https://www.nature.com/articles/s41467-018-08240-4
[images in online paper]
Published: 16 January 2019
Permafrost is warming at a global scale
Boris K. Biskaborn, Sharon L. Smith, […]Hugues Lantuit
Nature Communications volume 10, Article number: 264 (2019)
Abstract
Permafrost warming has the potential to amplify global climate change,
because when frozen sediments thaw it unlocks soil organic carbon. Yet
to date, no globally consistent assessment of permafrost temperature
change has been compiled. Here we use a global data set of permafrost
temperature time series from the Global Terrestrial Network for
Permafrost to evaluate temperature change across permafrost regions for
the period since the International Polar Year (2007–2009). During the
reference decade between 2007 and 2016, ground temperature near the
depth of zero annual amplitude in the continuous permafrost zone
increased by 0.39 ± 0.15 °C. Over the same period, discontinuous
permafrost warmed by 0.20 ± 0.10 °C. Permafrost in mountains warmed by
0.19 ± 0.05 °C and in Antarctica by 0.37 ± 0.10 °C. Globally, permafrost
temperature increased by 0.29 ± 0.12 °C. The observed trend follows the
Arctic amplification of air temperature increase in the Northern
Hemisphere. In the discontinuous zone, however, ground warming occurred
due to increased snow thickness while air temperature remained
statistically unchanged.
Introduction
One quarter of the Northern Hemisphere and 17% of the Earth’s exposed
land surface is underlain by permafrost1, that is ground with a
temperature remaining at or below 0 °C for at least two consecutive
years. The thermal state of permafrost is sensitive to changing climatic
conditions and in particular to rising air temperatures and changing
snow regimes2,3,4,5,6,7. This is important, because over the past few
decades, the atmosphere in polar and high elevation regions has warmed
faster than elsewhere8. Even if global air temperature increased by no
more than 2 °C by 2100, permafrost may still degrade over a significant
area9. Such a change would have serious consequences for ecosystems,
hydrological systems, and infrastructure integrity10,11,12. Carbon
release resulting from permafrost degradation will potentially impact
the Earth’s climate system because large amounts of carbon previously
locked in frozen organic matter will decompose into carbon dioxide and
methane13,14,15. This process is expected to augment global warming by
0.13–0.27 °C by 2100 and by up to 0.42 °C by 230015. Despite this,
permafrost change is not yet adequately represented in most of the Earth
System Models14 that are used for the IPCC projections for decision
makers. One major reason for this was the absence of a standardized
global data set of permafrost temperature observations for model validation.
Prior to the International Polar Year (IPY, 2007–2009), ground
temperatures were measured in boreholes scattered across permafrost
regions. However, a globally organized permafrost data network and a
standard reference period against which temperature change could be
measured did not exist. One key outcome of the IPY was strenghtening the
Global Terrestrial Network for Permafrost (GTN-P)16,4. This initiative
established a temperature reference baseline for permafrost and led to
an increase in the number of accessible boreholes used for temperature
monitoring.
To analyze the thermal change of permafrost we assembled a global
permafrost-temperature data set that includes time series of data
attributed to the IPY reference boreholes. We compiled a time series for
the decade from 2007 to 2016 that comprises mean annual ground
temperatures T¯
, determined from temperatures measured in boreholes within the
continuous and discontinuous permafrost zones in the Arctic (including
the Subarctic), Antarctica and at high elevations outside the polar
regions. The measurements were made at, or as close as possible to the
depth of zero annual amplitude Z*, where seasonal changes in ground
temperature are negligible (≤0.1 °C). Rates of permafrost temperature
change calculated for the 2007–2016 decade were indexed in each borehole
to suppress near-surface and deep geothermal changes. Regional and
global change rates were calculated as area-weighted means. To compare
single borehole sites, due to the higher availability of full-year
records after 2007, we ranked the temperature difference between the
biennial means of 2008–2009 and 2015–2016. We used linear regression on T¯
between 2007–2016 to estimate decadal change rates. To calculate annual
departures, we compared consecutive years to the reference mean of
2008–2009. We concluded, that ground temperature near the depth of zero
annual amplitude increased in all permafrost zones on Earth, that is
continuous and discontinuous permafrost in the Northern Hemisphere, as
well as permafrost in the mountains and in Antarctica. The observed
trend followed increased air temperature and snow thickness, each in
varying degrees depending on the region.
