-air
pressure column).
Overview of TCCON measurement sites used for the evaluation of
chemical transport models. Abbreviations of the site names, information about
geographical location, and number of measurement days in 2010 are provided.
TCCON siteAbbreviationAltitudeLatitudeLongitudeDaysReferenceSodankylä (Finland)SOD188 m67.4∘ N26.6 ∘ E78Kivi et al. (2014)Białystok (Poland)BIA180 m53.2∘ N23.0∘ E120Deutscher et al. (2014)Karlsruhe (Germany)KAR110 m49.1∘ N8.4∘ E79Hase et al. (2014)Orléans (France)ORL130 m48.0∘ N2.1∘ E91Warneke et al. (2014)Garmisch (Germany)GAR743 m47.5∘ N11.1∘ E120Sussmann et al. (2014)Park Falls (USA)PAR440 m46.0∘ N90.3∘ W155Wennberg et al. (2014a)Lamont (USA)LAM320 m36.6∘ N97.5∘ W299Wennberg et al. (2014b)Izaña (Tenerife)IZA2370 m28.3∘ N16.5∘ W50Blumenstock et al. (2014)Darwin (Australia)DAR30 m12.4∘ S130.9∘ E64Griffith et al. (2014a)Wollongong (Australia)WOL30 m34.4∘ S150.9∘ E142Griffith et al. (2014b)Lauder (New Zealand)LAU370 m45.0∘ S169.7∘ E142Sherlock et al. (2014a, b)
These XCH4 retrievals are corrected a posteriori for known
air-mass-dependent biases and calibrated to account for air-mass-independent
biases, which can, among other errors, arise from spectroscopic uncertainties
(Wunch et al., 2011). The air-mass-independent calibration factor, which 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), allows for a calibration of TCCON XCH4
retrievals to in situ measurements on the WMO scale. Furthermore, 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
(2σ) for XCH4 is < 0.3 % (< 5 ppb) for single
measurements. For the year 2010, XCH4 observations are available from 11
TCCON sites, listed in Table 2. Knowing that TCCON XCH4 accuracy can be
affected by a strong polar vortex (Ostler et al., 2014), we exclude
high-latitude observations at Sodankylä within the early spring period
(March, April, May) from the analysis. 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 from this database.
Satellite-based data sets of stratospheric methane
In order to correct modeled stratospheric CH4 fields, we use
satellite-borne MIPAS measurements covering the stratosphere. As a
Fourier-Transform Infrared Spectrometer aboard the Environmental Satellite
(Envisat), MIPAS detected atmospheric emission spectra in the mid-infrared
region via limb sounding (Fischer et al., 2008). Profiles of various
atmospheric trace gas concentrations are derived by the research processor
developed by the Karlsruhe Institute of Technology, Institute of Meteorology
and Climate Research (KIT IMK) and the Instituto de Astrofísica de
Andalucía (CSIC) (von Clarmann et al., 2003). The MIPAS CH4 data
set comprises zonal monthly means with a horizontal grid resolution of
5∘ latitude. In the vertical, the resolution of the MIPAS CH4
fields range from 2.5 to 7 km; see Plieninger et al. (2015) for more
details. As an additional quality criterion, we only select MIPAS data points
that are averaged over more than 300 profile measurements. As a result, our
MIPAS CH4 data set typically covers altitudes higher than ∼ 10 km
at midlatitudes and heights above ∼ 15 km in the tropics. This
implies that we do not use a thermal or chemical tropopause definition, but
use the MIPAS data where they are available. Therefore, we cannot exclude
that our MIPAS-based CH4 fields contain some upper tropospheric MIPAS
values; i.e., our definition of stratospheric CH4 is not strict from a
meteorological point of view.
The corrected model CH4 profiles rely on original model CH4 fields
that are merged with MIPAS-based zonal CH4 fields (monthly means)
interpolated to the model grid. Merging original model CH4
fields/profiles with zonal monthly means implies that we lose some spatial
and temporal variability in the corrected model CH4 fields. For example,
vertical shifts of the tropopause can cause significant variations in
XCH4 of ∼ 25 ppb even within a day (Ostler et al., 2014). As
these XCH4 changes can be positive but also negative (tropopause shifted
upwards and downwards), we expect that dynamically induced XCH4
variations should be negligible from a statistical point of view as used in
this study. For our aim – investigating the overall impact of model
stratospheric CH4 fields on the quantity XCH4 – a monthly mean
representation of stratospheric CH4 in the corrected model fields is
sufficient.
