# Seasonal patterns in greenhouse gas emissions from lakes and ponds in a High Arctic polygonal landscape

Vilmantas Prèskienis, Isabelle Laurion, Frédéric Bouchard, Peter Douglas,

Michael Billett, Daniel Fortier, Xiaomei Xu

► To cite this version:

Vilmantas Prèskienis, Isabelle Laurion, Frédéric Bouchard, Peter Douglas, Michael Billett, et al. Seasonal patterns in greenhouse gas emissions from lakes and ponds in a High Arctic polygonal landscape. Limnology and Oceanography, 2021, 66 (S1), pp.S117-S141. 10.1002/lno.11660. hal-04505252

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LIMNOLOGY
and
OCEANOGRAPHY

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Limnol. Oceanogr. 66, 2021, S117-S141
© 2021 Association for the Sciences of Limnology and Oceanography
doi: 10.1002/lno.11660

# Seasonal patterns in greenhouse gas emissions from lakes and ponds in a High Arctic polygonal landscape

Vilmantas Prėskienis<sup>1,2*</sup> Isabelle Laurion<sup>1,2</sup> Frédéric Bouchard<sup>1,2,3</sup> Peter M. J. Douglas,<sup>4</sup> Michael F. Billett,<sup>5</sup> Daniel Fortier,<sup>2,6</sup> Xiaomei Xu<sup>7</sup>

## Abstract

Lakes and ponds can be hotspots for CO₂ and CH₄ emissions, but Arctic studies remain scarce. Here we present diffusive and ebullition fluxes collected over several years from 30 ponds and 4 lakes formed on an organic-rich polygonal tundra landscape. Water body morphology strongly affects the mixing regime—and thus the seasonal patterns in gas emissions—with ice-out and autumnal turnover periods identified as hot moments in most cases. The studied thermokarst lake maintained relatively high ebullition rates of millennia-old CH₄ (up to 3405 ¹⁴C YBP). Larger and deeper kettle lakes maintained low fluxes of both gases (century to millennium-old), slowly turning into a CO₂ sink over the summer. During winter, lakes accumulated CO₂, which was emitted during the ice-out period. Coalescent polygonal ponds, influenced by photosynthesizing benthic mats, were continuous CO₂ sinks, yet important CH₄ emitters (modern carbon). The highest fluxes were recorded from ice-wedge trough ponds (up to 96 mmol CO₂ equivalent m⁻<fnref n="2" /> d⁻<fnref n="1" />). However, despite clear signs of permafrost carbon inputs via active shore erosion, these sheltered ponds emitted modern to century-old greenhouse gases. As the ice-free period lengthens, scenarios of warmer and wetter conditions could favor both the production of CO₂ and CH₄ from thawing permafrost carbon, and CH₄ production from recently fixed carbon through an atmospheric CO₂-to-CH₄ shunt at sites in which primary production is stimulated. This must be carefully considered at the landscape scale, recognizing that older carbon stocks can be mineralized efficiently in specific locations, such as in thermokarst lakes.

High Arctic regions are experiencing pronounced climate change (Bintanja and Krikken 2016), affecting air and permafrost temperatures (Biskaborn 2019), and snowfall (Bokhorst 2016). Lowlands are widespread in this part of the world, and known for abundance (up to 90% of surface area in

certain regions; Pienitz et al. 2008) and diversity of water bodies. Lakes located in organic-rich permafrost substrate can be sites of intense carbon dioxide (CO₂) and methane (CH₄) emissions to the atmosphere (Abnizova et al. 2012; Sepulveda-Jauregui et al. 2015; Matveev et al. 2016). Small ponds have been identified as hot spots of biogeochemical activity (Holgerson and Raymond 2016), and this holds true for tundra ponds (Bouchard et al. 2015). Recent field and modeling studies showed that CO₂ and CH₄ emissions from Arctic lakes are significant and could increase with global warming (Kosten et al. 2010; Tan and Zhuang 2015; Wik et al. 2016a; Tan et al. 2017).

The carbon-based greenhouse gases (GHG; CO₂ and CH₄) produced from old carbon pools mobilized through permafrost thawing could act as a positive feedback on climate warming (Schuur et al. 2015), although the magnitude of this mechanism remains largely unknown (reviewed by Dean

Additional Supporting Information may be found in the online version of this article.

**Author Contribution Statement:** V.P., I.L., F.B., D.F., and M.B. have participated in study planning and data collection in the field. V.P. and I. L. performed the data analyses, with M.B., P.D., and X.X. adding their expertise in analyzing and interpreting the isotope data. V.P. wrote the manuscript under the supervision of I.L.. All authors have contributed to the writing or revision of the manuscript.

**Special Issue:** Biogeochemistry and Ecology across Arctic Aquatic Ecosystems in the Face of Change. Edited by: Peter J. Hermes, Suzanne E. Tank and Ronnie N. Glud

<footnote n="1"><sup>1</sup>Centre Eau Terre Environnement, Institut national de la recherche scientifique, Québec, Canada</footnote>

<footnote n="2"><sup>2</sup>Centre d'études nordiques (CEN), Université Laval, Québec, Canada</footnote>

<footnote n="3"><sup>3</sup>Géosciences Paris-Saclay (GEOPS), Université Paris-Saclay, Orsay, France</footnote>

<footnote n="4"><sup>4</sup>Department of Earth and Planetary Sciences and Geotop, McGill University, Montréal, Canada</footnote>

<footnote n="5"><sup>5</sup>Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, UK</footnote>

<footnote n="6"><sup>6</sup>Département de Géographie, Université de Montréal, Montréal, Canada</footnote>

<footnote n="7"><sup>7</sup>Department of Earth System Science, University of California, Irvine</footnote>

<footnote>*Correspondence: isabelle.laurion@ete.inrs.ca</footnote>
et al. 2018). The majority of organic-rich permafrost areas developed during the Holocene (Treat and Jones 2018), and in certain regions this led to the sequestration of large amounts of carbon (Hugelius 2014). The Holocene carbon storage may represent a feedback to climate change if recent permafrost erosion accelerates its transfer to the atmosphere, although most studies involving GHG dating up to now have taken place in yedoma regions (late Pleistocene deposits; e.g., Walter et al. 2008; Elder et al. 2019). Many recent studies have found that microorganisms predominantly mineralize freshly produced organic matter in high-latitude lakes (Bouchard et al. 2015; Elder et al. 2018; Bogard et al. 2019), except at sites strongly affected by deep permafrost thaw (Mann et al. 2015; Turetsky et al. 2020).

Studies presenting GHG fluxes from lakes and ponds of the Arctic tundra environment remain scarce, even though their importance was emphasized at least 30 years ago (Sebacher et al. 1986; Kling et al. 1992). To our knowledge, there are only six areas where GHG emissions from tundra lakes or wetlands have been studied: the Alaskan North Slope and Yukon Delta in the United States, the Lena Delta and Yamalia in the Russian Federation, and the Eastern Nunavut and Northwest Territories in Canada (Table 1). Furthermore, Arctic studies are often strongly limited temporally for logistical reasons, with only a few measurement points of the fluxes, most often restricted to July or August, or estimated from bubbles trapped in ice or from under-ice measurements (e.g., Langer et al. 2015; Matveev et al. 2019). Despite that, the few studies looking at spatiotemporal variations in CO₂ or CH₄ emissions

revealed strong seasonality, as well as dependence on the type of organic substrate (Sepulveda-Jauregui et al. 2015; Elder et al. 2018, 2019; Hughes-Allen et al. 2020), lake morphology (Abnizova et al. 2012), and lake–river connectivity (Cunada et al. 2018). Thus, more studies covering the whole open-water season and accounting for winter and summer storage fluxes (respectively, defined as the emission during the ice-out and autumnal mixing period of the GHG accumulated in the water column during preceding months) are needed to better assess the contribution of aquatic tundra ecosystems to the global carbon cycle, and their sensitivity to climate change.

Bylot Island is characterized by a Holocene organic-rich tundra landscape, where lake and pond limnology has been studied for over a decade, with initial CO₂ and CH₄ flux data presented in Breton et al. (2009) and Laurion et al. (2010), and later complemented with a few radiocarbon dates on samples collected in July (Negandhi et al. 2013; Bouchard et al. 2015). These studies showed high CO₂ and CH₄ emissions, primarily composed of modern carbon. Exploiting data collected from late May to mid-August, the present study aims to identify the key mechanisms behind the variability observed in CO₂ and CH₄ fluxes from lakes and ponds that characterize polygonal tundra landscapes. We investigated how water body morphology affects seasonal trends in fluxes of these gases through its influence on mixing regime, and which sources of carbon are predominantly mineralized in the water bodies by exploiting the gas isotopic signatures. This is particularly relevant since morphology can be recognized by spatially resolved remote

<table><caption>**Table 1.** Reported CO₂ and CH₄ fluxes from tundra lakes and ponds across the Arctic. Only studies on water bodies above the tree-line were selected. The ranges presented for Bylot Island are composed of averaged seasonal values from all studied water bodies, and include summer storage flux estimated from dissolved concentrations measured at the hypolimnion. Negative values represent CO₂ uptake from the atmosphere.</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">References</th><th colspan="3">Reported flux (mmol m<sup>-2</sup> d<sup>-1</sup>)</th><th rowspan="2">Notes*</th></tr><tr><th>Diff CO<sub>2</sub></th><th>Diff CH<sub>4</sub></th><th>Ebul CH<sub>4</sub></th></tr></thead><tbody><tr><td rowspan="4">Alaskan North Slope, United States</td><td>Elder et al. (2018)</td><td>0.2–82.0</td><td>0.16–0.56</td><td>n.a.</td><td>DG, HE; Apr, Aug</td></tr><tr><td>Sepulveda-Jauregui et al. (2015)</td><td>6.7–50.3</td><td>0.46–1.63</td><td>0.20–4.5</td><td>HE, SR; Apr, Jun–Jul</td></tr><tr><td>Kling et al. (1992)</td><td>-5.5 to 59.8</td><td>0.23–1.02</td><td>n.a.</td><td>DIC, HE; Jul</td></tr><tr><td>Sebacher et al. (1986)</td><td>n.a.</td><td>2.1–16.6</td><td>n.a.</td><td>CH; Aug</td></tr><tr><td>Bylot Island, Canada</td><td>This study, Laurion et al. (2010),<br>Negandhi et al. (2013),<br>Bouchard et al. (2015)</td><td>-10.4 to 81.5</td><td>0.02–8.9</td><td>0.01–16.9</td><td>HE, SR, CH; Jun–Aug</td></tr><tr><td>Ellesmere Island, Canada</td><td>Emmerton et al. (2016)</td><td>-6.5 to 5.1</td><td>0.01–0.56</td><td>0.01–0.43</td><td>HE, SR, EC; Jun–Aug</td></tr><tr><td>Lena Delta, RF<sup>†</sup></td><td>Abnizova et al. (2012)</td><td>9.0–25.9</td><td>n.a.</td><td>n.a.</td><td>EC; Aug–Sep</td></tr><tr><td>Northwestern Canadian Arctic</td><td>Cunada et al. (2018)</td><td>n.a.</td><td>0.12–57.0</td><td>n.a.</td><td>HE; May–Aug</td></tr><tr><td></td><td>Buell (2015)</td><td>-0.08 to 3.9</td><td>n.a.</td><td>n.a.</td><td>CH, HE, SR; Jun–Aug</td></tr><tr><td>Yamalia, RF<sup>†</sup></td><td>Golubyatnikov and Kazantsev (2013)</td><td>n.a.</td><td>0.36–0.79</td><td>n.a.</td><td>CH; Aug</td></tr><tr><td>Yukon Delta, USA</td><td>Bartlett et al. (1992)</td><td>n.a.</td><td>3.0–229.6</td><td>n.a.</td><td>CH; Jul</td></tr></tbody></table>

<footnote>*Methods for acquiring diffusive flux: CH, gas chamber; DG, degassing membrane contactor; DIC, obtained from dissolved inorganic carbon; EC, eddy covariance flux tower; HE, headspace equilibration; SR, sensors measuring concentration directly in the field. The months roughly indicate when field sampling was done.</footnote>

<footnote>†Russian Federation.</footnote>
sensing tools (as in Elder et al. 2020), making such relation-
ships informative for upscaling exercises.

