ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-143-2017Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport modelsBelikovDmitry A.dmitry.belikov@ees.hokudai.ac.jphttps://orcid.org/0000-0002-2114-7250MaksyutovShamilhttps://orcid.org/0000-0002-1200-9577GanshinAlexanderhttps://orcid.org/0000-0002-2835-3145ZhuravlevRuslanhttps://orcid.org/0000-0002-8254-2756DeutscherNicholas M.https://orcid.org/0000-0002-2906-2577WunchDebrahttps://orcid.org/0000-0002-4924-0377FeistDietrich G.https://orcid.org/0000-0002-5890-6687MorinoIsamuhttps://orcid.org/0000-0003-2720-1569ParkerRobert J.https://orcid.org/0000-0002-0801-0831StrongKimberlyhttps://orcid.org/0000-0001-9947-1053YoshidaYukiohttps://orcid.org/0000-0002-3515-1488BrilAndreyOshchepkovSergeyBoeschHartmutDubeyManvendra K.https://orcid.org/0000-0002-3492-790XGriffithDavidhttps://orcid.org/0000-0002-7986-1924HewsonWillKiviRigelhttps://orcid.org/0000-0001-8828-2759MendoncaJosephNotholtJustusSchneiderMatthiashttps://orcid.org/0000-0001-8452-0035SussmannRalfVelazcoVoltaire A.https://orcid.org/0000-0002-1376-438XAokiShujiNational Institute for Environmental Studies, Tsukuba, JapanNational Institute of Polar Research, Tokyo, JapanFaculty of Mechanics and Mathematics, Tomsk State University, Tomsk, RussiaCentral Aerological Observatory, Dolgoprudny, RussiaCentre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, AustraliaInstitute of Environmental Physics, University of Bremen, Bremen, GermanyCalifornia Institute of Technology, Pasadena, CA, USAMax Planck Institute for Biogeochemistry, Jena, GermanyEarth Observation Science, University of Leicester, Leicester, UKDepartment of Physics, University of Toronto, Toronto, ON, CanadaInstitute of Physics of the National Academy of Sciences, Minsk, BelarusEarth System Observations, Los Alamos National Laboratory, Los Alamos, New MexicoFinnish Meteorological Institute, Sodankylä, FinlandAgencia Estatal de Meteorología (AEMET), CIAI, Santa Cruz de Tenerife, SpainKarlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, GermanyCenter for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai, Japancurrently at: Faculty of Environmental Earth Science, Hokkaido University, Sapporo, JapanDmitry A. Belikov (dmitry.belikov@ees.hokudai.ac.jp)3January20171711431578March20164May201625November20161December2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/143/2017/acp-17-143-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/143/2017/acp-17-143-2017.pdf
The Total Carbon Column Observing Network (TCCON) is a network of
ground-based Fourier transform spectrometers (FTSs) that record near-infrared
(NIR) spectra of the sun. From these spectra, accurate and precise
observations of CO2 column-averaged dry-air mole fractions (denoted
XCO2) are retrieved. TCCON FTS observations have previously been used to
validate satellite estimations of XCO2; however, our knowledge of the
short-term spatial and temporal variations in XCO2 surrounding the TCCON
sites is limited.
In this work, we use the National Institute for Environmental Studies (NIES)
Eulerian three-dimensional transport model and the FLEXPART (FLEXible
PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the
footprints of short-term variations in XCO2 observed by operational,
past, future and possible TCCON sites. We propose a footprint-based method
for the collocation of satellite and TCCON XCO2 observations and
estimate the performance of the method using the NIES model and five
GOSAT (Greenhouse Gases Observing Satellite)
XCO2 product data sets. Comparison of the proposed approach with a
standard geographic method shows a higher number of collocation points and
an average bias reduction up to 0.15 ppm for a subset of 16 stations for the
period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved,
non-uniform and significantly different from a geographical rectangular area,
the differences between these approaches are more noticeable. This emphasises
that the collocation is sensitive to local meteorological conditions and flux
distributions.
Introduction
Satellite observations of the column-averaged dry-air mole fraction of
CO2 (XCO2) have the potential to significantly advance our
knowledge of carbon dioxide (CO2) distributions globally and provide new
information on regional CO2 sources and sinks. Observations of XCO2
are available from space-based instruments such as the SCanning Imaging
Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY; data
available for the period 2002–2012; Bovensmann et al., 1999), the Greenhouse
Gases Observing Satellite (GOSAT; data available since 2009; Kuze et al.,
2009, 2016; Yokota et al., 2009) and the Orbiting Carbon Observatory-2
(OCO-2; available since the middle of 2014; Crisp et al., 2004). These satellites
provide unprecedented spatial coverage of the variability in XCO2 around
the world, with the exception of polar regions and areas with dense clouds.
These observations are, however, limited by the orbit of the satellites,
which typically measure in the local afternoon.
Ground-based Fourier transform spectrometer (FTS) observations available
from the Total Carbon Column Observing Network (TCCON) (Deutscher et al., 2010;
Geibel et al., 2010; Messerschmidt et al., 2010, 2012; Ohyama et al., 2009;
Washenfelder et al., 2006; Wunch et al., 2011,
2015) provide dense temporal resolution and are more precise and accurate
than space-based instruments. However, the number of ground-based FTS sites
is limited, with just 23 operational sites and several approved for the
future. These sites are sparsely distributed, and Siberia, Africa, South
America and the oceans from middle to high latitudes are poorly covered.
Despite this limitation, FTS observations are used to validate satellite
retrievals in order to assess bias, variability and other key parameters
(e.g. Wunch et al., 2011; Lindqvist et al., 2015).
The spatial and temporal coverage of satellite observations over TCCON sites
is sparse in space and time due to cloud and aerosol filters, retrieval
selection criteria and post-retrieval data quality filters. To obtain
satellite observation data at the location and time of interest it is
necessary to apply a collocation method for aggregating neighbouring soundings.
All collocation methodologies implement interpolation techniques. It is
important to minimise the interpolation errors, which cause an uncertainty
that is incorporated into the variability of the colocated/validation data
comparison (Nguyen et al., 2014). Currently available methods for XCO2
collocation include geographical (e.g. Cogan et al., 2012; Inoue et al.,
2013; Reuter et al., 2013), T700 (it implies that the air with the same
history of transport derived from the 700 hPa potential temperature has the
same XCO2; Wunch et al., 2011), model-based (Guerlet et al., 2013) and
geostatistical approaches (Nguyen et al., 2014).
In the geographical collocation method a spatial region around a TCCON site is
selected together with a temporal window for selecting the satellite data.
Inoue et al. (2013) used daily mean observations within a 10∘× 10∘
area, Reuter et al. (2013) selected the monthly median
of all observations within a 10∘× 10∘ area, and
Cogan et al. (2012) implemented narrower limits, using a 2 h mean period
within a ±5∘×±5∘ area.
To increase the number of soundings, the spatial region may be expanded and
additional selection criteria imposed. In the T700 collocation method proposed
by Wunch et al. (2011), all observations within ±30∘ longitude,
±10∘ latitude, and ±2 K of the selected
TCCON location and within ±5 days window are employed.
The model-based method proposed by Oshchepkov et al. (2012) and improved by
Guerlet et al. (2013) uses daily mean values within 0.5 ppm of the 3-day-averaged model XCO2 values and is located within ±25∘ longitude and
±7.5∘ latitude of a TCCON site.
Nguyen et al. (2014) developed a geostatistical collocation methodology that
selects observations using a “distance” function, which is a modified
Euclidian distance in terms of latitude, longitude, time and
mid-tropospheric temperature at 700 hPa.
