Inaccurate representation of atmospheric processes by transport
models is a dominant source of uncertainty in inverse analyses and can lead
to large discrepancies in the retrieved flux estimates. We investigate the
impact of uncertainties in vertical transport as simulated by atmospheric
transport models on fluxes retrieved using vertical profiles from aircraft
as an observational constraint. Our numerical experiments are based on
synthetic data with realistic spatial and temporal sampling of aircraft
measurements. The impact of such uncertainties on the flux retrieved using
the ground-based network and those retrieved using the aircraft profiles
are compared. We find that the posterior flux retrieved using aircraft
profiles is less susceptible to errors in boundary layer height, compared
to the ground-based network. This finding highlights a benefit of utilizing
atmospheric observations made onboard aircraft over surface measurements for
flux estimation using inverse methods. We further use synthetic vertical
profiles of CO
Reliable prediction of climate change scenarios requires a thorough
understanding the carbon–climate feedbacks in the earth system, and
accurately estimating current sources and sinks of carbon is of prime
importance. While it is impossible to measure these sources and sinks
directly everywhere around the globe, we may estimate these using the
“top–down” approach employing atmospheric observations in combination with
knowledge of atmospheric transport and prior knowledge of the fluxes by
inverse modelling. The inverse modelling scheme exploits the fact that the
spatial and temporal variations of atmospheric trace gases like CO
Atmospheric transport models use meteorological input, like wind fields, to link the observed atmospheric concentrations of tracers to the estimated fluxes at the surface of the earth. These models are not able to perfectly simulate atmospheric transport processes, which results in uncertainties in the retrieved surface fluxes (Law et al., 1996, 2008; Gerbig et al., 2003; Stephens et al., 2007; Lauvaux et al., 2009; Houweling et al., 2010). One of the dominant sources of transport model uncertainty is the inaccurate representation of the vertical mixing near the surface of the earth and hence the boundary layer height (Stephens et al., 2007; Gerbig et al., 2008). An accurate simulation of the vertical mixing in the boundary layer is critical, since it is this part of the atmosphere that lies closest to the carbon sources and sinks and where most observations are made. Hence, misrepresentation of transport in the boundary layer can lead to significant biases in modelled tracer mixing ratios as well as the retrieved fluxes (Denning et al., 1996, 2008; Yi et al., 2004; Ahmadov et al., 2009).
Furthermore, a weak observational constraint due to insufficient atmospheric data is also an important factor that causes large errors in retrieved fluxes. Lack of measurements in the atmosphere or an unevenly distributed network of observation sites can result in a poorly constrained regional carbon budget (Gurney et al., 2002). Hence, in addition to improved transport models, an enhanced global network of atmospheric measurements is indispensable for more accurate and precise estimation of surface fluxes using inverse modelling.
The current global measurement network of greenhouse gases combines in situ measurements made by the ground-based stations and satellite instruments measuring total column mixing ratios remotely. While ground-based measurements are highly precise, the main limitation of these measurements is the sparse and uneven spatial coverage (Bousquet et al., 2006; Marquis and Tans, 2008). While parts of Europe and North America dispose of a fairly high data coverage from the surface-based observation network, the tropical regions of Amazonia, Africa, remote regions of tundra and Siberia are not adequately covered, sometimes even lacking measurements entirely. In addition, these measurements, except those obtained from tall towers, are often not representative of large areas and provide information only at the local scale (Haszpra, 1999). Satellites largely overcome this drawback of ground-based measurements since they have the ability to provide information around the world using a single instrument. However, they have their limitations too, which limits their use for accurate flux estimation using inverse methods. Space borne measurements are still somewhat limited by higher measurement uncertainty and systematic errors as well as temporal heterogeneity in their sampling (Ehret and Kiemle, 2005; Galli et al., 2014; Checa-Garcia et al., 2015)
The use of passenger aircraft as platforms for obtaining information about
the physical and state and chemical composition of the atmosphere is a
rather new concept. IAGOS (In-service Aircraft for a Global Observing
System) is a European research infrastructure that deploys sensors on
commercial airliners that make regular in situ measurements of the
atmosphere. The project is an extension and continuation of the MOZAIC
(measurement of ozone and water vapour by Airbus in-service aircraft)
project (Marenco et al., 1998) that was initiated in the year 1993. Detailed
and continuous measurements are made during long-distance flights by
