Longterm direct CO2 measurements over a boral lake: Five years of eddy covariance data. Geophys. Res

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Long‐term direct CO
2
flux measurements over a boreal lake:
Five years of eddy covariance data
Jussi Huotari,
1
Anne Ojala,
2
Elina Peltomaa,
2
Annika Nordbo,
3
Samuli Launiainen,
3,4
Jukka Pumpanen,
5
Terhi Rasilo,
5
Pertti Hari,
5
and Timo Vesala
3
Received 4 July 2011; revised 18 August 2011; accepted 22 August 2011; published 16 September 2011.
[1] Significant amounts of terrestrial carbon are processed in
lakes and emitted into the atmosphere as CO
2
. However, due
to lack of appropriate measurements the absolute role of lakes
in the landscape as sinks or sources of CO
2
is still uncertain.
We conducted the first long‐term, ecosystem‐level CO
2
flux
measurements with eddy covariance technique in a boreal
lake within a natural‐state catchment covering 5 years. The
aim was to reveal the natural level of CO
2
flux between a
lake and the atmosphere and its role in regional carbon
cycling. On average, the lake emitted ca 10% of the
terrestrial net ecosystem production of the surrounding old‐
growth forest and the main immediate drivers behind the
fluxes were physical rather than biological. Our results
suggest that lakes are an integral part of terrestrial carbon
cycling. Citation: Huotari, J., A. Ojala, E. Peltomaa, A. Nordbo,
S. Launiainen, J. Pumpanen, T. Rasilo, P. Hari, and T. Vesala
(2011), Long‐term direct CO
2
flux measurements over a boreal lake:
Five years of eddy covariance data, Geophys. Res. Lett.,38, L18401,
doi:10.1029/2011GL048753.
1. Introduction
[2] The importance of inland waters in carbon cycling has
only recently been recognized [Cole et al., 2007; Battin et al.,
2008; Tranvik et al., 2009]. Globally the majority of lakes
have surface water CO
2
concentrations higher than the
equilibrium with the atmosphere and thus they are net sources
of CO
2
[Cole et al., 1994]. Generally, this surplus CO
2
is
attributed to in‐lake heterotrophic respiration fuelled by
organic carbon of terrestrial origin [Jonsson et al., 2003;
Sobek et al., 2003]. Lakes also store carbon effectively in their
sediments, but in the boreal zone the annual CO
2
emissions
are 17–43 times higher than the net sedimentation of carbon
[Kortelainen et al., 2006]. A distinct feature of the majority of
boreal lakes is the brown water color, implying high loads of
allochthonous dissolved organic carbon (DOC). Carbon
enters lakes also in inorganic form (DIC), but the transport of
DIC from the catchment to lakes is largely unknown. These
lateral transport processes from the terrestrial to aquatic
ecosystems are not yet routinely included in network of
micrometeorological EC (eddy covariance) flux towers,
which are becoming the standard of CO
2
flux studies
[Baldocchi et al., 2001] and there are only a few lakes
equipped with EC towers. However, reliable assessment of
the total terrestrial net ecosystem production (NEP) and cal-
culation of terrestrial carbon balance requires information on
the lateral transport processes of DIC and DOC. Thus,
accurate knowledge of CO
2
fluxes to the atmosphere from
inland waters is a prerequisite for precise estimates of ter-
restrial carbon sinks.
[3] In aquatic sciences, flux estimates are usually based on
discrete samples and indirect models heavily relying on
wind‐based gas transfer coefficients [Wanninkhof et al.,
1985; Cole and Caraco, 1998], or chamber measurements
that are very labor‐intensive when high temporal resolution
is needed. Hence, the natural dynamics and level of CO
2
exchange in lakes have thus far been somewhat uncertain.
Here we present unique data on CO
2
exchange at ecosystem
scale measured with the most reliable and accurate method
available, namely the direct EC measurement technique,
over five consecutive ice‐free periods (2003–2007) in a
small, stratifying polyhumic headwater lake (Valkea‐Koti-
nen) and relate the flux dynamics to the possible drivers.
