We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European
biogenic CO
Accurate information about the terrestrial biogenic CO
Atmospheric inversions, which exploit atmospheric CO
This paper aims at studying the skill of a regional inversion system in
Europe, which is equipped with a relatively large number of ground-based
atmospheric measurement stations, for estimating NEE at the continental and
country scales, down to 0.5
Europe is a difficult application area for atmospheric inversion because of
the very heterogeneous distribution of vegetation types, land use, and
agricultural and industrial activities inside a relatively small domain,
and, consequently, because of the need for solving for fluxes at high
resolution. Furthermore, its complex terrain also requires a high resolution
of the topography when modeling the atmospheric transport (Ahmadov et al.,
2009). However, the Integrated Carbon Observing System (ICOS) infrastructure
is setting up a dense network of standardized, long-term, continuous and
high precision atmospheric and flux measurements in Europe, with the aim of
understanding the European carbon balance and monitoring the effectiveness
of greenhouse gas (GHG) mitigation activities
(
Several inversion studies have focused on the estimate of European NEE based
on measurements from the CarboEurope-IP atmospheric stations, most of which
are planning to join the ICOS atmospheric network (Peters et al., 2010;
Broquet et al., 2011). Broquet et al. (2013) have demonstrated, based on
comparisons with independent flux tower measurements, that there is a high
confidence in the Bayesian estimate of the European NEE and of its
uncertainty at the 1-month and continental scale based on their variational
system, which uses the CHIMERE mesoscale transport model run at
0.5
Here, we apply the system of Broquet et al. (2011, 2013) to assess the
potential of the near-term and realistic future configurations of the ICOS
continuous measurements of CO
The Bayesian statistical framework chosen here provides estimates of the
posterior uncertainties as a function of the prior uncertainties, of the
atmospheric transport and of the combination of statistical errors, which are
not controlled by the update of the prior NEE by the inversion (like the
measurement errors and the atmospheric transport errors). Even though the
prior uncertainty can potentially depend on the value of the prior NEE, the
actual values of the prior NEE or of the measurement data to be assimilated
are not formally involved in the estimation of the posterior uncertainty due
to the linearity of the atmospheric transport of CO
Using synthetic data in an OSSE framework has been a common way to assess
the utility of new GHG observing systems for the monitoring of the GHG
sources and sinks at large scales based on global inversion systems with
coarse-resolution transport models (e.g., Rayner et al., 1996; Houweling et
al., 2004; Chevallier et al., 2007; Kadygrov et al., 2009; Hungershoefer et
al., 2010). This approach now plays a critical role in the recent emergence
of regional inversion systems supporting strategies for the deployment of
regional observation networks and assessing the potential of regional
inversion for assessing the GHG fluxes at a relatively high resolution (Tolk
et al., 2011; Ziehn et al., 2014). Such a use of OSSEs today is not specific
to the GHG inversion community. The OSSEs are increasingly used by the air
quality community (e.g., Edwards et al., 2009; Timmermans et al., 2009a, b,
2015; Claeyman et al., 2011) and they are still extensively used by the
meteorological community (e.g., Masutani et al., 2010; Riishøjgaard et
al., 2012; Errico et al., 2013; see also
In OSSEs, twin experiments are often used to derive a single realization of
the uncertainties (Masutani et al., 2010) while our Monte Carlo approach
explores the uncertainty space much more extensively. Further, in common
(linear) CO
The manuscript first documents the potential of different configurations of
the ICOS network for constraining NEE, through the use of a state-of-the-art
inversion setup, which solves the NEE at high spatial and temporal
resolution, and which has been submitted to a high level of evaluation. This
inversion setup is based on a variational atmospheric inversion system. We
study the potential of the 23 station (hereafter ICOS23) network containing existing sites and other
stations that could be installed on tall towers over Europe in the coming
years. We also consider two longer-term ICOS configurations with 50 stations
(hereafter ICOS50) and 66 stations (hereafter ICOS66). For the time domain,
we consider results for NEE aggregated at the 2-week scale, for two
different periods of the year (in July and in December). Shorter aggregation
scales, like a day, result in a significant dependency of NEE on specific
synoptic events. Longer timescales require computing resources that are
beyond the scope of this study with its high-resolution inversion system. We
pay special attention to the analysis of the results at different spatial
scales, from the native transport model grid scale of about 50 km
The paper is organized as follows. Section 2 describes the mesoscale inversion experimental framework focusing on the Monte Carlo estimate of uncertainties. Section 3 analyzes the scores of posterior uncertainties and the uncertainty reduction compared to the prior uncertainties in order to assess the potential of the near-term framework and of future improvements of the network or of the inversion setup. The last section synthesizes the results and discusses them.
