Greenhouse gas observation network design for Africa Nickless, Alecia; Scholes, Robert J.; Vermeulen, Alex ...
Tellus. Series B, Chemical and physical meteorology,
01/2020, Letnik:
72, Številka:
1
Journal Article
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An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the ...uncertainty in emissions across three of the most important GHGs: CO
2
, CH
4
, and N
2
O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO
2
was driven by seasonality in net primary productivity. The solution for N
2
O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH
4
was consistent over different seasons. All solutions for CO
2
and N
2
O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zoétélé (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH
4
solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10ºN and 25ºS. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO
2
, 34.3% for CH
4
, and 32.5% for N
2
O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa.
Carbon monoxide (CO), carbon dioxide (CO2), and radiocarbon (14CO2) measurements have been made in Heidelberg from 2001 to 2004 in order to determine the regional fossil fuel CO2 component and to ...investigate the application of CO as a quantitative tracer for fossil fuel CO2 (CO2(foss)). The observations were compared with model estimates simulated with the regional transport model REMO at 0.5° × 0.5° resolution in Europe for 2002. These estimates are based on two available emissions inventories for CO and CO2(foss) and simplified atmospheric chemistry of CO. Both emissions inventories appear to overestimate fossil fuel emissions in the Heidelberg catchment area, in particular in summer and autumn by up to a factor of 2. Nevertheless, during meteorological conditions with high local source influence the CO/CO2(foss) emission ratios compared well with the observed atmospheric CO/CO2(foss) ratios. For a larger catchment area of several 100 km the observed CO/CO2(foss) ratio compared within better than 25% with the ratios derived from model simulations that take the transport from the sites of emission to the measurement station into account. Non‐fossil‐fuel CO emissions, production by volatile organic compounds, and oxidation, as well as soil uptake, turned out to add significant uncertainty to the application of CO as a quantitative fossil fuel CO2 surrogate tracer, so that 14CO2 measurements seem to be indispensable for reliable estimates of fossil fuel CO2 over the European continent.
The ICOS (Integrated Carbon Observation System) network of atmospheric measurement stations produces standardized data on greenhouse gas concentrations at 46 stations in 16 different European ...countries (March 2023). The placement of instruments on tall towers and mountains results in large influence regions ("concentration footprints"). The combined footprints for all the individual stations create a "lens" through which the network sees the European CO.sub.2 flux landscape. In this study, we summarize this view using quantitative metrics of the fluxes seen by individual stations and by the current and extended ICOS networks. Results are presented from both country level and pan-European perspectives, using open-source tools that we make available through the ICOS Carbon Portal. We target anthropogenic emissions from various sectors, as well as the land cover types found across Europe and their spatiotemporally varying fluxes. This recognizes different interests of different ICOS stakeholders. We specifically introduce "monitoring potential maps" to identify which regions have a relative underrepresentation of biospheric fluxes. This potential changes with the introduction of new stations, which we investigate for the planned ICOS expansion with 19 stations over the next few years.
Detailed 222radon (222Rn) flux maps are an essential pre-requisite for the use of radon in atmospheric transport studies. Here we present a high-resolution 222Rn flux map for Europe, based on a ...parameterization of 222Rn production and transport in the soil. The 222Rn exhalation rate is parameterized based on soil properties, uranium content, and modelled soil moisture from two different land-surface reanalysis data sets. Spatial variations in exhalation rates are primarily determined by the uranium content of the soil, but also influenced by soil texture and local water-table depth. Temporal variations are related to soil moisture variations as the molecular diffusion in the unsaturated soil zone depends on available air-filled pore space. The implemented diffusion parameterization was tested against campaign-based 222Rn soil profile measurements. Monthly 222Rn exhalation rates from European soils were calculated with a nominal spatial resolution of 0.083∘ × 0.083∘ and compared to long-term direct measurements of 222Rn exhalation rates in different areas of Europe. The two realizations of the222Rn flux map, based on the different soil moisture data sets, both realistically reproduce the observed seasonality in the fluxes but yield considerable differences for absolute flux values. The mean 222Rn flux from soils in Europe is estimated to be 10 mBq m-2 s-1 (ERA-Interim/Land soil moisture) or 15 mBq m-2 s-1 (GLDAS (Global Land Data Assimilation System) Noah soil moisture) for the period 2006–2010. The corresponding seasonal variations with low fluxes in winter and high fluxes in summer range in the two realizations from ca. 7 to ca. 14 mBq m-2 s-1 and from ca. 11 to ca. 20 mBq m-2 s-1, respectively. These systematic differences highlight the importance of realistic soil moisture data for a reliable estimation of 222Rn exhalation rates. Comparison with observations suggests that the flux estimates based on the GLDAS Noah soil moisture model on average better represent observed fluxes.
