Critical data selection is essential for determining
representative baseline levels of atmospheric trace gases even at remote
measurement sites. Different data selection techniques have been used around
the world, which could potentially lead to reduced compatibility when
comparing data from different stations. This paper presents a novel
statistical data selection method named adaptive diurnal minimum variation
selection (ADVS) based on CO2 diurnal patterns typically occurring at
elevated mountain stations. Its capability and applicability were studied on
records of atmospheric CO2 observations at six Global Atmosphere Watch
stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick
(Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland
(Germany), and Hohenpeissenberg (Germany). Three other frequently applied
statistical data selection methods were included for comparison. Among the
studied methods, our ADVS method resulted in a lower fraction of data
selected as a baseline with lower maxima during winter and higher minima during
summer in the selected data. The measured time series were analyzed for
long-term trends and seasonality by a seasonal-trend decomposition technique.
In contrast to unselected data, mean annual growth rates of all selected
datasets were not significantly different among the sites, except for the
data recorded at Schauinsland. However, clear differences were found in the
annual amplitudes as well as the seasonal time structure. Based on a pairwise
analysis of correlations between stations on the seasonal-trend decomposed
components by statistical data selection, we conclude that the baseline
identified by the ADVS method is a better representation of lower free
tropospheric (LFT) conditions than baselines identified by the other methods.
Introduction
Continuous in situ measurements of greenhouse gases (GHGs) at remote
locations have been established since 1958 (Keeling, 1960). Knowledge of
background atmospheric GHG concentrations is key to understanding the global
carbon cycle and its effect on climate, as well as the GHG responses to a
changing climate. A critical issue when using data from remote stations
remains the identification of time periods that are representative of larger
spatial areas and their differentiation from periods influenced by local and
regional pollution. If these two regimes are well disaggregated, the
available datasets can represent more reliable information about long-term
changes of undisturbed atmospheric GHG levels or be used to investigate
local and regional GHG sources and sinks when specifically analyzing
deviations from baseline conditions. In this study, the baseline
conditions refer to a selected subset of data from the validated dataset,
representing well-mixed air masses with minimized short-term external
influences (Elliott, 1989; Calvert, 1990; Balzani Lööv et al., 2008;
Chambers et al., 2016).
Measurement results depend on sampling methods, analytical instrumentation,
and data processing. Validated data (labeled as VAL in this study to
differentiate from the selected data) are usually obtained after signal
correction, for example due to interferences from other GHGs such as water
vapor, calibration accounting for sensitivity changes of the analyzer, and
validation based on plausibility checks. Baseline data selection starts with
validated data and identifies in subsequent steps a final subset of the
validated dataset based on predefined criteria for specific qualities such
as representativeness. These data will be referred to as “selected baseline
data” or simply as “selected data” in the following.
Data selection methods can be categorized into meteorological, tracer, and
statistical selection methods (Ruckstuhl et al., 2012; Fang et al., 2015).
Meteorological data selection makes use of the meteorological information at
the measurement sites, which provides valuable information about the
surrounding environment as well as air mass transport (Carnuth and Trickl,
2000; Carnuth et al., 2002). Forrer et al. (2000), Zellweger et al. (2003),
and Kaiser et al. (2007) intensively studied the relationship between
measured trace gases (such as O3, CO, and NOx) and meteorological
processes at Zugspitze, Jungfraujoch, Sonnblick, and Hohenpeissenberg. For
CO2, the most common parameters applied in the literature are wind
speed and wind direction. They can provide information on critical
variations at stations with sources and sinks in their vicinity, while these
parameters are less suited at stations in largely pristine environments. For
example, Lowe et al. (1979) performed a pre-selection on the CO2 record
at Baring Head (New Zealand) using periods with southerly winds only (clean
marine air). Massen and Beck (2011) found that the CO2 versus wind
speed plot can be valuable for baseline CO2 estimation without a local
influence of continental measurements. Another widely used data filtering
method is fixed time window selection, by selecting data in a certain time
interval of the day based on local and mesoscale mechanisms of air mass
transport. For selecting well-mixed air at elevated mountain sites,
nighttime is usually chosen with a special focus on the exclusion of
afternoon periods due to the influence of convective upward transport
(Bacastow et al., 1985). Brooks et al. (2012), for example, limited their
mountaintop CO2 results in the Rocky Mountains (USA) by “time-of-day”
from 0 a.m. till 4 a.m. local time (LT) to increase the likelihood of
sampling the free tropospheric environment at the station. Apart from this,
modeling techniques such as backward trajectories are very helpful for
analyzing the origins and transport processes of air masses
arriving at the station in detail (Cui et al., 2011). Uglietti et al. (2011) focused
on the origins of atmospheric CO2 at Jungfraujoch (Switzerland) by the
FLEXible PARTicle dispersion model. Using tracers, data selection can be
performed by investigating the correlations between the air components of
interest. Many tracers have been tested and compared with CO2.
Threshold limits of 300 ppb for CO and 2000 ppb for CH4 were defined by
Sirignano et al. (2010) to perform a regional analysis of CO2 data at
Lutjewad (the Netherlands) and Mace Head (Ireland). Similar approaches with
black carbon and CH4 were performed by Fang et al. (2015) at Lin'an
(China). Moreover, Chambers et al. (2016) applied a data selection technique
to identify baseline air masses using atmospheric radon measurements at the
stations Cape Grim (Australia), Mauna Loa (Hawaii, USA), and Jungfraujoch
(Switzerland).
