ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-2311-2018A synthesis dataset of permafrost-affected soil thermal conditions for Alaska, USASynthesis data of permafrost-affected soils, AlaskaWangKangkang.wang@colorado.eduhttps://orcid.org/0000-0003-3416-572XJafarovElchinhttps://orcid.org/0000-0002-8310-3261OvereemIrinahttps://orcid.org/0000-0002-8422-580XRomanovskyVladimirhttps://orcid.org/0000-0002-9515-2087SchaeferKevinClowGaryhttps://orcid.org/0000-0002-2262-3853UrbanFrankhttps://orcid.org/0000-0002-1329-1703CableWilliamhttps://orcid.org/0000-0002-7951-3946PiperMarkSchwalmChristopherZhangTingjunKholodovAlexanderSousanesPamelahttps://orcid.org/0000-0002-4650-8200LosoMichaelhttps://orcid.org/0000-0001-8414-2310HillKennethCSDMS, Institute of Arctic and Alpine Research and Department of Geological Sciences, University of Colorado Boulder, Boulder, CO 80309, USALos Alamos National Laboratory, Los Alamos, NM 87545, USAGeophysical Institute Permafrost Laboratory, University of Alaska, Fairbanks, AK 99775, USANational Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USAU.S. Geological Survey, Lakewood, CO 80225, USAWoods Hole Research Center, Falmouth, MA 02540, USAMOE Key Laboratory of Western China's Environmental Systems, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, ChinaNational Park Service Arctic Central Alaska Inventory and Monitoring Networks Fairbanks, AK 99709, USAAlfred Wegener Institute Helmholtz Center for Polar and Marine Research, 14473 Potsdam, GermanyUniversity Cooperation for Polar Research (UCPR), Beijing 100875, ChinaKang Wang (kang.wang@colorado.edu)21December20181042311232816April20189May201810October201830November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/2311/2018/essd-10-2311-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/2311/2018/essd-10-2311-2018.pdf
Recent observations of near-surface soil temperatures over the circumpolar
Arctic show accelerated warming of permafrost-affected soils. The
availability of a comprehensive near-surface permafrost and active layer
dataset is critical to better understanding climate impacts and to
constraining permafrost thermal conditions and its spatial distribution in
land system models. We compiled a soil temperature dataset from 72 monitoring
stations in Alaska using data collected by the U.S. Geological Survey, the
National Park Service, and the University of Alaska Fairbanks permafrost
monitoring networks. The array of monitoring stations spans a large range of
latitudes from 60.9 to 71.3∘ N and elevations from near sea level to
∼1300 m, comprising tundra and boreal forest regions. This dataset
consists of monthly ground temperatures at depths up to 1 m,
volumetric soil water content, snow depth, and air temperature during
1997–2016. These data have been quality controlled in collection and
processing. Meanwhile, we implemented data harmonization evaluation for the
processed dataset. The final product (PF-AK, v0.1) is available at the Arctic
Data Center (https://doi.org/10.18739/A2KG55).
Introduction
Permafrost is frozen ground that remains at or below
0 ∘C for at least two consecutive years and may be found within
about a quarter of the terrestrial land area in the Northern Hemisphere and
80 % of the land area in Alaska
. A continuous increase in
near-surface air temperatures over the Alaskan Arctic
causes warming and thawing of permafrost,
which is expected to continue throughout the 21st century with impacts on
ecosystems and infrastructure
.
Thaw may have global consequences due to the potential for a significant
positive climate feedback related to newly released carbon previously stored
within the permafrost . Modeling
studies indicate that greenhouse gas emissions following thaw would amplify
current rates of atmospheric warming . However, large
uncertainties exist regarding the timing and magnitude of this
permafrost–carbon feedback, in part due to challenges associated with the
representation of permafrost processes in the climate models and the lack of
comprehensive permafrost datasets with which to test such models
. There is an immediate need for ready-to-use
reliable near-surface permafrost datasets, including ground temperatures,
soil moisture, and related climatic factors (such as air temperature and snow
depth), which can serve as benchmarks for the modeling community and help
evaluate potential physical, societal, and economic impacts.
The permafrost extent map by is one of the most widely used
metrics for comparing permafrost model results against ground-based data
. Another widely used dataset in model
validation is the Russian soil temperature dataset of daily ground
temperature measurements at different depths ranging from 0 to 3.2 m
for 51 years . An additional ground temperature
dataset includes daily-mean ground temperatures at various depths from 0 to
3.2 m at more than 800 stations in China, which for selected
locations date back to the 1950s . In addition to shallow
borehole ground temperatures data (i.e., depths less than 3 m) there
are datasets that archive temperatures from much deeper boreholes (generally
>5m) . Moreover, the Circumpolar
Active Layer Monitoring network measures active layer thickness – the maximum soil depth above permafrost that thaws every
summer and refreezes in the winter . Here,
we consolidated data from shallow borehole ground monitoring stations across
Alaska from multiple government agencies. Shallow borehole data are important
because they record the most immediate response to the changing environmental
conditions, whereas deep ground temperatures take extensive time to respond.
A typical permafrost monitoring station consists of an air temperature
sensor, a snow depth sensor, soil moisture sensors, and soil temperature
sensors. In situ observations of ground temperatures from the Alaskan Arctic
region have been dispersed over different monitoring efforts, which are
spread over varying time spans, and are observed at non-standardized depths.
The maximum depth of a typical monitoring station ranges from 1 to
3 m below the ground surface. However, not all stations use this
design. For example, the National Park Service of Alaska network does not
collect soil moisture data. Also, data from permafrost monitoring stations
are not archived in a common standardized format and are hosted by different
academic and government agencies, such as the Arctic Data Center, the Global
Terrestrial Network for Permafrost (GTN-P), the Long Term Ecological Research
Network (LTER), and the U.S. Geological Survey (USGS). Thus, we compiled a
ready-to-use permafrost dataset in order to allow for efficient data
retrieval and processing for permafrost-related analyses.
We compiled the first integrated shallow ground temperatures dataset for
permafrost-affected soils across Alaska from the three most reliable
monitoring networks operating over the past several decades: the Geophysical
Institute Permafrost Laboratory at the University of Alaska Fairbanks
(GI-UAF), the National Park Services in Alaska (NPS), and the USGS. This
synthesis permafrost dataset for Alaska (PF-AK, version 0.1) includes
measured air and ground temperatures at depth intervals up to 1.0 m,
snow depth, and soil volumetric water content (VWC) for 72 permafrost
monitoring stations across the state of Alaska. Detailed information and
metadata are provided for the compiled dataset so that potential users can
have a full understanding of the data and their associated limitations.
