ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-999-2019On the diurnal, weekly, and seasonal cycles and annual trends in atmospheric
CO2 at Mount Zugspitze, Germany, during 1981–2016Weekly, seasonal cycles and annual trends in atmospheric CO2 at Mount ZugspitzeYuanYeyuan@wzw.tum.deRiesLudwighttps://orcid.org/0000-0001-8545-6397PetermeierHannesTricklThomasLeuchnerMichaelCouretCédrichttps://orcid.org/0000-0002-1874-9883SohmerRalfMeinhardtFrankMenzelAnnetteDepartment of Ecology and Ecosystem Management, Technical University
of Munich (TUM), Freising, GermanyGerman Environment Agency (UBA), Zugspitze, GermanyDepartment of Mathematics, Technical University of Munich (TUM),
Garching, GermanyInstitute of Meteorology and Climate Research, Atmospheric
Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
Garmisch-Partenkirchen, GermanySpringer Nature B.V., Dordrecht, the NetherlandsGerman Environment Agency (UBA), Schauinsland, GermanyInstitute for Advanced Study, Technical University of Munich (TUM),
Garching, GermanyYe Yuan (yuan@wzw.tum.de)25January2019192999101214August201830August201819December201810January2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/19/999/2019/acp-19-999-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/999/2019/acp-19-999-2019.pdf
A continuous, 36-year measurement composite of atmospheric carbon dioxide
(CO2) at three measurement locations on Mount Zugspitze, Germany, was
studied. For a comprehensive site characterization of Mount Zugspitze,
analyses of CO2 weekly periodicity and diurnal cycle were performed to
provide evidence for local sources and sinks, showing clear weekday to
weekend differences, with dominantly higher CO2 levels during the daytime
on weekdays. A case study of atmospheric trace gases (CO and NO) and the
passenger numbers to the summit indicate that CO2 sources close by did
not result from tourist activities but instead obviously from anthropogenic pollution
in the near vicinity. Such analysis of local effects is an indispensable
requirement for selecting representative data at orographic complex
measurement sites. The CO2 trend and seasonality were then analyzed by
background data selection and decomposition of the long-term time series into
trend and seasonal components. The mean CO2 annual growth rate over the
36-year period at Zugspitze is 1.8±0.4 ppm yr-1, which is in
good agreement with Mauna Loa station and global means. The peak-to-trough
amplitude of the mean CO2 seasonal cycle is 12.4±0.6 ppm at
Mount Zugspitze (after data selection: 10.5±0.5 ppm), which is much
lower than at nearby measurement sites at Mount Wank (15.9±1.5 ppm)
and Schauinsland (15.9±1.0 ppm), but following a similar seasonal
pattern.
Introduction
Long-term records of atmospheric carbon dioxide (CO2) improve our
understanding of the global carbon cycle, as well as long- and short-term
changes, especially at remote background locations. The longest continuous
measurements of atmospheric CO2 started in 1958 at Mauna Loa, Hawaii,
initiated by investigators of the Scripps Institution of Oceanography (Pales
and Keeling, 1965). The measurements were performed on the north slope of
the Mauna Loa volcano at an elevation of 3397 m above sea level (a.s.l.),
thus at long distances from CO2 sources and sinks. Later, additional
measurement sites were established for background studies of global
atmospheric CO2, such as the South Pole (Keeling et al., 1976), Cape
Grim, Australia (Beardsmore and Pearman, 1987), Mace Head, Ireland (Bousquet
et al., 1996), and Baring Head, New Zealand (Stephens et al., 2013). Along
with sites located in Antarctica or along coastal/island regions,
continental mountain stations offer excellent options to observe background
atmospheric levels due to high elevations that are less affected by local
influences, for example, Mount Waliguan, China (Zhang et al., 2013), Mount
Cimone, Italy (Ciattaglia, 1983), Jungfraujoch, Switzerland, and Puy de
Dôme, France (Sturm et al., 2005).
Although mountainous sites experience less impact from local pollution and
represent an improved approach to background conditions compared with
stations at lower elevations, we cannot fully dismiss the influence of local
to regional emissions. This influence largely depends on air-mass transport
and mixing within the moving boundary layer height. Lidar measurements show
that air from the boundary layer is orographically lifted to approximately
1–1.5 km above typical summit heights during daytime in the warm season
(Carnuth and Trickl, 2000; Carnuth et al., 2002). A 14-year record of
atmospheric CO2 at Mount Waliguan (3816 m a.s.l.), China, reveals
significant diurnal cycles and depleted CO2 levels during summer that
are mainly driven by biological and local influences from adjacent regions,
although the magnitude and contribution of these influences are smaller than
those at other continental or urban sites (Zhang et al., 2013). At the Mt.
Bachelor Observatory (2763 m a.s.l.), USA, atmospheric CO2
variations were studied in the free troposphere and boundary layer
separately, where wildfire emissions were observed to drive CO2
enhancement at times (McClure et al., 2016). However, it still remains
unclear to exactly what extent elevated mountain sites are influenced by
local activities and how to characterize better local sources and sinks at
such stations. It is difficult to make quantitative conclusions on the
anthropogenic and biogenic contributions to these measurements (Le
Quéré et al., 2009). Analyzing weekly periodicity may be a potential
indicator since periodicity represents anthropogenic activity patterns
during 1 week (7 days) without the influence of natural causes
(Cerveny and Coakley, 2002). From the perspective of modeling and satellite
observational systems, studies have shown that the weekly variability has
implications on the quantification and verification of anthropogenic
CO2 emissions, as well as diurnal variability (e.g., Nassar et al.,
2013; Liu et al., 2017). Regarding in situ measurements, results from Ueyama
and Ando (2016) clearly indicate the presence of elevated weekday CO2
emissions compared with weekend and/or holiday CO2 emissions at two
urban sites in Sakai, Japan. Cerveny and Coakley (2002) detected
significantly lower CO2 concentrations on weekends than on weekdays at
Mauna Loa, which was assumed to result from anthropogenic emissions from
Hawaii and nearby sources.
