ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-11-221-2019A long-term (2002 to 2017) record of closed-path and open-path eddy covariance CO2 net ecosystem exchange
fluxes from the Siberian ArcticLong-term eddy covariance CO2 fluxes from the Siberian ArcticHollDaviddavid.holl@uni-hamburg.dehttps://orcid.org/0000-0002-9269-7030WilleChristianhttps://orcid.org/0000-0003-0930-6527SachsTorstenhttps://orcid.org/0000-0002-9959-4771SchreiberPeterRunkleBenjamin R. K.https://orcid.org/0000-0002-2583-1199BeckebanzeLutzLangerMoritzBoikeJuliahttps://orcid.org/0000-0002-5875-2112PfeifferEva-MariaFedorovaIrinaBolshianovDimitry Y.GrigorievMikhail N.KutzbachLarshttps://orcid.org/0000-0003-2631-2742Institute of Soil Science, Center for Earth System Research and
Sustainability (CEN), Universität Hamburg, Hamburg, GermanyHelmholtz-Zentrum Potsdam – Deutsches GeoForschungsZentrum (GFZ),
Potsdam, GermanyAlfred Wegener Institute Helmholtz Centre for
Polar and Marine Research, Potsdam, GermanyDepartment of
Biological & Agricultural Engineering, University of Arkansas, Fayetteville,
USASaint Petersburg State University – Institute of Earth
Sciences, St. Petersturg, RussiaArctic and Antarctic Research Institute – Russian Federal
Service for Hydrometeorology and Environmental Monitoring, St. Petersburg, RussiaMelnikov Permafrost Institute – Russian Academy of Sciences, Siberian Branch, Yakutsk, RussiaHumboldt-Universität zu Berlin, Geography Department,
Berlin, GermanyDavid Holl (david.holl@uni-hamburg.de)18February201911122124015August20188October20181February20195February2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/11/221/2019/essd-11-221-2019.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/11/221/2019/essd-11-221-2019.pdf
Ground-based observations of land–atmosphere fluxes are
necessary to progressively improve global climate models. Observed data can
be used for model evaluation and to develop or tune process models. In arctic
permafrost regions, climate–carbon feedbacks are amplified. Therefore,
increased efforts to better represent these regions in global climate models
have been made in recent years. We present a multi-annual time series of
land–atmosphere carbon dioxide fluxes measured in situ with the
eddy covariance technique in the Siberian Arctic (72∘22′ N,
126∘30′ E). The site is part of the international network of eddy
covariance flux observation stations (FLUXNET; site ID: Ru-Sam). The data set
includes consistently processed fluxes based on concentration measurements of
closed-path and open-path gas analyzers. With parallel records from both
sensor types, we were able to apply a site-specific correction to open-path
fluxes. This correction is necessary due to a deterioration of data, caused
by heat generated by the electronics of open-path gas analyzers.
Parameterizing this correction for subperiods of distinct sensor setups
yielded good agreement between open- and closed-path fluxes. We compiled a
long-term (2002 to 2017) carbon dioxide flux time series that we additionally
gap-filled with a standardized approach. The data set was uploaded to the
Pangaea database and can be accessed through 10.1594/PANGAEA.892751.
Introduction
The release of the Arctic's belowground carbon (C) pools to the atmosphere
can potentially act as a positive feedback on climate change. Organic
material that is now stored in the permanently frozen soil and largely
inaccessible for microbial decomposition might become available under a
warming climate resulting in an increased release of greenhouse gases from
Arctic regions . At the same time, the Arctic vegetation
responds to ongoing warming with a greening trend , probably
enhancing summer carbon assimilation. Although the importance of permafrost
carbon pools for a potential amplification of climate change has been widely
recognized e.g.,, the earth system models analyzed
for the Fifth Assessment Report (AR5) of the Intergovernmental Panel on
Climate Change (IPCC) did not include permafrost carbon emissions.
While efforts to include permafrost dynamics in global climate models have
been made recently e.g.,, models can be improved by using
ground-based flux measurements for calibration and validation.
assessed the carbon balance of the Arctic tundra combining
ground-based observations and process and atmospheric inversion models. The
authors found that the uncertainty with which a carbon balance can be
quantified is still very large, with upper and lower uncertainty bounds
indicating the Arctic tundra as a sink for carbon at one and as a C source at
the other bound. conclude that reducing uncertainties in
regional estimates based on observational data relies on high-quality
ground-based measurements that should be placed strategically, e.g., along
hydrological or vegetation gradients. In situ gas flux measurements
from the Arctic are, however, still scarce. Moreover, the available data are
biased towards Alaska; observations from the Eurasian Arctic are even more
scarce . To be able to distinguish climate-change-related
flux responses from interannual variability, long-term data sets are
essential as recently argued by .
Within the scope of this publication, we aimed at creating a high-quality,
long-term CO2 flux data set from a polygonal tundra site in the
Russian Arctic. We had the opportunity to analyze a 16-year record of eddy
covariance data that includes periods with simultaneous measurements from two
different (closed-path and open-path) CO2 gas analyzer types. Our
objective was to consistently process the data while following standardized
quality control methods to allow for comparability between the different
years of our record and with other data sets. We additionally aimed at
cross-calibrating open-path and closed-path CO2 fluxes and at
gap filling the data set by employing the method of
that is widely used in the FLUXNET community.
Site description
The investigation site is located on Samoylov Island in the southern central
part of the Lena River delta at 72∘22′ N, 126∘30′ E
(see Fig. ). The fan-shaped delta covers an area of roughly
30 000 km2 and is
characterized by a network of channels and more than 1500 islands
. Being the largest delta in the Arctic and one of the
largest worldwide , it lies in the continuous permafrost
zone with permafrost depths of about 500 to 600 m . Mean annual permafrost temperatures range around
-9∘C at 10 m depth , making the Lena River delta one of the coldest permafrost regions on earth.
inferred an annual mean soil temperature of -8.6∘C at
10.7 m depth from a 2006 to 2011 time series of temperature
measurements in a borehole on Samoylov Island. Based on long-term
hydrological observations in the delta area, found an
increase in discharge as well as in sediment flux indicating recently
intensified thawing of ice complex sediments in the region.
Location of Samoylov Island (center of b) in the Lena River delta (a). Map data from OpenStreetMap contributors, under Open
Database License.
divides the delta area into three main geomorphological
units. The oldest, ice-rich river terrace consists of fine-grained sediments
with high organic content. It developed as an eroded Pleistocene plane
characterized by polygonal ground and thermokarst processes. The second
largest unit consists of Late Pleistocene to Early Holocene sandy sediments
with low ice content and covers 23 % of the northwestern part
. Samoylov Island is part of the third unit, the Mid- to
Late Holocene river terrace , which makes up about two-thirds of the delta .