Results
Permafrost temperature changes
Measurements from borehole sites established prior to the IPY generally
indicated warming driven by higher air temperatures (Fig. 1)4,17,18. Our
new data set contains 154 boreholes of which 123 allow calculation of
decadal temperature change rates based on adequate time series. The
remaining 31 boreholes provide additional information on annual
departures. Our results show that in the decade after the IPY permafrost
warmed within 71 boreholes, cooled in 12, and remained unchanged (within
measurement accuracy) in the remaining 40 (Fig. 2). The ground
temperature rose above 0 °C in five boreholes, indicating thawing at the
measurement depth of 10 m at Z*. The largest increase of T¯
over the observed reference decade between 2007 and 2016 was
0.39 ± 0.15 °C dec−1Refin the Arctic continuous permafrost zone. The
greatest permafrost temperature changes observed in individual boreholes
(ΔT¯b) since 2008–2009 were 0.93 and 0.90 °C in northwestern Siberia
(Marre Sale, 10 m) and northeastern Siberia (Samoylov Island, 20.75 m),
respectively. The discontinuous permafrost zone experienced warming of
0.20 ± 0.10 °C dec−1Ref. The largest ΔT¯b since 2008–2009 of 0.95 °C was
observed in southeastern Siberia, Magadan (Olsky pass, 10 m). Permafrost
at this site started thawing after the IPY period at the measurement depth.
Mountain permafrost in the data set is mainly represented by boreholes
in the European Alps, the Nordic countries, and central Asia. Although
absolute T¯
values in mountain permafrost are highly heterogeneous, depending on
elevation, local topography, snow regime, and subsurface
characteristics, changes in mountain permafrost temperatures were
analyzed for all regions and settings19 as one group. They can vary
considerably, however, between sites of low and high ground ice content
at temperatures just below 0 °C.
Mountain permafrost T¯
increased20,21 by 0.19 ± 0.05 °C dec−1Ref. The greatest ΔT¯b
since 2008–2009 was 1.15 °C, observed in the Aldan mountain tundra of
southern Yakutia, Siberia (Taezhnoe, 25 m).
On average, permafrost across zones warmed by 0.33 ± 0.16 °C over the
reference decade in northern Asia and by 0.23 ± 0.11 °C dec−1Ref
in North America. This difference is most likely due to stronger warming
of the atmosphere over North Asia compared to North America, as
indicated by reconstructed decadal air temperature changes (1998–2012)
that showed cooling in Alaska22.
Similar to warming of the Arctic continuous permafrost zone, the
Antarctic permafrost warmed by 0.37 ± 0.10 °C dec−1Ref
. However, the remoteness of the continent and its limited accessibility
resulted in far fewer boreholes drilled to Z* compared to the Northern
Hemisphere. Consequently, permafrost temperature departures and trends
were statistically not significant and had large uncertainty bands (Fig.
3d).
Air temperature changes
The relation between air and soil temperature development in permafrost
regions is not straightforward due to highly variable buffer layers such
as vegetation, active layer soils, or snow cover. To compare permafrost
temperature changes to those in the atmosphere, we applied the same
calculation method for each borehole site using mean annual air
temperatures (T^
) at 2-m height above ground level (Fig. 4a, d), spatially interpolated
from the ERA Interim gridded reanalysis data set23. We calculated
general snow thickness changes for Arctic sites in Fig. 4a, b. However,
there is not, as yet, a reliable consistent data set on snow thickness
applicable for high elevation regions or Antarctica.
The propagation of temperature change in the atmosphere downward to the
depth of Z* can take up to several years, but the time varies depending
on the surface characteristics, the subsurface ice content, and the soil
thermal diffusivitiy24,25. We took this lag into account by averaging
over the previous 4 years for each year considered, but there was no
significant correlation at an annual resolution between permafrost
temperature departures at Z* depth and 2-m air temperature anomalies
derived from ERA Interim data alone (Fig. 4). This lack of correlation
can be attributed to the discrepancy between the scale at which borehole
observations are conducted and the spatial resolution of 80 km for the
gridded air-temperature reanalysis data26 and because in permafrost
regions, the reanalysis output is more dependent on the model structure
and data assimilation methods than in data-rich regions27; local micro-
and secondary climate effects28; and buffering layers at the air-ground
interface5 that influence the thermal response of permafrost to
short-term changes in air temperature.
Previous studies have shown that these surface effects, along with the
thermal diffusivity of the underlying materials, act as a buffer that
reduces the effect of short-term climate variation2,3,5,6,7,29. Thus,
short-term meteorological phenomena are increasingly attenuated and
delayed with depth, and the mean permafrost temperature changes near the
depth of Z* generally follow the atmosphere’s long-term trend. Mean
surface air temperature changes calculated from ERA Interim data at the
borehole locations (Fig. 5b) are similar to those for permafrost
temperature with respect to direction and order of magnitude. The
decadal change rates of air temperature were estimated to 0.86 ± 0.84 °C
per reference decade in the Arctic continuous permafrost zone,
0.63 ± 0.91 °C dec−1Ref
in the Arctic discontinuous permafrost zone, and 0.1 ± 0.50 °C dec−1Ref
in mountain permafrost. Air temperature trends in Antarctica (annual
mean 0.10 ± 0.55°C dec−1Ref, June–August mean –0.48 ± 0.91 °C dec−1Ref,
unweighted median –0.12, Fig. 5b), however, do not match the observed
strong permafrost warming. This discrepancy is due to large climatic
differences between the Antarctic Peninsula and eastern Antarctica30,31,
the small number of boreholes that fulfill the quality criteria, and the
principal climate model bias in Antarctica32.