In our study we use the strongly revised MIPAS CH4 data product for the
MIPAS reduced-resolution period from January 2005 to April 2012. This new
data set (version V5R_CH4_224/V5R_CH4_225) was recently introduced by
Plieninger et al. (2015) with an emphasis on retrieval characteristics.
Plieninger et al. (2015) showed that CH4 mixing ratios are reduced in
the lowermost stratosphere when using the new retrieval settings. This
finding implies that the high bias of the older CH4 data version in the
lowermost stratosphere, which was determined by Laeng et al. (2015), has been
partially alleviated. Nevertheless, a recent comparison study by Plieninger
et al. (2016) suggests a remaining positive bias (100–200 ppb) relative to
other satellite measurements such as ACE-FTS observations.
For this reason, a second satellite CH4 data set was constructed by
adjusting MIPAS stratospheric CH4 mixing ratios to ACE-FTS (Boone et al., 2013) measurements
of CH4. Given the sparse data coverage of ACE-FTS observations for the
year 2010, we did not use ACE-FTS measurements directly. Instead, the MIPAS
CH4 fields were adjusted by offsets relative to ACE shown in Fig. 1,
yielding the second satellite-based CH4 data set abbreviated by
MIPAS_ACE. We used collocated pairs of CH4 profiles from MIPAS and
ACE-FTS to derive a CH4 offset as a function of altitude and latitude
for the year 2010. The collocation criteria are based on a maximum radius of
500 km and a maximum temporal deviation of 5 h, which is identical to
Plieninger et al. (2016). Furthermore, the MIPAS averaging kernels were
applied to ACE-FTS CH4 profiles. ACE-FTS operates in solar occultation
mode (Bernath et al., 2005) and also provides retrievals of several trace
gases including CH4. Here, we use ACE-FTS data from a research version
of the 3.5 retrieval described in Buzan et al. (2016).
Figure 1 shows the CH4 offset functions computed as mean differences
between MIPAS and ACE-FTS for 30∘ latitudinal bands. Figure 1
confirms the findings by Plieninger et al. (2016) that MIPAS is biased
positive by ∼ 150 ppb relative to ACE-FTS within the lowermost
stratosphere. For higher altitudes (> 25 km), mean differences between
MIPAS and ACE-FTS are larger for the tropical domain (up to 100 ppb)
compared to higher latitudes (up to 50 ppb).
Mean CH4 differences between collocated MIPAS and ACE-FTS
CH4 profiles measured in the year 2010. Mean CH4 differences in
parts per billion (ppb) are derived for 30∘ latitudinal bands
indicated by different colors.
MIPAS-observed mean age
Besides MIPAS CH4 observations, we also use MIPAS data sets of
stratospheric mean age inferred from SF6 measurements. Here, we use the
new MIPAS mean age data set presented by Haenel et al. (2015). This new mean
age data set contains several improvements compared to the previous version
introduced by Stiller et al. (2012). For MIPAS, the mean age is calculated as
the average transport time from the tropical troposphere to a certain
location in the stratosphere using NOAA (National Oceanic and Atmospheric
Administration) observations as reference. The mean age of stratospheric air
is of special interest for climate research because the distributions of
greenhouse gases like ozone critically depend on possible changes in the
stratospheric transport pathways (Engel et al., 2009). Mean age can be
inferred from observations of clock tracers (concentrations monotonically
increasing with time) like SF6 or CO2, and can also be simulated by
models. For this reason, it is a well-known diagnostic for stratospheric
transport and is very suitable for the evaluation of model transport
characteristics (Waugh and Hall, 2002). The combined MIPAS data set of
stratospheric CH4 and mean age is used for the evaluation of model
transport characteristics in Sect. 5.1.
Model–TCCON comparison of column-averaged methane
Figure 2 shows model biases in XCH4 with respect to TCCON observations,
where each TCCON site is represented by its geographical latitude. For each
CTM a triplet of model CH4 fields (uncorrected, MIPAS and MIPAS_ACE
corrected) yields a triplet of model XCH4 biases. All site-specific
XCH4 model biases are individually listed in Table 3. In addition,
Table 4 provides an average XCH4 bias for each model data set, computed
as the mean of absolute site-specific biases.