Site location for the different ICOS network configurations used
in this study:
We consider three successive phases of deployment of the ICOS atmospheric network. The initial ICOS23 configuration includes 23 sites among which there are 10 tall towers. This minimum network configuration is based on existing stations, most of them being operational in the CarboEurope-IP FP6 project. The ICOS network is expected to further expand during the next 5 years according to the country declarations at the ICOS Interim Stakeholder Council and to the ICOS European Research Infrastructure Consortium 5-year financial plan. Using possible locations for the future stations, including sites that have already been discussed with the ICOS consortium during the ICOS preparatory phase FP7 project (European Union's Seventh Research Framework Programme, grant agreement no. 211574), we derived two plausible ICOS configurations: ICOS50 with 50 sites including 27 tall towers and ICOS66 with 66 sites including 39 tall towers.
The locations and details on the sites of the three configurations are
summarized in Table A1 and in Fig. 1. Here, the existing and future ICOS
CO
The estimate of uncertainties related to the different ICOS networks is
based on an ensemble of inversions with the variational inversion system of
Broquet et al. (2011), assimilating synthetic hourly averages of the
atmospheric CO
Peylin et al. (2011) indicate that uncertainties in anthropogenic fluxes
yield errors when simulating CO
In order to simulate the full amount of CO
We define the control vector
With this theoretical framework,
The inversion system derives an estimate of
In this framework, a common performance indicator is the theoretical
uncertainty reduction for specific budgets of the NEE estimates (averaged
over specified periods of time and over specified spatial domains), defined
by
Due to the size of the observation and control vectors in this study, we
could not afford the analytical computation of Eq. (2) based on the full
computation of the
In this study, the operator
In this study, we use the European domain shown in Fig. 1a, which covers most
of the European Union and some of eastern Europe, with a land surface area
of 6.8
The setup of the error covariance matrix
Broquet et al. (2011) analyzed the periods of time during which the CHIMERE
European configuration bears transport biases that are too high, so that
measurements from ground-based stations such as ICOS sites should not be
assimilated to avoid erroneously projecting such biases into the corrections
to the fluxes. In agreement with common practice, they concluded that
observations at low altitude sites (approximately below 1000 m above
sea level, m a.s.l.; see Broquet et al., 2011, for the exact definition of the
different types of sites used for the time selection of the data and the
configuration of the observation error), which include almost all of the ICOS
tall towers, should be assimilated during daytime (12:00–20:00) while the
observations at high altitude stations (approximately above 1000 m a.s.l.)
should be used only during the night (00:00–06:00). This generally yields
larger uncertainty reduction during daytime than during nighttime (Broquet
et al., 2011). However, this does not raise a potential bias related to a
better constraint on daytime inverted NEE (when the ecosystems are generally
a sink of CO
The observational error covariance matrix
Broquet et al. (2011) derived a quantitative estimation of the model error
(depending on the station height) including transport and representativeness
errors based on comparisons between simulations and measurements of CO
We use 12 iterations of minimization for each variational inversion of the Monte Carlo ensemble experiments. This number is similar to that from Broquet et al. (2011) where they considered a longer time period for the inversions but far smaller observation networks and a smaller inversion domain, which reduces the dimension of the minimization problem. However, here, 12 iterations were still found to be sufficient for converging toward the theoretical minimum of the cost function, i.e., the number of assimilated data divided by 2 (Weaver et al., 2003), with less than 10 % relative difference to this theoretical minimum except for a few cases (for these cases, 18 iterations were used to reach a relative difference to the theoretical minimum that is smaller than 10 %).