Air temperature trends in the Arctic continuous permafrost zone
correspond well with permafrost temperature change rates (Figs. 3a and
4a), suggesting that enhanced warming of permafrost in the High Arctic
reflects the polar amplification of recent atmospheric warming22.
However, in the Arctic discontinuous permafrost zone, air temperatures
were statistically unchanged between 2006 and 2014 while permafrost
temperatures increased. We found that snow dynamics, the time lag
between air and ground temperature, and the latent heat effect serve as
concurrent explanations for this phenomenon.
Snow thickness changes
The snow cover reduces the upward transfer of energy from the ground to
the air during winter33,34. Distinct peaks in the mean snow depth in
2009, 2011 and from 2013 onward (Fig. 4a, b) suggest that the observed
continued warming of discontinuous permafrost is facilitated by
increasing snow thickness. Compared to the Arctic continuous permafrost
zone, the mean snow cover in the discontinuous zone arrived about 1 week
later, reached its maximum insulation 1 month earlier, and also
disappeared half a month earlier. Compared to 2007–2009 the snow cover
in 2014–2016 in the discontinuous zone started to form 13.7 days
earlier, reached its maximum insulation effect 37.7 days earlier, and
disappeared 9.3 days earlier (Fig. 4f). It was shown previously that a
difference of only 10 days caused significant warming in Alaska35.
Increases of shrub height and density that trap wind drifting snow is
likely also a contributing factor36. All of these changes provide
evidence of increased protection of the ground from low temperatures
during winter37,38. Snow timing differences within the continuous zone
are less distinct but show a generally similar trend (Fig. 4e, f).
Discussion
An important factor that explains the general discrepancy between mean
annual temperature changes at Z* in permafrost and the atmosphere is
that permafrost progressively with depth “remembers” the surface
temperature history of the past several years25,39. The temporal
dimension of episodes with lower air temperatures between 2009 and 2013
in the Arctic (Fig. 4a, b), and around 2012 in the mountains (Fig. 4c),
relative to preceding period of higher air temperatures, however, was
not large enough to sustainably impact the general warming trend of
permafrost.
We partly attribute the difference in ground temperature change between
the continuous permafrost and the discontinuous permafrost zones to the
latent heat effect. In this process, the ice-water phase change
associated with warmer permafrost in the discontinuous zone (Fig. 2a, b)
reduces the response of ground temperature to changes in air
temperature4. Cold permafrost therefore exhibits a greater response to
changing air temperature compared to permafrost with a temperature close
to 0 °C4,40.
The warming of permafrost observed since IPY continues the trends
documented prior to IPY41. Our global analysis suggests that the future
increases in air temperature projected under current climate scenarios42
will result in continued permafrost warming. The duration of our
time-series, however, does not yet permit predictive analysis of
non-linear climate-permafrost relations as the latent heat effect is
stronger near 0 °C and surface characteristics are not constant.
However, observations of thaw at some of the observation sites
demonstrate that the latent heat requirement cannot indefinitely delay
permafrost warming down to depths of about 15 m observed in this study
(Fig. 6), nor prevent the eventual thawing of permafrost. This could
have wide implications in terms of permafrost degradation and release of
greenhouse gases from decomposition of organic matter.
The SWIPA 2017 report41 gave an estimate of 0.5 °C warming of permafrost
in very cold areas such as the High Arctic since IPY (2007–2009). This
is similar to our network observations of strong warming within the
Arctic continuous permafrost zone and of continued warming elsewhere.
The assessment of permafrost temperature trends presented in this paper
can facilitate validation of models to project thawing of permafrost
down to the depth of Z* and associated impacts with respect to feedbacks
to the climate system.
The current global coverage of permafrost temperature monitoring is not
yet ideal, due to the limited sampling in regions such as Siberia,
central Canada, Antarctica, and the Himalayan and Andes mountains.
Furthermore, even though the data used were quality checked and are as
complete as possible, logistical challenges during fieldwork caused gaps
in the time series. Better assessments of the evolution of the thermal
state of permafrost, including consideration of non-linear system
behavior, will benefit from ongoing efforts to enhance the global
network spatially and extend the length of the record. Enhancing
existing monitoring sites through co-location with meteorological
stations could further improve understanding of microclimate and
buffer-layer influences, and would also provide the data necessary for a
comprehensive assessment of permafrost responses to ongoing climate change.
