Sparse data coverage in the Arctic hampers our
understanding of its carbon cycle dynamics and our predictions of the fate
of its vast carbon reservoirs in a changing climate. In this paper, we
present accurate measurements of atmospheric carbon dioxide (CO2) and methane (CH4) dry air
mole fractions at the new atmospheric carbon observation station Ambarchik,
which closes a large gap in the atmospheric trace gas monitoring network in
northeastern Siberia. The site, which has been operational since August 2014, is located
near the delta of the Kolyma River at the coast of the Arctic Ocean. Data
quality control of CO2 and CH4 measurements includes frequent
calibrations traced to World
Meteorological Organization (WMO) scales, employment of a novel water vapor
correction, an algorithm to detect the influence of local polluters, and
meteorological measurements that enable data selection. The available
CO2 and CH4 record was characterized in comparison with in situ
data from Barrow, Alaska. A footprint analysis reveals that the station is
sensitive to signals from the East Siberian Sea, as well as the northeast
Siberian tundra and taiga regions. This makes data from Ambarchik highly
valuable for inverse modeling studies aimed at constraining carbon budgets
within the pan-Arctic domain, as well as for regional studies focusing on
Siberia and the adjacent shelf areas of the Arctic Ocean.
Introduction
Detailed information on the distribution of sources and sinks of the
atmospheric greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) is a prerequisite
for analyzing and understanding the role of the carbon cycle within the
context of global climate change. The Arctic plays a unique role in the
carbon cycle because it hosts large carbon reservoirs preserved by cold
climate conditions (Hugelius
et al., 2014; James et al., 2016; Schuur et al., 2015). However, the net budgets
of both terrestrial (Belshe et al., 2013;
McGuire et al., 2012) and oceanic (Berchet
et al., 2016; Shakhova et al., 2014; Thornton et al., 2016) carbon
surface–atmosphere fluxes are still highly uncertain, as are the mechanisms
controlling them. Furthermore, the Arctic is subject to faster warming than
the global average at present and this is predicted to continue in the coming decades (IPCC,
2013). Thus, a considerable fraction of terrestrial (Schuur
et al., 2013) and subsea (James et al., 2016)
permafrost carbon reservoirs is at risk of being degraded and released under
future climate change. The fate of further carbon reservoirs in the Arctic
seabed is also uncertain under warmer conditions. A substantial release
of the stored carbon in the form of CO2 and CH4 would constitute a
significant positive feedback, enhancing global warming. Therefore, improved
insight into the mechanisms that govern the sustainability of Arctic carbon
reservoirs is essential for the assessment of Arctic carbon–climate
feedbacks and the simulation of accurate future climate trajectories.
A key limitation for understanding the carbon cycle in the Arctic is limited
data coverage in space and time (Oechel et al.,
2014; Zona et al., 2016). Besides infrastructure limitations, the
establishment of long-term, continuous, and high-quality measurement programs
at high latitudes is severely challenged by the harsh climatic conditions,
especially in the cold season (Goodrich
et al., 2016). During the Arctic winter, even rugged instrumentation may
fall outside its range of applicability, and measures may be required to
prevent ice buildup and instrument failure without compromising data quality
(Kittler et al., 2017a). Furthermore, many sites are difficult to
access for large parts of the year, complicating regular maintenance and
therefore increasing the risk of data gaps due to broken or
malfunctioning equipment.
A widely used approach to quantify carbon fluxes on a regional scale builds
on measurements of atmospheric CO2 and CH4 mole fractions and
inverse modeling of their transport in the atmosphere (Miller
et al., 2014; Peters et al., 2010; Rödenbeck et al., 2003; Thompson et
al., 2017). The performance of inverse models to constrain
surface–atmosphere exchange processes depends on the accuracy of atmospheric
trace gas measurements. Because biases in the measurements (e.g., drift in
time or bias between stations) translate into biases in the retrieved fluxes (Masarie
et al., 2011; Peters et al., 2010; Rödenbeck et al., 2006), the World
Meteorological Organization (WMO) has set requirements for the
interlaboratory compatibility of atmospheric measurements: ±0.1 ppm
for CO2 in the Northern Hemisphere and ±0.05 ppm in the Southern
Hemisphere, and ±2 ppb for CH4 (WMO, 2016).
Atmospheric inverse modeling has a high potential for providing insights
into regional to pan-Arctic-scale patterns of CO2 and CH4 fluxes,
as well as their seasonal and interannual variability and long-term trends.
The technique could also serve as a link between smaller-scale,
process-oriented studies based, e.g., on eddy-covariance towers (Euskirchen
et al., 2012; Kittler et al., 2016; Zona et al., 2016) or flux chambers (e.g.,
Kwon et al., 2017; Mastepanov et al., 2013) and the coarser-scale
satellite-based remote sensing retrievals of Arctic ecosystems and carbon
fluxes (e.g.,
Park et al., 2016). However, to date, sparse data coverage limits the
spatiotemporal resolution and the accuracy of inverse modeling products at
high northern latitudes. To improve inverse model estimates of high-latitude
GHG surface–atmosphere exchange processes, the existing atmospheric carbon
monitoring network (Fig. 1) needs to be expanded
(McGuire et al., 2012).
In this paper, we present the new atmospheric carbon observation station
Ambarchik, which improves data coverage in the Arctic. The site is located
in northeastern Siberia at the mouth of the Kolyma River (69.62∘ N,
162.30∘ E) and has been operational since August 2014. In Sect. 2, we introduce the station location and
instrumentation, and in Sect. 3 the quality control
of the data is presented. We characterize which areas the station is sensitive to in
Sect. 4, and present a signal characterization of
the available record in Sect. 5. Section 6 contains concluding remarks.
Stations observing atmospheric CO2 and CH4 in northeastern
Siberia (including Barrow, Alaska). At all stations but Yakutsk, continuous
in situ monitoring takes place. At Yakutsk, flasks are sampled monthly
onboard an aircraft. At Tiksi and Barrow, flasks are sampled by NOAA in
addition to the continuous in situ measurements.
Station descriptionArea overview
Ambarchik is located at the mouth of the Kolyma River, which opens to the
East Siberian Sea (69.62∘ N, 162.30∘ E;
Fig. 2). The majority of the landscape in the
immediate vicinity of the locality is wet tussock tundra. At the ecoregion
scale, Ambarchik is bordered by Northeast Siberian Coastal Tundra ecoregion
in the west, the Chukchi Peninsula Tundra ecoregion in the east, and the
Northeast Siberian Taiga ecoregion in the south
(ecoregion definitions from Olson et al.,
2001). Major components contributing to the net carbon exchange processes in
the area are tundra landscapes including wetlands and lakes, as well as the
Kolyma River and the East Siberian Arctic Shelf.
Ambarchik station location. Background based on Copernicus Sentinel
data from 2016.
Site overview
Ambarchik hosts a weather station operated by the Russian meteorological
service (Roshydromet), whose staff is the entire permanent population of the
locality. The closest town is Chersky (∼100 km to the south, with a
population of 2857 as of 2010), with no other larger permanent settlement
closer than 240 km. Thus, the site does not have any major sources of
anthropogenic greenhouse gas emissions in the nearby. The only regular
anthropogenic CO2 and potential CH4 sources that may influence
the measurements are from the Roshydromet facility, including the building
that hosts the power generator and the inhabited building.