Site-specific model XCH4 biases with respect to TCCON
observations in parts per billion (ppb) for the year 2010. Different colors
indicate different stratospheric CH4 fields used for the calculation of
model XCH4.
Site-specific model XCH4 biases with respect to TCCON
observations in 2010. The model–TCCON agreement in XCH4 is evaluated
with different stratospheric CH4 model fields: the original model
distribution (orig), the MIPAS-based stratospheric CH4 (MIPAS), and the
MIPAS-based stratospheric CH4 adjusted to ACE-FTS observations
(MIPAS_ACE). XCH4 biases and corresponding 2σ standard errors
(in brackets) are in parts per billion (ppb).
The original XCH4 bias for ACTM lies between 18.8 and 51.3 ppb (see
Fig. 2a and Table 3). This high bias is significantly reduced when ACTM
stratospheric CH4 fields are replaced by satellite-based CH4
fields. The model correction with MIPAS CH4 reduces the average ACTM
XCH4 bias from 38.1 to 13.7 ppb (see Table 4). Site-specific XCH4
biases are ranging from 4.8 to 19.9 ppb (see Table 3). The model correction
with MIPAS_ACE reduces the average ACTM XCH4 bias further from 38.1 to
3.3 ppb (see Table 4), with values in an interval between -9.9 and 3.5 ppb
(see Table 3); values similar to that were expected from the comparison with ACTM
simulations with tropospheric measurements (Patra et al., 2016).
For the original TM5 we detect negative site-specific XCH4 biases with
values between -17.6 and -3.7 ppb (see Fig. 2b and Table 3). When TM5
CH4 fields are corrected with MIPAS observations, this negative
XCH4 bias is reduced from -8.7 to -4.3 ppb on average (see
Table 3). The corresponding site-specific XCH4 biases are then between
-11.1 and 8.1 ppb (Table 3). If the MIPAS_ACE is applied to TM5 then the
site-specific TM5 XCH4 biases are shifted further to the negative
direction with values between -18.3 and -3.7 ppb. In this case the
average XCH4 bias increased from 8.7 to 10.8 ppb (Table 4).
Average model XCH4 bias with respect to TCCON observations in
2010 computed as mean of absolute site-specific biases (see Table 3). Average
XCH4 biases in ppb are derived for different model stratospheric
CH4 fields.
Mean XCH4 bias Model stratospheric CH4 fieldACTMTM5LMDzOriginal model38.18.76.8MIPAS13.74.34.3MIPAS_ACE3.310.820.0
With respect to TCCON observations LMDz produces both negative and positive
XCH4 biases ranging from -11.9 ppb (Wollongong) to 13.0 ppb
(Sodankylä); see Fig. 2c and Table 3. The average LMDz XCH4 bias is
slightly reduced from 6.8 to 4.3 ppb if LMDz is corrected with MIPAS
CH4 fields (see Table 4). After this correction, site-specific LMDz
XCH4 biases lie between -2.9 and 9.1 ppb. Using MIPAS_ACE CH4
fields for the LMDz model correction produces LMDz XCH4 biases between
-13.8 and -31.1 ppb. At the same time, the average LMDz XCH4 bias
is increased from 6.8 to 20.0 ppb (Table 4).
Model–MIPAS differences of stratospheric CH4 volume mixing
ratios (vmr) in parts per billion (ppb). Zonally averaged CH4 vmr
differences are annual means for the year 2010.
Overall, our results confirm that the model–TCCON agreement in XCH4
depends very much on the model representation of stratospheric CH4. It
is obvious that the XCH4 offset between ACTM and TCCON is significantly
reduced with stratospheric CH4 fields based on satellite data. In contrast, for TM5 and LMDz, the impact of the model correction on the
model–TCCON agreement is ambiguous, in that the model–TCCON agreement can be
improved (with MIPAS), but can also be reduced (with MIPAS_ ACE). In order
to understand this inter-model spread we look at the differences between
modeled and satellite-retrieved CH4 fields. Figure 3 shows zonal and
annual averaged CH4 mixing ratio differences between MIPAS and each CTM.
