ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-1653-2016Ability of the 4-D-Var analysis of the GOSAT BESD XCO2
retrievals to characterize atmospheric CO2 at large and
synoptic scalesMassartSébastiensebastien.massart@ecmwf.inthttps://orcid.org/0000-0002-7576-6188Agustí-PanaredaAnnaHeymannJensBuchwitzMichaelhttps://orcid.org/0000-0001-7616-1837ChevallierFrédérichttps://orcid.org/0000-0002-4327-3813ReuterMaximilianhttps://orcid.org/0000-0001-9141-3895HilkerMichaelBurrowsJohn P.https://orcid.org/0000-0003-1547-8130DeutscherNicholas M.FeistDietrich G.https://orcid.org/0000-0002-5890-6687HaseFrankSussmannRalfDesmetFilipDubeyManvendra K.https://orcid.org/0000-0002-3492-790XGriffithDavid W. T.https://orcid.org/0000-0002-7986-1924KiviRigelhttps://orcid.org/0000-0001-8828-2759PetriChristofhttps://orcid.org/0000-0002-7010-5532SchneiderMatthiashttps://orcid.org/0000-0001-8452-0035VelazcoVoltaire A.https://orcid.org/0000-0002-1376-438XEuropean Centre for Medium-Range Weather Forecasts, Reading, UKInstitute of Environmental Physics, University of Bremen, Bremen, GermanyLaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif sur Yvette, FranceMax Planck Institute for Biogeochemistry, Jena, GermanyKarlsruhe Institute of Technology, IMK-ASF, Karlsruhe, GermanyKarlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, GermanyDepartment of Chemistry, University of Antwerp, Antwerp, BelgiumEarth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, USACentre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, AustraliaFinnish Meteorological Institute, Arctic Research, Sodankylä, FinlandSébastien Massart (sebastien.massart@ecmwf.int)12February20161631653167123July201528September201517December201516January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/1653/2016/acp-16-1653-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/1653/2016/acp-16-1653-2016.pdf
This study presents results from the European Centre for Medium-Range Weather
Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the
atmospheric CO2 is controlled through the assimilation of
column-averaged dry-air mole fractions of CO2 (XCO2) from the
Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to
a free-run simulation (without assimilation of XCO2), and they are
both evaluated against XCO2 data from the Total Carbon Column
Observing Network (TCCON). We show that the assimilation of the GOSAT
XCO2 product from the Bremen Optimal Estimation Differential Optical
Absorption Spectroscopy (BESD) algorithm during the year 2013 provides
XCO2 fields with an improved mean absolute error of 0.6 parts per
million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively)
and an improved estimated precision of 1 ppm compared to the GOSAT
BESD data (3.3 ppm). We also show that the analysis has skill for
synoptic situations in the vicinity of frontal systems, where the GOSAT
retrievals are sparse due to cloud contamination. We finally computed the
10-day forecast from each analysis at 00:00 UTC, and we
demonstrate that the CO2 forecast shows synoptic skill for the
largest-scale weather patterns (of the order of 1000 km) even up to
day 5 compared to its own analysis.
Introduction
Carbon in the atmosphere is present mostly in the form of carbon
dioxide (CO2). Its amount is relatively small compared to the
amount of carbon present in other reservoirs like the ocean
. Being well mixed, atmospheric CO2 is
nevertheless easier to monitor by measurements than other carbon
reservoirs. To improve the monitoring of atmospheric CO2, one
can combine atmospheric CO2 measurements with a numerical
model. This paper describes such a system, which has been developed
for the Copernicus Atmosphere Monitoring Service (CAMS).
Rather than using the relatively sparse network of the surface
air-sample measurements, here we explore the measurements from
satellite sounders in order to have a more global picture of the
atmospheric CO2. To extract information on the CO2
content in the atmosphere, passive atmospheric remote sounders
measure in the thermal infrared (TIR) or in the near infrared/short-wave infrared (NIR/SWIR).
The Atmospheric Infrared Sounder (AIRS), measuring in the TIR,
detects thermal radiation emitted by the Earth's surface and the
atmosphere . The assimilation of the AIRS observed
radiances was developed by at the European Centre for
Medium-Range Weather Forecasts (ECMWF) using a four-dimensional
variational (4-D-Var) data assimilation scheme. Their results showed
the potential of data assimilation to constrain atmospheric
CO2. They also showed the limitations of the assimilation of
AIRS radiances, in particular due to the vertical sensitivity of the
sounder. Due to the low thermal contrast between the Earth's surface
and the air masses above, AIRS measurements have limited or no
sensitivity to the lower troposphere and higher sensitivity to the
middle atmosphere. Because the signals of the CO2 surface
sources and sinks are the largest in the near-surface and lower
troposphere than in the middle atmosphere, AIRS measurements were not
able to capture these signals.
In contrast, column-averaged dry-air mole fractions of CO2
(or XCO2) with a high near-surface sensitivity are retrieved
from NIR/SWIR measurements based on scattered and back-scattered
solar radiation; however, the NIR/SWIR measurements also have their
limitations. They need sunlight and are therefore limited to daytime
observations. Sufficiently cloud-free conditions and a low aerosol
optical depth are also needed for accurate XCO2 retrievals.
The aim of this study is to document the assimilation of XCO2
products from NIR/SWIR measurements in order to constrain atmospheric
CO2 and to document how the assimilation impacts the simulated
atmospheric CO2 concentration. For that purpose, we
assimilated the XCO2 products derived from the NIR/SWIR
spectra of the Greenhouse gases Observing Satellite
GOSAT;. The assimilation system is based on the
ECMWF system of , which has lately evolved for CAMS in
order to assimilate retrieved products instead of observed radiances
.
The assimilation system provides an analysis of the atmospheric
CO2 concentration that is then integrated in time using
a forecast model. The CO2 forecast model used in this study
is documented by . In this model, the production and
loss of CO2 at the surface is based on surface fluxes that
are partially prescribed and partially modelled. These CO2
surface fluxes are not directly constrained by observations and they
may deviate from reality. The accumulation of surface fluxes errors
then leads to biases in the atmospheric CO2. On the other
hand, the strength of the CO2 forecast model is its ability
to provide a realistic CO2 synoptic variability. The first
objective of this study is to determine the quality of the
XCO2 fields resulting from the assimilation of GOSAT
XCO2 data with a CO2 forecast model where the
CO2 surface fluxes are not constrained.
The atmospheric CO2 synoptic variability on a regional scale
is related to the passage of frontal systems . These
events are difficult to capture with the GOSAT measurements as the
availability of the data is limited due to cloud contamination.
Therefore, the second objective of this study is to document whether the
assimilation helps improve the simulation of atmospheric CO2
for synoptic events despite the lack of measurements nearby frontal
systems.