The atmospheric carbon observation station Ambarchik began operation in
August 2014. It consists of a 27 m tall tower with two air inlets and
meteorological measurements, while the majority of the instrumentation is
hosted in a rack inside a building. The rack is equipped for temperature
control, but due to the risk of overheating, it is open most of the time and
thus in equilibrium with room temperature (room and rack temperature are
monitored). Atmospheric mole fractions of CH4, CO2, and H2O
are measured by an analyzer based on the cavity ring-down spectroscopy
(CRDS) technique (G2301, Picarro Inc.), which is calibrated against
WMO-traceable reference gases at regular intervals (Sect. 3.2). The tower is located 260 m from the
shoreline, with a base elevation of 20 m a.s.l. (estimated based on
GEBCO_2014 (Weatherall et al., 2015), which
in this region is based on GMTED2010; Danielson and Gesch, 2011).
Gas handling
The measurement system allows for switching between two different air inlets and
four different calibration gas tanks (Fig. 3).
Component manufacturers and models of the individual components are listed
in Table A1.
Air inlets with rain guards are mounted on the tower at 27 (“top”) and 14
(“center”) m a.g.l., respectively, and are equipped with 5 µm
polyester filters (labels F1 and F2 in Fig. 3). The
two air inlets are probed in turns (15 min top; 5 min center).
Signals from the center inlet are mainly used for quality control purposes
(Sect. 3.4). Air is drawn from the inlets (I1, I2)
through lines of flexible tubing (6.35 mm outer diameter) by a piston pump
located downstream of the measurement line branch (PP1). The cycles of the
pump are smoothed by a buffer with a volume of about 5 L. The combined
flow through both inlet lines is about 17 L min-1, monitored by a flowmeter
(FM1) and limited by a needle valve (NV1). The tubing enters the house at a
distance of about 15 m from the tower. The air passes 40 µm stainless
steel filters (F3, F4), behind which the sample line is branched from the
high flow line. A solenoid valve (V1) is used to select between the two
inlets.
The sample line (between filters F3/F4 and the CRDS analyzer) is composed
exclusively of components made of stainless steel; they include tubing (ss
tube 1/8′′), two 2 µm filters (F5, F6), a needle valve for sample flow
regulation (NV2, usually fully open), a pressure sensor (P1), and a flowmeter (FM2). Air is drawn from the high flow line into the sample line by a
membrane pump downstream of the CRDS analyzer (MP1). The nominal flow rate
in the sample line is 170 mL min-1. The residence time of sample air in
the tubing between inlets and CRDS analyzer is on the order of 12 s.
Calibration gases also pass through a line composed exclusively of stainless
steel components. Air from gas tanks (“high”, “middle”, “low”, and “target”)
passes through pressure regulators (RE1–4), reducing their pressure
to roughly ambient pressure. This way, the CRDS analyzer can cope with the pressure
difference between sample air and calibration air from the tanks without an
open split, which would normally be installed to equilibrate the line with
ambient pressure. This setup was chosen in order to conserve calibration
air. The lines from the gas tanks are connected to a multiposition valve
(MPV1), which is used to select between gas tanks. Downstream of the
multiposition valve, the calibration gas line is connected to the sample
line by a solenoid valve (V3). The solenoid valves V2 and V3 are used to
select between sample air from the tower and calibration air.
During calibrations, the part of the measurement line that is not part of
the calibration line is continuously flushed by the high flow pump (PP1)
through the purge line, which comprises solenoid valve V4 (which shuts off
air flow from the gas tanks through the purge line in case of a power outage
during a tank measurement), needle valve NV3 (which is used to match the
purge flow to the usual sample flow), and flowmeter FM3 (which monitors the
purge flow).
The flowmeters (FM1–3) and pressure sensor (P1) are used to diagnose
problems such as weakening pump performance, clogged filters, leaks, or
obstructions.
The gas handling system was tested for leaks after installation. This was
done by capping the tubing and evacuating it using a hand pump to pressures
of 0.3–0.4 bar (normal operating pressure is around 0.7 bar). The leak rate
was then computed from the pressure increase over several hours, corrected for
temperature fluctuations measured in the lab. To mitigate the effect of
inhomogeneous temperature fluctuations throughout the tubing and increase
sensitivity of the pressure to small leaks, the experiments were limited to
the small tubing volume inside the laboratory, ignoring the tubing on the
tower. This is the part that is most susceptible to leaks, due to the number
of tubing connections and the potentially higher CO2 mole fractions.
The results of several such experiments indicated leak rates on the order of
no more than 1.3×10-6 mbar L s-1. At this rate, CO2
and CH4 contamination is negligible even with extremely high mole
fractions in the laboratory. During later maintenance visits, simpler leak
tests, which did not require opening tubing connections, were performed by
breathing on individual connectors and observing the CO2 mole fraction
measured by the gas analyzer. No indications of leaks were observed during
these tests.
Air flow diagram of Ambarchik greenhouse gas measurement system.
See Sect. 2.3 for a description of component
abbreviations. “ss” refers to stainless steel throughout the diagram.
Meteorological measurements
Meteorological measurements performed by the Max Planck Institute for Biogeochemistry (MPI-BGC) at Ambarchik include wind
speed and direction at 20 m a.g.l., air temperature and humidity at 20 and 2 m a.g.l., and air pressure at 1 m a.g.l. (instruments listed in
Table A2). The measurements mainly serve to
monitor atmospheric conditions like wind and stability of atmospheric
stratification for quality control of the GHG data (described in Sect. 3.4). The 2-D sonic anemometer, which is used to
measure wind speed and direction, features a built-in heating to prevent
freezing. The heating is switched on if the temperature decreases below 4.5 ∘C and relative humidity is higher than 85 %, and switched off
when temperatures increase above 5.5 ∘C.
Power supply
Power is supplied by the diesel generator of the Roshydromet meteorological
station. Power consumption of the MPI-BGC measurement system is about 350 W,
and an additional 125 W is required when the heating of the sonic
anemometer is switched on. In order to avoid loss of power during routine
generator maintenance, an uninterruptible power supply (9130 UPS, Eaton) was
installed, which is able to buffer power outages of up to about 40 min
(the heating of the sonic anemometer is not powered by the UPS). In the case of
a longer power loss, the UPS initiates a controlled shutdown of the CRDS
analyzer.
Data logging
Trace gas measurements and related data are logged by the factory-installed
software of the CRDS analyzer. All other measurements are logged by an
external data logger (CR3000, Campbell Scientific). The logger samples all
variables every 10 s. Raw samples are stored for wind measurements as
well as flow and pressure in the tubing (FM1–FM3, P1). Of the remaining
meteorological measurements, room and rack temperature, and diagnostic
variables, 10 min averages are stored. The data are transferred from the
external data logger to the hard drive of the CRDS analyzer daily. All data
are backed up to an external hard drive hourly. The internal clocks of the
CRDS analyzer and the data logger are synchronized with a GPS receiver (GPS
16X-HVS, Garmin) once per day.