Figure 3a illustrates that stratospheric CH4 mixing ratios are generally
much higher in ACTM than in MIPAS. The ACTM–MIPAS differences in CH4 are
increasing from negligible values within the lowermost stratosphere up to
450 ppb in the upper stratosphere. Furthermore, the ACTM–MIPAS difference in
CH4 also shows a latitudinal dependence, with middle and upper
stratospheric values increasing towards higher latitudes. The positive bias
in stratospheric ACTM CH4 mixing ratios causes a positive ACTM bias in
XCH4. In contrast to that, we find negative model–MIPAS differences in
stratospheric CH4 mixing ratios for TM5 (Fig. 3b), resulting in a small
negative XCH4 bias. We identify two altitude regions, where TM5 modeled
CH4 mixing ratios are smaller than MIPAS CH4 mixing ratios: the
lower stratosphere with differences in CH4 mixing ratios of up to
-100 ppb, and the upper stratosphere (> 30 hPa) with maximum CH4
differences of ∼-150 ppb. Figure 3c shows the CH4 mixing ratio
differences between LMDz and MIPAS with noticeable negative CH4
differences of up to -200 ppb within the tropical upper stratosphere.
Negative CH4 differences (∼-100 ppb) are also visible in the upper
stratosphere of the midlatitude and high-latitude region. In contrast to this, we
identify positive CH4 differences of up to 100 ppb within the middle
stratosphere (∼ 50 hPa) of the midlatitudes and high latitudes. The negative
and positive CH4 differences partially cancel out in XCH4. Similarly to Fig. 3, the CH4 differences between model and MIPAS_ACE
fields are illustrated in Fig. 4. Given the offset adjustment of MIPAS to
ACE-FTS (see Fig. 1), the MIPAS_ACE CH4 fields comprise lower CH4
mixing ratios compared to MIPAS, mostly in the lower stratosphere. Hence, the
ACTM–satellite CH4 difference is larger for MIPAS_ACE fields than for
MIPAS fields. For TM5 and LMDz, model–satellite CH4 differences are
shifted into the positive direction (Fig. 4b and c). In other words, modeled
stratospheric CH4 mixing ratios appear to be too high when compared to
MIPAS and too low in comparison to MIPAS_ACE.
Model–MIPAS_ACE differences of stratospheric CH4 volume mixing
ratios (vmr) in parts per billion (ppb). Zonally averaged CH4 vmr
differences are annual means for the year 2010.
Zonal XCH4 differences resulting from model–satellite
differences of stratospheric CH4 volume mixing ratios. Mean XCH4
differences are shown as solid lines for the summer period (June, July, and
August) and as dashed lines for the winter period (December, January, and
February).
Average XCH4 differences between model simulations and model
CH4 fields with satellite-based stratospheric CH4 fields. Annual
mean differences as XCH4 bias (with 1σ SD) and minimum–maximum
range of zonal XCH4 differences are in ppb.
The zonal difference fields between model and satellite-based CH4 data
sets have also been converted to XCH4 differences and are shown in
Fig. 5. Two main features can be found in Fig. 5: (i) the XCH4
difference range between the two satellite-based data sets MIPAS (dark red)
and MIPAS_ACE (light red), which is ∼ 27 ppb (1σ
standard deviation (SD) = 4 ppb) on annual mean basis; and (ii) the model–satellite XCH4
differences, which indicate the latitudinal dependence of ACTM (Fig. 1a) and LMDz
(Fig. 1c). For example, ACTM–satellite XCH4 differences are clearly
increasing toward higher latitudes. In contrast to this, the TM5–satellite
XCH4 difference does not show a latitudinal dependence. These findings
on the latitudinal dependence of model–satellite XCH4 differences are
supported by Table 5, which provides some statistical results. For example, the SDs and the
minimum–maximum ranges of model–satellite XCH4 differences are much smaller for
TM5 compared to the other models. Besides that, Fig. 5 also shows that the
model–satellite XCH4 differences for the year 2010 only slightly depend
on season. A noticeable seasonal variation in the model–satellite XCH4
differences can be found in the tropical/subtropical region of the Northern
Hemisphere. However, in order to analyze seasonal variations, a more thorough
analysis is needed, including model and satellite-based XCH4 data sets
with a larger time period than used in this study. Furthermore, in the
context of seasonality the role of TCCON station elevation needs to be
considered in more detail. Since we only apply 1 year of model and
satellite data, the focus of this study is not on the seasonal agreement
between model and satellite-based XCH4 data sets.
