Methane (CH4) emissions from biogenic sources,
such as Arctic permafrost wetlands, are associated with large uncertainties
because of the high variability of fluxes in both space and time. This
variability poses a challenge to monitoring CH4 fluxes with the eddy
covariance (EC) technique, because this approach requires stationary signals
from spatially homogeneous sources. Episodic outbursts of CH4
emissions, i.e. triggered by spontaneous outgassing of bubbles or venting of
methane-rich air from lower levels due to shifts in atmospheric conditions,
are particularly challenging to quantify. Such events typically last for only
a few minutes, which is much shorter than the common averaging interval for
EC (30 min). The steady-state assumption is jeopardised, which potentially
leads to a non-negligible bias in the CH4 flux. Based on data from
Chersky, NE Siberia, we tested and evaluated a flux calculation method based
on wavelet analysis, which, in contrast to regular EC data processing, does
not require steady-state conditions and is allowed to obtain fluxes over
averaging periods as short as 1 min. Statistics on meteorological conditions
before, during, and after the detected events revealed that it is atmospheric
mixing that triggered such events rather than CH4 emission from the
soil. By investigating individual events in more detail, we identified a
potential influence of various mesoscale processes like gravity waves,
low-level jets, weather fronts passing the site, and cold-air advection from
a nearby mountain ridge as the dominating processes. The occurrence of
extreme CH4 flux events over the summer season followed a seasonal
course with a maximum in early August, which is strongly correlated with the
maximum soil temperature. Overall, our findings demonstrate that wavelet
analysis is a powerful method for resolving highly variable flux events on
the order of minutes, and can therefore support the evaluation of EC flux
data quality under non-steady-state conditions.
Introduction
Methane (CH4) is one of the most important greenhouse gases
, but unexpected changes in atmospheric CH4
budgets over the past decade emphasise that many aspects regarding the role
of this gas in the global climate system remain unexplained to date
e.g..
Atmospheric CH4 increased in concentration from 722 ppb in the year
1850, i.e. before industrialisation started, to 1810 ppb in the year 2012
. Current concentration levels are the highest
reached in 800 000 years , and emissions and concentrations
are likely to continue increasing, making CH4 the second most
important greenhouse gas (after CO2) that is strongly influenced by
anthropogenic emissions . In comparison to
CO2, CH4 is characterised by a shorter atmospheric lifetime
and a higher warming potential 34 times greater, referring to a period
of 100 years and including feedbacks;. With management of
CH4 emissions being identified as a realistic pathway to mitigate
climate change effects , quantitative and qualitative
insights into processes governing CH4 sources and sinks need to be
improved in order to better predict its future feedback with a changing
climate.
The Arctic has been identified as a potential future hotspot for global
CH4 emissions , but the effective impact of rapid
climate change on the mobilisation of the enormous carbon reservoir currently
stored in northern high-latitude permafrost soils remains unclear
e.g.. Under
warmer future conditions, increased thaw depths in Arctic permafrost soils as
well as geomorphologic processes such as thermokarst lake formation are
expected to mobilise carbon pools from deeper layers ,
while at the same time the activity of methanogenic microorganisms may be
promoted. Both factors would contribute to a potential increase in
CH4 emissions from permafrost wetlands . However,
complex feedback mechanisms between climate change, hydrology, vegetation, and
microbial communities may partly counterbalance these increased emissions
. In order to improve the reliability of
simulated Arctic CH4 emissions under future climate scenarios,
several process-based modelling frameworks for predicting CH4
emissions have been improved in recent years , but the confidence in the results remains low, which can
also be attributed to a lack of high-quality observational datasets for
CH4 emissions from Arctic permafrost wetlands .
The eddy covariance (EC) method allows for accurate and continuous flux
measurements at the ecosystem scale, but strict theoretic assumptions need to
be fulfilled to ensure high-quality observations. Besides the requirement of
steady-state conditions and a fully developed turbulent flow field
, the observation of CH4 fluxes in high latitudes
requires some special considerations. These include technical challenges
related to harsh climate conditions in remote areas of the high northern
latitudes , and also problems related to atmospheric
phenomena such as very stable stratification that inhibits turbulent exchange
during polar winter. Methodological difficulties specific to CH4 also
play a role: since net CH4 emissions are not only dependent on the
production conditions for CH4 in the soil, but also on the transport
processes from soil to atmosphere, they are characterised by higher temporal
variability, compared to CO2. CH4 release through ebullition
, i.e. episodic outgassing in the form
of bubbles, typically occurs in events of only a few minutes in length, much
shorter than the common averaging interval for EC (30 min). CH4
ebullition events that simultaneously occur within large fractions of the
tower footprint thus hold the potential to violate the steady-state
assumption for EC. Also, continuous emissions may lead to the accumulation of
methane pools close to the ground during periods of very stable
stratification, and their instantaneous venting towards higher levels linked
to changes in atmospheric conditions may cause pronounced spikes in the
signal that violate the EC assumptions. Both cases would lead to systematic
biases in EC flux calculations because of an incorrect Reynolds
decomposition. As a consequence, high-emission events are likely to be
discarded from the time series as very low quality data, or outliers, which
has the potential to systematically underestimate long-term CH4
budgets .
To constrain potential systematic biases in EC data that are related to the
aforementioned effects, a direct comparison with other observation
techniques such as ecosystem chambers can be used. Experiments involving
parallel observations with both approaches have been conducted
e.g..
Chamber measurements are capable of resolving small-scale CH4
emissions properly, but in most cases they cover only a small area on the
order of up to a few metres squared. Furthermore the installation of the chamber as
well as its operation could introduce disturbances to the study area, which
might lead to biased results. Upscaling approaches from the chamber to the EC
footprint scale already exist e.g., but until now no
method has been presented that aims to calculate CH4 fluxes
directly from high-frequency EC measurements with a time resolution of about
1 min.
As a second approach to evaluate potential systematic biases in EC
CH4 fluxes, a different calculation method can be applied to high-frequency atmospheric observations that does not require the theoretic
assumptions that limit the applicability of EC .
Wavelet analyses provide this option e.g., since they can be applied to calculate fluxes for time windows
smaller than 10 to 30 min due to wavelet decomposition in time and frequency
domain without ignoring flux contributions in the low-frequency range.
Moreover, wavelet transformation does not require steady-state conditions
but can also be applied on time series containing
non-stationary power e.g.. As a drawback, the
calculation of fluxes using wavelet transform requires considerably more
computational resources even when a windowed approach is used.