Methods

Description of the study site

The study was conducted on Bylot Island, on the eastern side of the Canadian Arctic Archipelago, in Sirmilik National Park, Nunavut. The core of the island is composed of the Byam Martin Mountains, consisting of igneous and metamorphic rocks and covered by ice caps. The surrounding undulating lowlands, covered by graminoid-moss tundra (Duclos et al. 2006), are made up of poorly consolidated sedimentary rocks, where previous glaciations have carved a series of valleys, and left behind a cover of glacigenic sediments (Klassen 1993). All the lakes and ponds presented in this study are located in one of these valleys, named Qarlikturvik (glacier C-79; 73°09′N, 79°58′W), located on the western side of the island and dominated by a braided river fed by glacial meltwater. A 3 to 5-m thick syngenetic permafrost terrace, a mixture of windblown loess and peat, has been accumulating since the mid-Holocene (Fortier et al. 2006). These peaty loess deposits contain excess pore ice (> 100% dry weight) and their gravimetric organic matter content can reach more than 50%. The active layer depth in these deposits generally ranges from 40 to 60 cm (Fortier and Allard 2004). This continuous permafrost terrain is characterized by a complex network of ice-wedge polygons with well-defined ridges and troughs.

The region experiences a polar climate characterized by short cool summers, long cold winters, and low precipitation. The climate normal is based on 1981-2010 data provided by the Environment Canada meteorological station in Mittimatalik (Pond Inlet), located 85 km southeast of the study site. For this time period, mean annual air temperature was -14.6°C and precipitation 189 mm (91 mm of rain) (Environment Canada, https://climate.weather.gc.ca/climate normals/index_e.html). In recent decades, the climate in the Eastern Canadian Arctic has been experiencing one of the most rapid changes in the Arctic. The greatest warming trends are observed for autumn and winter seasons (with warming in some areas exceeding 1.7°C per decade) leading to shorter snow-covered periods, yet with increased total winter precipita- tion (Bell and Brown 2018).

During the thawing period, large portions of the valley
retain snowmelt water in closed depressions, creating thou-
sands of shallow (0.1–1.5 m depth) water bodies of diverse
shapes and sizes (Fig. 1). These aquatic systems freeze
completely during winter as the average maximum ice thick-
ness at the study site is 2 m ± 20 cm (measured in 2015 and
2016), which makes their annual biogeochemical cycle very dif-
ferent from the larger and deeper lakes present in the valley.
We used this distinction to define lakes (> 2 m depth) with a
fraction of the water column remaining unfrozen in winter,
and ponds (< 2 m depth) that freeze to the bottom. This

distinction is in agreement with other studies in the Arctic clas-
sifying lakes and ponds based on area (Langer et al. 2015;
Polishchuk et al. 2018)—the majority of the ponds at our site
are smaller than 500 m², and all studied lakes are larger than
10,000 m².

Water body selection

In total, 30 ponds and 4 lakes were studied for their limnology, biogeochemistry, and fluxes of CO2 and CH4. Some lakes and ponds have been sampled sporadically since 2008, but the majority of the data presented here is from four consecutive summers (2014–2017), in 2015 starting late May and in 2016 extending until mid-August. The chosen water bodies are well represented at the study site (Fig. 1).

Two of the chosen lakes (Table 2) were formed in kettle basins and are mainly surrounded by inorganic glaciofluvial deposits and till. This type of glacial lake is common across the Arctic (Wik et al. 2016a). Since they fill depressions left by melted glacial ice, they tend to be deeper (> 5 m) than other types of freshwater bodies in the area. The valley also contains several thermokarst lakes formed within the above-mentioned organic-rich peat-loess complex. Most of these lakes are shallow (< 3 m), which is typical for thermokarst lakes outside yedoma regions (Grosse et al. 2013). One of the chosen thermokarst lakes, BYL123, has an elongated shape (Fig. 1) and two deeper basins (2–2.5 m deep). Lake BYL66 is the largest and deepest thermokarst lake in the valley, presenting a large central zone with a bumpy bottom (~ 4 m deep), and a wide shallow littoral zone (< 2 m deep) that continues to expand by thermokarst and thermo-erosional wave action (see Bouchard et al. 2020 for details on the bathymetry and temporal evolution of this lake).

The study ponds were chosen to represent the most fre-
quent morphologies observed at the site (Table 2). They can
be roughly divided into two categories: (1) narrow (0.5–5.0 m
wide; 0.4–1.5 m deep), elongated, channel-like ponds formed
above degrading ice wedges, hereafter referred to as ice wedge
trough (IWT) ponds, and (2) wider ponds (5–35 m wide;
0.5–0.9 m deep) with steep shores and flat bottoms, often
occupying several polygons, hereafter referred to as coalescent
polygonal (CP) ponds. The IWT ponds have dark, humic, tur-
bid waters, rich in allochthonous organic matter originating
from shore erosion and linked to their large soil-water contact
areas relative to their volume. In contrast, CP ponds are more
transparent, warmer, and shallower, with a flat bottom cov-
ered by benthic mats mainly composed of cyanobacteria
(*Oscillatoria* sp.; Vézina and Vincent 1997) and brown mosses
(*Drepanocladus* sp.; Ellis et al. 2008). We studied 17 IWT ponds
(with BYL24, BYL27, and BYL117 in more details) and 13 CP
ponds (with BYL30 and BYL80 in more details). Shallow
(0.1–0.3 m maximum depth) ponds forming in depressions of
low-centered polygons during the snowmelt period (known as
polygonal ponds) are also common in the study area. They are
often short-lived or too shallow for common sampling
approaches; nevertheless, a few were sampled for the diffusive
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location map for Bylot Island and the study site, showing the abundance and diversity of water bodies in this landscape, including ice-wedge trough (IWT) and coalescent polygonal (CP) ponds. The zoomed area illustrates the complexity of pond structures found in tundra wetlands.</figcaption></figure>

fluxes and were included in the same group as CP ponds, due
to their similarities in morphology and biogeochemistry.

Limnological data

The thermal structure of a few ponds and lakes (BYL27,
BYL37, BYL66, and BYL80) was monitored for a minimum of
2 years (5-s to 1-h intervals) using submersible temperature
loggers (RBR SoloT, Vemco Minilog-II-T, or Hobo U12,
depending on the year and water body; specifications for all
sensors are given in Table S1). In addition to these loggers
recording annual cycles, manual measurements of tempera-
ture and dissolved oxygen (DO; ProODO, YSI Inc.) were car-
ried out on all studied lakes and ponds with varying
frequency during the field seasons. Limnological seasons were
defined based on changes in the thermal structure of the water
column (Table 3), also using seasonal patterns in DO, CO₂,
and CH₄ to validate and adjust the limits of these periods. The

length and timing of ice-out, mixing, and stratified periods
can differ by 1–10 days between individual water bodies and
years; hence for simplicity, these seasons are differentiated
only between lakes and ponds. Briefly, spring corresponds to
the period when ice is melting and the water column is
mixing, early summer to the period when water is warming
and the water column is stratifying (except for CP ponds,
which are always mixed), and late summer to the period when
the surface water is cooling but stratification is still present.
Autumn corresponds to the turnover period before the ice-
cover starts to form, which defines the start of the winter sea-
son. We acknowledge that these Arctic limnological seasons
are different from climatological seasons, as well as seasons
defined for temperate lakes.

Samples for other limnological characteristics, including
concentrations of dissolved organic carbon (DOC), chromo-
phoric fraction of dissolved organic matter (CDOM,
<table><caption>**Table 2.** Morphological and limnological characteristics of the studied water bodies, including surface area, maximum depth, mean depth, dissolved organic carbon concentration (DOC), absorbance coefficient at 320 nm (a<sub>320</sub>), absorption of DOM at 254 nm per unit DOC (SUVA), total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), and chlorophyll *a* concentration (Chl *a*). Median values of surface waters are presented for the four studied lakes and the most studied ponds, with additional group medians for all ice wedge trough ponds and coalescent polygonal ponds studied in the valley. Water bodies in bold corresponds to the one presented in Fig. 3. CP ponds, coalescent polygonal ponds; IWT ponds, ice-wedge trough ponds.</caption><thead><tr><th>Water body type</th><th>Water body</th><th>Area</th><th>Max depth</th><th>Mean depth</th><th>DOC</th><th>a<sub>320</sub></th><th>SUVA</th><th>pH</th><th>TSS</th><th>TN</th><th>TP</th><th>Chl a</th></tr><tr><td></td><td></td><td>m<sup>2</sup></td><td>m</td><td>m</td><td>mg L<sup>-1</sup></td><td>m<sup>-1</sup></td><td>L m<sup>-1</sup> mgC<sup>-1</sup></td><td></td><td>mg L<sup>-1</sup></td><td>µg L<sup>-1</sup></td><td>µg L<sup>-1</sup></td><td>µg L<sup>-1</sup></td></tr></thead><tbody><tr><td rowspan="2">Kettle lake</td><td>BYL36</td><td>31,123</td><td>12.0</td><td>3.1</td><td>4.3</td><td>5.3</td><td>1.8</td><td>7.8</td><td>1.1</td><td>223.1</td><td>8.5</td><td>1.5</td></tr><tr><td>dimictic</td><td>BYL37</td><td>209,120</td><td>11.9</td><td>3.8</td><td>5.3</td><td>18.4</td><td>3.3</td><td>8.1</td><td>1.1</td><td>253.1</td><td>17.4</td><td>1.6</td></tr><tr><td rowspan="2">Thermokarst lake</td><td>BYL66</td><td>116,172</td><td>4.1</td><td>1.9</td><td>4.5</td><td>16.9</td><td>3.6</td><td>7.2</td><td>1.6</td><td>261.3</td><td>14.7</td><td>1.6</td></tr><tr><td>polymictic</td><td>BYL123</td><td>11,509</td><td>2.5</td><td>1.0</td><td>8.1</td><td>8.6</td><td>1.7</td><td>n.d.</td><td>1.3</td><td>584.6</td><td>20.3</td><td>n.d</td></tr><tr><td rowspan="2">IWT pond</td><td>BYL27</td><td>&gt; 400*</td><td>1.4</td><td>0.6</td><td>11.8</td><td>42.7</td><td>3.6</td><td>7.0</td><td>4.5</td><td>821.7</td><td>36.0</td><td>1.7</td></tr><tr><td>mainly stratified</td><td>BYL24</td><td>256</td><td>1.2</td><td>0.5</td><td>10.5</td><td>34.5</td><td>3.6</td><td>7.8</td><td>4.9</td><td>660.8</td><td>28.5</td><td>1.9</td></tr><tr><td></td><td></td><td>Avg</td><td>20–300</td><td>0.8</td><td>0.4</td><td>12.7</td><td>37.6</td><td>3.1</td><td>7.3</td><td>5.4</td><td>828.4</td><td>30.7</td><td>2.0</td></tr><tr><td rowspan="2">CP pond</td><td>BYL80</td><td>212</td><td>0.8</td><td>0.6</td><td>10.9</td><td>12.7</td><td>1.8</td><td>8.9</td><td>2.8</td><td>915.1</td><td>22.2</td><td>0.8</td></tr><tr><td>continuously mixed</td><td>BYL30</td><td>125</td><td>0.7</td><td>0.4</td><td>12.2</td><td>24.9</td><td>2.3</td><td>8.6</td><td>2.7</td><td>798.6</td><td>19.8</td><td>1.6</td></tr><tr><td></td><td></td><td>Avg</td><td>50–500</td><td>0.7</td><td>0.5</td><td>10.1</td><td>15.5</td><td>1.9</td><td>8.6</td><td>2.9</td><td>698.8</td><td>21.6</td><td>1.9</td></tr></tbody></table>