Within CAMS, ECMWF is providing a CO2 analysis based on the
assimilation of GOSAT XCO2 data with a delay of 5 days
behind real time. A 10-day forecast is then issued from the analysis
in order to provide the atmospheric CO2 field in real time
and for the next few days. The last objective of this study is to
assess the quality of this forecast. The forecast quality as
a function of the lead time and the season is evaluated against the
analysis.
This paper is structured as follows. Section
introduces the data sets used in this study. Section
describes our atmospheric CO2 simulations with and without
assimilation of the GOSAT XCO2 data, and how we compared them
with independent measurements. Sections
to present the global evaluation of
our simulations, a case study and the evaluation of the CO2
forecast based on the analysis. Finally, Sect.
presents our conclusions.
Data sets
In this study, we used two sets of data. The first one is the
measurements from the GOSAT's Fourier transform spectrometer and the
XCO2 product retrieved from these measurements by the
University of Bremen (UoB) and is described in
Sect. . The second one is the collection of
measurements provided by the Total Carbon Column Observing Network
(TCCON) and is described in Sect. .
GOSAT XCO2
GOSAT is a joint effort between the Japanese Aerospace
Exploration Agency (JAXA), the National Institute for Environmental
Studies (NIES) and the Japanese Ministry of the Environment (MOE) as
part of the Global Change Observation Mission (GCOM) programme of
Japan. GOSAT was launched on 23 January 2009 and carries
the thermal and near-infrared sensor for carbon observations, which
consists of a Fourier transform spectrometer (TANSO-FTS) and a cloud
and aerosol imager (TANSO-CAI).
In this study, we used XCO2 retrieved from TANSO-FTS
measurements of the upwelling radiance at the top of the atmosphere
by the Bremen Optimal Estimation DOAS (differential optical
absorption spectroscopy) (BESD) algorithm of UoB. The BESD
algorithm was initially developed to retrieve XCO2 from nadir
measurements of the SCanning Imaging Absorption spectroMeter for
Atmospheric CHartographY (SCIAMACHY) remote sensing spectrometer on
the ENVIronment SATellite ENVISAT;. The
BESD algorithm has been modified to also retrieve XCO2 from
GOSAT measurements. A detailed description of the GOSAT BESD algorithm
can be found in . In brief, the algorithm uses three
fitting windows, the O2-A band
(12 920–13 195 cm-1), a weak CO2 absorption band
(6170–6278 cm-1) and a strong CO2 band
(4804–4896 cm-1) from both the medium- and high-gain
(respectively M-gain and H-gain) GOSAT nadir modes. An optimal
estimation-based inversion technique is used to derive the most likely
atmospheric state from every individual GOSAT measurement using
a priori knowledge. The BESD algorithm explicitly accounts for
atmospheric scattering by clouds and aerosols, reducing potential
systematic biases. The scattering information on cloud and aerosols
is mainly obtained from the O2-A and strong CO2
absorption bands.
We used an inhomogeneous GOSAT BESD XCO2 data set in this study
as the GOSAT BESD algorithm was still under development. This
intermediate version of the GOSAT BESD XCO2 data is referred
to as MACC GOSAT BESD XCO2 (MACC standing for Monitoring
Atmospheric Composition and Climate, the precursor of CAMS).
Nevertheless, from the beginning of 2014 onwards, we have been
assimilating the current version of the GOSAT BESD data
v01.00.02; in near-real time.
The TANSO-FTS detector has a circular field of view of 10.5 km
when projected on the Earth's surface (at exact nadir). In 2013, it
measured in a mode with three measurements across track, and the
footprints were separated by ∼263km across track and
∼283km along track. GOSAT can also operate
in target mode, resulting in a finer sampling distance. For these
specific situations, we further thinned the observations on
a 1∘×1∘ grid by removing all the
observations but one chosen at random. This procedure avoids having
several measurements in the same model grid cell during the
assimilation. This thinning, plus the characteristics of the
instrument (measurement only during sunlit periods) and the processing
of the level-2 data procedure (retrievals for clear-sky conditions and
only over land), reduces the number of GOSAT XCO2 data to
about 100 per day. The assimilation window of 12 h means that about 50 GOSAT XCO2 data points are
assimilated during each time window.
Example of the distribution of the assimilated GOSAT BESD
XCO2 data: July 2013 (top panel, about 3400 retrievals)
and October 2013 (bottom, about 1270 retrievals). The monthly data
are here aggregated on a 2∘×2∘ grid and
averaged. The blue/red represents the low/high averaged
XCO2 values in ppm.
The geographic distribution of these data is dependent on the season
and the atmospheric conditions as illustrated by
Fig. . For example, in July 2013, GOSAT BESD data
are available up to 75∘ N, and in October 2013 they are
available only up to 60∘ N. The reason for this is the solar
geometry and the filtering of measurements under high solar zenith
angle (SZA) conditions, where XCO2 is more challenging to
retrieve as the impact of atmospheric scattering becomes larger
compared to low-SZA conditions. Other data gaps are due to the strict
cloud filtering and other types of filtering, like those based on the
quality of the spectral fits, on scattering parameters, on the
meteorological state, and on the measurement geometry.
The MACC GOSAT BESD XCO2 data sets have been bias-corrected
using the TCCON data. As this data set is delivered in near-real time
and the TCCON data are delivered with a delay of few months, it was
not possible to directly compare the two data sets. Instead, the
TCCON data from the previous year were used and they were corrected
assuming a 2 parts per million (ppm) global atmospheric growth of CO2. A
global offset was then computed and applied to the MACC GOSAT BESD
XCO2 based on the comparison between this data set and the
corrected TCCON data set of the previous year. Moreover, with this
procedure the TCCON data used in this study (same year as for the
MACC GOSAT BESD XCO2 data set) can be considered as independent
data.
For the assimilation, the observation error covariances have to be
specified. In this study, we assumed that the observation errors are
not correlated in space and time. For the standard deviation of the
observation error, we used the uncertainty of the BESD XCO2
product provided together with the data. The BESD XCO2
uncertainty product accounts for the various sources of uncertainty
of the retrieval process. It varies in time and space around an
average value of 2 ppm. We furthermore
established that the specified observation error based on the
XCO2 uncertainty globally matches the expected observation
error using diagnostics posterior to the analysis (not shown).
TCCON XCO2
The TCCON is a network of ground-based Fourier transform spectrometers
recording direct solar spectra in the near infrared spectral region
(http://tccon.ornl.gov/). The column-averaged dry-air mole
fractions of CO2 are retrieved from these spectra together
with other chemical components of the atmosphere . In
2014, the version GGG2014 of the TCCON data was released. The errors
on the retrieved XCO2 are documented to be below 0.25 %
(∼1ppm) until the solar zenith angles are larger than
82∘
(Wunch et al., 2015).