onboard instruments, thus providing a view of the horizontal and vertical
distribution of the measured trace gases at high temporal and spatial
resolution. The last MOZAIC aircraft was deactivated in October 2014;
currently, six IAGOS aircraft are flying. IAGOS provides observations with
applications in the field of atmospheric modelling and for validation of
satellite observations. There are a number of species that are currently
being measured by IAGOS aircraft, like CO, O
Some recent studies have utilized measurements made onboard commercial
aircrafts in order to better understand their impact on the dynamics of the
carbon cycle. Niwa et al. (2012) examined the impact of passenger aircraft
based on measurements from CONTRAIL (Comprehensive Observation Network for
Trace gases by Airliner) on the overall carbon budget constraint and the
flux uncertainties. Patra et al. (2011) used measurements from the CARIBIC
(Civil Aircraft for the Regular Investigation of the atmosphere Based on an
Instrument Container) project as well as the CONTRAIL project to estimate
regional CO
In this paper we employ synthetic data to investigate theoretical impacts of transport model uncertainties associated with boundary layer height on the fluxes retrieved by using passenger aircraft profiles in an inverse modelling set-up. The synthetic data are generated using a forward run of the TM3 transport model (Heimann and Körner, 2003) and have the temporal and spatial sampling of the measurements made during the MOZAIC project. We examine how closely the posterior flux obtained using the synthetic aircraft measurements as constraint captures the trends and variability in the flux is used to generate the synthetic data. This allows us to estimate the impact of the inaccurate, simulated vertical mixing.
In the second part of this work, we assess the potential of CO
The paper is organized as follows: Section 2 describes the methods used that include estimation of the model representation error (Sect. 2.1), description of the inversion scheme (Sect. 2.2) and the experimental set-up (Sect. 2.3); Section 3 presents the results from the simulations, and the conclusions are discussed in Sect. 4.
The Jena inversion scheme (Rödenbeck, 2005)
is a Bayesian inversion
framework that is used to estimate trace gas fluxes at the surface of the
earth from measured atmospheric concentrations and knowledge of atmospheric
transport. It employs the global atmospheric tracer model TM3 to simulate
atmospheric transport (Heimann and Körner, 2003). In this study, our
model simulations are carried out at a 4
In the following paragraphs, we provide a brief description of the inversion
system described in more detail in Rödenbeck (2005). Observed
atmospheric mixing ratios,
The existing observation network consists of a number of ground-based stations that measure at different temporal frequencies. While stations based on flask observations have measurements made once per day or once per week, there also exist a growing number of continuously measuring stations with data provided typically half hourly or hourly. For the aircraft profiles, the profile measurements are made over a period of approximately 30–40 min during the ascent or descent of the aircraft. Therefore, many of the measurements made by surface stations in a single day or in a single aircraft profile cannot be treated as independent of each other. This means that the errors of such measurements are likely to be correlated with each other over certain temporal scales. To account for this fact in the simulations, the error of correlated measurements is enhanced (or “inflated”), so that their contribution to the cost function is reduced. In this way the impact of continuous observations from a single station has a comparable impact on the cost function to less frequent flask observations from another station.
In the Jena inversion scheme, these error correlations between measurements
are accounted for using a data density “de-weighting” scheme. It assigns a
weight to the error associated with every measurement computed based on
certain pre-defined criteria. For surface network sites, to avoid a higher
impact of the more frequent continuous observations compared to the less
frequent flask observations, the data density weighting considers, for every
observation, the number of observations,
The aircraft is a moving platform, which means that the aircraft profiles
span a considerable horizontal and vertical distance while making
measurements. Therefore, in contrast to a fixed station, the CO
Temporal de-correlation length is taken to be 1 week, to be consistent with the treatment of the station data.
Horizontal spatial de-correlation distance is set at
We use these values of spatial correlation lengths since they are comparable to the grid size that we use for our simulations and sub-grid scale processes cannot be resolved by the transport model. The 700 mbar pressure level represents approximately the maximum of a typical boundary layer height and separates the boundary layer part of the atmospheric column (which is more closely coupled to surface fluxes by fast vertical mixing and hence has a shortened correlation length) from the free troposphere part of the column.