characterized by the absorption coefficient at 320 nm and the specific UV absorbance index at 254 nm or SUVA), pH, total suspended solids (TSS), nutrients (total phosphorus, total nitrogen) and chlorophyll *a* (Chl *a*, as a proxy to phytoplankton biomass) were usually collected once a year in mid-July. Standard protocols were applied as described in Bouchard et al. (2015).

## Dissolved gas concentrations and diffusive flux estimations

Water samples for dissolved gas concentrations were obtained from the surface of all water bodies, sampling directly with the gas exchange bottle (see below). Deeper waters were also sampled (1) in ponds at 10-cm depth intervals using a thin-layer laminar-flow sampler that had two plates set 63.5 mm apart and connected to a peristaltic pump (Matveev et al. 2016), and (2) in lakes at 0.5-m depth intervals using a van Dorn water sampler (horizontal configuration, integrating 15 cm depth).

A water volume of 2 L was collected and equilibrated with 20 mL (2008–2015) or 30 mL (2016–2017) of atmospheric headspace for 3 min, using a low-density polyethylene Nalgene bottle with tubulation at the bottom connected to a syringe (Hesslein et al. 1991). The equilibrated gaseous headspace was injected into 6 mL (in 2008–2015) or 12 mL (in 2016–2017) gas-tight glass Exetainer vials, previously flushed with helium (2008–2014) or nitrogen (2015–2017) and vacuumed. In the laboratory, the headspace was analyzed by gas chromatography (GC) in a Varian 3800 from 2008 to 2015 (COMBIPAL headspace injection system, CP PoraPLOT Q 0.53 mm ID × 25 m,

flame ionization detector), or in a Thermo 1310 in 2016 and 2017 (TRI-Plus Head-Space auto-sampler, HSQ 80/100 1.6 mm × 30.5 m in series with MS 5A 1.6 mm × 18.3 m, thermal conductivity and flame ionization detectors). Dissolved gas concentrations were calculated using Henry's Law, and departure from saturation was obtained subtracting the gas concentration in the water at equilibrium with the atmosphere (C<sub>eq</sub>) using global annual average values of atmospheric partial pressures for each year based on data provided by the Earth System Research Laboratory (Dlugokencky and Tans 2017).

The diffusive flux (F<sub>D</sub>, in mmol m<sup>-2</sup> d<sup>-1</sup>) was estimated using surface dissolved gas concentrations (C<sub>sur</sub>) and empirically estimated gas transfer coefficient (k<sub>600</sub>). Estimations of k<sub>600</sub>, standardized to a Schmidt number (Sc) of 600 (Wanninkhof 1992), were calculated with the wind-based model proposed by Vachon and Prairie (2013): k<sub>600</sub> = 2.51 + 1.48 U<sub>10</sub> + 0.39 U<sub>10</sub> log<sub>10</sub> LA, where U<sub>10</sub> is the wind speed at 10 m above the ground (from a local meteorological station; CEN 2020) and LA is the lake surface area (km<sup>2</sup>); and applying the equation F<sub>D</sub> = k (C<sub>sur</sub> - C<sub>eq</sub>), where k is the gas transfer coefficient for a given gas calculated as k = k<sub>600</sub> (Sc/600)<sup>-1/2</sup> for wind velocities > 3.7 m s<sup>-1</sup>, otherwise as k = k<sub>600</sub> (Sc/600)<sup>-2/3</sup>.

The study site is not accessible during the autumnal mixing period; hence, the latest (mid-August) gas concentrations in the water column were used to estimate the summer storage flux, assuming that all CO<sub>2</sub> and CH<sub>4</sub> accumulated in deeper layers would have sufficient time to mix with surface waters and equilibrate with the atmosphere before the ice cover

<footnote>*The limits of this pond are unclear due to the seasonal water level changes and persistent connections to other ponds, which may open or close from year to year depending on shore erosion.</footnote>
<table><caption>**Table 3.** Definition of the limnological seasons used in this study. Due to significant depth and volume differences among the water bodies, the timing of seasonal shifts differs between lakes and ponds. CP, coalescent polygonal; IWT, ice-wedge trough. Typical thermal structures are shown in Fig. 2.</caption><thead><tr><th rowspan="2">Seasons on Bylot Island</th><th colspan="2">Duration (± 5 d) and description</th></tr><tr><th>Lakes</th><th>Ponds</th></tr></thead><tbody><tr><td><strong>Spring</strong><br><em>Melting and mixing period</em></td><td><strong>Jun 15–Jul 10.</strong> This period begins when the ice cover starts to crack and melt, and ends when the last floating blocks of ice disappear. Water column keeps mixing and stays close to 4°C. Due to their larger volume, kettle lakes take up to a week longer to fully mix compared to thermokarst lakes. As the central part of the lakes remains ice-covered for a longer time, it was not possible to measure GHG concentration and ebullition flux during this period.</td><td><strong>Jun 15–Jul 1.</strong> The completely frozen ponds start to thaw from the surface towards the bottom. The water level remains high as ponds are fed by melting snow but their lower section is still frozen, leading to a complex interconnected network of ponds (Fig. 8a). The shallow water column of IWT ponds quickly stratifies, despite the deeper parts being still frozen.</td></tr><tr><td><strong>Early summer</strong><br><em>Warming and stratification period</em></td><td><strong>Jul 10–Aug 5.</strong> Period of continuous warming of the water column. While thermokarst lakes maintain a well-mixed water column, deeper kettle lakes begin to form a thermal stratification in their deep basins (up to 5–7°C difference in late July).</td><td><strong>Jul 1–Aug 1.</strong> Period during which the water column and sediments present a steady warming trend. The maximum surface water temperature is reached during the last week of July (17°C for IWT ponds; 23°C for CP ponds). CP ponds remain well-mixed. The water column of IWT ponds is strongly stratified (thermal gradient reaching 12°C m<sup>-1</sup>); the stratification can be only partially broken on cool, cloudy days when wind is aligned with the pond axis (but bottom waters never rise above 4°C).</td></tr><tr><td><strong>Late summer</strong><br><em>Cooling period</em></td><td><strong>Aug 5–Aug 25.</strong> This period begins with the warmest water column of the year, after which it slowly starts cooling down. The thermokarst lakes continue mixing, but weak stratification is possible during calm and warm weather (discontinuous cold polymictic lakes). Kettle lakes continue having stratified deeper basins.</td><td><strong>Aug 1–Aug 15.</strong> With the sun starting to sink below the horizon, the difference in surface water temperatures between mornings and evenings increases, with an occasional thin ice cover observed in the morning. This period is marked with a general drop in water column temperatures, and in less stable stratification in IWT ponds.</td></tr><tr><td><strong>Autumn</strong><br><em>Mixing period before freezing</em></td><td><strong>Aug 25–Oct 1.</strong> Water temperature drops significantly at the beginning of this period and stays low (0–5°C). Water column keeps mixing. The period ends when the ice cover becomes permanent.</td><td><strong>Aug 15–Sept 15.</strong> Period marked by a significant drop in water temperature, followed by intermittent mixing in IWT ponds lasting up to 2 weeks. The period ends when the ice cover becomes permanent.</td></tr><tr><td><strong>Winter</strong><br><em>Ice-covered period</em></td><td><strong>Oct 1–Jun 15.</strong> A layer of water remains under ice. Water temperatures keep dropping during this period (down to 2°C in kettle lakes and close to 0°C in the thermokarst lake).</td><td><strong>Sept 15–Jun 15.</strong> Freezing progresses from all directions (inward freezing). Hydraulic pressure fractures the ice and forces water to spring out to the surface, creating ice mounds (Fig. 6d). Ponds remain completely frozen from October until next spring.</td></tr></tbody></table>

forms. The theoretical autumnal surface concentration was calculated by mixing epilimnetic with hypolimnetic gas concentrations, with the volume of each water layer calculated from hypsographic curves. Similarly, the CO₂ and CH₄ measurements in the ice cover and under the ice (obtained during late-winter campaign from 22 May to 2 June 2015) were used to

calculate the winter storage flux for the two lakes (BYL66 and BYL36), again assuming a full transfer of the accumulated gas to the atmosphere and taking into account the volumetric proportions of the ice and underlying water mass. Storage fluxes were estimated using the equations above, with wind velocities averaged over the mixing period of each lake or pond.
The majority of dissolved CO₂ and CH₄ data comes from day-
time sampling; out of the 453 measurements included in the
study (all water bodies and dates combined), 78 values were from
night-time sampling (9 p.m. to 6 a.m., period defined when solar
irradiance was below ~33% of zenith), allowing a fair assessment
of diurnal variations. A formal diurnal cycle following surface
concentrations of CO₂ and O₂ over a 6-d period was additionally
obtained from IWT pond BYL24 in July 2008 using a continuous
gas monitoring system, further described in Bastien et al. (2008)
and Laurion et al. (2010). Briefly, the system was equipped with a
CO₂ infrared gas analyzer (LI820, LI-COR) and an O₂ sensor
(Qubit Systems), both installed on a gas stream equilibrated with
the source water. Surface water was pumped at 20 cm depth via a
reversible peristaltic pump controlled with solenoid valves, fil-
tered using layers of Nitex of 250, 100, 53, and 10 µm, and pas-
sed through a bundle of porous polypropylene tubes that act as a

water-air exchanger. Water and air temperatures were also
recorded. Measurements were taken automatically every 3 h.

