Increased mean annual temperatures in 2014–2019 indicate permafrost thaw in Alaskan national parks

ABSTRACT

Rising temperatures in the Arctic can result in thaw of permafrost, with widespread implications for ecosystems and infrastructure. We analyzed mean annual air and ground temperatures in the eight northernmost national parks in Alaska using data from thirty-three National Park Service climate monitoring stations and eight National Weather Service stations. Mean annual air temperatures (MAATs) from 2014 to 2019 increased in a stepwise fashion relative to the preceding thirty-year period by at least 1°C at all locations in the study area; the increase was near 2°C in Denali National Park and most of the Arctic Alaska parks and 3°C in the far western coastal areas of the Arctic parks. The increase in mean annual ground temperatures (MAGT) was approximately equal to the increase in MAAT in windswept tundra areas with minimal snow, whereas under deeper taiga and alpine snowpacks the increase in MAGT was about half as large as the increase in MAAT. If the warm temperatures observed during 2014 to 2019 persist, there will be widespread degradation of permafrost in portions of these national parks and in similar environments across Alaska.

KEYWORDS:

• Permafrost
• climate change
• temperature
• n-factors

Introduction

High latitudes and mountain environments are currently undergoing rapid change (Box et al. 2019; Richter-Menge, Druckenmiller, and Jeffries 2019), with Alaska experiencing unprecedented increases in temperatures (Bieniek et al. 2014; Lader et al. 2017; Walsh et al. 2017; Markon et al. 2018; Overland, Hanna et al. 2019). The largest temperature increases have occurred during the cold season (Bieniek et al. 2014) and are attributed to both global climatic warming and regional changes in atmospheric circulation (Walsh and Brettschneider 2019). The Arctic and central Alaskan national parks are at the forefront of these changes. Gonzalez et al. (2018) described the disproportionate impacts of climate change on the country’s national parks and found that the highest mean annual temperature increases for the period 1901 to 2009 were in Alaska.

Temperature changes in Alaska have been nonmonotonic, with several warm and cool phases associated with atmospheric and oceanic circulation patterns, most notably the El Nino–Southern Oscillation and the Pacific Decadal Oscillation (Papineau 2001; Bieniek et al. 2014; Wendler, Moore, and Galloway 2014; Markon et al. 2018). A marked shift to warmer temperatures occurred in the mid-1970s due to a change in the Pacific Decadal Oscillation (Trenberth and Hurrell 1994; Mantua et al. 1997; Papineau 2001; Hartmann and Wendler 2005). Subsequently, much of interior Alaska displayed a generally flat or even slight cooling trend for several decades after 1980 (Hartmann and Wendler 2005; Wendler, Chen, and Moore 2012). This local cool phase came to an end with a series of record warm years beginning in 2014 (Overland, Dunlea et al. 2019). The four warmest years to date in Alaska all occurred in the period 2014 to 2019 (NOAA National Centers for Environmental Information [NCEI] 2020). This unprecedented warm spell was linked to anomalously warm sea surface temperatures, low sea ice extent, and a long ice-free season in the surrounding ocean (Walsh et al. 2018; Thoman et al. 2020). Climate models suggest that conditions as warm as those observed in recent years could become the norm by the 2050s (Walsh et al. 2017).

Warming of permafrost and thaw-related landform changes have been widely reported in Alaska in recent decades, with the most dramatic effects in the Arctic region north of the Brooks Range (Jorgenson et al. 2001; Osterkamp 2005, 2007; Jorgenson, Shur, and Pullman 2006; Osterkamp et al. 2009; Belshe, Schuur, and Grosse 2013; Frost et al. 2015; Liljedahl et al. 2016). Permafrost is widespread in the national parks of central and northern Alaska (Jorgenson et al. 2008b; Panda, Marchenko, and Romanovsky 2014a, 2014b, 2016). Where permafrost is present, it has a strong influence on ecosystems and human infrastructure (Chapin et al. 2006; Schuur et al. 2015; Harris, Brouchkov, and Guodong 2017). Thus, the National Park Service (NPS) monitors permafrost landforms and ground temperatures as a part of its long-term monitoring program (Swanson 2017a, 2017b; Loso 2018). Shallow (1 m depth or less) ground temperatures are measured at the network of climate monitoring stations maintained by the NPS (Sousanes and Hill 2017; Sousanes 2018). These climate monitoring stations were installed in remote mountainous areas of the national parks, a historically underrepresented area of weather and climate observations for Alaska. The NPS measurements began in 2005 for most stations in the central Alaskan NPS units and 2012 in the Arctic Alaskan NPS units and continue to the present (Figure 1).