Model representation error or model data mismatch (mdm) can be defined as the mismatch between point observations assimilated in the model and the model-simulated spatial averages (Engelen et al., 2002). This error needs to be pre-specified in the inversion framework. In our model, we use a representation error that varies with altitude. This is because the mismatch is likely to be higher for measurements that lie closer to the surface while the models perform better for higher altitudes that are not affected as directly by the fluxes. The functional dependency of the mismatch with altitude is computed using data from the CONTRAIL project (Machida et al., 2008).
We compute the dependency of the mismatch on altitude using data from the
CONTRAIL project (Machida et al., 2008). For this, we compare observations
from CONTRAIL against TM3 “reanalysed CO
Box plot showing the model data mismatch between the TM3-analysed
CO
Synthetic data at the times and locations of the MOZAIC profiles and the ground network sites are generated to both investigate the impact of boundary layer height errors and assess the impact that the addition of aircraft observations has on flux retrievals. For the forward run, we use fluxes from the Biome-BGC biosphere model (Thornton et al., 2005) in order to get realistic mixing ratios at the locations of aircraft profiles and the surface stations. These fluxes form our “true flux”. The MOZAIC aircraft profiles consist of measurements provided at approximately every 150 m altitude starting at 75 m and going typically up to an altitude of 9–10 km. We choose not to use the cruise level data for this study because of the fact that most of these measurements are made around the tropopause region, and the model skill in accurately representing the transport at that altitude and linking those measurements via vertical transport to fluxes at the surface is limited (Deng et al., 2015).
Since the profiles generated by the forward run of the transport model use
the ERA-Interim meteorology, the boundary layer height represented by these
profiles is that of ERA-Interim. We call this the “true” boundary layer
height, BLH
The effect of vertical-mixing errors in transport models on flux retrieval
is analysed with three groups of experiments.
simulation with only the surface-based observation network. simulation using only the IAGOS aircraft profiles. simulation with the combined network – the surface-based observation
network augmented with the measurements from IAGOS. Original profiles (control case). Reshuffled profiles.
For each of these simulations, we further carry out two types of inversions:
Experiments S (a), A (a) and C (a) represent scenarios where the boundary
layer height is well known. Experiments S (b), A (b) and C (b) simulate the
realistic case where the vertical mixing in the transport model is imperfect.
The monthly posterior fluxes are analysed for 1 year (2000). The surface
network consists of 49 sites (Fig. 2a), and the IAGOS observation network
consists of measurements from five IAGOS aircraft (Fig. 2b). The prior flux
used for the inverse simulations is different and independent from the true
flux used to generate the pseudo data and is obtained from the
Lund–Potsdam–Jena (LPJ) dynamic global vegetation model (Sitch et al., 2003).
In the second part of the study, we estimate the reduction in posterior flux
uncertainty brought about by the use of IAGOS vertical profiles as a
constraint on the carbon budget. We carry out simulations where the
surface-based observation network is augmented by one or more IAGOS
aircraft. These simulations do not require the synthetic data that are used
in the first part of this study, since the inversion system solves for the
resultant posterior flux uncertainties based upon only the measurement time,
location and the uncertainties of the prior fluxes and the measurements
(model data mismatch). The uncertainty reduction is computed for the monthly
mean posterior fluxes aggregated over the TransCom3 land regions (Gurney et
al., 2000). It is expressed as the following:
Figure 3 shows the prior uncertainty used by the Jena inversion scheme for the different TransCom3 regions. We focus on the years 1996–2004 because of sufficient data availability from MOZAIC during this period. This period also has some data gaps representing times when one or more aircraft are not flying. This helps give a more realistic quantification of the uncertainty reduction brought about by the use of these data.
Prior flux uncertainty for the TransCom3 regions (in
PgC year
We analyse monthly posterior fluxes for the TransCom3 land regions and compare them to our true flux, which is the flux that is used to generate our pseudo data. We concatenate the time series of the posterior flux for all regions to form a single time series in order to obtain a single diagnostic metric for the whole globe. The statistics for comparison between the different simulations are represented on a Taylor diagram as shown in Fig. 4.