The focus of the present study is on the interpretation of CH4
emission events detected by a wavelet software package
, which has already successfully
been applied to the non-steady-state fluxes during a solar eclipse
or to CH4 fluxes from a shallow lake containing
ebullition . This approach, which builds on the raw data
sampled by EC towers, allows us to resolve fluxes not only over 30 min
averaging periods, but also for an averaging interval of 1 min. Such a
higher temporal resolution facilitates detection of the exact time and
duration of non-stationary CH4 release events. The obtained results
can be directly compared against EC fluxes, where a good agreement has been
shown for times with well-developed turbulence conditions. We present an
analysis of whether peak CH4 emission events at timescales on the
order of minutes can be found in the results, and what their basic
characteristics are. Finally the study aims to find meteorological triggers
that could cause the observed events to occur.
Material and methodsStudy site
Field work was conducted at an observation site within the floodplain of the
Kolyma River (68.78∘ N, 161.33∘ E, 6 m above sea level),
situated about 15 km south of the town of Chersky in north-eastern Siberia
. The site is classified as wet tussock
tundra underlain by continuous permafrost, with very flat topography.
Averaged over the period 1960–2009, the mean annual temperature was
-11∘C, and the average annual precipitation amounts to 197 mm
.
Two EC towers were installed in summer 2013 about 600 m apart, one of them
(tower 1) focusing on an artificially drained section of the tundra site, the
other (tower 2) serving as a control site to monitor undisturbed conditions.
Both systems were equipped with the same instrumentation set-up, including a
heated sonic anemometer (uSonic-3 scientific, METEK GmbH) and a closed-path
gas analyser (FGGA, Los Gatos Research Inc.), and feature about the same
observation height (tower 1: 4.9 m a.g.l.; tower 2: 5.1 m a.g.l.).
Due to their proximity, both towers are also exposed to the same
meteorological conditions. Inter- and intra-annual variability of the
exchange fluxes of CO2 and CH4, including an analysis of
related environmental controls, are presented by . For
more details on the instrumentation set-up, please refer to
.
Raw data processing and flux calculation
The raw data on the high-frequency fluctuations of wind and mixing ratios
were collected using the software EDDYMEAS at a sampling
rate of 20 Hz. Ancillary meteorological data were acquired at 1 Hz
frequency through the LoggerNet software (Campbell Scientific Inc., Logan,
Utah, USA) on a CR3000 Micrologger (Campbell Scientific). Both programs were
running on-site on a personal computer, using the local time zone (Magadan
time, MAGT: UTC+12 h). The mean local solar noon is UTC+13 h. Within the context of
this study, datasets within the period 1 June to 15 September 2014 were
analysed.
As a first approach to calculate turbulent CH4 fluxes, we employed
the EC method using recent recommendations on correction methods and quality
assurance measures . A coordinate rotation into the
streamlines was not applied due to the very flat and
homogeneous terrain at both towers. There was no tilt in the alignment of the
sonic anemometers at both towers and after a careful inspection of the raw
data no disturbances of the streamlines due to the terrain or other
influences could be found. This allows the assumption that w¯=0 for
well-developed turbulence. We used the software package TK3
for this purpose, which includes all
necessary corrections, data quality tests , and a spike
detection test using the median absolute deviation
MAD,. TK3 has been demonstrated to compare
well with other available packages . As the
standard for the EC method, we derived turbulent fluxes with an averaging
period of 30 min.
Because highly non-steady-state conditions were expected for CH4
fluxes at this observation site, which potentially causes a serious violation of the basic
assumptions linked to the EC method , we applied a
wavelet-based calculation method as a second flux processing approach in
addition to the standard EC data processing. have
developed a method for wavelet-based flux computation that offers the
possibility of determining fluxes with a user-defined time resolution that
can be as low as about 1 min. Within the context of this study, we applied
their calculation tool with a continuous wavelet transform using the Mexican
hat wavelet (WVMh), which provides an excellent resolution of the
flux in the time domain. It should therefore be the preferred mother wavelet
to obtain an exact localisation of single events in time without losing
information in the frequency domain . For more details
on the direct implementation of the method refer to
.
For wavelet analysis the spike-corrected raw data of both
vertical wind speed w and CH4 mixing ratio c were used. The
time series was corrected for a time lag between these parameters by
maximisation of the covariances by cross-correlation for every 30 min
interval . As also stated for EC, a coordinate rotation
was not applied. Small tilt errors have no significant influences on scalar
fluxes . The cone of influence COI;
was estimated and all results are based on data not affected by edge effects.
For steady-state conditions, the wavelet and EC method have been shown to be
in very good agreement . In the case of non-steady-state conditions with contributing periods >30min, the EC
quality control tests should flag those cases to be excluded
. Additionally, in those cases the ogive test
also yields contributions to the flux
for periods >30min. Besides that, the Mexican hat wavelet will
nonetheless yield correct and trustworthy fluxes, also for periods >30min, if the chosen integration interval in the period domain is
big enough . In this study, the upper
integration limit λmax in the period domain was set to
33min. To account for low-frequency contributions in the case
study in Sect. , a second calculation was conducted,
where λmax=184min.
Detection and classification of eventsDetection of events
While spikes within the 20 Hz raw data were already identified in the
MAD test , in a first stage of the wavelet-based event
detection we conducted an additional MAD test on processed fluxes similar to
:
〈d〉-q⋅MAD0.6745≤di≤〈d〉+q⋅MAD0.6745,
where
di=xi-xi-1-xi+1-xi
parameterises the difference of the current value xi to the previous and
next value in time. 〈d〉 denotes the median of all those
double differenced values and
MAD=〈|di-〈d〉|〉.
Due to its robustness the median absolute deviation is a very good measure of
the variability of a time series and substantially more resilient to outliers
than the standard deviation . The test was applied to
Mexican hat wavelet flux with a time step of Δt=30min. If a
value di in the time series exceeded the given range in
Eq. (), it was detected as an event. A threshold
value of q=6 was found to be suitable to reliably separate events from
periods with a regular exchange flux between surface and atmosphere.
The same MAD test calculations have also been applied to the flux with
averaging interval Δt=1min. The purpose of this higher-resolution analysis was first to precisely constrain the duration of an event
down to the resolution of minutes, and second to allow the detection of exact
start and end times of events. We defined here a minimum duration of 2 min
for an event, since this way we could avoid labelling a sequence of
high-frequency spikes, which sometimes pass the TK3 spike detection
threshold, as an event.