Lake Valkea‐Kotinen represents the lakes in natural‐state
areas within the boreal part of the Precambrian Shield in
Northern Europe and North America, where as a result of
the latest glacial period, numerous lakes with low alkalinity
and hence low pH were formed in the ancient bedrock. The
lake is surrounded by an old‐growth forest, and hence the
study demonstrates the truly natural dynamics and level of
lacustrine CO
2
flux.
2. Methods
[4] The study lake, Valkea‐Kotinen (61°14′N, 25°04′E)
is situated within a nature reserve area in Evo, Southern
Finland. Surface area of the lake is 0.041 km
2
and maximum
and mean depths are 6.5 m and 2.5 m, respectively. Details
of the lake are given by Kankaala et al. [2006], Vesala et al.
[2006], and Huotari et al. [2009]. The CO
2
fluxes were
measured with EC, as described by Vesala et al. [2006],
with some modifications in data postprocessing introduced
by Nordbo et al. [2011]. Upward fluxes were defined to be
positive representing net CO
2
emission into atmosphere.
Footprint modeling and data quality selection ensured that
the measurements were representative of lake‐atmosphere
exchange [Vesala et al., 2006; Nordbo et al., 2011]. Due to
advances in data postprocessing and quality control the flux
estimates presented here for 2003 diverge slightly from
those by Vesala et al. [2006], and are regarded more reli-
able. Quality selection retained 10% of all measured CO
2
fluxes in analysis. The percentage is quite low since we kept
1
Lammi Biological Station, University of Helsinki, Lammi, Finland.
2
Department of Environmental Sciences, University of Helsinki,
Lahti, Finland.
3
Department of Physics, University of Helsinki, Helsinki, Finland.
4
Finnish Forest Research Institute, Joensuu, Finland.
5
Department of Forest Sciences, University of Helsinki, Helsinki,
Finland.
Copyright 2011 by the American Geophysical Union.
0094‐8276/11/2011GL048753
GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L18401, doi:10.1029/2011GL048753, 2011
L18401 1of5

the quality criteria strict for this micrometeorologically non‐
ideal site [Vesala et al., 2006]. However, the amount of
collected data is much larger than using traditional methods
instead of the EC technique. The partial pressure of surface
water CO
2
(pCO
2
) was calculated from weekly samples of
DIC and pH, using Henry’s law. Temperature stratification
in the lake was measured at least at hourly intervals at dif-
ferent depths and the strength of stratification was calculated
as the Brunt‐Väisälä stability frequency (N
s
) between the
surface water (0.2 m) and the depth of 1.5 m [see Huotari
et al., 2009]. The precipitation data were provided by the
Finnish Meteorological Institute and the DOC, DIC and pH
was taken from the sampling of International Cooperative
Programme on Integrated Monitoring of Air Pollution
Effects on Ecosystems (ICP IM) [Keskitalo and Salonen,
1994]. All data were averaged over full calendar months
from June to September, and from ice melt until 31 May
(spring) and from 1 October until freeze over (autumn). The
annual CO
2
flux estimates were further integrated by mul-
tiplying the daily averages of the monthly periods by the
number of days in the corresponding period and summing
the periods during the year. The relationships between the
pCO
2
or CO
2
fluxes and the chemical and physical variables
measured by ICP IM were studied graphically, and using
Pearson’s correlation analysis. The dependences of the pCO
2
and the CO
2
flux on N
s
and CO
2
flux on pCO
2
were studied,
using monthly averaged values with curve estimation
regression analysis. PASW Statistics 18.0.0 software (SPSS
Inc., Chicago, IL, USA) was used for all the analyses.
3. Results and Discussion
3.1. Temporal Dynamics of CO
2
Flux
[5] Lake Valkea‐Kotinen was a source of CO
2
to the
atmosphere with a clear annual pattern in CO
2
flux dynam-
ics. Most of the CO
2
was emitted to the atmosphere in late
summer, when the thermocline was deepening, and during
the autumn turnover in September‐October (Figure 1). The
mean daily CO
2
fluxes (±SD) during these time periods were
from 0.52 (±0.18) to 0.56 (±0.22) g C m
−2
d
−1
(Figure 2), and
they contributed together up to 77% of the annual fluxes. The
time of ice melt and the following spring turnover, which
was often incomplete and short, was also distinct in the
annual pattern (Figure 1). As a consequence of the rapid
vernal development of strong stratification, the contribution
of spring turnover to annual fluxes was small. The mean
daily CO
2
flux in spring, averaged over the period from ice
melt until 31 May, was 0.31 (±0.16) g C m
−2
d
−1
(Figure 2),
and the spring period contributed 13.4% (±6.3%) to the
annual flux.