Ebullition flux measurements

Ebullition gas samples were collected using submerged funnels (as in Bouchard et al. 2015; based on Wik et al. 2013) equipped with a 140-mL polypropylene plastic syringe and deployed for a period of 6 h to 2 weeks depending on the flux rate (5 d as median duration; 15% of cases exceeding 10 d). The possibility of a slow dissolution of the CH₄-enriched gas accumulated in the syringe back into water on long deployments was tested by looking at the relationship between ebulition collection time and the CO₂ to CH₄ ratio, which was not significant (*p* value = 0.640). Most importantly, long deployments in kettle lakes still showed on average seven times higher CO₂ concentration in the syringe compared to

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Examples of typical temperature and dissolved oxygen (DO) profiles for the three water body types presenting a vertical structure. Late winter and late summer are presented for the kettle lake BYL36 (6 June 2015 and 28 July 2016) and the thermokarst lake BYL66 (2 June 2015 and 3 August 2016), while spring and late summer are presented for the IWT pond BYL27 (27 June 2015 and 3 August 2016).</figcaption></figure>
surface waters, despite the high solubility of CO₂ in water. The small headspace-water contact area in the syringe likely slowed down dissolution rate.

The same vials described above were used to collect gas samples for GC analysis. Ebullition flux ($F_E$, in mmol m⁻² d⁻¹) was calculated as $F_E = pGHG \times V/A \times V_m \times t$, where pGHG is the partial pressure of CO₂ or CH₄ measured with the GC, V is the total collected gas volume over the accumulation time t, A is the funnel area, and $V_m$ is the molar gas volume at ambient air temperature. Ebullition was monitored during the ice-free period in all 4 studied lakes and in 14 ponds (9 IWT

and 5 CP ponds) that were deep enough for the installation of funnels. Ebullition could not be assessed in lakes during the melting period (due to moving ice floes); therefore, their spring flux consists only of the diffusive flux. In comparison to the diffusive fluxes that are biased toward daytime hours, ebullition fluxes typically integrate many days and thus comprise both day-time and night-time emissions.

## Radiocarbon and stable isotopes analyses

At the beginning and end of each field season, a fraction of the ebullition samples collected from the funnels were

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Seasonal variations in CO₂ and CH₄ concentrations (values presented in the water column, in µM) and fluxes (values above the arrows, in mmol m⁻² d⁻¹) from the four studied water body types. The CO₂ fluxes (or concentrations) are given by the arrows (or numbers) on the left of each water body, and CH₄ fluxes (or concentrations) by the arrows (or numbers) on the right. The dissolved gas concentration values, when available, are presented for the surface (upper 10 cm) and bottom (lower 10% of the total depth) waters, and in the case of lakes, are further distinguished between shallow and deep zones. Filled arrows indicate the recorded fluxes (sum of diffusion and ebullition, based on in situ measurements), whereas empty arrows denote the estimated storage fluxes (i.e., not directly measured). The size of the arrows is proportional to the strength of the fluxes, but not using a linear scale, with the CH₄ scale amplified by two relative to CO₂. Water body shapes are not to scale between the different types. The water body filling represents water (light blue) or ice (grey). The limnological seasons used here are further explained in detail in Table 3.</figcaption></figure>
transferred into 50-mL glass bottles closed with thick butyl rubber stoppers (bottles were acid-washed, precombusted, helium-flushed and vacuumed) and sent to the Keck Carbon Cycle AMS facility at the University of California, Irvine for radiocarbon ($^{14}$C) and $^{13}$C isotopic ratio analyses for CO$_2$ and CH$_4$ (detailed protocol description in Pack et al. 2015).

Dissolved gas samples were also collected for $^{14}$C and $^{13}$C analyses. Dissolved CO$_2$ samples were collected “passively” using zeolite molecular sieve traps installed in selected water bodies (3 lakes and 9 ponds) over a period of 51 d (25 June–15 August 2015). The method, which slowly traps dissolved CO$_2$ in the water column over an extended time period is described in detail in Garnett et al. (2012). The zeolite pellets are contained within a glass tube sealed at both ends by gas-permeable, water-

impermeable hydrophobic filters. The molecular sieves, enclosed within a robust, protective, open polyvinyl chloride tube, were installed at 25 cm depth in the selected water bodies. After placing below the water surface, a clip is removed to allow ingress of CO$_2$; when the sampling period was over, the clip was replaced underwater to avoid contamination by atmospheric CO$_2$. The molecular sieves and their protective housing were returned to the NERC Radiocarbon Laboratory in Scotland, where the CO$_2$ was recovered by heating and cryogenic purification, prior to the determination of $^{13}$C and $^{14}$C. Discrete samples of CH$_4$ were also collected using the “super headspace” method (Garnett et al. 2016a) involving equilibrating several large volumes of water samples with ambient air in 15-L collapsible water containers from three ponds where CH$_4$ concentration was

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Temporal variations in CO$_2$ and CH$_4$ fluxes from seven water bodies, in which sufficient data were available to present trends in diffusive flux (dark grey area) and ebullitive flux (dashed area on top of diffusive flux). Estimated late-season fluxes are also added (pale grey area). The fluxes are 5-d period averages merging different years together; hence, the error bars representing the standard deviations illustrate both the intra and inter-annual variations. Littoral and pelagic values for lakes BYL66 and BYL36 were averaged, taking into account their proportions to the total lake surface area. Ebullition fluxes are missing prior to the 195th Julian day for the two lakes, because the ice floes hindered the installation of bubble traps. Note that flux scales differ among water bodies.</figcaption></figure>
<table><caption>**Table 4.** Average greenhouse gas diffusive and ebullition fluxes for the studied water bodies (specific values and standard deviations are given in Table S2). These averages are based on discrete flux measurements (i.e., storage fluxes not included). The total GHG flux normalized by the global-warming potential of each gas (named the GWP index, given in CO₂ equivalent, CO₂ eq.) includes both diffusive and ebullition fluxes, where CH₄ was assumed to be 32 times more potent than CO₂ (on a gram-to-gram basis over 100 years; Nisbet et al. 2019). Water bodies in bold correspond to the ones presented in Fig. 3. CP ponds, coalescent polygonal ponds; IWT ponds, ice-wedge trough ponds; N, number of measurements.</caption><thead><tr><th rowspan="2">Water body</th><th rowspan="2">Type</th><th rowspan="2">Name</th><th colspan="3">Diffusive flux (mmol m<sup>-2</sup> d<sup>-1</sup>)</th><th colspan="3">Ebullition flux (mmol m<sup>-2</sup> d<sup>-1</sup>)</th><th rowspan="2">GWP<sub>100</sub> index (mmol CO<sub>2</sub> eq. m<sup>-2</sup> d<sup>-1</sup>)*</th></tr><tr><th>n</th><th>CO<sub>2</sub></th><th>CH<sub>4</sub></th><th>n</th><th>CO<sub>2</sub></th><th>CH<sub>4</sub></th></tr></thead><tbody><tr><td rowspan="2">Kettle lakes</td><td rowspan="2"></td><td><b>BYL36</b></td><td>16</td><td>4.988</td><td>0.360</td><td>18</td><td>0.004</td><td>0.131</td><td>10.8</td></tr><tr><td>BYL37</td><td>11</td><td>23.39</td><td>0.108</td><td>6</td><td>0.005</td><td>0.040</td><td>15.2</td></tr><tr><td rowspan="2">Thermokarst lakes</td><td rowspan="2"></td><td><b>BYL66</b></td><td>22</td><td>3.455</td><td>0.213</td><td>29</td><td>0.013</td><td>1.462</td><td>27.1</td></tr><tr><td>BYL123</td><td>2</td><td>-9.251</td><td>0.430</td><td>10</td><td>0.011</td><td>0.186</td><td>-0.2</td></tr><tr><td rowspan="4">IWT ponds</td><td rowspan="4"></td><td><b>BYL27</b></td><td>75</td><td>16.09</td><td>0.955</td><td>31</td><td>0.408</td><td>3.837</td><td>60.5</td></tr><tr><td>BYL117</td><td>13</td><td>15.16</td><td>0.930</td><td>21</td><td>0.420</td><td>5.255</td><td>93.9</td></tr><tr><td>BYL24</td><td>45</td><td>3.599</td><td>0.613</td><td>19</td><td>0.010</td><td>0.326</td><td>14.3</td></tr><tr><td>Average 17 ponds</td><td>224</td><td>12.74</td><td>0.890</td><td>84</td><td>0.261</td><td>2.844</td><td>n/a</td></tr><tr><td rowspan="3">CP ponds</td><td rowspan="3"></td><td><b>BYL80</b></td><td>68</td><td>-4.110</td><td>0.479</td><td>39</td><td>0.106</td><td>2.550</td><td>49.1</td></tr><tr><td>BYL30</td><td>18</td><td>-0.119</td><td>0.775</td><td>10</td><td>0.017</td><td>0.489</td><td>16.7</td></tr><tr><td>Average 13 ponds</td><td>161</td><td>-4.051</td><td>0.555</td><td>61</td><td>0.070</td><td>1.844</td><td>n/a</td></tr></tbody></table>

<table><caption>**Table 5.** Radiocarbon contents of dissolved organic carbon (DOC), and dissolved and ebullition CO₂ and CH₄. ¹⁴C ages are given in YBP (numbers above) and fraction modern (numbers below); each given range is composed of two dates only. Measurement errors can be found in data repository Prèskienis et al. (2020). Gas samples from littoral (L) and pelagic (P) zones were analyzed for the thermokarst lake BYL66, but in the kettle lake BYL36 only the littoral zone provided enough ebullition carbon for ¹⁴C analysis. CP ponds, coalescent polygonal ponds; IWT ponds, ice-wedge trough ponds; n.d., not determined.</caption><thead><tr><th rowspan="2">Water body</th><th rowspan="2"></th><th rowspan="2"></th><th colspan="6">¹⁴C age (YBP above, and fraction modern under)</th></tr><tr><th>DOC</th><th>Diss. CO<sub>2</sub></th><th colspan="2">Ebul. CO<sub>2</sub></th><th colspan="2">Ebul. CH<sub>4</sub></th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>Early July</th><th>Late July</th><th>Diss. CH<sub>4</sub></th><th>Early July</th><th>Late July</th></tr></thead><tbody><tr><td rowspan="2">Kettle lake</td><td rowspan="2"></td><td rowspan="2">L</td><td>756</td><td>441</td><td>n.d.</td><td>970</td><td>n.d.</td><td>n.d.</td><td>125-2015</td></tr><tr><td>0.910</td><td>0.947</td><td></td><td>0.886</td><td></td><td></td><td>0.778-0.984</td></tr><tr><td rowspan="2">Thermokarst lake</td><td rowspan="2"></td><td rowspan="2">L</td><td>108</td><td>282</td><td>390-540</td><td>1790</td><td>n.d.</td><td>1555-2775</td><td>1500</td></tr><tr><td>0.987</td><td>0.966</td><td>0.935-0.952</td><td>0.801</td><td></td><td>0.708-0.824</td><td>0.734</td></tr><tr><td rowspan="4">IWT ponds</td><td rowspan="4"></td><td rowspan="2">P</td><td></td><td></td><td>735-765</td><td>510-1960</td><td>n.d.</td><td>3105-3380</td><td>2480-3405</td></tr><tr><td></td><td></td><td>0.909-0.913</td><td>0.939-0.784</td><td></td><td>0.657-0.679</td><td>0.655-0.829</td></tr><tr><td>34</td><td>Modern</td><td>35-165</td><td>Modern-170</td><td>Modern</td><td>Modern-5</td><td>Modern</td></tr><tr><td>0.996</td><td>1.013</td><td>0.996-0.979</td><td>0.979-1.009</td><td>1.009</td><td>0.999-1.037</td><td>1.006-1.015</td></tr><tr><td rowspan="4">CP ponds</td><td rowspan="2">BYL117</td><td></td><td>47</td><td>24</td><td>240</td><td>240</td><td>Modern</td><td>Modern</td><td>Modern</td></tr><tr><td></td><td>0.994</td><td>0.997</td><td>0.97</td><td>0.97</td><td>1.007</td><td>1.003</td><td>1.006</td></tr><tr><td rowspan="2">BYL24</td><td></td><td>Modern</td><td>Modern</td><td>Modern</td><td>Modern-1340</td><td>Modern</td><td>Modern</td><td>Modern</td></tr><tr><td></td><td>1.037</td><td>1.065</td><td>1.005</td><td>0.846-1.049</td><td>1.035</td><td>1.035</td><td>1.041-1.043</td></tr><tr><td rowspan="3">CP ponds</td><td rowspan="2">BYL80</td><td></td><td>Modern</td><td>Modern</td><td>Modern</td><td>245-1220</td><td>n.d.</td><td>Modern</td><td>Modern</td></tr><tr><td></td><td>1.015*</td><td>1.034*</td><td>1.046</td><td>0.859-0.97</td><td></td><td>1.063-1.067</td><td>1.059-1.076</td></tr><tr><td>BYL30</td><td></td><td>Modern</td><td>Modern</td><td>Modern</td><td>20</td><td>n.d.</td><td>Modern</td><td>Modern</td></tr><tr><td rowspan="2">Polygonal ponds (n = 3)</td><td rowspan="2"></td><td></td><td>Modern</td><td>Modern</td><td>n.d.</td><td>n.d.</td><td>n.d.</td><td>n.d.</td><td>n.d.</td></tr><tr><td></td><td>1.066</td><td>1.049</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote>*To calculate the GWP₁₀₀ index, individual flux values were first converted to CO₂ equivalent before computing the average. These values do not include the estimated spring and autumn fluxes from cumulated gases under the ice or in the hypolimnion, but only directly measured fluxes.</footnote>