Unlike most of the methods mentioned above, which require additional data or
advanced transport modeling, statistical data selection only relies on the
time series of interest and typically investigates the variability of
signal. It is usually assumed that the most representative CO2 data are
found during well-mixed conditions revealing small variations in time
(Peterson et al., 1982) and in space (Sepúlveda et al., 2014). For
continuous measurements, it is possible to investigate within-hour and
hour-to-hour variability in the datasets. The within-hour variability is
often expressed as the standard deviation of the measured data within 1 h.
The hour-to-hour variability compares the differences between hourly
averaged concentrations either during a certain time period, or from one
hour to the next. Pales and Keeling (1965) marked ambient data as
“variable” when the within-hour variability for the air sample was
significantly larger than the within-hour variability for the reference gas.
Consequently, they only considered CO2 data to belong to background
conditions when the concentrations were in “steady” conditions for 6 h or
more. Similarly, Peterson et al. (1982) rejected sampled CO2 data
values for adjacent hours when the hour-to-hour variability exceeded 0.25
ppm. Thoning et al. (1989) combined these two strategies using an iterative
approach by selecting data according to deviations of daily averages from a
spline curve fit. Ruckstuhl et al. (2012) developed a method based on robust
local regression, called “Robust Extraction of Baseline Signal”, to estimate
the baseline curves generalized for atmospheric compounds, which is
available in the R package IDPmisc (Locher and Ruckstuhl, 2012).
Locations of six European elevated mountain stations. Symbols from
left to right stand for: IZO – Izaña, Spain; SSL –
Schauinsland, Germany; JFJ – Jungfraujoch, Switzerland;
HPB – Hohenpeissenberg, Germany; ZSF –
Schneefernerhaus-Zugspitze, Germany; SNB – Sonnblick, Austria.
The present study focuses on the comparison of results from previous
statistical data selection methods with the new adaptive diurnal minimum
variation selection (ADVS) method proposed in this study. The ADVS is seen
as a possible alternative to already known data selection methods as
discussed above. The results obtained with ADVS for the atmospheric CO2
records from six European mountain stations are compared with those derived
from three other statistical data selection methods. To investigate the
potential influences of trend and seasonality, further analyses focus
on the decomposition of validated and selected datasets into trend and
seasonal components. Finally, differences between ADVS and other data
selection methods are assessed by correlation analysis.
MethodsCO2 measurements at elevated European sites
CO2 measurements from six European mountain stations (see Fig. 1) within
the Global Atmosphere Watch (GAW) network were used. The data were taken from
mountain stations due to their remote locations, being subjected to limited
anthropogenic influence and this provided increased representativeness. Three high alpine
measurement sites were included: Zugspitze-Schneefernerhaus (ZSF,
DE, 47∘25′ N, 10∘59′ E, 2670 m a.s.l.), Jungfraujoch
(JFJ, CH, 46∘33′ N, 7∘59′ E, 3580 m a.s.l.),
and Sonnblick (SNB, AT, 47∘03′ N, 12∘57′ E,
3106 m a.s.l.). They are often above the planetary boundary layer (PBL) and
thus exposed to free and presumably clean lower tropospheric air masses, but
periodically influenced by regional emissions from lower altitudes.
Additionally, to test data selection for a less remote environment, CO2
measurements were investigated from Schauinsland (SSL, DE, 47∘55′ N,
7∘55′ E, 1205 m a.s.l.) at a much lower elevation, in the
mid-range Black Forest. Data selection was also applied to
three recently started CO2 time series from different sampling heights
above ground on a tall tower at the Hohenpeissenberg observatory
(HPB, DE, 47∘63′ N, 11∘01′ E, 934 m a.s.l.),
located in the northern foothills of the Alps. Henne et al. (2010) presented
a method of categorizing site representativeness based on the influence and
variability of population and deposition by the surface fluxes. JFJ
and SNB were classified as “mostly remote,” while ZSF was
considered as “weakly influenced, constant deposition,” and SSL
and HPB were considered as “rural” (Henne et al., 2010). Finally,
the station Izaña on Tenerife Island (IZO, ES,
28∘19′ N, 16∘30′ W, 2373 m a.s.l.) in the North
Atlantic was chosen as a reference due to its location above the subtropical
temperature inversion layer, which means that the station is rarely affected
by any local or regional CO2 sources and sinks (Gomez-Pelaez et al.,
2013).
Information of measured CO2 datasets at six GAW mountain
stations.