Classification of events
The approach described above only detects 1 min steps belonging to an event,
but does not provide any knowledge about typical structures of such
contiguous single events. The term “structure” in this context refers to
the specific sequence of consecutive 1 min flux values that together form
the event: in a simple case, flux rates regularly increase until reaching a
plateau, then drop back to their starting values, with no events directly
before or afterwards. More complex events appear as clusters; i.e. during a
prolonged period of time several shorter events occur close to each other.
Since events with different structure may also be triggered by different
atmospheric conditions, we developed a basic classification to differentiate
types of events consisting of adjacent 1 min steps.
Based on the single event minutes identified by the MAD test, a manual search
for characteristic, repeating patterns within all half-hour intervals that
contained events resulted in the definition of three typical event
structures. In this context, it was found that the MAD test for a threshold
value of 4 or 6 was not always able to resolve the whole event (blue
plus signs within grey shaded event duration in
Fig. ), and thus in such cases the actual
starting and ending times of an event were corrected manually.
We labelled the first event type a single “peak event”. For this
category, in the simplest case the flux increased monotonically up to one
maximum event peak or a plateau with high flux rates, followed by a monotonic
decrease back to base level. No other events were detected within 30 min
before or after the single-peak event. As the example
(Fig. a) shows, such an
ideal sequence cannot be expected in general, but in all cases a pattern of
coherent single event minutes showing the tapering to one peak or a few
subsequent local maxima clearly suggested the classification of a peak
event. Peak events can occur as either negative or positive outliers from
the baseline flux. If a positive peak was followed or preceded by a negative
one or vice versa, both were combined into a single peak event as long as
the magnitude of the second peak was lower than one quarter that of the main
peak.
We termed the second event class “down–up” events. Down–up events had
the same basic properties as single-peak events, but in contrast they
consisted of one sharp negative and positive peak each, which were of similar
magnitude (Fig. b). If
the order of the two peaks was reversed, the process was called an
“up–down” event. Typically the two extremes within a down–up event
were separated by several minutes (e.g. 04:58 and 05:01 in
Fig. b), and such
(non-extreme) transition periods were frequently not labelled as events by
the MAD test because they did not exceed the threshold for event detection.
In this case these event minutes needed to be manually added to form a
coherent down–up/up–down event.
Examples for peak
events (a), down–up events (b), and clustered
events (c) identified using the Mexican hat wavelet flux. Data
points marked with a yellow vertical line were detected as event minute using
the MAD test with threshold q=6, while all other non-event data were marked
with blue plus signs. The manually detected event length is shaded in grey
colour.
The third class of events in our classification scheme was called
“clusters”. In this category we collected all events that did not meet
the criteria defined above for single-peak events or down–up events, instead
showing a coherent pattern but not an unambiguous structure. This was
generally the case for longer event periods that were potentially formed by
the merging of several consecutive shorter events
(Fig. c). However, in
these cases a clear distinction between individual events was impossible due to
the close succession of events over time, and the associated partial overlap.
Accordingly, the identification of meteorological triggers for single events
(see also Sect. ) was also impeded, since more than one
trigger may have been involved. We therefore handled the classification of
events very conservatively, assigning single-peak or up–down/down–up events
only in very clear cases, while all remaining events were labelled clusters.
Linking events to meteorological conditions
For all events detected within the observation period, computed flux rates as
well as prevalent meteorological conditions before, during, and after the
event were collected in a database. These conditions were available as
parameters in four different aggregation time steps: (1) CH4 flux
rates from both EC and wavelet processing as well as friction velocity
(u*) were used at 30 min intervals. (2) Longwave radiation budget (I),
air temperature (T), relative humidity (RH), and air pressure (p) came in
10 min time steps. (3) 1 min CH4 flux rates were available from the
high-resolution wavelet processing. Finally (4) wind speed (U), CH4
mixing ratios (cCH4), and wind direction (WD) were taken from
20 Hz raw data. Averages for the period during the event were aggregated
between start and end times of the detected event, while for the periods
before and after the event mean values were derived for 10 min intervals
before the event start or after the event end, respectively. Regarding the
coarser resolution datasets (1) and (2), in each case the time step that
overlapped most with the target time frame before, during, and after the
event was chosen.
ResultsEvent statistics
Most statistics in this section are based on the number of minutes that were
identified as part of an event. Using a flux averaging interval of Δt=1min, these minutes were defined as values failing the MAD test.
For this analysis, the study period from 1 June to 15 September 2014 was
split into seven blocks with a length of half a month each.
Our event detection algorithm identified 49 events for each site during the
given observation period. Of these events, 28 (tower 1) and 23 (tower 2) were
classified as clusters, while at both towers 6 events showed the typical
shape of an up–down or down–up event. Including interpolation between event
minutes detected by the MAD test, the cluster events covered a combined
period of 65 (tower 1) and 49 h (tower 2), with a minimum duration of 49 and
31 min, and a maximum duration of 410 and 329 min. All clusters and
up–down/down–up events occurred exclusively during night-time
(21:00–09:00 MAGT).
The remaining 15 (tower 1) and 20 (tower 2) events were characterised as
single-peak events. Only 4 of these occurred during daytime
(09:00–21:00 MAGT), on 12 and 15 June 2014, while all other events occurred
at night. The duration of these peak events ranged between 2 and 43 min,
while about half of them lasted between 9 and 21 min. All peak events
occurred simultaneously with an event at the other tower, i.e. a
corresponding counterpart event at the other tower was observed at about the
same time. We will subsequently refer to simultaneous events (one from each
tower) as a “pair” of events, while “event” still denotes one event from
a single tower. For 13 event pairs, both events were classified as “peak
events”, while the majority of the remaining peak and up–down events were
paired with cluster events at the other tower. The absolute
number of detected event minutes differed strongly between the two towers. At
tower 1, their cumulative duration exceeded that observed at tower 2 by a
factor of 1.4 (first half of September) to 2.8 (first half of August). As one
example, in the first half of August 462 min were identified by the MAD test
as being part of an event at tower 1, surpassing just 165 event minutes
detected at tower 2 by a wide margin. Summed up for the period 1 June to
15 September, a total of 1078 event minutes were detected for tower 1, more
than doubling the cumulative sum at tower 2 (539 min). An explanation for
this difference can be found in the statistical characteristics of the
wavelet flux for both towers: at tower 1 7.7 % of all data were out of the
range from Q1–1.5(Q3–Q1) to Q3+1.5(Q3–Q1), where Q1
denotes the 25 % quantile and Q3 the 75 % quantile. At tower 2 5.4 %
of the data are out of this range, i.e. tower 2 had 2.3 % more extreme
outliers (values that exceeded the interquartile range by a factor of 1.5)
compared to tower 1. As the median absolute deviation is resilient regarding
outliers, the MAD test is a robust outlier classifier even if one dataset
contains more outliers than another one .