When we downloaded the GGG2014 data in November 2015, 20 TCCON
stations were providing data within the time period we are interested
in (year 2013). Not all the stations were used in this study. First we
removed JPL 2011 (USA), Pasadena/Caltech (USA) and Tsukuba (Japan), as
they are not background stations and are associated with significant
representativity errors. We also removed Edwards (USA). This station
started to retrieve data from the middle of the year 2013, and we
assumed that this was not long enough to provide information on the
seasonal variation of the error in our simulations. Additionally, we
removed Eureka (Canada) from the list of stations as the site was
providing data during only 3 days in 2013. This selection of the
TCCON stations left 16 stations for the study (Table ).
Orléans (France) had a specific treatment compared to the other
stations. The averaging kernels were not specified in the GGG2014
release. Therefore, we decided to use the same information as for Lamont
(USA) as advised in the previous release of the TCCON data (version
GGG2012).
List of the TCCON stations used, ordered by latitude from
north to south.
SiteLatLongStarting dateReferenceSodankylä (sodankyla01)67.3726.636 Feb 2009Białystok (bialystok01)53.2323.021 Mar 2009Bremen (bremen01)53.108.856 Jan 2005Karlsruhe (karlsruhe01)49.108.4419 Apr 2010Orléans (orleans01)47.972.1129 Aug 2009Garmisch (garmisch01)47.4811.0616 Jul 2007Park Falls (parkfalls01)45.94-90.2726 May 2004Four Corners (fourcorners01)36.80-108.481 Mar 2011Lamont (lamont01)36.60-97.496 Jul 2008Saga (saga01)33.24130.2928 Jul 2011Izaña (izana01)28.30-16.4818 May 2007Ascension Island (ascension01)-7.92-14.3322 May 2012Darwin (darwin01)-12.43130.8928 Aug 2005Réunion Island (reunion01)-20.9055.496 Oct 2011Wollongong (wollongong01)-34.41150.8826 Jun 2008Lauder 125HR (lauder02)-45.05169.682 Feb 2010Experimental setup
We ran two model simulations for the year 2013. The first is similar
to the operational CAMS CO2 forecast and is
referred to as the “free run”. This simulation is used as the
reference to assess the impact of the assimilation of the GOSAT BESD
XCO2 data. The second simulation is the analysis in which the
GOSAT XCO2 data are assimilated and is referred to as the
“analysis”. The configuration of both simulations is described in
Sect. . The simulations were evaluated against
each other and also against the TCCON data.
Section introduces the methodology used in
comparison of simulations and the TCCON data.
Model simulations
The global simulations of atmospheric CO2 are performed within
the numerical weather prediction (NWP) framework of the Integrated
Forecasting System (IFS). The CO2 mass mixing ratio is
directly transported within IFS as a tracer and is affected by surface
fluxes. The transport is computed online and is updated each
12 h, benefiting from the assimilation of all the operational
observations within the IFS 4-D-Var assimilation system. The
terrestrial biogenic carbon fluxes are also computed online by the
carbon module of the land surface model Carbon-TESSEL or
CTESSEL;, while other prescribed fluxes are read from
CO2 surface fluxes inventories (see , for
more details).
The ability to assimilate retrieval products from GOSAT was included
in IFS and is detailed in for the assimilation of
methane data. The system used in this study is similar to the one of
and is based on fixed background errors derived from
the National Meteorological Center (NMC) method . The
standard deviation of the background error is constant for each model
level and slowly increases from the upper troposphere to the lower
troposphere with values from about 1 to about 5 ppm, and then
rapidly increases to reach a value of about 40 ppm at the
surface. The correlation of the background errors varies over the
whole domain and vertically with a representative length scale of
about 250 km. The system does not account for the spatial or
temporal correlation between the errors of the observations.
We chose in this study to have a horizontal resolution of TL255 on
a reduced Gaussian grid (∼80km×80km),
and 60 vertical levels from the surface up to 0.1 hPa. This
resolution is sufficient for resolving the large- and synoptic-scale
horizontal structures (∼1000km) of the atmospheric
CO2 fields.
Comparison with TCCON
To evaluate the quality of the model simulations (free run and
analysis), we have extensively used the TCCON data in this study. The
comparison is performed in the TCCON space using the TCCON a priori
and averaging kernel information (see
Appendix for more details). In order to
have a decomposition of the errors of the model column-averaged
CO2 against the TCCON measurement, we computed for each TCCON
station k for k∈1,N, the mean difference (or
bias) δk and the standard deviation of the difference (or
scatter) σk over the Mk times ti for i∈1,Mk when we have a TCCON observation for the
station k. If c^koti for i∈1,Mk is the observed TCCON XCO2 time
series for the station k, and if c^kti
for i∈1,Mk is the model equivalent time
series, then the bias δk and scatter σk are
defined by
δk=1Mk∑i=1Mkc^kti-c^koti,σk=1Mk-1∑i=1Mkc^kti-c^koti-δk2.
Additionally, we computed the correlation coefficient rk between
c^kti and c^koti for i∈1,Mk.
Hovmöller diagram (latitude vs. time) of the smoothed bias (in
ppm, negative/positive in blue/red) of the simulated XCO2
against the data of the TCCON
network, from 1 January to 31 December 2013. (a) Free-run simulation. (b) Analysis.
Following , we also computed the model offset δ,
the mean absolute error (MAE) Δ, the station-to-station
bias deviation σ and the model precision π for the N
TCCON stations
δ=1N∑k=1Nδk,Δ=1N∑k=1N|δk|,σ=1N-1∑k=1Nδk-δ2,π=1N∑k=1Nσk.
The statistics for the comparisons of the simulations against the
TCCON data have some gaps in time due to gaps in the availability of
the TCCON data. They are also valid only where the TCCON sites are
located, i.e. 16 points distributed over the globe. To have a more
global overview of the model bias and scatter against the TCCON data,
we smoothed these statistics in time and space (see
Appendix for more details). In summary, for the
bias we averaged all the model–measurement differences for each
TCCON site using a 1-week time bin. We then fit the time evolution of
the weekly bias with a function that combines a linear and a harmonic
component for each station. The second step is an extrapolation in
space. For each week, the weekly biases of every station are
extrapolated using a quadratic function of latitude. This results in
a Hovmöller diagram of the bias as a function of time and latitude.
A similar process is applied for the scatter (see
Figs. and ).
Statistics of the XCO2 difference between the
simulations (free run and analysis) and the average hourly
TCCON data (model–TCCON): bias (δk, in
ppm), scatter (σk, in ppm) and
correlation coefficient (rk). Also shown are the mean,
the mean absolute error (MAE) and the deviation of the
stations bias (respectively δ, Δ and
σ, in ppm), the mean scatter (π, in ppm)
and the mean r (last three rows). The second column (N) is the number of data used
for computing the statistics.