Figure 1. (a) Locations of the climate monitoring stations and park units in the NPS Arctic Inventory and Monitoring Network (ARCN): Bering Land Bridge National Preserve (BELA), Cape Krusenstern National Monument (CAKR), Gates of the Arctic National Park and Preserve (GAAR), Kobuk Valley National Park (KOVA), and the Noatak National Preserve (NOAT). (b) Stations in the NPS Central Alaska Inventory and Monitoring Network (CAKN): Denali National Park and Preserve (DENA), Wrangell–St. Elias National Park and Preserve (WRST), and Yukon–Charley Rivers National Preserve (YUCH).

Temperature measurements in permafrost provide direct evidence for permafrost stability or thaw (Osterkamp 2003). However, temperature measurements from deep boreholes in permafrost are much sparser than measurements of atmospheric climate, and they are entirely lacking on NPS-administered lands in Alaska. Fortunately, permafrost temperatures are closely related to mean annual air temperatures (MAAT) and shallow mean annual ground temperatures (MAGT), which are much more easily and widely measured (Brown 1973).

The amplitude of ground temperature variations declines with distance below the surface, converging at depth on a value close to the mean annual temperatures of the air and ground above (Brown 1973). Thus, the MAATs of the air and the ground near the surface can be used to estimate deep ground temperatures and thus the stability of permafrost. However, the match between permafrost temperatures and mean annual air or shallow ground temperatures is not exact, and the difference between them may be understood as the sum of two “offsets”: the “surface offset” and “thermal offset” (Smith and Riseborough 2002). The sum of these two offsets can vary over short distances as a result of soil and site conditions, resulting in local variations in permafrost temperatures and local presence vs. absence of permafrost, even under a generally uniform macroclimate (Brown 1973). The mean annual temperature at the ground surface in cold regions is usually warmer than the MAAT (as measured about 2 m above the ground surface), mainly as a result of the insulating effect of snow during the cold seasons (the surface offset). The MAGT at the surface is usually warmer than the MAGT at the top of the permafrost as a result of the different thermal conductivities of the active layer when frozen vs. thawed (the thermal offset). The thermal offset (TO) is greatest in wet materials (i.e., lowland peat or fine-grained mineral soils) and negligible in dry materials (gravelly soils or bedrock; Goodrich 1978; Burn and Smith 1988). As a result of TO, it is not unusual in wet soils to have an MAGT at the permafrost table about 1°C colder than at the surface (Burn and Smith 1988). If a TO is present, the MAGT typically declines from the surface before leveling off at some depth within, or at the bottom of, the active layer (Romanovsky and Osterkamp 1995). For this reason, MAGTs in wet soils from within the active layer—for example, at 50 cm depth as in our study—may slightly overestimate the permafrost temperatures below.

The surface offset is often quantified by the use of “n-factors” (Lunardini 1978): the freezing n-factor (n_f) is the ratio between the annual sum of negative degree days at the ground surface to the annual sum of negative degree days in the air (the latter is also known as the air freezing index, I_fa). The thawing n-factor (n_t) is a similar ratio for the annual sum of positive degree days at the surface and in the air (the latter is known as the air thawing index, I_ta). The n_f for many natural surfaces in cold climates is 0.5 or less due to the effect of snow, whereas the value of n_t is often close to one (Lunardini 1978; Way and Lewkowicz 2016). The mean annual temperature at the top of the permafrost T_p can be modeled from air temperature data, n-factors, and the thermal offset (T_o) as follows (Romanovsky and Osterkamp 1995; Way and Lewkowicz 2016; Garibaldi, Bonnaventure, and Lamoureux 2020):