Furthermore, two types of data evaluation were implemented: (i) testing for
inconsistencies between air and ground temperature trends and (ii) the use of
the snow and heat transfer metric (SHTM) to
validate the relations between seasonal temperature amplitudes and snow
depth. These technical evaluations are useful for proving data harmonization
and reusing these data.
Locations of the Geophysical Institute at the University of Alaska
Fairbanks (GI-UAF), the U.S. Geological Survey (USGS), and the National Park
Services (NPS) permafrost monitoring stations in Alaska. The basemap shows
the permafrost distribution of Alaska compiled by .
Data sources and processingPermafrost monitoring networks
Our synthesis permafrost dataset for Alaska (Fig. and
Table ) is based on observed in situ data collected by the USGS,
NPS, and GI-UAF teams. In the late 1990s, researchers at the GI-UAF
established a near-surface permafrost monitoring system consisting of 27
stations across Alaska, primarily along the Trans-Alaskan Highway
(Fig. ) . Similarly, the USGS installed
permafrost stations to monitor permafrost conditions within the two federally
managed areas on the North Slope, the National Petroleum Reserve Alaska and
the Arctic National Wildlife Refuge. Since August 1998, the USGS has
maintained 17 automated stations in the area, spanning latitudes from 68.5 to
70.5∘ N and longitudes from 142.5 to 161∘ W
(Fig. ) . NPS has monitored ground temperatures
since 2004 at several sites in national parks . All
monitoring stations are installed on undisturbed land (Fig. ) at a
minimum specified distance from nearby infrastructure. This installation
protocol ensures no biases occur associated with anthropogenic or ecosystem
disturbances, which is one of the main differences with traditional
meteorological stations which are often associated with airstrips and
villages in Alaska. A brief description of environmental characteristics of
each site, including dominant soil and vegetation type, is summarized in
Table . Due to the differences in the station design and
description used by the various teams, the soil and vegetation descriptions
may not be fully comparable and are not available at all sites.
Overview of the data from the permafrost monitoring stations in Alaska.
NameLatitudeLongitudeOnsetLastNumber of available annual statistics SnowSourcedepthMAATMAGSTMAGTMAGTMAGTMAGT0.25 m0.5 m0.75 m1 mAwuna169.17-158.01199820043222221USGSAwuna269.16-158.03200320157111115USGSCamden Bay69.97-144.7720032015711111USGSDrew Point70.86-153.91199820151112121212128USGSEast Teshekpuk70.57-152.97200420151111111USGSFish Creek70.34-152.051998201514151515151511USGSIkpikpuk70.44-154.37200520159455USGSInigok69.99-153.0919982015127111114USGSKoluktak69.75-154.621999201596111111111USGSLake145Shore70.69-152.632007201545USGSMarsh Creek69.78-144.7920012015121777712USGSNiguanak69.89-142.982000201514141414141411USGSPiksiksak70.04-157.08200420151711118USGSRed Sheep Creek68.68-144.84200420157166667USGSSouth Meade70.63-156.84200320151811118USGSTunalik70.20-161.08199820151381414141413USGSUmiat69.40-152.141998201514131313131311USGSBarrow 271.31-156.66200220164988864GI-UAFBoza Creek 164.71-148.29200920166166665GI-UAFBoza Creek 264.72-148.2920092016666666GI-UAFChandalar Shelf68.07-149.5819972016111114142GI-UAFDeadhorse70.16-148.471997201633444GI-UAFFox64.95-147.62200120163554GI-UAFFranklin Bluffs69.67-148.721997201613113138GI-UAFFranklin Bluffs boil69.67-148.72200720164888GI-UAFFranklin Bluffs69.67-148.72200620166976GI-UAFinterior boilFranklin Bluffs wet69.68-148.722006201633335GI-UAFGalbraith Lake68.48-149.502001201666666GI-UAFHappy Valley69.16-148.8420012016688884GI-UAFImnaviat68.64-149.352006201688888GI-UAFIvotuk 368.48-155.742006201322222GI-UAFIvotuk 468.48-155.74199820166555416GI-UAFPilgrim Hot Springs65.09-164.90201220162222223GI-UAFSag1 MNT (moist69.43-148.6720012016731212121GI-UAFnonacidic tundra)Sag2 MAT (moist69.43-148.7020012016111111113GI-UAFacidic tundra)Selawik Village66.61-160.02201220163333333GI-UAFSmith Lake 164.87-147.8619972016999999GI-UAFSmith Lake 264.87-147.8620062016979999GI-UAFSmith Lake 364.87-147.86199720161255888GI-UAFSmith Lake 464.87-147.8620062016774447GI-UAFUAF Farm64.85-147.86200720167677554GI-UAFWest Dock70.37-148.5520012016941111113GI-UAFGakona 162.39-145.1520092016555555GI-UAFGakona 262.39-145.1520092016555553GI-UAFASIA267.47-162.27201220163332NPSCCLA265.31-143.132004201611911118NPSCHMA267.71-150.592012201633322NPSCREA262.12-141.8520042016115115511NPSCTUA261.27-142.622004201611511119NPSDKLA263.27-149.5420042016944447NPSDVLA266.28-164.5320112016433NPSELLA265.28-163.82201220163331NPSGGLA261.60-143.012005201615915NPSHOWA268.16-156.90201120163221NPSIMYA267.54-157.08201220163331NPS
Continued.
NameLatitudeLongitudeOnsetLastNumber of available annual statistics SnowSourcedepthMAATMAGSTMAGTMAGTMAGTMAGT0.25 m0.5 m0.75 m1 mKAUA267.57-158.43201220163331NPSKLIA267.98-155.01201220162221NPSKUGA268.32-161.49201420161111NPSMITA265.82-164.5420112016NPSMNOA267.14-162.992011201642221NPSPAMA267.77-152.16201220162222NPSRAMA267.62-154.3420122016111NPSRUGA262.71-150.542008201642NPSSRTA265.85-164.71201120164223NPSSRWA267.46-159.84201120161112NPSSSIA268.00-160.402011201643322NPSTAHA267.55-163.572011201631113NPSTANA260.91-142.90200520165223NPSTEBA261.18-144.34200520168556NPSTKLA263.52-150.0420052016118NPSUPRA264.52-143.202005201693664NPSWIGA263.81-150.11201320162221NPS
Brief description of vegetation and soil type
of monitoring stations in Alaska.
