Figure 2 . Circumpolar percentage coverage of the five adapted
BAWLD terrestrial land cover types (Boreal Forests, Non-permafrost
Wetlands, Permafrost Bogs, Dry Tundra, and Tundra Wetlands) used for
ecosystem-based upscaling of GHG flux budgets in this study. Note that
these maps show the distributions across the full BAWLD domain as
presented by Olefeldt et al (2021), not the more limited extent of the
RECCAP2 permafrost BAWLD domain used in this study.
The land cover mean GHG flux (Fjx) were obtained for
each of the five terrestrial land cover classes after homogenising and
analysing three comprehensive GHG flux datasets: Virkkala et al. (2022)
for CO2 fluxes; Kuhn et al. (2021a) for
CH4 fluxes; and Voigt et al. (2020) for
N2O fluxes. Additional data was extracted from
literature for Boreal Forest N2O fluxes (Schiller and
Hastie, 1996; Simpson et al., 1997; Kim and Tanaka, 2003; Morishita et
al. 2007; Matson et al. 2009; Ullah et al. 2009; Köster et al. 2018a),
since the N2O flux dataset from Voigt et al. (2020) does
not cover Boreal Forest ecosystems. These datasets comprise roughly 1000in-situ growing-season and annual observations (including
multiple observations from some sites) of terrestrial fluxes obtained
from more than 200 sites using chamber (for CH4,
N2O, and CO2), diffusion (for
CH4 and CO2), and eddy covariance (for
CO2 and CH4) methods. The growing season
length was defined as June to August (90 days) for the tundra and
permafrost bogs sites, and May to September (150 days) for the Boreal
Forests and Non-Permafrost Wetlands. The CO2 dataset
comprises year-round measurements of net ecosystem exchange (NEE), which
we used to calculate growing season and annual NEE. Average fluxes were
calculated based on 93 sites and 403 observations for growing season NEE
and 54 sites and 222 observations for annual NEE. The
CH4 and N2O datasets provide
growing-season measurements based on 98 sites and 458 observations of
CH4 exchange and 47 sites and 91 observations of
N2O exchange. For sites with incomplete growing season
measurements, we multiplied average daily fluxes to the length of the
growing season. Annual CH4 fluxes were estimated
assuming that growing season emissions accounted for 64% of annual
emissions (Treat et al. 2018), except for boreal forests were we assumed
growing season emissions accounted for 100% of annual emissions as the
sites averaged net CH4 growing season uptake and
available data for winter season fractions only covers
CH4-emitting ecosystems (Treat et al. 2018). Our Boreal
Forest annual estimate should therefore be considered conservative.
Annual N2O fluxes were estimated assuming that growing
season emissions accounted for 50% of annual emissions as reported in
Voigt et al. (2020). For all three GHGs, only sites with no record of
large-scale upland hillslope abrupt thaw disturbance in the metadata
were included in the flux estimates to avoid double-counting emissions
from upland hillslope abrupt thaw (see methodology for disturbances).
However, although scarce, we included other disturbed sites in our
CO2 estimates to account for ecosystem
CO2 losses following disturbances and their different
successional stages (e.g., 4 sites reporting thermokarst; Virkkala et
al. 2022). Sites from the above-mentioned GHG flux datasets were
classified into one of the five terrestrial land cover classes using the
metadata provided in each of the datasets. More details on how ecosystem
flux upscaling was performed can be found in the supporting information.
While the focus of this study is the period 2000-2020, we include allin-situ measurements obtained between 1991 and 2020 in order to
overcome the limited amount of flux measurements in some of the
ecosystems and therefore ensure adequate spatial representation of
ecosystem fluxes. A separate analysis of decadal CO2fluxes from 1991 to 2020 revealed no differences, suggesting that the
extension of time series to 1991 does not impact our findings (Table
A2).
2.4 GHG fluxes from inland waters
Similarly to the method used to calculate GHG emissions from terrestrial
land cover types, GHG fluxes from inland waters were calculated by
upscaling mean GHG fluxes from lakes and rivers (see below) using the
estimated surface area of these aquatic classes from the BAWLD
classification (Olefeldt et al. 2021), adjusted to the study region (see
supplementary Table A1 for estimated aerial extent of inland waters).
GHG budgets for rivers
Atmospheric riverine GHG fluxes were calculated in different ways for
each GHG, depending on available source data, and when possible scaled
across the region using riverine area from the permafrost region (0.12 x
106 km2), reported in BAWLD.