Station (GAW ID)Sampling elevation (a.s.l.)Time period (yyyy.mm)Data providerHohenpeissenberg (HPB)984/1027/1065 m2015.09–2016.06DWDSchauinsland (SSL)1210 m2010.01–2015.12UBA-DeIzaña (IZO)2403 m2010.01–2015.12AEMETZugspitze-Schneefernerhaus (ZSF)2670 m2010.01–2015.12UBA-DeSonnblick (SNB)3111 m2010.01–2015.12UBA-AtJungfraujoch (JFJ)3580 m2010.01–2015.12Empa
For this study, unless otherwise indicated, hourly data were used
consistently for the purpose of evaluating the data selection method since
the method should be easily applicable to data obtained from standard data
centers such as the World Data Centre for Greenhouse Gases (WDCGG) where
data are commonly stored with hourly resolution. The validated CO2
hourly averages from all stations were downloaded from WDCGG
(http://ds.data.jma.go.jp/gmd/wdcgg/). Data with higher time resolution
required for some sensitivity analysis in this study were provided directly
by the station investigators. All time stamps refer to the beginning of the
averaging interval. Descriptions of the sampling elevation and time period
of available data are given in Table 1. Further information on each station
can be found in Schmidt et al. (2003) for SSL, Gilge et al. (2010) for HPB and
SNB, Gomez-Pelaez et al. (2010) for IZO, Risius et al. (2015) for ZSF, and Schibig et
al. (2015) for JFJ. Practical data selections and analyses in this study were
performed using the R Statistical Environment (R Core Team, 2017).
ADVS
ADVS is a tool for automated and systematic analysis of diurnal CO2
cycles at elevated mountain stations in order to select consecutive time
sequences with minimum variation, which can be regarded as representing
well-mixed air conditions. Even though such measurement sites are remotely
located, the CO2 levels are still influenced by local sources and
sinks. For example, at ZSF, these can be characterized by episodic CO2
enhancements due to anthropogenic emissions, detectable especially in winter
during the day, whereas in summer the convective upwind transport results in
episodes with depleted CO2 concentrations due to photosynthetic uptake
of CO2 at lower altitudes. Although high altitude mountain stations do
not have vegetation in their surroundings, mountain stations at lower
altitudes that are still in the vegetation zone may be influenced by plant
respiration, especially at night. As these effects of upward transport
photosynthesis and respiration all vary diurnally, the basic strategy that
we follow in this study is to identify the most stable time periods of the
day, i.e., periods with minimum variation, which in turn can be used for
selecting representative data. However, the duration of this time window
during the day varies with the season and from day to day because of
variations in the dynamics of transport to the site (e.g., Birmili et al.,
2009; Herrmann et al., 2015). In summer, larger variabilities in the
CO2 signal are observed due to more prevalent convective boundary-layer
air-mass injections influencing the diurnal pattern, resulting in shorter
periods of stable conditions, whereas in winter, significantly longer stable
periods occur. No upwind air masses with depleted CO2 levels due to
photosynthesis by vegetation are recorded in winter. To preserve as much
representative data as possible, it is desirable to select the time window
dynamically. ADVS is constructed to select a subset from the measured data,
being best representative for baseline conditions with an adaptive selection
time window specific for every day.
The algorithm is based on two basic assumptions. First, air masses measured
at elevated stations represent well-mixed air, closest to baseline levels,
within a certain time window of several hours during the day. For the
elevated mountain stations discussed in this paper, this time interval is
around midnight. Different diurnal patterns are apparent at each station, so
the selection time window should be adjusted accordingly. Second, it is
assumed that real baseline conditions are not subject to local influences
and thus represent unperturbed lower free tropospheric air masses. This
indicates that the variability of the measured CO2 signal should be
minimal within this selection time window. The methodological steps of ADVS
are introduced in detail below in the two sections “starting selection” and “adaptive selection”.
Starting selection
For a given validated hourly dataset, ADVS starts data selection by finding
a start time window for all days. The standardized selection procedure for the start time window results from
site-specific parameters. This time interval is set as the most stable
period from the diurnal variation. The step is referred to as starting selection. It begins by
analyzing the mean diurnal cycle of the data input.
Step 1: detrending is done by subtracting a 3-day average for each
day, including the neighboring two days. It is the shortest possible time
window to remove sudden changes in the time series related to the previous
and posterior days while preserving the diurnal pattern.
Step 2: the overall mean diurnal variation, d‾i (i=0
to 23 h), is calculated from the complete set of detrended data.
Step 3: the standard deviations sΔj from the overall
mean diurnal variation d‾i are calculated on a moving window
Δj (j=6 h). To be able to place a full set of 24 moving time
windows over the overall mean diurnal variation, time windows across
midnight (e.g., 6 h from 11 p.m. to 4 a.m. LT) are also included, that is,
its first j hours are appended to the end of the 24 h in the overall mean
diurnal variation. The time window with the smallest standard deviation is
selected as the start time window.
Result: the start time window istart,…,iend.
With the focus on elevated mountain stations, starting selection is purposely designed with
the moving window Δj of 6 h, and the starting hour istart to
be between 6 p.m. and 5 a.m. LT for this study. For other stations with
possibly different diurnal patterns, starting selection can be adjusted accordingly. For
instance, at urban stations or stations completely within the continental
PBL, the start time window can be chosen based on their best mixing conditions, which often
occur in the afternoon with a shorter moving window, when the PBL reaches
its maximum depth after “ingesting” free tropospheric air during its
growth. Being aware that calculating the start time window from all data could differ from
the start time windows calculated by season, the overall generated start time windows have been compared with
seasonally generated start time windows for high altitude mountain stations (see Supplement
Sect. S1.1). Because these differences were mostly small to moderate and this work
aims at a methodical comparison under identical conditions, the start time windows are always
derived from overall data.