The newly compiled GTN-P data set has facilitated assessment of trends
in permafrost temperatures and can also contribute to improved
representation of permafrost dynamics in climate models and the
reduction of uncertainty in the prediction of future conditions.
Methods
Field observations of permafrost temperatures
Boreholes were established and temperatures were recorded during
annually repeated fieldwork campaigns in polar and high-elevation areas.
Temperature was measured either by lowering a calibrated thermistor into
a borehole, or recorded using permanently installed multi-sensor
cables43. Measurements were recorded either manually with a portable
temperature system or by automated continuous data logging. At some
borehole sites, permafrost thawed at the measurement depth during the
period of observations. The criterion to include non-permafrost sites in
the global change calculation was that ground temperatures near the
depth of the Z* were below 0 °C until the end of the IPY reference
period in 2009.
Compiling permafrost temperature data
Permafrost temperature data are assembled in the Global Terrestrial
Network for Permafrost (GTN-P) Database16. They are then transferred to
a global data set after a 1-year embargo to allow authors to publish
their local findings first. Within the GTN-P Data Management System the
data presented were harmonized, quality checked and filtered to generate
a standardized global permafrost borehole data set. Data standardization
was performed during data entry into the database following
international geospatial metadata standards ISO 19115/2 and TC/221. The
data management system is based on an object-oriented data model,
accessible online at http://gtnpdatabase.org. The GTN-P mean annual
ground temperature T¯
compilation is accessible online at https://doi.org/10.1594/PANGAEA.884711.
A total of 154 boreholes with 1264 T¯
values were used in this study. Data analyses of decadal permafrost
temperature change were based on 123 boreholes and 1033 T¯
values calculated from > 105 sensor observations.
Calculating permafrost temperature change
We used the R environment44 to calculate the mean permafrost temperature
change for every borehole from quality-filtered T¯
data. The same measurement depth was used each year for a borehole. The
depth was chosen to be the nearest available sensor to the depth of Z*,
the depth at which seasonal changes in temperature are ≤0.1 °C (Fig. 7).
The nearest depth to Z* was detected by either an algorithm calculating
the difference between annual maximum (summer) and minimum (winter)
temperature in the original data starting from the shallowest depth
downwards and using cubic spline interpolation between thermistors and a
threshold set to sensor accuracy, or by visual inspection of annual
maximum and minimum temperature measurements plotted versus depth (Fig.
7). Because the depth of Z* varies over time as temperature changes, we
used an average estimated for the observation period. The data revealed
that 19.5% of measurements were from above Z*. 59.8% of measurements
represented Z* and 20.7% were from below Z*. Measurements from boreholes
that had no reliable indication of Z* had a mean depth of 17.1 m, which
is well below the average of all indicated Z* values (mean 14.1 m,
median 12 m). Thus, the data distribution represents an approximation to
Z* which minimizes the potential bias caused by seasonal fluctuations.
We created a data set that reflects long-term climate change and avoids
large temperature fluctuations caused by seasonal phenomena, e.g., in
Antarctica, by excluding data from shallow boreholes that did not reach
Z*. Because Z* could not be determined in all boreholes the minimum
depth was set to 10 m. However, five boreholes with depths between 6.7 m
and 10 m were included (GTN-P ID’s16: 137, 860, 861, 877, and 1192),
because their depths were equal to Z*, and seasonal fluctuations were
less than the instrument precision and accuracy. Boreholes that
fulfilled the quality criteria but were not included in this analysis
due to depth constraints, represented 22.6% of the original data set.
8.6% were excluded from the Arctic continuous data set; 23.4% from the
Arctic discontinuous data set; 30.0% from the mountain data set; and
57.1% from the Antarctic data set. Statistically indifferent temperature
trends of the remaining shallow (≤12 m) and deeper (>12 m, max. 40 m)
boreholes in the utilized data set confirm that the observed depths near
Z* (Fig. 6b) provide a representative sample tracking climate
variability coherently.
We applied different methods to extract information on permafrost
temperature changes in single years, in single boreholes and for decadal
changes in the permafrost regions, described as follows: We define a set
i = {2007,...,2016} to identify the years. To identify the boreholes b
we use the GTN-P Database ID. Continuous (full-year) records started at
a large number of borehole sites in 2008, the second year of the 4th
International Polar Year (IPY). To base the reference period for the
annual departure calculation on the largest possible number of boreholes
we exclude 2007 and estimate the annual differences in T¯
in year y∈i
and borehole b as
ΔT¯y,b=T¯y,b−1/2(T¯2008,b+T¯2009,b)
(1)
The last term on the right-hand side of Eq. (1) serves as our mean value
for the reference period. We compare this reference period to the latest
available mean value period and calculateΔT¯b
to rank total temperature differences among boreholes.