The majority of collocation methods described above have a common
disadvantage; i.e. they work with a rectangular spatial domain, which is
convenient for technical handling but does not reflect the impact of surface
sources or sinks of CO2 and the local meteorology in the area of
interest. The spatial domains in collocations should take into account these
features to ensure that only appropriate observations are selected.
Keppel-Aleks et al. (2011, 2012) showed that the largest gradient in
XCO2 is formed mainly by the north–south flux distribution, with
variations in XCO2 caused mainly by large-scale advection. TCCON and
satellite XCO2 observations have pronounced temporal variability and
are thus important in studies of short-term variations in XCO2.
In this paper we study short-term variations in XCO2 observed at TCCON
sites. Although the XCO2 is derived from column-averaged concentrations
of CO2, XCO2 observations are most sensitive to near-surface
fluxes. The XCO2 variations are thus related to changes in the CO2
mole fraction occurring near the surface surrounding the TCCON sites
(hereafter known as the footprints of the TCCON sites).
The remainder of this paper is organised as follows: an overview of the
method for estimating the footprints of TCCON sites is presented in Sect. 2.
The results of the footprint estimation and a new method for collocation
are presented and discussed in Sects. 3 and 4, and the conclusions are given
in Sect. 5.
Method
To estimate the footprints of TCCON sites we used forward simulations
employing the NIES Eulerian three-dimensional transport model (TM) and
backward trajectory tracking using the FLEXPART LPDM model.
The key features of the NIES TM are as follows: a reduced horizontal
latitude–longitude grid with a spatial resolution of 2.5∘× 2.5∘
near the equator (Belikov et al., 2011), a vertical flexible
hybrid sigma–isentropic (σ–θ) grid with 32 levels up to
the level of 5 hPa (Belikov et al., 2013b), separate parameterisation of the
turbulent diffusivity in the PBL and free troposphere (provided by the
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim
reanalysis) and a modified Kuo-type parameterisation scheme for cumulus
convection (Belikov et al., 2013a).
The NIES model has previously been used to study the seasonal and
interannual variability in CO2. Belikov at al. (2013b) reported that
the NIES model is able to successfully reproduce the vertical profile of
CO2 as well as the seasonal and interannual variability in XCO2.
A comparison of modelled output with TCCON observations (Belikov et al.,
2013b) revealed model biases of ±0.2 % for XCO2; on this basis
we assume that the NIES TM is able to successfully reproduce the vertical
profile of CO2 at the locations of TCCON sites.
Firstly we run NIES TM for the target period (January 2010–February 2011)
using 10 year's spin-up to ensure reduction of initialization errors. Then
NIES TM CO2 concentrations sampled at the location of TCCON sites at the
level of 1 km above ground at 13:00 local time were used to initialise
backward tracer simulations with the FLEXPART model.
FLEXPART is used to identify the source–receptor relationship of the CO2
tracer. The CO2 emission is the “source”, and the TCCON site is the
“receptor”. Like other Lagrangian particle dispersion models (LPDMs),
FLEXPART approximates a plume of atmospheric tracer by a cloud of particles.
An efficient way of calculating sensitivity at the receptor is by solving the adjoint
equation of tracer transport, which requires backward transport (Hourdin and
Talagrand, 2006). Lagrangian models provide a efficient tool for backward
transport modelling of a compact plume of particles, one plume representing a
single observation. By tracking the pathway of each individual particle back
in time and counting the particle residence times in the mixed layer at each
grid cell the sensitivity coefficient or the footprint can be obtained
(Stohl et al., 2009). The sensitivity S of CO2 concentration
C to emissions F is the ratio of the change in C
to an incremental change of F : S=∂C/∂F.
Surface emissions change the
concentration in the surface layer, while FLEXPART sensitivity to
concentration in a surface grid cell at a given time is given by the number
of particles that reside in the each surface grid cell divided by the total
number of particles released.
The level of 1 km above ground typically corresponds to the top of the
daytime planetary boundary layer (PBL). The PBL is the lowest part of the
atmosphere and its behaviour is directly influenced by its contact with the
planetary surface. Turbulence causes intensive vertical mixing of the air
within the PBL, so CO2 released from the surface is roughly uniformly
distributed throughout the column of air in the PBL at local noon, when the
maximum extent of vertical mixing occurs. The selected sampling time is also
favourable for minimising errors in the initial CO2 concentration
calculated by NIES TM, as this type of chemical transport model has proved
to be successful in resolving the diurnal vertical profiles of tracers
(Belikov et al., 2013a).
To run the NIES TM and FLEXPART models we use fluxes obtained with the GELCA-EOF
(Global Eulerian–Lagrangian Coupled Atmospheric model with Empirical
Orthogonal Function) inverse modelling scheme (Zhuravlev et al., 2013). A
priori fluxes consist of four types:
the Open-source Data Inventory of
Anthropogenic CO2 (ODIAC) (Oda et al., 2011) and the Carbon Dioxide
Information Analysis Center's (CDIAC) (Andres et al., 2011) anthropogenic
fluxes;
the Vegetation Integrative SImulator for Trace gases (VISIT)
(Ito, 2010) biosphere fluxes;
the offline Ocean Tracer Transport Model
(OTTM) (Valsala et al., 2013) oceanic fluxes;
the Global Fire
Emissions Database (GFED) (Van der Werf et al., 2010) biomass burning
emissions. Both models are driven by the Japanese Meteorological Agency
Climate Data Assimilation System (JCDAS) data sets (Onogi et al., 2007).
Details of operational TCCON sites.
NumberSiteLatitudeLongitudeAltitude(degrees)(degrees)(km)1Anmyeondo, Korea36.54126.330.032Ascension Island7.92-14.330.033Białystok, Poland53.2323.030.184Bremen, Germany53.108.850.035Caltech, USA34.14-118.130.236Darwin, Australia-12.42130.890.037Edwards, USA34.96-117.880.708Eureka, Canada80.05-86.420.619Garmisch, Germany47.4811.060.7410Izaña, Tenerife28.30-16.502.3711Karlsruhe, Germany49.108.440.1212Lamont, USA36.60-97.490.3213Lauder, New Zealand-45.04169.680.3714Ny Ålesund, Spitsbergen78.9011.900.0215Orléans, France47.972.110.1316Park Falls, USA45.95-90.270.4417Paris, France48.852.320.1018Reunion Island, France-20.9055.490.0919Rikubetsu, Japan43.46143.770.3620Saga, Japan33.24130.290.0121Sodankylä, Finland67.3726.630.1922Tsukuba, Japan36.05140.120.0323Wollongong, Australia-34.41150.880.03
Variations in TCCON XCO2 are influenced by a large-scale processes.
Keppel-Aleks et al. (2012) presented a robust relationship between weekly and
monthly aggregated total column CO2 and local net ecosystem exchange,
while column drawdown has only a weak correlation with the regional flux on
daily timescales. Thus the maximum trajectory duration for the FLEXPART was,
therefore, set to 1 week. The FLEXPART model was run with resolution of 1∘
and 2 h time step for a 14-month period from January 2010 to February 2011.
Global distribution of the sensitivity of CO2 concentrations
(ppm (µmol (m2 s)-1)-1) with respect to the
concentrations in adjacent cells, calculated using the FLEXPART model with a
resolution of 1.0∘ for the 23 TCCON operational sites:
(a) tracer simulation initialised at the level of 1000 m,
(b) tracer simulation initialised at the level of 3000 m that
corresponds to 700 hPa based on the International Standard Atmosphere for
dry air.
Distribution of the sensitivity of CO2 concentrations
(ppm (µmol (m2 s)-1)-1) in Europe with respect to
the concentrations in adjacent cells, calculated using the FLEXPART model
with a resolution of 1.0∘ for TCCON operational sites within Europe,
using a tracer simulation initialised at the level of 1000 m.