The island itself consists of two morphological units, an annually flooded,
modern floodplain (1.49 km2) in the west and a Late Holocene river
terrace (2.85 km2) in the east, which lies 10 to 16 m a.s.l. and
is not flooded regularly . The data presented
here were collected with eddy covariance systems installed on the elevated
river terrace. In contrast to the modern floodplain, the river terrace's
surface is patterned due to frost action that formed a wet polygonal tundra
landscape consisting of mostly low-centered and some high-centered ice-wedge
polygons as well as thermokarst lakes and channels. Due to the underlying
permafrost and thereby hampered drainage, water-saturated soils or ponds form
in the polygon centers, whereas on the rims, which can be elevated by up to 50 cm above the centers, a drier, moderately moist water regime prevails
. Accordingly, the vegetation community in
the wetter centers is dominated by hydrophytic sedges (Carex aquatilis, Carex chordorrhiza, Carex rariflora) and mosses
(e.g., Limprichtia revolvens, Meesia longiseta, Aulacomnium turgidum). Mesophytic dwarf shrubs (e.g., Dryas octopetala, Salix glauca), forbs (e.g., Astragalus frigidus) and mosses (e.g.,
Hylocomium splendens, Timmia austriaca) dominate on the rims
. Maximum summer leaf coverage was
estimated by to be 0.3 for vascular plants and 0.95 for
mosses and lichens at both polygon centers and rims. The river terrace as a
whole is composed of polygon rims with a coverage of 60 % to 65 % and of
depressed surfaces (including vegetated and water-filled polygon centers as
well as lakes and channels) that cover the remaining 35 % to 40 % of area
.
An arctic–continental climate with low mean annual temperatures prevails in
the Lena River delta. Although precipitation is low as well, the climate can
be considered humid as evaporation rates are low due to low ambient
temperatures, and relative humidity is high . Based on long-term (1998 to 2017) in situ
measurements on Samoylov Island, inferred an annual mean
air temperature of -12.3∘C, the coldest and warmest months being
February and July with mean temperatures of -32.7 and 9.5 ∘C, respectively. For the period from 1998 to 2011, estimated
total annual precipitation to be composed of 124±57 mm summer rainfall
and 65±35 mm snowfall. Interannual variability in rainfall was,
however, very high, with a maximum of 199 mm and a minimum of
48 mm. Snowmelt usually starts in mid-May and lasts until early
June. Snow accumulation typically commences between late September and early
October. Between 1998 and 2011, the snow season lasted on average 224±18 days and the snow-free period 138±18 days. Snow depth was reported by
, averaging 0.3 m between 2002 and 2017 with a
maximum of 0.8 m in 2017. Beginning in early to mid-June, the soil
starts to thaw from the top, forming the so-called active layer.
report a mean active layer depth in August of 49 cm
with a maximum of 79 cm between 1998 and 2011. The closest WMO (World
Meteorological Organization) weather station is located on the continent,
around 110 km southeast of Samoylov Island in the city of Tiksi
(WMO ID 21824). Between 1936 and 2017 the mean air temperature reported from
Tiksi is -12.74∘C, mean annual precipitation amounts to
304.5 mm. While the mean air temperature in Tiksi is
very similar to the 20-year mean from Samoylov Island, average annual
precipitation appears to be much higher in Tiksi than in the delta region.
explain this divergence with the fact that Tiksi is located
on the coast of the Laptev Sea and surrounded by mountains.
The soils of Samoylov Island were classified as Gelisols by
based on work by according to the
US Soil Taxonomy . At a subgroup level, typical soils of the
river terrace are Glacic Aquiturbels, which developed on the polygon
rims and are characterized by the translocation of soil material due to
freeze–thaw processes (cryoturbation). In the wetter polygon centers
Typic Historthels formed. On the more sand-rich active floodplain,
Typic Aquorthels and Typic Psammorthels dominate. According
to the FAO World Reference Base for Soil Resources , the
diverse soils of Samoylov Island belong to the reference soil group of
Cryosols. estimated the soil organic carbon
(SOC) stocks for the upper meter of the island's two major landscape units to
be 29±10kgm-2 for the river terrace and 14±7kgm-2 for the active floodplain.
Eddy covariance (EC) tower positions on the river terrace of
Samoylov Island and surface class distribution according to
. Photographic image of the entire island (top right
corner) from .
MethodsInstrumentation
We used the eddy covariance (EC) technique to determine half-hourly gas and
energy fluxes. The EC method requires high-frequency (typically >10Hz) raw gas concentration and three-dimensional wind velocity
measurements. A comprehensive description of the EC approach is given, for
example, by . We recorded carbon dioxide (CO2)
and water vapor concentrations as well as three-dimensional wind velocity
with changing instrumentation on three different tower structures, all
located on the central river terrace of Samoylov Island between 2002 and 2017
(see Fig. ). We deployed open-path (OP) as well as
closed-path (CP) gas analyzers, at times simultaneously. Models,
manufacturers and years of deployment are given in
Table . Between the different setups, CP intake tube
lengths varied from 5 to 8 m. OP analyzers were always installed
inclined by about 10∘ from the vertical, as suggested in the analyzer
manuals. Raw data were recorded at 20 Hz except for the periods
22 August 2009 to 19 July 2010 (10 Hz) and 31 August 2012 to
17 May 2013 (5 Hz). Until 29 April 2014, all raw data were recorded
on a CR3000 data logger (Campbell Scientific, UK). From then on, CP analyzer
and anemometer data were logged on a CR3000, whereas OP analyzer and
anemometer data were recorded on a LI-7550 data logger (LI-COR Biosciences,
USA). Although data coverage is biased towards the growing season, the data
set contains considerably more shoulder season and winter fluxes in its
second half from 2010 to 2017 (see Table ). The availability of year-round ancillary meteorological data, also
increasing, resulted
in gap-filled flux time series covering each half hour of the two years 2010
and 2016 (see Fig. ).
List of deployed instrument types. All infrared gas analyzers were
manufactured by LI-COR Biosciences (USA), R3 sonic anemometers were built by
Gill Instruments Ltd. (UK) and CSAT3 anemometers by Campbell Scientific Ltd.
(UK).