Quality controlWater correction
In order to minimize maintenance efforts and reduce the number of components
prone to failure, CO2 and CH4 mole fractions are measured in humid
air. Hence, the values reported by the analyzer have to be corrected for the
effects of water vapor to obtain dry air mole fractions. This is done by
applying a water correction function to the raw data:
cdry=cwethfch
Here, cwet is the mole fraction of CO2 or
CH4 in humid air reported by the analyzer, h is the water vapor mole
fraction (also measured by the CRDS analyzer), fch is the water correction function, and cdry
is the desired dry air mole fraction. Picarro Inc. provides a factory water
correction based on Chen et al. (2010), but to achieve
accuracies within the WMO goals for water vapor mole fractions above 1 %
H2O, custom coefficients must be obtained for each analyzer
(Rella et al.,
2013). Here, we employ the novel water correction method by Reum et al. (2019). In Reum et al. (2019), data from gas washing bottle
experiments (explained in Appendix B) with the CRDS
analyzer in Ambarchik were analyzed in the context of the new method
(labeled “Picarro no. 5” therein). Here, we use these data and
data from additional experiments to derive water correction coefficients for
application to the complete Ambarchik record. The results of this
procedure are briefly summarized here, and more details are given in
Appendix B.
Water correction experiments were performed in 2014, 2015, and 2017.
Differences between the water corrections based on the different experiments
were on the order of magnitude of the WMO goals
(Fig. 4). Here, we chose the WMO internal
reproducibility goals as a reference, which correspond to half of the
interlaboratory compatibility goals (WMO, 2016). The motivation
for this choice is that keeping biases of observations with respect to the
calibration scale within these goals ensures that biases between stations
are within the interlaboratory compatibility goals. Given the small number
of water correction experiments conducted so far, it is unknown whether
these differences represent drifts over long timescales, short-term
variations, and/or systematic differences between the experimental methods.
Stavert et al. (2019) found
that variability among weekly water correction tests over 3 months was
similar to that of annual tests over 2 years. This indicates that the
differences of the Ambarchik analyzer could be short-term variations. In the
absence of evidence for trends, water correction coefficients were derived
based on the averages of the individual water correction function responses
for each species (see Appendix B). The maximum
deviations of the individual functions to these synthesis functions were
0.018 % CO2 at 3 % H2O, which corresponds to 0.07 ppm at 400 ppm dry air mole fraction, and 0.034 % CH4 at 2.7 % H2O,
which corresponds to 0.7 ppb at 2000 ppb dry air mole fraction
(Fig. 4).
Differences between individual water correction functions and the
synthesis water correction function at dry air mole fractions of 400 ppm CO2 and 2000 ppb CH4. The dashed lines correspond to the WMO
internal reproducibility goals, in the case of CO2 in the Northern
Hemisphere (WMO, 2016).
Calibration
Calibrations are performed with a set of pressurized dry air tanks filled at
the Max Planck Institute for Biogeochemistry (Jena, Germany). The levels of
GHG mole fractions of these tanks have been traced to the WMO scales X2007
for CO2 and X2004A for CH4 (Table C1).
Three calibration tanks (in order high, middle, and low) are probed once every
116 h for 15, 10, and 10 min, respectively. The longer probing time
of the first (high) tank serves to flush out residual water vapor due to
water molecules that adhere to the inner tubing walls. Thus, residual water
vapor during tank measurements is well below 0.01 % H2O. From these
three tanks, coefficients for linear calibration functions are derived. Due
to the scatter of the coefficients over time, the coefficients are smoothed
using a tricubic kernel with a width of 120 d
(Fig. C1). Individual measurements are calibrated
by applying the smoothed coefficients, interpolated linearly in time. The
impact of the smoothing on the calibration of ambient mole fractions is
smaller than 0.02 ppm CO2 and 0.3 ppb CH4 (1 standard
deviation). The fourth tank (target) is probed every 29 h for 15 min. Its calibrated CO2 and CH4 mole fraction measurements
(Fig. 5) serve as quality control of the
calibration procedure (Sect. 3.3). Uncertainties
associated with the calibration procedure, as well as possible future
improvements, are discussed and quantified in
Appendix E.
Target tank bias over time for CO2 and CH4. As in
Fig. 4, the dashed lines correspond to the WMO
internal reproducibility goals.
Uncertainty in CO2 and CH4 measurements
Measurement uncertainties in the CO2 and CH4 data arise from
instrument precision, the calibration, and the water correction. We estimated
time-varying uncertainties of hourly trace gas mole fraction averages based
on the method by Andrews et al. (2014),
with some modifications. Details of the procedure are given in
Appendix E.
Average uncertainties at the 1σ level were 0.085 ppm CO2 and 0.77 ppb CH4. Both were dominated by the variability between the water vapor
correction experiments. The contribution of analyzer signal precision for
averages over 1 h to these uncertainties was 0.013 ppm CO2 and
0.25 ppb CH4. These numbers may be used to distinguish analyzer signal
precision from atmospheric variability.
Data screening
After water correction and calibration, invalid data are automatically
removed before calculating hourly averages using filters for bad analyzer
status (Sect. 3.4.1), flushing of lines (Sect. 3.4.2), times of calibration and maintenance,
contamination from local polluters (Sect. 3.4.3),
and water vapor spikes (Sect. 3.4.4). In the case
of contamination from local polluters, CO2 and CH4 averages are
also computed with the flagged data to allow for assessment of the impact of the
filter. Additional variables reported in the hourly averages allow for
further data screening, e.g., for using the data in inverse models
(Table 1). Details on the gradient of virtual
potential temperature are given in Sect. 3.4.5.
Variables for data screening and an example of a strict filter for
background conditions that was used to infer average growth rates in Sect. 5.1.
VariableBackground filter exampleMole fractions without removing CO2 spikesRemove flagged spikesDifference between inlets|ΔCO2| < 0.1 ppm; |ΔCH4| < 2 ppbIntra-hour variabilityσ(CO2) < 0.2 ppm; σ(CH4) < 4 ppbGradient of virtual potential temperatureΔTv,p < 0 KWind speedwv > 2 ms-1Time of day13:00–16:00 LST (local solar time)Analyzer status diagnostics
Picarro Inc. provides the diagnostic flags INST_STATUS and
ALARM_STATUS that monitor the operation status of the
analyzer. The values in Table 2 indicate normal
operation. The flag ALARM_STATUS indicates both exceeding
user-defined thresholds for high mole fractions (ignored here), and data
flagged as bad by the data acquisition software. The code reported in
INST_STATUS contains, among other indicators, thresholds for
cavity temperature and pressure deviations from their target values. We
created stricter filters for these two values based on their typical
variation during normal operation of this particular measurement system.
Occasionally, small numbers (< 5) of outliers are recorded after a
period of lost data (e.g., due to high CPU load). These are removed manually.
Diagnostic values indicating normal status of the CRDS analyzer.
Air from the two inlets at the tower and the calibration tanks flows through
some common tubing (Fig. 3). Hence, air measured
immediately after a switch is influenced by the previous air source. We
remove the first 30 s from the record after a switch between inlets to
avoid sample cross-contamination. Air from calibration tanks exhibits larger
differences in humidity and mole fractions to ambient air. Hence, the first
5 min of ambient air measurements after tank measurements are removed
from the record.