(1) $T_p=\frac{\left(n_t{I}_{ta}-n_f{I}_{fa}\right)}{365}+T_o$(1)

It can be shown algebraically that if the air freezing or thawing indices change while the n-factors and thermal offset remain constant, the change in MAAT (ΔT_a) and change in mean annual temperatures at the top of the permafrost (ΔT_p) are related by the simple relationship

(2) $\mathrm{\Delta }{T}_p=\bar{n}\mathrm{\Delta }{T}_a$(2)

where $\bar{n}$ is the mean of the freezing and thawing n-factors, weighted by the proportion of the change in the sum of air annual degree days that is due to the change in I_f and I_t, respectively. Thus, in a deep-snow environment where n_f is less than 0.2 (e.g., Way and Lewkowicz 2016), we can expect 1°C of warming in MAATs to result in considerably less than 1°C of warming in the MAGT, whereas in a windswept, nearly snow-free environment both n_f and n_t should be close to one and the rise in MAGTs is expected to track the rise in MAATs in a nearly 1:1 fashion.

Modeling of ground temperatures by Panda, Marchenko, and Romanovsky (2014a, 2014b, 2016) suggests that substantial areas of Alaska’s national parks have permafrost that is within a few degrees of thawing (Figure 2, Table 1). Warming of the magnitude predicted by global circulation models in the current century would result in large areas of permafrost becoming thermally unstable and thawing from the surface downward (Panda, Marchenko, and Romanovsky 2014a, 2014b, 2016). These maps of current permafrost temperatures also provide a baseline from which to judge the areas of permafrost thaw that would be expected from any ground warming measured at observing stations within these parks. For example, with a widespread increase in ground temperatures of 0.5°C, the permafrost in Denali National Park (DENA) that has mean annual temperatures of 0.5°C or warmer (red areas in Figure 2b) would be expected to begin thawing; these areas cover 19 percent of the park (Table 1).

Figure 2. Mean annual ground temperatures (MAGT) at the top of the permafrost, from modeling by Panda, Marchenko, and Romanovsky (2014a, 2014b, 2016). (a) The Arctic parks (see Figure 1 for the park acronyms). (b) Denali National Park and Preserve. (c) Wrangell–St. Elias National Park and Preserve.

Table 1. Summary of permafrost temperatures in Alaska’s national parks.^a

		Percentage of park area with permafrost temperatures^b
Network	Park unit	0°C to −0.5°C	0°C to −1°C	0°C to −2°C	0°C to −3°C
CAKN	DENA	19	34	47	55
CAKN	WRST	11	19	33	48
ARCN	BELA	0	0	0	33
ARCN	CAKR	0	0	0	13
ARCN	GAAR	2	4	11	21
ARCN	KOVA	1	6	18	40
ARCN	NOAT	0	0	1	6

^aSee Figure 1 for NPS network and park unit abbreviations and locations.

^bModeled mean decadal ground temperatures at the top of the permafrost for 2000 to 2009 by Panda, Marchenko, and Romanovsky (2014a, 2014b, 2016).

In the present study we investigate the effects of recent warming on air and shallow ground temperatures in the national parks of central and northern Alaska, and we interpret these data in terms of permafrost stability. We summarize the magnitude of recent MAAT increases at NPS climate monitoring stations and at nearby National Weather Service (NWS) stations with long-term records. We summarize MAGT trends at NPS monitoring stations for the available period of record and their relationship to changes in MAAT. Finally, we compare the observed increases in MAGT to maps of modeled permafrost temperature in the study area to locate areas where destabilized permafrost is likely to be present.

Study area

The study area includes eight national parks in northern and central Alaska, where NPS maintains thirty-three long-term climate monitoring stations (Figure 1, Supplementary Table S1). This vast region includes approximately half of the lands managed by NPS in the United States. The northern five NPS units of the Arctic Inventory and Monitoring Network (ARCN) are in a region of mostly arctic tundra, with boreal forest present in the southern portions of all parks except Bering Land Bridge National Preserve (BELA). Permafrost in ARCN is continuous, except in the southernmost portions of Kobuk Valley National Park (KOVA) and Gates of the Arctic National Park and Preserve (GAAR), where it is discontinuous (Jorgenson et al. 2008b). Taliks (permafrost-free areas) in the discontinuous permafrost zone of ARCN are mainly on active river floodplains and coarse-grained slope deposits with south aspects below 500 m elevation (Panda, Marchenko, and Romanovsky 2016).