Event seasonality
For both towers, the relative distribution of events over the summer season
showed similar patterns: the largest proportion of all events was detected in
the first half of August (37.9 % and 30.6 % at towers 1 and 2). Earlier
in the growing season, we observed a gradual increase in event occurrence
from only a few percent in the first half of June to 19.3 % (tower 1) and
16.5 % (tower 2) in the second half of July. Following the maximum in early
August, the appearance of events decreased rapidly to a range between 5.9 %
and 15.4 % per half-month in late August–September.
Seasonal courses in event frequency appear to be linked to trends in soil
thermal conditions, as indicated by, for example, the simultaneous drop in both
event minutes and mean soil temperatures in late August. At the control site,
the median half-monthly soil temperature at -8cm depth gradually
increases from 3.6 ∘C in the second half of June to its maximum at
5.1 ∘C in the first half of August, followed by the aforementioned
steep drop to 3.3 ∘C in the second half of August details
infor example. Both the general shape of the seasonal course and the timing of the peak agree with the detected seasonality in event
flux percentages.
The observation from Sect. that peak events were
exclusively detected simultaneously with an event at the other tower suggests
that events are typically not triggered by local changes in soil effluxes,
but rather by mesoscale meteorological effects. The correlation found between
event frequency and soil thermal conditions does not contradict that: a
higher CH4 emission rate from soil in times where the ground layers
are (partly) decoupled from the EC level will result in a bigger amount of
pooled CH4 in a certain time – and consequently also cause a bigger
flux when flushed up to the EC system.
Links between events and meteorological conditions
Due to their precise temporal delimitation, the class of peak events allowed
a clear characterisation of conditions for the periods before, during, and
after events. Accordingly, based on the study of peak events we were able to
correlate event occurrence with short-term shifts in meteorological
conditions that may be responsible for triggering the observed peak events.
The following paragraphs list statistics on the most relevant potential
influence factors.
The air temperature (T) measured at the top of the towers
monotonically decreased in at least 60 % of all peak events (21 of 35).
This temperature drift usually started more than 10 min before the event,
and persisted until at least 10 min after the event. Temperature change in
time in this context ranged between -0.04Kmin-1 within an
18 min interval and -0.27Kmin-1 within a 22 min interval.
The opposite case of increasing air temperatures during a peak event was
detected only once. For the relative humidity (RH) at the top of the
tower, in at least 29 % (10 of 35) of all peak event cases a monotonic
increase was observed within the timespan of at least 10 min before and
after the event. Increase rates for this subset of events are within the
range +0.67%min-1 within 9 min to
+0.86%min-1 within 22 min. To give an example, during the
peak event that started on 13 July at 22:39 MAGT, and had a total length of
22 min, the temperature dropped by 5.9 K in total, while the
relative humidity increased by 19 %. No case was observed where the
relative humidity decreased significantly during an event.
The wind speed (U) increased in 83 % of all cases (29 of 35)
during a peak event, in comparison to the last 10 min before the occurrence.
In 48 % (14 of 29) of these situations, however, U decreased again right
after the event. The largest increase in wind speed was found to be
7.4 m s-1, while for the majority of cases the difference between the
time before and during the event ranged from 0.2 to 2.1 m s-1. The
vertical wind speed (w), which is a direct part of all flux
calculation methods, remained very close to the ideal value of zero in all
these cases. Still, minor variations within a very narrow range of absolute
values showed a very similar pattern, i.e. in 74 % (26 of 35) of the peak
events a temporary increase was observed, followed by a decrease in 54 % of
these cases (14 of 26). The friction velocity (u*) increased at
the beginning of 94 % (33 of 35) of all peak events, and decreased again
right afterwards in 76 % (25 of 33) of these cases. For half of these
events, only a moderate increase in the friction velocity was observed
(<0.1 to 0.3 m s-1), while the full range of shifts lay between <0.01 and 0.7 m s-1.
For the stability of atmospheric stratification (zL-1, with z
as measurement height and L as Obukhov length), no general pattern for the
conditions before, during, and after a peak event could be found. In 43 % of
all events (15/35) there was no change in stability over time while the
event occurred. For 7 cases, the stability during the 30 min interval where
the event occurred shifted towards more unstable stratification, while for
8 cases a change in the opposite direction was observed. About 23 % (8 of
35) of all events occurred during unstable stratification
(zL-1<-0.0625), exceeding the average data fraction of unstable
stratification during night time (13 % for tower 1, 18.5 % for tower 2).
Due to the site being located in the high Arctic latitudes, (slightly)
unstable stratification was also likely to occur at night as long as the
shortwave downwelling radiation K↓>20Wm-2. The
stability before, during, and after daytime events was always neutral
(-0.0625≤zL-1≤0.0625).
Summarising, since the majority of events were detected during the night
(21:00–09:00 MAGT), it could be expected that a large number of cases would be
subject to systematically falling temperatures, and associated increases in
relative humidity. On the other hand, the high percentage of peak events that
are characterised by an increase and subsequent decrease in wind speed and
friction velocity indicates that turbulence intensity in the atmospheric
surface layer is a major influence factor. With a higher-than-average
fraction of cases with neutral atmospheric stability associated with peak
events, it can be speculated that such stratification conditions promote the
impact of sporadic increases in mechanically generated turbulence that lead
to the high CH4 emissions.
Case study: night-time advection
To demonstrate the characteristics of a typical peak event, as well as the
approach we used herein to analyse and interpret it, the following
sub-sections provide a detailed description of a case study during the night
from 2 to 3 August 2014. That event was already described by
to show that wavelet analysis is able to resolve that
event. discussed the calculation method, comparing
both the Mexican hat and the Morlet mother wavelet, and showed that the
Mexican hat was able to resolve the event precisely in time. Based on that
finding, we show the meteorological conditions and analyse them to identify
the underlying triggering mechanism. We chose this particular event because
conditions are well documented through photographs taken by the observer,
which strongly support our theory about the underlying triggering mechanism
as described later in this section.