Taylor diagram showing the correlation coefficient, standard
deviation and root-mean-square difference of the concatenated time series of
the monthly posterior fluxes from the TransCom3 land regions. Standard
deviation of the time series is depicted on the vertical axis while the
correlation coefficient with respect to the true flux time series is shown on
the circular arc of the diagram. The root-mean-square difference of the time
series is shown on the green arcs. Points S, A and C represent the simulations
using measurements from only the surface stations, only the aircraft profiles
and the combined network (surface
Spatial maps showing the reduction in monthly CO
We see that the transport model errors related to vertical mixing, as
simulated using the reshuffling method, affect the flux retrieved from
measurements made at surface stations differently than those retrieved using
aircraft profiles. We observe that there is a large impact of the simulated
vertical-mixing errors on the flux retrieved using the surface measurements
with and without the boundary layer height uncertainties incorporated in the
experiments as shown by points Sb and Sa respectively. The posterior flux
standard deviation, root-mean-square difference and correlation coefficient
values with respect to the true flux change from 1.90, 0.65 and
0.95 PgC year
Points Ca and Cb in Fig. 4 show the impact of the boundary layer error on the flux retrieved using the combined observation network that uses measurements from both the surface network and the aircraft profiles. By using the combined observation network, a similar sensitivity of the posterior flux to boundary layer uncertainty is observed as by the surface-based network alone (Points Sa and Sb). This similarity in sensitivity of posterior flux between simulation types C and S, shows that the effect of the surface network dominates the flux retrieval from the observations using the combined network and indicates that the surface network stations largely contribute to the sensitivity of the retrieved flux to the uncertainty of the boundary layer height. It can also be seen that the addition of aircraft measurements leads to an improved estimate of the surface flux. This is shown by points Ca and Cb being closer to the true flux than points Sa and Sb respectively. It implies that the addition of the aircraft measurements to the surface-based network improves the constraint on the carbon budget as compared to the surface network alone.
In this section, we evaluate the utility of aircraft measurements of
CO
Figure 5a shows the flux uncertainty reduction of the monthly mean flux over the TransCom3 regions when only the surface-based observational network is used in the inversion. The largest constraint due to the surface network alone is observed in Europe and North America. The European and temperate North American regions have a dense and extensive network of surface observations and hence the reduction in flux uncertainty is as high as about 85 %. In addition, remote observations are also responsible for bringing about a constraint on the fluxes in the neighbouring regions due to the effect of wind transport (horizontal advection). For instance, the value of the uncertainty reduction over North American boreal regions (75 %) is high despite insufficient surface stations in that region. This can be attributed to the impact of the westerly winds flowing over temperate North America. West winds mean that observations in these regions are sensitive to boreal fluxes. Using the same argument, dense observations over Europe can help constrain surface fluxes from the Eurasian boreal region due to the effect of transport (advection) by the westerlies.
Figure 5b shows the uncertainty reduction only due to the pseudo profiles from five simulated IAGOS aircraft. Europe and the temperate North American regions show an uncertainty reduction of about 70 %. These regions are where most of the aircraft profiles are measured due to large air traffic between the two continents by the airlines participating in MOZAIC–IAGOS. These measurements are also able to constrain boreal North America (70 %) and boreal Eurasia (55 %), regions with little or no MOZAIC–IAGOS measurements. The African continent shows a high reduction in flux uncertainty (75 %). Regions of South America and tropical Asia exhibit a low constraint ranging between 20 and 35 %, due to fewer aircraft profiles measured in these regions in addition to the impact of advection by the easterly winds.