In this study, we present a composite 36-year record of atmospheric CO2
measurements (1981–2016) at Mount Zugspitze, Germany (2962 m a.s.l.). The
objective of this study is to achieve an improved measurement site
characterization with respect to historical CO2 data in terms of
diurnal and weekly cycles, and to produce a consistent overall analysis of
CO2 trend and seasonality. The CO2 measurements were performed at
three locations on Mount Zugspitze: at a pedestrian tunnel (ZPT), at the
summit (ZUG), and at the Schneefernerhaus (ZSF) on the southern face of the
mountain. In addition, CO2 measurements were taken at the nearby lower
mountain station, Wank Peak (WNK), but for a shorter time period. Short-term
variations of weekly CO2 periodicities and diurnal cycles were
evaluated for Mount Zugspitze. In addition, a case study combining
atmospheric CO and NO measurements and records of passenger numbers was used
to examine weekday–weekend influences. Then the results for the CO2
annual growth rates and seasonal amplitudes were studied separately via seasonal-trend decomposition and compared with CO2 data for the
comparable time period (1981–2016) at the Global Atmospheric Watch (GAW) regional observatory
Schauinsland, Germany (SSL), and the GAW global observatory Mauna Loa, Hawaii
(MLO), as well as the global CO2 means calculated by the NOAA/ESRL and
the World Data Centre for Greenhouse Gases (WDCGG).
(a) Map showing the study area (GAP – Garmisch-Partenkirchen; WNK
– Mount Wank; ZPT – pedestrian tunnel at Mount Zugspitze; ZUG – Zugspitze
summit; ZSF – Zugspitze Schneefernerhaus). (b) A photograph showing the
locations (ZPT, ZSF, and ZUG) on Mount Zugspitze at which atmospheric CO2
measurements were performed.
Experimental methods and dataMeasurement locations
Mount Zugspitze is located approximately 90 km southwest of Munich, Germany.
The nearest major town is Garmisch-Partenkirchen (GAP; 708 m a.s.l.).
Measurements of CO2 were first performed between 1981 and 1997 at a
southward-facing balcony in a pedestrian tunnel (ZPT; 47∘25′ N, 10∘59′ E; 2710 m a.s.l.) situated about 250 m below the summit of Mount Zugspitze, which joined the ancient summit
station of the first Austrian cable car to the Schneefernerhaus (Reiter et
al., 1986). The Schneefernerhaus was a hotel until 1992 when it was rebuilt
into an environmental research station. From 1995 until 2001, a new set of
measurements were made at a sheltered laboratory on the terrace of the
summit (ZUG; 47∘25′ N, 10∘59′ E; 2962 m a.s.l.). These two measurement periods were performed by the
Fraunhofer Institute for Atmospheric Environment Research (IMK-IFU), and,
since 1995 these measurements have been carried out on behalf of the German
Environmental Agency (UBA). Since 2001, to continue contributing to the GAW
program, CO2 measurements have been performed at the Environmental
Research Station Schneefernerhaus (ZSF; 47∘25′ N,
10∘59′ E; 2656 m a.s.l.). Approximately 100 m below the
Schneefernerhaus, the glacier plateau Zugspitzplatt can be reached from the
valley via cable cars or cogwheel trains. The Zugspitzplatt descends
eastward via a moderate to steep slope across the Knorrhütte towards the
Reintalangerhütte as shown in Fig. 1 (Gantner et al., 2003).
Instrumental setup and data processing
CO2 mole fractions were processed separately because of different
measurement locations and time periods at Mount Zugspitze as described
above. Information on the first time period (ZPT) was collected based on
personal communication with corresponding staff, logbooks, and literature
research (Reiter et al., 1986). The CO2 measurement at ZPT was
continuously performed with different instrument models used consecutively
(i.e., the URAS-2, 2T, and 3G) employing the nondispersive infrared (NDIR) technique.
The measured values were corrected by simultaneously measured air pressure
with a hermetically sealed nitrogen-filled gas cuvette due to no flowing
reference gas being used. Two commercially available working standards (310 and
380 ppm of CO2 in N2) were used for calibration every day at
different times. The CO2 concentration in this gas bottle was compared
in short intervals with a reference standard provided by UBA which was
adjusted to the Keeling standard reference scale.
At ZUG the sampling line consisted of a stainless steel tube with an inner
core of borosilicate glass and a cylindrical stainless steel top cup to
prevent intake of precipitation. The inlet was mounted on a small mast
(approximately 4 m high) on the top of the laboratory building, which is
situated on the Zugspitze summit platform (see Fig. 1b). Inside the
laboratory a turbine with a fast real-time fine control ensured a constant
sample inflow of 500 L min-1
of in situ air. The borosilicate glass tube
(about 10 cm diameter) continued inside the laboratory, providing a number
of outlets from which the instruments could get the sample air for their own
analyses. The measurement and calibration were performed with a URAS-3G
device and an Ansyco mixing box. The mixing controller allowed automatic
switching for up to four calibration gases and sampling air by a
self-written calibration routine using Testpoint software. The linear
two-point calibration enveloping the actual ambient values with low and high
CO2 concentrations was taken every 25th hour. Every 6 months
the working standards were checked and readjusted, when required, according to the
standard reference scale using intercomparison measurements with the station
standards.
At ZSF the same construction principle was applied for atmospheric sampling.