NameVegetationSoil typeDrew PointMoist meadow, tussock-tundra complexSiltFish CreekMoist meadow, tussock-tundra complexSiltInigokMoist meadow, tussock-tundra complexSiltTunalikMoist meadow, tussock-tundra complexSilty sandUmiatMoist tussock tundraSiltBarrow 2Graminoid-moss tundra (wet and moist acidic)Typic Histoturbel, Typic AquiturbelBoza Creek 1Open black spruce forestPergelic CryaqueptsBoza Creek 2–Chandalar ShelfAlpine meadow with low shrubsRuptic-Histic AquiturbelDeadhorseGraminoid-moss tundra and graminoid, prostrate-dwarf-shrub,Terric Aquiturbelmoss tundra (wet and moist nonacidic)Franklin BluffsGraminoid-moss tundra and graminoid, prostrate-dwarf-shrub,Ruptic-Histic Aquorthelmoss tundraFranklin Bluffs wetGraminoid-moss tundra and graminoid, prostrate-dwarf-shrub,–moss tundraGalbraith LakeGraminoid-moss tundra and graminoid, prostrate-dwarf-shrub,Ruptic-Histic Aquiturbelmoss tundra (wet and moist nonacidic)Happy ValleyTussock-graminoid, dwarf-shrub tundra and low-shrubRuptic-Histic Aquiturbeltundra (moist acidic)ImnaviatTussock-graminoid, dwarf-shrub tundra and low-shrubTypic Histoturbel,tundra (moist acidic)Typic AquorthelIvotuk 3Horsetail-rich variation of nonacidic tundra–Ivotuk 4Moss dominated–Sag1 MNT (moistMoist nonacidic tundraPergelic Cryaquolls (43 %), P. Cryaquepts (18 %),nonacidic tundra)P. Cryoborolls (14 %), others (25 %)Sag2 MAT (moistMoist acidic tundraPergelic Cryaquepts (79 %),acidic tundra)Histic Pergelic Cryaquepts (21 %)Selawik VillageUpland dwarf birch-tussock shrub–Smith Lake 1White spruce forest with high canopy–Smith Lake 2Dense diminutive black spruce forest–Smith Lake 3Forest surrounded by black spruce trees and tussock shrubs–Smith Lake 4Hummocks of sedges (tussocks) and shrubby vegetation–with sparse black spruceWest DockMoist to wet tundraTypic AquahaplelASIA2Dryas octopetalaLithic HaplogeleptDVLA2Arctagrostic latifolia, Petasites frigidus, Carex bigelowii,Aquic MolliturbelEmpetrum hermaphroditum, Ledum palustre,Vaccinium uliginosum, Arctous alpina,Hylocomium splendens, Lupinus arcticus, Salix pulchraELLA2Umbilicaria, Alectoria nigricans, CarexTypic HaploturbelHOWA2Dryas octopetala, Salix phlebophyllaTypic GelorthentIMYA2Dryas octopetala, Hierochloe alpine, Salix phlebophyllaTypic GelorthentKAUA2Dryas octopetala, Vaccinium uliginosumTypic GelorthentKUGA2Betula, Empetrum hermaphroditum, Ledum palustre,Typic GelorthentVaccinium vitis-idaeaMNOA2Dryas integrifolia, Potentilla bifloraTypic HaploturbelSRTA2Betula, Ledum palustre, Loiseleuria procumbens, Stereocaulon,Typic HaplogeleptFlavocetraria cucullata, Vaccinium uliginosumSRWA2Betula, Dryas octopetalaTypic GelorthentSSIA2Dryas octopetala, Arctous alpinus, Lupinus arcticus, Rhytidium rugosumTypic HaplorthelTAHA2Betula, Dryas octopetala, Vaccinium uliginosum,Typic GelorthentUPRA2Betula, Empetrum hermaphroditum, Ledum palustre, Picea glaucaTypic Dystrogelept
These networks utilize radiation-shielded thermistors (Campbell Scientific
CSI 107 temperature probes) to monitor air temperature. In the GI-UAF and NPS
network, the air temperature sensors were installed at 1.5 or 2.0 m
above the ground surface, whereas the USGS network monitors air temperature
at 3.0 m above the ground surface in order to minimize damage by
wildlife.
Typical permafrost observing stations. (a) Imnaviat site
(68.64∘ N, 149.35∘ W) in the GI-UAF network (source:
http://permafrost.gi.alaska.edu/site/im1, last access: 15 December
2018); (b) the Drew Point station (70.86∘ N,
153.91∘ W) in the USGS network (source:
http://pubs.usgs.gov/ds/0977/DrewPoint/DrewPoint.html, last access:
15 December 2018); (c) the Wigand site (63.81∘ N,
150.109∘ W) in the NPS network.
Instruments used in ground temperature monitoring are specified in
Table . To monitor near-surface ground temperatures, the networks
use either a probe with several thermistors embedded within a single rod,
typically 1.0 to 1.5 m long, or several individual Campbell
Scientific 107 thermistors anchored at specified depths within a single hole.
The thermistor temperature sensors are designed to record temperatures
ranging from -30 to 75 ∘C, with the exception of the 107 sensors,
which record temperatures from -35 to 50 ∘C.
Summary of ground temperature instruments from the USGS, GI-UAF, and
NPS networks of Alaska, USA.
NetworkTemperatureData loggerMeasurement depthsTemperatureAccuracyMaintenancesensor(m)ranges(∘C)visits(∘C)USGSMRC thermistorCR10X or CR1000Surface, 0.10, 0.20, 0.25, 0.30, 0.45,-30 to 750.01July, August0.70, 0.95, and 1.20 m (except forLake145Shore, where only0.25 m was available)GI-UAFCampbell Scientific 107CR10x or CR1000Surface to >1 m, but various in stations-35 to 500.02July, AugustMRC thermistorCR10x or CR1000Surface to >1 m, but various in stations-30 to 750.01July, AugustNPSCampbell Scientific 107CR-1000 XTSurface, 0.10, 0.20, 0.50, 0.75, and-35 to 500.02July, August1.00 m, but various in stations
An ice-bath calibration is a required procedure before installation of the
GI-UAF temperature probes. This calibration includes placing the sensors into
an insulated container filled with a mixture of ice shavings and distilled
water, measuring the temperature, and recording the offset from
0 ∘C. The measured offset is then used to correct the temperature
measurements. The average accuracy of these sensors is ±0.01 ∘C
. For the USGS network, the thermistor sensors are
installed inside a tight-fitting fluid-filled plastic tube, 1.25 m long, to
measure ground temperatures at depths of 0.05, 0.10, 0.15, 0.20, 0.25, 0.30,
0.45, 0.70, 0.95, and 1.20 m . Newer USGS ground sensors
are calibrated in the USGS temperature calibration facility while the older
ones were calibrated in situ using an inversion . The NPS
has three to four soil temperature sensors (CSI-107) installed in individual
holes at depths of 0.10, 0.20 and 0.50 m, and at several locations an
additional sensor is located at 1.00 m. The ground-measurement depths
vary station by station within the GI-UAF network, typically ranging from the
ground surface (i.e., 0 m) to 1 m below the ground surface.