Estimates of river and stream CO2 flux were calculated
from gridded monthly flux data estimated by Liu et al. (2021;
https://doi.org/10.5061/dryad.d7wm37pz9; Dryad) from river water
dissolved CO2 pressure and gas transfer velocity. We
combined the monthly fluxes from the start of May to the end of October,
assuming that this corresponds to the ice-free season (when water-to-air
gas transfer can occur). This time extent (184 days) is nine days longer
than the duration Liu et al. (2022) cite as the mean ice-free period for
Arctic lakes (175 days). This data is delivered as unprojected global
grids with a 0.0083 degree resolution (which is ca 1*0.2 km pixels in
the high Arctic). The global grids were clipped to the extent of BAWLD
and then reprojected to an equal area grid at 100*100 m resolution.
Calculations from this data yields a total stream and river area of
0.069*106 km2, and a total flux of
94 Tg CO2-C yr-1. Assuming the mean
river flux (1,370 g C m-2 yr-1) can
be scaled also to smaller streams and rivers, we applied the area of
streams and rivers in BAWLD (0.12*106 km2). Because
spatially explicit estimates of uncertainty are not available, we report
a coefficient of variation proportional to the global uncertainty
reported by Liu et al. (2022). Riverine CH4 emissions
were determined using the mean CH4 diffusive flux
reported in the MethDB (Stanley et al. 2016). Stanley et al. (2016)
found that diffusive CH4 emissions did not statistically
differ across latitudes and scaled global river CH4emissions using one mean value. Given the limited number of reported
CH4 fluxes for rivers in the Arctic (e.g. Zolkos et al.
2020), we used the same approach as Stanley et al. (2016) and applied a
global mean diffusive flux of 135 mg CH4m-2 d-1 to the river area. Because
there are few studies that measure CH4 emissions upon
ice-out, we applied for CH4 a conservative estimate that
17% of annual fluxes occur during the ice-free period (Denfield et al.
2018; consistent with the approach by Liu et al. 2022). Ebullition was
not included for river CH4 emission estimates due to few
available measurements in the literature for this region (Stanley et al.
2016). Estimates of river N2O flux were derived from
gridded annual N2O flux estimated by a mechanistic mass
balance model developed globally for inland waters by Maavara et al.
(2019). These data was reprojected from an original 0.5 degree
unprojected grid to an equal area grid at 1 km resolution and clipped to
the BAWLD extent. As the original lake and river surface area was not
known, no correction of inland water surface area was made.
Uncertainties for river GHG budgets were determined using the standard
error and coefficient of variance reported by Liu et al. (2022), Stanley
et al. (2016) and Maavara et al. (2019), respectively, for
CO2, CH4, and N2O.
GHG budgets for lakes
CH4 fluxes (diffusion and ebullition) were extracted
from the BAWLD-CH4 aquatic ecosystem dataset and classified based on
classes (yedoma lakes, peatland ponds, and glacial/post-glacial organic
poor lakes and ponds) and sizes, from large
(> 10 km2) to midsize (0.1 to
10 km2) to small lakes
(< 0.1 km2) (Kuhn et al. 2021a; total area =
1.255 106 km2; Table A3). Notably,
no minimum size for lakes was considered in the BAWLD dataset, as the
dataset gives an estimate of the overall area covered by lakes in each
size-class (Olefeldt et al. 2021). Conceptually, any area which is
likely to be inundated >50% of the growing season period
(long term average) is considered part of the lake land cover classes.
Ice-free days were determined based on averages of reported ice-free
days for each lake type and this information was used to determine
ice-free season fluxes (supplementary Table A1). In addition to ice-free
emissions, spring ice-out emissions (i.e. winter contribution) were
considered to be 23% of the annual total (Wik et al. 2016).
Estimated lake CO2 fluxes were compiled from multiple
available sources based on a literature search made in May 2022 (Humborg
et al. 2010; Rocher-Ros et al. 2017; Karlsson et al. 2013;
Sepulveda-Jauregui et al. 2015; Pelletier et al. 2014; Rasilo et al.
2014; Korteliane et al. 2006) and are summarised in Table A4). The
studies report lake CO2 fluxes as mean flux values for
various binned lake surface areas. We took these averages and grouped
them by the lake size classes included in BAWLD (<0.1, 0.1-10,
>10 km2). We found no statistical
differences in fluxes between the size groups and thus used one mean
lake CO2 flux to scale across the year and the region
(315 ± 196 mg C m-2 d-1). We applied
the same number of ice-free days used to scale lake CH4emissions (ice-free days reported in the literature for each lake
class).