There, the mast height is about 2.5 m above the pavement of the research
terrace on the fifth floor at an altitude of 2670 m a.s.l. Measurements
of CO2 at Schneefernerhaus continued thereafter until the present with a
modified HP 6890 using a gas chromatograph (GC), with an intermediate
upgrade in 2008 (Bader, 2001; Hammer et al., 2008; Müller, 2009). In
2012 and 2013, because of an instrumental failure of the GC, CO2 data
were recorded with a cavity ring-down spectrometer (CRDS; Picarro
EnviroSense 3000i) connected to the same air inlet, which had been installed
in parallel since 2011. The GC calibrations were carried out at 15 min
intervals using working standards (near-ambient), which had been calibrated
with station standards from the GAW Central Calibration Laboratory (CCL)
operated by the NOAA/ESRL Global Monitoring Division. The GC data
acquisition system (see Supplement Fig. S1) produced a calibration value
every 15 min and two values from the sampled air based on one
chromatogram every 5 min. For continuous quality assurance the GC was
checked daily for flows, retention times, gas pressures, and the structure
of chromatograms. Calibration factors and metadata were used to convert raw
data into the final data product. Invalid and unrepresentative data due to
local influences were flagged according to a logged list of local pollution
from working activities in the research station. The measurement quality was
controlled by comparison with simultaneous measurements of identical gas
(CRDS) or with measurements of other trace substances and meteorological
data, and additional support from station logbooks and checklists. The data
were flagged according to quality control results. In principle, the
acquisition system stores all measured data (flagged or not) and never
discards them. Drifts in the working standards were controlled by a second
target (measured approximately 25 times per day) and a regular 2-month
intercomparison between the working standard and NOAA station standards,
performing corrections as needed. Calibration for CRDS was performed
automatically, with three different concentrations every 12 h. Until 2013
the calibrations were performed automatically every 24 h with one
concentration, very close to the ambient value. Every 2 months the
concentrations were rechecked according to the station reference standards.
Detailed description of atmospheric CO2 measurement techniques
(NDIR is the nondispersive infrared, GC is gas chromatography, and CRDS is cavity ring-down spectroscopy). At ZSF, CO2 data from GC measurements
were not available from 2012 to 2013 due to an instrumental failure; thus
data from CRDS measurements were used in these 2 years for this study.
However, CRDS measurements were performed in parallel from the same air
inlet from 2011.
Additional atmospheric CO2 measurements throughout the GAP area were
performed between 1978 and 1996 at Mount Wank summit (WNK; 47∘31′ N, 11∘09′ E; 1780 m a.s.l.) using a URAS-2T
instrument. Wank Observatory is located in an alpine grassland just
above the tree line (Reiter et al., 1986; Slemr and Scheel, 1998). Detailed
information on the CO2 measurements at Schauinsland (SSL;
47∘55′ N, 7∘54′ E; 1205 m a.s.l.) and Mauna Loa, Hawaii (MLO;
19∘28′ N,
155∘35′ W; 3397 m a.s.l.), which we use to compare
the results of this study with, can be found in Schmidt et al. (2003) for
SSL and Thoning et al. (1989) for MLO. The CO2 data from these
measurement sites and from Mount Zugspitze locations were considered as
validated data set (Level 2: calibrated, screened, and artefacts and outliers
removed), without any further data processing prior to the selection of
representative data. The different instruments and calibration scales used
at each location are summarized in Table 1.
Offset adjustment
According to NOAA CMDL (http://ds.data.jma.go.jp/wcc/co2/co2_scale.html; last access: 23 January 2019),
no significant offsets are documented between the calibration
scales WMO X74 and WMO X85 and the current WMO mole fraction scale. However,
for the 3-year parallel CO2 measurements at ZPT and ZUG
(1995–1997), clear offsets of -5.8±0.4 ppm (CO2,ZPT minus
CO2,ZUG, 1 SD) were observed. The major reason for this
bias is assumed to be the pressure-broadening effect in the gas
analyzers used and the different gas mixtures used in the standards (Table 1),
CO2/N2 vs. CO2/air, the so-called “carrier gas
correction” (CGC) (Bischof, 1975; Pearman and Garratt, 1975). It is known from
previous studies that the measured CO2 concentration, when using
CO2/N2 mixtures as reference, is usually underestimated by several
parts per million for the URAS instruments, and such offsets vary from different types of
analyzers (Pearman, 1977; Manning and Pohl; 1986). The carrier gas effect
varies even between the same type of analyzer as well as with replacement of
parts of the analyzer (Griffith et al., 1982; Kirk Thoning, personal
communication, 1 August 2018). Since we have insufficient information to
determine a physically derived correction to the ZPT CO2 data, an
offset adjustment was made for further analyses based on the offsets in data
computed in the overlapping years. A single correction factor
G=0.956+0.00017⋅CZPT
was applied to the ZPT data, where CZPT denotes the CO2
concentrations at ZPT. Because of the same calibration mixtures, an
additional adjustment was applied to the CO2 concentrations at WNK by
calculating the CO2 differences between ZPT and WNK. A detailed
description on the offset adjustment of CGC with potential errors is given
in the Supplement. Two similar CGCs by Manning and Pohl (1986) at Baring
Head, New Zealand, and Cundari et al. (1990) at Mt. Cimone, Italy, were
comparable in magnitude to our offset adjustment.
On the other hand, there were 9 consecutive months, from April to December
2001, of parallel atmospheric CO2 measurements at both ZUG and ZSF,
based on which an intercomparison between the two series was made. The
offset between these two records attained an average of 0.1±0.4 ppm
(CO2,ZUG minus CO2,ZSF, 1 SD), which fulfills the
requirement of the GAW data quality objective (DQO; ±0.1 ppm) for
atmospheric CO2 in the Northern Hemisphere. Therefore, no adjustments
regarding this offset were applied to the data sets.
In this study, we took CO2 measurements during the corresponding time
intervals at ZPT (1981–1994), ZUG (1995–2001), and ZSF (2002–2016) to
assemble a composite time series for Mount Zugspitze over 36 years.