Similarly to Broquet et al. (2011), 60 members are used in each Monte Carlo ensemble experiment. This is also the typical number of members that Bousserez et al. (2015) used for their Monte Carlo simulations. Broquet et al. (2011) found a satisfactory convergence of the estimate of the uncertainties in Europe and 1-month-average NEE with an ensemble size of 60, which is confirmed here (the estimates using 50 and more members are within 6 % of the results with 60 members).
Three and five Monte Carlo ensembles of inversions are conducted for
December and July, respectively. For each season, three ensembles using the
default setup of
There is a steady increase in the resolution of the atmospheric transport
models used for atmospheric inversions, with corresponding improvements of
the simulation precision (e.g., Law et al., 2008). In this test we simulate
the effect of potential future transport model improvement on the posterior
flux uncertainties by reducing the default observation error standard
deviations in
The test of the sensitivity of the inversion system to the prior uncertainty
is focused on that of the sensitivity to the spatial correlation length in
In this section, the performance of the inversion relying on the default configuration and on the ICOS23 initial state network (i.e., the reference inversion) is analyzed as a function of the spatial scale, highlighting the main patterns of the uncertainty reduction obtained from the pixel scale to the regional (national, European) scales.
Figure 2a and b show the uncertainty reduction for estimates of a 2-week
average NEE at 0.5
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at 0.5
Standard deviations (g C m
The spatial structure of the uncertainty reduction and the underlying spatial extrapolation from a site is a complex combination of transport influence and of the structure of the prior uncertainty. Due to varying transport conditions, standard deviation of the prior uncertainty at the grid scale (which is larger in summer; see below the comments on Fig. 3), and observation error (which is larger in winter), the spatial distribution of uncertainty reduction is found to vary from summer to winter. Because the prior uncertainties are larger and the observation errors are smaller in July than in December, there is generally a larger uncertainty reduction in July (especially in western Europe). But variations in meteorology alter (limiting or enhancing) this general behavior. The lower vertical mixing (which strengthens the sensitivity of the near-ground measurements to the local fluxes) partly balances the higher observation error in December and the range of local uncertainty reductions overlaps between July and December. The observations from the Angus tall tower (tta site, Table A1) in Scotland or from Pallas (pal site, Table A1) in Finland contribute differently to the uncertainty reduction during July and December (using meteorological conditions from 2007), showing better performance at the grid scale during summer. This also comes from the different weather regimes, with different dominant wind directions, different average wind speed and different vertical mixing in summer and winter. Regions lacking stations in ICOS23 have an uncertainty reduction that is more sensitive to the atmospheric transport than regions with a dense network. The uncertainty reduction in December is significantly larger in the east and in the southeast part of domain compared to July, due to more occurrences of winds from the east during December than during July.
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at the country scale for July
Complementing the uncertainty reduction, Fig. 3 shows prior and posterior
uncertainty standard deviations at the grid scale in order to illustrate the
precision of the estimates of NEE that should be achievable with the
reference inversion using the ICOS23 network. As already stated, prior
uncertainties are up to
Figure 4a and b show the uncertainty reduction for a 2-week- and country-mean NEE in July and December, respectively. The countries and corresponding estimates of prior and posterior uncertainties are listed in Table A2. The results suggest the ability of the mesoscale inversion framework to derive estimates of the NEE at the national scales with relatively low uncertainties. The uncertainty reduction is particularly large for countries such as Germany, France and the UK, e.g., more than 80 % for France during July. It is larger than 50 % for a large majority of the countries in western Europe and Scandinavia both in July and December.