Contamination from local polluters
Possible frequent contamination sources in the immediate vicinity of the
tower are the building hosting the power generator of the facility (65 m
northwest of the tower), the heating and oven chimneys of the only inhabited
building (30 and 20 m northeast, respectively), and waste disposal. These
local polluters can cause sharp and short increases in CO2 and CH4
mole fractions on the timescale of seconds to a few minutes. These features
cannot be modeled by a regional or global atmospheric transport model and
should therefore be filtered out. We developed a detection algorithm to
identify spikes based on their duration, gradients, and amplitude in the raw
CO2 data. Spike detection algorithms are often compared to manual
flagging by station operators (El Yazidi et al., 2018).
Parameters of our algorithm were tuned in this way based on the first year
of data. The algorithm is described in Appendix D.
The impact of the CO2 spike flagging procedure is shown in
Table 3. Impacts on the hourly mole fractions are
small, more so when considering only data that pass other quality filters.
We observed that large CH4 spikes were much less frequent than and
often coincided with CO2 spikes. Hence, the spike detection algorithm
developed for CO2 was used to flag CH4 as well. This strategy may
remove some unpolluted CH4 signals and, in rare cases, leave
contaminated CH4 signals undetected. However, given the small impact of
filtering flagged CO2 spikes and the smaller frequency of large
CH4 spikes, we think that contamination of CH4 independent of
CO2 is a negligible source of error in Ambarchik data. Furthermore, due
to the large variability of natural CH4 sources, a spike detection
algorithm for CH4 may bear the risk of flagging natural signals. In addition, contamination of CH4 data may also be flagged based on other criteria, in particular
their intra-hour variability. For these reasons, we
decided that a common filter for both CO2 and CH4 works best at
Ambarchik.
Fraction of hourly averages of all data from the top inlet that
contain flagged CO2 spikes, and impact of removing them before
averaging (ΔCO2, ΔCH4).
During winter, the CRDS analyzer occasionally records H2O spikes with
durations of a few seconds. The spikes typically exhibit much higher mole
fractions than possible given ambient air temperature. This suggests that
they are caused by small amounts of liquid water in the sampling lines in
the laboratory upon evaporation. As we observed the phenomenon
exclusively during the cold season, we speculate that it is caused by small
ice crystals that may form on the air inlet filters (F1, F2), detach, are
trapped by one of the filters inside the laboratory, and evaporate.
Due to the fact that fast water vapor variations deteriorate the accuracy of the water
vapor correction, we remove the spikes before creating hourly averages.
Spikes are identified using a flagging procedure similar to the one for
CO2 contamination described in Appendix D,
with parameters adapted to the different shape of the H2O spikes.
Virtual potential temperature
Regional- and global-scale atmospheric tracer transport models rely on the
assumption that the boundary layer is well-mixed
(e.g., Lin et al., 2003). This requirement
is not satisfied when the air is stably stratified due to a lack of
turbulent mixing (Stull, 1988). This may occur
when the virtual potential temperature increases with height. To detect
these situations, sensors for temperature and relative humidity are
installed at 2 and 20 m above ground level on the measurement tower
(Table A2). Based on these measurements, the
virtual potential temperature is calculated for both heights, and the
difference can be used as an indicator for stable stratification of the
atmospheric boundary layer at the station (e.g.,
Table 1 and Sect. 5.1).
Atmospheric tracer transport to Ambarchik
The predominant wind directions at Ambarchik were southwest and northeast
(Fig. 6) over the analyzed period (August 2014–April 2017). Southwesterly winds dominated from October to March, while
northeasterly winds dominated from April to August. September and October
were a transitional period.
Wind distribution at Ambarchik for wind speeds > 2 ms-1 for the period from August 2014 to April 2017.
We used an atmospheric transport model (Henderson et
al., 2015) to determine regions within the Arctic that influence the
atmospheric signals captured at Ambarchik. For the case studies shown here,
15 d back trajectories were calculated for the period from August 2014 to
December 2015. Atmospheric transport was modeled using STILT
(Lin et al., 2003) driven by WRF
(Skamarock et al., 2008), for which boundary
and initial conditions were taken from MERRA reanalysis fields
(Rienecker et al., 2011). The resolution
of the transport model in our domain was mostly 10 km horizontally with 41
vertical levels. Based on these trajectories, the sensor source weight
functions (“footprints”) were calculated on a square-shaped Lambert
azimuthal equal area grid with a resolution of 32 km and an extent of 3200 km centered on Ambarchik. To better visualize the representativeness of
Ambarchik data to different origins of air masses, we aggregated these
footprints over seasons. Furthermore, we sorted the aggregated footprints
into bins each covering a quartile of the cumulative footprint
(Fig. 7). Footprints covered adjacent northeast
Siberian tundra and taiga ecoregions as well as the East Siberian Arctic
Shelf, with seasonally varying influences. In winter, spring, and summer, the
top quartile of the footprint concentrated on a few grid cells (order of
∼100 km) around Ambarchik, with a slightly larger spread in
fall. The two central quartiles had a focus on easterly directions in spring
and on the north in summer.
Cumulative Ambarchik footprints based on 15 d back trajectories
for August 2014–December 2015. The footprints were aggregated over the winter (December–January–February), spring (March–April–May), summer
(June–July–August), and fall (September–October–November) seasons, and sorted into
bins covering 25 % of the cumulative influence each. Shown here is a zoom in of the center of the 3200 km × 3200 km domain on which the
footprints were computed, covering 1600 km × 1600 km.
Greenhouse gas signals at AmbarchikAmbarchik time series in comparison with Barrow, Alaska
In order to provide a context for the characteristics of greenhouse gas
signals measured at Ambarchik, we compared the time series from Ambarchik
with in situ CO2 (NOAA, 2015) and CH4
(Dlugokencky et al., 2017) mole fractions observed at
Barrow Observatory, Alaska, which is located close to the village of
Utqiaġvik (71.32∘ N, 156.61∘ W). Data from Barrow
were chosen for the comparison because of the station's proximity to
Ambarchik (distance ∼1500 km, latitudinal difference
1.7∘; cf. Fig. 1), and because they have
been used in many studies on both global and regional greenhouse gas fluxes (e.g.,
Berchet et al., 2016; Jeong et al., 2018; Rödenbeck, 2005; Sweeney et
al., 2016). The analyzed period was August 2014 to December 2016.
For the comparison, afternoon data (13:00–16:00 LST) for which the wind speed was
above 2 ms-1 were used (gaps in the MPI-BGC wind measurements were
filled with Roshydromet 10 m wind speed data). In addition, Ambarchik data
were filtered out when the virtual potential temperature increased with
height. This filter was omitted for Barrow, because it would have removed
most of the data from October to April, including data classified as
“background” signals (which occurred throughout the year). Barrow data
were filtered according to their three-character quality flag. For CO2, data with
quality flags “…”, “.D.”, “.V.”, and “.S.” were included. For
CH4, data with quality flags “…” and “.C.” were included. Data
with flags beginning with a character other than a “.” in the first column were removed as invalid. Quality flags differing with respect to the second or third character from those listed above were excluded, as their number was negligible. We inferred average growth rates and
seasonal cycles for the analyzed period based on the curve fitting procedure
by Thoning et al. (1989): linear trends and four harmonics
representing the seasonal cycles were fitted to the data, and a low-pass
filter was applied to the residuals. We emphasize that the purpose of this
procedure was not to infer baselines, which would not be suitable for
CH4. Instead, the fitted curves were smooth representations of the time
series, including regional signals. To minimize the influence of interannual
variations on the estimated average growth rates at Ambarchik, they were
estimated with additional strict filters for background conditions applied
to Ambarchik data (Table 1). Given the short
duration of the Ambarchik record, we estimated seasonal cycle amplitude and
timing based on the harmonic part of the fit function, which was more robust
than including smoothed residuals.