ΔT¯b=1/2(T¯2015,b+T¯2016,b)−1/2(T¯2008,b+T¯2009,b)
(2)
Equations (1) and (2) require data to be available in each of the
observation years.
To calculate the rate of temperature change per decade we follow a third
approach using the primary mean annual ground temperature data set T¯b
for all available years in i and perform linear regression, according to
the following attribution of our data in the regression equation:
T¯regb=ab+cbx
(3)
where T¯regb
is the regression estimate of T¯b
, ab is the vertical intercept (the starting temperature in a borehole),
cb is the slope of the regression line, and x is the range of years
involved.
The requirement to perform linear regression on b was that i included at
least one value y in the IPY period (2007, 2008, or 2009), one value in
the modern reference period (2015 or 2016) and a minimum of five values
in total. We calculated the rate of temperature change in each borehole
as the slope of the linear regression cb using the linear model function
(lm) in the R environment. To generate decadal change values, we
extrapolated 37.7% of the borehole data in the Arctic continuous zone,
47.3% in the Arctic discontinuous zone, 29.3% in the mountain zone and
100% in Antarctica for 1–3 years.
The consistency of temperature time series in boreholes depends on
sustained data collection at remote sites. At some boreholes,
instrumentation was destroyed, damaged or malfunctioned leading to
interruptions in data collection45. To avoid broken data runs affecting
the annual means, measurements at frequencies greater than monthly (e.g.
daily or hourly), were aggregated to monthly means before calculating
annual means. Mean annual values were based on at least monthly primary
data. Data points based on fewer than one measurement every month were
allowed only if the sensor depth was equal to or below the depth of zero
annual amplitude. Annual means were calculated from original
measurements as calendar-year means in the GTN-P Database.
Meteorological years in permafrost areas depend on the onset and
termination of the freezing and thaw periods, and in previous studies
varied spatially. We therefore indicated the starting month of the
period in the data set. Mean values contain only the available valid T¯
data in each year, and thus the number of borehole temperatures included
in change-rate calculations varies between years.
To evaluate temperature changes in the Arctic continuous and
discontinuous permafrost zones, in the mountain permafrost and in
permafrost in Antarctica, we applied a spatial de-clustering prior to
calculating mean values of temperature changes from the boreholes. The
spatial de-clustering reduces the bias in the calculation of means
caused by an inhomogeneous (clustered) spatial distribution of the
boreholes. We grouped the boreholes into ten world zones (Fig. 8) and
defined the areas underlain by permafrost by correlating the boreholes
with the International Permafrost Association (IPA) permafrost zones46.
Arctic continuous permafrost represents the mean of four different
zones: Arctic continuous permafrost West (2.41 × 106 km2), Arctic
continuous permafrost West islands (1.57 × 106 km2), Arctic continuous
permafrost Europe (0.22 × 106 km2), and Arctic continuous permafrost
East (Asia) (6.62 × 106 km2). Arctic discontinuous permafrost is
averaged over three zones: Arctic discontinuous permafrost West
(3.91 × 106 km2), Arctic discontinuous permafrost East (Asia)
(3.86 × 106 km2), and Arctic discontinuous permafrost Europe
(0.28 × 106 km2). Mountain permafrost is averaged over two zones:
Chinese mountains (2.07 × 106 km2), and Other mountains (2.33 × 106 km2)
including the Alps and other sites with high elevations >1000 m a.s.l.
such as in Scandinavia and the North American Cordillera. Antarctica is
treated as one zone (0.05 × 106 km2 6,47). For comparing temperature
trends between North American and north Asian permafrost we define two
separate data sets by excluding southern, European, and central Asian
boreholes. Within the zones, clusters of boreholes close together were
grouped when the sum of longitude and latitude differences were <0.1
decimal degree and the T¯
values of adjacent boreholes were averaged before calculating the mean
temperature change.
To estimate the mean annual temperature change in each zone we applied
area-weighted arithmetic averaging of T¯ values in boreholes. To
preserve the signal of local outlier trends showing atypical temperature
change directions and magnitudes (e.g., in parts of Antarctica and in
Québec, Canada), we did not use medians. To suppress near-surface and
geothermal changes indices of boreholes were distributed as three
possible integers to multiply the sites before averaging, according to
the following criteria: (i) T¯ is available in each year of the
reference periods indicated in Eqs. (1) and (2), and (ii) T¯
depth is equal to the depth of Z* and >10 m (few exceptions were made
according to the depth of Z* as described above).