Meteorological conditions during event period
Within the given night, at both tower 1 (Fig. ) and tower 2
(similar general patterns, data not shown) no signs of an upcoming event
could be registered until 23:30 MAGT. Starting at 23:00 MAGT, a light
breeze from the south-east with a maximum wind speed around 1.5 m s-1
gradually decreased to a calm. The mean CH4 concentrations in this
half-hour were 2102 and 2112 ppb at towers 1 and 2, and the friction
velocity as a proxy measure for aerodynamically generated turbulent motion
was very low (<0.1 m s-1). At 23:31 MAGT, both towers registered an
increase in CH4 concentrations, associated with a minor increase in
the wind speed. A temporary shift in wind direction to the north-west was
reversed back to the south-east after a few minutes.
Meteorological conditions observed at tower 1 during the case study
event of 2–3 August 2014. Wind velocity U, vertical wind speed w, and
CH4 mixing ratio c as well as wind direction ϕ are shown in a
time resolution of 20 Hz. The friction velocity u* was averaged to
30 min, while all other data were averaged to 10 min: relative humidity RH
and air temperature T (both in each 2.0 and 4.5 m a.g.l.) as well as the
longwave radiation balance I↓+I↑, the shortwave downwelling
radiation K↓, and air pressure p. The bottom panel shows a
legend for ϕ.
Around 23:45 MAGT, the wind speed continued increasing to about
1.5 m s-1, and a few minutes later the wind direction turned to the
east-north-east. The onset of the event itself was detected at 23:55
(tower 1, Fig. ) and 23:59 MAGT (tower 2,
Fig. ), and this period of high fluxes lasted until
00:18 (tower 1) and 00:07 MAGT (tower 2). During the time interval 23:30 to
23:59 MAGT when the event started, the half-hourly averaged friction
velocity u* increased substantially, disrupting the previously existing
decoupling of surface and higher atmosphere due to stable stratification.
This increased turbulence intensity potentially vented CH4 pools that
had accumulated near the ground towards the EC systems at tower top. Shortly
after the end of the event, the wind direction at both towers changed from
the east back to the south-east, i.e. the same direction as before the event.
The CH4 concentrations also decreased. Wind speeds, on the other
hand, did not decrease, while the friction velocity decreased marginally.
Wavelet cross-scalogram and flux rates computed for tower 1 during
the case study event on 2–3 August 2014. The colours in the wavelet
cross-scalogram between w and c denote the flux intensity (wavelet
coefficient), where intensive colours indicate higher flux contributions
downwards (negative, dark red) or upwards (positive, blue). The whole
scalogram is outside the cone of influence (COI). Atmospheric stratification
zL-1 for every 30 min interval was denoted as a blue
(neutral) or pink (stable) colour bar right below the cross-scalogram. Grey-coloured intervals in the line labelled RNCov refer to best
steady-state (RNCov<30 %) conditions according to
. The quality classes 1–9 in the bottom panel for EC refer
to the overall flux flagging system after .
Wavelet fluxes during event period
The mean Mexican hat CH4 flux rate during the event was calculated as
181nmolm-2s-1 at tower 1 (tower 2:
392nmolm-2s-1). This value is substantially higher than
the 7nmolm-2s-1 observed in the 20 min period before
the event (tower 2: 26nmolm-2s-1) as well as the
19nmolm-2s-1 in the 20 min period after the event
(tower 2: 88nmolm-2s-1). The relatively high mean flux
rate after the event at tower 2 is caused by a short period of higher fluxes
up to 00:20 MAGT. In addition to the average flux rates, the standard deviation of
fluxes at tower 1 (118nmolm-2s-1) also significantly
exceeded the values before (53nmolm-2s-1) and after
(31nmolm-2s-1) the event (tower 2 showed similar overall
behaviour).
The exact times when the flux peaks occurred coincided with the highest
energy and most positive contribution to the wavelet flux, as indicated in
the wavelet cross-scalograms of both towers (Figs. ,
). Sensitivity studies revealed that the choice of the
upper wavelet scale integration limit JEq. 13
in and thus the maximum wavelet period λmax
significantly impacts the flux computation: an extension of the upper period
integration limit to λmax=184min showed a significant
increase in the wavelet flux. Still, we did not find any indication that
hinted at an influence of gravity waves during this particular case study.
The Mexican hat cross-scalogram
(Fig. ) generated a sharp
temporal transition between periods of high and low flux contributions, and
this separation allowed us to precisely constrain the duration of the event,
where the low-frequency periods from 40 to 180 min contributed most to the
total flux.
Wavelet cross-scalogram and flux rates computed for tower 2 during
the case study event on 2–3 August 2014. The colours in the wavelet
cross-scalogram between w and c denote the flux intensity (wavelet
coefficient), where intensive colours indicate higher flux contributions
downwards (negative, dark red) or upwards (positive, blue). The whole
scalogram is outside the cone of influence (COI). Atmospheric stratification
zL-1 for every 30 min interval was shown as coloured
bars right below the cross-scalogram and stable (pink coloured) during the
complete time. Grey-coloured intervals in the line labelled RNCov
refer to best steady-state (RNCov<30 %) conditions according
to . The quality classes 1–9 in the bottom panel for EC
refer to the overall flux flagging system after .
Mexican hat wavelet cross-scalogram for the case study event on
2–3 August 2014 at tower 1. The right axis numbers the period, while plotted
lines refer to the left axis. Solid lines show the flux for an integration
over all periods from λ=2⋅δt to λ=33min
(Mh: Mexican hat), while the dashed line gives the flux up to
λ=184min. The colours in the wavelet cross-scalograms
between w and c denote the flux intensity, where intensive colours
indicate higher flux contributions downwards (negative, dark red) or upwards
(positive, green).
For both flux processing approaches compared herein, average CH4 flux
rates for the 30 min interval that contained the peak event are summarised
in Table . These results indicate that for the
chosen event period, the Mexican hat wavelet yielded systematically lower
fluxes compared to the EC reference. These differences from the EC fluxes
suggest that regular EC data processing yielded biased results caused by
non-stationary conditions, if these EC periods were not filtered out and gap
filled.
Mean flux rates during the 30 min period that hosted the peak event
discussed in the case study, as detected by two different flux processing
approaches. All flux values are given in nmolm-2s-1.
ApproachTower 1Tower 2Eddy covariance161213Mexican hat wavelet109179Cold-air advection from mountains
Around 23:45 MAGT, the first signs of a developing ground fog were observed and
also documented by photographs. Additional pictures were taken during the
following minutes near tower 2
(Fig. , top), i.e. around the time
the events began. All pictures demonstrate a ground fog moving in from the
north-east, where the ridges of two nearby hills, Mount Rodynka
(351 m a.s.l.) and Mount Panteleicha (632 m a.s.l.,
Fig. ), are located. The time at
which this fog reached tower 1 coincided well with the onset of the events.