Nevertheless, we always treat measurements from each location separately for
further analyses. At WNK, as well as at SSL and MLO, we used measured
CO2 data starting from 1981 for time consistency with measurements at
Mount Zugspitze.
ADVS data selection
Adaptive diurnal minimum variation selection (ADVS), a recently published,
novel statistical data selection strategy, was used to ensure that the data
were clean and consistent with respect to the state of a locally unaffected
lower free troposphere at the measurement sites (Yuan et al., 2018). ADVS,
which was originally designed to characterize mountainous sites, selects
data based on diurnal patterns, with the aim of selecting optimal data that
can be considered representative of the lower free troposphere. To achieve
this, variations in the mean diurnal CO2 were first evaluated and a
time window was selected based on minimal data variability around midnight,
at which point data selection began. The data outside the starting time
window were examined on a daily basis both forward and backward in time for
the day under consideration, by applying an adaptive threshold criterion.
The selected data represent background CO2 levels at the different
measurement sites.
(a) Time series plot of 30-min averaged CO2 concentrations
measured at Mount Zugspitze (ZPT, ZUG, and ZSF) and Wank (WNK), and hourly
averaged CO2 concentrations measured at Schauinsland (SSL) and Mauna
Loa (MLO) with ADVS selection. Grey and black colors are used for the
unselected and selected results. (b) Detrended mean diurnal cycles with
starting time windows (in grey) for ADVS data selection.
ADVS data selection was applied to all CO2 records based on the same
threshold parameters, followed by examining the starting time window and
calculating the percentages of the ADVS-selected data. Figure 2a shows the
CO2 time series before and after ADVS data selection. We also evaluated
the starting time windows resulting from ADVS data selection with the
detrended mean diurnal cycles as described in Yuan et al. (2018) for each
measurement site in Fig. 2b. The number of ADVS-selected data is
summarized as percentage per hour in the total number of all CO2 data
in Fig. 3. A detailed description and discussion is given in Sect. 3.1.
Mean symmetrized residual
Weekly periodicity was calculated using the mean symmetrized residual
(MSR) method, which was originally applied to atmospheric CO2 data
(Cerveny and Coakley, 2002). The MSR method focuses on variations in mean
values for the days of the week. Daily deviations from the 7-day
(consecutive) averages are calculated without ADVS selection to account for
the most likely emission cycles. Then, the MSR values are derived by
averaging the differences for each single day. Additionally, only the MSR
values with no data gaps in all the seven differences are considered as
valid. Finally, all the MSR values are aggregated into overall mean values
for each day of the week. In addition, the MSR values are standardized so
that the sum of all the seven values is equal to 0 (Cerveny and Coakley,
2002).
STL decomposition
The seasonal-trend decomposition technique (STL) was applied to decompose
the CO2 time series into trend, seasonal, and remainder components
individually (Cleveland et al., 1983, 1990), which, in
previous studies, has been a commonly applied method (e.g., Stephens et al.,
2013; Hernández-Paniagua et al., 2015). Locally weighted polynomial
regressions were iteratively fitted to all monthly values in both an outer
and an inner loop. According to Cleveland et al. (1990) and Pickers and
Manning (2015), we set the trend and seasonal smoothing parameters to 25 and
5, respectively. The CO2 time series at each site or location were
aggregated into monthly averages and, then, decomposed using STL. Missing
monthly values were substituted using spline interpolation.
To study the trend and seasonality, we firstly intended to apply STL
decomposition to the ADVS-selected time series. However, due to multiple
occurrences of consecutively missing values in the ADVS-selected monthly
averages, especially for measurement sites at lower elevations (WNK and
SSL), it was more practical to use the original CO2 time series without
ADVS data selection for STL decomposition, to preserve time series
continuity (Pickers and Manning, 2015). There is one missing 6-month time
interval at ZUG in 1998 (July to December). Thus STL was performed
separately for the time periods before (January 1995–June 1998) and after
(January 1999–December 2001) the gap. Nevertheless, we still applied STL decomposition
to the ADVS-selected data sets from Mount Zugspitze and Mauna Loa, since
these selected time series were applicable. At ZPT, due to larger time gaps
of missing data at the beginning (1981 and 1982) of the ADVS-selected data
set, the ADVS-selected and STL-decomposed results were only studied starting
from 1983. Individual figures of each STL-decomposed component at all
stations can be found in the Supplement.
For annual growth rates we did not include the WNK time series due to
shorter time periods of available data. Monthly trend components were first
aggregated into annual mean values. Then, the annual CO2 growth rates
were calculated as the difference between the CO2 value of the current
year and the value from the previous year (Jones and Cox, 2005). The mean
seasonal cycle was aggregated directly from the monthly seasonal components
by month. To observe potential deviations on the regional and global scale,
we compared the trend and seasonality derived from the STL-decomposed
components at Zugspitze with other measurement sites. We
included the globally averaged marine surface monthly mean data from NOAA
(https://www.esrl.noaa.gov/gmd/ccgg/trends/; last access: 23 January 2019) and data for the global mean mole
fractions from WDCGG (WMO Greenhouse Gas Bulletin, 2018) as references, and processed these data
based on the identical STL decomposition routine. All the statistical
analyses described above (including ADVS, MSR, and STL) were performed in
the R environment (R Core Team, 2018).
Frequency of the percentages of the number of ADVS-selected
CO2 data for each hour (0 to 23) in the total number of CO2 data
for that hour as shown in greyscale.