Calculating air temperature change
The set of air temperature data monitored at borehole sites is
incomplete. To develop data comparable to the permafrost temperature
data, we calculated mean annual air temperatures (T^
) from ERA Interim 2 m air temperature data set with 80 km spatial
resolution. We derived the reanalysis time series for each borehole from
linear interpolation of the four nearest grid points surrounding the
borehole coordinates. Mean annual values were calculated from December
until November. Given, that the propagation of atmospheric temperature
change downward to the depth of Z* takes up to several years25,37,
depending on the local thermal diffusivity24, we extended the time
series shown in Fig. 4 backwards to 2000 and used the standard reference
period 1981–2010 to estimate anomalies.
We define a set j = {1981,...,2016} to identify the years being
considered. We use the coordinates of boreholes b defined in Eq. (1) and
calculate the annual difference for specific years y∈j
in T^
as
ΔT^y,b=T^y,b−130∑j=19812010T^j,b
(4)
Based on the average propagation of surface temperature towards Z* of 4
years25 we calculated 4-year end-point running means to compare air
temperature with permafrost temperature changes. To calculate the rate
of temperature change over a decade, we apply linear regression on T^y,b
for all y∈j using the linear model function in the R environment and the
slope of the linear regression in an annual array between 2004 and 2016
and multiplied the annual change rates by 10. Data analyses of air
temperature change were based on 137 borehole sites and 4932 T^
values.
Calculating snow thickness change
We calculated the mean annual snow thickness (S^
) for the Arctic continuous and the discontinuous permafrost zone from
the Canadian Meteorological Centre (CMC) daily snow depth analysis data
with 24 km spatial resolution48. We derived the reanalysis time series
for each borehole from linear interpolation of the four nearest grid
points surrounding the borehole coordinates. Mean values were calculated
from December until February for each year in the data set. To identify
winters we use subsequent years, e.g. in the time series we assign the
1999–2000 winter to 2000.
Given that 1999 is the earliest available year in the data set we define
a set k = {1999,...,2016} to identify the winter years, where y∈k
. We use the coordinates of boreholes b defined in Eq. (1) and calculate
the annual difference in S^
as
ΔS^y,b=S^y,b−112∑k=19992010S^k,b
(5)
The onset snow has an impact on the ground thermal regime. To assess the
onset of snow cover, we assemble a set of snow depths dates at daily
resolution between 1 September and 30 April in a set of days
l = {1,2,3,...,242} for every year in k. In leap years
l = {1,2,3,...,243}. To calculate the onset date of snow SO we use the
first day dSOk,b
reaching 6 cm in l for which the following 5 days, adding up to a
synoptic time scale of 6 days, retain a daily snow cover of at least 6 cm49.
The insulation maximum of snow SIM is reached when the snow cover
accumulated to a thickness between 40 and 50 cm33,37. Accordingly, we
set SIM based on the first day dSIMk,b
in l reaching 50 cm, or, if it is not reached, take the day representing
the maximum snow cover in l (below 50 cm).
To assess the end of snow cover SE, we assemble the snow depth dates at
daily resolution between 1 September and 30 August in a set
m = {1,2,3,...,365} for every year in k. In leap years
m = {1,2,3,...,366}. To calculate SE we use the first day dSEk,b
in m reaching down to less than 1 cm after a decreasing gradient of at
least 8 cm over 6 days, or, if this gradient is not reached, the first
day of at least 6 subsequent snow free (<1 cm) days.
Measurement accuracy
The reported measurement accuracy of our temperature observations,
including manual and automated logging systems, varied from ±0.01 to
±0.25 °C with a mean of ±0.08 °C. Previous tests have shown the
comparability of different measurement techniques to have an overall
accuracy of ±0.1 °C3. Thermistors are the most commonly used sensors for
borehole measurements. Their accuracy depends on (1) the materials and
process used to construct the thermistor, (2) the circuitry used to
measure the thermistor resistance, (3) the calibration and equation used
to convert measured resistance to temperature, and (4) the aging and
resulting drift of the sensor over time. Thermistors are typically
calibrated to correct for variations due to (1) and (2). About 20% of
the boreholes are visited once per year and measured at or below Z*
using single thermistors and a data logger. In this case the system is
routinely validated in an ice-bath allowing correction for any
calibration drift. The accuracy of an ice-bath is ~± 0.01 °C50. Using
the offset determined during this validation to correct the data greatly
increases the measurement accuracy near 0 °C, an important reference
point for permafrost. The remaining systems are permanently installed
and typically ice-bath calibrated at 0 °C before deployment. The
calibration drift is difficult to quantify as thermistor chains are not
frequently removed for re-calibration or validation. In many cases
removal of thermistor chains becomes impossible some time after
deployment, e.g. because of borehole shearing.