Shortly after midnight, another photograph (00:11 MAGT) demonstrates that the fog
had largely disappeared, well aligned with the sharp decrease in flux
magnitude that indicates the end of the event.
The observed ground fog was also reflected in the meteorological data
(Fig. ). During the slow build-up of the ground fog in the
period between 23:20 and 23:50 MAGT, the temperature at 2 m a.g.l.
decreased by 1.3 K, while the relative humidity showed a small increase in
the same timespan. Within the same period, the longwave net radiation, which
is a good measure of the temperature difference between the sky and the
ground, decreased to minimum values of 23 Wm-2, which implies a
low temperature difference between the surface and the clouds, indicating
very low clouds or fog.
Photos of the study site directly at tower 1 (top and bottom left)
and on the boardwalk between the power station at Ambolyka river and tower 2,
taken between 2 August 2014 at 23:57 MAGT and 3 August 2014 at 00:11 MAGT.
Flow path span of potential cold-air drains from the ridge between
Mount Rodynka and Panteleicha through the flat floodplains of Kolyma river to
the study site (map modified from http://www.openstreetmap.org, last
access: 24 June 2015, copyright by OpenStreetMap contributors under Creative
Commons License CC-BY-SA).
Event triggers
Our statistics on meteorological conditions before, during, and after the
detected peak events reveal a common pattern for all event situations,
regardless of the mechanism that actually triggered the event: during a
period of weak turbulence, the surface was at least partially decoupled from
the lower atmosphere where the flux sensors were positioned. CH4 that
was emitted from the soil during this period could not properly be mixed up
to the sensor level, therefore likely forming a CH4-rich layer of air
near the ground. In all event cases, either a general change in atmospheric
conditions or a short-term meteorological phenomenon broke up the decoupling
between the layers. As a consequence, the CH4 pool in near-surface
air layers was vented up to the EC level, and therefore detected as a
pronounced peak in the flux rate.
This sequence of conditions strongly suggests that atmospheric mixing, and
not CH4 emissions processes from the soil, is the dominating
mechanism behind the flux peak events as detected by our algorithm. Since we
did not observe a single case study where a strong flux peak was detected
within a previously well-mixed situation, our findings indicate that
ebullition events, which can for example be detected at smaller scales with
soil chambers e.g., are usually too small as individual
emissions, or not coordinated enough spatially across the relatively large
footprint area (approx. 4000m2 at neutral stratification) to be
detected by this EC set-up with a sensor height ≥4.9m a.g.l.
Following the detailed description of the case study presented in the
preceding section, in the subsections below we briefly discuss several
typical meteorological situations that were also observed to trigger events.
Although there were no additional boundary layer or gradient measurements
available, all identified mesoscale phenomena in the following subsections
were always clearly visible at both towers, which supports the findings.
Cold-air drainage
At the Chersky floodplain sites, about 50 % of all events occurred with
wind directions from the E-NE, while only 3 % of all events fell into the
S-SE (Table ). These observations are in stark contrast
to the local wind climatology, which lists just 16.2 % of cases in the E-NE
sector, while the S-SE sector dominates with 37.9 % (values based on
observations from tower 1, averaged for whole observation period). An
explanation for this discrepancy can be found in the mesoscale wind field at
this particular location, which may be prone to katabatic winds from the E-NE
sector at night: typically, night-time events from these sectors are
characterised by decreases in the longwave net radiation I to values around
or below 20 Wm-2 exactly during or a short time after the event.
This observation indicates that temperature differences between above and
below the net pyrgeometer rapidly decreased, which could be a sign for
low-level fog layers moving through.
Night-time frequency (21:00–09:00 MAGT) of the wind directions over the
whole measuring period for both towers in percent. The last row gives the
frequency of wind directions observed for night-time peak events. Percentages
greater than 20 % are denoted in italics.
Weather fronts are typically associated with substantial shifts in, for
example, air temperature, wind speed, or wind direction. As an example, we
observed such signs of a weather front passing the site on 12 June 2014,
where the previously falling air pressure started increasing rapidly by
1 hPa per hour, combined with a wind speed increase from about 5 to
10 m s-1. With the stability of atmospheric stratification being
neutral during this daytime event, it is unlikely that the mechanical
turbulence associated with the frontal passage ejected CH4 pools that
had previously been accumulated close to the ground. Instead, it can be
speculated that pressure fluctuations associated with the stronger turbulence
washed out CH4 from micropores within the top soil layers. However,
particularly at night an accumulated CH4 pool close to the surface
should be the most likely source for a peak event, as observed during the
night of 13 June 2014 for example. Here the wind speed increased rapidly from
about 1 to 4 m s-1, breaking up decoupled air layers between the
surface and sensor level, and in the process venting the CH4 that had
previously been accumulated over time. This event was registered as rapidly
shifting CH4 mixing ratios at the tower top, which decreased within
10 min, while the wind speed continuously remained high.
Atmospheric gravity waves
For one pair of events occurring on 12 July 2014, conditions at both towers
indicated low atmospheric turbulence intensity (u*<0.3 m s-1),
associated with a vertical temperature inversion and very low horizontal wind
speeds. These conditions were interrupted at 03:10 MAGT, when the wind
speeds first rapidly increased to 2.5 m s-1, only to drop to the
previous low level (∼0.5 m s-1) immediately afterwards. This step
change was followed by both CH4 concentration and vertical wind
speed, where the former showed a sharp increase within seconds from around
2500 up to 5067 ppb (tower 1). For this situation, the Morlet cross-wavelet
spectrum showed a period of around 5 to 10 min that contributed most to the
observed flux. This information, together with the characteristics of the
high-frequency data, are indications that this particular event may have been
triggered by an atmospheric gravity wave reaching the ground
; however, lacking soundings of the
vertical structure of the atmospheric boundary layer, this assumption remains
speculative.