The smallest uncertainty reduction applies to southeastern European countries where it can be smaller than 10 % (e.g., for Greece in July) indicating that the presence of stations very close to or within a given country is a requisite for bringing significant improvement to the estimates of NEE in this country. In general, the differences of the inversion skill between July and December look consistent with what has been analyzed at the pixel scale. In particular the uncertainty reduction is higher in July for western European countries but higher in December for eastern European countries for the same reasons as that given when analyzing the same behavior at the pixel scale (see Sect. 3.1.1).
Table 1 shows that the uncertainty in a 2-week-mean NEE in July averaged
over the full European domain (6.8
Uncertainty reduction in a 2-week- and European-mean NEE for July and
December as a function of the observation network and of the configuration of
the inversion parameters (
In order to examine here the dependency of the NEE uncertainty reduction to
increasing spatial scales of aggregation for the analyses in July and
December, we choose five locations at which we define centered areas with
increasing size for which uncertainties in the average NEE are derived.
These stations are located using the green circles in Fig. 1c. The five
locations correspond to three observing sites of ICOS23: Trainou (TRN),
Ochsenkopf (OXK) and Plateau Rosa (PRS); one site of ICOS50: SMEAR II-ICOS
Hyytiälä (HYY); and one point in Sweden, which does not correspond to
any site of the ICOS networks tested here, called SW1 hereafter (Fig. 1c).
We compute the uncertainty reductions of the 2-week-mean NEE for July and
December over five squares centered around each site and of increasing size
(in square degrees): 1.5
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE in July and December 2007 using ICOS23 and the
reference configuration of the inversion, as a function of the size
(logarithmic scale) of the spatial averaging area (in km
The five locations used for this analysis are representative of the diversity of the situation regarding the differences between grid scale uncertainty reduction in July and in December. While the uncertainty reduction is slightly larger in July than in December for TRN and much larger in July for PRS and HYY, it is slightly larger in December at OXK and much larger in December at SW1. Furthermore, the values for these grid scale uncertainty reductions range from 15 to 50 % in July and from 7 to 47 % in December at these locations (Fig. 5).
The maximum scores of uncertainty reduction occur for spatial scales of
aggregation ranging from 10
The convergence between the results around TRN, PRS and OXK in December and
July (which tend to nearly 65 % uncertainty reduction when the averaging
area reaches the western European domain), between the results around all
sites in December (which tend to 66 % uncertainty reduction when the
averaging area reaches the whole of Europe) or between the results around
all sites in July (which tend to nearly 53 % uncertainty reduction when
the averaging area reaches the whole of Europe), starts between the
10
Correlations of the posterior uncertainties in a 2-week-mean NEE
between Germany and the other European countries in July
The similarity of the results for the western European domain despite differences at the grid scale in July and December can be explained by differences of correlations between areas at scales similar to or larger than the national scale in the posterior uncertainties (since the correlations of the prior uncertainties aggregated at the national scale or at larger scales are very close for July and December). Figure 6 illustrates the variations of such correlations of the posterior uncertainty at the national scale between July and December using the example of correlations between Germany and other countries. These correlations are usually more negative in December, which indicates a larger difficulty in December than in July to distinguish in the information from the measurement network the separate contributions of the different neighboring countries (or of different areas of larger size). This can be attributed to the stronger winds in December, which increase the extent of the flux footprints of the concentration measurements. Such an increase of the footprints in December limit the ability to solve for the fluxes in the vicinity of the measurement sites but increase the ability to solve for the fluxes at large scales.