Correlations of nighttime atmospheric methane (CH4) and
222radon (222Rn) observations in Heidelberg, Germany, were
evaluated with the radon tracer method (RTM) to estimate the trend of annual
...nocturnal CH4 emissions from 1996–2020 in the footprint of the
station. After an initial 30 % decrease in emissions from 1996 to
2004, there was no further systematic trend but small inter-annual variations were
observed thereafter. This is in accordance with the trend of total
emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings
of Heidelberg and provides a fully independent top-down verification of the
bottom-up inventory changes. We show that the reliability of total
nocturnal CH4 emission estimates with the RTM critically depends on
the accuracy and representativeness of the 222Rn exhalation rates
estimated from soils in the footprint of the site. Simply using
222Rn fluxes as estimated by Karstens et al. (2015) could lead to
biases in the estimated greenhouse gas (GHG) fluxes as large as a factor of 2. RTM-based GHG flux estimates also depend on the parameters chosen for the
nighttime correlations of CH4 and 222Rn, such as the
nighttime period for regressions and the R2 cut-off value for the
goodness of the fit. Quantitative comparison of total RTM-based top-down flux
estimates with bottom-up emission inventories requires representative
high-resolution footprint modelling, particularly in polluted areas where
CH4 emissions show large heterogeneity. Even then, RTM-based
estimates are likely biased low if point sources play a significant role in
the station footprint as their emissions may not be fully captured
by the RTM method, for example, if stack emissions are injected above the top
of the nocturnal inversion layer or if point-source emissions from the
surface are not well mixed into the footprint of the measurement
site. Long-term representative 222Rn flux observations in the
footprint of a station are indispensable in order to apply the RTM method for
reliable quantitative flux estimations of GHG emissions from
atmospheric observations.
Correlations of nighttime atmospheric methane (CH.sub.4) and .sup.222 radon (.sup.222 Rn) observations in Heidelberg, Germany, were evaluated with the radon tracer method (RTM) to estimate the trend ...of annual nocturnal CH.sub.4 emissions from 1996-2020 in the footprint of the station. After an initial 30 % decrease in emissions from 1996 to 2004, there was no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of total emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg and provides a fully independent top-down verification of the bottom-up inventory changes. We show that the reliability of total nocturnal CH.sub.4 emission estimates with the RTM critically depends on the accuracy and representativeness of the .sup.222 Rn exhalation rates estimated from soils in the footprint of the site. Simply using .sup.222 Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gas (GHG) fluxes as large as a factor of 2. RTM-based GHG flux estimates also depend on the parameters chosen for the nighttime correlations of CH.sub.4 and .sup.222 Rn, such as the nighttime period for regressions and the R.sup.2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down flux estimates with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH.sub.4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station footprint as their emissions may not be fully captured by the RTM method, for example, if stack emissions are injected above the top of the nocturnal inversion layer or if point-source emissions from the surface are not well mixed into the footprint of the measurement site. Long-term representative .sup.222 Rn flux observations in the footprint of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.
We present an analysis of atmospheric transport impact on estimating CO.sub.2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm ...(LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO.sub.2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO.sub.2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO.sub.2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between -0.72 and 0.20 PgC yr.sup.-1, which is larger than the a priori uncertainty of 0.47 PgC yr.sup.-1 . The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr.sup.-1 ), in comparison with 0.23 and 0.10 PgC yr.sup.-1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO.sub.2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.
We present an analysis of atmospheric transport impact on
estimating CO2 fluxes using two atmospheric inversion systems
(CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm ...(LUMIA)) over Europe in 2018. The main focus of
this study is to quantify the dominant drivers of spread amid CO2
estimates derived from atmospheric tracer inversions. The Lagrangian
transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of
mesoscale transport. The impact of lateral boundary conditions for CO2
was assessed by using two different estimates from the global inversion
systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with
an ensemble of eight inversions differing in the regional and global
transport models, as well as the inversion systems, show a relatively large
spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is
larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies
in annual budget are primarily caused by differences in the mesoscale
transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion
systems, respectively. Additionally, varying the mesoscale transport caused
large discrepancies in spatial and temporal patterns, while changing the
lateral boundary conditions led to more homogeneous spatial and temporal
impact. We further investigated the origin of the discrepancies between
transport models. The meteorological forcing parameters (forecasts versus
reanalysis obtained from ECMWF data products) used to drive the transport
models are responsible for a small part of the differences in CO2
estimates, but the largest impact seems to come from the transport model
schemes. Although a good convergence in the differences between the
inversion systems was achieved by applying a strict protocol of using
identical prior fluxes and atmospheric datasets, there was a non-negligible
impact arising from applying a different inversion system. Specifically, the
choice of prior error structure accounted for a large part of
system-to-system differences.