Low-level jets
Low-level jets appeared to be the triggering mechanism for two pairs of
events with distinctive characteristics. In one example, on 31 July 2014,
very low wind speeds (∼0.5 m s-1) from NW to N resulted in a
stably stratified lower atmosphere and a strong temperature inversion. In the
period before the event occurred, the longwave net radiation decreased from
about 30 to <15Wm-2, which could indicate that low stratus
clouds were moving in. The onset of the event itself was marked by a rapid
increase in the wind speed and a shift in wind direction by at least
45∘ to S to SW, which led to a sharp rise in CH4
concentration with maximum values around 4120 ppb (tower 1). The flux rate
also substantially increased for 5 min. Within the next half-hour, the wind
speed gradually decreased, then the wind switched back to the direction
before the event. Under nocturnal stable stratification with a typically
shallow stable boundary layer, the observed sudden increase in wind speed in
combination with a change in wind direction are indicators for a significant
vertical wind shear associated with a low-level jet, which was found to be
connected with a significant increase in gas fluxes . But, as already mentioned for gravity waves, additional
boundary layer measurements would be necessary to validate this assumption.
Onset of turbulent flow
The three remaining event pairs were detected under stable or neutral
conditions and characterised by a gradually increasing, non-fluctuating wind
speed, but no change in flux rates just before the event occurred. One
example from 11 July demonstrated that only when the increase in winds
finally started to yield fluctuations in wind speed did the event occur and
the CH4 concentration increased by about 500 ppb within 15 min.
After the event peak was reached, the concentration decreased quickly, while
the wind speed fluctuations did not change. These patterns indicate that,
before the event, vertical decoupling of the shallow boundary layer resulted
in a laminar wind flow at sensor height, which explains the dampened
fluctuations in wind speed. With the shift from laminar to turbulent flow,
the previously accumulated CH4 near the ground could be transported
up the sensor height, resulting in the observed flux peak. This observed
change from laminar to turbulent flow is very similar to the conditions
associated with a low-level jet, but due to the missing shift in wind
directions we decided to separate both triggering mechanisms herein.
DiscussionAdvective contributions to flux events
The EC method is based on the assumption that observations of turbulent
fluctuations at a single point in space within the atmospheric surface layer
can be used to obtain a representative flux rate from the ecosystem
surrounding the flux tower. It is therefore of crucial importance for the
interpretation of the impact of events for calculation of the local flux
budgets whether the emitted CH4 was produced locally and just
temporarily pooled near the surface, or horizontally advected towards the
measurement location. Advective transport would bias the local mass balance
of CH4 and any other atmospheric constituent to be monitored,
therefore seriously undermining the theoretic assumptions that the EC
technique relies on . If the fluxes detected by the
instruments do not originate from the target area if advection is present,
they should not be considered in the local flux budget. Accordingly, the
detection of advection as a triggering mechanism behind an event deserves
special attention, since inclusion of such data in the flux budget would
lead to a systematic overestimation of fluxes from the local ecosystem.
To differentiate between events with and without advective flux
contributions, the extension of the wavelet integration period provides
essential information. For all methods compared herein, peak events are
characterised by an intensive high-frequency turbulent component within an
integration interval of up to 30 min, which explains the increase in the
flux. In addition to this, events that were influenced by advection also
showed significant flux contributions from longer integration periods. This
finding indicates that the elevated flux rates were not exclusively driven by
turbulence and the venting of local CH4 pools near the ground, but
also contained contributions from mesoscale motions spanning periods of
minutes to hours.
The correlation in temporal trends of turbulence intensity and CH4
mixing ratios after the event can also be taken as an indicator for the
source of the CH4. If the excess CH4 that created a peak flux
during a detected event was coming from a limited source, i.e. local
emissions that had been pooled in air layers close to the ground, the
increased CH4 concentrations usually dropped to lower levels after
only a few minutes. In this case, elevated flux rates also lasted for only a
few minutes, while the increased turbulent mixing that initiated an event
often persisted for a long time thereafter. In contrast, if the triggering
mechanism had been advective transport, both CH4 concentrations and
turbulence intensity should remain high for an extended period of time. Here,
the reservoir that feeds the peak CH4 fluxes is substantially larger,
since it is originating from a different region and is transported to the
tower by katabatic winds. However, the differentiation is not as clean as
that based on the wavelet integration periods, since the maximum amount of
CH4 that can be vented from a local source close to the surface in
the absence of advective contributions depends on many factors. Most
importantly, the time since decoupling and the time since the last event took
place influences how much CH4 can have re-accumulated, but the
current CH4 emission rate from the ground and the intensity of the
vertical mixing with the onset of the event also play a role in how long it
will take until a local source will be depleted. To summarise, based on the
length of an event alone a clean distinction between events with and without
advective flux contributions cannot be performed.
Implications for designing an optimum observation strategy
Statistics for the Chersky site show that, on average for the observation
period in summer 2014, an event occurred about every other day (0.46 events
per day). With the longer cluster events lasting for up to several hours, the
average time covered by an event per day is 36.4 min at tower 1, and
27.5 min at tower 2. Assuming that such events lead, at best, to lower-quality rating of the EC fluxes, and in the worst case constitute systematic
biases to flux budgets determined through the EC technique, their net impact
on longer-term flux budgets may be substantial, which should be investigated
in a subsequent study. Our results demonstrate two major pathways through
which events can systematically disturb the flux budget determined through
the conventional EC approach, outlined in the following two paragraphs.
In the absence of advection, an event such as, for example a peak event that produces
a short but intense outburst of CH4 with a duration of
(significantly) less than the common integration interval for EC (30 min)
constitutes a substantial violation of the steady-state assumption. As a
consequence, the Reynolds decomposition that separates the high-frequency
signal into a mean and turbulent component may produce incorrect positive and
negative fluctuations of both vertical wind and trace gas concentrations.
Depending on the nature of the event, the observation may in part be
discarded as a spike, or the entire 30 min interval may be flagged as very
low quality data and in turn be sorted out during data analysis, to be
replaced by gap-filled values. In both cases, provided that the event was not
caused by advection, the high-emission event would disappear from the
long-term CH4 flux budget, effectively leading to a systematic
underestimation of net emissions. As a second potential scenario, the
incorrect Reynolds decomposition may lead to both positive and negative flux
biases, again dependent on the nature of the event, while a medium-quality
flag will lead to the inclusion of this flux in long-term budget
computation. In summary, the presence of events will introduce additional
uncertainty into long-term flux observations, and in the case of CH4
is likely to lead to a systematic underestimation of flux budgets since peak
events are likely to be sorted out by the processing software.