The effect on local (grid scale) uncertainty reduction of assimilating data
from new sites in the ICOS network depends on the coverage of the area by
the initial ICOS23 network, as illustrated by the comparison of the results
using ICOS23, ICOS50 and ICOS66 and the reference configuration of the
inversion (see Figs. 2 and 7). For example, adding one new site in Sweden or
Finland yields a stronger increase of the uncertainty reduction than adding
one site in the central part of western Europe, where the network is already
rather dense. Since most of the new sites from ICOS23 to ICOS50 and then
ICOS66 are located in western Europe, the improvements due to adding 27 or
43 sites to ICOS23 do not thus appear to be as critical as what can been
achieved using the 23 sites of ICOS23. The changes from ICOS23 to ICOS50
significantly enhance the uncertainty reduction at 0.5
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at 0.5
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at the country scale in July
The impact on the scores of uncertainty reduction of the increase of the
ICOS network is also significant at the national (cf. Figs. 4 and 8)
and European scales (see Table 1 and Fig. 9) when comparing results with
ICOS50 or ICOS66 to those obtained with ICOS23. The ICOS66 network delivers
uncertainty reductions as high as 80 % for countries like France and
Germany in July. For Europe, the uncertainty reduction when using ICOS66
reaches 79 % down to
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE for July 2007 as a function of the size (in
logarithmic scale) of the spatial averaging area (same as for Fig. 5)
centered on
Figure 9 illustrates the diversity (depending on the space locations) of the evolution of the impact of increasing the network as a function of the NEE averaging spatial scale. For a low altitude site already present in the dense part of ICOS23, the impact of adding new sites increases when increasing the spatial scale of the analysis up to areas where ICOS23 is less dense (mainly in eastern Europe) and where new sites are included in ICOS50. Conversely, the impact of the addition of new sites can decrease when increasing the NEE spatial aggregation scale, e.g., at HYY where a new site is specifically added in ICOS50.
The impact of reducing the correlation
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at 0.5
Even though these reductions can be very large, it is important to keep in
mind that they refer to uncertainty reductions compared to a prior
uncertainty, which is decreased by the new configuration of
Uncertainty reduction (theoretically comprised between 0 and 1)
for a 2-week-mean NEE at the country scale in July when modifying the
inversion configuration from the reference one by using
The impact of dividing the standard deviation of the observation error by two in the inversion configuration is tested using ICOS50 in July (cf. Figs. 7a and 10b, Figs. 8a and 11b and the corresponding curves in Fig. 9). The decrease of observation error increases the weight of the measurements in the inversion and the resulting uncertainty reduction. This increase is visible at all spatial scales for the aggregation of the NEE, and relatively constant as a function of these spatial scales except at the European scale (for which the uncertainty reduction is equal to 67 % when dividing the observation error by 2 instead of 64 % when using the default configuration of this error). This provides the highest scores of uncertainty reduction of this study at any spatial scales, the impact of division of the observation error by 2 being larger than that of increasing the ICOS network configuration from ICOS50 to ICOS66.
We assessed the potential of CO
Despite the absence of seasonal variation for the uncertainty in the average NEE over western Europe (at least according to our results for the year 2007) significant seasonal variations at higher resolution or for the full European domain reveal the influence of the atmospheric transport on the scores of uncertainty reduction. Using ICOS66 instead of ICOS23 does not limit this behavior because few sites are added between ICOS23 and ICOS66 in eastern Europe, where the largest seasonal variations of the uncertainty reduction occur. The larger wind speed in December than in July explains that there is a similar uncertainty reduction in July and December for western Europe. This is another illustration of the influence of the atmospheric transport on the scores of uncertainty reduction. It demonstrates that such scores and their sensitivity to the network extension can hardly be anticipated based on a simple analysis of the site locations and on the knowledge of the typical spatial scale of a station footprint. Their derivation requires the complex application of an inversion system as in this study.
These scores of uncertainty reduction result in posterior uncertainties
lower than 1.8 g C m
Standard deviations (g C m
The comparison of the sensitivity of the results in July to changes in the observation network, correlation lengths of the prior uncertainty and observation error (in the range of tests conducted in this study) indicates a hierarchy of the impact of such changes, which depends on the spatial scales. Increasing the network from ICOS23 to ICOS50 yields the largest change in posterior uncertainty due to a significantly better monitoring of the eastern part of Europe. However, for western European countries, at the grid to national scales, the impact of changing the inversion parameters is generally larger than that of the increase of the network size. Given the range of spatial correlations in the prior uncertainty that are investigated here, the spacing of ICOS sites in western Europe is already sufficiently narrow to ensure that this full domain is significantly constrained by the measurements from ICOS23. The weight of this constraint at grid to national scales in western Europe is more directly modified by dividing the observation errors by 2 or shortening them by nearly half the correlation length of the prior uncertainties than by doubling the number of monitoring sites.