ResultsSensitivity of TCCON site footprints
We analysed two groups of TCCON sites: operational sites (Table 1; Figs. 1
and 2) and past, future and possible sites (Table 2; Fig. 3). We included
Arrival Heights (Antarctica) and Yekaterinburg (Russia) in the second group,
though the status of these monitoring stations is unclear. The footprint
estimation is restricted to the summer season for high-latitude sites
(Arrival Heights, Eureka, Ny Ålesund, Poker Flat and Sodankylä),
due to limitations relating to the solar zenith angle.
Past, future and possible TCCON sites.
NumberSiteLatitudeLongitudeAltitude(degrees)(degrees)(km)1Arrival Heights, Antarctica-77.83166.660.252Burgos, Philippines18.50120.850.103East Trout Lake, Canada54.35-104.980.494Four Corners, USA36.80-108.481.645Manaus, Brazil-3.10-60.020.096Oxfordshire, UK51.57-1.320.077Paramaribo, Suriname5.80-55.200.058Poker Flat, USA65.12-147.470.219Yekaterinburg, Russia57.0459.550.30
Global distribution of the sensitivity of CO2 concentrations
(ppm (µmol (m2 s)-1)-1) with respect to the
concentrations in adjacent cells, calculated using the FLEXPART model with a
resolution of 1.0∘ for 9 past, future and possible TCCON operational
sites, using a tracer simulation initialised at the level of 1000 m.
Operational sitesNorth America
The five active American sites are located in the US and Canada, so they are
sensitive to the western and central parts of North America, the northern
part of Canada and Greenland and the eastern part of the Pacific Ocean.
There are no TCCON sites in Alaska or on the east coast of North America,
which is a region of intense anthropogenic activity (Fig. 1).
European sites
The European region contains eight operational sites (Fig. 2). We also
include Izaña, which does not belong to this region but is located very
close to it. This region has a good spatial coverage of operational TCCON
sites; however, most sites are located near the coast and are thus very
sensitive to the Atlantic and Arctic oceans. The maximum footprint
sensitivity occurs in western Europe where there is a high density of
operational TCCON sites; five sites (Bremen, Garmisch, Karlsruhe,
Orléans and Paris) are concentrated within a small area. The
sensitivity decreases quite rapidly towards the east and south, and only
parts of eastern Europe and northern Africa are covered.
Asia
The footprints of Asian sites mainly span countries bordering the Sea of
Japan, i.e. Japan, Korea, the Russian Far East and eastern China. These sites
are also able to capture signals from Mongolia, eastern Siberia and
South-east Asia. Although the coverage of these sites is relatively small,
the main industrial centres in the region are included.
Australia and New Zealand
The footprint sensitivity of TCCON sites in this region covers almost all of
Australia. Chevallier et al. (2011) show that TCCON data could constrain flux
estimates over Australia equally as well as the existing in situ measurements.
Our footprint estimations are, however, more sensitive to the ocean regions
between Australia and New Zealand as well as adjacent coastal areas.
Oceanic sites: Ascension Island and Reunion Island
Ascension Island is in the trade wind belt of the tropical Atlantic, ideally
located to measure the South Atlantic marine boundary layer. The
south-eastern trade winds, which are almost invariant and are derived from the deep South
Atlantic Ocean with little contact with Africa. Surface measurements of
CO2 at Ascension Island are used as a background (Gatti et al., 2010).
However, above the trade wind inversion (TWI), at about 1200–2000 m above
sea level, the air masses are very different, coming dominantly from
tropical Africa and occasionally South America (Swap et al., 1996). The
FLEXPART simulation with tracers released at an altitude of 3000 m detected
some hotspots in Africa (Fig. 1b). The study of biomass burning in Africa is
essential, but lies outside of the scope of this paper.
Reunion Island is situated in the Indian Ocean, about 800 km east of
Madagascar. For this site the seasonal trend of wind mainly remains in the
easterly sector, so the footprint covers mainly ocean regions. The Reunion Island site is further discussed in Sect. 4.3.2.
Past, future and possible TCCON sites
The footprints of past, future and possible TCCON sites are presented in
Fig. 3. The Oxfordshire site enhances the sensitivity of the region, which
is already well covered by existing TCCON sites in Europe. The East Trout
Lake, Four Corners and Poker Flat sites fill sensitivity gaps in the
Canadian boreal forest, the south-western US, northern Mexico and Alaska.
Nevertheless, there are no TCCON sites near the Atlantic coast of North
America, which is a key region of interest.
In South America, the Manaus site (briefly in operation during 2014 and will
operate after reconstruction) was ideally located in central Amazonia.
However, meteorological conditions meant that a signal was only detected in
a very narrow section towards the east. Observations at this site are more
sensitive to anthropogenic activity on the Atlantic coast of South America,
compared with the surrounding Amazonian biosphere. Additional use of CO
observations will be necessary to isolate the net primary production signal
in central Amazonia (Keppel-Aleks et al., 2012). Another site in this region
is Paramaribo, located in Suriname, which is part of Caribbean South America.
The footprint of the Paramaribo site is narrowly focused towards the
Atlantic Ocean due to site location and meteorological conditions as stated
above.
Burgos in the northern Philippines extends the Asian footprint southward.
The location of the Yekaterinburg site is ideal, as it quite evenly covers a
large area of western Russia. The site reduces the gap between the European
and Asian TCCON domains. The Arrival Heights site is located on the
Antarctic coast and currently cannot be used for satellite data validation.
Given the air circulation near the South Pole, this site can be useful for
measuring the background value of XCO2.
In general, the operational stations cover some regions well (North America,
Europe, the Far East, South-east Asia, Australia and New Zealand), and the
planned sites will improve this coverage. However, on a global scale there
are major gaps that highlight the difficulty in generalising the available
data along latitude for bias correction.
The short-term variations in CO2 in the near surface and free
troposphere (< 3000 m) have the same form, but different
intensities (Fig. 1b), as a smaller number of tracers from the middle troposphere
reached the surface during the simulation time.
Footprints for different seasons for Ascension Island, Białystok,
Darwin, Izaña, Manaus, Park Falls and Tsukuba, for (a) the
summer (June, July and August) of 2010 and (b) the winter
(December, January and February) of 2010–2011.
Seasonal variability in footprints
Some TCCON stations have strong seasonal variations in their footprint due
to changes in wind direction, i.e. Białystok, Darwin, Izaña, Park
Falls and Tsukuba (Fig. 4). For other sites (e.g. Ascension Island and Manaus)
the weather conditions are less variable throughout the year. The depth of
the PBL changes with season and is thus an important factor that influences
the footprint. In winter weak vertical mixing causes the shallow PBL. This
leads to enhanced horizontal tracer transport and a wider spatial coverage
of the footprints.
Averaged results of different collocation methods implemented for
XCO2 from NIES TM calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumberof discardedcorrelationvalue ofstandardof cells*coincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.5355.330.960.751.01C2Footprint limit log10(x) =-1.01605.480.960.810.98C3Footprint limit log10(x) =-1.55075.900.970.850.97C4Footprint limit log10(x) =-2.010716.970.970.880.96C5Within area of 2.5∘× 2.5∘15.760.960.761.03C6Within area of ±5.0∘×±5.0∘165.360.960.791.00C7Within area of ±5.0∘×±10.0∘325.220.960.790.98C8Within area of ±7.5∘×±22.5∘1085.110.970.800.97
* The number of FLEXPART cells with resolution
1.0∘× 1.0∘ is counted for methods based on the
footprint (1–4), while for other methods NIES TM cells
(2.5∘× 2.5∘) are used.