<footnote>*There are no dates available from BYL80 for DOC and dissolved GHG; however, dates were obtained on a few coalescent ponds with a similar morphology, therefore the average (n = 3) CP ponds values are presented here.</footnote>
known to be sufficiently elevated. Headspace CH₄ concentration was measured using a portable Detecto Pak-Infrared gas analyzer, and the CH₄-enriched headspace (4 L) stored in large 10-L air-tight gas sample bags. The gas sample bags were sent to the NERC radiocarbon laboratory for analysis. Further details of the recovery of CO₂ and CH₄ for isotope analysis are given in Garnett et al. (2016a,b).

An additional series of gas samples collected from melted ice, ebullition funnels, lake sediments or dissolved in water were analyzed for isotopic composition (¹³C of CO₂, and ¹³C and ²H of CH₄) using gas chromatography-isotope ratio mass spectrometry (Thermo GC-IsoLink coupled to a Delta V Plus) at the McGill University Isotope Biogeochemistry Laboratory, following methods described by Kinnaman et al. (2007) and Ai et al. (2013). A volume of 10–500 µL of gas was injected into the GC using a pressure and temperature variable

injector. Gases were separated using a GS Carbon Plot column at 35°C following the method of Ai et al. (2013), and CH₄ was either combusted to CO₂ at 1000°C (for δ¹³C) or pyrolysed to H₂ at 1400°C (for δ²H). Isotope values were converted to the VPDB and VSMOW scales using a two-point calibration based on isotopic standards (AirLiquide Inc.).

DOC was also collected for isotopic analyses of ¹⁴C and ¹³C at the Woods Hole NOSAMS facility (2013 dates), the NERC radiocarbon facility (2015 dates), or the Lalonde AMS laboratory (2017 dates). Isotopic analysis of DOC involved acidification of the sample to pH 4 to remove any inorganic carbon, rotary evaporation, and freeze drying, prior to combustion at 900°C to CO₂ followed by graphitization.

A portion of the presented radiocarbon and stable isotope data (from 2013 and 2014 field campaigns), published in Bouchard et al. (2015), are used here in combination with data

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Variations among water body types in CH₄ signatures and fluxes (a); relationship between CH₄ radiocarbon and stable isotopic signatures (b), between radiocarbon signatures of CH₄ and CO₂ with the dashed diagonal line representing a ratio of 1 (c), and between CO₂ radiocarbon and stable isotopic signatures (d). The same colours are used in all four graphs to represent the different water body types, with triangles indicating ebullitive GHG and circles representing dissolved GHG. Note that due to the wide range in CH₄ flux (a), the x-axis has been transformed to logarithmic scale. Horizontal and vertical grey dash lines represent fraction modern = 1 (¹⁴C age = 0), with fraction modern > 1 indicating the presence of bomb ¹⁴C.</figcaption></figure>
from samples collected in 2015 and 2016, allowing a better
exploration of seasonal trends.

Results

The thermal structure of the different water body types is illustrated in Fig. 2. Temporal and spatial trends in CO2 and CH4 are presented in Fig. 3 (seasonal trends of gas concentrations and fluxes in four water bodies, differentiating pelagic and littoral zones of the lakes) and in Fig. 4 (detailed temporal trends in diffusive and ebullition fluxes in seven water bodies), while averages are presented in Table 4 (and seasonal averages with standard deviations in Table S2). All individual data can

be retrieved in Nordicana D data repository Prèskienis et al. (2020).

Kettle lakes

The CO₂ fluxes from the kettle lakes were highest during spring, remained high early summer, and then decreased progressively, with negative CO₂ fluxes (small sinks of CO₂) becoming increasingly frequent by the end of July (Fig. 4). Emission of CH₄ remained generally low (0.1–0.8 mmol m⁻² d⁻¹) and principally occurred through diffusion (on average 80% of total CH₄ emissions); the only exception being early summer along the shores of the smaller kettle lake, BYL36,

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Radiocarbon signature (fraction modern) of ebullition GHG along the open-water season. The temporal patterns of fraction modern of CO₂ are given on left panels, and of CH₄ on right panels. Each point denotes a single measurement. Analytical errors vary from 0.0011 to 0.0109, not visible in the plots.</figcaption></figure>
<table><caption>**Table 6.** Stable isotopic compositions of dissolved organic carbon (DOC), and dissolved and ebullition greenhouse gases. Water bodies in bold correspond to the ones presented in Fig. 3. Averages of all available data are presented for both ponds types. CP ponds = coalescent polygonal ponds; IWT ponds, ice-wedge trough ponds; *n*, number of values composing the average.</caption><thead><tr><th rowspan="2">Type</th><th rowspan="2">Name</th><th colspan="3">DOC</th><th colspan="3">Dissolved GHG</th><th colspan="3">Ebullition GHG</th></tr><tr><th>δ<sup>13</sup>C</th><th>δ<sup>13</sup>C</th><th>CO<sub>2</sub></th><th>CH<sub>4</sub></th><th>δ<sup>2</sup>H</th><th>CH<sub>4</sub></th><th>δ<sup>13</sup>C</th><th>CO<sub>2</sub></th><th>CH<sub>4</sub></th></tr></thead><tbody><tr><td rowspan="2">Kettle lakes</td><td>BYL36</td><td>-27.6</td><td>-14.6</td><td>-51.9</td><td>-357.0</td><td>-16.3</td><td>-62.3</td><td>-350.0</td><td></td><td></td></tr><tr><td>BYL37</td><td>-28.3</td><td>-17.3</td><td>n.d.</td><td>n.d.</td><td>-18.5</td><td>n.d.</td><td>n.d.</td><td></td><td></td></tr><tr><td>Thermokarst lake</td><td>BYL66</td><td>-28.0</td><td>-21.3</td><td>-53.8</td><td>-324.2</td><td>-24.0</td><td>-63.4</td><td>-328.4</td><td></td><td></td></tr><tr><td rowspan="3">IWT ponds</td><td>BYL27</td><td>-27.5</td><td>-15.8</td><td>-50.5</td><td>-345.5</td><td>-16.1</td><td>-59.7</td><td>-407.1</td><td></td><td></td></tr><tr><td>BYL117</td><td>-28.1</td><td>-17.6</td><td>-52.0</td><td>-344.6</td><td>-17.5</td><td>-62.3</td><td>-389.8</td><td></td><td></td></tr><tr><td>BYL24</td><td>-28.1</td><td>-18.2</td><td>-52.3</td><td>-319.5</td><td>n.d.</td><td>-60.3</td><td>-374.8</td><td></td><td></td></tr><tr><td rowspan="3">IWT ponds</td><td>Average</td><td>-27.9 (n = 4)</td><td>-17.0 (n = 20)</td><td>-49.3 (n = 19)</td><td>-349.2 (n = 11)</td><td>-17.0 (n = 3)</td><td>-60.7 (n = 13)</td><td>-394.7 (n = 8)</td><td></td><td></td></tr><tr><td>CP ponds</td><td>BYL80</td><td>-25.9</td><td>n.d.</td><td>n.d.</td><td>n.d.</td><td>-3.8</td><td>-58.9</td><td>-359.7</td><td></td><td></td></tr><tr><td>BYL30</td><td>-26.5</td><td>-14.1</td><td>-46.2</td><td>-310.9</td><td>-17.2</td><td>-60.0</td><td>-355.6</td><td></td><td></td></tr><tr><td rowspan="3">IWT ponds</td><td>Average</td><td>-26.4 (n = 6)</td><td>-15.0 (n = 15)</td><td>-46.3 (n = 9)</td><td>-333.0 (n = 8)</td><td>-10.5 (n = 4)</td><td>-59.9 (n = 18)</td><td>-353.9 (n = 11)</td><td></td><td></td></tr><tr><td>CP ponds</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

where ebullition represented 60% of total CH₄ emissions (Fig. 3). Although the stratified water column (Fig. 2) allowed GHG accumulation at the bottom, it did not result in increased autumnal emissions due to the small volume of the hypolimnion (2% of the total lake volume).

The kettle lake BYL36 presented the oldest radiocarbon age for DOC (756 YBP) and dissolved CO₂ (441 YBP) among all aquatic systems sampled in the valley (Table 5). Ebullition rates were often too low to collect sufficient carbon for radiocarbon analysis; however the few dates obtained along the littoral zone were in the same range (125–2015 YBP) for ebullition gases.