The increase of the ICOS network from ICOS23 to ICOS50 or to ICOS66 follows
two strategies: a densification of the European network in the west and its
extension in the poorly monitored area, mainly in the east. The results of
this study indicate that the extension should presently focus on the east
because notional targets for the posterior uncertainty in national scale NEE
(derived from the CarbonSat report for mission selection) are reached in
western Europe when using ICOS23, as the posterior uncertainties from the
national scale to the 0.5
Some limitations of the calculations in this paper should be kept in mind when analyzing the results more precisely. The convergence of the calculations, as a function of the number of minimization iterations during the inversion or as a function of the number of inversions in each Monte Carlo ensemble experiment, has been assessed based on average diagnostics. Locally, some results have not converged. Additionally, the use of ICOS50 or ICOS66 should require more minimization iterations to converge to the same extent as when using ICOS23 or ICOS50 due to the increase in the dimension of the inversion problem. As an example, this results in very slight increases in the posterior uncertainty for Sweden or for Europe when extending ICOS50 to ICOS66. This problem of convergence slightly changes the scores of uncertainty reduction only for specific areas, but it is not significant enough to impact the typical range of values analyzed and the subsequent conclusions in this study.
Another point to note is that the confidence in the reference configuration
of the inversion has been built based on the diagnostics of the errors in
NEE simulated with the ORCHIDEE model at the local scale from Chevallier et
al. (2012), and at the monthly and Europe-wide scale from Broquet et al. (2013). A simple model is used to represent the correlations of the prior
uncertainty in NEE and thus the prior uncertainty in NEE at the intermediate
scales. The modeling of the prior uncertainties may need to be refined to
better account for the heterogeneity of the European ecosystems with a
potential impact on the results of posterior uncertainty at fine scales.
Furthermore, the assumption that the uncertainties in CO
This study focuses on results for 2-week-mean fluxes, while a critical
target of the inversion should be related to annual-mean fluxes. This and
the strong influence of the variations of the meteorological conditions on
the inversion results (which limits the ability to extrapolate the results
to the annual scale) encourage the setup of 1-year-long experiments.
However, this study already gives qualitative insights on such results and
on their sensitivity to the observing network or to the accuracy of
different components of the system, which should support future network
design studies in Europe. By demonstrating the capability of deriving
scores of uncertainty reductions for NEE at 6 h and 0.5
Atmospheric measurement sites for the different ICOS network configurations considered in this study with associated observation errors in the reference configuration of the inversion. Two values are given for the observation error at a given site for low altitude sites: that for temporal window 12:00–18:00 (left) and temporal window 18:00–20:00 (right), and one value for temporal window 00:00–06:00 at high altitude sites. Type column represents the way of the station installation: on ground sites (G) or on tall towers (TT). Height corresponds to the vertical location of the site above the ground level (m a.g.l.) and elevation corresponds to its vertical location above sea level (m a.s.l.).
Continued.
NEE uncertainty budget for European countries for July 2007
estimated using the reference inversion configuration and different
atmospheric CO
Standard deviations (g C m
Standard deviations (g C m
Standard deviations (g C m
This study was co-funded by the European Commission under the EU Seventh Research Framework Programme (grant agreement no. 283080, geocarbon project) and under the framework of the preparatory phase of ICOS. It was also co-funded by the industrial chair BridGES (supported by the Université de Versailles Saint-Quentin-en-Yvelines, the Commissariat à l'Energie Atomique et aux Energies Renouvelables, the Centre National de la Recherche Scientifique, Thales Alenia Space and Veolia). We also would like to thank the partners of the ICOS infrastructure for providing a list of potential locations for future ICOS atmospheric sites. Edited by: W. Lahoz