Carbon dioxide
In spring, CO2 mole fractions observed at Ambarchik closely tracked
those measured at Barrow (Fig. 8), which was likely
due to the absence of local to regional sources and sinks during this
period. In summer, Ambarchik recorded a stronger seasonal drawdown of
CO2 mole fractions compared with Barrow, leading to a lower minimum value
that occurred 12 d earlier. In fall, CO2 rose faster at Ambarchik,
reaching the midpoint between minimum and maximum 21 d earlier than
Barrow. The mole fraction maxima in winter were at similar values. Carbon
dioxide mole fractions at Ambarchik were more variable than at Barrow in
summer and fall, which indicates stronger local and regional sources and
sinks captured by the Ambarchik tower. The annual amplitude of CO2 was
slightly larger at Ambarchik (20 ppm vs. 18 ppm) because of the lower summer
minimum. The average growth rates were (2.77±0.09) and (2.82±0.05) ppm CO2 yr-1 at Ambarchik and Barrow, respectively. Note
that despite the good agreement of these growth rates, their uncertainties
are larger than the statistical uncertainties given here, as the
estimates depended on data selection and were based on less than 3 years
of data. We note that in November and December 2016, exceptionally high
CO2 mole fractions were measured at Ambarchik. However, analysis of
individual signals is beyond the scope of this paper.
Atmospheric CO2 and CH4 measurements from Ambarchik and
Barrow. Points are quality-controlled hourly averages; lines are the results
of a curve fit plus smoothed residuals (see text for details).
Same as Fig. 8, but for CH4.
Methane
Similar to CO2 mole fractions, in spring, CH4 mole fractions at
Ambarchik matched those at Barrow and had low variability
(Fig. 8). Throughout the rest of the year, CH4
mole fractions at Ambarchik were higher and more variable than at Barrow,
which is reflected by the larger annual amplitude of 72 ppb at Ambarchik,
compared with 47 ppb at Barrow. The summer minimum of the harmonics occurred
70 d earlier at Ambarchik. By contrast, the minimum of the visual
baseline of hourly data occurred much later, and was close in values and
timing to the Barrow measurements (Fig. 9). This discrepancy was due to the fact that the harmonics fitted to
Ambarchik CH4 data were influenced by large positive CH4
enhancements starting in early summer, which were likely caused by strong
regional sources. Such CH4 enhancement events were also recorded
throughout most of the winters. Estimated average growth rates of CH4
were 6.4±1.0 ppb yr-1 at Ambarchik and 10.0±0.7 ppb yr-1 at Barrow. Note that, as for CO2, the true uncertainties of
these growth rates are larger than the statistical uncertainties given here,
as the estimates depended on the data selection.
Carbon dioxide anomalies plotted against wind direction. The
dashed circle is the baseline (anomaly 0 ppm). The (gray) points are the
median, boxes the first and third quartile, and whiskers the first and ninth
decile. Shown here are data that passed the filters for low wind speeds and
temperature inversions (Table 1). The color of
boxes and whiskers indicates the number of measurements available in each
bin.
Same as Fig. 10, but for CH4.
Angular distribution of regional CO2 and CH4 anomalies
Ambarchik is located at a junction of several different ecoregions, and in
particular at the coast of the East Siberian Sea. Therefore, the dependence
of CO2 and CH4 signals on wind direction could provide insights
into CO2 and CH4 exchange between these different regions and the
atmosphere. We examined this dependence based on CO2 and CH4
anomalies representative of fluxes inside the domain introduced in Sect. 4 (3200 km × 3200 km, centered on
Ambarchik). These anomalies were computed following a standard method in
regional inverse modeling of atmospheric tracer transport, i.e., by
subtracting the contribution of CO2 and CH4 transported into the
domain (the background signal) from the observations. Therefore, the anomalies represent the atmospheric signature of sources and sinks inside
the domain. The background signal was computed by sampling global
atmospheric CO2 and CH4 mole fraction fields at the end points of
the back trajectories introduced in Sect. 4. The
global CO2 fields were based on Rödenbeck (2005, version
10.17871/CarboScope-s04_v3.8.), and the CH4 fields
were based on the code by Rödenbeck (2005) modified by Tonatiuh Guillermo Nuñez Ramirez (personal communication, 2018). Both fields were optimized for station sets that
included Ambarchik data. We analyzed the data that passed the filters for
low wind speeds and temperature inversions (see
Table 1) grouped by season, and focused the
interpretation on the signals from the predominant wind directions, as
sample sizes from other sectors were small.
Carbon dioxide
The most pronounced CO2 signals from predominant wind directions were
positive anomalies during southwesterly winds in fall and winter. During
summer, CO2 anomalies from the predominant wind direction (northeast)
were small. During spring, almost no CO2 anomalies were observed.
Methane
The strongest CH4 enhancements were observed from westerly winds in
summer, and southwesterly winds in fall and winter. The predominant
northeasterly winds in summer carried comparatively small CH4
enhancements. The overall variability of CH4 was highest in summer and
fall, with considerable enhancements especially from the southwest in
winter. Like CO2, CH4 showed almost no anomalies in spring.
Discussion and conclusions
In this paper, we presented the first years (August 2014–April 2017) of
CO2 and CH4 measurements from the coastal site of Ambarchik in
northeastern Siberia. The site has been operational without major downtime
since its installation. Greenhouse gas measurements are calibrated about
every 5 days using dry air from gas tanks with GHG mole fractions traced
to WMO scales. Mole fractions of CO2 and CH4 are measured in humid
air and corrected for the effects of water vapor using a novel water vapor
correction method. An algorithm was developed to remove measurements
influenced by local polluters, which affected a small fraction of the
measurements. Measurements of the gradient of the virtual potential
temperature and the two sampling heights allow for detection of stable
stratifications of the atmospheric boundary layer at the station.
Uncertainties of the GHG measurements, which were inferred from measurements
of dry air from calibrated gas tanks and water correction experiments, were
0.085 ppm CO2 and 0.77 ppb CH4 on average. We continue work on
improvements of the accuracy of the calibrations and uncertainty estimates
and will adapt them as additional information becomes available (e.g., based
on post-deployment calibration of used gas tanks).
A footprint analysis indicates that Ambarchik is sensitive to trace gas
emissions from both the East Siberian Sea and terrestrial ecosystems. Both
CO2 and CH4 anomalies were large during southwesterly and westerly
winds and small during northeasterly winds. This suggests that the larger
signals originated from terrestrial rather than oceanic fluxes and
demonstrates the value of sampling at the Ambarchik location for
distinguishing fluxes from different source regions and, thus, insights into
carbon cycle processes in this region. In comparison with Barrow, Alaska,
Ambarchik recorded larger CO2 and CH4 anomalies, which resulted in
larger seasonal cycle amplitudes as well as earlier minima and fall growth.
We interpret the stronger CO2 and CH4 signals at Ambarchik as
stronger local and regional fluxes compared with those captured at Barrow.