Adaptive selection
The second component, adaptive selection, is designed to determine the most suitable time window for
each day, based on the data variability. Through this method, the length of the start time window is
expanded in both directions in time. Adaptive selection is performed on a daily basis,
starting with the first day of the given dataset. The following steps only
describe the forwardadaptive selection. ADVS also runs the backwardadaptive selection in an analogous manner but backwards in
time.
Step 1: the mean molar fraction x‾i, standard deviation
si, and the proportion of missing values πmissing are
calculated from data in the start time window istart,…,iend.
Step 2: if si≤0.3 ppm (CO2) and πmissing≤0.5, ADVS continues to advance in time, examine
whether the next data point xf can be included in the selection time
window W with f=iend+1. Otherwise, it is considered that the start time window does
not fulfill the assumptions. In this case, no baseline data is selected for
the present day and the algorithm proceeds to the next day.
Step 3: the absolute difference between xf and x‾i
is calculated, and the following threshold criterion is applied: xf-x‾i≤κ⋅si, where κ is the
threshold parameter. If this criterion holds, xf is included in W and
ADVS continues. Otherwise, ADVS stops for this day with only the start time window, and
proceeds to the next day.
Step 4: mean x‾W and standard deviation sW for the
new selection time window W are calculated. If sW≤0.3 ppm (CO2), ADVS continues with the next data point xf with
f=f+1. Otherwise, ADVS stops for this day with the previous selection time
window and proceeds to the next day.
Step 5: the new absolute difference between xf and
x‾W is calculated, as well as the new threshold criteria. If
condition xf-x‾W≤κ⋅sW holds,
xf is included in W and ADVS goes back to Step 4. Otherwise,
ADVS stops for this day and proceeds to the next day.
When data selection for all days is finished, ADVS continues with backward adaptive selection.
Afterwards, it proceeds to the result.
Result: this is the final selection time window, which is a
combination of Wforward and Wbackward for the day in question.
The following limitations of the forward and backward expansions of the time
window should be considered. ADVS always runs for no longer than 24 h
including the start time window, i.e., f≤24⋅ tr, where tr
is the time resolution in data points per hour of the input data. This
sometimes results in an overlap of “selected” and “unselected” data for
two consecutive days. We always label the data as “selected” once it has
been selected by ADVS. The threshold parameter κ is the controlling
factor for the length of the selection time window. As κ increases,
the length of the selection time window increases. A value of 2 was chosen
heuristically for this study as a compromise between selecting as many data
points as possible and achieving the least data variability. Similar values
of sensitivity-controlling parameters in other data selection methods can be
found (Thoning et al., 1989; Sirignano et al., 2010; Uglietti et al., 2011;
Satar et al., 2016). In Step 2, values of 0.3 ppm and 0.5 indicate the
threshold values for si and πmissing. We denote them as
si,threshold and πmissing,threshold. Less remote
stations at lower altitudes may require a larger value than 0.3 ppm because
of different mixing conditions. When performing ADVS data selection at lower
sites such as HPB and SSL, we recommend a higher
si,threshold, such as 1.0 ppm. However, throughout this study
we used the described parameter setting (0.3 ppm) for a methodical
inter-comparison of selection methods at all stations. Potential influences
of these parameter sizes (si,threshold and tr) are discussed in
Supplement Sect. S1.2 and S1.3.
Other statistical data selection methods for comparison
We compared ADVS with three statistical data selection methods. The first
method named SI is based on “steady intervals” (Lowe et al., 1979;
Stephens et al., 2013). Steady intervals, which are considered as baseline
conditions, are defined by a standard deviation being lower than or equal to
0.3 ppm for six or more consecutive hours. Although this method has some
similarity with ADVS, it treats all hours of the day equally without giving
preference to hours where the variability is, on average, the smallest.
Second, we adopted a method applied by NOAA ESRL, which originated from
Thoning et al. (1989). This selection routine has been applied specifically
for measurements of background CO2 levels at Mauna Loa. This method
(referred to as THO) was applied as described on the website:
http://www.esrl.noaa.gov/gmd/ccgg/about/co2_measurements.html. The first step of THO examines the within-hour
variability by selecting hours with hourly standard deviation less than 0.3 ppm. For the hourly data used in this study, the within-hour variability is
not applicable so that the first step is skipped. Second, it computes hourly
averages and checks the hour-to-hour variability by retaining any two
consecutive hourly values where the hour-to-hour difference is less than
0.25 ppm. The last step is based on the diurnal pattern (similar to ADVS),
by excluding data from 11 a.m. to 7 p.m. LT due to transported air
influenced by photosynthesis.
The last method compared is a moving average technique (MA). A moving time
window of 30 days and a threshold criterion of two standard deviations from
the moving averages were applied to discard outliers. Afterwards, new moving
averages and new threshold criteria were calculated for data exclusion. This
step is repeated until no more outliers were found. A more detailed
description can be found in Uglietti et al. (2011) and Satar et al. (2016).