Gas analyzer Anemometer Data coverage YearClosed pathOpen pathModelHeight, mDate rangeDays2002LI-7000n/aR33.6512 July to 3 September532003LI-7000n/aR33.6519 July to 22 October952004LI-7000n/aR33.6528 May to 20 July532005LI-7000n/aR3417 July to 1 September462006LI-7000n/aR345 June to 19 September1062007n/aLI-7500CSAT32.411 July to 23 August362008n/aLI-7500CSAT32.422 April to 26 September1572009 In/aLI-7500CSAT32.410 April to 14 June652009 IIn/aLI-7500CSAT34.1515 July to 29 December1672010LI-7000LI-7500CSAT34.151 January to 31 December3592011LI-7000LI-7500CSAT34.151 January to 22 August2332012n/aLI-7500CSAT34.1513 July to 10 November1202013LI-7000LI-7500ACSAT34.154 May to 5 November1852014LI-7000LI-7500ACSAT34.1521 February to 29 October2502015LI-7000LI-7500ACSAT34.156 May to 31 December2392016LI-7000LI-7500ACSAT34.151 January to 19 November3232017LI-7000LI-7500ACSAT34.151 January to 30 September272Flux processingPrior considerations
Due to the contrasting designs of OP and CP analyzers, these sensor types
have distinct signal response characteristics that we considered during data
processing. The most apparent constructional difference between OP and CP gas
analyzers is the presence or absence of a housing for the measurement cell
that contains the optical path. In a CP instrument, the measurement cell is
housed, whereas the optical path of an OP analyzer is exposed to the
atmosphere. CP systems are typically more bulky and installed at the base of
an EC tower, from where tubing leads to an intake close to the anemometer.
Sample air is drawn into the cell with a pump. OP sensors are commonly
installed in close proximity to the anemometer and do not require a pump,
which greatly reduces the power consumption of OP instruments compared to CP
setups. Due to the tubing acting as a low-pass filter, the response to
high-frequency concentration variations is systematically attenuated in CP
setups as opposed to OP systems . Moreover, the severity
of frequency dampening can vary nonlinearly with environmental conditions,
especially with relative humidity .
Infrared gas analyzers typically measure gas densities and report the number
of molecules per volume of air. To be able to refer the mass of a gas to the
mass of air, gas densities are transformed to mixing ratios using air
density. However, as the optical path of an OP gas analyzer is exposed to the
varying temperature, pressure and humidity conditions of the atmosphere, air
density in the measurement cell fluctuates mainly due to thermal
expansion/contraction and water dilution/concentration. This effect, which leads to faulty concentration readings of OP instruments and thereby to
incorrect flux estimates, has first been described by . The
authors proposed two flux correction terms to compensate for these density
fluctuation effects that are referred to as Webb–Pearman–Leuning (WPL) terms
and have since been verified experimentally and theoretically and are
routinely applied in OP EC studies. Especially at times of low gas fluxes,
WPL terms can become orders of magnitude larger than raw gas fluxes
. CP analyzers have the advantage of controlled temperature
and pressure conditions in the measurement cell, allowing for the sample-wise
calculation of mixing ratios rather than molar densities
and thereby avoiding the need to apply air density fluctuation correction
terms after raw flux calculation.
Major drawbacks of OP instruments, especially in harsh environments, are
(1) their downtime during adverse weather conditions (e.g., precipitation) and
(2) flux biases due to sensor self-heating . The
OP self-heating effect was first recognized due to apparent
off-season CO2 uptake in flux time series obtained with LI-7500
(LI-COR Biosciences, USA) OP gas analyzers. However,
recently found that this effect is not limited to cold conditions but extends
throughout all seasons. The necessary corrections can be substantial but
decrease greatly when the sensor is not mounted vertically but inclined
instead as shown by and .
Processing steps
We performed separate flux processing steps on OP and CP data sets and
computed half-hourly fluxes using the software EddyPro (LI-COR Biosciences,
USA). An overview of the processing steps is given in
Table . We detected and removed raw data spikes
according to , with a maximum of 1 % accepted spikes and
a maximum of three samples as consecutive outliers. We applied an angle of
attack correction, i.e., compensation for flow distortion induced by the
anemometer frame , on wind velocity data collected with the
R3 (Gill Instruments Ltd., UK) anemometer. The majority of the wind velocity
records come, however, from a CSAT3 (Campbell Scientific, UK) instrument, for
which this correction is not necessary. Coordinate rotation to align the
anemometer x axis to the current mean streamlines was calculated as double
rotation according to . For OP fluxes, we compensated for
air density fluctuations due to thermal expansion/contraction and water
dilution/concentration following . Because simultaneous water
vapor concentration, cell temperature and cell pressure measurements from
inside the CP analyzer were available, CO2 concentrations from this
sensor could be converted directly into mixing ratios, i.e., concentrations
referring to dry air of constant temperature ,
making further corrections for density fluctuations unnecessary. We
compensated for CP time lags by using the automatic time lag optimization option
in EddyPro. For this procedure, prior to processing the complete data set,
time lags were determined for a subperiod of raw data by covariance
maximization . A searching window around the median of the time lags found (nominal time lag, Tnom) is defined by
Tnom±3.5×MAD, where MAD is the
median absolute deviation of the time lags found. When processing the
complete data set, EddyPro performed a covariance maximization of vertical
wind velocity and the scalar of interest for each half hour and then checked whether the time lag found fell within the searching window defined before.
If not, Tnom was used as the time lag. Water vapor concentration time
series were binned in 10 relative humidity (RH) classes, and the procedure was applied to each
class, resulting in 10 different nominal time lags. CO2
concentrations were not binned in humidity classes. We computed CP time lag
statistics annually and within a year if pump speeds or instrumental setups
varied. OP time lags were determined by covariance maximization within a
searching window of -10 to 10 s. We evaluated OP time lags statistics,
binned in classes of wind direction sectors, later on in the course of
quality filtering.
Eddy covariance flux processing steps. Partly differing processing
was applied to raw data from closed- and open-path analyzers. OP and CP fluxes
were computed consistently for the whole period from 2002 to 2017.
Setup-dependent statistics (for time lags and in situ spectral
correction methods) were evaluated annually or if tower position, CP pump
speed or any other analyzer metadata changed.