Free run Analysis SiteNBiasScatterrBiasScatterrSodankylä20441-1.591.350.91-0.551.350.92Białystok16063-2.681.960.81-1.661.800.77Bremen4883-1.621.520.79-0.411.270.82Karlsruhe4201-1.261.720.80-0.251.540.82Orléans8444-0.381.360.850.091.210.91Garmisch10371-0.921.590.82-0.291.620.80Park Falls27991-1.692.060.81-0.601.450.90Four Corners198720.691.760.580.571.430.74Lamont43731-0.202.090.59-0.041.350.80Saga10349-1.191.610.75-0.641.330.83Izaña44630.270.800.900.400.620.94Ascension Island71112.311.290.240.721.270.21Darwin291941.571.120.78-0.021.040.79Réunion Island188800.560.730.76-0.770.600.78Wollongong275620.301.050.71-1.081.060.65Lauder535000.010.830.86-0.970.590.85Mean16-0.361.430.75-0.341.220.78MAE161.08––0.57––Deviation161.27––0.61––
Same as Fig. but for the standard deviation,
with yellow/red for low/high values.
Time series of XCO2 (in ppm) at (a) Sodankylä,
Finland; (b) Karlsruhe, Germany; (c) Park
Falls, USA; and (d) Lauder, New Zealand, between 1
January and 31 December 2013. For each station, the top panel
presents the daily averaged data from TCCON (black dots),
the daily averaged data from GOSAT co-located in time and
space with the station (yellow squares), the simulated
XCO2 (solid lines) and the daily averaged simulated
XCO2 in the observation space (coloured dots). The
bottom panel presents the weekly averaged bias of the
simulated XCO2 against the TCCON data (coloured
dots) and the smoothed bias (solid lines). Blue
represents the free run, while red is for the
analysis.
Global evaluation of the analysis
In this section we first present the characteristics of the
XCO2 derived from the free-run simulation when compared to the
TCCON data. Second, we present the impact of the assimilation of the
MACC GOSAT BESD XCO2 comparing the XCO2 from the
analysis against the XCO2 from the free run. Then, we discuss
whether the analysis represents an improvement compared to the free run in
terms of statistics against the TCCON data. Finally, we discuss the
merits of the analysis compared to the MACC GOSAT BESD data using the
TCCON data as a reference.
Free-run simulation vs. TCCON
When compared with the TCCON data, the free-run simulation has a mean
offset δ of -0.36 ppm and a mean absolute error
Δ of 1.08 ppm (Table ).
However, the individual station bias δk spans a range from
2.3 ppm at Ascension Island (Saint Helena, Ascension and
Tristan da Cunha) to -2.9 ppm at Białystok (Poland). The
station-to-station bias deviation σ of the free-run simulation then
has a value of 1.27 ppm.
The variations of the bias as well as the seasonal cycle of the bias
are highlighted in the Hovmöller diagram displayed in
Fig. a. First, it shows that the initial condition
of the free run has a positive bias of about 2 ppm over the
tropical region (region between 23∘ S and 23∘ N)
when compared to the TCCON data. This bias is reduced during the
spring and reappears the next summer. It reaches its highest values in
autumn with more than 2 ppm. These results are slightly
different from those of , where the model bias was
found to be more constant in the tropical region when comparing the
background CO2 in the marine boundary layer with the National
Oceanic and Atmospheric Administration (NOAA) GLOBALVIEW-CO2. Here,
the evaluation of the bias in the tropics is driven by the comparison
with XCO2 measurements from the TCCON station of Ascension
Island. For this station, the values of the bias from July to
September result from the interpolation process as no measurements
were reported during this period (Fig. S1 of the Supplement).
In contrast to the situation at the tropics, the initial condition of
the free run has a negative bias at northern mid-latitudes (region
between 23 and 66∘ N) and reaches almost
4 ppm at the latitude of Sodankylä (Finland,
67∘ N) when compared to the TCCON XCO2. This value
is the result of the smoothing process as we do not have data for
that period (Fig. a). The negative bias at
these mid-latitudes is nevertheless confirmed by the comparison with
other stations, like Karlsruhe (Germany) and Park Falls (USA), where
we have some data at the beginning of the year
(Fig. b and c).
The negative bias at northern mid-latitudes remains high during the
whole year, with an absolute value generally greater than
1 ppm at the end of spring, and in June and December. This can
be explained by the fact that the model does not release enough
CO2 before and after the growing season, i.e. March to May
and October to December, and by the fact that, in the model, the
onset of the CO2 sink associated with the growing season
starts too early in the season .
The precision π of the free run measured by the average scatter
between the simulation and the TCCON data is 1.4 ppm
(Table ). Similarly to the bias, the
scatter varies in time and space as highlighted by the Hovmöller
diagram of the scatter (Fig. a). The scatter has its
highest values of more than 1 ppm at the northern
mid-latitudes during May–June–July. This increase in the scatter
is driven by the behaviour of the free run at Sodankylä. There, the
simulation has a larger variability than the measurements. For
example, at the end of June, the simulation presents a decrease of about
7 ppm in 36 h, whereas the measurements show a decrease
of about 4 ppm (Fig. a).
Elsewhere, there is also an increase in the scatter between May and
July which is during the Northern Hemisphere growing season. This
increase could be explained by the difficulty for CTESSEL to model
the terrestrial biogenic carbon fluxes during the growing season,
which leads to higher variability in the simulated atmospheric
CO2.
Analysis vs. free run
To assess the impact of the assimilation of the MACC GOSAT BESD
XCO2, we compared the evolution of XCO2 from the
analysis with XCO2 from the free simulation.
Figure presents the Hovmöller diagram
(time vs. latitude) of this difference. It shows that the first region
where the analysis impacts XCO2 is the tropics. There,
compared to the free run, the analysis continuously decreases
XCO2 by up to 1 ppm in June and by more than
2 ppm from September to December. The assimilation of the
GOSAT data consequently causes an improvement as the free run has
a positive bias in this region in autumn compared to the TCCON data.
The analysis also decreases XCO2 over the southern extra
tropics (region between 23 and 66∘ S) when
compared to the free run (Fig. ). The
decrease extends to the southern high latitudes (≥
66∘ S) even when no GOSAT data were assimilated in this
region. This decrease results mainly from the transport of
CO2 from the equatorial region and southern mid-latitudes
towards southern high latitudes. Unfortunately, there are no
independent XCO2 data available at southern high latitudes to
assess the merits of the analysis there.
Despite the fact that some GOSAT data are assimilated in the northern
mid-latitudes during the first months of the simulation, the analysis
only starts to differ significantly from the free run from March
onwards. In this region, north of 30∘ N, the analysis has
higher values of XCO2 than the free run, with a difference of
more than 2 ppm during the northern summer. Again, the
assimilation of the GOSAT data improves the simulated XCO2 as
the free run shows a strong negative bias there. Similar to the
behaviour discussed for the southern high latitudes, the change in
the CO2 concentration at northern mid-latitudes is
transported northward to higher latitudes. There is, nevertheless,
a difference between the two hemispheres. For the Northern Hemisphere
we have more data at high latitudes, especially during the summer, when
the northernmost GOSAT measurements' cover goes up to 80∘ N.