Seasonal-trend decomposition STL
To analyze the results from different data selection methods and compare
them with the original validated datasets, we applied the seasonal-trend
decomposition technique based on locally weighted regression smoothing
(Loess), named STL (Cleveland, 1979; Cleveland et al., 1990). STL has been
widely applied to measurements of atmospheric CO2 and other trace gases
(Cleveland et al., 1983; Carslaw, 2005; Brailsford et al., 2012;
Hernández-Paniagua et al., 2015; Pickers and Manning, 2015). It
decomposes a time series of interest into a trend component T, a seasonal
component S, and a remainder component R, which allows detailed separate
analyses of trend and seasonality. Two recursive procedures are included in
the STL technique: an inner loop where seasonal and trend smoothing based on
Loess are performed and updated in each pass, and an outer loop that
computes the robustness weights to reduce the influences of extreme values
for the next run of the inner loop (Cleveland et al., 1990).
For this study, we used the implemented function stl in R (R Core Team,
2017). Owing to functional limitation of stl, full time coverage of monthly
data is needed in order to reduce the risk of large time gaps or unequal
spacing (Pickers and Manning, 2015). All data were first aggregated to
monthly averages. Then, missing data were substituted by linear
interpolation, using R function na.approx (Zeileis and Grothendieck, 2005). For the application of STL, two parameters
need to be specified, which are the seasonal smoothing parameter
n(s) (s-window in function stl) and the trend smoothing
parameter n(t) (t-window in function stl). As n(s)
and n(t) increase, the seasonal and trend components get smoother
(Cleveland et al., 1990). For optimal compatibility in this study, the same
parameters were chosen for all stations as n(s)=7 and
n(t)=23, based on the recommendation of Cleveland et al. (1990).
Another parameter combination of n(s)=5 and n(t)=25
was also tested according to Pickers and Manning (2015), but with no
significant differences in results.
Results and discussionStart time window
ADVS was applied to the validated hourly averages from all six stations with
the parameter settings as described above. The detrended mean diurnal cycles
were obtained together with the start time window for each station by starting selection (see Fig. 2, for
conventional mean diurnal plots see Supplement Sect. S2). The observed differences
in the start time windows, as well as in the widths of the confidence intervals (gray shades),
reflect the characteristics of differently situated measurement sites and
different sampling levels. The first subplot column (HPB50, HPB93, and HPB131), representing
the three sampling heights at HPB, shows similar detrended diurnal patterns
with similar start time windows. The slightly different start time window at HPB131 potentially indicates different
dynamics of the atmospheric transport at higher elevation. The decreasing
amplitude with increasing sampling height indicates that the higher the
sampling inlet is above the ground, the less it is affected by the local
surface fluxes. The three start time windows suggest that the most stable period at HPB occurs
during the last few hours of a day, including midnight. However, in
contrast to all other stations covering at least a full year, HPB data are only
from September of 2015 to June of 2016. The results may not be fully
comparable, but instead it shows that the data selection method is also
applicable to data with time periods shorter than one year.
Detrended mean diurnal cycles of validated CO2 datasets (black)
with 95 % confidence intervals (gray) from six GAW stations (hours in
LT). Measurements at HPB are differentiated by the sampling heights
(e.g., HPB50 for 50 m a.g.l.). The covered time periods (top
text), resulting start time windows (middle text, also in light blue
shades), and mean diurnal amplitudes (bottom text) are shown in each
subplot.
Regarding the second subplot column (SSL, SNB, and IZO), the start time windows can be found from
midnight on or later in the morning. The start time window for SSL encompasses its diurnal
maximum, indicating that data variability is considerably smaller in the
early morning than in the afternoon because of its vicinity to the Black
Forest region, which has strong influence due to local photosynthetic
activity (Schmidt et al., 2003). A similar diurnal pattern can be found at
SNB. The influence of CO2 sources is not as prominent as the effect of
distant CO2 sinks, since it is situated at the isolated summit peak of
Hoher Sonnblick surrounded only by mountains and glaciers, with a negligibly
small number of tourists, thus anthropogenic activities are minimal. IZO is a
special case, since it is located on a remote mountain plateau on the Island
of Tenerife above the strong subtropical temperature inversion layer. Even
though the start time window is limited to 6 h, IZO presents an ideal mean diurnal cycle for
data selection from a potentially much longer time window.
In the right column of the figure, both ZSF and JFJ find their start time windows around midnight
(including hours after midnight). ZSF shows higher diurnal CO2 amplitude
than JFJ, but the two sites show similar diurnal patterns. For the choice of
the start time window from the mean diurnal variation, relatively close or even local
anthropogenic sources may influence the CO2 at these two stations,
possibly due to touristic influences.
Percentage of selected data
Starting from the initial start time windows, ADVS selected the baseline data for all stations
(see Fig. 3). In addition, we calculated the percentages of the complete
datasets selected by ADVS as baseline data, which are listed in the first
column of Table 2. The higher the percentage the more well-mixed air is
measured at the station, which is assumed to be a representation of lower
free tropospheric conditions. This holds especially for IZO, where a larger
percentage of 36.2 % was selected as baseline data. The sites with
intermediate percentages are JFJ (22.1 %), SNB (19.3 %), and ZSF (14.8 %). For
the three sampling heights at HPB, only 3.2 % (50 m), 4.8 % (93 m), and
6.2 % (131 m) of the data were selected by ADVS. Finally, a similarly low
percentage was found for SSL (4.0 %), probably due to its higher data
variability.
Time series plots of validated CO2 datasets (gray), and
selected datasets by ADVS (black) at six GAW stations.