Monthly average residuals of modelled XCO2 compared with TCCON
ground-based FTS for methods C1, C4, C5 and C8, for (a) Darwin and
(b) Garmisch.
(a) Annual average footprint for the Darwin TCCON
observation site; ACOS GOSAT XCO2 observations selected using
(b) the geostatistical method within an area of
±7.5∘×±22.5∘ and (c) the
footprint-based method with the limit log10(x) =-2.0.
Averaged results of different collocation methods implemented for XCO2
from the GOSAT ACOS product calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumber ofof discardedcorrelationvalue ofstandardobservationscoincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.511909.850.930.651.18C2Footprint limit log10(x) =-1.030467.750.920.611.21C3Footprint limit log10(x) =-1.548807.820.930.621.15C4Footprint limit log10(x) =-2.060167.060.930.641.12C5Within area of 2.5∘× 2.5∘97610.290.930.811.11C6Within area of ±5.0∘×±5.0∘20428.680.920.671.19C7Within area of ±5.0∘×±10.0∘31118.180.920.651.19C8Within area of ±7.5∘×±22.5∘50027.270.930.641.16
Difference (denoted as d[]) in correlation coefficients, mean bias
(ppm), SD (ppm) and number of observational points between methods C4 (the
collocation domain size is determined by sensitivity values
(ppm (µmol (m2 s)-1)-1) with the limit of
log10(x) equal to -2.0) and C8 (the collocation domain size is
rectangular with dimension
±7.5∘×±22.5∘) using ACOS, NIES, PPDF,
RemoTeC and UoL GOSAT products near the Darwin site. Please note that the scale of
number of observational points is 105.
(a) Annual average footprint for the Reunion Island TCCON
observation site; ACOS GOSAT XCO2 observations selected using
(b) the geostatistical method within an area of
±7.5∘×±22.5∘ and (c) the
footprint-based method with the limit log10(x) =-2.0.
Averaged results of different collocation methods implemented for XCO2
from the GOSAT NIES product calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumber ofof discardedcorrelationvalue ofstandardobservationscoincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.5104910.490.890.631.14C2Footprint limit log10(x) =-1.0289011.130.920.521.20C3Footprint limit log10(x) =-1.548239.700.920.601.19C4Footprint limit log10(x) =-2.059228.410.920.561.16C5Within area of 2.5∘× 2.5∘90711.680.890.631.17C6Within area of ±5.0∘×±5.0∘184510.350.910.561.15C7Within area of ±5.0∘×±10.0∘297610.040.930.581.15C8Within area of ±7.5∘×±22.5∘48749.760.920.601.17
Averaged results of different collocation methods implemented for
XCO2 from the GOSAT PPDF product calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumber ofof discardedcorrelationvalue ofstandardobservationscoincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.53577.800.840.501.11C2Footprint limit log10(x) =-1.08709.070.860.621.12C3Footprint limit log10(x) =-1.515367.810.810.731.16C4Footprint limit log10(x) =-2.019116.460.810.671.17C5Within area of 2.5∘× 2.5∘3317.020.860.661.02C6Within area of ±5.0∘×±5.0∘7497.530.850.641.15C7Within area of ±5.0∘×±10.0∘11148.460.830.691.19C8Within area of ±7.5∘×±22.5∘17337.430.860.681.17
Averaged results of different collocation methods implemented for XCO2
from the GOSAT RemoTeC product calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumber ofof discardedcorrelationvalue ofstandardobservationscoincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.579510.200.810.711.17C2Footprint limit log10(x) =-1.018989.630.830.661.19C3Footprint limit log10(x) =-1.532129.190.830.611.22C4Footprint limit log10(x) =-2.040918.120.830.591.21C5Within area of 2.5∘×2.5∘76911.200.900.871.15C6Within area of ±5.0∘×±5.0∘14919.910.850.631.18C7Within area of ±5.0∘×±10.0∘23259.460.860.701.19C8Within area of ±7.5∘×±22.5∘38188.570.860.641.25
Averaged results of different collocation methods implemented for XCO2
from the GOSAT UoL-FP product calculated for 16 TCCON sites.
MeanMean numberMeanAbsoluteMeannumber ofof discardedcorrelationvalue ofstandardobservationscoincidentcoefficientmean biasdeviationCaseMethod of collocationobs. (%)C1Footprint limit log10(x) =-0.563411.040.880.781.31C2Footprint limit log10(x) =-1.0145412.780.870.761.34C3Footprint limit log10(x) =-1.5245010.880.880.801.28C4Footprint limit log10(x) =-2.0301710.220.890.701.23C5Within area of 2.5∘× 2.5∘62911.900.860.731.33C6Within area of ±5.0∘×±5.0∘121513.150.880.761.30C7Within area of ±5.0∘×±10.0∘185213.580.860.741.27C8Within area of ±7.5∘×±22.5∘279911.930.850.721.25Applying the model-derived footprints to the collocation of XCO2
In the next two sections we assess the performance of the footprint-based
method of collocating TCCON XCO2 against the NIES model and GOSAT product
data sets. The collocation domain size for each site is determined by
sensitivity values (ppm (µmol (m2 s)-1)-1) with the limits
of log10(x) equal to -0.5, -1.0, -1.5 and -2.0 (cases C1–C4). These
sensitivity values were selected to approximately correspond to the domain
sizes in standard geographical collocation techniques, which have rectangular
dimensions of 2.5∘× 2.5∘,
±5.0∘×±5.0∘,
±5.0∘×±10.0∘ and ±7.5∘×±22.5∘
(cases C5–C8). Only coincident observations were used, and observations with
differences of ≥ 3 ppm were discarded from the comparison. The considered
period for comparison is January 2010 and January 2014.
UoL-FP GOSAT XCO2 observations selected using (a) the
geostatistical method within an area of
±7.5∘×±22.5∘ and (b) the
footprint-based method with the limit log10(x) =-2.0.
TCCON observations were used from 16 sites: Białystok, Caltech, Darwin,
Eureka, Garmisch, Izaña, Karlsruhe, Lamont, Lauder (125HR), Orléans,
Park Falls, Reunion Island, Saga, Sodankylä, Tsukuba (125HR) and
Wollongong. These observations were obtained from the 2014 release of TCCON
data (GGG2014), available from the TCCON Data Archive
(http://tccon.ornl.gov).
Comparison of collocation methods C4 and C8 using ACOS, NIES, PPDF,
RemoTeC and UoL GOSAT products near the Darwin site.
Comparison of collocation methods C4 and C8 using ACOS, NIES, RemoTeC and
UoL GOSAT products near the Reunion Island site. The PPDF GOSAT product does
not include any observations near the Reunion Island site.
GOSATCaseCorrelationMeanStandardNumber ofProductcoefficientbiasdeviationobservationsACOSC40.820.700.8311 873C80.830.650.769640NIESC40.700.251.077720C80.730.451.026505RemoTeCC40.510.921.072482C80.611.161.043414UoLC40.450.750.94860C80.360.711.002239Collocation of XCO2 from TCCON and the NIES model
The TCCON and NIES TM data sets are initially compared using a geographical
collocation of 2.5∘× 2.5∘, which corresponds to
selecting the nearest NIES TM cell (Table 3). The resolution of the model
grid is rather coarse, so we observe that the results depend mainly on the
size of the collocation area but not on the form. As the size of the
collocation area increases, the correlation between XCO2 from TCCON and NIES TM
slightly increases from 0.96 to 0.97 and the standard deviation decreases
from 1.1 to 0.96 ppm. This is due to an increase in the number of
observations.