Analysis vs. TCCON data
When compared with the TCCON data, the GOSAT BESD XCO2 analysis has
an offset δ of -0.34 ppm and a mean absolute error Δ
of 0.57 ppm (Table ). The offset is
similar to that of the free run (-0.36 ppm), but the mean absolute
error is improved (1.08 ppm for the free run). The individual station
bias is moreover more constant in time for the analysis compared to the free
run. For example, the trend of the free-run bias is 2.08 ppmyr-1
for Lauder (New Zealand) (Table S1 of the Supplement), and it improves to
0.47 ppmyr-1 for the analysis (Table S2 and
Fig. c).
By increasing XCO2 in the northern mid-latitudes as discussed
before, the analysis considerably reduces the bias. A residual
seasonal cycle in the bias is still present, with values usually in
the range of 0 to 3 ppm (Fig. b). This
could be explained by the fact that we correct the atmospheric state
of CO2 and not the CO2 fluxes. During the seasons when
the CO2 fluxes are the main driver of the atmospheric
CO2, the optimization of the atmospheric state only may not be
enough.
The analysis has a more constant bias in time than the free run. It
is also more accurate in space, with a station-to-station bias
deviation σ that is largely reduced compared to the free run
with a value of 0.61 ppm against 1.27 ppm
(Table ). The assimilation of the MACC
GOSAT BESD XCO2 thus helps to significantly improve the accuracy
of the model. The assimilation also helps improve the precision π,
with the mean scatter improved by 15 %, reduced to a value of
1.22 ppm. The scatter of the analysis is reduced for all
TCCON stations compared to the free run except for Garmisch (Germany),
where the scatter remains essentially unchanged. The Hovmöller
diagram of the scatter shows that the main reduction is in the
northern high latitudes in May (Fig. ). In
particular, the analysis shows less spurious variability than the free
run at Sodankylä (Fig. a).
Analysis vs. MACC GOSAT BESD data
The analysis is much more accurate and more precise than the free run
when compared to the TCCON data. The analysis also fills the gaps in
time and space of the MACC GOSAT BESD data. In this section, we
evaluate the analysis against the MACC GOSAT BESD data once more using
the TCCON data as a reference.
The MACC GOSAT BESD data were compared to the TCCON data using
a geolocation criterion of 5∘ in space and a time window
of ±2h. Before computing the difference between each
GOSAT–TCCON pair, following , we added a correction
to the GOSAT-retrieved value in order to account for the use of
different a priori CO2 profiles in the two products. Moreover,
we only kept the stations where more than 30 GOSAT–TCCON pairs were
found in order to have more robust statistical results. This procedure
removes Izaña (Spain), Ascension Island, Réunion Island (France)
and Lauder from the list of the used TCCON stations in the comparison
and reduces the number of stations to 12
(Table ).
Statistics of the XCO2 differences between the
MACC GOSAT BESD data set and the average hourly TCCON data
(left block, GOSAT–TCCON) or the analysis and the average
hourly TCCON data (right block, model–TCCON): bias
(δk, in ppm), scatter (σk, in
ppm) and correlation coefficient (rk). The
analysis has been sampled similarly to the GOSAT data set in
time and space. Also shown are the mean, the mean absolute
error (MAE) and the deviation of the stations bias, the mean
scatter (all in ppm) and the mean r (last three rows). The second
column (N) is the number of data points used for
computing the statistics.
MACC GOSAT data set Analysis SiteNBiasScatterrBiasScatterrSodankylä90-0.264.500.390.241.410.92Białystok58-0.283.450.321.061.990.17Bremen411.192.340.530.540.860.81Karlsruhe911.452.740.520.890.740.88Orléans520.202.440.341.290.570.84Garmisch761.643.100.551.171.060.77Park Falls631.503.220.71-0.081.030.95Four Corners102-0.003.790.640.650.810.89Lamont340-1.014.050.570.051.010.91Saga610.402.950.760.140.880.90Darwin234-1.273.370.42-0.110.810.84Wollongong221-3.033.860.31-1.541.070.74Mean120.043.320.500.361.020.80MAE121.02––0.65––Deviation121.31––0.74––
Hovmöller diagram (latitude vs. time) of the difference in ppm
(negative/positive in blue/red) between XCO2 from
the analysis and from the free-run simulation, from 1
January to 31 December 2013. The horizontal dotted lines
represent the latitude of the northernmost and the
southernmost TCCON station, respectively.
The grey shaded areas are where GOSAT does not provide observations.
For each GOSAT–TCCON pair, we extracted the CO2 profile from
the analysis at the same location and time as the GOSAT measurement
before computing the difference between the model and the TCCON data.
In this way, we have a fair comparison between the analysis and the
MACC GOSAT BESD data with respect to the TCCON data.
The resulting subset of the analysis minus TCCON differences has
a different offset than the full data set but a similar mean absolute
error, station-to-station bias deviation and precision
(Tables and
). The difference in the offset is
mainly due to a difference in the sampling between the subset and the
full data set over the Northern Hemisphere. Due to few or no pairs
occurring in spring for the subset, the sampling misses the negative
bias of the analysis there. Missing the negative bias of the analysis
results in an increased offset. In that respect, the mean absolute
error is less sensitive to the used data set (subset or full data set).
The analysis has a lower mean absolute error Δ than the one
from the MACC GOSAT BESD data (0.65 ppm vs. 1 ppm,
Table ), a station-to-station bias
deviation σ almost half of the one from GOSAT data
(0.7 ppm vs. 1.3 ppm) and has an improved precision
π (1 ppm vs. 3.3 ppm). The mean correlation
coefficient is also higher in the analysis than in the satellite data
with a value of 0.8 compared to 0.5. The statistics of the MACC
GOSAT BESD data found here are different than those of
, who used a more recent version of the GOSAT BESD
product. With the successive improvements in the BESD algorithm, the
latest version has a station-to-station bias deviation of ∼0.4ppm and a precision of ∼2ppm.
The better precision (lower value of π) and the lower value of the
mean absolute error Δ and station-to-station bias deviation
σ of the analysis compared to the MACC GOSAT BESD data set shows
that the analysis is capable of smoothing the scatter of the satellite
data. Moreover, the analysis is able to fill the gaps of the satellite
data in time and space.
Case study of a cold front over Park Falls
The CO2 concentration could be strongly affected by frontal
systems. As an illustration, such a situation occurred at the end of
May 2013, close to the TCCON station of Park Falls, Wisconsin, USA,
when a cold front came from the northwest. On 31 May, the
XCO2 dropped from 398.62 ppm at 08:15 LT
(local time) to 395.97 ppm at 12:53 LT
(Fig. , top panel). This sudden decrease of
2.65 ppm in less than 5 h occurs after the arrival of
a cold front, which is associated with a decrease in the surface
pressure and a decrease in the temperature at 500 hPa
(Fig. , lower panel).