### Thermokarst lakes

The CO₂ fluxes from the larger thermokarst lake BYL66 were also highest during spring, but in contrast to the kettle

lakes, they decreased right after the ice-out and later showed a rising trend (Fig. 4). Early in the season, the shallow zone of Lake BYL66 tended to be a sink of CO₂ more often than a source (from -7 to +5 mmol m⁻² d⁻¹); however, positive fluxes to the atmosphere became more common as summer progressed. For comparison, the more transparent thermokarst Lake BYL123 (higher DOC but much lower SUVA; Table 2) continued to uptake CO₂ even later in summer. Since these lakes are well-mixed throughout the warm season (BYL66 qualified as a discontinuous cold polymictic lake), bottom and surface CO₂ concentrations followed the same trend and slowly increased during summer (Fig. 3). Dissolved CH₄ concentrations remained relatively low and did not show any clear trends over time. However, the dominant CH₄ emission pathway from Lake BYL66 was via ebullition (26–54% for the

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** The seasonal increase in concentrations of CO₂ (left) and CH₄ (right) in hypolimnetic waters of the most studied IWT pond BYL27.</figcaption></figure>

<figure><img src="image_11.png" /></figure>
littoral zone, and 88–91% for the deep zone). The deep central
basin had similar ebullition rates as ponds (on average
1.9 mmol of CH₄ m⁻² d⁻¹), while in the littoral zone of the
lake (< 2 m) rates were lower (< 0.58 mmol of CH₄ m⁻² d⁻¹;
Fig. 3) and comparable to fluxes measured from Lake BYL123
(Table 4).

The ebullition flux from Lake BYL66 was not only relatively
high, but also characterized by a more depleted ¹⁴C signature
of CH₄ compared to the other studied water bodies (Fig. 5a).
In general, ebullition gases were much older than DOC or dis-
solved CO₂ (108 and 282 YBP, respectively). Ebullitive CO₂
radiocarbon age ranged from 400 to 2000 YBP (Table 5), with

no clear spatial variation (deep and shallow basins emit CO2
of similar ages), but with a slightly older signature later in the
summer (Fig. 6). The ¹⁴CH4, on the other hand, did not pre-
sent any temporal trend, but deeper sections of the lake emit-
ted older CH4 (2500–3500 YBP) compared to the shallow
zones (1400–2800 YBP). Moreover, BYL66 had lower δ¹³CO2
values compared to the other water body types (–28‰ to
–17‰; Fig. 5d). The range of δ¹³CH4 values was close to that
of kettle lakes (–67‰ to –54‰) with one ebullition value
dropping to –71‰ (Fig. 5b). The δ²H of CH4 included some
of the highest values recorded for this study site (–387‰ to
–269‰, Table 6).

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Photos illustrating the landscape differences between snow-melting season (a) and peak summer (b) near CP pond BYL30; an example of a stable IWT pond colonized by brown mosses (c); and a frost mound formed in an IWT pond (d; photo taken during spring melt). IWT ponds can interconnect with each other, as well as with CP ponds (a). With summer progressing, ponds become isolated (b). The dark orange photosynthesizing microbial mat forming on the ice surface can be seen (a).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Diurnal variations in concentrations of CO₂ and dissolved oxygen (DO) in surface waters of the IWT pond BYL24, during mid-July (a). Increasing CH₄ concentration in the CP pond BYL80 during daytime (b); the data set is composed of discrete values taken over many days (of July) and several years.</figcaption></figure>

## Ice-wedge trough ponds

Elevated dissolved CO₂ and CH₄ concentrations and high ebullition rates were recorded immediately after the ponds started to thaw in spring (thawing occurs from top to bottom in ponds, with the ice layer on the pond bottom in spring, as illustrated in Figs. 2, 3). Diffusive fluxes increased over time toward the autumn, while ebullition rates slowed down progressively during the summer (Fig. 4). Therefore, the contribution of ebullition to the total CH₄ flux decreased over time (from 60–90% in early summer to 1–50% in late summer). Due to the continuous thermal stratification observed in IWT ponds (typical thermal structure illustrated in Fig. 2), bottom water GHG concentrations rose exponentially during July (Fig. 7). This led to extremely high summer storage fluxes estimated for the autumnal mixing period (Figs. 3, 4), which lasted from 4 to 14 d depending on individual ponds and specific years (obtained from the continuous measurements of

the thermal structure, data not shown, but mixing regime summarized in Table 3), before the ponds started to freeze.

Continuous CO₂ and DO measurements over a 6-d period in mid-July at the surface of BYL24, an IWT pond rich in vegetation (Fig. 8c), showed a clear diurnal pattern (Fig. 9a). Even though the highs and lows were not observed at the same time each day (CO₂ maximum occurred from 1 to 9 a.m., and the minimum from 6 to 9 p.m.), the pond still showed strong diurnal cycles in both gases, with DO inversely related to CO₂ ($r^2 = -0.921$, *p* value < 0.001).

Despite the high ebullition fluxes measured from IWT ponds (CH₄ flux averages varied from 0.1 to 5.9 mmol m⁻² d⁻¹, depending on pond and year, with a maximum of 31.8 mmol m⁻² d⁻¹), CH₄ collected for ¹⁴C analysis was modern in age (Fraction modern [Fm] > 1, with ¹⁴C from mid-20th century atmospheric thermonuclear weapons tests; Table 5 and Fig. 5a). However, ponds with more stable shores (less
erosion, more vegetation) emitted CH₄ that was slightly more enriched in ¹⁴C (BYL24, average Fm = 1.037 ± 0.005) compared to those presenting active shore erosion (BYL27, BYL117; average Fm = 1.011 ± 0.011). The same spatial trend was obtained for CO₂ (Fm = 0.991 vs 0.983 ± 0.024, respectively, for the pond with stable shores vs. ponds with active erosion). While the ¹⁴CH₄ remained similar throughout the ice-free season, the ¹⁴CO₂ changed over time, with slightly older CO₂ emitted in late June compared to early August (Fig. 6); thus, showing an opposite trend compared to the thermokarst lake. IWT ponds had the same range of δ¹³CO₂ values (−22‰ to −11‰) as the other water bodies studied on Bylot Island (Fig. 5d). However, δ¹³CH₄ values were higher, particularly for dissolved CH₄ (−62‰ to −40‰), with no clear difference between erosive and stable IWT ponds (Fig. 5b). While δ²H of dissolved CH₄ was similar to the other water bodies (−396‰ to −305‰), ebullition gas values were conspicuously low (−448‰ to −323‰).

## Coalescent polygonal ponds

During most of the open-water season, CP ponds were CO₂ sinks. The CO₂ uptake rate increased with time (on average from 2.5 mmol m⁻² d⁻¹ in late June to 4.1 mmol m⁻² d⁻¹ in late July), and then slowed in August with the onset of cooler and darker nights. The pattern was opposite for the CH₄ flux; it was highest in spring and decreased during summer (Fig. 4). Since there was no storage at the bottom of the water column of these well-mixed ponds, and because the autumnal fluxes were estimated using the same method as for stratified ponds, the presented values were similar to late summer fluxes (i.e., still negative in the case of CO₂; Fig. 3). Ebullition contributed a larger fraction of total CH₄ emissions in CP ponds compared to the other water bodies, reaching 91% in spring (Fig. 4).

The majority of ebullition samples collected from two CP ponds showed a modern radiocarbon signature (i.e., after 1950; Table 5). The available data point suggest a seasonal trend, with CH₄ being slightly less enriched in ¹⁴C in spring (i.e., a greater contribution of older carbon), and a rising trend in Fm throughout the open-water season (Fig. 6). Conversely, the emitted CO₂ was more enriched in ¹⁴C in spring (younger than modern), with a decreasing trend towards the autumn, and centuries-old carbon emitted by the end of summer. In general, the radiocarbon age of DOC and dissolved CO₂ was modern (Table 5). Despite showing little variation in terms of GHG fluxes, CP ponds were characterized by highly variable stable isotope signatures (dissolved and ebullition δ¹³CO₂ values ranging from −22‰ to −0.1‰; Table 6 and Fig. 5d). There was a clear distinction between dissolved δ¹³CH₄ (−54‰ to −42‰) and ebullition δ¹³CH₄ (−71‰ to −52‰).

## Discussion

The four morphologically distinct water body types studied on Bylot Island showed different seasonal patterns in CO₂ and

CH₄ concentrations, fluxes, and isotopic signatures, partly dictated by their morphology and mixing regime. The biogeochemistry of Arctic lakes is expected to be quite different from their subarctic counterparts in terms of permafrost extent, parent material, history of past thaw events, and seasonality (Tank et al. 2020). The observed ranges for most part concur with previous studies, although comparative studies remain scarce in Arctic tundra regions. The studied polygonal landscape represents an Arctic wetland environment, characterized by numerous water bodies (Fig. 1), relatively lush tundra vegetation and organic-rich soils (Minke et al. 2007; Gauthier et al. 2011; Abnizova et al. 2012; Lara et al. 2015; Muster et al. 2017). Overall, the variability was quite large over a small area (1 km²); therefore, not taking into account these patterns can lead to large upscaling errors, and to misleading interpretations when comparing studies.

## Winter storage and spring flux

While the spring season is brief for lakes and ponds on Bylot Island (unfreezing or ice-out period lasts ~ 10 d for ponds and up to 3 weeks for lakes; Table 3), it is the period during which considerable amounts of GHG are emitted from these water bodies (Figs. 3, 4). For example, CP ponds more often showed positive CO₂ fluxes (i.e., represented a net source) as compared to the following summer period (net sink; Table S2). Also, most of the studied ponds experienced high ebullition rates during this period, with up to 52% of all CH₄ emitted during the open-water season occurring in spring, when normalized for the season lengths.

For lakes, between 40% and 80% (BYL66 and BYL36, respectively) of all annual CO₂ emissions occurred during the spring, but only 3% to 12% of annual CH₄ emissions. This can be related to the high CO₂ but relatively low CH₄ concentrations observed under the ice cover (Fig. 3). Such a discrepancy could be attributed to persistent methanotrophic activity in the water column during winter and spring, despite the low temperatures. This has been reported from boreal lakes where winter methanotrophy was found to be equally important to that of summer (Kankaala et al. 2006; Elder et al. 2019). The extent of methanotrophic oxidation affects the net CH₄ emissions from lakes and ponds; a better assessment of the importance of this mitigating effect and its response to changes in ice cover length, water temperature, mixing regime and oxygen level is therefore needed (Bartosiewicz et al. 2019; Woolway and Merchant 2019).

The presented GHG emission rates from lakes in spring may still be conservative for many reasons. Firstly, a portion of under-ice winter storage in lakes was likely in the form of gas pockets trapped under the ice cover, which rapidly escaped as soon as the ice started to break apart (Walter Anthony et al. 2010). Secondly, the GHG storage within the ice cover was likely underestimated as it was based on concentrations from the top portion of the ice cover (60 cm out of about 2 m of ice), while CH₄ concentrations in the ice has been shown
to increase with depth (Langer et al. 2015). Also, when conditions finally allowed ebullition flux measurements, a fraction of the gases trapped in the ice cover, water column or sediment pore water had likely already escaped. The discrepancy observed between dissolved GHG measured under the lake ice cover in early June and at the lake surface in early July (winter CO₂ concentration up to 14 times higher) highlights the importance of the spring flux, and underlines the need to develop better methods to capture this critical and challenging period. This was also highlighted by Jammet et al. (2015) who found that spring flux accounted for 53% of annual emission from a subarctic lake.

The CO₂ concentrations calculated in the ice cover (at 60 cm) were similar to July surface water concentrations in the IWT pond BYL27, but not in the CP pond BYL80 (Fig. 3). Similar concentrations may indicate the release of ice-trapped gases to the water column upon melting (and later its transfer to the atmosphere), while the early onset of photosynthetic activity could explain the difference observed in CP ponds. Microbial mats have indeed been observed to form on the ice surface soon after the ice started to melt (Fig. 8a,d). On the other hand, the early rise in CH₄ emissions from CP ponds was mainly through the ebullition pathway (91% of total spring flux). During this period, the ice was still present at the bottom; therefore, the emitted CH₄ originated from gases trapped in the ice since the previous autumn. The spring ebullition rate in CP ponds was three times higher than that in the early summer period (Fig. 4), with the flux decreasing as soon as ponds were free of ice (moment identified from thermistors placed in pond sediments).