To estimate lake fluxes of N2O, gridded global data of
annual flux from Lauerwald et al. (2019) were used. This estimate is
based on the nitrous oxide (N2O) emission model
developed by Maavara et al. (2019) and the HydroLAKES database and was
reprojected from an original 0.5 degree unprojected grid to an equal
area grid at 1 km resolution and clipped to the BAWLD extent. As the
original lake and river surface area was not known, no correction of
inland water surface area was made. Uncertainties for lake
N2O were determined using the coefficient of variance
reported for regions north of 50 deg latitude in Lauerwald et al.
(2019).
2.5 Disturbances - fires and abrupt thaw
Monthly GHG fire emissions were extracted for the study region from the
Global Fire Emission Database version 4s (GFED; van der Werf et al.
2017). The GFED4s spans from 1997-2016 and estimates of burned areas are
based on remote sensing data at a spatial resolution of 0.25 degrees
(van der Werf et al. 2017). GHG emissions in the GFED4s are derived from
the multiplication of burned area and fuel consumption per unit burned
area, the latter being the product of modelled fuel loads per unit area
and combustion completeness. For our purpose, we extracted mean annual
GHG emissions from burned areas for the period 2000-2016 and assumed
similar rates for the period 2016-2020.
Localised, but widespread, disturbances associated with abrupt thaw are
thought to contribute significantly to GHG emissions from permafrost
(Abbott and Jones, 2015, Yang et al. 2018, Walker et al. 2019, Turetsky
et al. 2020; Holloway et al. 2020, Marushchak et al. 2021, Runge et al.
2022). Abrupt thaw includes thawing processes that affect permafrost
soils in periods of days to several years (Grosse et al. 2011), and is
typically associated with thermokarst and thermoerosion processes that
lead to the formation of hillslope erosional features (thaw slumps,
thermo-erosion gullies and active layer detachments), thermokarst lakes,
and thermokarst wetlands (i.e., collapse scar bogs and fens). We report
abrupt thaw areas and derived annual CO2 and
CH4 emissions using the inventory-based abrupt thaw
model by Turetsky et al. (2020), in which atmospheric emissions are
estimated for three generalised types of abrupt thaw terrains:
mineral-rich lowlands, upland hillslopes, and organic-rich wetlands. In
the abrupt thaw model, abrupt thaw areas are based on synthesised field
observations and remote sensing measurements. GHG emissions from abrupt
thaw were synthesised for each ecosystem state within each abrupt thaw
type from the literature (ca. 20 published papers). The abrupt thaw
model was initialised for a historical assessment period (1900-2000) to
provide the model with a spin up and prevent the regional carbon fluxes
starting at zero at the beginning of the dynamic measurement period.
Thaw rates were generally in equilibrium with succession and recovery of
surface permafrost during this initialization period. Changes in the
area of each successional state were tracked over time by multiplying
initial starting areas by transition rates. Estimates of abrupt thaw GHG
emissions following the historical assessment period were done by
increasing rates of abrupt thaw through time. This increase in thaw rate
was prescribed to follow the average output of ‘permafrost-enabled’ land
surface models, all of which were forced by atmospheric climate
anomalies from the Community Climate System Model 4 (CCSM4) Earth system
model under an RCP8.5 projection. For our purpose, we ran the abrupt
thaw model for the period 2000-2020 and extracted cumulative
CO2 and CH4 emissions from active and
stabilised abrupt thaw features, and derived annual fluxes for each
abrupt thaw terrain for the time period 2000-2020. We used the reported
uncertainty ranges of ± 40% on the upland hillslope areas, ± 30% on
the mineral-rich lowland areas, and ± 35% on the organic-rich wetland
areas as in Turetsky et al. (2020). Additional details on the inventory
model can be found in Turetsky et al. (2020). Since GHG datasets that we
used for ecosystem upscaling partly account for abrupt thaw and to
prevent double counting GHG fluxes, CO2 and
CH4 fluxes from abrupt thaw were added as a sub-flux
(not added to the total) of terrestrial and inland water land cover
fluxes and their contribution to the total GHG budget is discussed. Due
to the lack of in situ observations of abrupt thaw impacts on
N2O fluxes in the used datasets, no N2O
budget is presented for abrupt thaw.
2.6 Lateral fluxes and geological emissions
Lateral C and N fluxes from riverine transport and coastal erosion (i.e.