The free run is able to capture the sudden decrease in XCO2,
highlighting the skill of the model for such a situation
(Fig. , upper panel). The flow during this
period is mainly a descent of cold air from Canada towards the
midwestern and eastern US. This cold air mass is depleted in
CO2 relative to the background
(Figs. e and f). When it moves towards Park
Falls, it results in decreasing XCO2 as observed and
simulated, but the decrease in the free run is too strong by 2 to
3 ppm compared to the measurements.
Situation over Park Falls (USA) between 30 May and 2 June. Top
panel: evolution of XCO2 (in ppm) from
hourly averaged TCCON data (black dots), the free run (blue
line and dots) and the analysis (red line and dots). The
dots are the values of the model in the observation space.
Lower panel: evolution of the mean sea level pressure
(in hPa, black line) and the temperature at
500 hPa (in K, magenta line). The vertical
dotted lines represent 31 May, at 00:00 UTC and at
12:00 UTC, and 1 June, at 00:00 UTC.
Situation around Park Falls (black triangle), Wisconsin, USA, end of
May
2013. (a) Average increment in terms of XCO2 (in
ppm, negative/positive in blue/red) on 30 May 2013 (contours) and location of the GOSAT
measurements during this day (black rectangles). (b, c, d)XCO2 (in ppm) on 31 May at
00:00 UTC, at 12:00 UTC and on 1 June
at 00:00 UTC, respectively, from the analysis. (e, f)XCO2 (in ppm) on 31 May at 12:00 UTC and on
1 June at 00:00 UTC from the free run (below/above
background value in blue/red). For (b) to
(f) the dark contours are the values of the geopotential at
500 hPa.
We investigated whether the assimilation of the GOSAT data helps
improve the simulated evolution of the CO2 concentration for
such situations even if the number of BESD GOSAT data is limited in
the vicinity of a frontal system due to the strict cloud filtering.
Frontal systems are associated with clouds formed when moist air
between the cold and warm fronts is lifted.
On 30 May, we have a few GOSAT measurements over the
north and northeast region of North America
(Fig. a). These measurements have the effect of
increasing the XCO2 in this region
(Fig. b–d). The cold air mass is then richer in
CO2 in the analysis compared to the free run, and when it moves
towards Park Falls, the decrease is weaker and closer to the observed
decrease. The assimilation of the GOSAT data helps improve the simulation by
correcting the large-scale structure upstream and by improving the large-scale atmospheric XCO2 horizontal gradient.
The XCO2 decrease continues the next day on 1 June in both
simulations as the cold front continued its descent. Unfortunately,
likely due to the presence of clouds, no TCCON measurements are
available during this period to corroborate the simulated XCO2
decrease.
Anomaly correlation coefficient (ACC) of the forecast compared to
its own
analysis as a function of the forecast lead time and for each month:
(a) global ACC, (b) ACC for the Northern Hemisphere
(20–90∘ N),
(c) ACC for the tropics (20∘ S–20∘ N),
and
(d) ACC for the Southern Hemisphere (90–20∘ S). Each month is represented by
a different colour (see inset legends).
Forecast based on the analysis
Within CAMS, we are receiving the GOSAT BESD data for a given day with
a delay of 5 days behind real time. The analysis for this day is run as soon as the data are received. A 10-day forecast is
then subsequently run based on the resulting analysis.
In this section, we aim to evaluate the forecast as a function of its
lead time by comparing the forecast to the analysis valid for the
same time. This comparison informs us about how long the information
provided by the analysis remains in the forecast. Assuming perfect
transport and perfect surface fluxes, the analysis and the forecast
(valid for the same time) should be similar given that the analysis
accurately corrects the atmospheric concentration of CO2. In
practice, the differences observed between the analysis and the
forecast could come from either the transport, the surface fluxes or
the analysis.
To compare a forecast with the analysis valid for the same time, we
computed the anomaly correlation coefficient (ACC) for XCO2
(see Appendix for more details). The ACC can be regarded
as a skill score relative to the climatology: the higher the ACC, the
better the forecast. In the framework of NWP, an ACC reaching
50 % corresponds to forecasts for which the error is the
same as for a forecast based on a climatological average. An ACC of
about 80 % indicates valuable skill in forecasting
large-scale synoptic patterns.
We computed the ACC for each month individually as we know that the
surface fluxes, drivers of the difference between the forecast and the
analysis, have a strong seasonal cycle. We also computed it for
different domains (globe, tropics and mid- to high latitudes) and for
several forecast lead times, from 12 h up to 10 days.
We found that the ACC is globally more than 90 % for day 3
and almost always more than 85 % for day 5 for each single
month (Fig. a). This means that the forecast for today
based on the analysis of 5 days ago shows the same
large-scale synoptic XCO2 patterns as the analysis. The
information of the analysis therefore lasts long enough in the
forecast to provide a good quality 5-day forecast for today (compared
to the analysis). The information lasts longer in the tropics than
in the Northern Hemisphere and slightly longer in the Northern
Hemisphere than in the Southern Hemisphere (Fig. b to
d). This difference between the two hemispheres may reflect the fact
that the CO2 variability is much weaker in the Southern
Hemisphere.
For forecasts longer than 5 days, globally, there are two particular
months for which the ACC decreases faster than the others, i.e. July
and December. For example, for these two months the ACC at day 5 is
similar to the ACC at day 10 for October. This means that for July and
December, the medium-range XCO2 forecast (between 5 and 10 days) should be used more carefully. For July, the drop in skill
occurs mainly over the Northern Hemisphere. The main reason is that
the CO2 fluxes are an even more important driver of the
CO2 concentration than the initial CO2 concentration
for this month. To better understand the impact of the surface fluxes,
let us assume that in July we have too little release or, similarly,
too much uptake of CO2 in the atmosphere in the model over the
Northern Hemisphere (as confirmed by Fig. a). This
induces a negative bias of the CO2 surface fluxes in the
model. In the meantime, the analysis increases the CO2
concentration helped by the GOSAT BESD data
(Fig. ). However, the next 12 h
short-term forecast (used as the background for the next analysis)
will not increase the CO2 concentration enough due to the
negative bias of the CO2 fluxes. This opposition between the
analysis and the short-term forecast explains the reduction in skill
during the periods when the surfaces fluxes are the most important
driver of the CO2 concentration in the atmosphere.
The global drop in skill for December is not directly related to
a particular region as for July. It is nonetheless the second worst
month for the tropics (after January) and the third worst for the
Northern Hemisphere (together with September). Over the tropics during
the winter, the reduction in skill is due to the opposite effect as
for July over the Northern Hemisphere: the CO2 fluxes are
important and there is a positive bias in the fluxes (too much release
or too little uptake of CO2 in the atmosphere) in the model.
For these situations when the CO2 fluxes are the main driver
of the atmospheric CO2, the only solution to improve the
skill would be to optimize the CO2 fluxes together with the
CO2 initial conditions.
