Within the NDACC (Network for the Detection of Atmospheric Composition
Change), more than 20 FTIR (Fourier-transform infrared) spectrometers, spread
worldwide, provide long-term data records of many atmospheric trace gases. We
present a method that uses measured and modelled
The Network for the Detection of Atmospheric Composition Change (NDACC – formerly called Network for the Detection of Stratospheric Change, NDSC)
first started measurements of atmospheric components in 1991
In this context, it is helpful to refer to TCCON (Total Carbon Column
Observing Network), which is another network of ground-based FTIR
spectrometers and closely affiliated to the Infrared Working Group of NDACC.
The major difference between the TCCON and NDACC is that for the former, solar
spectra in the near infrared (NIR) are recorded
In this paper, we propose using the total column dry-air mole fractions of
In the following section, we present our simple
This study uses more than 17000 individual observations made since 1996 on
more than 6000 days and during different periods recorded at 10
globally distributed NDACC/FTIR sites. These are the sites that are currently
contributing to the MUSICA project (multi-platform remote sensing of
isotopologues for investigating the cycle of atmospheric water,
The ground-based FTIR systems measure solar absorption spectra using
high-resolution Fourier transform spectrometers. For our analysis we apply
the retrieval code PROFFIT and the included radiative transfer code PROFFWD
The spectral microwindows that are used for this study are shown in
Fig.
The four spectral microwindows used for the ground-based FTIR retrieval. Shown
is an example of a typical measurement at Karlsruhe (04 June 2010, 07:36 UT, solar
elevation 38.15
Spectral microwindows chosen for the NDACC
An important aspect of our study is that we use a rather
simple retrieval setup (fixed a priori and simple scaling retrieval). This
assures that the method can be correctly and consistently applied for
different sites and time periods, without any risk of inconsistency due to a
priori and constraints. The spectral windows, as depicted in
Fig.
For the TCCON retrievals the
The a priori assumptions affect the retrieval results. For sites where NDACC
and TCCON measurements are made simultaneously, we made two different
retrievals with the NDACC MIR spectra. First we applied our simple retrieval
recipe (fixed WACCM a priori), and then we calculated the results when using
the TCCON strategy (daily varying a priori). Figure
NCEP (National Centers for Environmental Prediction) analysis data at 12 UT
are used for daily temperature and pressure profiles for all sites (e.g.
A priori profiles for Karlsruhe: WACCM v6 (used as a priori for NDACC retrieval) and some examples used as a priori for TCCON retrieval.
Column-averaging kernel for Karlsruhe (NDACC; 04 June 2010, 07:36 UT, solar elevation 38.15
The assumptions we made for our error calculations are listed in Table
The errors, estimated with the error calculation implemented in PROFFIT for a
typical measurement at Karlsruhe station (04 June 2010, 07:36 UT, solar
elevation 38.15
Uncertainty sources used for our error estimation. The second column gives the assumed uncertainty value and the third column the assumed partitioning between statistical and systematic sources.
Statistical and systematic errors in the Karlsruhe total
Overview of collaborating ground-based NDACC/TCCON FTIR stations.
NDACC
This method is not expected to be as precise as the TCCON method which uses
the measured
As reference for the
The only inputs required for the model in addition to the Mauna Loa time
series and CarbonTracker are the time, latitude and typical surface pressure
of the measurement site. The
For approximating the actual atmospheric
Due to the fact that the model does not include meteorological fields, the calculated values can only be valid on a monthly time scale and not on a synoptic or daily time scale. To account for that, we only compare monthly mean data, which are calculated from daily means and we require that the standard error of the so calculated monthly mean is smaller than 5 ‰.
As in
TCCON is a network of ground-based Fourier transform spectrometers
that record direct solar spectra in the NIR. It was founded in 2004
and operates around 20 spectrometers spread worldwide. From these
spectra, accurate and precise column-averaged abundances of
atmospheric constituents including
To retrieve trace gas columns from the measured spectra, the software package GGG2012,
developed by G. Toon (JPL), is used
TCCON uses time-dependent
TCCON data products are column-averaged dry-air mole fractions, which
for e.g.
Correlation of TCCON vs.
There have been several calibration campaigns
TCCON data sets (GGG2012) have been downloaded from the TCCON database
(
The TCCON sites have been chosen to fit to our actual set of NDACC
sites (Table
Figure
The middle graph of Fig.
The correlation of the de-seasonalised yearly means is plotted in the
right graph (it is the yearly mean as calculated from the data after
removing the seasonal cycle). On this inter-annual time scale the
agreement is very good (
As Fig.
As Fig.