In contrast to the other water body types, spring was not an exceptional period for IWT pond emissions, as we found relatively constant averaged CH₄ emissions from spring to the end of summer, close to 2 mmol m⁻² d⁻¹. However, it is very likely that we missed sporadic bubbling events during the ice-melting period. Intense and sudden bubbling events lasting up to 20 min were observed several times (see the video presented in the data repository Prèskienis et al. 2020).

## Summer diffusive flux

The limnological summer, lasting about 6 weeks on Bylot Island, is the most widely studied period in the Arctic. This seasonal bias, mostly caused by logistical constraints, nevertheless allows a relatively well-documented comparison between studies. In general, our results fall within the range of CO₂ and CH₄ fluxes recorded from lakes and ponds across the Arctic (Table 1; see also Table 3 in Matveev et al. 2016, and Table 4 in Bouchard et al. 2015 for circumpolar comparisons), underlining the importance of including the tundra biome in global estimates (Wik et al. 2016a). The highest CO₂ flux measured on Bylot are from IWT ponds, and particularly from ponds subject to active erosion (e.g., on average 53 mmol m⁻² d⁻¹ over the late summer period for BYL27, maximum recorded flux of 118 mmol m⁻² d⁻¹). These values are among

the highest CO₂ fluxes reported for tundra lakes (Table 1). However, higher CO₂ diffusive fluxes were reported from subarctic thermokarst lakes (> 350 mmol m⁻² d⁻¹ from yedoma landscape in Eastern Siberia, Hughes-Allen et al. (2020); 242 mmol m⁻² d⁻¹ from Quebec palsa peatlands, Matveev et al. 2016).

IWT ponds are also large CH₄ emitters (late summer average 1.8 mmol m⁻² d⁻¹ for BYL27, maximum recorded flux of 5 mmol m⁻² d⁻¹ from BYL67), with high diffusive fluxes observed throughout the stratified summer period (Fig. 4). These high fluxes suggest that respiration and methanogenesis were relatively important in the warmer and organic-rich epilimnetic water column and littoral sediments, considering that pelagic and littoral zones are in close proximity for water bodies of a few square meters (the pelagic zone in IWT ponds being defined as the water column showing a persistent stratification with an epilimnion and hypolimnion, as opposed to the littoral zone that is shallower than the epilimnion). Elevated diffusive flux under stratified conditions can also result from the upwelling of deeper, GHG-enriched waters through partial mixing caused by wind and especially by nocturnal cooling in sheltered IWT ponds (Walter Anthony and MacIntyre 2016, MacIntyre et al. 2018; see also section below on diurnal cycles). However, these mixing events were apparently not long or deep enough to vent out all accumulated gases until autumnal cooling occurs, since the hypolimnetic concentrations remained high during summer (ranging from 25 to 233 µM in different IWT ponds; Fig. 7). In other tundra regions, higher CH₄ fluxes were mainly reported for systems studied late in summer (August; Sebacher et al. 1986), potentially during the mixing period when stored GHG are released, or at sites close to the tree-line, receiving DOM inputs from large river systems (Bartlett et al. 1992; Cunada et al. 2018).

CP ponds remained CO₂ sinks throughout the summer. This likely reflects the particularly efficient photosynthetic uptake by microbial mats in these water bodies, combined with an efficient transfer of CO₂ from the atmosphere as the water column is permanently mixed in these ponds. Their shallow, well-mixed, and transparent water column (low turbidity and CDOM due to negligible levels of shore erosion; Table 2) creates an environment well suited for the establishment of thick benthic cyanobacterial mats, dominated by *Oscillatoria* sp. (Vézina and Vincent 1997; Negandhi et al. 2014). Continuous daylight during early summer allows active photosynthesis even at night (although limited by lower irradiance), making these water bodies almost constant CO₂ sinks (Fig. 4).

The highest uptake rates of CO₂ recorded at Bylot were measured in CP ponds; they were also notably higher than other reported values in the Arctic (Table 1). Moreover, these uptake rates were potentially underestimated because we did not account for the chemical enhancement of CO₂ uptake, particularly induced when pH rises above 9.5 under low wind conditions (Hoover and Berkshire 1969). This effect has recently been reported to be strong during summer for
shallow, high-pH (generally > 9) “alas” lakes typical of yedoma landscapes in eastern Siberia (Hughes-Allen et al. 2020). The pH of Bylot CP ponds rarely reached such high values (Table 2), and we estimated that the chemical enhancement varied between 1 and 3.6 (on average 1.2, N = 142). Therefore, the CO₂ uptake in these ponds may have been about 20% higher than reported, a factor that should be taken into account.

## Summer ebullition flux

Methane ebullition rates obtained on Bylot Island were comparable to values reported for tundra lakes on the Alaskan North Slope (Table 1). Although the contribution of ebullition to total CH₄ emissions was highly variable and strongly dependent on water body morphology and season (varying from 0.1% to 99%), it remains an important emission pathway for Bylot lakes and ponds (global average of 52% for water bodies presented in Fig. 4). When factoring in the global warming potential of CH₄ over 100 years (GWP₁₀₀ index, in CO₂-equivalent; Table 4), ebullition contributed 60% to 95% of total emissions, with the exception of kettle lakes (2–15% of GWP₁₀₀) and ponds with thick brown moss coverage (such as IWT pond BYL24 and CP pond BYL30).

We never observed point-source ebullition from Bylot Island water bodies—except when escaping from the melting and cracking ice in ponds at the end of June—nor did we observe large bubble clusters on the ice transects carried out in the first week of June. Therefore, we suspect that background ebullition (as defined by Walter et al. 2006) is the main mode of bubble evasion at the studied site. The ¹³C isotopic fractionation factor between CH₄ and CO₂ (αc) in the collected samples (mean value of 1.048) was similar to the background ebullition signature obtained from lakes in West Siberian tundra (Walter et al. 2008). Since background ebullition is typically less spatially variable, it makes the small area coverage of the ebullition funnels (1–2 in ponds, 4 in lakes) less of an issue. Nevertheless, we cannot exclude the possibility that point-source events occurred, making our estimates conservative (Wik et al. 2016b).

In general, kettle lakes showed the lowest ebullition rates of the study site; they are more transparent (lower DOM) and less productive (lower nutrients) than other water bodies. The majority of ebullition gases were collected from the littoral zones, where benthic vegetation was observed. Ebullition rates decreased by three orders of magnitude when moving to the pelagic zone, where less bubbles are expected to reach the surface due to the longer travel path (11 m) and greater hydrostatic pressure (Bastviken et al. 2004; McGinnis et al. 2006), especially for background ebullition (smaller bubbles). Glacial and post-glacial lakes (which include kettle lakes) are known to be small emitters per m², but as they are abundant north of ~50°N (79% of total lakes and ponds by area), they are considered to be important CH₄ contributors globally (about 50% of annual CH₄ emissions north of 50°N as estimated by Wik

et al. 2016a). The shallow thermokarst lake BYL123 and the littoral zone of BYL66 presented similar ebullition rates as the kettle lakes, while the deep zone of thermokarst Lake BYL66 was characterized by 10 times higher rates. These shallow zones lacked a substantial gyttja layer, and are directly underlain by terrestrial peaty silt, while the deep basin of BYL66 is covered by a ~10 cm layer of gyttja (Bouchard et al. 2020) underlain by an organic-rich talik, fuelling CH₄ production (more details below in section “The carbon source of emitted GHG”).

Overall, both CP and IWT ponds experienced similar ebullitions rates (Table 4). However, while CP ponds maintained rather constant rates every year, IWT ponds showed much higher interannual variability. For example, Pond BYL27 emitted 17.4 mmol CH₄ m⁻² d⁻¹ at mid-July in 2014, while in 2015 flux was barely reaching 0.2 mmol CH₄ m⁻² d⁻¹ over the same period. This is most likely due to the complex bathymetry of IWT ponds and their discontinuous and erratic shore erosion events, whereas methanogens in CP ponds are likely supplied by more constant benthic primary production. The observed difference may be further magnified by the depth constraint on funnel deployment; to avoid sediment disturbance, only sites deeper than 40 cm were selected. For CP ponds with a relatively flat bottom, funnels were likely a better representation of their ebullition rates, whereas for IWT ponds, funnel deployment was biased toward deeper sections which thaw later and maintain colder bottom sediments, thus likely underestimating total ebullition. Considering the large amount of small and shallow water bodies in polygonal landscapes, and that shallow waterlogged permafrost areas are often reported as important CH₄ emitters (e.g., Olefeldt et al. 2013), other methods will have to be applied to assess the carbon footprint of these systems at the landscape scale, for example using eddy co-variance flux towers (Abnizova et al. 2012; Sturtevant and Oechel 2013).

## Summer storage flux

Both the kettle lakes and the IWT ponds became stratified during the summer, and accumulated CO₂ and CH₄ in their hypolimnion. However, only the hypolimnion volume of IWT ponds was large enough (e.g., 15% of total volume for BYL27) to generate a significant increase in GHG flux during the autumnal mixing period (3.1 × larger CO₂ flux and 8.3 × larger CH₄ flux for BYL27). Summer storage flux in shallow ponds remains overlooked by most studies, perhaps because these systems are not generally considered as stratified.

As expected, under the steady turbulent mixing occurring in the thermokarst lakes and CP ponds, venting of GHG occurred throughout the summer, and thus summer storage fluxes estimated for these water bodies were comparable to summer fluxes (Fig. 4). However, autumnal fluxes comprise not only the venting of GHG stored in the hypolimnion (summer storage flux), but also autumnal production in the water column and sediments (not measured in the present study),
which is expected to be significant considering the time lag
before bottom waters and sediments cool down. Moreover,
the estimated summer storage fluxes did not include ebulli-
tion, which has been shown to continue year-round in
thermokarst lakes of Alaska and Siberia (Walter Anthony
et al. 2010), and even peak in October–January in deeper lakes
(Zimov et al. 1997). Therefore, our flux estimates for the
autumnal period are conservative.

Diurnal cycles in GHG concentrations

The diurnal cycles obtained with the continuous measuring
system installed in IWT pond BYL24 indicate that surface CO2
peaked in the morning and DO peaked in the evening
(Fig. 9a), even though the pond maintained a stratified water
column for most of the open-water season, with significant
temperature differences between surface and bottom waters
(about 10°C in July). Such opposing cycles in CO2 and DO
may have resulted from (1) the balance between night-time
respiration and day-time photosynthesis, and (2) a diurnal
pattern in mixing regime. Surface waters cooling at night cre-
ate small eddies, slowly mixing with the deeper, GHG-
enriched, water mass, thus creating a potentially important
escape pathway (Walter Anthony and MacIntyre 2016).