The drift rate among bead thermistors from different manufacturers was
<0.01 °C per year during a 2 year experiment at 0, 30, and 60 °C51. The
calibration drift of glass bead thermistors was found to be 0.01 mK per
year52, at an ambient temperature of 20 °C. A single drifting thermistor
in a chain is detectable through its anomalous temporal trend. Such data
were excluded from our data set. The absolute accuracy of borehole
temperature measurements, in terms of their representativeness of the
temperature distribution in undisturbed soil, also depends on the depth
accuracy of the sensors’ positions in the borehole. This study is
concerned with temperatures at Z*, where temperature gradients are
typically small (<0.1 °C m−1). Consequently, mm-level positioning
accuracy does not significantly impact measurement accuracy. Finally, as
this study is concerned with annual averages, adequate chronometry is
ensured.
The above discussion of accuracy relates to the absolute temperature
values measured, but the detection of temperature change is more
accurate because errors in calibration offset have no impact, sensor
nonlinearities are generally small and not of concern. We therefore
consider <0.1 °C a conservative average estimate of the accuracy of
temperature change on an individual sensor basis.
Confidence intervals and statistical significance
Permafrost and air temperature departure from 2008 until 2016 (ΔT¯i,b
and ΔT^y,b
) and the regression from 2007 until 2016 of each borehole were used to
calculate the 95% confidence intervals within each world zone using a
Student t-test in the R environment (52% p < 0.05, 48% p > 0.05, mean
|t| = 3.4). The upper and lower confidence boundaries were calculated
from de-clustered and indexed boreholes. Mean confidence intervals for
composite permafrost zones (global, Arctic continuous, Arctic
discontinuous, mountain, Asian and American) were area-weighted.
Antarctica consists of one zone and thus area-weighting is not
applicable. Given a non-normal, unimodal, and only slightly skewed
distribution of data in similarly shaped subsets (regions) gained by eq.
3 (Figs. 5, 6), we performed a Wilcoxon Signed-Rank test and a
Kruskal–Wallis test to assess the significance of the difference to zero
and the differences between medians, respectively. To consider false
positives, we performed a False Discovery Rate adjustment of the
p-values, resulting in 43.3% p < 0.05, 56.6% p > 0.05, median 0.08 in a
data matrix of 9 years (eq. 1) versus 10 permafrost world zones
indicated in Fig. 8. Boxplots represent 25–75% quartiles and whiskers
are 1.5 interquartile ranges from the median.
Data availability
The GTN-P global mean annual ground temperature data for permafrost near
the depth of zero annual amplitude (2007–2016) is accessible online at
https://doi.org/10.1594/PANGAEA.884711.
Additional information
Journal peer review information: Nature Communications thanks the
anonymous reviewers for their contribution to the peer review of this
work. Peer reviewer reports are available.
Publisher’s note: Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
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Acknowledgements
This research would not have been possible without the long-term
commitment of all observers to site maintenance, data collection, and
their willingness to share permafrost borehole data. All data were
compiled by the Global Terrestrial Network for Permafrost (GTN-P). We
thank the International Permafrost Association for financial support. We
thank Jerry Brown for initiating the borehole metadata collection and
Christina Roolfs for mathematical review. This research was supported by
grants from (in alphabetical order) AGAUR ANTALP #2017-SGR-1102
(Catalonia); BMBF PALMOD #01LP1510D (Germany); ERC PETA-CARB #338335
(EU); FCT #PERMANTAR2017-18/PROPOLAR (Portugal); Formas #214-2014-562
(Sweden); HGF COPER #VH-NG-801 (Germany); Horizon 2020 Nunataryuk
#773421 (EU); JSPS KAKENHI #25350416, #21310001 (Japan); MESC
#RFMEFI58718X0048, #14.587.21.0048-SODEEP (Russia); MeteoSwiss in the
framework of GCOS Switzerland, FOEN and SCNAT for the Swiss Permafrost
Monitoring Network PERMOS (Switzerland); Natural Resources Canada; NNSF
#41690144, #41671060 (China); NRC TSP #176033/S30, #157837/V30,
#176033/S30, #185987/V30 (Norway); NSERC #2014-04084, #2015-05411
(Canada); NSF OPP #1304271, #1304555 #1836377; ICER #1558389, #1717770
(USA); PNRA #16_00194 (Italy); Ramon y Cajal #RYC-2015-17597 (Spain);
RAS PP #15, #51, #55, GP #AAAA-A18-118022190065-1, #18-218012490093-1
(Russia); RFBR #18-05-60004, #18-55-11003, #16-05-00249,
#16-45-890257-YaNAO, #18-55-11005 AF_t(ClimEco), #18-05-60222-Arctica
(Russia); RSCF #16-17-00102 (Russia); National Research Foundation, SNA
#14070874451 (South Africa).
Author information
Affiliations
Alfred Wegener Institute Helmholtz Centre for Polar and Marine
Research, Potsdam, 14473, Germany
Boris K. Biskaborn, Heidrun Matthes, Julia Boike, William L.