It is important to note that for most of the installed probes, frost heave
occurs with time, and heaving depths are adjusted accordingly by subtracting
the heaving values yearly. The USGS and NPS teams estimate frost heave by
using ground temperature data from the topmost thermistor (at a depth of 0.05
or 0.10 m). If the temperature of the top thermistor during the thaw
period exceeds air temperature, then the sensor is considered exposed or
partly exposed to solar radiation. The GI-UAF team measures frost heave at
every site and then subtracts heave depth from known sensors depths to
correct for heaving . Each team corrects for heaving
every summer, and corrections are applied before releasing data. Our
presented data thus already account for frost heave and consist of corrected
ground temperatures.
Both the USGS and the GI-UAF networks measure liquid soil moisture using a
HydraProbe sensor developed by Stevens Water Monitoring Systems Inc. The
Stevens HydraProbe has a reported accuracy of ±0.03m3m-3. Each volumetric water content sensor was calibrated
in accordance with the manufacturer's recommendations. Uncertainties
associated with the sensor's sensitivity still exist under certain specific
conditions, e.g., for peat. The measured liquid soil moisture from a
HydraProbe cannot be directly compared with the total soil moisture content
values produced by land system models because in most of the models, soil
moisture includes both ice and liquid water, whereas HydraProbe sensors only
measure liquid soil moisture. The USGS network measures soil moisture at one
depth, approximately 0.15 m below the ground surface in all cases.
The soil moisture sensors depths vary between stations for the GI-UAF network
because they are installed at representative depths depending on the soil
profile and texture within the active layer. The GI-UAF network measures soil
moisture typically at three different depths within the active layer, ranging
from 0.10 to 0.60 m. The NPS network does not include moisture probes
at any of their monitoring stations. Our processed dataset only presents the
upper layer (up to 0.25 m) soil water content.
Snow depth is measured once per hour with a SR50 or SR50A ultrasonic distance
sensor (Campbell Sci. Inc.) at all of the available stations. This
downward-looking sensor is mounted on a crossarm typically at 2.5 m
above the ground surface for the USGS and NPS networks, and 1.5 m
above the ground surface for the GI-UAF network. The factory evaluated
accuracy is ±0.01m or 0.4 % of the distance to the ground
surface. It is important to note that vegetation at the ground surface might
influence shallow snow depth measurements.
Data processing workflow
All three networks apply data processing and quality-control checks before
release. Typically, quality control occurs shortly after annual summer field
campaigns; the fully processed and quality-controlled data become publicly
available a year after the data collection. In the present version of the
permafrost dataset, we use the USGS Data Series 1021, which includes data
through July 2015 (10.3133/ds1021; USGS data through July 2016 were
released after the analysis presented in this paper ). The
latest available quality-controlled data for the GI-UAF and NPS networks is
through August 2016. The GI-UAF data are available at
http://permafrost.gi.alaska.edu/sites_map (last access: 15 December
2018), while NPS data are available from
https://irma.nps.gov/DataStore/Reference/Profile/2240059 (last access:
15 December 2018) and
https://irma.nps.gov/DataStore/Reference/Profile/2239061 (last access:
15 December 2018).
Schematic representation of the data processing workflow used to
compile the permafrost dataset in the Alaska.
Figure shows a schematic representation of the data processing
workflow used to compile our synthesis dataset. To standardize the ground
temperature depths in the dataset, we linearly interpolate ground
temperatures for target depths: 0.25, 0.50, 0.75, and 1.00 m. We
only implemented interpolation for those stations with measurements at
least four depths, which assures a relatively small interval around the
specified target depths. In addition, soil temperatures were not extrapolated
beyond the maximum observed depth at any site; ground surface temperature is
only calculated when supporting measurements are indeed available. Then, the
calculated soil temperature at a specific depth depends on the linear slope
between the observations at adjacent depths. Therefore, using a linear
interpolation method does not necessarily result in a linear prediction from
the ground surface to 1 m. We examined the uncertainty resulting from our
linear interpolation method for the most data-sparse case, i.e., when we only
have observations at four depths. To do so we selected the entire year of
data without any missing values or depths and used linear interpolation to
predict temperatures at five depths. Then we randomly selected only four
depths, and interpolated again by using these four depths. This analysis
demonstrates that while missing depths would reduce the number of available
interpolation results, the influence from missing depths is limited.
The USGS and NPS network releases data at hourly resolution, whereas the
GI-UAF network releases data at daily resolution. Since the most common model
data output intervals of the land system and global climate models are
monthly, the monthly means were calculated for all variables, including air
and ground temperatures, snow depth, and soil water content. In addition to
monthly data, annual means were calculated to allow evaluation of the
relationship between air and ground temperatures. Thus, the dataset also
provides annual statistics, including mean-annual air temperature (MAAT);
mean-annual ground surface temperature (MAGST); mean-annual ground
temperature at 1 m (MAGT at 0.25, 0.50, 0.75, and 1.00 m); mean and
maximum seasonal snow depth (SND); and maximum, mean, and minimum soil
volumetric water content (VWC).
Data from many sites have gaps and discontinuities due to harsh environmental
conditions and wildlife that may interrupt the monitoring. There are various
methods for calculating monthly means from incomplete time series data. For
example, the USGS standards allow only 5 % of missing values for both
monthly and annual mean temperature data . The World
Meteorological Organization (WMO) does not allow gaps of more than three
consecutive days or more than 5 days total from each monthly data series
. Other researchers are more tolerant of missing data,
acknowledging the difficulty of data collection in remote cold regions.
allow up to 10 missing days in a monthly time series.
calculated monthly averages using at least 15 days. Here
we calculated monthly means for any station which has at least 20 days of
measurements for that specific month. The annual means were calculated from
daily data. Due to the scarcity of the data, we only calculate the annual
means for those years with a coverage of at least 90 % of the daily data.
For this reason, we separately present annual means for air and ground
temperatures as well as soil moisture, derived from daily data.