Overall, the model and measurements agree very well. According to the
scatter between the model and the TCCON data, the model is able to
predict the
In this section, we want to check the quality of the
NDACC
In the following, we present two types of comparison. First, we
generate an
Figure
We observe a good correlation between the two data sets. The scatter
is about 4 ‰ on a monthly time scale and 3 ‰ on
a yearly time scale. However, there is a significant systematic
difference. The NDACC
As Fig.
Correlations of the yearly means. Left: NDACC vs. model NDACC (1996–2012), right: TCCON vs. model (2005–2012).
The comparison of the seasonal variations (central panel) also reveals
good agreement. When using the same a priori as TCCON, the NDACC
measurements can reproduce the TCCON seasonal variation within
2.6 ‰ (scatter between the two data sets). A detailed
overview on the seasonal cycles for the different sites is given in
the Appendix
The main interest of this study are monthly or longer time scales,
which are decisive for the reliability of trend analyses. These time
scales can be well captured by the model
(Sect.
Figure
On seasonal time series, variations in the
Time series of the differences of the yearly means between
measurement and respective model. Top: NDACC-model
The central panel of Fig.
In this section, we check the NDACC/FTIR time series
for long-term consistency by comparing the NDACC
The reduced seasonality due to the fixed a priori used for the NDACC
retrievals must be taken into account; i.e. we apply the damping
factor
As the subset of NDACC/FTIR sites we are using within this study is
representative of nearly all latitudes (Table
Finally, to document the network-wide long-term stability, we compare
annual means, not to be mistaken for the de-seasonalised annual means
shown in the comparisons Fig.
Mid-infrared high resolution solar absorption spectra have been recorded for many years and at many sites around the globe. Most of these activities are organised within the NDACC and have a high potential for investigating the long-term change of our atmosphere on a global scale. However, such investigations require data that are very consistent throughout many years and between the different sites.
In this work, we present a method that allows an assessment of the
consistency of any mid-infrared high-resolution solar absorption
measurement (2600–3000
The
We apply the developed method to the NDACC/FTIR spectra that have so far been contributing to the project MUSICA. These spectra have been measured since 1996 at 10 stations that are distributed around the globe. We found a scatter between the yearly mean NDACC data and the model of about 3 ‰. This provides strong evidence for the very good long-term data consistency between these NDACC/FTIR sites and is a good reliability and consistency test for the long-term trends of tropospheric species measured at these sites.
All seasonal cycles determined from the different data sets are
plotted in Fig.
To consider this, the damping factor
Seasonal cycles. Black: NDACC with fixed WACCM a priori, red: NDACC with same a priori as TCCON, green: TCCON and blue: model.
As Fig.
Depicted in Fig.
There is a higher correlation for northern sites, where there is more
variability (
We would like to note that the consistency of NDACC and TCCON
Correlation between (NDACC
Listed in Table
Overview of all yearly correction factors used in our
We would like to thank the many different technicians, PhD students, post-docs and scientists from the different research groups what have been involved in the NDACC-FTIR activities during the last two decades. Thanks to their excellent work (maintenance, calibration, observation activities, etc.), high-quality long-term data sets can be generated.
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, PCSP and ORF. The authors wish to thank Rebecca Batchelor, Rodica Lindenmaier, PEARL site manager Pierre F. Fogal, the CANDAC operators and the staff at Environment Canada's Eureka weather station for their contributions to data acquisition and logistical and on-site support.
We thank the Alfred Wegener Institut Bremerhaven for support in using the AWIPEV research base, Spitsbergen, Norway. The work has been supported by EU-Project NORS.
We would like to thank Peter Völger for technical support at IRF Kiruna.
The University of Liège contribution has been supported by the A3C PRODEX program (Belgian Science Policy Office, Brussels), by the GAW-CH program of MeteoSwiss (Zürich), by the F.R.S.-FNRS and the Fédération Wallonie-Bruxelles. We thank the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) for supporting the facilities needed to perform the observations.
E. Sepúlveda enjoyed a pre-doctoral fellowship thanks to the Spanish Ministry of Education.
Measurements at Wollongong are supported by the Australian Research Council, grant DP110103118.
Measurements at Lauder and Arrival Heights are core funded through New Zealand's Ministry of Business, Innovation and Employment. We would like to thank Antarctica New Zealand and the Scott Base staff for providing logistical support for the NDACC-FTIR measurement program at Arrival Heights.
TCCON data were obtained from the TCCON Data Archive, operated by
the California Institute of Technology from the website at
This study has been conducted in the framework of the project MUSICA, which is funded by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/ERC Grant agreement number 256961.
We acknowledge the support by the Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of the Karlsruhe Institute of Technology.
The service charges for this open-access publication have been covered by a Research Centre of the Helmholtz Association. Edited by: H. Worden