DOC and DON losses from the permafrost region to the ocean) are taken
from Terhaar et al. (2021), representative for all land north of 60° N.
They estimated riverine lateral fluxes for the six largest Arctic rivers
(Mackenzie, Yukon, Kolyma, Lena, Ob, Yenisei) from the Arctic Great
River Observatory (ArcticGRO) dataset and extrapolated to the entire
Arctic catchment. Emissions from coastal erosion were calculated by
multiplying spatially resolved estimates of coastal erosion rates by
estimates of C content in coastal soils provided in Lantuit et al.
(2012).
Estimates of geological emissions of CH4 (from
subsurface fossil hydrocarbon reservoirs) are taken from an upscaled
circumpolar permafrost region estimate for gas seeps along permafrost
boundaries and lake beds made by Walter Anthony et al. (2012). We note
that there is some risk of double counting such fluxes, especially in
sites where eddy covariance flux towers may have unknowingly been placed
close to seeps of geological CH4 emissions. No separate
estimates of geological emission for CO2 or
N2O are available for the permafrost region. For
CO2, the full global geological emissions are estimated
to 0.16 Pg CO2-C yr-1 (Mörner and
Etiope 2002).
3 Results and Discussion
3.1 Net GHG exchange from terrestrial land cover types
Terrestrial ecosystems represented a decadal-scale sink for
CO2, and source for CH4 and
N2O (Table 1, Fig. 3). The mean annual
CO2 flux was a net sink, but could not be distinguished
from CO2 neutral when the 95% confidence interval was
considered (-339.6 (-835.5, 156.3) Tg CO2-C
y-1). The broad uncertainty interval can be attributed
both to the large natural variability in CO2 fluxes
across sites and to the heterogeneity of ecosystem types included in
each of the land cover classes defined in the BAWLD classification.
Boreal Forests and Non-permafrost Wetlands were CO2sinks (-270.3 and -69.4 Tg CO2-C y-1,
respectively) while Tundra Wetlands and Permafrost Bogs were close to
neutral (-2.7 and -0.05 Tg CO2-C y-1,
respectively). Dry Tundra was the only ecosystem type classified as an
annual ecosystem CO2 source (2.9 Tg
CO2-C y-1), but the very broad
uncertainty range (-147.6, 153.5 Tg CO2-C
y-1) indicates low confidence in the sign of this
flux. Terrestrial ecosystems were overall a net sink of
CO2 during the growing season (-1611 (-2148, -1074) Tg
CO2-C gs-1), with the strongest sink
in the boreal forest (-1034 (-1305, -763) Tg CO2-C
gs-1) (Table 2).
Annual terrestrial CO2 flux budgets have been reported
for high-latitudes in recent papers using different upscaling
approaches. While closely related due to overlap in flux data, a higher
NEE uptake is reported by both Virkkala et al. (2021) and Watts et al.
(2023) (-419 (95% CI of -559 to -189) Tg CO2-C
y-1 and -601 (standard error of
± 1138) Tg CO2-C y-1, respectively).
However the estimated NEE uptakes for the permafrost region solely are
weaker, with an uptake of -181 (-305, 32) Tg CO2-C
y-1 and -230 (± 22) Tg CO2-C
y-1, respectively). The difference between the later
NEE uptakes and our results relates to the subset of data included in
the analyses (exclusively eddy covariance tower fluxes in Watts et al.
(2023)), the different years covered in the analyses (Virkkala et al.
2021: 1990-2015, Watts et al. 2023: 2003-2015), the different spatial
extents, and the upscaling approach applied (Arctic Terrestrial Carbon
Flux Model (TCFM-Arctic) in Watts et al. (2023), and statistical
upscaling in Virkkala et al. (2021)). Both of these studies as well as
the previous RECCAP synthesis (1990-2006, McGuire et al. 2012) report
the tundra as a weak CO2 sink (-13 (-81, 62); -16
(±84–270); and -16 (-42, 10) Tg CO2-C
y-1, respectively) although they also show that annual
tundra budgets cannot be distinguished from CO2 neutral
when taking into account the uncertainty range. Dry Tundra
CO2 budget was also identified as a source of 10 (-27,
47) Tg CO2-C y-1 in McGuire et al.
(2012).