As a second major pathway to disturb EC flux budgets, events hold the
potential to bias the local mass balance through advective flux
contributions. Our statistics demonstrate that cold-air drainage is the
responsible trigger for about half of the peak events detected by our
algorithm at the Chersky observation site. Wind statistics and regional
topography structure support the assumption that these events are associated
with horizontal advection of CH4 that contributes a significant
portion of the excess flux. Based on overall event statistics, this means
that the site experiences on average about 2–3 events per month with
potential advective flux contributions during the growing season. For several
reasons, the potential bias of this effect on the EC flux budget cannot be
quantified yet. First, the total flux during an event triggered by cold-air
drainage will be a composition of local CH4 emissions pooled near the
surface and advected CH4. Second, a portion of the affected events
will be sorted out by the EC quality flagging procedure, and (in this case
rightfully) removed from the long-term budget computation. Therefore, as for
the violation of steady-state conditions, advective events need to be
considered as a potential cause for systematic biases, in this case
overestimation, of EC flux budgets.
To facilitate a differentiation between these pathways, it would be important
to validate these mesoscale triggering mechanisms in future field
experiments. Influences by low-level jets or gravity waves could be verified
by additional measurements of the atmospheric boundary layer, e.g. using a
well established technique like SODAR/RASS (SOnic Detection And Ranging/Radio
Acoustic Sounding System). The conceptual model of katabatic winds from the
hill ridge located north-north-east of the study site could be investigated
by installing additional nocturnal temperature measurements at heights of 20
to 50 cm in the hills and optionally also between the site and the hills. In
order to visualise the events and to achieve a better understanding of how
the accumulated CH4 is mixed up to the sensor during an event, it
could be helpful to use the high-resolution fibre-optic temperature sensing
approach, which was newly developed by and has already
been established for studies on cold-air layers in the nocturnal stable
boundary layer . Additional vertical and horizontal
CH4 concentration profiles could also be useful to visualise the
flushing process of previously stored CH4 below the EC measuring
level.
Role of cluster events
The potential role of events classified as “clusters” (coherent pattern,
but no uniform shape) on potential systematic biases in flux budgets was
excluded from this study. Clustered events, which made up the vast majority
of event minutes detected by our algorithm, hold the potential to yield very
different results between EC and wavelet methods; however, a uniform
classification of for example environmental conditions and flux patterns was
not conducted here, because this study focused on events that occur at short
timescales, i.e. last only for minutes or some tens of minutes. Therefore a
detailed investigation needs to be carried out as a follow-up field study
including additional boundary layer measurements that will be exclusively
dedicated to this phenomenon. It is very likely that these clusters were a
result of recurring events, and complex recirculation of air masses enriched
in trace gases.
Conclusions
We showed that wavelet analysis can serve as a suitable method to resolve
events of the order of minutes, which typically occurred at night and were
not caused by ebullition or other local processes in the soil, but by
different mesoscale meteorological phenomena. The signs of those phenomena
were always visible at both towers (distance: 600 m) simultaneously. The EC
method failed to resolve the events correctly, because the steady-state
assumption was not fulfilled, but it can be assumed that during regular EC
processing these times usually would be filtered out and gap filled.
In detail, this study demonstrates that events which represent a violation
of the basic assumption for the application of the EC technique are a
regularly occurring phenomenon at the observation site Chersky in
north-eastern Siberia. The exact localisation of these events in time as well
as measurement of their duration and magnitude was made possible using wavelet analysis.
All events evaluated in this study started with a similar general setting:
CH4, as emitted from the soil, accumulated near the ground because
the surface layer was decoupled from the overlaying air during time periods
of low turbulence. The break-up of these conditions was triggered by different
mechanisms on the mesoscale. These mechanisms included the passage of fronts,
atmospheric gravity waves, low-level jets, and katabatic winds. All events
were characterised by sudden peaks in CH4 mixing ratios, often
connected with increased horizontal wind speeds. This led to turbulent mixing
and thus to short-term events with increased CH4 fluxes. It is very
unlikely that the observed peaks were the result of sudden, simultaneous
CH4 releases from the soil.
We found a strong positive correlation of short-term extreme CH4 flux
events during the season with high soil temperatures and high median
CH4 rates. This conjunction was likely formed by an increased
CH4 production during times of high soil temperatures, which
facilitated the accumulation of substantial CH4 pools when the
surface layer was decoupled from the air above. Further, we found that events
that were triggered by katabatic winds advected further CH4 to the
site, which must have been emitted at a remote place within the flow path of
the advection. As half of all events within our dataset were linked to
advection, the peaks therefore do not necessarily represent the
characteristics of the local CH4 production. This leads us to
conclude that the respective flux events do not necessarily reflect the
conditions at the site or within the EC flux footprint.
The portability of these results to other flux observation sites, within the
Arctic and beyond, depends largely on prevalent local and regional
atmospheric transport and mixing conditions. Particularly at sites where low
winds at night-time frequently enable an efficient decoupling of the surface
layer, it is likely that similar phenomena may occur. As this study focused
on the characterisation of single non-stationary events, the net impact of
such events on the long-term CH4 budget as well as a comparison with
typical EC gap-filling approaches still needs to be quantified, particularly
since a large fraction of events were present in the form of clusters that
proved difficult to classify and analyse. Such an analysis will be the subject of
a follow-up study that is currently in progress.
Data availability
The dataset containing all necessary data to calculate
methane fluxes for the case study of Sect. 3.4 is publicly available at
10.1594/PANGAEA.873260. The data of the other
examples are available upon request from Mathias Göckede.
Author contributions
All authors conceived and designed the research. CS prepared and performed
the wavelet flux calculation as well as the data analysis and wrote the
majority of the text. FK prepared and calculated the eddy covariance
flux. MG, FK, and TF revised the initial manuscript. All of the authors
discussed the results and contributed to the final research article. MG and
TF supervised the study.
Competing interests
The author declares that there is no conflict of
interest.
Acknowledgements
This work has been supported by the European Commission (PAGE21 project,
FP7-ENV-2011, grant agreement no. 282700, and PerCCOM project,
FP7-PEOPLE-2012-CIG, grant agreement no. PCIG12-GA-2012-333796), the German
Ministry of Education and Research (CarboPerm-Project, BMBF grant no.
03G0836G), and the AXA Research Fund (PDOC_2012_W2 campaign, ARF fellowship
Mathias Göckede). Furthermore the German Academic Exchange Service (DAAD)
provided financial support for the travel expenses. Additionally we thank
Andrew Durso for text editing of an earlier version of the paper.The article processing
charges for this open-access publication were covered by the
Max Planck Society.
Review statement
This paper was edited by Laurens Ganzeveld and reviewed by
Norbert Pirk and one anonymous referee.
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