Zonal XCH4 differences as a result of model–satellite
differences of stratospheric CH4 volume mixing ratios. Solid lines refer
to the merged model–satellite CH4 fields, including discontinuities at
the model–satellite transition zone around the tropopause. Dashed lines refer
to merged model–satellite CH4 fields that have been smoothly
interpolated at the model–satellite transition zone.
Modeled stratospheric CH4 fields have been directly replaced by
satellite data sets. As a result, there can be discontinuities in the merged
CH4 fields around the tropopause, where the lowest satellite-based
CH4 mixing ratios strongly deviate from the original modeled CH4
mixing ratios. In order to quantify the impact of these discontinuities on
the XCH4 data sets, we have also performed a smoother replacement
method. For this purpose we defined a vertical transition range of 75 hPa,
starting at the lowest vertical MIPAS data grid point. From this position the
model vertical profile of CH4 mixing ratios was linearly interpolated to
the satellite-based CH4 mixing ratio profile, starting at the upper
boundary of this transition range. This method was applied to each
latitudinal MIPAS grid point corresponding to a vertical profile of CH4
mixing ratios. The method was not used if the model–satellite difference of
CH4 mixing ratios was smaller than 30 ppb at the lower boundary of the
transition range. Consequently, we also computed XCH4 differences
between the original model and the smoothed satellite-based data sets. Figure
6 then shows model–satellite XCH4 differences resulting from the force
replacement (solid lines) and from the smoothly interpolated replacement
(dashed lines). From Fig. 6 it is obvious that the impact of the smoothly
interpolated replacement on the model–satellite XCH4 differences is
small; i.e., differences between solid and dashed lines are typically smaller
than 4 ppb. For this reason we expect that the impact of discontinuities in
the merged model–satellite CH4 fields on the results of the XCH4
validation against TCCON is negligible.
Discussion
Our analysis shows that the model–TCCON agreement in XCH4 critically
depends on the model representation of stratospheric CH4, which is
diverse for the presented CTMs. In the following we discuss possible causes
for the inter-model spread in stratospheric CH4. In addition to that,
we evaluate the findings of our XCH4 model–TCCON comparison with
respect to satellite data uncertainty.
Model transport characteristics as possible cause for inter-model spread
in stratospheric methane
An inter-model spread in stratospheric CH4 fields has already been
detected by Patra et al. (2011) despite applying uniform fields of OH, Cl,
and O1D for all models. Their findings, therefore, suggested a
predominant role of transport in the simulation of CH4 vertical
distributions. For this reason, here we tested whether differences in the
modeling of stratospheric transport are noticeable. To do this, we follow the
approach of Strahan et al. (2011) who sought to understand chemistry–climate
model ozone simulations using transport diagnostics. This method is based on
the compact relationship between a long-lived stratospheric tracer and mean
age in the lower stratosphere. In their work, they compared simulations and
air-borne observations of N2O/mean age correlations, in order to
evaluate the model transport characteristics. Here, we use the MIPAS data of
CH4 and mean age as a reference to identify model-to-model differences
in the simulation of stratospheric transport. The MIPAS data are not used to
evaluate whether modeled stratospheric circulations are realistic or not,
given the uncertainties of MIPAS CH4 and mean age data. For example, the
MIPAS mean age range may be too large because MIPAS mean age can be up to
0.8 years too old due to the impact of mesospheric SF6 loss (Stiller et
al., 2012). This loss process was not included in the models used for this
study. Moreover, the MIPAS CH4 data significantly differ from ACE-FTS
CH4 data within the lower stratosphere (see Fig. 1).
Model–MIPAS differences of mean age for the tropical lower. Mean age
data in years (yr) are calculated as annual means on the MIPAS
pressure–latitude grid.
In analogy to Strahan et al. (2011) the model transport diagnostics are
focused on the tropical domain because tropical diagnostics quantities allow
a better assessment of the individual transport processes' ascent and mixing.