During the dataset compilation, we identified similarly named sites with
different installation times and locations that do not match precisely. It is
important to note that these sites, even when located nearby each other, may
have considerably different environmental conditions, and thus, different
ground temperature thermodynamics. A unique name is assigned to each site.
Deadhorse site, maintained by GI-UAF, and Awuna site, maintained by USGS, have
new monitoring stations, and the old ones have been decommissioned. The new
and retired systems ran simultaneously for a few months in order to evaluate
the data consistency. The environmental conditions for the newer Deadhorse
station remained the same, assuring data consistency. Environmental conditions
between two monitoring stations at Awuna are quite different: the original
Awuna site was located on a ridge, whereas the new site is in a valley
1.9 km away. Nevertheless, the temperature data are consistent
between the old and new station at Awuna. The old site (Awuna1) did not
monitor soil moisture, which would be expected to be more site-specific and
spatially variable. Thus, in this dataset, we present both the new and old
sites' records.
Derived variables
We calculated three derived variables from monthly temperature curve at each
site: (i) degree days of freezing (DDF), (ii) degree days of thawing (DDT),
and (iii) frost number (FN). and have
demonstrated that these variables calculated from monthly data closely
correspond to those calculated from daily data. DDT and DDF are given by
DDT=∫T(t)dt,T(t)>0∘C
and
DDF=∫T(t)dt,T(t)≤0∘C.
The FN index was calculated for both air temperature and ground temperatures
following :
FN=DDFDDF+DDT.
Here, dt is a day. FN serves as a simplified index for the
likelihood of permafrost occurrence. A FN index of 0.5 implies equal freezing
and thawing index. When the FN index is >0.5, it indicates that the annual
period of freezing dominates thaw, implying climate conditions that promote
permafrost.
Data evaluation
Despite the fact that individual station observations had originally been
quality controlled, we still need to examine our own results for data
harmonization. Here we implemented two methods of evaluation. The first one
compares the trends in air and ground temperature trends, while the second
method examines the effects of snow on the ground's thermal state.
The primary objective of the trend analysis is to evaluate the consistency
between trends at each station (for different depths) and between stations
rather than inform interannual variability. Most of the estimated trends
have a short observational period (see Table ). We chose to show
trends only for those stations with more than 10 available annual means.
Currently, some of the time series are still too short to provide significant
trends. As more data become available in the future, a more rigorous
analysis will be possible. It is well known that climatic trend analysis
requires more than 30 years of time series . On the other
hand, showed that 15 years is sufficient for interannual
variability diagnosis to be statistically significant. Since the time series
for most of the stations do not exceed 15 years, we calculate trends for
temperatures at different depths to determine inconsistencies between air and
ground temperature trends in terms of signs' differences.
Summary of the air, ground surface, ground temperature at
1 m, volumetric water content, and snow depth over the entire
observation period.
SiteAir temperature Ground surface Ground VWC Snow (∘C) temperature temperature (m3 m-3) depth (∘C) at 1 m (∘C) (m) MinMeanMaxMinMeanMaxMinMeanMaxMinMeanMaxMeanMaxAwuna1-28.51-10.619.62-11.30-4.162.79-9.38-4.52-0.930.390.61Awuna2-30.47-9.8811.60-13.21-3.348.10-10.84-4.43-0.640.020.210.430.370.54Camden Bay-28.89-10.356.92-14.47-7.49-1.200.200.26Drew Point-28.62-10.846.04-20.60-7.634.74-16.02-7.84-1.680.180.29East Teshekpuk-28.19-10.277.79-17.97-6.264.07-14.20-6.91-1.900.010.180.420.230.32Fish Creek-29.07-10.558.81-16.85-6.024.50-14.11-6.82-1.170.010.170.410.200.28Ikpikpuk-29.15-10.279.21-18.08-5.495.600.220.37Inigok-29.98-10.5810.55-16.28-4.807.73-12.68-5.58-0.600.000.120.330.220.33Koluktak-30.02-10.1811.64-15.20-3.778.75-13.77-4.691.160.020.130.360.200.30Lake145Shore-28.72-10.507.300.060.210.410.280.42Marsh Creek-26.51-8.6510.20-16.87-5.285.26-14.39-6.11-0.820.030.160.410.190.25Niguanak-27.80-9.978.48-18.13-6.094.66-14.87-6.72-1.020.150.21Piksiksak-29.21-9.9310.71-17.65-5.766.21-13.44-5.94-0.870.100.16Red Sheep Creek-23.94-6.8112.88-10.04-2.768.84-8.78-3.56-0.360.020.250.740.230.38South Meade-29.90-10.429.35-19.91-6.455.89-15.74-7.19-1.120.190.29Tunalik-28.26-10.179.15-21.58-7.126.81-16.18-7.35-0.920.170.28Umiat-28.67-9.8411.18-14.24-4.664.71-10.96-5.14-1.040.320.44Barrow 2-26.55-10.235.09-19.17-6.875.33-15.46-7.41-1.590.020.160.390.140.22Boza Creek 1-25.00-3.2016.03-9.171.1312.93-4.58-1.27-0.290.000.200.550.180.36Boza Creek 2-23.60-2.1816.31-3.622.2812.00-0.460.091.230.060.220.40Chandalar Shelf-23.66-7.6411.41-9.54-1.297.740.000.220.74Deadhorse-28.04-9.978.27-14.89-3.657.130.030.160.38Fox-26.02-2.9916.030.080.240.40Franklin Bluffs-30.15-10.6210.74-14.65-3.898.380.020.190.47Franklin Bluffs boil-18.04-4.1511.99Franklin Bluffsinterior boil-16.85-3.6611.12Franklin Bluffs wet-28.56-10.4910.84-14.52-3.3610.28Galbraith Lake-28.77-9.3510.72-14.38-3.459.34Happy Valley-30.01-9.4912.30-9.31-1.637.190.020.140.310.270.47Imnaviat-22.95-6.8110.57-8.48-0.818.54Ivotuk 3-29.85-10.1211.30-9.97-1.146.99Ivotuk 4-29.10-9.7011.23-9.21-1.248.26-5.16-1.89-0.530.000.270.770.430.60Pilgrim Hot Springs-16.78-2.0414.63-11.950.0813.52-7.56-2.30-0.270.000.300.730.060.21Sag1 MNT-26.72-8.3910.68-17.14-4.279.48-13.50-5.000.240.040.200.40Sag2 MAT-15.11-3.769.01-11.03-4.49-0.450.020.260.63Selawik Village-20.26-3.7214.91-11.16-0.7412.18-7.99-3.09-0.450.050.12Smith Lake 1-23.88-3.0616.06-11.29-0.1112.98-2.02-0.73-0.260.020.140.31Smith Lake 2-24.91-3.7415.98-7.321.1012.86-4.10-1.110.000.070.290.59Smith Lake 3-27.29-4.7014.68-3.492.5711.51-0.330.000.880.070.230.40Smith Lake 4-26.15-3.5818.20-15.81-2.279.68-10.32-3.81-0.62UAF Farm-22.09-1.4816.57-10.910.6813.00-0.831.185.430.280.47West Dock-28.82-10.536.81-20.30-6.685.460.010.200.550.040.09Gakona 1-23.06-2.7613.70-5.291.5511.26-1.62-0.63-0.22Gakona 2-23.01-2.4514.00-5.541.359.63-0.72-0.180.75ASIA2-15.10-3.2012.240.020.07CCLA2-27.39-4.5215.900.330.52CHMA2-15.97-5.249.810.040.08CREA2-16.41-3.878.57-12.35-1.7811.22-6.00-2.130.350.120.21CTUA2-14.15-2.528.61-12.83-1.0912.430.080.16DKLA2-17.19-3.3210.72-3.331.227.030.390.64DVLA2-21.84-5.3810.77ELLA2-17.18-4.819.930.290.43GGLA2-13.51-2.019.13-1.502.5412.180.901.45HOWA2-23.29-6.6410.180.050.11IMYA2-15.30-5.198.960.150.26
Continued.