There are several reasons for the larger discrepancy (≥ 3 ppm) of GOSAT
observations. Systematic errors due to imperfect characterisation of clouds
and aerosols dominate the error budget. Other effects, such as
spectroscopy errors, pointing errors, imperfect radiometric and spectral
characterisation of the instrument are clearly present in retrievals.
Additional real-world issues, such as forest canopy effects, partial
cloudiness, cloud shadows and plant fluorescence will further increase the
retrieval errors (O'Dell et al., 2012). The mean number of discarded coincident
observation is about 5–7 %.
For Darwin, Eureka, Izaña, Lauder, Reunion Island, Sodankylä and
Wollongong, the residuals between the data sets are small and similar for all
methods (see Fig. 5a for Darwin; cases C1, C4, C5 and C8). Here, XCO2
is under the influence of global long-term variations that are included in
the NIES TM. The low sensitivity of the model to local sources does not
cause a significant difference between the collocation methods. For the
second group (non-operational sites), local sources are essential and even
coarse-grid models can capture their signal. As a result, the shape of the
collocation area is important (see Fig. 5b for Garmisch; cases C1, C4, C5
and C8).
Collocation of XCO2 from TCCON and GOSAT products
A comparison of collocation methods was performed for five GOSAT XCO2
products: NIES v02.11 (Yoshida et al., 2013) and the photon path length
probability density function method retrievals (PPDF-S v02.11; Oshchepkov et
al., 2013) from the NIES, Japan; the NASA Atmospheric CO2 Observations
from Space retrieval (ACOS B3.4; O'Dell et al., 2012); the Netherlands
Institute for Space Research/Karlsruhe Institute of Technology, Germany
(RemoTeC v2.11; Butz et al., 2011; Guerlet et al., 2013); and the University
of Leicester Full Physics retrieval (UoL-FP v4; Boesch et al., 2011; Cogan et
al., 2012). The mean percentage of discarded coincident TCCON–GOSAT
observation is around 7–14 %. Results from PPDF and UoL-FP methods are
closer to lower and upper limits.
The results of the comparison of eight collocation methods employed for the
five GOSAT XCO2 products are presented in Tables 4–8. Only coincident
observations were used, and observations with differences of ≥ 3 ppm
were discarded from the comparison. The number of observations selected for
collocation between the methods with the smallest areas (C1 and C5) and
largest areas (C4 and C8) differs approximately by a factor of 5. There is,
however, no clear dependence of the collocation efficiency on the number of
observations. The correlation coefficient and standard deviation are within
0.81–0.93 and 1.02–1.22 ppm, respectively, regardless of the method used.
Mean bias values are within 0.50–0.87 ppm, with the footprint method
typically having a slightly lower bias by 0.02–0.15 ppm and a higher number
of collocations. For individual stations, these statistics may lie slightly
outside the specified ranges.
Case study
In this section we demonstrate the developed collocation method for GOSAT
observations over the Darwin and Reunion Island TCCON sites.
Darwin site
The Northern Territory of Australia has two distinctive climate zones: the
northern and southern zones. The northern zone, including Darwin, has three
distinct seasons: the dry season (May–September), the build-up season
(high humidity, but little rain: October–December) and the wet season,
associated with tropical cyclones and monsoon rains (December–April). The
average maximum temperature is remarkably similar all year round. The
southern zone is mainly desert with a semi-arid climate and little rain. To
the north of Darwin, the territory is bordered by the Timor Sea, the Arafura
Sea and the Gulf of Carpentaria. The Northern Territory, therefore, has a
pronounced seasonal variability that affects the spatial and temporal
distribution of CO2 and thus the footprint (Figs. 4 and 6a).
Figure 6b and c show the locations of GOSAT observations selected using a
geographical method within an area of ±7.5∘×±22.5∘
and a footprint-based method with the limit log10(x) =-2.0.
Sizes of selected collocation areas (C4 and C8 methods) are close to
ones used in others works (Wunch et al., 2011; Guerlet et al., 2013; Inoue et
al., 2013; Reuter et al., 2013; Nguyen et al., 2014).
For ACOS, NIES and RemoTeC GOSAT products the distributions of XCO2 data
sets for the Darwin site are similar and cover an area to the west of Darwin,
including ground-based observations from central Australia (Fig. 6c). The
comparison of collocation methods shows that the footprint-based method (C4)
outperforms the geographical method (C8) for these three GOSAT products
(Fig. 7, Table 9), with approximately three times as many observations.
Difference (denoted as d[]) in correlation coefficients, mean bias
(ppm), SD (ppm) and number of observational points between methods C4 (the
collocation domain size is determined by sensitivity values
(ppm (µmol (m2 s)-1)-1) with the limit of
log10(x) equal to -2.0) and C8 (the collocation domain size is
rectangular with dimension
±7.5∘×±22.5∘) using ACOS, NIES,
RemoTeC and UoL GOSAT products near the Reunion Island site. Please note
scale of number of observational points is 104.
Although currently the UoL GOSAT XCO2 version 6 includes ocean-glint
observations, in this study we use the slightly outdated UoL-FP GOSAT
product v4, which has only overland points. In this case the difference
between collocation subsets is the observations towards the south over land,
which provide a similar distribution to the ACOS product, but without marine
observations (Fig. 6b and c). These differences in the covered areas
have a significant negative effect on the result (Fig. 7). From that it can
be concluded that XCO2 patterns towards the south over land are rather
different from those around Darwin, the sun-glint observation over the ocean
are important and must be included into analysis. Thus, XCO2 at the
Darwin site is under the influence of the three different fluxes coming from
surrounding land area, central part of Australia and oceanic regions. The
oceanic observation over the Coral Sea is quite important, though
substantially removed from the station.
Reunion Island site
Reunion Island is a small island east of Madagascar surrounded by the Indian
Ocean. The nearest land territory to Reunion Island is Mauritius, located
∼ 175 km to the north-west. The meteorological conditions in
the region mean that the footprint of the Reunion Island site mostly covers
a large area of ocean to the south-east of the island and a small area of
northern Madagascar (Fig. 8).
The geographical collocation method does not take into account local
conditions. Therefore, despite the fact that the site is predominantly
oceanic, the geographical method includes observations made over land in
Madagascar and the south-east coast of Africa (Fig. 8b). In contrast, the
footprint method takes into account the local meteorology, so observations
are predominantly taken from the ocean (Fig. 8c). Since the UoL-FP data set
has no observations over the sea, the observations for this data set are
located only over Madagascar (Fig. 9).
Unlike Darwin, Reunion Island receives clean air from the ocean and thus has
very little CO2 variation. The selection of areas for collocation,
therefore, did not reveal any significant advantages of the footprint-based
method, with the exception of a slightly smaller bias for the NIES and
RemoTec products (Fig. 10, Table 10). The comparison of the UoL-FP product
for method C4 and method C8 shows that the XCO2 cycles over Madagascar
and the eastern coast of Africa are quite different (Fig. 10). This
highlights that the exclusion of marine observations leads to poor results
over marine-based TCCON sites.
A comparison of TCCON data and NIES model results for Darwin and Reunion
shows that XCO2 for these sites is controlled mainly by large-scale
changes. However, analysis of GOSAT products emphasises that the influence of
local fluxes is also important (Liu et al., 2015). The geographical method of
collocation assumes a fairly even distribution of GOSAT observations near
TCCON sites, while the calculated footprints have strongly curved shapes and
an uneven distribution. We therefore expect the proposed footprint method to
be useful for other sites with rather curved and non-uniform footprints, such
as the Ascension Island and Manaus sites.