Results and discussionADVS selection and diurnal variation
The resulting ADVS-selected CO2 data showed a clear linkage of the
percentage of selected data and the altitude of the measurement site. Among
the continental stations, the percentage increased with altitude. A lower
percentage indicates higher data variability due to lower elevation and
proximity to local sources and sinks. At Schauinsland, the percentage of
CO2 data by the ADVS selection was 6.3 %, while the percentages at
Mount Zugspitze reached 9.9 % (ZPT), 19.5 % (ZUG), and 13.6 % (ZSF),
respectively. A moderate percentage of 6.3 % was also derived at Mount
Wank. However, regarding the elevated mountain station Mauna Loa on the
island of Hawaii, a much higher percentage (40.0 %) of CO2 data was
selected using ADVS as being representative of its background concentration, mainly
due to the very limited nearby anthropogenic sources as well as mostly
clean, well-mixed air arriving there. A similar result for an island
mountain station can be found in Yuan et al. (2018), in which a percentage of
36.2 % was computed for the CO2 measurements at Izaña station
on the island of Tenerife (28∘19′ N, 16∘30′ E; 2373 m a.s.l.). This can also be explained by the detrended
mean diurnal cycles shown in Figs. 2b and 3. The mean diurnal cycle at
MLO only exhibits a clear trough during daytime, especially starting from
12:00 local time (LT), which is believed to be influenced by the vegetation
activity (photosynthesis) in the surroundings. The same effect can be seen
at WNK and SSL, but with larger magnitudes and earlier occurrences of the
minima because of their lower locations closer to CO2 sinks. In
contrast, at these two sites the CO2 maxima in the diurnal cycles were
not as clearly noticeable as at Mount Zugspitze due to anthropogenic sources
and high biogenic respiration. At the three locations on Mount Zugspitze,
the CO2 peaks in the mean diurnal cycles are driven by the late-morning
convective upslope wind, which was relatively obvious at both ZUG and ZSF.
However, from the perspective of data selection, a significantly higher
percentage of CO2 data was selected at ZSF compared with ZPT, although
there is only a small difference in altitude of around only 70 m. This
proves that ZSF is capable of capturing more background conditions than ZPT
during the day. Nevertheless, based on the starting time window computed for
ADVS selection, we found that, in general, most stations exhibited similar
starting time windows beginning around midnight, and the ADVS data selection
was applied systematically by including more data around these hours (see
Fig. 3), which confirmed our assumption of background conditions during
midnight for the ADVS data selection (Yuan et al., 2018).
(a) Mean MSR CO2 values at Mount Zugspitze and MLO as a
function of the day of the week. Mean MSR values are adjusted such that they sum to
0. (b) Detrended mean CO2 diurnal cycles at ZSF by day of the week from 2002 to
2016. Uncertainties at the 95 % confidence interval are shown by the shaded
areas.
Weekly periodicity
For a better characterization of the differences among the measurement
locations at Mount Zugspitze, the mean CO2 weekly cycles were analyzed
as a function of mean MSR values (see Fig. 4a). The mean MSR values at the
MLO for the corresponding time intervals were also calculated. Most weekly
cycles exhibited no clear peaks or patterns for both sites. However, the
magnitude of MSR data variability is mostly higher at Zugspitze, with a
maximum on Thursdays. The only significant weekday–weekend difference is
observed at ZSF in terms of the 95 % confidence interval, which shows
weekly maxima and weekly minima on Thursday and Saturday, respectively
(peak-to-trough difference: 0.76 ppm). Gilge et al. (2010) observed similar
phenomena when studying O3 and NO2 concentrations at Alpine
mountain stations, including Zugspitze. Clear weekly cycles, with enhanced
O3 levels on working days, were observed at ZSF in summer, with weekly
maxima and minima on Thursdays and Sundays, respectively. For NO2,
maximum mixing ratios on working days and minimum ratios on Sundays at
neighboring stations were observed, generally suggesting an anthropogenic
impact at all elevations.
We obtained more insights into the weekly CO2 cycle at Mount Zugspitze
by comparing the mean diurnal cycles of weekdays and weekends (see Fig. 4b).
Detrended mean diurnal cycles at ZSF, from Sunday to Saturday, were
calculated by subtracting the daily averages from the daily data between
2002 and 2016. In the morning around 09:00 to 10:00 LT the CO2 levels at
ZSF are higher on weekdays than weekends, while CO2 diurnal patterns
during the rest of the week are relatively stable. Such weekly cycles are
not observable at ZPT and ZUG, nor at WNK and SSL (see Fig. S18). At ZPT, there are fewer variations in the diurnal cycle compared to
ZSF, indicating that this location does not receive the effect of regular
local anthropogenic working activities and hence it is more representative
of lower free tropospheric conditions regarding this aspect. The
weekday–weekend differences at ZSF are possibly due to local working
patterns, whereas the absence of this pattern at lower sites may indicate
influences from a more regional reservoir. In fact, ZSF is closed on the
weekends and, thus, is influenced by less immediate anthropogenic activities.
Mean diurnal plots at ZSF during 2016 by day of the week for (a) CO and (b) NO,
and (c) the standardized daily passenger number at the Zugspitzplatt and
Zugspitze summit combined.
Case study on atmospheric CO and NO and passenger numbers at
Zugspitze
To study the potential sources and sinks for such weekday–weekend
differences in the CO2 diurnal cycles at ZSF further, we analyzed atmospheric
CO and NO data at ZSF and the daily combined number of cable car and train
passengers to Zugspitzplatt and to the Zugspitze summit in 2016. Atmospheric
CO and NO are known to be good indicators of local anthropogenic influences
due to highly variable short-term signals and are thus helpful to identify
potential CO2 sources (Tsutsumi et al., 2006; Sirignano et al., 2010;
Wang et al., 2010; Liu et al., 2016). In this study, we used atmospheric NO
due to its short lifetime based on rapid atmospheric NO2 formation with
resulting altitude-dependent O3 surplus, indicating the presence of
sources at closer distances. The CO and NO data shown in Fig. 5 include data
that were flagged during data processing because for the delivery to GAW
World Data Centres the logged and recognized work-dependent concentration
peaks are flagged. A clear weekday–weekend difference is observed for both
CO and NO. Only weekdays are characterized by multiple short-term
atmospheric CO events and higher atmospheric NO peaks during the daytime
(mostly around 09:00 LT), which fits perfectly with daytime peaks in
CO2 diurnal cycles. A general fluctuating pattern in NO throughout the
week is thought to originate from heating of the Zugspitzplatt and changing
work with combustion engines. On the other hand, the daily number of
passengers at Zugspitze (see Fig. 5c) shows a clear weekday–weekend pattern,
with a higher number of passengers on the weekends. However, increased numbers
of passengers on the weekends do not correspond to higher levels of CO and
CO2, indicating that measured CO2 levels are not significantly
influenced by tourist activities nearby. Instead, it is more likely that
anthropogenic working activities are the main driver of weekly periodicity.