[6] The midsummer CO
2
fluxes were usually small and
were affected by sporadic physical events (Figure 1). In June
and July, the fluxes were only 0.08 (±0.17) and 0.19 (±0.10) g
Cm
−2
d
−1
, respectively (Figure 2), and the surface water CO
2
concentration was sometimes under atmospheric equilibrium,
Figure 1. Half‐hourly CO
2
fluxes over open‐water periods
of 2003–2007. Positive values indicate upward transport
(emission). Capital letters M and F represent times of ice
melt and freeze‐over, respectively. Upward arrows represent
bursts of CO
2
during summer stratification in June‐July, as
discussed in the text.
Figure 2. Seasonality of CO
2
fluxes. Spring and autumn
periods are from ice melt until 31 May and from 1 October
until freeze‐over, respectively. Vertical bars represent stan-
dard deviation.
HUOTARI ET AL.: CO
2
FLUXES OF A BOREAL LAKE L18401L18401
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presumably as a consequence of vigorous primary production
[Huotari et al., 2009]. Thus, during the summer months the
lake acted occasionally as a CO
2
sink, which is hardly ever
reported for boreal polyhumic lake before. However, spo-
radic bursts of CO
2
, comparable to fluxes during turnover,
were also detected. They were associated with event‐type
deepenings of the epilimnion due to convection [cf. Eugster
et al., 2003] after cooling of the air and sometimes a simul-
taneous increase in wind speed or precipitation. The summer
bursts of CO
2
in 2004 may also have resulted from extreme
rain events flushing CO
2
from the catchment, as reported
from a nearby larger lake [Ojala et al., 2011]. Due to dif-
ferences in data quality screening night time influx into the
lake in summer evidenced by Vesala et al. [2006] could not
be detected in this study. In general, the fluxes in June and
July had only a small annual contribution (2.5% ± 5.7% and
7.5% ± 4.0%, respectively).
[7] The CO
2
flux was best explained by pCO
2
(Figure 3).
The pCO
2
and consequently the CO
2
flux were clearly
dependent on the strength of stratification in the water col-
umn, i.e., the more stable the stratification the lower the
pCO
2
(Figure 3) and the flux (R
2
= 0.341, P= 0.001, n= 30).
Due to the high DOC concentration and rapid light attenu-
ation, the euphotic zone and the mixing depth were restricted
during stratification within the top 1‐m layer, below which
there was a large storage of CO
2
[Vesala et al., 2006; Huotari
et al., 2009]. Hence, when the mixing depth increased,
resulting from a decreasing Brunt‐Väisälä frequency, CO
2
was supplied from the metalimnion to the surface. Simulta-
neously, the planktonic primary producers were mixed dee-
per in the water column, which deteriorated their light
climate and hence productivity, i.e., uptake of inorganic
carbon decreased. Stratification determined how the bio-
logical activity was reflected in the surface water CO
2
con-
centration and thus, physical rather than biological processes
had the immediate control over the surface water CO
2
con-
centration in Lake Valkea‐Kotinen [Huotari et al., 2009]
and, further, over the flux to the atmosphere.
[8] The annual fluxes were 97, 74, 74, 74 and 68 g C
m
−2
yr
−1
in 2003, 2004, 2005, 2006 and 2007, respectively.
The differences between the annual fluxes in Lake Valkea‐
Kotinen were small and only the efflux in 2003 was slightly
higher. This may have been due to the longer winter in 2002–
2003, since in autumn 2002 the lake froze over 1 month
earlier than normally, and the ice melt occurred rather late in
spring 2003. Thus, a month’s efflux from autumn 2002 was
trapped below the ice cover and evaded in 2003. The date of
freeze‐over was more variable than the time of ice melt, and
thus the length of the ice‐covered period determined how
large an efflux was transferred to the next year, i.e., there
was a positive correlation between the annual fluxes and the
length of the preceding ice‐covered period (R = 0.994, P=
0.001, n= 5). The lake water DOC concentration or pre-
cipitation did not explain the fluxes, although summer 2004
was very wet as a consequence of which the DOC con-
centration increased by one third, i.e., monthly averages
were 12.6 in 16.9 mg L
−1
in June and August, respectively.