Summary
We have developed a method for assessing the footprints of short-term
XCO2 variations observed by TCCON ground-based FTS sites. The method is
based on 1-week FLEXPART backward trajectory simulations that are
initiated at an altitude of 1 km (the upper border of the PBL) in the
afternoon using the vertical CO2 distribution calculated by the NIES
transport model.
We applied this method to estimate footprints of the operational, past,
future and possible TCCON sites, and revealed some basic patterns. Most
sites located near coastal regions are strongly influenced by ocean regions;
thus, there is a large seasonal variability in footprints for Białystok,
Darwin, Izaña, Park Falls and Tsukuba. The Ascension Island, Manaus
and Reunion Island sites have very narrow footprints that show small
seasonal variations.
We proposed the footprint-based method for the collocation of satellite
observations with TCCON sites, and assessed the performance of the method
using the NIES model and GOSAT product data sets. The collocation footprint
area is determined by yearly averaged sensitivity values with limits of
log10(x) equals -0.5, -1.0, -1.5 and -2.0. These were selected to
approximately correspond to the areas of standard geographical collocation
techniques that have rectangular shapes of 2.5∘× 2.5∘,
±5.0∘×±5.0∘,
±5.0∘×±10.0∘ and
±7.5∘×±22.5∘,
respectively. A comparison of the proposed
method with the geographical method showed similar but smaller biases for a
subset of 16 stations for the period from January 2009 to January 2014. Case
studies of the Darwin and Reunion Island TCCON sites revealed that the
footprint has a very different collocation area to that of the geographical
method, especially near marine coast.
The geographical collocation (and other similar methods) is based on tracking
long-term trends of tracers (i.e. derived from global model calculations) and
is therefore less sensitive to the influence of local sources. This approach
shows good performance for current precision of satellite XCO2 retrievals,
but it has its limitations and works up to a certain accuracy threshold. Given that the GOSAT XCO2 products are sensitive to local
sources, the proposed footprint method is promising and requires further
fine-tuning. The potential for further improvement includes moving from gross
annual averaging to more accurate seasonal or monthly averaging. In addition,
it is possible to study the sensitivity of XCO2 observations using the
adjoint of the global Eulerian–Lagrangian coupled atmospheric transport
model (Belikov et al., 2016), which can resolve long-term, synoptic and
hourly variation patterns.
We believe, however, that the footprint analysis should be considered
important in the appraisal of new TCCON sites, along with assessments of the
number of cloudless days, the surrounding landscape and the reflectivity of
the earth's surface.
Data availability
The data sets are available at
ftp://tccon.ornl.gov/2014Public/documentation/ (Wunch et al., 2015).
The JRA-25/JCDAS meteorological data sets used in the simulations were
provided by the Japan Meteorological Agency. The computational resources were
provided by NIES. This study was performed by order of the Ministry for
Education and Science of the Russian Federation No. 5.628.2014/K and was
supported by The Tomsk State University Academic D. I. Mendeleev Fund Program
in 2014–2015 and by the GRENE Arctic project.
TCCON data were obtained from the TCCON Data Archive, hosted by the Carbon
Dioxide Information Analysis Center (CDIAC) at Oak Ridge National
Laboratory, Oak Ridge, Tennessee, USA, http://tccon.ornl.gov.
The Ascension Island site has been funded by the
Max Planck Society. The Bremen, Białystok and Orléans TCCON sites are
funded by the EU projects InGOS and ICOS-INWIRE, and by the Senate of
Bremen. 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 programme for technical support at the Darwin TCCON
site. Nicholas Deutscher was supported by an Australian Research Council
fellowship, DE140100178.
The Eureka measurements were made at the Polar Environment Atmospheric
Research Laboratory (PEARL) by the Canadian Network for the Detection of
Atmospheric Change (CANDAC) led by James R. Drummond, and in part by the
Canadian Arctic ACE Validation Campaigns led by Kaley A. Walker. They were
supported by the AIF/NSRIT, CFI, CFCAS, CSA, EC, GOC-IPY, NSERC, NSTP, OIT,
ORF and PCSP.
The University of Leicester data were obtained with funding from the UK
National Centre for Earth Observation and the ESA GHG-CCI project, using the
ALICE High Performance Computing Facility at the University of Leicester.
R. Parker was funded by an ESA Living Planet Fellowship.
Acknowledgements
Authors thank Paul Wennberg for insightful discussions and suggestions
regarding the manuscript.Edited by: Q. Errera
Reviewed by: two anonymous referees
References
Andres, R. J., Gregg, J. S., Losey, L., Marland, G., and Boden, T.: Monthly,
global emissions of carbon dioxide from fossil fuel consumption, Tellus,
63B, 309–327, 2011.Belikov, D., Maksyutov, S., Miyasaka, T., Saeki, T., Zhuravlev, R., and
Kiryushov, B.: Mass-conserving tracer transport modelling on a reduced
latitude-longitude grid with NIES-TM, Geosci. Model Dev., 4, 207–222,
10.5194/gmd-4-207-2011, 2011.Belikov, D. A., Maksyutov, S., Krol, M., Fraser, A., Rigby, M., Bian, H.,
Agusti-Panareda, A., Bergmann, D., Bousquet, P., Cameron-Smith, P.,
Chipperfield, M. P., Fortems-Cheiney, A., Gloor, E., Haynes, K., Hess, P.,
Houweling, S., Kawa, S. R., Law, R. M., Loh, Z., Meng, L., Palmer, P. I.,
Patra, P. K., Prinn, R. G., Saito, R., and Wilson, C.: Off-line algorithm for
calculation of vertical tracer transport in the troposphere due to deep
convection, Atmos. Chem. Phys., 13, 1093–1114, 10.5194/acp-13-1093-2013,
2013a.Belikov, D., Maksyutov, S., Sherlock, V., Aoki, S., Deutscher, N. M., Dohe,
S., Griffith, D., Kyro, E., Morino, I., Nakazawa, T., Notholt, J.,
Rettinger, M., Schneider, M., Sussmann, R., Toon, G. C., Wennberg, P. O.,
and Wunch, D.: Simulations of column-average CO2 and CH4 using the
NIES TM with a hybrid sigma–isentropic (σ–θ)
vertical coordinate, Atmos. Chem. Phys., 13,
1713–1732, 10.5194/acp-13-1713-2013, 2013b.Belikov, D. A., Maksyutov, S., Yaremchuk, A., Ganshin, A., Kaminski, T.,
Blessing, S., Sasakawa, M., Gomez-Pelaez, A. J., and Starchenko, A.: Adjoint
of the global Eulerian–Lagrangian coupled atmospheric transport model
(A-GELCA v1.0): development and validation, Geosci. Model Dev., 9, 749–764,
10.5194/gmd-9-749-2016, 2016.Boesch, H., Baker, D., Connor, B., Crisp, D., and Miller, C.: Global
characterization of CO2 column retrievals from shortwave-infrared
satellite observations of the Orbiting Carbon Observatory-2 Mission, Remote
Sens., 3, 270–304, 10.3390/rs3020270, 2011.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S.,
Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission
objectives and measurement modes, J. Atmos. Sci., 56, 127–150, 1999.Butz, A., Guerlet, S., Jacob, D. J., Schepers, D., Galli, A., Aben, I.,
Frankenberg, C., Hartmann, J.-M., Tran, H., Kuze, A., Keppel-Aleks, G.,
Toon, G. C., Wunch, D., Wennberg, P. O., Deutscher, N. M., Griffith, D. W.