Conclusions
The Copernicus Atmosphere Monitoring Service (CAMS) greenhouse gases
data assimilation within the numerical weather prediction (NWP)
framework of the Integrated Forecasting System (IFS) is designed to
correct the atmospheric concentration of CO2 instead of the
surface fluxes in order to constrain the atmospheric CO2. This
requires the use of a short assimilation window so as to neglect the
model errors of the short-term forecast (lasting the length of the
assimilation window). In the case of atmospheric CO2, model
errors are related to potentially inaccurate surface fluxes or
transport.
This article demonstrates the benefit of the assimilation of
XCO2 data derived from the Greenhouse gases Observing
Satellite (GOSAT) by intermediate versions of the Bremen Optimal
Estimation DOAS (BESD) algorithm of the University of Bremen (UoB).
The assimilation of the GOSAT BESD XCO2 provides a
CO2 analysis that was compared to a free-run forecast where
the CO2 concentration is not constrained by any CO2
observation. The comparison was 1 year long (year 2013) and both
simulations (analysis and free run) were evaluated against
measurements from the Total Carbon Column Observing Network (TCCON).
We showed that the free run has a negative bias at northern
mid-latitudes and a large positive bias in the tropical region with
strong seasonal variations in both regions. These results are
consistent with the biases documented by and mainly
associated with biogenic fluxes.
The analysis significantly reduces these biases without completely
removing them, with a remaining mean offset of -0.34 ppm and
a mean absolute error of 0.57 ppm compared to the TCCON data.
However, the accuracy estimated with the station-to-station bias
deviation is 0.61 ppm. This represents a large improvement
compared to the free run, for which the accuracy is 1.27 ppm.
The precision of the analysis estimated with the mean scatter is
1.22 ppm, slightly better than for the free run with a value
of 1.43 ppm.
The analysis produced in this paper was compared to the assimilated
MACC GOSAT BESD data using TCCON data as a reference. This comparison
showed that the analysis has a lower station-to-station bias deviation
than the assimilated data (0.7 ppm compared to
1.3 ppm). The precision is much better for the analysis, with
a scatter of 1 ppm, while the assimilated data have a scatter
of 3.3 ppm. The precision of the analysis is also better than
the documented precision of other GOSAT XCO2 products. The
precision of the NIES product extracted from is
1.8 ppm. The precision of the University of Leicester
product and of the SRON Netherlands Institute for Space Research
product is respectively 2.5 and 2.37 ppm. The CO2 analysis is consequently an
alternative to the standard XCO2 GOSAT products as it provides
a lower or similar station-to-station bias deviation and a better-precision XCO2 product compared to TCCON. Moreover, it has a
uniform spatio-temporal resolution.
The pre-operational CAMS CO2 analysis is similar to the
analysis presented in this paper, having nevertheless a higher
horizontal resolution (TL511 on a reduced Gaussian grid, ∼40km×40km), and a higher vertical
resolution with 137 vertical levels. It currently assimilates the
most recent version of the GOSAT BESD data presented by
in near-real time. These data have an improved bias
deviation (∼ 0.4 ppm) and an improved precision
(∼ 2 ppm) compared to those used in this study. The
near-real-time CAMS CO2 analysis should therefore have an
improved station-to-station bias deviation and precision than the
analysis presented in this paper.
We corrected the atmospheric concentration by only constraining the
atmospheric concentration and not the surface fluxes. When and where
the surface flux is a significant driver of the atmospheric
concentration and if the assimilated data are not good enough or not
numerous enough (in time and space), then constraining only
atmospheric CO2 does not compensate for the error in the
surface flux. The next step is to further improve the carbon module
CTESSEL in order to reduce the bias of the model. Another long-term
solution would be to constrain the surface flux at the same time as
the concentration.
One strength of the CO2 model used in this study is its
ability to represent CO2 variations associated with synoptic
weather systems . By correcting the large-scale
XCO2 patterns and removing part of the model bias, we showed
with a case study that the analysis is able to better represent the
CO2 variations associated with these situations. The
variations in the atmospheric reservoir of CO2 are the result
of changes in the surface fluxes to and from the atmosphere. If the
characteristics of the analysis are found to be satisfactory in terms
of bias and precision, the analysis could be included into a flux inversion
system to infer surface fluxes.
The horizontal resolution of this study is half the horizontal
resolution of the pre-operational analysis and the vertical
resolution of the pre-operational analysis is also higher. One
should expect an even better representation of the CO2
variability in the pre-operational analysis. In the future, the
horizontal resolution could be increased even further toward the
ECMWF operational resolution of about 16km×16km.
The quality of the analysis is considered to be sufficient to assess
the quality of the forecast as a function its lead time. We showed
that the forecast for day 3 and day 5, which will be the valid range
for today's forecast, has an anomaly correlation coefficient of 90 and
85 %, respectively. This means that we are providing a
CO2 forecast with accurate synoptic features for today. With
a good representation of the variability and a bias mostly under
1 ppm, the CAMS atmospheric CO2 promises to become
a useful product, for example, for planning a measurement campaign.
It could also be used as the a priori in the satellite or TCCON
retrieval algorithms or be used to evaluate the retrieval products
from the Orbiting Carbon Observatory-2 (OCO-2,
oco.jpl.nasa.gov).
Comparing the model against TCCON
For the comparison with the TCCON data, one has to account for the
a priori information used in the retrieval that links c^o,
the TCCON-retrieved XCO2 to xt, the true (unknown)
CO2 profile ,
c^o=cb+aTxt-xb+ε,
where xb is an a priori profile of CO2, a
is a vector resulting from the product of the averaging kernel matrix
with a dry-pressure weighting function vector (for the vertical
integration), cb is the column-averaged mixing ratio computed
from xb, and ε is the error in the retrieved
column-averaged mixing ratio. This error includes the random and
systematic errors in the measured signal and in the retrieval
algorithm.
To compare the model with the TCCON-retrieved value, we used the same
a priori information, so that the model profile x is converted
to a column-averaged mixing ratio c^ by
c^=cb+aTx-xb.
The comparison between the simulation and TCCON occurs in the
observation space with the difference between the model column-averaged mixing ratio
c^ of Eq. () and the TCCON column-averaged mixing ratio
c^o of Eq. (),
c^-c^o=aTx-xt-ε.
Let us define η=aT(x-xt) as the model error in terms of the
column-averaged mixing ratio. It accounts for numerous errors, for
example the errors directly linked to the model processes like the
transport, the errors in the surface fluxes, the representativity
error and the error due to the assimilation of the GOSAT XCO2
data for the analysis. The difference between the smooth model
column-averaged mixing ratio c^ and the TCCON column-averaged
mixing ratio c^o is, therefore, the sum of the model
error η and the error in the retrieved column-averaged mixing
ratio ε.