Annual means of age for modeled as well as MIPAS-observed fields were
calculated for the lower stratosphere (30–100 hPa) of the tropical domain
(10∘ S–10∘ N), and of the northern hemispheric
midlatitude region (35–50∘ N), respectively. Subsequently,
vertical profiles of mean model–MIPAS differences were calculated to provide
insight into the tropical transport characteristics. Figure 7 illustrates
that the model–MIPAS difference of tropical mean age is almost identical for
all models; i.e. the model simulations produce similar mean ages that are
younger than MIPAS-observed mean ages. Knowing that mean age represents the
combined effects of ascent and mixing, we separately look at the tropical
ascent rate, which is assessed by the horizontal mean age gradient, calculated
as the difference between midlatitude and tropical mean ages. The
model–MIPAS difference of the tropical ascent rate is shown in Fig. 8,
indicating that ACTM and LMDz simulate tropical ascent in a similar way. The
TM5-modeled tropical ascent is faster compared to ACTM and LMDz. Finally,
these model transport diagnostics indicate model-to-model differences in the
simulation of tropical ascent, which are likely to cause an inter-model
spread in model stratospheric CH4 fields.
Indeed, model-to-model differences affecting the simulation of stratospheric
transport are present in the vertical/horizontal resolution, sub-grid-scale
physical parameterizations, advection schemes, and numerical methods, etc.
Furthermore, the simulation of stratospheric transport depends on the
reanalysis data used to drive the model meteorology; e.g., the ECMWF
reanalysis data set ERA-Interim leads to an improved representation of the
stratospheric circulation in comparison to the older ERA-40 reanalysis data
(Monge-Sanz et al., 2007, 2013;
Diallo et al., 2012). The ERA-Interim data are used by TM5 and LMDz, whereas
ACTM applies the JRA-25 reanalysis data (Onogi et al., 2007), which are known
to have several deficiencies compared to the newer JRA-55 data (Ebita et al.,
2011). However, testing ACTM with both ERA-Interim/40 and JRA-25/55 has not
produced significant differences in CH4 simulations (P. Patra, personal
communication, 2016). Besides that, we do not expect that the poor
representation of stratospheric CH4 by ACTM (with 67 vertical levels) is
impacted by a coarse vertical model grid resolution, as seen for an older
version of LMDz (Locatelli et al., 2015).
Model–MIPAS differences of the mean age gradient as a transport
diagnostics for tropical ascent. The mean age gradient was calculated as
the difference between the lower stratospheric mean ages averaged over
35–50∘ N and 10∘ S–10∘ N. Mean age data in years
(yr) are calculated as annual means on the MIPAS pressure–latitude grid.
Significance of satellite data range
The model correction with satellite-based CH4 fields has an impact on
the XCH4 model–TCCON agreement, but the significance of this impact is
diverse for the models. For ACTM, both satellite-based CH4 fields, in
particular MIPAS_ACE, clearly yield an improved model–TCCON agreement. For
TM5 and LMDz, the model–TCCON agreement can be slightly improved (with
MIPAS), but also reduced (with MIPAS_ACE). Thereby, we assert that original
XCH4 simulations from TM5 and LMDz lie inside the range that is spanned
by the two satellite-based CH4 fields. The most prominent feature of the
satellite data range lies within the lower stratosphere where MIPAS-retrieved
CH4 mixing ratios are up to 200 ppb higher than ACE-FTS-retrieved
CH4 mixing ratios. Plieninger et al. (2016) also found a similar high
bias for MIPAS CH4 data in comparison to satellite-based CH4
observations from SCIAMACHY or HALOE (HALogen Occultation Experiment).
Furthermore, they showed that surface measurements provide CH4 mixing
ratios with slightly lower values than MIPAS-retrieved CH4 mixing ratios
of the upper troposphere, a finding that is against expectation. For these
reasons, it is likely that our satellite data range is dominated by high
biased lower stratospheric MIPAS CH4 data. Thus, the model correction
with ACE-FTS-based CH4 fields seems more reliable. However, a definite
assessment of the satellite data accuracies is not possible yet due to the
lack of an extensive observational data set based on stratospheric in situ
measurements.
Summary and conclusions
This study analyzed the importance of uncertainties in stratospheric
CH4 in comparisons of modeled and TCCON observed XCH4. Modeled
stratospheric CH4 fields were substituted by satellite-retrieved
CH4 fields from MIPAS and ACE-FTS. Original and satellite-corrected
model CH4 fields were converted to XCH4 and subsequently evaluated
by comparison to TCCON XCH4 observations from 11 sites. This approach
and the statistical analysis of XCH4 model–TCCON residuals were
conducted with three well-established CTMs: ACTM, TM5 and LMDz.