Strong CH4 enhancements were recorded at Ambarchik well into the
winter, which is evidence for the relevance of cold season emissions (Kittler
et al., 2017b; Mastepanov et al., 2008; Zona et al., 2016). While the
average growth rate of CO2 at Ambarchik matched that at Barrow, the
growth rate of CH4 at Ambarchik was smaller. We attribute the
discrepancy to the short analysis period, which makes the growth rate
estimate sensitive to interannual variability and differences in the timing
of the annual maximum and minimum.
The accuracy of the CO2 and CH4 data obtained at Ambarchik, and
their sensitivity to sources and sinks of high-latitude terrestrial and
oceanic ecosystems make the Ambarchik station a highly valuable tool for
carbon cycle studies focusing on both terrestrial and oceanic fluxes from
northeastern Siberia.
Data availability
Quality-controlled hourly averages of data from Ambarchik are available upon
request from Mathias Göckede. We plan to publish continuous updates to
the data to an open access repository in the future. For access to data from Barrow, see NOAA (2015) and Dlugokencky et al. (2017).
Hardware manufacturers and models
Gas handling components.
DescriptionLabelManufacturerModelCRDS analyzerCRDS analyzerPicarroG2301Membrane pumpMP1PicarroPicarro vacuum pumpPiston pumpPP1Gardner Denver Thomas617CD32FlowmeterFM1OMEGAFMA1826AFlowmeterFM2OMEGAFMA1814A-STFlowmeterFM3OMEGAFMA1812AMultiposition valveMPV1VICIValco EMT2CSD6MWMSolenoid valveV1–V4SMCVDW350-6W-2-01N-H-X22-QNeedle valveNV1–NV3SwagelokSS-2MGGas tanksHigh, middle, low, targetLuxfer Gas Cylinders20 l T-PED cylinders, Type P3056ZPressure regulatorRE1–4 (incl. pressure gauges P2–P9)TESCOM44-3440KA412-SPressure sensorP1KellerPAA-21YStainless steel tubingss tube 1/16′′VICIVICI Jour JR-T-625-40Stainless steel tubingss tube 1/8′′VICIVICI Jour JR-T-626-00Flexible tubingflex tube 1/4′′SERTOSERTOflex 6.35SInlet filterF1, F2SolbergF-15-100FilterF3, F4SwagelokSS-4TF-40FilterF5, F6SwagelokSS-4FW-2
Meteorological measurements by MPI-BGC at Ambarchik.
MeasurandManufacturerModelHeight (a.g.l.)/locationWind speed, directionMETEKuSonic-220 m/towerAir temperature, relative humidityMelaKPK1_6-ME-H3820 and 2 m/tower(inside ventilated radiation shield)Air pressureSetraType 2781 m/laboratoryDerivation of water correction coefficients
The influence of water vapor on CO2 and CH4 measurements was
corrected for based on several water correction experiments and a novel
water correction model, which we describe in the following paragraphs. For
more details, please refer to Reum et al. (2019). As stated in Sect. 3.1, data from gas washing bottle experiments
(explanation below) with the CRDS analyzer located in Ambarchik were
analyzed in Reum et al. (2019) in the
context of the new water correction method (labeled “Picarro no. 5”
therein). Here, we use these data and data from additional
experiments to derive water correction coefficients for application to
the complete Ambarchik record.
Experiments were performed with two different humidification methods. For
the so-called droplet method, a droplet of deionized water (ca. 1 mL) was
injected into the dry air stream from a pressurized air tank and measured
with the CRDS analyzer. The gradual evaporation of the droplet provided
varying water vapor levels. By contrast to the droplet method, the gas
washing bottle method was designed to hold water content in the sampled air
at stable levels. For this purpose, the air stream from a pressurized tank
was humidified by directing it through a gas washing bottle filled with
deionized water, resulting in an air stream saturated with water vapor. The
humid air was mixed with a second, untreated air stream from the same tank.
Different water vapor levels were realized by varying the relative flow
through the lines using needle valves.
Synthesis water correction coefficients. Uncertainties are
approximated by the maximum difference between the coefficients of the
individual water correction functions and the coefficient of synthesis
function.
Initial experiments were performed using the droplet method, but
systematic biases in the resulting dry air mole fractions at H2O
< 0.5 % led to further experiments with the gas washing bottle
method and the development of an improved water correction model:
fch=1+ac⋅h+bc⋅h2︸fcparah+dc⋅ehhp-1
Here, fcparah
corrects for dilution and pressure broadening (Chen et al., 2010).
The parameters dc and hp correct for a sensitivity of pressure
inside the measurement cavity of Picarro analyzers to water vapor
(Reum et al., 2019).
Three droplet experiments were performed in 2014, and one gas washing
bottle experiment was performed in 2015 and 2017, respectively. The droplet results
proved unsuitable to derive the pressure-related coefficients
dc and hp due to fast
variations of water vapor, which typically occurred below 0.5 % H2O (Reum et al., 2019). Therefore, from the
droplet experiments only the data with slowly varying water vapor were used,
and dc and hp were only based
on the gas washing bottle experiments. For each species, a synthesis water
correction function was derived by fitting coefficients to the average
response of the individual functions (Table B1).
Calibration scale and coefficients
Calibrated dry air mole fractions of the air tanks in use at
Ambarchik over the period covered in this paper. For a discussion of the
uncertainties, see Appendix E2.
Coefficients of linear fits to the high, middle, and low tanks. The
smoothed coefficients are used for calibrating data.
Spike detection algorithm for CO2
The CO2 spike detection algorithm is a multistep process. First,
candidates for CO2 spikes are identified. In subsequent steps, false
positives are removed. Parts of the algorithm are based on Vickers and Mahrt (1997).
Step 1. Identifying spike candidates based on variation of
differences between CO2 measurements
For this step, data are processed in intervals spanning 1.5 h.
Candidates for CO2 spikes are identified based on the variability of
differences between individual consecutive CO2 measurements.
Measurements with differences that exceed 3.5 standard deviations from
non-flagged data are flagged as spike candidates. As flagging the data
changes the standard deviation of the non-flagged data, flagging is
repeatedly applied until changes between standard deviations of the
non-flagged data between the last and second-last loop are less than
10-10 ppm CO2. In some cases, this procedure flags the complete
interval as spikes. This happens when the variations throughout the interval
are rather uniform. This might be the case both in the presence of spikes
throughout the interval, or in the absence of spikes altogether. To avoid false
positives, all flags are removed, and the interval is considered to have no
spikes. Cases with many spikes throughout the interval can be filtered based
on the intra-hour variability flag.
Step 2. Blurring
Around the top of a spike, differences between individual CO2 soundings
are often small; thus, these measurements are not captured as part of a
spike in step 1. To unite the ascending and descending parts of spikes, the
20 data points before and after a flagged measurement are flagged. From here
on, each group of consecutive flagged measurements is considered a spike
candidate.
Step 3. Unflagging individual outliers
Step one often identifies individual or very few consecutive data points as
spikes, spanning a few seconds. We regard these very small groups of flagged
data points as noise misidentified as spikes. After blurring (step 2), these
individual outliers form groups of at least 41 data points. In step 3, spike
candidates consisting of less than 45 data points are unflagged.
Step 4. Baseline and detrending
For each spike candidate, the baseline is identified as a linear fit to the
unflagged measurements within 5 min of any data point of the spike
candidate. Using this baseline, the data in this interval are detrended,
including the spike candidate.