SiteAir temperature Ground surface Ground VWC Snow (∘C) temperature temperature (m3 m-3) depth (∘C) at 1 m (∘C) (m) MinMeanMaxMinMeanMaxMinMeanMaxMinMeanMaxMeanMaxKAUA2-21.65-6.4710.010.150.25KLIA2-19.10-7.667.380.070.10KUGA2-16.74-3.5613.640.180.59MITA2MNOA2-18.78-3.7912.470.140.37PAMA2-18.00-4.4911.020.070.11RAMA2-17.93-5.4210.77RUGA2-9.49-0.5310.450.500.83SRTA2-21.96-4.6911.770.060.16SRWA2-17.35-3.1513.890.340.68SSIA2-21.85-5.8611.270.020.06TAHA2-20.09-4.4811.580.090.20TANA2-13.83-2.029.911.011.55TEBA2-17.27-1.9211.540.751.34TKLA2-18.48-3.1511.39-6.931.6313.170.150.25UPRA2-21.39-4.9111.36-13.19-1.6912.800.330.48WIGA2-17.84-1.5513.210.100.15
The second evaluation effort examines the physical mechanism among air
temperature, snow cover, and ground thermal states, which is an auxiliary
evaluation of the dataset. Seasonal snow cover will keep the ground warm by
reducing cooling (or heat loss) during the winter .
Considering a semi-infinite column, the damping of the ground temperature
annual cycle is dependent on both snow depth and soil thermal properties. In
this study, the snow period is defined as October through March. We averaged
the snow depth measurements over the period to obtain the effective snow
depth (SNDeff) . The amplitudes of air
temperature (Ampair) and ground surface temperature
(Ampgnd) were calculated following , for those
stations with available snow depth data. The snow and heat transfer metric
(SHTM) captures the correlation between the normalized temperature amplitude
difference (ΔAmpnorm) (i.e.,
Eqs. –) and SNDeff. Quantities
Ampair, Ampgnd, and ΔAmpnorm
are given by
Ampair=[Max(Tair)-Min(Tair)]2Ampgnd=[Max(Tgnd)-Min(Tgnd)]2ΔAmpnorm=Ampair-AmpgndAmpair.
ResultsOverview of this dataset
Table presents an overview of the data compiled in the dataset
for Alaska. Our dataset comprises 41 667 data points in total. There are
significant missing data (e.g., some stations do not have soil moisture
sensors installed) and there are different observational periods for each
sensor (e.g., air temperature sensors were installed often earlier than other
sensors in some cases). Excluding the missing time series when certain
instruments were not installed, the percentage of complete data is about 77 %.
Figure shows an annual summary of our core variables, including
mean annual air temperature, ground surface temperature, and ground
temperatures at 0.25, 0.50, 0.75, and 1.00 m. Overall, mean-annual
air temperatures are colder than -10∘C in the Alaskan Arctic, while
in the southern mountain tundra regions they are close to freezing point
(-0.5∘C at RUGA2 site). Mean-annual ground surface temperatures
for 46 available sites range from -7.6∘C through
2.5 ∘C, which, as expected, is considerably warmer than the
mean-annual air temperature. For most of the sites, ground temperatures could
be determined at depths of 0.25 and 0.50 m (69 and 67 sites, respectively). Ground temperatures at depths of 0.25 and 0.50 m range
roughly from -7.8 to 3.3 ∘C. Mean-annual ground temperature at
0.75 m varies from -7.5 to 1.2 ∘C over 49 available sites.
Ground temperatures at 1 m could only be determined at 32 sites, most
of which are located in the southern portion of the Alaskan Arctic (∼62∘ N). Mean-annual ground temperatures at this depth
range from -7.8 to 1.2 ∘C.
Overview of spatial distribution of mean annual air temperature,
ground surface temperature, and ground temperatures at 0.25, 0.50, 0.75, and
1.00 m.
The VWC shown in Table is from the upper part of the soil (i.e.,
depth of up to 0.25 m). The VWC measurements are mainly available from the
North Slope of Alaska. Maximum VWC is important for understanding active
layer dynamics during summer. Notably, the spatial variance of the maximum
VWC is 3 times larger than that of the annual means. Three sites,
Chandalar Shelf, Pilgrim Hot Springs, and Red Sheep Creek, were much wetter
than other sites (maximum VWCs exceeding 0.7 m3m-3). This is
mainly because these sites are close to a water body.
Snow depth is spatially variable over Alaska, although with a general trend
of increasing snow depth in the southern part of the state, according to the
synthesis dataset (Fig. ). In the Alaskan Arctic, snow cover is
shallower than in the southeast region. The maximum seasonal snow depth was
>1.5m at the Gates Glacier station (which is located near the
glacier) in Wrangell St. Elias National Park. The lowest maximum snow depth
occurs at West Dock near the Beaufort Sea in Prudhoe Bay, with only
0.09 m in 2010. Similar magnitudes of snow thickness were
reported at West Dock during the period 1983–1993 . The
other two sites, Asik in Noatak National Park and Serpentine in Bering Land
Bridge National Preserve, also showed a shallow snow cover in recent years.