Processing stepMethod Closed-path dataOpen-path dataSpike detectionraw data spike removal and removalAngle of attackfrom 2002 to 2006 during Gilln/a, sensor was not deployedcorrectionanemometer deploymentbetween 2002 and 2006Axis rotationDouble rotation Detrendinglinear Correction for airsample-wise conversion of rawapplication of WPL termsdensity fluctuationsdata to mixing ratiosto fluxes Time lag compensationcovariance maximization withcovariance maximizationnominal time lag from statisticsSpectral corrections forhigh-pass filteringanalytic low-pass filteringin situ/analytic analytic instrument separationn/aEddyPro version≥6.0.0
Spectral attenuation in the high- and the low-frequency spectral range was
compensated for according to the following methods. Low-frequency signal loss due
to the finite averaging time used for flux calculations (30 min) and due to
linear raw data detrending was corrected for following the method of
for both OP and CP fluxes. High-frequency signal loss of
OP fluxes due to path and volume averaging of the sonic anemometer and the
gas analyzers as well as due to the separation between the two instruments
were corrected for with the analytical approach of .
High-frequency signal loss of CP fluxes due to spectral attenuation by the
intake tube and volume averaging in the measurement cell were corrected for
using the in situ method of . For each measurement
period with a unique instrumental setup and CP pump speed, we determined the
cutoff frequency of a first-order low-pass filter from ensemble means of
30 min power spectra of CO2 concentration and sonic temperature
time series data. The spectral correction factor was then parameterized as a
function of the cutoff frequency found and the mean wind speed for stable
and unstable atmospheric conditions as described by . Before
using them for ensemble spectra estimations, the 30 min power spectra were
quality-filtered by applying the scheme of and by
omitting half hours that were assigned quality class 2 according to
. High-frequency noise was removed from the ensemble means
of CO2 concentration power spectra before the determination of the
cutoff frequency where it was deemed necessary. High-frequency signal losses
due to crosswind and vertical separation of the sample air tube intake and
the anemometer were corrected for according to .
Quality filtering
We set EddyPro to calculate quality flags according to
that represent flux quality in three classes (0, 1 and 2), with 0 denoting the
highest- and 2 denoting the lowest-quality class. This quality evaluation is
based on tests for stationarity and developed turbulence and thereby
indicates whether general EC assumptions about atmospheric conditions were
met during a flux calculation period. Flux quality assessment was largely
based on the scheme of . In the data set available for
download, we included one column for each analyzer type containing this
quality flag. Additionally, we applied six further screening steps and
flagged fluxes of low quality. If a flagged flux was not already assigned to
class 2 according to Mauder and Foken (2004), we set the quality flag to 2.
In our opinion, fluxes of quality class 2 should be omitted from further
analysis. They are included in the reported data set for the sake of
completeness. We performed the six additional flagging steps in the following
sequence. An overview of these filtering steps including the number of
flagged values is given in Table .
Additional quality flagging steps after flux processing. Flagged
fluxes were assigned to quality class 2 if not in this class already
according to the quality assessment. As CP time lag
detection quality had been addressed earlier during flux processing in
EddyPro, it was not screened at this stage.
Applied to No. of flagged fluxes StepOP fluxesCP fluxesOPCP1: raw data skewness/kurtosisyesyes23 769 (23 %)12 043 (18 %)2: instrument signal strengthyesno6951 (7 %)n/a3: time lag detection qualityyesno20 277 (20 %)n/a4: absolute concentration limitsyesyes223 (0.2 %)2261 (3 %)5: exclusion of outliers when simul-yesn/a346 (0.3 %)n/ataneous CP fluxes close to zero6: absolute flux limitsyesyes634 (0.6 %)102 (0.6 %)
In step 1, skewness and kurtosis were computed with EddyPro for the
half-hourly high-frequency raw data time series of CO2
concentration, vertical wind speed and sonic temperature. If any of these
statistics was outside certain intervals (skewness: [-2,2]; kurtosis:
[1,8]; equivalent to the hard flag defined by ),
CO2 flux values were flagged.
In step 2, OP fluxes were additionally filtered for an instrument
signal strength indication (AGC) recorded from the LI-7500
sensor. Along with a software upgrade, this diagnostic value was renamed RSSI, and its definition was changed. We therefore recalculated
the AGC values for sensors not running on firmware version 6.6 and
above (before July 2013). According to the old AGC definition in
the LI-7500 manual, typical clean window values range between 55 % and 65 %. As dirt accumulates on the windows (or anywhere in the optical path),
the AGC value will increase up to 100 %. The new RSSI
value takes 100 % for clean windows and decreases as windows get dirtier.
In order to obtain one consistent diagnostic variable for the cleanness of
the optical path, AGC was converted to the RSSI range.
AGC values smaller than 44 were set to 44, then AGC values were
mapped to the RSSI range as follows.
RSSI(AGC)=188-2⋅AGC.
We flagged OP CO2 flux values when RSSI≤60.
As quality control of the half-hourly time lag detection results was not
applied during OP flux processing in EddyPro, we additionally screened OP
time lags to identify low-quality flux values in step 3. We divided
the time lag data set into subsets of different instrumental setup and
binned the time lags of these subsets in 36 10∘ wind direction
sectors. We used the 25th and 75th percentiles per class as filter
thresholds. We flagged OP flux values with associated time lags outside the
range spanned by these thresholds. Because we computed CP fluxes in EddyPro
considering and compensating for low time lag detection quality, we did not
perform this type of filtering step on CP fluxes.
In step 4, we flagged CP as well as OP fluxes when 30 min average
concentration measurements were larger than 450 ppm or smaller than 300 ppm.
CO2 concentrations outside this range indicate dirty OP gas
analyzer optics or technical problems of the CP air sampling system (sudden
pump speed changes due to brownouts, blocked filters, etc.).
To filter dubious, large OP fluxes that coincided with reasonable CP fluxes,
we selected all OP fluxes when simultaneously measured CP values ranged
between -2 and 2 µmol m-2 s-1. Step 5 only
affected OP data from this subset. We calculated the 99th and 1st percentile
of this group and flagged fluxes from it when they lay outside this
percentile range.
In step 6, we flagged remaining outliers in both the CP and OP data
sets by using the 0.1st and 99.9th percentile (-3.5423 and
3.3473 µmol m-2 s-1) of the CP time series after the
concentration limits filter as absolute limits to define an acceptable range
of OP and CP flux values.
Open-path self-heating correction
To account for self-heating errors induced by the LI-7500 sensor electronics,
we corrected OP fluxes as described by . The authors use
WPL-corrected fluxes and add a correction term that
accounts for self-heating effects of vertically installed instruments. In
their approach, use a scaling factor ξ, taking values
between 0 and 1, to trim the correction for inclined analyzer setups. With
simultaneously available CP fluxes, we were able to estimate this scaling
factor specifically for our site and periods of unique instrumental setups.