T., Macatangay, R., Messerschmidt, J., Notholt, J., and Warneke, T.: Toward
accurate CO2 and CH4 observations from GOSAT, Geophys. Res. Lett.,
38, 2–7, 2011.Chevallier, F., Deutscher, N. M., Conway, T. J., Ciais, P., Ciattaglia, L.,
Dohe, S., Frohlich, M., Gomez-Pelaez, A. J., Griffith, D., Hase, F., Haszpra,
L., Krummel, P., Kyro, E., Labuschagne, C., Langenfelds, R., Machida, T.,
Maignan, F., Matsueda, H., Morino, I., Notholt, J., Ramonet, M., Sawa, Y.,
Schmidt, M., Sherlock, V., Steele, P., Strong, K., Sussmann, R., Wennberg,
P., Wofsy, S., Worthy, D., Wunch, D., and Zimnoch, M.: Global CO2 fluxes
inferred from surface air-sample measurements and from TCCON retrievals of
the CO2 total column, Geophys. Res. Lett., 38, L24810,
10.1029/2011GL049899, 2011Cogan, A. J., Boesch, H., Parker, R. J., Feng, L., Palmer, P. I., Blavier,
J.-F. L., Deutscher, N. M., Macatangay, R., Notholt, J., Roehl, C., Warneke,
T., and Wunch, D.: Atmospheric carbon dioxide retrieved from the Greenhouse
gases Observing SATellite (GOSAT): Comparison with ground-based TCCON
observations and GEOS-Chem model calculations, J. Geophys. Res.-Atmos., 117,
D21301, 10.1029/2012JD018087, 2012.
Crisp, D., Atlas, R. M., Bréon, F.-M., Brown, L. R., Burrows, J. P.,
Ciais, P., Connor, B. J., Doney, S. C., Fung, I. Y., Jacob, D. J., Miller,
C. E., O'Brien, D., Pawson, S., Randerson, J. T., Rayner, P., Salawitch, R.
S., Sander, S. P., Sen, B., Stephens, G. L., Tans, P. P., Toon, G. C.,
Wennberg, P. O., Wofsy, S. C., Yung, Y. L., Kuang, Z., Chudasama, B.,
Sprague, G., Weiss, P., Pollock, R., Kenyon, D., and Schroll, S.: The
Orbiting Carbon Observatory (OCO) mission, Adv. Space Res., 34, 700–709,
2004.Deutscher, N. M., Griffith, D. W. T., Bryant, G. W., Wennberg, P. O., Toon,
G. C., Washenfelder, R. A., Keppel-Aleks, G., Wunch, D., Yavin, Y., Allen, N.
T., Blavier, J.-F., Jiménez, R., Daube, B. C., Bright, A. V., Matross, D.
M., Wofsy, S. C., and Park, S.: Total column CO2 measurements at Darwin,
Australia-site description and calibration against in situ aircraft profiles,
Atmos. Meas. Tech., 3, 947–958, 10.5194/amt-3-947-2010, 2010.Gatti, L. V., Miller, J. B., D'Amelio, M. T. S., Martinewski, A., Basso, L.
S., Gloor, M. E., Wofsy, S., and Tans, P.: Vertical pro- files of CO2
above eastern Amazonia suggest a net carbon flux to the atmosphere and
balanced biosphere between 2000 and 2009, Tellus B, 62, 581–594,
10.1111/j.1600-0889.2010.00484.x, 2010.Geibel, M. C., Gerbig, C., and Feist, D. G.: A new fully automated FTIR
system for total column measurements of greenhouse gases, Atmos. Meas. Tech.,
3, 1363–1375, 10.5194/amt-3-1363-2010, 2010.Guerlet, S., Butz, A., Schepers, D., Basu, S., Hasekamp, O. P., Kuze, A.,
Yokota, T., Blavier, J.-F., Deutscher, N. M., Griffith, D. W., Hase, F.,
Kyro, E., Morino, I., Sherlock, V., Sussmann, R., Galli, A., and Aben, I.:
Impact of aerosol and thin cirrus on retrieving and validating XCO2
from GOSAT shortwave infrared measurements, J. Geophys. Res. Atmos., 118,
4887–4905, 2013.Hourdin, F. and Talagrand, O.: Eulerian backtracking of atmospheric tracers.
I: Adjoint derivation and parametrization of subgrid-scale transport,
Q. J. Roy Meteor. Soc., 132: 567–583, 10.1256/qj.03.198.A, 2006.Inoue, M., Morino, I., Uchino, O., Miyamoto, Y., Yoshida, Y., Yokota, T.,
Machida, T., Sawa, Y., Matsueda, H., Sweeney, C., Tans, P. P., Andrews, A.
E., Biraud, S. C., Tanaka, T., Kawakami, S., and Patra, P. K.: Validation of
XCO2 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft
measurement data, Atmos. Chem. Phys., 13, 9771–9788,
10.5194/acp-13-9771-2013, 2013.
Ito, A.: Changing ecophysiological processes and carbon budget in East Asian
ecosystems under near-future changes in climate: Implications for long-term
monitoring from a process-based model, J. Plant Res., 123, 577–588, 2010.Keppel-Aleks, G., Wennberg, P. O., and Schneider, T.: Sources of variations
in total column carbon dioxide, Atmos. Chem. Phys., 11, 3581–3593,
10.5194/acp-11-3581-2011, 2011.Keppel-Aleks, G., Wennberg, P. O., Washenfelder, R. A., Wunch, D.,
Schneider, T., Toon, G. C., Andres, R. J., Blavier, J.-F., Connor, B.,
Davis, K. J., Desai, A. R., Messerschmidt, J., Notholt, J., Roehl, C. M.,
Sherlock, V., Stephens, B. B., Vay, S. A., and Wofsy, S. C.: The imprint of
surface fluxes and transport on variations in total column carbon dioxide,
Biogeosciences, 9, 875–891, 10.5194/bg-9-875-2012, 2012.Kuze, A., Suto H., Nakajima M., and Hamazaki, T.: Thermal and near infrared
sensor for carbon observation Fourier-transform spectrometer on the
Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl.
Opt., 48, 6716–6733, 10.1364/AO.48.006716, 2009.Kuze, A., Suto, H., Shiomi, K., Kawakami, S., Tanaka, M., Ueda, Y., Deguchi,
A., Yoshida, J., Yamamoto, Y., Kataoka, F., Taylor, T. E., and Buijs, H. L.:
Update on GOSAT TANSO-FTS performance, operations, and data products after
more than 6 years in space, Atmos. Meas. Tech., 9, 2445–2461,
10.5194/amt-9-2445-2016, 2016.Lindqvist, H., O'Dell, C. W., Basu, S., Boesch, H., Chevallier, F.,
Deutscher, N., Feng, L., Fisher, B., Hase, F., Inoue, M., Kivi, R., Morino,
I., Palmer, P. I., Parker, R., Schneider, M., Sussmann, R., and Yoshida, Y.:
Does GOSAT capture the true seasonal cycle of carbon dioxide?, Atmos. Chem.
Phys., 15, 13023–13040, 10.5194/acp-15-13023-2015, 2015.Liu, J., Bowman, K. W., and Henze, D. K.: Source-receptor relationships of
column-average CO2 and implications for the impact of observations on flux
inversions, J. Geophys. Res. Atmos., 120, 5214–5236,
doi:10.1002/2014JD022914, 2015.Messerschmidt, J., Macatangay, R., Notholt, J., Petri, C., Warneke, T., and
Weinzierl, C.: Side by side measurements of CO2 by ground-based Fourier
transform spectrometry (FTS), Tellus B, 62, 749–758,
10.1111/j.1600-0889.2010.00491.x, 2010.Messerschmidt, J., Chen, H., Deutscher, N. M., Gerbig, C., Grupe, P.,
Katrynski, K., Koch, F.-T., Lavrič, J. V., Notholt, J., Rödenbeck,
C., Ruhe, W., Warneke, T., and Weinzierl, C.: Automated ground-based remote
sensing measurements of greenhouse gases at the Białystok site in
comparison with collocated in situ measurements and model data, Atmos. Chem.