Figure 5c shows the uncertainty reduction map for the case when pseudo
profiles IAGOS aircraft are added to the surface-based network. The combined
observation network almost completely constrains the regions of Europe and
temperate North America, with the uncertainty reduction value being close to
90 %. Tropical Asia is the least constrained by the combined network
since it is not adequately covered by either of the networks – surface or the
passenger aircraft. The net impact of adding the profiles from IAGOS to the
existing network is shown in Fig. 5d, which is the difference between the
uncertainty reduction values for the TransCom3 land regions with and without
the aircraft profiles. Tropical and Eurasian temperate regions show the
greatest change in the uncertainty reduction of the posterior fluxes on
the addition of pseudo observations from IAGOS (about 7 to 10 %). These are
regions that are poorly constrained by the surface-based network. So,
the addition of aircraft measurements results in the largest improvement in
posterior flux uncertainty in these regions. On the other hand, for regions
already well constrained by the surface network, for example North America
and Europe, the simulated constraint due to the IAGOS CO
Plots showing change in uncertainty reduction (with respect to the
surface network) against the number of measurements from IAGOS aircraft for
the
We further investigated the constraint due to the aircraft measurements on
aggregated spatial scales by examining the change in uncertainty reduction on
the addition of pseudo measurements from IAGOS for the Northern Hemisphere
(30 to 90
Transport models that drive the inversion schemes often have a poor representation of the near-surface vertical mixing causing large errors in the retrieved fluxes. In this study, we investigate the impact of such transport model uncertainties on the fluxes simulated using aircraft profiles as constraint in an inverse modelling set-up. We focus only on errors in near-surface vertical mixing. Those due to imperfect representation of other processes, like advection and deep convection, have not been accounted for. Our simulations show that the flux retrieved using aircraft profiles when the boundary layer height is well known has the same statistical metrics as the flux retrieved when the boundary layer height is erroneous. This shows that posterior fluxes retrieved using aircraft profiles show no sensitivity to the boundary layer height errors as simulated in our experiments. We compare this behaviour of the retrieved flux to that obtained using the surface measurements as constraint. These measurements are usually in the boundary layer part of the atmosphere; therefore, we find a much higher mismatch between the flux retrieved using correct versus erroneous boundary layer height in terms of the standard deviation, root-mean-square difference and correlation parameters. In other words, this mismatch shows that the transport model uncertainties related to boundary layer height are very likely to be translated to the posterior flux when surface measurements are used as constraint in the inversion, while these errors are not propagated to the retrieved flux when the aircraft profiles are used. This difference in the response of the flux retrieved using the two observation networks is likely to be due to the fact that vertical transport, whose effect we simulate by the redistribution of the tracer mass in the model profile at the location of the airports and surface stations, only redistributes the tracer mass between the boundary layer height and the free tropospheric part keeping the total tracer mass constant. The loss (or gain) of the tracer mass in the profile in the boundary layer part of the profile is compensated by the gain (or loss) in the free tropospheric part of the profile. Since aircraft profile measurements extend all the way from the surface to the free tropospheric part of the atmosphere, the net impact of the complete reshuffled profile remains comparable to that of the original. This effect of redistribution, on the other hand, is not observed for the surface station measurements since they are made within the boundary layer, and hence the error in the estimation of the boundary layer height will impact the modelled mixing ratio that constrains the inversion. These results demonstrate the benefit of aircraft measurements over those made by ground-based stations for flux estimation using transport models that cannot resolve the boundary layer perfectly. Although we only account for errors in fluxes due to vertical mixing in our simulations, we can say that flux estimation using aircraft profiles is expected to be more robust when aircraft profiles are used as constraint, since the contribution of the boundary layer height uncertainty to the overall transport model error is likely to decline. While improved transport models are an imperative for achieving more accurate estimates of surface fluxes, the potential benefit of aircraft profiles over ground-based measurements, as shown by our simulations, provides a simple and flexible approach to dealing with and eliminating the impact of boundary layer height uncertainties due to vertical mixing and diminishing the overall impact of transport model errors on retrieved fluxes. In addition to this, aircraft profiles would also provide valuable information to drive model development.
Furthermore, upon estimating the impact that the CO
We must bear in mind that since the MOZAIC–IAGOS aircraft profiles are
measured near the airports, which form areas of high anthropogenic emissions,
it is likely that these observations are not truly representative of large
areas. This fact has been taken into account, in this study, in a
conservative way by estimating the model data mismatch uncertainty using the
difference between CO
In summary, our results demonstrate the benefit and application of aircraft profile measurements in an inverse modelling framework. In the near future, increased numbers of aircraft profiles of greenhouse gases are expected to be available. Hence, exploiting the potential advantage of this new data stream for inverse modelling studies can go a long way to developing a better understanding of carbon cycle dynamics in hitherto under-sampled regions of the world.
The MOZAIC/IAGOS flight tracks and profile data can be accessed
at
The authors declare that they have no conflict of interest.
The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 312311 for the IGAS project (IAGOS for the GMES Atmospheric Service). The article processing charges for this open-access publication were covered by the Max Planck Society. Edited by: Y. Qian Reviewed by: two anonymous referees