As suggested by , we optimized this parameter with a
nonlinear least squares method in Matlab (v. 9.2). We determined ξ for
periods of different instrumental setups and separately for night (incoming
shortwave radiation <20Wm-2) and day (incoming shortwave
radiation ≥20Wm-2) conditions using the following
equation:
Fc=Fc,WPL+ξ(Ts-Ta)ρcraTa,
where Fc (kgm-2s-1) is the true CO2 flux,
Fc,WPL (kgm-2s-1) is the WPL-corrected OP
CO2 flux, Ts (K) is the instrument surface
temperature, Ta (K) the ambient air temperature,
ra (sm-1) the aerodynamic resistance and ρc
(kgm-3) the ambient CO2 density. Prior to ξ
optimization, we also estimated the instrument surface temperature Ts
following the parameterization of separately for
nighttime and daytime:
Ts,day=0.93(Ta-T0)+3.17+T0andTs,night=1.05(Ta-T0)+1.52+T0,
with Ts,day (K) and Ts,night (K) as
instrument surface temperature estimates and T0 set to 273.15 K. We
determined the scaling factor as a parameter of Eq. (2), being the modified
approach from . For function fitting, we
assumed CP fluxes of quality classes 0 and 1 as true fluxes. We used
WPL-corrected OP quality class 0 fluxes and the surface
temperature estimates described above as independent variables. Before parameter
optimization, we quality-screened the correction term
(expression to the right-hand side of ξ in Eq. 2) and removed spikes
ranging within the uppermost or lowest percent of its distribution.
Throughout all years, ξ is larger during the daytime than at nighttime but
generally small, adding mostly below 1 % of the full correction term to the
uncorrected flux (see Table ). In four of the seven available
years with simultaneous CP and OP fluxes, nighttime ξ optimization
converged to values below zero. Before applying the correction models to
these periods, we set nighttime ξ estimates to the median of the years
yielding parameter values that, including their 95 % confidence bounds,
ranged above zero. We used this value and the median of all daytime model
optimizations to calculate corrected OP fluxes at times without parallel CP
measurements. We did not correct OP fluxes when radiation measurements or
correction term estimates were not available. Correlation between CP and OP
fluxes improved throughout all quality classes by applying the self-heating
correction (see Table ), while fluxes indicating net
CO2 uptake were affected more strongly than fluxes above zero (see
Fig. ).
Effect of the self-heating correction on the correlation between
open-path (OP) and closed-path (CP) fluxes (a). Correlations were
quantified using Spearman's rank correlation coefficient rs and
Pearson's correlation coefficient r. Only quality class 0 is shown.
Negative fluxes are affected more strongly by the correction than positive
fluxes (b).
Estimates of scaling factor ξ±95 % confidence intervals
used for open-path flux correction. ξ describes the portion of the
self-heating correction term, given by for vertically
installed instruments, that is needed to correct OP fluxes determined with
inclined gas analyzers. The scaling factor was optimized as a parameter of a
nonlinear function where CP data were regarded as true fluxes. It was
therefore determined for years when parallel CP and OP measurements were
available. In the case of an optimization converging to unreasonable values
(below 0), we used the median of the remaining ξ estimates.
Spearman's rank correlation coefficient rs and Pearson's
correlation coefficient r between closed-path (CP) and open-path (OP)
fluxes with and without the applied self-heating correction. The agreement
between CP and OP fluxes increases throughout all quality classes after OP
correction.
Quality class 0Quality classes 0, 1Quality classes 0, 1, 2rsOP uncorrected0.8960.8660.508OP corrected0.9070.8710.512rOP uncorrected0.8940.8710.042OP corrected0.9040.8770.055Carbon dioxide flux gap filling
We used the CP and the corrected OP fluxes (see
Fig. ) to compile a CO2 flux time series.
We aimed at keeping as many measured data points as possible, while omitting
records with large uncertainty. We accepted all CP values of quality classes
0 and 1. At time steps where no CP fluxes were available, we selected OP
values of the same quality classes. The resulting time series contains 75 921
data points. Additionally, we filled the remaining gaps in the time series
using the marginal distribution sampling (MDS) method as first presented by
. This method employs two types of model value
calculations. The environmental variables global radiation, air temperature
and water vapor pressure deficit are binned in classes and combined in a
lookup table (LUT). In the case of a gap, flux values related to similar
environmental conditions can be looked up and used for averaging and gap
filling. The setup of different LUTs for fixed time periods was first
described by . This process can be refined by the use of
moving time windows around gaps, as applied by
. The second model type implemented in the MDS algorithm
exploits the commonly high autocorrelation of gas flux time series. The mean
diurnal variation (MDV) technique was also first described by
and uses the average of available gas flux measurements
from adjacent days at the same hour of day to fill a flux gap. The MDS method
has found wide application, as it has, for example, been the standard technique
within the processing pipeline of the FLUXNET2015 data set, which includes
over 1500 site years of data. The algorithm of
combines a screening procedure of the available data for similar
environmental conditions (lookup table steps) and the use of an MDV method
(diurnal cycle steps) if a gap could not be filled within the lookup table
steps. Both techniques include moving windows with variable sizes that are
increased until a solution can be found. Large gaps are skipped. To run the
gap-filling algorithm, we used the REddyProc routine that is accessible
through a web-based service hosted by the Department of Biogeochemical
Integration at Max Planck Institute Jena. The R routine that is executed on
this server is a further-developed and extended version of the
approach and is described by . We
did not use the friction velocity filter or the flux partitioning
capabilities of the REddyProc online tool. Gap filling resulted in 131 908
data points. The provided data set includes quality flags for each gap-filled
value that depend on the method used and time window size, as defined by
. These flags take values between 0 and 3, with 0
denoting measurement data, 1 indicating the most reliable and three least reliable
gap-filled fluxes. To assess the overall quality of the gap-filling result,
the MDS algorithm, in a stepwise manner, treats single available values as
gaps and fills them according to the described scheme. Pearson's correlation
coefficient between our compiled CO2 flux time series and the MDS
quality assessment run, where these values were treated as artificial gaps,
is 0.92, with a root mean squared error of
0.31 µmol m-2 s-1.
Multi-annual carbon dioxide flux time series compiled from fluxes
measured with closed-path and open-path sensors on Samoylov Island's river
terrace. Fluxes of quality class 2 are not shown. Self-heating errors in the
OP data set have been corrected for. Additionally, the result from gap filling
this time series with the MDS method is shown. The number of values given for
the gap-filled time series include measured fluxes.
Flux uncertainty estimation
Flux uncertainty can be regarded as a combination of a systematic and a
random part. While the attempt should be made to remove systematic biases,
random errors cannot be corrected for . However,
statistical methods exist to estimate the uncertainty in a flux measurement
due to random errors. We used three different approaches from the literature to
quantify random uncertainty and addressed fluxes with a suspected large bias
by correcting for it during processing or by filtering in the course of
quality assessment.