Phys., 12, 6741–6755, 10.5194/acp-12-6741-2012, 2012.Nguyen, H., Osterman, G., Wunch, D., O'Dell, C., Mandrake, L., Wennberg, P.,
Fisher, B., and Castano, R.: A method for collocating satellite XCO2
data to ground-based data and its application to ACOS-GOSAT and TCCON,
Atmos. Meas. Tech., 7, 2631–2644, 10.5194/amt-7-2631-2014, 2014.Oda, T. and Maksyutov, S.: A very high-resolution (1 km × 1 km)
global fossil fuel CO2 emission inventory derived using a point source
database and satellite observations of nighttime lights, Atmos. Chem. Phys.,
11, 543–556, 10.5194/acp-11-543-2011, 2011.O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C.,
Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie,
J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor,
T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval
algorithm – Part 1: Description and validation against synthetic
observations, Atmos. Meas. Tech., 5, 99–121, 10.5194/amt-5-99-2012,
2012.Ohyama, H., Morino, I., Nagahama, T., Machida, T., Suto, H., Oguma, H.,
Sawa, Y., Matsueda, H., Sugimoto, N., Nakane, H., and Nakagawa, K.:
Column-averaged volume mixing ratio of CO2 measured with ground-based
Fourier transform spectrometer at Tsukuba, J. Geophys. Res., 114, D18303,
10.1029/2008JD011465, 2009.
Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika,
H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S.,
Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., and Taira, R.: The
JRA-25 Reanalysis, J. Meteor. Soc. Jpn., 85, 369–432, 2007.Oshchepkov, S., Bril, A., Yokota, T., Morino, I., Yoshida, Y., Matsunaga,
T., Belikov, D., Wunch, D., Wennberg, P. O., Toon, G. C., O'Dell, C. W.,
Butz, A., Guerlet, S., Cogan, A., Boesch, H., Eguchi, N., Deutscher, N. M.,
Griffith, D., Macatangay, R., Notholt, J., Sussmann, R., Rettinger, M.,
Sherlock, V., Robinson, J., Kyrö, E., Heikkinen, P., Feist, D. G.,
Nagahama, T., Kadygrov, N., Maksyutov, S., Uchino, O., and Watanabe, H.:
Effects of atmospheric light scattering on spectroscopic observations of
greenhouse gases from space: Validation of PPDF-based CO2 retrievals
from GOSAT, J. Geophys. Res., 117, 1–18, 2012.Oshchepkov, S., Bril, A., Yokota, T., Yoshida, Y., Blumenstock, T.,
Deutscher, N. M., Dohe, S., Macatangay, R., Morino, I., Notholt, J.,
Rettinger, M., Petri, C., Schneider, M., Sussman, R., Uchino, O., Velazco,
V., Wunch, D., and Belikov, D.: Simultaneous retrieval of atmospheric
CO2 and light path modification from space-based spectroscopic
observations of greenhouse gases: methodology and application to GOSAT
measurements over TCCON sites, Appl. Optics, 52, 1339–1350, 2013.Patra, P. K., Houweling, S., Krol, M., Bousquet, P., Belikov, D., Bergmann,
D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Corbin, K.,
Fortems-Cheiney, A., Fraser, A., Gloor, E., Hess, P., Ito, A., Kawa, S. R.,
Law, R. M., Loh, Z., Maksyutov, S., Meng, L., Palmer, P. I., Prinn, R. G.,
Rigby, M., Saito, R., and Wilson, C.: TransCom model simulations of CH4
and related species: linking transport, surface flux and chemical loss with
CH4 variability in the troposphere and lower stratosphere, Atmos. Chem.
Phys., 11, 12813–12837, 10.5194/acp-11-12813-2011, 2011.Reuter, M., Bösch, H., Bovensmann, H., Bril, A., Buchwitz, M., Butz, A.,
Burrows, J. P., O'Dell, C. W., Guerlet, S., Hasekamp, O., Heymann, J.,
Kikuchi, N., Oshchepkov, S., Parker, R., Pfeifer, S., Schneising, O.,
Yokota, T., and Yoshida, Y.: A joint effort to deliver satellite retrieved
atmospheric CO2 concentrations for surface flux inversions: the
ensemble median algorithm EMMA, Atmos. Chem. Phys., 13, 1771–1780,
10.5194/acp-13-1771-2013, 2013.Stohl, A., Seibert, P., Arduini, J., Eckhardt, S., Fraser, P., Greally, B.
R., Lunder, C., Maione, M., Mhle, J., O'Doherty, S., Prinn, R. G., Reimann,
S., Saito, T., Schmidbauer, N., Simmonds, P. G., Vollmer, M. K., Weiss, R.
F., and Yokouchi, Y.: An analytical inversion method for determining
regional and global emissions of greenhouse gases: Sensitivity studies and
application to halocarbons, Atmos. Chem. Phys., 9, 1597–1620,
10.5194/acp-9-1597-2009, 2009.Swap, R., Garstang, M., Macko, S. A., Tyson, P. D., Maenhaut, W., Artaxo, P.,
Kållberg, P., and Talbot R.: The long-range transport of southern African
aerosols to the tropical South Atlantic, J. Geophys. Res., 101, 23777–23791,
10.1029/95JD01049, 1996.Valsala, V. and Maksyutov, S.: Interannual variability of the air–sea
CO2 flux in the north Indian Ocean, Ocean Dynam., 63, 165–178,
10.1007/s10236-012-0588-7, 2013.Van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M.,
Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and Van Leeuwen,
T. T.: Global fire emissions and the contribution of deforestation, savanna,
forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10,
11707–11735, 10.5194/acp-10-11707-2010, 2010.Washenfelder, R., Toon, G., Blavier, J., Yang, Z., Allen, N., Wennberg, P.,
Vay, S., Matross, D., and Daube, B.: Carbon dioxide column abundances at the
Wisconsin Tall Tower site, J. Geophys. Res, 111, D22305,
10.1029/2006JD007154, 2006.Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The
Total Carbon Column Observing Network, Phil. Trans. R. Soc. A, 369,
2087–2112, 10.1098/rsta.2010.0240, 2011.Wunch, D., Toon, G. C, Sherlock, V., Deutscher, N. M., Liu, C., Feist, D. G.,
and Wennberg, P. O.: The Total Carbon Column Observing Network's GGG2014 Data
Version. Technical report, Carbon Dioxide Information Analysis Center, Oak
Ridge National Laboratory, Oak Ridge, Tennessee, USA,
10.14291/tccon.ggg2014.documentation.R0/1221662, 2015.
Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and
Maksyutov, S.: Global concentrations of CO2 and CH4 retrieved from
GOSAT: First preliminary results, SOLA, 5, 160–163,
10.2151/sola.2009-041, 2009.Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A.,
Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P.
O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J.,
Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R., Ahonen,
P., Heikkinen, P., Kyrö, E., Mendonca, J., Strong, K., Hase, F., Dohe,
S., and Yokota, T.: Improvement of the retrieval algorithm for GOSAT SWIR
XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas.
Tech., 6, 1533–1547, 10.5194/amt-6-1533-2013, 2013.Zhuravlev, R. V., Ganshin, A. V., Maksyutov, S. S., Oshchepkov, S. L., and
Khattatov, B. V.: Estimation of global fluxes of CO2 using
ground-station and satellite (GOSAT) observation data with empirical
orthogonal functions, Atmos. Ocean. Opt., 26, 388–397, 2013.