However, mineralization of the DOC of allochthonous
origin is slow [e.g., Wetzel, 2001] and the stratification
dynamics determined when the CO
2
produced was released.
The highest daily flux (0.96 g C m
−2
d
−1
) was recorded in
August 2005 and probably resulted from mineralization of
the DOC already flushed to the lake in 2004.
3.2. Direct Flux Measurements Versus Modeled Flux
[9] The mean annual flux over the 5‐year measuring
period was 77 (±11 SD) g C m
−2
yr
−1
. This value is lower
than estimated with the gas flux model [Cole and Caraco,
1998] for a large sample of statistically selected lakes in
Finland, where the CO
2
flux from small lakes (<0.1 km
2
) was
102 g C m
−2
yr
−1
[Kortelainen et al., 2006]. Those estimates
were based on only four samples of surface water CO
2
per
year, whereas the continuous measurements from Lake
Valkea‐Kotinen show that the annual course of CO
2
flux is
dynamic and partly behind sporadic events (Figure 1). On the
other hand, our directly measured CO
2
fluxes were higher
than the values of 44 and 30 g C m
−2
yr
−1
for Lake Valkea‐
Kotinen in 2005 and 2006, respectively [Huotari et al.,
2009], which are based on continuous surface water CO
2
measurements and calculated with the wind‐based gas flux
model [Cole and Caraco, 1998]. MacIntyre et al. [2010]
have suggested divergent wind‐based gas transfer equa-
tions for times when lakes are cooling and when they are
heating. We determined times of cooling and heating from
the change in heat storage [Nordbo et al., 2011] and applied
those equations to hourly averages of continuous surface
water CO
2
measurements for 2006 [Huotari et al., 2009].
This resulted in annual flux estimate of 60 g C m
−2
yr
−1
, i.e.,
much closer to EC values than attained with flux model of
Cole and Caraco [1998]. Gas transfer coefficient (k
600
),
computed according to Jonsson et al. [2008] from the EC
and the continuous surface water CO
2
concentration data
Figure 3. (a) Relationship between CO
2
flux and surface
water partial pressure of CO
2
(pCO
2
); CO
2
flux = 0.3921
ln (pCO
2
)−2.3944. The pCO
2
explained 45% of the varia-
tion in CO
2
flux (P= 0.000). (b) Linear relationship between
pCO
2
and Brunt‐Väisälä stability frequency (N
s
), which is a
measure of the strength of stratification. The relationship is
in the form of pCO
2
=−16 783 N
s
+ 1944.7. N
s
explained
77% of the variation in pCO
2
(P= 0.000). Each point repre-
sents a monthly average (n= 30).
HUOTARI ET AL.: CO
2
FLUXES OF A BOREAL LAKE L18401L18401
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for the year 2006 [Huotari et al., 2009], was 1.5 times
higher than obtained with the wind‐based equation of Cole
and Caraco [1998] from Huotari et al. [2009], i.e., 3.8 ±
0.8 cm h
−1
vs. 2.5 ± 0.05 cm h
−1
(±95% CI), respectively.
Since the relationship between k
600
and wind speed is non-
linear the wind‐based models where the regressions are
derived from data over longer periods of time, underestimate
the importance of short‐term changes in wind speed captured
by the EC method [Cole et al., 2010]. Also other sources of
turbulence besides wind shear, such as heat loss, enhance gas
transfer across the air‐water interface [MacIntyre et al.,
2010] and most likely affected the results in the steeply
stratifying Lake Valkea‐Kotinen. Wind‐based flux models
may not adequately describe the gas transfer across the air‐
water interface and perhaps other models, such as surface
renewal models would be better [MacIntyre et al., 2010].