The thin snow cover is probably due to wind exposure.
Overview of spatial distribution of snow depth, including annual
mean snow depth and maximum snow depth.
Overview of spatial distribution of freezing–thawing index from air,
ground surface temperature, and ground temperature at 0.50 m. Frost
number (FN) was derived from the freezing–thawing index according to
.
Data evaluation
In this dataset, we derived the FN index for air and ground temperatures at
various depths (Fig. and Table ). Because many
stations do not have sensors at depths >1m, we report the
DDT–DDF indices of air, ground surface, and 0.5 m below the ground
surface in Fig. , with all available results listed in
Table . Overall, almost all stations have an air FN above 0.5.
Stations on the North Slope have both air and ground surface FNs exceeding
0.6. In interior and southern Alaska, air FNs are above 0.5, although the
ground surface FNs are much lower due to the thicker snow cover in this
region. In the Alaskan Arctic, DDTs at ground surface are generally lower
than air according to the station observations. There are 13 stations with a
zero DDT based on ground temperature data at 0.5 m. These results
indicate a shallow active layer (<0.5m) at these sites. Another
five stations have a DDT of 0.5 m ground temperature less than
10 ∘C days. The calculated frost number indices are
consistent with the existing permafrost distribution map over Alaska
.
We examined the consistency among the trends of MAAT, MAGST, and MAGT at 1 m
depth. Typically, if MAAT has a long-term positive trend, then MAGST is
expected to have a positive trend, even if the rate is dampened
. Similarly, signs of trends in MAGST and MAGT at the
depth of 1 m and MAAT and MAGT at 1 m depth are hypothesized to be consistent
. Here we show the annual mean temperatures at four
stations, Drew Point, Fish Creek, Niguanak, and Tunalik, with 10 or more
years of data (Fig. ). Mean-annual air, ground surface, and ground
temperature at 1 m indicates consistent warming at rates of
0.07–0.18, 0.14–0.23, and 0.12–0.22 ∘Cyear-1,
respectively. A notable feature is that at Fish Creek, ground surface
temperature and ground temperature at 1 m showed amplified warming rates
compared to the magnitude of the air temperature increases, which can be
explained by the significant increase of seasonal snow depth over the same
period. There are six stations with relatively long records (≥10 years)
of air, ground surface, and ground temperature at 0.5 m for the same
period. In other words, at these sites, the data used to estimate linear
trends of air, ground surface, and ground temperature at 0.5 m were
collected over corresponding years. Figure shows that air
temperature, ground surface, and ground temperature at 0.5 m have
consistently positive trends. Furthermore, the trends in ground surface and
0.5 m were generally close.
Summary of freezing index (DDF, ∘C days), thawing
index (DDT, ∘C days), and frost number (FN, unitless) of air
and ground temperatures over the entire observation period.
SiteAir Ground surface Ground 0.25 mGround 0.50 mGround 0.75 mGround 1.00 mDDFDDTFNDDFDDTFNDDFDDTFNDDFDDTFNDDFDDTFNDDFDDTFNAwuna142177690.7017501960.751862100.93187801.00188001.00188001.00Awuna244179750.6817408070.5919392330.74208670.95212101.00209501.00Camden Bay44934820.7526841000.84285801.00287301.00286001.00Drew Point45214000.7732213270.763291460.89328001.00324801.00323101.00East Teshekpuk42985760.7328152790.762964180.93298201.00295101.00293901.00Fish Creek43766770.7225823280.742813120.94282101.00280401.00278901.00Ikpikpuk43567180.7127124340.7126852250.78Inigok44048580.6922687080.642454600.86249101.00244901.00242301.00Koluktak43379840.6820348560.6122426180.6623093250.7323401530.802355540.87Lake145Shore44305220.74Marsh Creek38368600.6825264080.7128311590.812863200.92280101.00277601.00Niguanak41796540.7227983390.742952540.88296010.98293401.00290001.00Piksiksak42638860.6925945060.692700660.86270701.00265701.00261101.00Red Sheep Creek324912300.6212089890.5216373240.691715580.84171001.00166701.00South Meade44777270.7130064470.723186450.89321401.00318701.00307801.00Tunalik42137250.7132305350.7132581380.83322580.95316001.00312001.00Umiat41389480.6821143740.702306140.93227101.00221601.00218901.00Barrow 242413250.7829253980.732996850.86307201.00304901.00311201.00Boza Creek 1327016340.5995916460.436765810.52832810.7691710.9788801.00Boza Creek 2303617040.5727818080.282248390.341665500.351033080.37Chandalar Shelf328510490.6411848550.541352550.83130201.00138801.00Deadhorse42366280.7220706540.6421062610.7421441010.82223630.96Fox344116180.591924420.40214210.7619101.00Franklin Bluffs44208790.6919648200.6120962370.752114610.85228910.98Franklin Bluffs boil233912340.5822937920.6321174140.6920181930.76Franklin Bluffsinterior boil219211450.5821324980.6721662880.7320731110.81Franklin Bluffs wet41429070.68187311000.5717336350.6217346890.611702680.83Galbraith Lake41908950.6818759550.5820501670.782110140.92212301.00Happy Valley429310610.6711677810.5512452110.711337360.86140401.00Imnaviat32129540.6599410050.5010174600.6010532180.691086930.77Ivotuk 343329480.6812737290.5711341270.75131230.95131201.00Ivotuk 442099480.6811059330.5211425790.5812481200.76129060.94103801.00Pilgrim Hot Springs202516320.53134616310.4817231680.761583180.90146510.97142701.00Sag1 MNT (moistnonacidic tundra)38409120.6723139140.6122095210.6722272020.772259360.89242550.96Sag2 MAT (moistacidic tundra)20129000.6022071860.782287440.882281120.93209830.96Selawik Village255615790.56126614520.4816261480.77169501.00160801.00154201.00Smith Lake 1308616590.58127315810.47488700.7346910.9642901.0041501.00Smith Lake 2325416240.5971217230.397793920.598101200.72781130.8974810.96Smith Lake 3351014820.6127517390.282277730.351145140.32603240.30361370.34Smith Lake 4338419340.5720849660.5918153530.692064390.88208201.00199601.00UAF Farm277917730.56121615990.4749910430.412799590.351359490.27518910.19West Dock44914750.7531084000.743181220.92318601.00312101.00Gakona 1306813610.6048315730.364343030.54443350.7843701.0033601.00Gakona 2304614020.6056413110.404285780.462612940.491602330.451391450.49ASIA2186113390.54165711500.55161710300.56CCLA2365615590.6014305510.621162230.88111330.95CHMA221049810.5922229360.6118374780.6615373580.67CREA222488170.62148112740.5214127250.5812673960.6411311290.751046150.89CTUA218808680.60151014380.5114348700.5613107510.57DKLA2226410840.5972513500.4256612160.4142810980.383219970.36DVLA2303110100.6317423600.6917241430.78ELLA222989750.61154510300.5515307600.59GGLA217539530.587920280.161718240.09416420.05HOWA232929010.6632956780.6931115160.71IMYA220388800.6018499950.5818875470.65KAUA230279040.6517646230.6316744520.66KLIA227636240.6822013660.7122572080.77KUGA2205714910.54125514180.48124510660.52MITA2
There are several sites in a small area that indicated inconsistency in air
temperature trends. The inconsistency is mainly due to different
observational periods and the relatively short duration of records. For
example, there are several Smith Lake (SL) permafrost monitoring stations
which are located north of the University of Alaska Fairbanks campus and west
of Smith Lake with varying environmental conditions. (SL1 is in a white spruce
forest with high canopy; SL2 is in a dense diminutive black spruce forest;
and SL3 is located at the edge of the forest surrounded by black spruce trees
and tussock shrubs; and SL4 is characterized by hummocks of sedges (tussocks)
and shrubby vegetation with sparse black spruce.) The environmental
conditions at the SL3 site provide favorable conditions for permafrost
existence. The SL3 site has the longest air temperature record, indicating a
cooling trend over the observational period (Fig. a). After
calculating the differences between measured data for all three sites, we
applied corresponding corrections and extend the data at all three sites. The
overlap period (2006–2012) showed a consistent variation with the roughly
constant offset between SL2 and SL3. By using the offset, we extended the
records at SL3 to 2015. Figure b shows that extending the time
series reduces the trend magnitude and changes the negative sign of the SL3
trend to positive, demonstrating the important difference between trends
derived from a complete longer time series and those derived from a sparse time series.