Our model–TCCON XCH4 intercomparison reveals an inter-model spread in
XCH4 bias caused by an inter-model spread in stratospheric CH4. For
ACTM we find a large average XCH4 bias of 38.1 ppb, in contrast to
small average XCH4 biases of 8.7 ppb for TM5 and 6.8 ppb for LMDz. The
ACTM XCH4 bias is reduced by the model correction to 13.7 ppb with
MIPAS, and to 3.3 ppb with MIPAS adjusted to ACE-FTS, respectively. For TM5
and LMDz the impact of the model correction with satellite-based CH4
fields is ambiguous, in that the model XCH4 bias can be slightly reduced
to 4.3 ppb with MIPAS, but can also be increased to 10.8 ppb for TM5 and
20.0 ppb for LMDz with MIPAS adjusted to ACE-FTS. This implies that for TM5
and LMDz the model representation of stratospheric CH4 is located within
the satellite data range mapped by MIPAS and ACE-FTS observations. The annual
mean differences between the two satellite-based stratospheric CH4 fields
yield a global XCH4 difference range of ∼ 27 ppb.
Possible causes for the inter-model spread in stratospheric CH4 have
been discussed with an emphasis on model transport characteristics. Applying
tropical transport diagnostics suggests that the poor representation of
stratospheric CH4 by ACTM originates from errors in the simulation of
transport pathways into and within the stratosphere. However, this is only
an interpretation based on a diagnostic and requires more process-oriented
model evaluation of stratospheric transport. The inter-model spread in
stratospheric CH4 could be quantitatively investigated with a main
focus on model-to-model differences in the simulation of stratospheric
transport (physical parameterizations, reanalysis data sets,
vertical/horizontal resolution); e.g., model simulations could be performed
with different reanalysis data sets, and/or different physical
parameterizations, resulting in a model ensemble for each CTM or a
multi-model ensemble consisting of multiple CTM data sets. This would allow
the individual model errors in stratospheric CH4 to be assessed more
precisely.
Overall we state that there is a need for improvement in modeling of
stratospheric CH4 and, thus, XCH4. At the same time, a better
quantification of model errors in stratospheric CH4 is limited by the
uncertainty of satellite data products as used in this study. This implies
that more stratospheric CH4 in situ observations are required to
validate both satellite-retrieved and modeled CH4 data. A more accurate
evaluation of modeled stratospheric CH4 fields is particularly
reasonable as these CTMs are used to invert CH4 emissions from XCH4
data. As surface emission signals in XCH4 are small compared to
co-resident XCH4 atmospheric background levels, it is necessary to
identify minor XCH4 biases in the model as done in this study. Of
course, an analogous quality requirement is also needed for ground-based and
satellite-borne XCH4 data. Indeed, as long as unallocated and poorly
understood differences of several parts per billion remain between satellite-borne
XCH4 data and optimized model fields, it is difficult to make full
benefit of satellite XCH4 data to robustly retrieve regional methane
emissions.
Data availability
TCCON data are publicly available at http://www.tccon.caltech.edu/;
please follow the data use policy described there. For obtaining the model
data used in this work, contact Prabir Patra (prabir@jamstec.go.jp) for ACTM,
Sander Houweling (S.Houweling@uu.nl) for TM5, and Philippe Bousquet
(philippe.bousquet@lsce.ipsl.fr) for LMDz. MIPAS and ACE satellite data are
available from the official websites after signing a data protocol.
Acknowledgements
We thank H. P. Schmid (KIT/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. 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 Darwin and Wollongong TCCON sites are funded by NASA
grants NAG5-12247 and NNG05-GD07G, and Australian Research Council grants
DP140101552, DP110103118, DP0879468, LE0668470, and LP0562346. We are
grateful to the DOE ARM program for technical support at the Darwin TCCON
site. The Białystok and Orléans TCCON sites are funded by the EU
projects InGOS and ICOS-INWIRE, and by the Senate of Bremen. Nicholas
Deutscher was supported by an Australian Research Council fellowship,
DE140100178. We are also grateful to P. O. Wennberg for providing TCCON data.
The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a
Canadian-led mission mainly supported by the Canadian Space Agency and the
Natural Sciences and Engineering Research Council of Canada.The article processing charges for this open-access
publication were covered by a Research Centre
of the Helmholtz Association. Edited by:
H. Worden Reviewed by: C. Frankenberg and one anonymous
referee
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