Table 2 clearly indicates that the percentage of baseline data increases
with altitude for all methods, suggesting measurements at higher altitudes
can capture progressively well-mixed and hence representative air. Based on
this finding, a linear least squares regression was applied between the
absolute altitudes and the percentages of selected data for continental
stations. IZO is on a remote island and therefore not comparable. This approach
reveals a significant positive linear trend (see coefficient in Table 2).
The related figure of linear regression can be found in Supplement Sect. S3.1.
To examine the characteristic growth of the percentages of selected data by
ADVS during the selection process, we additionally calculated percentages
after completing both the starting selection and adaptive selection steps mentioned in Sect. 2.2 (see
Supplement Sect. S3.2). All results of percentages show an order of stations
similar to that above, and the percentages increase steadily step by step
for all stations. The percentages of selected data by ADVS were then
compared with those of the mentioned statistical data selection methods SI,
THO, and MA (see Table 2, with the corresponding figure shown in Supplement
Sect. S3.3).
Percentage of selected data in all data by different data selection
methods. The bottom shows the linear regression coefficients of station (HPB
is represented by HPB50; IZO is excluded) altitudes
and the percentages of selected data at the significance level of 0.05
(∗∗∗).
Station IDADVSSITHOMAHPB503.213.921.779.8HPB934.818.525.079.4HPB1316.221.327.379.8SSL4.017.925.483.2IZO36.282.256.060.5ZSF14.847.140.879.0SNB19.358.744.276.9JFJ22.162.146.377.6Linear regression0.996∗∗∗0.992∗∗∗0.985∗∗∗0.645coefficient (γ2)
Since the percentages of selected data indicate not only the amount of data
declared as representative but also show the characteristics of the
selection methods, this criterion is used for further assessment. All other
methods except for MA result in higher percentages for higher altitude
stations (IZO, ZSF, SNB, and JFJ) than for those of lower altitudes (HPB and SSL). ADVS always
performs the strictest filtering in all cases. Based on the stepwise study
(see Supplement Sect. S3.2), these low percentages are primarily due to the
restrictive definition of the start time window requiring data with a standard deviation of
less than 0.3 ppm. With adaptive selection, the percentages of selected data increase but
remain lower than those of the other methods. SI and THO, in comparison,
show differences between stations at high and low elevations. Compared with
SI, THO is higher at stations at lower elevations, but lower at high ones. A
major limitation of SI seems to be the requirement for consecutive hours, in
our case of 6 h with 0.3 ppm standard deviation threshold, which might be
too restrictive for stations at lower elevations. However, this criterion
results in a fairly large percentage for stations at high elevations. At
ZSF, SNB, and JFJ, it results in the second largest, and even the largest in the case
of IZO.
STL decomposition results from VAL (black), SI-selected (brown),
THO-selected (yellow), and ADVS-selected (green) datasets at five GAW
stations.
The highest percentages of selected data (approximately 80 %) were
obtained with MA at most stations except for IZO. However, IZO obtains the largest
percentages from all other selection methods. This is probably caused by the
very low variability of CO2 at IZO, resulting in overly strict moving-average
thresholds for the MA method. Thus, we conclude that MA does not work
properly in the case of very well-mixed air (IZO). At all other stations, it is
possible that MA declares too much data as representative. Therefore, MA was
excluded from further analyses.
STL components
STL was applied to the validated datasets before and after baseline
selection with SI, THO, and ADVS, except for HPB due to its limited length of
time (less than one year). Depending on data availability, STL was performed
on CO2 data from 2012 to 2015 at SNB, while data inputs at SSL, IZO, ZSF, and JFJ cover
the whole period from 2010 to 2015. Figure 4 gives an overview of the
decomposition by STL. The following sections discuss the resulting
components obtained by STL, namely the trend component, the seasonal
component, and the remainder component.
Trend component
From the trend components, the mean annual growth rates were estimated by
linear regression (see Table 3). Based on the 95 % confidence intervals
for the slope, positive trends i.e., increasing CO2 concentrations are
observed. Owing to the overlap of the confidence intervals, differences in
the mean annual growth rates among VAL and selected datasets at the same
station are all in good agreement. This indicates that the trend component
is not significantly influenced by the statistical data selection method,
which agrees well with the finding of Parrish et al. (2012) from a study of
baseline ozone concentrations that there were no significant differences of
the long-term changes between the baseline and unfiltered datasets.
Moreover, the following fact is observed for all sites except for SSL. Compared
to unselected data (VAL), the mean annual growth rates based on selected
datasets are systematically higher approaching the growth rates at IZO. IZO can be
considered as better representing the lower free tropospheric conditions and
agrees well with the mean annual global CO2 growth rates (2.31 ppm)
during the same time period (2010–2015) based on data from
https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html. The exception at
SSL is probably caused by stronger local influences as a result of its lower
elevation. In addition, the confidence intervals of the mean annual growth
rates are always smaller after data selection, which improves the precision
of trends.
Mean annual growth rates (ppm yr -1) with 95 % confidence
intervals from linear regression, applied on the trend components by STL over
2010 to 2015, except for SNB. Data at SNB were decomposed over 2012 to 2015
due to missing data from 2010 to 2011 and thus shown in italic font.