Normalized mean contributions of the surface classes defined by
to the eddy covariance footprint. Values were averaged
over each subperiod and normalized to sum up to 1. Additionally, the average
non-normalized sum of all surface class contributions is given as the column
“median image contribution”. These values indicate how sufficient the
classified area is to describe the EC footprint. Non-normalized half-hourly
contributions of the single classes are given in the provided data
set.
YearTundra Water Median imageDryWetOvergrownOpencontribution20020.710.170.070.050.8820030.700.170.070.050.8720040.710.160.070.060.8820050.710.170.070.050.8720060.700.170.070.060.8620070.540.370.060.020.7320080.530.340.090.040.772009 I0.540.320.080.060.722009 II0.640.190.090.080.7120100.650.180.090.080.7320110.670.180.080.070.7920120.670.180.080.070.8020130.690.170.080.060.8320140.660.180.080.070.7720150.660.180.080.080.7820160.650.180.090.080.7420170.670.180.080.070.82
Description of columns included in the data set file.
Column nameUnit/formatDescriptionDate/time (Local)yyyy-mm-ddTHH:MMTime stamp referring to end of 30 min flux calculation period in local time (UTC +9 h).Date/time (UTC)yyyy-mm-ddTHH:MMTime stamp referring to end of 30 min flux calculation period in UTC.CP CO2 fluxµmol m-2 s-1Closed-path CO2 fluxQC CP CO2 fluxdimensionlessClosed-path CO2 flux quality classes 0, 1 and 2CP CO2 flux rand uncµmol m-2 s-1Closed-path CO2 flux random uncertainty estimate OP CO2 fluxµmol m-2 s-1Open-path CO2 fluxOP corr CO2 fluxµmol m-2 s-1Corrected open-path CO2 flux QC OP CO2 fluxdimensionlessOpen-path CO2 flux quality classes 0, 1 and 2OP CO2 flux rand uncµmol m-2 s-1Open-path CO2 flux random uncertainty estimate CO2 flux compµmol m-2 s-1Time series compiled of open- and closed-path quality class 0 and 1 fluxesCO2 flux gfµmol m-2 s-1Gap-filled CO2 flux time seriesQC CO2 flux gfdimensionlessQuality flag of gap-filled fluxes, between 0 and 3 CO2 flux gf stdµmol m-2 s-1Standard deviation of gap-filled flux estimates, calculated from the data used for averagingFP CC drydimensionlessContribution of surface class “dry tundra” to the eddy covariance footprintFP CC wetdimensionlessContribution of surface class “wet tundra” to the eddy covariance footprintFP CC ovedimensionlessContribution of surface class “overgrown water” to the eddy covariance footprintFP CC watdimensionlessContribution of surface class “open water” to the eddy covariance footprint
Most importantly, systematic errors are introduced when underlying EC
assumptions are not met. Using the method of that combines
an assessment of well-developed turbulence and steady-state conditions, we
identified biased fluxes and flagged them. Other sources of systematic errors
that we addressed include, for example, the angle of attack correction of
faulty sonic anemometer readings, filtering for low instrument signal
strength, the OP self-heating correction, and compensations for high-frequency
loss and air density fluctuations (see Sect. 3.2.2, 3.3 and 3.4). Although we
are confident that we applied corrections for systematic errors both
rigorously and carefully enough, biases were certainly not always removed
efficiently. The quality flags included in the data set, reflect a level of
confidence based on the assessment of general EC assumptions and our six
additional quality filtering steps (see Sect. 3.3).
To be able to include a random uncertainty estimate for each individual OP
and CP flux in the provided data set, we set EddyPro to calculate random
uncertainty estimates following . The authors
developed a method that aims at quantifying flux uncertainty associated with
turbulence sampling errors. These errors can contribute largely to the total
random error as they refer to the insufficient sampling of large eddies with
high spectral energy. Due to the stochastic nature of turbulence, this type
of error is random. To estimate its magnitude, the so-called integral
turbulence timescale (ITS) is first determined by expressing the covariance
of vertical wind velocity and gas concentration as a function of a lag time
between these two time series. The ITS is then given by integrating the
cross-correlation function theoretically from 0 to infinity, in practice,
however, until an upper lag time limit is reached. The upper limit can be
defined in three different ways in EddyPro. We used the definition of the
normalized cross-correlation function reaching a value of 1/e=0.369 to
determine an upper lag time limit used for integration. While the normalized
cross correlation should reach zero with increasing lag time in theory, in
practice it sometimes does not. The setting we used on the one hand provides
the least conservative estimate of the ITS but on the other hand offers
computational efficiency and makes sure that an upper limit for integration
can be reliably found. With the ITS, a flux uncertainty can be determined by
calculating the variance of an EC flux or, as put it,
by calculating the variance of the covariance. This ensemble variance would
approach zero with the averaging time approaching infinity. In the data set
available for download, a random uncertainty estimate calculated with the
method of is given for each OP and CP flux (see
Table ). Random uncertainties based on ITS estimation
observations increase with absolute fluxes with mean values of 0.16 and
0.05 µmol m-2 s-1 for OP and CP fluxes (see
Fig. ). OP random uncertainty estimates are generally larger and
more scattered with respect to the corresponding flux values.
Random uncertainty estimates for all closed-path (CP) and open-path
(OP) CO2 fluxes calculated using estimates of the integral
turbulence timescale (ITS), the successive-observations approach and
results from gap filling (GF), and the paired-observations approach during
periods with simultaneous OP and CP records.
As the random uncertainty estimate described above specifically addresses the
turbulence sampling error, other sources of random flux errors such as the
noise introduced by the different components of the measurement system are
neglected. With simultaneous measurements from two sensors, we could
additionally estimate random errors for the measurement system as a whole
during times when the data sets from both sensors overlapped. We followed the
paired-observations approach as presented by and
calculated a random error estimate ϵ as
ϵ=12⋅(FCP-FOP),
with the closed-path and open-path CO2 fluxes FCP and
FOP of quality classes 0 and 1 in
µmol m-2 s-1. The distribution of ϵ estimates
is shown in Fig. . The ϵ values calculated with OP
fluxes corrected for the self-heating error have a mean close to zero and are
distributed more symmetrically than the ϵ values calculated with
uncorrected OP fluxes. The mean of this distribution is shifted from its mode
as well as from zero, indicating a much stronger systematic component within
the measurement error. This result increases our confidence that the OP
self-heating correction we applied was successful in removing a systematic
bias from the data. Further following , we used the
ϵ system error data set from the overlap period to generate flux
uncertainty estimates for bins of increasing OP flux ranges. We sorted the
ϵ values into 20 corresponding flux bins between -2 and
2 µmol m-2 s-1 and calculated an uncertainty estimate
for each bin σ(ϵ)i as
σ(ϵ)i=21Nj∑j=0Nj|ϵi,j-ϵ‾i|.