Step 5. Spike height
From the detrended data from step 4, the maximum deviation from the baseline
(“spike height”) is calculated. Spike candidates smaller than 8 standard
deviations of the baseline measurements are unflagged.
Example of a series of flagged CO2 spikes from 4 December 2016. All times shown are UTC.
Step 6. Unflagging abrupt but persistent changes
Until the previous step, the algorithm flags abrupt CO2 changes even if
they are persistent. This pattern occurs for example during changes of wind
direction and does not constitute an isolated spike. In this case, a trough
is present in the detrended spike. The minimum deviation from the baseline
is calculated (“trough depth”) and compared to the spike height. As
spike height and trough depths can be based on few data points, the
influence of noise is strong. To counteract, spike height and trough depth
are diminished by 2 standard deviations of the baseline. Spike candidates
with trough depths greater than one-fifth of the spike height are unflagged.
Step 7. Unflagging persistent variability changes
The procedure so far can flag the beginning or end of longer periods of
larger CO2 variability. To unflag these false positives, steps 4–5 are
applied again with the following changes: (1) a longer baseline of 30 min before and after the spike candidate (instead of 5 min) is
used, (2) baseline standard deviations are calculated separately for the
period before and after the spike candidate, (3) the spike height from step
5 is used instead of recalculated, and (4) the spike height must exceed the
maximum of the 2 baseline standard deviations by a factor of 6 instead of
8.
Step 8. Repeat
The result from steps 4–7 depends on unflagged data points surrounding a
spike candidate. Therefore, these steps are repeated until a steady state is
reached.
An example of flagged spikes is shown in Fig. D1.
In this example, removing flagged data reduced the hourly averages of the center
inlet data between 03:00 and 04:00 UTC by 0.5 ppm (CO2) and 7.0 ppb
(CH4). No top inlet data were flagged in this period. As small
spikes can be hard to distinguish from natural signals, some smaller
features that may be classified
as spikes upon visual inspection can pass the algorithm without being flagged, e.g., at 05:33 UTC in
Fig. D1. However, given that larger spikes alter
hourly averages by values on the order of magnitude of the WMO goals, the
impact of these features is likely negligible. In this particular example,
removing the detected spikes reduced average CO2 mole fractions between
05:00 and 06:00 UTC from the center inlet by 0.07 ppm. Removing the unflagged small
spike at 05:33 UTC would further reduce this average by 0.005 ppm, which is
inconsequential.
Measurement uncertainties
We adopted the uncertainty quantification method of Andrews et al. (2014).
Here, we summarize the main ideas of this approach, the modifications we
made, and quantify individual uncertainty components. A detailed description
of the nomenclature and method was omitted; please refer to Andrews et al. (2014).
Uncertainty estimation framework by Andrews et al. (2014) and
modifications
Andrews et al. (2014)
calculated the measurement uncertainty as the largest of four different
formulations (Eq. 9a–d therein). Formulations (a) and (b) were the
prediction interval of the linear regression of the calibration tanks, which
takes the standard error of the fit (sefit) and the
uncertainty in the analyzer signal into account. The difference between (a) and (b) was
the estimate of the uncertainty in the analyzer signal. In formulation (a),
this uncertainty was estimated from a model (σu) that accounts for analyzer
precision (up) and drift (ub), uncertainty of the water vapor
correction (uwv), equilibration after switching calibration tanks
(ueq), and extrapolation beyond the range covered by the calibration
tanks (uex). In measurement uncertainty formulation (b), the
uncertainty estimate of the analyzer signal was estimated from the residuals
of the linear fits of the calibration tank mole fractions (σy),
accounting for the fact that the assigned values of the calibration tanks
have non-zero uncertainty (σx):
σy′=σy2-mσx2
Here, m is the slope of the calibration function. Formulation (c) was the
bias of the target tank (uTGT), and formulation (d) was the uncertainty in
the assigned values of the calibration tanks (σx). In this
approach, uncertainty formulations (b), (c), and (d) only accounted for
uncertainties of dry air measurements. Hence, we modified them by adding the
uncertainty of the water correction to these formulations. Thus, the
analyzer precision model for uncertainty formulation (a) became
σu=up2+ub2+ueq2+uex2
Thus, the full uncertainty terms were as follows:
E3uM,a=zα,f2sefitm2+σu2+uwv2E4uM,b=zα,f2sefitm2+σy′m2+uwv2E5uM,c=uTGT2+uwv2E6uM,d=σx2+uwv2
Here, zα,f is a factor based on the quantile
function of Student's t distribution with confidence level α
(α=0.675 for prediction interval at 1σ level) and degrees of
freedom f. Calibration uncertainties were estimated based on the averaging
strategy for coefficients, i.e., using linear fits of weighted observations
from individual calibration episodes over a window of 120 d (Sect. 3.2), which usually contained about 25 calibration
episodes. The standard error of the fit (sefit) was computed based on
these weighted fits. In the notation of Andrews et al. (2014),
the equations for sefit become (cf. Taylor, 1997):
E7sefit=σmx-x‾2+σbmin2E8σm=σy∑wixi-x‾2E9σbmin=σy∑wixi-x‾2∑wi∑wixi-x‾2-∑wixi-x‾2E10σy=∑wiyi-yi,fit2df
Here, all quantities are as in Andrews et al. (2014),
with the addition of weights wi and degrees of freedom f, which
change with the number of calibration episodes in an interval.
Compared to calibrating based on single calibration episodes, this affected
the uncertainty because of the larger number of observations (reduction of
sefit and zα,f), and because of drift of
the analyzer signal over the averaging window (increase of sefit and
σy′).
Uncertainty components and estimates
In the following paragraphs, the individual components of the four
uncertainty estimates Eqs. (E3)–(E6) are
described. For numerical values of the components, see
Table E1. The time-varying uncertainty estimates
uM,a-d are shown in Fig. E1.
Water vapor (uwv)
For the water correction uncertainty uwv, we used the maximum of the
difference between individual water correction functions and the synthesis
water correction function, i.e., 0.018 % CO2 and 0.034 % CH4,
regardless of actual water content. This approach likely overestimates
uwv at low water vapor content, but was chosen because uwv was not
well constrained by the small number of water correction experiments
conducted so far.
Assigned values of calibration gas tanks (σx)
For the uncertainty of the assigned values of the calibration gas tanks
σx, we followed the approach by Andrews et al. (2014),
who set them to the reproducibility of the primary scales WMO X2007
(CO2) and WMO X2004 (CH4). Estimates based on the MPI-BGC
implementations of the primary scales yielded smaller uncertainties that
underestimated the mismatch between the CO2 mole fractions of the
calibration tanks.
Target tank (uTGT)
The uncertainty based on the target tank measurements uTGT was the same
as in Andrews et al. (2014),
but with the weighting and window we used for smoothing the calibration
coefficients.
Analyzer signal precision model (σu)
For the analyzer signal precision model σu, analyzer precision
(up) and drift (ub) were estimated jointly (up2+ub2) as the standard deviation of hourly averages of a gas tank
measurement over 12 d prior to field deployment. Note that sefit
also accounts for drift of the analyzer signal. However, the contribution of
drift on timescales significantly shorter than the averaging window of 120 d to sefit tends toward zero. As the estimate of ub was
based on 12 d of measurements, it represents drift over this shorter timescale in the prediction interval, which is why it was included in the model.