The resulting seasonal components show systematic differences between VAL
and selected datasets. The mean monthly variations were calculated on a
monthly scale over the entire period from the analyzed data. Figure 5a
and b present the results at stations ZSF and IZO. At most stations (except for
IZO), the seasonal amplitudes have been substantially reduced compared to VAL
(see also Fig. 4). At ZSF, the averaged peak-to-peak seasonal amplitude,
defined as mean seasonal maximum minus seasonal minimum, drops the most by
18.9 % from VAL with the ADVS selected dataset. An explanation of this
reduction is CO2 signal exclusion from local sources and sinks by data
selection. When taking a closer look at the monthly averages, lower CO2
values are found in the selected datasets in the winter months from October
to April, indicating that the CO2 concentrations estimated by VAL are
above the background levels because of more dominant anthropogenic
activities and no active vegetation. Higher values in the summer months from
May to September explain underestimation of VAL due to intensified upward
transport of photosynthetic signatures resulting from vegetation. Similar
patterns can be found at stations SSL, SNB, and JFJ (see Supplement Sect. S4). IZO always
shows the smallest seasonal amplitude and there is almost no difference
between VAL and selected datasets. Based on this consideration, it is very
likely that the lower free troposphere will react with a delay to CO2
concentration changes of effective sources and sinks on the ground, acting
like an atmospheric memory.
Mean monthly variation of the seasonal component decomposed by STL
at (a)ZSF and (b)IZO over the whole
period. For a better visualization of the results of selection methods, dots
have been separated horizontally and equidistantly. The 95 % confidence
intervals are shown as error bars.
A time delay of one month in the mean seasonal maximum is shown in Fig. 5a at ZSF with selected datasets by SI and ADVS (March), compared with the
maximum from the validated data (February). A similar time shift can also be
found by other selection methods at stations SSL (one-month delay from February
to March by SI and ADVS) and JFJ (two-month delay from February to April by SI,
THO, and ADVS). As for station IZO (April) in Fig. 5b and station SNB (March),
the seasonal maxima stay the same. The magnitude of these delays may be
related to mixing in the lower free troposphere. Rapid changes are usually
observed close to sources and sinks, e.g., from anthropogenic and biogenic
activities. Thus, the higher the station is above the boundary layer, the
later the maxima during the winter can be observed because of the late
response due to inhibited mixing. However, this delay does not occur for the
minima during the summer because of the very effective upward transport and
more favorable mixing conditions at that time of year. Consequently, no
change in the seasonal minima is observed at all measurement sites, which is
taken as an indicator of enhanced thickness of the mixing layer as good
mixing conditions. Taking ZSF as an example, Birmili et al. (2009) observed low
concentrations of particle numbers in winter and found it representative for
the free tropospheric air by analyzing the annual and diurnal cycles. From
spring onwards, the PBL rises with increasing temperatures. The intense
vertical atmospheric exchange during summer months results in a daily air
mass transport from the boundary layer to reach ZSF due to thermal convection
(Reiter et al., 1986; Birmili et al., 2009). Thus there are optimal
transport and mixing conditions. Therefore after data selection, the timing
of seasonal peaks corresponds better among the stations.
Pearson's correlation matrices of combinations of trend and seasonal
components (T+S, a), and only remainder components (R,
b) at stations SSL, IZO, ZSF, SNB, and JFJ by different selection
methods. Correlations with no significant coefficients at the 0.05
significance level were left blank.
Remainder component
The remainder component resembles random noise from
local influences in its structure, being different from site to site and
statistically uncorrelated with the general signal of CO2
concentrations in the lower free troposphere (Thoning et al., 1989). The
standard deviation of the remainder component is taken here as a measure for
external influences (see Fig. 4). Table 4 shows the calculated standard
deviations from the remainder components at each station. Comparable results
are derived from all selected datasets. SSL, as the lowest altitude station,
exhibits the largest variation. IZO with the smallest standard deviations in
the remainder component proves to be the station least influenced by its
surrounding environment. The three alpine measuring stations (ZSF, SNB, and JFJ)
exhibit intermediate variability. From this perspective, STL performs well in showing the site characteristics. Consequently, the noise of the remainder
components, given in Table 4, decreases with increasing altitude of the
continental mountain stations, which is in inverse relation to the
percentages of selected data (Table 2). IZO was excluded in both regressions
against altitude because of its maritime character.
Standard deviations of the remainder components by STL over 2010 to
2015, except for SNB. Data at SNB were decomposed over 2012 to 2015 due to
missing data from 2010 to 2011 and thus shown in italic font.
Station IDVALSITHOADVSSSL1.611.161.261.99IZO0.340.330.300.30ZSF0.890.750.720.73SNB0.660.560.550.70JFJ0.560.450.480.47Correlation analysis
As mentioned above, data selection is defined here as an approach of
extracting a group of data to be the best representative for the lower free
troposphere. Consequently, the selected CO2 datasets should have
properties that are well correlated between the sites. For evaluating this
hypothesis, we took the combination of the trend and seasonal components from
STL and examined the correlations between each pair of stations in a Pearson
correlation matrix (see Fig. 6a). The trend and seasonal components of all
VAL and selected datasets were first compiled, and then Pearson's correlation
coefficients were calculated assuming normal distribution of data examined by
the Anderson–Darling test (P < 0.05). The correlation matrices
are shown for each data selection method individually, in order to enable a
comparison between ADVS and other methods. Data used for correlation were
chosen only when available at all stations (2012–2015). In general, most
pairs show higher correlation coefficients with selected data irrespective of
the selection method, especially between the three Alpine stations
(ZSF, SNB, and JFJ). This evaluation
shows a
similar result to the method presented by Sepúlveda et al. (2014) for
identifying baseline conditions based on the correlation between distant
measuring stations. Pairs including IZO after data selection by ADVS
show a notable increase in the correlation coefficients, meaning better
coherence between the reference station IZO and the others.