Mean annual CO2 growth rates in ppm yr-1 at the 95 % confidence interval based on three time blocks for all measurement sites/locations studied
(SSL – Schauinsland; WNK – Mount Wank; ZPT – pedestrian
tunnel at Mount Zugspitze; ZUG – Zugspitze summit; ZSF – Zugspitze
Schneefernerhaus; MLO – Mauna Loa; WDCGG and NOAA – global means). ADVS
means the data were selected using the ADVS method. This comparison refers to data
from all years including the corresponding time period for all stations.
Measurement sites or locations where data are not available for calculating
the corresponding time blocks are shown by “–”.
Based on the STL-decomposed results, the mean annual growth rate of the
36-year composite record at Mount Zugspitze from the three measurement
locations is 1.8±0.4 ppm yr-1, which is consistent with the SSL
(1.8±0.4 ppm yr-1), MLO (1.8±0.2 ppm yr-1), and
global means (NOAA: 1.8±0.2 ppm yr-1; WDCGG: 1.8±0.2 ppm yr-1). The mean annual growth rates from the ADVS-selected data
sets at Mount Zugspitze and Mauna Loa also result in the identical value of
1.8 ppm yr-1. Then, we divided the entire time period (1981–2016) into
three time blocks, corresponding to the different locations at Mount
Zugspitze, in order to observe potential differences with respect to other
sites separately (see Table 2). The results show good agreement of each
location on Mount Zugspitze with other measurement sites (also for the
ADVS-selected results) as well as a clearly increasing trend of the annual
growth rates over these three time blocks. Only the mean annual growth rate
between 1995 and 2001 at ZUG is obviously lower than at the other sites.
This can be explained by the missing monthly values in 1998, and thus in turn
the annual growth rates of 1998 and 1999 were left out for the average.
However, the annual growth rates of these 2 years reached anomalous peaks
at most sites (see details later in Sect. 3.6). Möller (2017) also
mentioned that 1981 to 1992 growth rates at both German stations and MLO
were identical.
Seasonality
For the overall seasonality, Fig. 6 presents the mean seasonal cycles for
the STL-decomposed seasonal components. We observed similar patterns in the
SSL and WNK seasonal cycles, with mean peak-to-trough amplitudes of 15.9±1.0 and 15.9±1.5 ppm, respectively. The composite data set
at Mount Zugspitze results in a lower amplitude (12.4±0.6 ppm), but
still exhibits a similar seasonality influenced by active biogenic processes
(mainly photosynthesis) in summer compared with SSL and WNK (Dettinger and
Ghil, 1998). As vegetation grows with rising temperatures (approaching
summer), CO2 levels decrease due to more and more intense
photosynthetic activities till a minimum in August. In addition, with rising
temperatures, locally influenced air masses reach Mount Zugspitze more often
due to “Alpine pumping” (Carnuth et al., 2002; Winkler et al., 2006). As
such, air sampled in summer is more frequently mixed with air from lower
levels, which is characterized by lower CO2 concentrations,
intensifying the August minimum. Anthropogenic activities and plant
respiration dominate the increases in concentration in the winter (January
to April). This influence appears to be stronger at SSL and WNK than at
Mount Zugspitze. Lower levels of CO2 and a 1-month delay, from
February to March, of the seasonal maximum at Mount Zugspitze are in
agreement with the expectation of thermally driven orographic processes that
drive the upward transport of CO2 from local sources, as well as
limited human access to Mount Zugspitze and the prevailing absence of
biogenic activities at such high elevations. Regarding the resulting
seasonal cycles based on ADVS-selected Zugspitze data sets, similar patterns
were observed but with a lower amplitude (10.5±0.5 ppm) as well as a
2-month shift of the seasonal maximum to April.
Mean CO2 seasonal cycles from the STL seasonal component at
each measurement site or location. Uncertainties at the 95 % confidence
interval are shown by the shaded areas with corresponding colors.
The Mauna Loa CO2 record is characterized by a seasonal maximum in May
and a minimum in September, with a peak-to-trough amplitude of 6.8±0.1 ppm, which agrees with observations from Dettinger and Ghil (1998) and
Lintner et al. (2006). The ADVS-selected results for MLO also show a similar
pattern, with a lower amplitude of 6.6±0.1 ppm. Global means
exhibited the lowest seasonal amplitudes, 4.4±0.1 ppm (NOAA) and 4.8±0.0 ppm (WDCGG). Compared with WDCGG, the NOAA global mean better fits
the seasonal cycle of MLO supporting the presence of a typical marine
boundary layer (MBL) condition for the levels of background CO2 in the
atmosphere. On the other hand, the WDCGG global mean includes continental
characteristics for its calculation, thus exhibiting a slightly more
continental signature which can be equally seen in the seasonal cycles at
continental sites, such as Mount Zugspitze. April and October appear to be
the important months that indicate the switch of either CO2 source to
sinks or vice versa for the continent.
We then examine in more detail the seasonal cycles at ZPT, ZUG, and ZSF.