Cable, Bernhard Diekmann, Guido Grosse & Hugues Lantuit
Geological Survey of Canada, Natural Resources Canada, Ottawa,
ON-K1A 0E8, Canada
Sharon L. Smith
WSL Institute for Snow and Avalanche Research SLF, Davos, CH-7260,
Switzerland
Jeannette Noetzli & Marcia Phillips
CEG/IGOT, Universidade de Lisboa, Lisbon, 1600-276, Portugal
Gonçalo Vieira
George Washington University, Washington DC, 20052, USA
Dmitry A. Streletskiy
Institut de Géographie Alpine, Grenoble, F-38100, France
Philippe Schoeneich
University of Alaska Fairbanks, Fairbanks, AK-99775, USA
Vladimir E. Romanovsky, Alexander Kholodov & Kenji Yoshikawa
University of Ottawa, Ottawa, K1N 6N5, Canada
Antoni G. Lewkowicz
Institute of Physicochemical and Biological Problems of Soil
Science, RAS, Moscow, 142290, Russia
Andrey Abramov & Alexander Kholodov
Université Laval, Centre d’études nordiques, Québec, G1V 0A6, Canada
Michel Allard
Humboldt-Universität, Geography Department, Berlin, 10099, Germany
Julia Boike
The University Center in Svalbard, Longyearbyen, N-9171, Norway
Hanne H. Christiansen
University of Fribourg, Fribourg, CH-1700, Switzerland
Reynald Delaloye
University of Potsdam, Potsdam, 14469, Germany
Bernhard Diekmann, Guido Grosse & Hugues Lantuit
Earth Cryosphere Institute, Tyumen Scientific Centre SB RAS,
Tyumen, 625000, Russia
Dmitry Drozdov, Galina Malkova, Natalia Moskalenko & Alexander
Vasiliev
University of Oslo, Department of Geosciences, Oslo, N-0316, Norway
Bernd Etzelmüller
Insubria University, Department of Theoretical and Applied
Sciences, Varese, 21100, Italy
Mauro Guglielmin
Technical University of Denmark, Department of Civil Engineering,
Kgs. Lyngby, DK-2800, Denmark
Thomas Ingeman-Nielsen
Norwegian Meteorological Institute, Oslo, 0313, Norway
Ketil Isaksen
Hokkaido University, Sapporo, 060-0810, Japan
Mamoru Ishikawa
Lund University, Lund, 22362, Sweden
Margareta Johansson
Arctic Portal, Akureyri, 600, Iceland
Halldor Johannsson, Anseok Joo & Jean-Pierre Lanckman
Komi Science Centre, RAS, Syktyvkar, 167972, Russia
Dmitry Kaverin
Melnikov Permafrost Institute, RAS, Yakutsk, 677010, Russia
Pavel Konstantinov, Pavel Skryabin & Mikhail Zheleznyak
Free University Berlin, Geography Department, Berlin, 12249, Germany
Tim Kröger
University of Lausanne, Lausanne, 1015, Switzerland
Christophe Lambiel
Northwest Institute of Eco-environment and Resource, CAS, Lanzhou,
730000, China
Dongliang Luo & Qingbai Wu
Rhodes University, Grahamstown, 6140, South Africa
Ian Meiklejohn
University of Barcelona, Barcelona, 08001, Spain
Marc Oliva
Universidad de Alcalá, Madrid, 28801, Spain
Miguel Ramos
Stockholm University, Stockholm, SE-106 91, Sweden
A. Britta K. Sannel
Institute of Environmental Geoscience, RAS, Moscow, 101000, Russia
Dmitrii Sergeev
National Soil Survey Center, Lincoln, NE-68508, USA
Cathy Seybold
Tyumen State University, Tyumen, 625003, Russia
Alexander Vasiliev
Contributions
The study was initially conceived during a GTN-P workshop in 2015.
B.K.B. led the analyses and writing of the manuscript. S.L.S., J.N.,
H.M., G.V., D.S., P.S., V.E.R. and A.G.L. are principal co-authors.
A.A., M.A., J.B., W.L.C., H.H.C., B.D., R.D., D.D., B.E., G.G., M.G.,
T.I.-N., K.I., M.I., M.J., D.K., A.K., P.K., H.L., C.L., D.L., G.M.,
I.M., N.M., M.O., M.P., M.R., A.B.K.S., D.S., C.S., P.S., A.V., Q.W.,
K.Y. and M.Z. contributed with data collection and expert assessment of
borehole data. H.J., A.J., T.K. and J.-P.L. performed database coding,
data processing, and data analyses. All authors contributed to analysis
of the results and revision of the manuscript.
Competing interests
The authors declare no competing interests.
Corresponding author
Correspondence to Boris K. Biskaborn.
Supplementary Information
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