CP ponds were also expected to show strong diurnal cycles
in gas concentrations, but primary production by microbial
mats maintained CO₂ concentrations largely below atmo-
spheric levels and below the detection limit for the continuous
gas monitoring system. On the other hand, the discrete data
collected from Pond BYL80 over many days in July, when
pooled, showed a significant rising trend in dissolved CH₄ at

the surface from about 3:00 a.m. to midnight (Fig. 9b). This rise
in CH₄ concentration potentially resulted from two concurrent
processes linked to daily variations in sunlight: (1) increasing
photosynthetic activity by benthic mats providing labile
autochthonous matter to methanogens (Berg et al. 2014; West
et al. 2015), and (2) rising light inhibition of methanotrophs,
as shown in temperate lakes by Thottathil et al. (2018). We did
not observe such trend in IWT ponds, potentially because they
are light limited (higher CDOM and TSS) and highly stratified;
thus, surface CH₄ concentrations are more likely to be con-
trolled by a partial mixing with hypolimnetic waters rather
than by the production-consumption balance.

Methane production and oxidation

The stable isotopic composition of CH₄ (dissolved in surface
waters or emitted through ebullition) as well as the ¹³C isotopic
fractionation factor between CH₄ and CO₂ (α_c = 1.040–1.055)
both imply that acetoclastic methanogenesis is the major CH₄
production pathway in Bylot ponds and lakes (Fig. 10; Whiticar
et al. 1986). However, as the majority of stable isotope data
were collected in July, it is possible that the lower availability of
labile organic compounds in earlier or later months might
switch prevailing methanogenesis to hydrogenotrophy (CO₂
reducing; Alstad and Whiticar 2011). The hydrogenotrophic
methanogens were indeed identified as equally abundant in
genomic analyses of pond sediments at the study site
(Negandhi et al. 2013).

A portion of produced CH₄ can be consumed by met-
hanotrophs before being emitted to the atmosphere, and an
active methanotrophy has been associated with brown mosses

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** (a) Carbon (δ¹³C) and hydrogen (δ²H) isotope composition of CH₄ dissolved in surface water (circles) or emitted through ebullition (triangles) for coalescent ponds (CP), ice-wedge trough ponds (IWT), kettle lake (BYL36), thermokarst lake (BYL66), and BYL66 sediment pore water (lake sediment), after Whiticar et al. (1986). (b) Carbon isotope composition (δ¹³C) of CH₄ (x-axis) and CO₂ (y-axis) from the same samples. AM, acetoclastic methanogenesis; HM, hydrogenotrophic methanogenesis; MO, methanotrophy.</figcaption></figure>
in tundra ponds of the Lena Delta (Liebner et al. 2011). Brown mosses are also abundant in Bylot, in CP ponds and IWT ponds with stable shores, where the majority of dissolved gas isotopic data clearly showed a high level of CH₄ oxidation (based on enriched δ¹³C values of CH₄ and depleted values for CO₂; Figs. 5b,d, 10) compared to the IWT ponds with erosive shores (limited primary production). Therefore, this mitigating effect on CH₄ emission might not operate efficiently in erosive IWT ponds that are likely to become more abundant on polygonal landscapes affected by permafrost thawing.

Dissolved CH₄ concentration in the CP pond BYL80 was strongly correlated with water temperature (*p* value = 0.007; *r*² = 0.724; data averaged over 5-d intervals over the open-water season), but the ebullition rates were not (*p* value = 0.212; *r*² = 0.007; temperature averaged over each funnel accumulation period). This may imply that even though CH₄ production is temperature sensitive (e.g., Thanh-Duc et al. 2010; Wik et al. 2014; Yvon-Durocher et al. 2014), its evasion through ebullition is controlled by other factors. Without direct investigations of production (and consumption) rates in response to temperature and substrate availability, it is difficult to determine if CH₄ emissions will increase or decrease with longer and warmer summers as these conditions will also affect primary producers.

## The carbon source of emitted GHG

Most studies presenting radiocarbon dates on GHG evading from Arctic lakes have been conducted in yedoma regions, where carbon of pleistocene age was shown to be used by methanogens (e.g., Zimov et al. 1997; Walter et al. 2008). More recently, studies on thermokarst lakes from boreal Alaska on non-yedoma deposits showed that ebullition GHG were produced from carbon pools of the last millennia (2330 YBP for CH₄, 585 YBP for CO₂; Elder et al. 2019), and that dissolved gases in these lakes had a relatively recent carbon signature (mostly modern, but up to 3300 YBP for CH₄ and 1590 YBP for CO₂; Elder et al. 2018, 2019). This is consistent with what we observed on Bylot Island, where emissions primarily originated from recently fixed carbon (Table 5).

At one extreme, CP ponds, colonized by mosses and thick cyanobacterial mats and with no visible erosion, emit GHG principally deriving from recent primary production (averaged Fm 1.03, and enriched δ¹³CO₂, Table 6; Fig. 5d). Although most dates obtained from ponds were modern, ebullition CO₂ became slightly older (from decades to 1220 YBP) in CP ponds as summer progressed and the thaw front deepened. Methane showed the opposite trend, with a slightly older carbon (within the last century) during the ice-out compared to late summer (Fig. 6; Table 5). With CP ponds covered by actively photosynthesizing benthic mats, freshly produced organic matter becomes more available to methanogens as summer progresses. The methanogens may preferentially use this readily labile material as their metabolism is less efficient compared to aerobic microbes (Canfield et al. 2005). The relatively

older carbon of CO₂ later in season may be linked to the bacterial consumption of terrestrial organic matter leaching in pore water from deeper soils, and laterally transferred to ponds, depending on rain patterns and local topography.

In IWT ponds, the radiocarbon ages remained stable over time (Fig. 6), especially for CH₄, suggesting limited seasonal changes in the source of carbon exploited by microorganisms. On the other hand, carbon of CO₂, CH₄ and DOC in IWT ponds featuring actively eroding shores (BYL27 and BYL117) was relatively more ¹⁴C-depleted (average Fm < 1) compared to CP ponds and IWT pond colonized by primary producers (average Fm > 1; Fig. 5b,d; Table 5). These eroding ponds contain less autochthonous organic matter as their unstable shores and turbid waters are less favorable to primary producers. Furthermore, they receive a significant amount of ¹⁴C-depleted carbon from falling thawed peat (dated to 2210–2535 YBP at the base of the active layer). However, the contribution of this older carbon to GHG production may not be evident if its fractional contribution is small, because it can be strongly diluted by the presence of modern carbon, as suggested by Drake et al. (2018), who studied an Arctic fluvial network. Therefore, the mobilization and microbial utilization of old carbon in the system can be hidden. Further constraining the role of old carbon in these ponds requires more specific approaches, such as the compound-specific radiocarbon analysis of lignin phenols as applied by Feng et al. (2017), or a more detailed analysis of isotopic composition, such as the Keeling plot approach (Pataki et al. 2003).

The only sampled water body constantly emitting older carbon was the thermokarst lake BYL66. Methane emitted from the pelagic (deepest) zone of this lake was significantly more ¹⁴C-depleted (up to 3405 YBP for ebullition CH₄) compared with the littoral zone (1500–2775 YBP). Siberian and Alaskan thermokarst lakes showed the opposite pattern, where the littoral zones (thermokarst margins) presented the oldest CH₄ signatures (and highest ebullition rates; Walter et al. 2006; Walter Anthony et al. 2016). However, these yedoma lakes are located within thicker unconsolidated sediments that actively erode once the ice-rich permafrost thaws. In the case of the thermokarst lake on Bylot Island, younger CH₄ emitted from the littoral zone may be explained by two factors: (1) the active layer underneath the littoral zone is limited in depth as the water column in this section of the lake refreezes to the bottom each winter, restricting the methanogens to carbon from shallower (younger) peat and freshly deposited autochthonous OM, and (2) lake expansion to this area is relatively recent, hence the submerged peat-rich terrace was formed more recently than the one underlying the deep basin (Bouchard et al. 2020). The deep basin of BYL66 (~ 4 m depth) maintains a layer of water throughout the winter, creating a talik underneath, thus allowing methanogens to access deeper horizons. The oldest age obtained on BYL66 gyttja was 2073 YBP (Bouchard et al. 2020), while the age of ebullition CH₄ was older (Table 5), indicating that the underlying peat layer was being used as a carbon source.
Ebullition CO₂ from Bylot Island lakes showed younger ages than CH₄, as was also found in Alaskan thermokarst lakes (Elder et al. 2019). However, ponds showed the opposite trend (generally slightly older CO₂ compared to CH₄; Fig. 5c), potentially suggesting a decoupled carbon cycle between pathways producing CH₄ and CO₂ in sediments. In lakes, CH₄ can be produced from deeper (older) horizons (in taliks), while access to older organic matter is limited in ponds (no talik) and fresh organic matter is more readily available from benthic mats, brown mosses and aquatic plants.

## Conclusions

Water body morphology significantly influenced the CO₂ and CH₄ flux patterns observed during the open-water season on this typical lowland tundra polygonal landscape. While kettle lakes experienced their highest gas fluxes early in the season, the studied thermokarst lake maintained relatively high emissions during the whole open-water season and was an important emitter of millennial-aged carbon. Emissions from IWT ponds increased from spring to autumn, whereas CP ponds had their maximum CH₄ flux in spring and maintained a relatively high flux throughout the summer, whilst being constant CO₂ sinks. Ponds are a particularly critical component of GHG emissions in this permafrost landscape, as they present the highest warming potential index per area, and their global areal coverage is likely to expand in the near future. They are particularly responsive to variations in primary production, precipitation regime and erosion events. With increasing precipitation and shortening winters, conditions can favor both the growth of primary producers and the intensification of carbon-rich permafrost erosion. Therefore, although these ponds presently emit century-old to modern ¹⁴C, further studies will need to rely on radiocarbon dating and stable isotopes to track future changes in the predominantly mineralized carbon source. Palaeoclimatic studies are also required to determine if present-day thermokarst erosion has accelerated during the Anthropocene, which is a condition for considering Holocene carbon mineralization as part of the positive feedback mechanism. Although studying ponds is challenging due to their small size, complex bathymetry and mixing regime, variable seasonal patterns and sensitivity to environmental changes, they must be considered in assessing the effects of permafrost thaw on the global carbon cycle.

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## Acknowledgments

We are grateful to Parks Canada, the Centre for Northern Studies (CEN) and G. Gauthier for helping with logistics during fieldwork, A. Veillette, M. Tremblay, Y. Seyer, T. Pacoureau and F. Mazoyer for their assistance with collecting and processing of the samples, M. Bartosiewicz and S. MacIntyre for advice on data analyses, and S. Duval and T. Hao Bui for gas chromatography and stable isotope analyses. The study was funded by the Natural Sciences and Engineering Research Council Discovery program, Network of Centres of Excellence ArcticNet, Natural Resources Canada Polar Continental Shelf Program, International Polar Year, and Fonds de recherche du Québec—Nature et technologies team grant (IL), as well as the CREATE program EnviroNorth (VP) and the W. Garfield Weston Foundation (FB). We also thank the NERC Radiocarbon Facility and M. Garnett for supporting and contributing to ¹⁴C analysis through allocations 1857.1014 and 1995.0416.

## Conflict of interest

None declared.

Submitted 13 November 2019

Revised 28 June 2020

Accepted 18 November 2020

Associate editor: Suzanne E. Tank