However, these discrepancies emphasize the need of high‐
frequency flux measurements with EC to reveal the true flux
dynamics and to accurately estimate annual CO
2
fluxes.
3.3. Regional Importance
[10] The mean annual CO
2
flux of 77 g C m
−2
yr
−1
is
almost 30 times higher than the long‐term (postglacial)
carbon accumulation rate of 2.8 g m
−2
yr
−1
determined from
sediment core samples of Lake Valkea‐Kotinen [Pajunen,
2004]. The flux per unit area of the catchment, which can
be used when assessing the importance of a lake as a site for
remineralization of terrestrial carbon, is 11 (±1.4 SD) g C
m
−2
yr
−1
. The published values of NEP for the unmanaged
boreal forests corresponding to the annual temperature and
precipitation regime of Lake Valkea‐Kotinen range from
−50 to 200 g C m
−2
yr
−1
[Luyssaert et al., 2007] the mean
value being around 100 g C m
−2
yr
−1
. This means that on
average the CO
2
flux from the lakes decreases the carbon
sink of natural forests by 10%. This being valid for the whole
boreal zone, the carbon sink in boreal forests [Hari et al.,
2008] would be in order of magnitude of 100 Tg C yr
−1
smaller than assumed. However, in the managed forested
catchments in the boreal zone the carbon loss to the atmo-
sphere through lakes is estimated to be considerably less, i.e.,
1–4% of terrestrial net ecosystem exchange [Jonsson et al.,
2007; Ojala et al., 2011].
4. Conclusions
[11] The long‐term record of direct ecosystem‐scale CO
2
flux measurements in a lake illustrates a substantial natural
leakage of terrestrially fixed carbon back to the atmosphere
through aquatic conduits. Global change in terms of higher
temperatures and precipitation [Intergovernmental Panel on
Climate Change, 2007] will increase lateral carbon transport
and, e.g., the total organic carbon flux in the outlet brook of
Lake Valkea‐Kotinen is predicted to increase up to 26% by
the 2050s [Holmberg et al., 2006]. Thus, the importance of
inland waters as conduits of terrestrial carbon to the atmo-
sphere will increase. Global change also alters the stability
of the water column, which was shown here to be crucial for
gas fluxes. Increased DOC together with higher tempera-
tures strengthens the stratification in lakes, which results in
lower summertime fluxes, but since total carbon loadings
will be higher, the annual CO
2
efflux presumably increases.
[12] Warmer autumns already increase CO
2
loss from
terrestrial ecosystems in northern latitudes [Piao et al.,
2008; Vesala et al., 2010]. Supposedly, the loss of terres-
trial carbon through lakes is also enhanced, due to warmer
autumns, which emphasizes the role of autumns in the
annual pattern of the CO
2
flux. Nevertheless, these results
based on the most reliable and direct measuring technique
available suggest that natural inland waters are an integral
part of terrestrial carbon cycling and hence must be taken
into account in balance calculations and when considering
the strength of regional as well as global terrestrial carbon
sinks [Hope et al., 2001; Luyssaert et al., 2007; Battin et al.,
2008]. Besides the importance of autumn for the fluxes, the
results also highlight physical phenomena rather than bio-
logical processes as the drivers. The flux model must be
chosen with great care in situations when direct flux mea-
surements cannot be made. The obtained results can also be
used in representations and parameterizations of the lake‐
atmosphere CO
2
exchange in Earth system models.
[13]Acknowledgments. This study was funded by the Academy of
Finland, projects TRANSCARBO (1116347), FASTCARBON (130984),
213093 and the Centre of Excellence programme (project 1118615) and
ICOS (projects 137352 and 141518); EU projects GHG‐Europe, IMECC
and ICOS; TEKES and Vaisala Oyj through project CO2EKO. We thank
the Finnish Meteorological Institute for providing the precipitation data.
Rob Striegl and two anonymous reviewers are acknowledged for their valu-
able comments that improved the paper. Pasi Ala‐Opas is acknowledged
for his help in the field.
[14]The Editor thanks two anonymous reviewers for their assistance in
evaluating this paper.
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A. Nordbo and T. Vesala, Department of Physics, University of Helsinki,
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A. Ojala and E. Peltomaa, Department of Environmental Sciences,
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