Conversely, when selecting representative data more effectively, the results
should contain less local and regional influences. Therefore, we compared the
remainder components derived from STL pairwise to check whether the Pearson
correlation coefficients decreased after data selection (see Fig. 6b). The
number of insignificant correlations between the station pairings is the
greatest for ADVS. For the only two coefficients significant at the 0.05
significance level (ZSF-SNB and
ZSF-JFJ), they drop largely from 0.75 to 0.48, and from 0.75 to 0.40,
respectively, which cannot be observed by the other selection methods. This
means that by ADVS the combination of trend and seasonal components correlate
best and the remaining unselected data have the lowest correlation among the
methods. If these two criteria are used to separate the representative part
of the data from the unrepresentative part, the ADVS method produces the best
results.
Conclusions and outlook
We presented the novel statistical ADVS method for selecting representative
baseline data for CO2 measurements at elevated GAW mountain stations.
For assessment of the data selection procedure, we applied the method to six
CO2 datasets measured at GAW mountain stations in the European Alps.
The ADVS resulted in an increasing number of percentages of selected data
representing the background conditions with growing altitude of continental
measurement sites, which is reasonable due to the underlying atmospheric
dynamics. For comparison, three well-known statistical data selection
methods were applied to the same datasets and most methods yielded similar
increasing percentages with growing altitude. Among all the methods, ADVS is
the most restrictive in terms of the number of selected data in the overall
datasets.
In addition, we applied the time series decomposition method STL to all
datasets before and after data selection. All statistical data selection
methods resulted in the same annual trend within the 95 % confidence
interval of the datasets before selection, while the seasonal signal varied
substantially with smaller seasonal amplitudes and delayed occurrences of
seasonal maxima. We also presented an additional assessment of ADVS compared
with the other statistical data selection methods based on correlation
analysis. For the combination of trend and seasonal components by STL,
higher correlation coefficients between stations were found with ADVS data
selection than SI and THO. Inversely, ADVS resulted in lower correlation
coefficients in the remainder components than the other methods. Both
indicate a better performance of selecting baseline data by ADVS.
The presented method is useful for data selection of atmospheric CO2
data representative of the lower free troposphere. It requires only
data from a single measurement site, is easily adjustable to the local
conditions, and runs automatically. The method can also be applied to
historical datasets. The results provide evidence that the proposed ADVS
method confers the possibility of selecting data that are representative of
CO2 concentrations of a larger area of the lower free troposphere. This
is an elementary prerequisite for application of the method to a larger
number of different stations and an essential step towards generalization. It
directly supports the objective of GAW to extrapolate from a set of point
measurements from single stations to a larger representative area or region
in the lower free troposphere (WMO, 2017). In future, there is a need to
test whether such results could be used for additional applications, such as
ground calibration of satellite measurements. Finally, it would be very
interesting to test as a next step whether this presented method is
applicable to stations in other regions and on other continents. Moreover,
the issue of whether and how to include coastal stations in a systematic and
practically generalizable approach for selecting representative data at GAW
stations will be a particular concern.
Data availability
Hourly CO2 data can be downloaded from WMO's World
Data Centre for Greenhouse Gases
(http://ds.data.jma.go.jp/gmd/wdcgg/cgi-bin/wdcgg/catalogue.cgi; last
access: 15 March 2018), data with higher resolution can be requested from the
station data providers.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-1501-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by a scholarship from China Scholarship Council
(CSC) under grant CSC No. 201508080110. This work was supported by a MICMoR
Fellowship through KIT/IMK-IFU to Ye Yuan. 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. The CO2 measurements at
Zugspitze and Schauinsland were supported by the German Environment Agency
(UBA). We thank Markus Wallasch for providing CO2 data obtained at
Schauinsland and Ralf Sohmer for technical support. The CO2
measurements at Hohenpeissenberg were conducted by the German Meteorological
Service within the ICOS Atmospheric Station Network. The CO2
measurements at Jungfraujoch were supported by the Swiss Federal Office for
the Environment, ICOS-Switzerland, and the International Foundation High
Alpine Research Stations Jungfraujoch and Gornergrat. Martin Steinbacher
acknowledges funding from the GAW Quality Assurance/Science Activity Centre
Switzerland (QA/SAC-CH), which is supported by MeteoSwiss and Empa. The
Izaña (IZO) CO2 measurements were performed within the GAW Program at
the Izaña Atmospheric Research Center, financed by AEMET. Finally, we
also thank Wolfgang Spangl from the Austrian Environment Agency (UBA-At) for
providing CO2 data obtained at Sonnblick.
This work was supported by the German Research Foundation (DFG)
and the Technische Universität München within the funding programme Open Access Publishing.
Edited by: Dominik Brunner
Reviewed by: Jooil Kim and one anonymous referee
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