Despite the close proximity, there are differences in their seasonal
amplitudes (ZPT: 11.9±1.2 ppm; ZUG: 11.2±1.0 ppm; ZSF: 13.3±0.7 ppm). Good agreement is shown between CO2 seasonal cycles
from April to June and from October to December. However, significantly
higher levels of CO2 were evident at ZSF from January to March as well
as lower levels from July to September. After data selection with lower
seasonal amplitudes of 10.3±1.3 (ZPT_ADVS), 10.3±1.2 (ZUG_ADVS), and 10.9±0.6 ppm
(ZSF_ADVS), similar differences of the CO2 levels in the
seasonal cycles could be observed. These results indicate that factors such
as elevation and measurement surroundings strongly determine the air-mass
composition via local vertical transport. The amount of air-mass transport
via orographic lifting affects the three locations differently. The lower
elevation station, ZSF, apparently captures more mixed air masses due to a
daytime up-valley flow along the Reintal (Gantner et al., 2003) as well as a
slightly southeastern flow from the Inntal (see Fig. 1) that is less
frequent for the higher locations (ZPT or ZUG). In addition, comparably
postponed seasonal maxima at ZUG and ZPT from March to April show delayed
onset of convective upwind air-mass transport and changing planetary
boundary layer (PBL) compositions. On the other hand, these differences in
the seasonal amplitudes (even though not significant at the 95 %
confidence interval) might be influenced by a potential trend in the
seasonal amplitude over time. Such increasing trends of the seasonal
CO2 amplitudes (i.e., +0.32 % yr-1 at Mauna Loa
and +0.60 % yr-1 at Utqiaġvik, formerly Barrow, Alaska) were studied in Graven et al. (2013),
indicating an enhanced interaction between the biospheric and atmospheric
CO2 across the Northern Hemisphere.
(a) Annual ADVS-selected percentages. (b) Annual CO2 growth
rates and global means from the NOAA and the WDCGG. The calculated growth
rates are shown at the beginning of the year. Since the time period starts
in 1981, the values of growth rates start in 1982. WDCGG data are only
available starting in 1984. (c) Annual CO2 seasonal amplitudes.
Interannual variation
To study the interannual variability, we focused on the percentages of ADVS
selection, the growth rates, and the seasonal amplitudes. The annual
percentages from ADVS data selection are shown for years without missing
monthly averages (see Fig. 7a). An exceptionally high percentage at
Zugspitze in 2000 resulted from careful and intensive filtering of the
original CO2 data. The total number of original validated 30 min data
points in 2000 is only 4634, while the number of data for other years ranges
from 8754 to 15 339 (except for 1998, with only 6-month data, the total
number of 30 min CO2 data is 6441). As described in the previous
section, the annual growth rates are plotted in Fig. 7b. The annual CO2
seasonal amplitudes are calculated as the difference between the yearly
maximum and minimum monthly CO2 values from the STL-decomposed seasonal
components (see Fig. 7c).
Focusing on the annual percentages from ADVS-selected representative data
after 1990, we calculated the mean annual percentages at Mount Zugspitze
locations, for the time periods between 1990 and 2001 (2000 was not included
for ZUG) and 2002 and 2016. We observe significantly higher percentages at
ZPT and ZUG (18.5±2.4 %) than at ZSF (13.6±1.1 %) at the
95 % confidence interval. These percentages are different from SSL (4.2±0.5 % vs. 4.2±0.6 %) and MLO (43.5±1.4 % vs. 42.1±1.6 %). A likely explanation is that there are systematically
different air-mass transport characteristics reaching each of these
locations. Higher percentages at ZPT and ZUG indicate that these locations
are capable of capturing more air masses that have traveled over long
distances along the mountains. These air masses trap air that ascends from
many Alpine valleys, but also from remote source regions up to the intercontinental scale (Trickl et al., 2003; Huntrieser et al., 2005). On
the other hand, ZSF is dominated by mixing air masses that have traveled
along the Zugspitzplatt area, which contain higher levels of CO2 due to
daily, local anthropogenic sources during winter and convective upwind
transport during seasons without snow cover that are characterized by lower
concentrations of CO2 at lower altitudes. Such patterns in the data are
also evident in the annual growth rates and seasonal amplitudes. The overall
patterns at Mount Zugspitze agree with SSL and WNK. However, SSL and WNK
exhibit more variation in the annual growth rates and higher seasonal
amplitude levels (see Fig. 7b and c). In addition, slightly higher seasonal
amplitudes for the WDCGG global mean compared with the NOAA one can be
explained by the WDCGG global mean calculation method, which includes more
continental stations (WMO Greenhouse Gas Bulletin, 2018).
Anomalies in the annual growth rates are frequently observed, which are
possibly explained by climatic influences such as the El Niño–Southern
Oscillation (ENSO), volcanic activity, and extreme weather conditions
(Keeling et al., 1995; Jones and Cox, 2001; Francey et al., 2010; Keenan et
al., 2016). One of the largest positive annual growth rate anomalies
occurred in 1998 and is clearly seen in all the records (aside from ZUG with
missing values), which is attributed to a strong El Niño event (Watanabe
et al., 2000; Jones and Cox, 2005). Similar signals are found in 1988,
especially at MLO and in global means. Such anomalies are more clearly
observed in the global and seaside time series. Regarding continental sites,
interannual signals may be hidden by more intense land influences rather
than global effects. Moreover, positive consecutive anomalies between 2002
and 2003 are clearly observed at ZSF and SSL, which are potentially due to
anomalous climatic conditions, such as the dry European summer in 2003 that
led to an increasing number of forest fires. These events are also
observable in the MLO and global means but at a smaller scale (Jones and Cox,
2005). At all German sites, clear negative anomalies, due to violent
eruptions of the El Chichón and Mt. Pinatubo volcanoes and the
subsequent volcanic-induced surface cooling effect are observed after
stratospheric aerosol maxima above Garmisch-Partenkirchen in 1983 and 1992,
respectively (Lucht et al., 2002; Frölicher et al., 2011, 2013; Trickl et al., 2013). This effect is only slightly visible in
the MLO and global means despite the fact that volcanic aerosols spread over
the entire globe.