To compute the model column-averaged mixing ratio c^ of
Eq. () equivalent to each TCCON measurement, we
extracted the two model profiles that are closest to the measurement
time and at the nearest grid point to the measurement. The two
profiles are then interpolated in time in order to obtain the model
profile at the same time as the measurement. Finally, we computed the
column-averaged mixing ratio according to
Eq. ().
Smoothing the statistics against TCCON
In order to have a more global view of the bias and the scatter of
a simulation against the data from the TCCON network, we have
developed and used a two-step algorithm. The first step consists in
computing the statistics (bias and the standard deviation) for each
week of 2013 and for each TCCON station when the data are available.
The weekly statistics are then interpolated in time using a function
described in the following section (Sect. ). This
allows one to fill in the gaps in time when no data are available. We
therefore have a value for the bias at each station and for each week.
For the second step, we compute a quadratic function of latitude that
best fits the interpolated biases for each week
(Sect. ).
Time smoothing
For each TCCON station k and for each week wl for l∈1,52, we compute the mean difference δkl and
the standard deviation of the difference σkl between
every TCCON observation during this week and the model equivalent
value. The statistics are computed only when more than 10 TCCON
measurements are available during the week. The averaged difference
(or bias) is then interpolated in time t with the function
b̃kt that combines a linear growth and
a harmonic component,
b̃kt=akt+bk+αksintτ1+φk+βksintτ2+φk.ak, bk, αk, βk and φk are the
parameters of the function b̃kt obtained
by an optimization procedure that minimizes the distance between
b̃kt and the series of δkl
for l∈1,52. τ1 is chosen to be 6 months
and τ2 3 months. The form of the function of
Eq. () thus gives a linear growing bias and
allows seasonal variations. A similar function is used for the
standard deviation.
Spatial smoothing
The time smoothing allows us to fill in the gaps in the time series of
the bias for each station, when for a given week we do not have any
measurement to compare with. Following , we then
compute for each week wl the best fit of the interpolated biases
with a quadratic function of latitude b^l,
b^lϕ=alϕ2+blϕ+cl,
where ϕ is the sine of the latitude. al, bl and cl
are obtained by an optimization procedure that minimizes the distance
between b^l and the weekly interpolated biases
δkl for k∈1,N. A similar function
is used for the standard deviation.
Discussion
For some stations, the availability of the weekly differences is not uniform
in time and the time smoothing of Eq. () provides
spurious values. We solved this issue by fixing the coefficient αk
to a zero value (See Table S1).
With a root mean square error (RMSE) mostly under 0.7 ppm and
a correlation mostly over 0.8, the smoothed bias matches well with the weekly
bias (Table S1). The Hovmöller diagram (Fig. ) can, thus,
be considered as an accurate representation of the overall bias.
Compared to the bias, the fit between the time series of the weekly
scatter and the regression is not as good for the scatter. The
correlation coefficient is mostly between 0.5 and 0.7 (Table S1).
Anomaly correlation coefficient
The anomaly correlation coefficient (ACC) between the forecast f and the
analysis a is computed using the climatology c by
ACC=(f-c)(a-c)‾(f-c)2‾(a-c)2‾,
where the overline is the spatial and temporal average. For example,
for the forecast range 24 h, we take the XCO2 fields
from all the 24 h forecasts for a given month, all the
analyses valid for the same time, and a fixed climatology for this
month.
The climatology is based on a free-run simulation using the optimized
CO2 surface fluxes from , which simulated the
years from 2003 to 2012. For each month, we compute the average over
the 10 years of the simulation, rescaling the mean so that the
mean is the same as for the analysis, avoiding with this procedure the
issue of the increase in CO2 over time. The two-dimensional
climatology field for XCO2 for the month m is
cm=110∑y=200320121n(y,m)∑d=1n(y,m)Σy,m,d-Σ‾y,m,d+Σ‾anm,
where y is the year, n the number of days for the year y and the
month m, d is an index for the day, Σy,m,d
is the XCO2 field from the simulation for the year y, the
month m and the day d, and Σ‾ is a spatial average of
Σ and Σ‾anm is the
spatial and temporal average of the XCO2 fields from the
analysis for the month m (and the year 2013).
The Supplement related to this article is available online at doi:10.5194/acp-16-1653-2016-supplement.
S. Massart designed and carried out the experiments
with the help of A. Agustí-Panareda and advice from
F. Chevallier, J. Heymann and M. Buchwitz. J. Heymann, M. Reuter,
M. Hilker, M. Buchwitz and J. P. Burrows were responsible for
the design and operation of the BESD GOSAT XCO2 retrieval
algorithm. S. Massart prepared the manuscript with contributions
from A. Agustí-Panareda, J. Heymann, M. Buchwitz, F. Chevallier,
M. Reuter, M. Hilker, J.P. Burrows, D. G. Feist, and F. Hase.
N. M. Deutscher and R. Sussmann contributed to the ACP version of
the paper. F. Desmet is the co-investigator of the La Réunion
TCCON station. N. M. Deutscher and C. Petri are responsible for
the Białystok, Bremen and Orléans TCCON data. M. Dubey is the
PI of Four Corners TCCON station. D. G. Feist is the PI of the
Ascension TCCON station. D. W. T. Griffith and V. Velazco are the
PIs of Darwin and Wollongong stations. F. Hase is the PI of the
Karlsruhe TCCON station. R. Kivi is the PI of the Sodankylä
TCCON station. M. Schneider is the PI of Izaña TCCON station.
R. Sussmann is the PI of the Garmisch TCCON station.
Acknowledgements
This study was funded by the European Commission under the European
Union's Horizon 2020 programme. The development of the GOSAT BESD
algorithm received funding from the European Space Agency (ESA)
Greenhouse Gases Climate Change Initiative (GHG-CCI). TCCON data were
obtained from the TCCON Data Archive, hosted by the Carbon Dioxide
Information Analysis Center (CDIAC) – http://tccon.ornl.gov/.
Garmisch work was funded in part via the ESA GHG-CCI project. Four
Corners TCCON was funded by LANL's LDRD programme. Darwin and Wollongong
TCCON measurements are funded by NASA grants NAG5-12247 and
NNG05-GD07G and the Australian Research Council grants DP140101552,
DP110103118, DP0879468, LE0668470 and LP0562346. We are grateful to
the DOE ARM programme for technical support in Darwin, and Clare Murphy,
Nicholas Jones and others for support in Wollongong. TCCON
measurements in Białystok and Orléans are supported by
ICOS-INWIRE, InGOS and the Senate of Bremen. N. Deutscher is
supported by an ARC-DECRA fellowship, DE140100178. The authors are
grateful to Marijana Crepulja for the acquisition of the BESD GOSAT
data at ECMWF and the preparation of the data for the assimilation.
The authors would like to acknowledge Paul Wennberg, PI of the Lamont
and Park Falls TCCON stations. Finally, we would like to express our
great appreciation to William Lahoz, editor of this paper, for his
useful comments during the revision process.
Edited by: W. Lahoz
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