Examples of time series of mean-annual air, ground surface, ground
temperature at 1 m below ground surface, and snow depth. The black line
shows the data time series, while the blue line is the estimated linear trend.
Shading shows the standard error of the linear regression estimates. An
asterisk indicates that the trend has a p value <0.05.
(a) Stations with at least 10 years of identical period of
air, ground surface, and ground temperature at 0.5 m.
(b) Trend comparison of air temperature, ground surface temperature,
and ground temperature at 0.5 m over 1997–2016. Trends were
only estimated for those stations consisting of at least 10 years of data.
Error bars represent standard errors from the linear regression analysis.
Circles indicate trends with a p value ≤0.05; triangles indicate
trends with a p value >0.05.
Comparison between trends calculated using measured data at SL1,
SL2, and SL3 (a). Panel (b) shows merged data series and
corrected trends at SL3. Shading shows the standard error of the linear
regression estimates.
Finally, we examined the physical relations among air temperature, snow
cover, and ground thermal state (Fig. ). Across stations,
effective snow depth was generally less than 0.4 m. The normalized
temperature amplitude difference (ΔAmpnorm) that
calculates the temperature difference between air and ground surface shows a
positive linear relationship with effective snow depth. This correlation, the
so-called SHTM , implies that snow insulation effects
increase with effective snow depth, which is consistent with previous studies
. In addition,
while snow is considered an important factor in winter ground temperature,
vegetation can also affect the amplitude through its influence on summer
temperature.
Correlation between effective snow depth and normalized temperature
amplitude difference between air and ground surface. The mathematical
function of fit line follows the correlation showed in
.
The latest compiled dataset is available at the Arctic Data
Center (https://doi.org/10.18739/A2KG55, ).
Conclusions
Changes in near-surface ground temperatures over time are
important indicators of a changing climate because they provide vital
information on the response of the permafrost to climate change. In this
paper, we synthesize data of 72 monitoring stations in Alaska, spanning a
large range of latitudes from 60.9 to 71.3∘ N and elevations from
near sea level to 1327 m in tundra and boreal forest regions. This
dataset consists of monthly ground temperatures at 0.25 m depth intervals up
to 1 m, volumetric soil water content, snow depth, and air
temperature during 1997–2016. The remoteness of the sites and the harsh
environmental conditions inevitably result in missing data; our presented
dataset is 77 % complete and consists of 41 667 data points. We describe
the data compilation process, listing the workflow and the challenges
associated with preparing the synthesis permafrost dataset for Alaska. These
data were quality controlled during the data collection and processing
stages. We also implemented a data harmonization evaluation for this compiled
dataset. The PF-AK v0.1 can be easily integrated into model–data
intercomparison tools such as the International Land Model Benchmarking
(ILAMB) tool . Standard unified protocols developed nationally
and internationally to monitor near-surface permafrost thermal conditions
could significantly improve and simplify the development of permafrost
benchmark datasets such as that presented in this paper and reduce the
amount of time and effort required for data processing. This dataset should
be a valuable permafrost dataset that is worth maintaining in the future. It also
provides a prototype of basic data collection and management for other
permafrost regions.
KW, EJ, and IO designed this study. KW compiled this dataset
and wrote the draft. VR, WC, and AK provided the data and technical
description from the UAF-GI monitoring network. GC and FU provided support for
the USGS monitoring network. PS, ML, and KH supplied data from NPS network. All
authors discussed the results and contributed to the final
paper.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Water, ecosystem,
cryosphere, and climate data from the interior of Western Canada and other
cold regions”. It is not associated with a conference.
Acknowledgements
We appreciate three anonymous reviewers, David Swanson, and the editor Chris
DeBeer for their careful review and insightful comments which significantly
strengthened our paper. We also appreciate all organizations and individuals for producing and making their data available. This study
was supported by the National Science Foundation (award no. 1503559) and the
NASA CMAC-14 project (no. NNX16AB19G). Gary Clow and Frank Urban were
supported by the U.S. Geological Survey's Climate and Land Use Change
Program. National Park Service data collection is supported by the NPS
Inventory and Monitoring Program. GI-UAF Permafrost Lab data collection was
supported by the National Science Foundation (Awards OPP-0120736,
ARC-0632400, ARC-0520578, ARC-0612533, and ARC-1304271) and by the State of
Alaska. Tingjun Zhang was supported by the Strategic Priority Research
Program of the Chinese Academy of Sciences (no. XDA20100308). Any use of
trade, firm, or product names is for descriptive purposes only and does not
imply endorsement by the U.S. Government.
Edited by: Chris DeBeer
Reviewed by: three anonymous referees
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