Results show (see Fig. ) a similar data range and pattern of
uncertainty estimates in relation to associated fluxes like the half-hourly
values calculated following .
As a third method of random uncertainty estimation, we simplified the
successive-observations approach from by using results
of the quality run performed during MDS gap filling (see Sect. 3.5). We
selected the time steps when a flux observation and an MDS value that was
estimated using a 1-day window and the MDV technique were available. We
used the standard deviation of the fluxes measured at the same hour of day
within a 1-day window, as an uncertainty estimate of the observed flux.
Results are shown in Fig. and also increase with rising
absolute fluxes in the same ranges as random uncertainties due to turbulence
sampling error or measurement system error do.
We included the results obtained with ITS estimation in the uploaded data
set considering the similarity between the uncertainty–flux relations
calculated with independent methods as well as due to the advantage of a
distinct uncertainty estimate for each sensor and time step.
Distributions of the measurement system errors ϵ estimated
using the paired-observations approach for differences between closed path and
corrected (a) as well as uncorrected (b) open-path (OP)
fluxes.
Footprint modeling
In order to quantify the cumulative contribution of distinct surface classes
to the EC source area, we evaluated the two-dimensional analytical footprint
formulation described by in combination with a
0.14 m×0.14m resolution surface classification of
Samoylov Island's central river terrace provided by . The
authors divide the surface into four classes based on hydrology and
vegetation communities, as illustrated in Fig. .
presented an analytical solution to the
crosswind-distributed advection–diffusion equation described by
and . Using the analytical model of
, the authors solved the power-law profiles of horizontal
wind speed and eddy diffusivity by relating them to the Monin–Obukhov
similarity theory, including the stability dependence of the exponents in the
power laws at a certain height. We implemented the equations given in
as a Matlab (v. 9.2) function and added a quality filter,
omitting calculations when friction velocity was larger than
0.9 ms-1 or smaller than 0 ms-1, wind speed was
below zero or above 20 ms-1, the crosswind standard deviation
was below zero or above 3 ms-1, or Monin–Obukhov length was
smaller than 10-3m or larger than 104m. Prior to
half-hourly footprint calculations, we additionally determined roughness
length statistics for annual subsets of data and binned them in
2 ∘ wind direction classes. The medians of these classes were used
in the subsequent half-hourly footprint estimation, depending on the mean
wind direction during these 30 min. We evaluated the footprint model at the
same resolution that was used by to classify the surface
(i.e., 0.14 m×0.14m). We could thereafter assign a
probability of being the EC source area to each classified pixel and sum up
the probabilities of all pixels belonging to the same surface class to
estimate the contribution of each class. This process of combining an EC
source area estimation with a land cover classification is similar to what
has been applied and described in more detail by .
Mean surface class composition of the eddy covariance footprint
during 17 subperiods of four different tower setups at three locations on
Samoylov Island.
Discussion
Although we did our best to ensure the consistency and appropriateness of the
data processing workflow for the presented net ecosystem exchange (NEE) time series, due to technical
and logistical constraints during 16 years of field work, disparities in the
experimental setup exist which may challenge its integrity. The EC tower was
relocated twice, and the measurement height was changed three times (see
Fig. and Table ). These changes of
tower location and measurement height affected the source area and hence the
surface types sampled during flux measurements. Most notably, between July
2007 and June 2009, the EC tower was placed about 650 m southwest
of its original position at the center of Samoylov Island, in an area with an
increased coverage of the surface class “wet tundra”. This is revealed
by the footprint analysis (Fig. ). While the EC footprint is
dominated by the surface class “dry tundra” throughout the time
series, during subperiods 2007, 2008 and 2009 I the contributions of
“wet tundra” to the measured flux are significantly higher. To check
the effect of the shifts in tower location and measurement height on
cumulative CO2-C fluxes, we calculated flux sums for a period when
flux time series without gaps were available in most years. The overlapping
period covers days of year 200 to 234, i.e., part of the growing season in all
years except for 2004 (see Fig. ). Interannual variability
of cumulative C fluxes in years with constant tower location (and measurement
height) appears to be large and driven by a more complex set of variables
than shifts in surface class contributions only. Flux sums from the periods
when EC tower relocation led to a significant shift in EC footprint
composition are well within the range of the distribution of cumulated fluxes
from years with a more homogeneous EC fetch area. We therefore assume that,
at least with respect to budget calculations, the presented long-term time
series is not disrupted and can be regarded as representative of a polygonal
tundra site dominated by “dry tundra”. For a more in depth analysis of
flux dynamics, footprint information should and can be considered by users of
the data set. Recently, a comparison between surface class level NEE models
based on chamber measurements with EC fluxes, using the half-hourly footprint
information provided in this data set for scaling, yielded good agreement
between the results obtained with both methods . We
regard the availability of half-hourly footprint information in the presented
NEE data set an attribute that sets it apart from other studies and holds
possibilities for comprehensive analyses.
Apart from the changes in anemometer height, other deviations of the general
instrument setup occurred due to limitations in data storage during two
winter periods when the acquisition frequency was reduced to 5 and
10 Hz. demonstrated in a field
experiment that fluxes calculated from raw data recorded at frequencies below
20 Hz compare well with fluxes derived from high-frequency raw data.
Differences arise as an increase in random noise and not as a systematic
bias. High-frequency noise removal before ensemble spectra estimation in
EddyPro is effective in limiting the effect of increased noise on the quality
of transfer function estimation in the process of spectral correction.
Overall spectral correction in EddyPro is expressed as a spectral correction
factor (SCF) which comprises the effect of all applied compensations for
high- and low-frequency loss. Raw fluxes are multiplied with the respective SCFs
during processing. We compared the SCF distributions of the two abovementioned winter periods with statistics of the remaining parts of the time
series when data were recorded at 20 Hz. SCF deviations between the
different acquisition frequencies are minor (see Fig. ), which implies that systematic differences between fluxes calculated from raw data
of different temporal resolutions are in fact small; random uncertainties
increase, however.
Comparison of cumulative CO2 flux sums of different years
during the same day-of-year range.
Spectral correction factor statistics for periods with different
acquisition frequencies.