In this study, we
investigated the regional contributions of carbon dioxide (CO2) at the
location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland,
3580 m a.s.l.). To this purpose, we ...combined receptor-oriented atmospheric
transport simulations for CO2 concentration in the period 2009–2017
with stable carbon isotope (δ13C–CO2) information. We
applied two Lagrangian particle dispersion models driven by output from two
different numerical weather prediction systems (FLEXPART–COSMO and
STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on
regional CO2 fluxes, to estimate atmospheric δ13C–CO2, and to obtain model-based estimates of the mixed source
signatures (δ13Cm). Anthropogenic fluxes were taken from a
fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem
fluxes were based on the Vegetation Photosynthesis and Respiration Model
(VPRM). The simulations of CO2, δ13C–CO2, and δ13Cm were then compared to observations performed by quantum
cascade laser absorption spectroscopy. The models captured around 40 % of
the regional CO2 variability above or below the large-scale background
and up to 35 % of the regional variability in δ13C–CO2.
This is according to expectations considering the complex Alpine topography,
the low intensity of regional signals at JFJ, and the challenging
measurements. Best agreement between simulations and observations in terms
of short-term variability and intensity of the signals for CO2 and
δ13C–CO2 was found between late autumn and early spring.
The agreement was inferior in the early autumn periods and during summer.
This may be associated with the atmospheric transport representation in the
models. In addition, the net ecosystem exchange fluxes are a possible source
of error, either through inaccuracies in their representation in VPRM for
the (Alpine) vegetation or through a day (uptake) vs. night (respiration)
transport discrimination to JFJ. Furthermore, the simulations suggest that
JFJ is subject to relatively small regional anthropogenic contributions due
to its remote location (elevated and far from major anthropogenic sources)
and the limited planetary boundary layer influence during winter. Instead,
the station is primarily exposed to summertime ecosystem CO2
contributions, which are dominated by rather nearby sources (within 100 km).
Even during winter, simulated gross ecosystem respiration accounted for
approximately 50 % of all contributions to the CO2 concentrations
above the large-scale background. The model-based monthly mean δ13Cm ranged from − 22 ‰ in winter to − 28 ‰ in summer and reached the most depleted values of − 35 ‰ at higher fractions of natural gas combustion, as well as the
most enriched values of − 17 ‰ to − 12 ‰ when impacted by
cement production emissions. Observation-based δ13Cm
values were derived independently from the simulations by a moving
Keeling-plot approach. While model-based estimates spread in a narrow range,
observation-based δ13Cm values exhibited a larger scatter and
were limited to a smaller number of data points due to the stringent
analysis prerequisites.
In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580ĝ€¯mĝ€¯a.s.l.). To this purpose, ...we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009-2017 with stable carbon isotope (δ13C-CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART-COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C-CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C-CO2, and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. The models captured around 40ĝ€¯% of the regional CO2 variability above or below the large-scale background and up to 35ĝ€¯% of the regional variability in δ13C-CO2. This is according to expectations considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C-CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions due to its remote location (elevated and far from major anthropogenic sources) and the limited planetary boundary layer influence during winter. Instead, the station is primarily exposed to summertime ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100ĝ€¯km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50ĝ€¯% of all contributions to the CO2 concentrations above the large-scale background. The model-based monthly mean δ13Cm ranged from -ĝ€¯22ĝ€¯‰ in winter to -ĝ€¯28ĝ€¯‰ in summer and reached the most depleted values of -ĝ€¯35ĝ€¯‰ at higher fractions of natural gas combustion, as well as the most enriched values of -ĝ€¯17ĝ€¯‰ to -ĝ€¯12ĝ€¯‰ when impacted by cement production emissions. Observation-based δ13Cm values were derived independently from the simulations by a moving Keeling-plot approach. While model-based estimates spread in a narrow range, observation-based δ13Cm values exhibited a larger scatter and were limited to a smaller number of data points due to the stringent analysis prerequisites.
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