Our estimated annual net CH4 source of 25.6 (14.7, 36.4)
Tg CH4-C y-1 from terrestrial
ecosystems (Table 1) was largely driven by emissions from Non-permafrost
Wetlands (20.6 (14.3, 26.9) Tg CH4-C
y-1). As in Treat et al. (2018), Non-permafrost
Wetlands emitted more than Tundra Wetlands. Annual CH4flux estimates for Tundra Wetlands (3.3 (2.7, 3.9) Tg
CH4 y-1) and Dry Tundra (2.1 (-0.4,
4.5 Tg CH4-C y-1) were in the lower
range from the previous estimates provided in McGuire et al. (2012), in
which the tundra was estimated to release 11 (0, 22) Tg
CH4-C y-1 (between 1990 and 2006). Our
growing season CH4 budget was a source of 16 (8.6, 23.3)
Tg CH4-C gs-1 (Table 2) with
Non-permafrost Wetlands contributing 83%. All terrestrial ecosystems
except Boreal Forests were net CH4 emitters. Boreal
Forests were a net sink of CH4 (-1.1 (-2.3, 0.0) Tg
CH4-C gs-1). Our CH4annual budget was lower than the ones estimated for the northern high
latitude wetlands (>45°N) at 31, 32, and 35 Tg
CH4-C y-1 (depending on wetland
distribution maps) by Peltola et al. (2019) and 38 Tg
CH4-C y-1 by Watts et al. (2023).
However, our CH4 growing season budget estimate was
higher than the budget based on 93 observations presented in Treat et
al. (2018) except for the Tundra Wetlands where they remain within the
same range. Despite their large spatial coverage, Dry Tundra was a small
source of CH4 during the growing season (1.4 (-0.3, 2.9)
Tg CH4-C gs-1), although the low end
of the CI suggests that it could remain a sink. More measurements from
these drier ecosystems are needed.
Our N2O annual budget estimate of 0.55 (-0.03, 1.1) Tg
N2O-N y-1 (Table 1) suggests that
terrestrial ecosystems were a N2O source, although the
uncertainty range around N2O fluxes extends from a small
sink to a larger source. These high uncertainties partly relate to the
limited number of observations of N2O fluxes (47 sites
and 91 observations), which only includes growing-season observations.
Our estimated annual N2O budget is within the range of
the one previously reported by Voigt et al. (2020)(0.14-1.27 Tg
N2O-N y-1 median-mean-based estimate).
In our study, Dry Tundra was the largest N2O source
(0.23 (0.04, 0.42) Tg N2O-N y-1).
Boreal Forests were the second largest N2O source (0.14
(-0.01, 0.30) Tg N2O-N y-1) due to
their large area, although their fluxes per unit area were small (Table
A5, 52.43 ug N2O m-2d-1). Although they occupy a small portion of the
landscape (5%), Permafrost Bogs were the largest N2O
emitters per unit area (Table A5, 645.14 ug N2O
m-2 d-1) and their contribution to
the regional budget was 18%. The estimate for Permafrost Bogs includes
emissions from barren peat surfaces, where vascular plants are absent -
surfaces previously identified as N2O hot spots in the
Arctic due to ideal conditions for N2O production (Repo
et al. 209; Marushchak et al., 2011; Gil et al. 2017). A challenge
remains regarding the mapping of Permafrost Bogs and barren ground and
integration within land cover classifications. Therefore, we did not
differentiate between vegetated and non-vegetated Permafrost Bog areas
when upscaling. N2O emissions from Tundra Wetlands were
negligible (0.01 (0.00, 0.02) Tg N2O-N
y-1), which can be explained by the lack of nitrate
supply as an N2O precursor in reduced conditions and
reduction of N2O to N2 during
denitrification when the water table is high (Butterbach-Bahl et al.
2011; Voigt et al. 2017). Recent observations not included in the
N2O review dataset (Voigt et al 2020) show that wetlands
may also function as net N2O sinks in the Arctic
(Schulze et al. 2023).
Table 1. Greenhouse gas (GHGs - CO2, CH4, and N2O) budget for the
permafrost region based on ecosystem upscaling. Negative GHG emissions
represent an uptake while positive emissions represent a release. GHG
emissions from terrestrial ecosystems are reported as mean fluxes with
2.5 and 97.5% confidence intervals (CI). GHG emissions from inland
waters and fires are reported with 5 and 95% CI. GHG emissions from
abrupt thaw are reported with +-40% uncertainty range. *these fluxes
are estimated using the abrupt thaw model from Turetsky et al (2020) and
are considered as additive to the total for these categories (to avoid
double counting of fluxes). **includes CO2,
CH4 and lateral fluxes.