Scientific overview
While results on methane exchange fluxes and the soils' methane production
and oxidation potential are more prominent in the publication record
e.g.,, the literature on CO2 flux time series recorded with the same measurement
system presented in this publication is available for distinct years. Flux
processing has, however, been streamlined only now. The length of the time
series, the addition of detailed footprint information, the site-specific
correction of OP fluxes, and the coherent processing and quality filtering
distinguishes the data set at hand from past publications like the
contribution made to the FLUXNET2015 data set .
Ongoing analysis of the long-term data set (Kutzbach, unpublished)
inter alia confirms what has been found in the past
. The polygonal tundra of
Samoylov Island appears to be a robust growing season CO2-C sink,
whereas this sink strength can vary so much interannually that prolonged
low-level respiratory CO2-C loss during the cold season can offset
CO2-C uptake during the vegetation period. Reduced summer uptake has
been observed for both the coldest and warmest summers.
found that with frequent early season heat spells, the temperature-induced
increase in respiratory release can exceed the rise in photosynthetic uptake.
Recently, all data from this publication have been contributed to the Arctic
Data Center's chamber and EC synthesis project “Reconciling historical
and contemporary trends in terrestrial carbon exchange of the northern
permafrost-zone” that aims at identifying seasonal and interannual C flux
dynamics and its drivers based on a newly established pan-Arctic database.
In context with the improvement of earth system models (ESMs), carbon dioxide
fluxes from Samoylov Island can be especially of use due to the site's
comparably high moss cover. Using data from Samoylov,
found that current ESMs miss an observed early season CO2 uptake peak
suspected to be connected to the earlier onset of moss photosynthesis in
comparison with vascular plants. Although there have been advances and, e.g.,
developed a dynamic moss model for JSBACH
, noted that the simulated
CO2 uptake and release terms combining vascular vegetation and moss
carbon fluxes did not agree with observational data. The fact that the
Samoylov Island NEE data set has now been extended and its quality has been
greatly improved holds the opportunity to estimate the performance of updated
ESM versions that are set up to represent carbon fluxes in the moss layer
better.
The data set was uploaded to the Pangaea database
and can be accessed through 10.1594/PANGAEA.892751.
The included columns are given in Table . Ancillary
long-term time series of meteorological and soil variables from Samoylov
Island are available from and can be accessed through
10.1594/PANGAEA.891142.
Conclusions
We are confident that the presented carbon dioxide land–atmosphere flux data
set is of high quality and is likely to be of value to the scientific
community. We screened the data carefully and applied filtering rules to
identify erroneous data, taking into account sensor diagnostics, time lag
statistics and the presence of atmospheric conditions that allow for a robust
application of the EC method. We followed standardized processing and quality
control/assurance routines to allow for comparability between different years
from our site as well as with flux time series from other tundra
environments. With OP measurements being paralleled by CP measurements in
7 years, we had the opportunity to correct for self-heating errors in our
OP measurements with a site-specifically scaled correction term rather than
using default correction methods e. g.. We could
therefore address different sensor setups with different correction terms and
thereby improve our OP data set, as the self-heating effect has distinct
impacts on sensors installed at different inclinations. We quantified the
contribution of certain soil and vegetation community types to each
half-hourly EC footprint, taking into account varying roughness lengths
throughout different years and wind direction sectors. We estimated the
cumulative probability of being the EC source area for the four main surface
classes on Samoylov Island's river terrace by using a land cover
classification and by computing an analytical EC footprint model. Multi-annual
results show (see Table ) that on average the combination of
different surface classes within the EC footprint is representative of the
surface composition of the whole river terrace that developed as a polygonal
tundra landscape. According to , the river terrace is
composed of 65 % “dry tundra”, 19 % “wet tundra” and 16 %
ponds (sum of “open water” and “overgrown”). On average, the
surface class compositions within the EC footprint are very similar to these
values. Deviations arise, however, in the years between 2007 and 2009, when
the tower location was shifted from the center towards the southwestern
cliff of Samoylov Island. Nevertheless, the contributions of each surface
class to the EC footprint are not only available on average, as presented in
Table , but half-hourly in the uploaded data set, ensuring
that EC source area deviations are quantifiable by a potential user. A total of 16 years
of consistently processed and quality-controlled carbon dioxide fluxes from a
polygonal tundra landscape typical of Arctic lowlands are a valuable
addition to the already existing database of CO2 net ecosystem
exchange observations from the Arctic, especially because of the site's
location in Northern Siberia, from where only limited data are available up to
now. Furthermore, analysis of this NEE time series is not limited to the gas
flux data only. An extensive data stream of meteorological and soil variables
between 2002 and 2017 has recently been published by . The
authors made their records publicly accessible on the two long-term
repositories Pangaea (10.1594/PANGAEA.891142) and Zenodo
(https://zenodo.org/record/2223709, last access: 1 February 2019). The fact of ancillary ecosystem
variables available in parallel enables a potential user to put the gas flux dynamics reported in
this publication into context with the variability of other ecosystem
properties and potential flux drivers. We regard this type of analysis as
vital to understanding interannual variability of gas fluxes and are working on
it ourselves (Kutzbach, unpublished).
DB, JB, MG, IF, LK and EP
conceptualized and administered the research activity planning and execution
and acquired funds for it. LB, DH, LK, ML, BR, PS, TS and CW conducted the
investigation. DH and CW analyzed the data; DH created the visualizations. DH
wrote the original draft; DH, LK, BR, TS and CW reviewed and edited the
original draft.
The authors declare that they have no conflict of
interest.
Acknowledgements
Without the dedicated work of many scientists, logistics experts and
engineers over the years, we would not have been able to present this
long-term eddy covariance NEE data set. We want to thank Niko Bornemann, Tim
Eckhardt, Mauel Helbig, Lars Heling, Oliver Kaufmann, Zoé Rehder, Norman
Rößger, Norman Rüggen, Günter Stoof and Waldemar Schneider for
their commitment, diligence and ingenuity. We thank Jakob Sievers for
providing us with a starting point for the Matlab implementation of the
footprint model and Norman Rößger for sharing his
analysis of the long-term meteorological data from Tiksi with us. This work
was supported through the Cluster of Excellence CliSAP (EXC177),
Universität Hamburg, funded through the German Science Foundation (DFG), by
the European Commission through the project PAGE21 (FP7-ENV-2011, 282700), and
by the German Ministry of Education and Research (BMBF) through the projects
CarboPerm (03G0836A) and KoPf (03F0764A).
Edited by: David Carlson Reviewed by: two anonymous referees
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