The other components (σeq, σex) appeared negligible.
In particular, we found no conclusive evidence of non-negligible
equilibration errors (σeq) in our calibrations; however, this
remains the subject of future research (Appendix E4).
The extrapolation uncertainty (σex) applied only to a small
fraction of Ambarchik data, so we ignored this error.
Measurement uncertainty components. The nomenclature follows
Andrews et al. (2014).
For time-varying components, averages are reported and denoted with an
asterisk (*).
Uncertainty componentCO2 [ppm]CH4 [ppb]Water correction uwv*0.075*0.67Assigned values of calibration gas tanks σx0.030.31Analyzer signal (a) σu0.0130.25Analyzer signal (b) σy′*0.018*0.17Standard error of fit sefit*0.005*0.05Target tank deviation from laboratory value uTGT*0.038*0.32Maximum of estimates uM,a-d*0.085*0.77
Estimates of CO2 and CH4 measurement uncertainty as
defined in Eqs. (E3)–(E6). The dashed
lines are the WMO interlaboratory compatibility goals.
Random and systematic uncertainty components
The uncertainty components described in Sects. E1
and E2 are mostly independent of the averaging
period for which atmospheric data are reported (1 h). Rather, they
describe systematic uncertainties inherent to the calibration procedure and
long-term drift (σx, sefit, uTGT), and the water
correction (uwv). Thus, these uncertainty estimates would not be
smaller for atmospheric data averaged over longer periods. Exceptions are
the analyzer signal precision estimates σu and σy′,
which contain random uncertainties: the precision model σu was
estimated based on hourly averages and reflects both their uncertainty and
drift on the timescale of 12 d. Thus, it might change for different
averaging periods. The analyzer signal uncertainty estimate σy′ was sensitive to several timescales, i.e., 2 min (averaging
period of calibration data), 22 min (time span of data of one calibration
episode), 116 h (time between individual calibration episodes), and 120 d (averaging window for calibration coefficients). To investigate whether
uncertainties at these timescales were similar to those of the hourly
averages of atmospheric data, we computed the Allan deviations for CO2
and CH4. The uncertainties of averages over 2 min, 22 min, and
1 h were close (Fig. E2). In addition, the
analyzer precision deteriorated beyond 1 h. These results are similar
(qualitatively and quantitatively) to those documented by Yver Kwok et al. (2015) for several Picarro GHG
analyzers.
The analyzer signal precision estimates only accounted for a small fraction
of the total uncertainty (Table E1). Thus, the
random uncertainty components play a minor role in the calibration of
Ambarchik data, and averaging atmospheric data over different periods would
not change the total estimated uncertainty considerably.
Allan deviation of the CO2 and CH4 readings of the CRDS
analyzer in Ambarchik. Values are based on one 12 d measurement of dry air
from a gas tank in the lab prior to field deployment. The averaging time is
cut off where the error becomes too large for a meaningful interpretation of
the result. The vertical line denotes an averaging time of 1 h. The
dashed line corresponds to white noise (slope -0.5), scaled to coincide with
the first data point of the Allan deviation.
Potential improvements of the calibration accuracy
Several aspects to the accuracy of the calibration using regular gas tank
measurements are subject to future research. Here, we outline potential
calibration errors that could not be conclusively quantified, and how we
plan to address them in the future.
To investigate whether the regular probing time of the gas tanks was
sufficient for equilibration (e.g., due to flushing of the tubing), we fitted
exponential functions to the medians of the regular tank measurements.
Deviations between modeled equilibrium mole fractions and the averages used
for calibration were negligible (|ΔCO2| < 0.008 ppm; |ΔCH4| < 0.09 ppb)
and thus ignored. Furthermore, in two experiments, we investigated
equilibration error and other drifts (e.g., diffusion in the pressure
reducers) by measuring the calibration tanks in reversed order, and in
original order for up to 2 h. However, the experiments were
inconclusive. Based on the available data, we estimated the largest
conceivable biases for the ranges 350–450 ppm CO2 and 1800–2400 ppb CH4. They were up to 0.06 ppm CO2 and 0.5 ppb CH4 at the
edges of these ranges and vanished around their centers. An additional
source of bias might be inlet pressure sensitivity of the Picarro analyzer
as documented by Gomez-Pelaez et al. (2019). Using the sensitivities
reported therein, some of the gas tank measurements in Ambarchik could have
a bias of up to 0.03 ppm CO2 and 0.2 ppb CH4. More experiments are
necessary to rule out or confirm and assess these possible biases; hence, no
bias correction was implemented.
The CO2 bias of the water-corrected target tank mole fractions varied
from -0.06 to -0.01 ppm (Fig. 5, top panel). These
variations correlated with residual water vapor (which was much smaller than
0.01 %) and temperature in the laboratory during the target tank
measurements, as well as with ambient CO2 mole fractions sampled
before. This suggests that the variations may be due to insufficient
flushing during calibration. However, the correlations varied over time
without changes to the hardware or probing strategy. Therefore, further
investigation of this observation is required, and no correction was
implemented.
So far, possible drifts of the gas tanks could not be assessed and have therefore
not been included in our uncertainty assessment. This will be assessed only
when the gas tanks are almost empty, and shipped back to the MPI-BGC for
recalibration.
Author contributions
MH, SZ and MG conceptualized the study. JVL, MH, OK, NZ, FR, and MG designed
and set up the Ambarchik station. NZ and SZ coordinated setup and
maintenance of the Ambarchik station. FR and MP performed calibration
experiments. FR curated and analyzed the data. FR prepared the paper
with contributions from all authors. MG supervised the project, and reviewed
and edited the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “The 10th International Carbon Dioxide Conference (ICDC10) and the 19th WMO/IAEA Meeting on Carbon Dioxide, other Greenhouse Gases and Related Measurement Techniques (GGMT-2017) (AMT/ACP/BG/CP/ESD inter-journal SI)”. It is a result of the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21–25 August 2017.
Acknowledgements
The authors would like to thank Armin Jordan (MPI-BGC) for preparing
the gas cylinders. We would also like to thank Christian Rödenbeck (MPI-BGC)
for access to the Jena Inversion code for producing global CO2 mole
fraction fields, Tonatiuh Guillermo Nuñez Ramirez (MPI-BGC) for providing global CH4
mole fraction fields, and John Henderson (AER) for providing footprints for
Ambarchik. Lastly, the authors wish to thank NOAA GMD for making the CO2 and
CH4 data from Barrow available.
Financial support
This work was supported by the Max Planck Society, the European Commission
(PAGE21 project, FP7-ENV-2011, grant agreement no. 282700; PerCCOM project,
FP7-PEOPLE-2012-CIG, grant agreement no. PCIG12-GA-201-333796; INTAROS
project, H2020-BG-2016-2017, grant agreement no. 727890), the German
Ministry of Education and Research (CarboPerm Project, BMBF grant no.
03G0836G), the AXA Research Fund (PDOC_2012_W2
campaign, ARF fellowship Mathias Göckede), and the European Science
Foundation (TTorch Research Networking Programme, Short Visit Grant Friedemann
Reum).The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Christoph Zellweger and reviewed by two anonymous referees.
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