However, the reasons for some anomalies are still unclear. These include the
negative anomalies during 1985 and 1986 at all Germans sites. Certain
anomalies in the annual percentages and seasonal amplitudes also derive from
extremely low ADVS selection percentages beginning in 1984 and continuing
until 1990, with peaks in seasonal amplitudes between 1985 and 1986. This is
the reason why we calculated the mean annual ADVS selection percentage
beginning at 1990. We assume that local influences mask similar physical
mechanisms at the sites. However, annual percentages at the MLO also have
similar characteristics. Therefore, it is still unclear what triggers such
distinct interannual data variability across measurement sites. Another
clear negative annual growth rate anomaly occurred in 2014 across all sites.
Such anomalies still require further investigation, but are beyond the scope
of this study.
Conclusions
In this study, we presented a time series analysis of a 36-year composite
CO2 measurement record at Mount Zugspitze in Germany, together with a
thorough study of the weekly periodicity combined with diurnal cycles. Even
though it is challenging to quantify local sources and sinks, this study
shows that it is possible to gain information on variation in this regard.
Compared with the GAW regional observatories at Schauinsland and Wank Peak,
as well as the GAW global observatory at Mauna Loa, Mount Zugspitze proves
to be a highly suitable site for monitoring the background levels of air
components using proper data selection procedures. The long-term trend at
Zugspitze agrees well with that at Mauna Loa and global means. The
seasonality and short-term variations show similar patterns, but are
considerably less influenced by local to regional mechanisms than the lower
elevation stations at Schauinsland and Wank Peak. Interannual variations
also correlate well with anomalous global events. However, several anomalies
still exist across most stations that lack clear explanations. These
anomalies require further investigation, possibly by analyzing correlations
between extreme events and historical meteorological or hydrological data.
Finally, we conclude that, at Zugspitze, we cannot neglect local to regional
influences. Regarding the seasonal amplitude, Mount Zugspitze is
significantly more influenced by biogenic activity, mostly in the summer,
compared with Mauna Loa and global means. On the other hand, the weekly
periodicity analysis provides a clear picture of local CO2 sources that
potentially result from human working activities, especially at ZSF.
Overall, this study provides detailed insights into long-term atmospheric
CO2 measurements, as well as site characteristics at Mount Zugspitze.
We propose the application of this type of analysis as a systematic tool for
the physical and quantitative classification of stations with respect to
their lower free tropospheric representativeness. As an additional component
in this analysis, weekly periodicity can be used to analyze anthropogenic
influences. The systematic application of this approach to larger
continental or global regions can serve as a basis for more quantitative
analyses of global greenhouse gases trends such as CO2. Based on the
physical foundation of the methodology presented here, we suggest that these
techniques can be applied to other greenhouse gases such as SF6,
CH4, and aerosols.
NOAA global mean data are available at ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt
(last access: 23 January 2019).
WDCGG global mean data are available at https://gaw.kishou.go.jp/publications/global_mean_mole_fractions (WDCGG, 2019a).
CO2 records (also including CO and NO) of all GAW observatories which
were used in this study are available from the World Data Centre for
Greenhouse Gases (WDCGG) at https://gaw.kishou.go.jp/ (WDCGG, 2019b).
The daily passenger number data for Zugspitze were provided by the Bayerische Zugspitzbahn railway company.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-999-2019-supplement.
YY, LR, HP, and AM designed the study and YY performed the data analyses with help from LR and HP
for the data processing and code validation. Atmospheric measurement data were collected, preprocessed, and provided by
LR, TT, CC, RS, and FM. Information about data quality assurance and measurement site was provided by LR. YY prepared
the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of
interest.
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 19th
WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases, and Related Measurement Techniques (GGMT-2017), Empa Dübendorf, Switzerland, 27–31 August 2017.
Acknowledgements
This study was supported by a scholarship from the China Scholarship Council
(CSC) under grant CSC no. 201508080110. We acknowledge support from a
MICMoR fellowship through the KIT/IMK-IFU to Ye Yuan. Our thanks go to
Gourav Misra for the geographical map of the measurement locations. Our
thanks go to James Butler and Kirk Thoning from NOAA for their indispensable
discussions on the problematic nature of representing and comparing data on
different older and current CO2 scales. The CO2, CO, and NO
measurements at Zugspitze Schneefernerhaus and at Platform Zugspitze of the GAW
global observatory Zugspitze/Hohenpeissenberg and CO2 measurements at
Schauinsland are supported by the German Environment Agency (UBA). The
IMK-IFU provided data from the Zugspitze tunnel and summit. Our thanks go to
Hans-Eckhart Scheel from the IMK-IFU for his high-quality data measurement
until 2001 at the Zugspitze Summit (ZUG). For a long period, Hans-Eckhart Scheel,
who passed away in 2013, led the in situ measurement program at the
Zugspitze summit with a high level of expertise and diligence. Former IFU
staff members helped us to reconstruct details of the measurements. We would
also like to thank the operating team at the Environmental Research Station
Schneefernerhaus for supporting our scientific activities and the
Bavarian Ministry for Environment for supporting this High Altitude Research
Station. Finally, our gratitude goes to the Bavarian Zugspitze railway
company for the passenger data for 2016.
This work was supported by the German Research Foundation (DFG) and the Technical University of Munich
(TUM) in the framework of the Open Access Publishing Program.
Edited by: Rachel Law
Reviewed by: two anonymous referees
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