The three national parks in the Central Alaska Inventory and Monitoring Network (CAKN) have boreal forest vegetation at low elevations and alpine tundra above about 800 to 900 m elevation (Roland et al. 2019). Permafrost in CAKN is discontinuous (Jorgenson et al. 2008b). It is typically present at elevations above about 1,500 m; at lower elevations permafrost is present on slopes with north aspects and in lowlands where fine-grained or peat soils are present and persistent winter temperature inversions result in low mean annual temperatures (Ping et al. 2005; S; Panda, Marchenko, and Romanovsky 2014a, 2014b; PRISM Climate Group 2020).

Tundra vegetation in the study area includes a broad range of wet lowland herbaceous communities (Carex, Eriophorum), wet and dry shrublands (Alnus, Betula, Salix, Dryas, ericaceous shrubs), and sparsely vegetated barrens. Boreal forest vegetation consists of mostly open spruce (Picea mariana and P. glauca) and birch (Betula neoalaskana) forests and local forests of aspen (Populus tremuloides) and poplar (Populus balsamifera; Jorgenson et al. 2008a; Jorgenson et al. 2009). Permafrost occurs under most tundra and open wetland vegetation types (it is absent in some tundra and wetland areas of CAKN), under most P. mariana forests, and locally under the other forest species (Clark and Duffy 2006; Jorgenson et al. 2008a; Jorgenson et al. 2009).

The climate monitoring stations were preferentially placed in mountainous areas of the parks on local topographic high points to complement the existing NWS stations that are typically found in lowlands. Thus, many of the stations have coarse-grained soils, thick active layers, and thin (wind-scoured) snowpacks (Supplementary Table S2). Our data set also contains a variety of sites with other soil and snow conditions, including sites with fine-grained and organic soils and sites with deep snowpacks. We have twenty stations in the Arctic parks, with ground temperature monitoring at nineteen, and permafrost present at all except one (Kavet Creek). At Kavet Creek a deep thawed zone is present after a fire more than fifty years ago, and permafrost conditions at depth are unknown. All of the Arctic stations are in areas of tundra vegetation, except for Kavet Creek with shrubs and regenerating forest; elevations range from 72 to 1,326 m. Most of the Arctic sites (eighteen of twenty) had relatively thin and dense “tundra” snowpacks by the classification system of Sturm, Holmgren, and Liston (1995; Supplementary Table S2). Locations with “tundra” snowpacks where snow depth measurements were noisy or missing due to lack of snow or the median maximum snow depth was less than 0.2 m are labeled as “tundra-scoured” (n = 9) in Supplementary Table S2. The remaining three Arctic stations had “taiga” or “alpine” snowpacks. Taiga snowpacks are lower in density than tundra and are usually deeper (30–120 cm; Sturm, Holmgren, and Liston 1995). Alpine snowpacks are deeper still (75–250 cm), with layers of varying density and warmer temperatures at the ground surface.

At the central Alaska sites, permafrost was known or inferred to be present at five stations, absent at six stations, and unknown at the two stations without ground temperature data (Supplementary Table S2). Seven of the thirteen CAKN stations were above elevational treeline. Elevations ranged from 278 to 1,598 m. The CAKN stations had tundra-scoured (n = 1), tundra (n = 4), taiga (n = 3), or alpine (n = 5) snowpacks. Estimated active layers range widely, from about 0.5 m to nearly 4 m (Supplementary Table S2).

Methods

Climate monitoring

Each climate monitoring station consists of a sensor mast, datalogger, satellite transmitter, and power system. Stations have redundant research-grade air temperature sensors including YSI thermistors (accuracy ±0.2°C), FTS humidity and temperature sensors (accuracy ±0.1°C), and Campbell Scientific Inc. (CSI) 109 probes (accuracy ±0.2°C). Sensors are mounted 2.0 m above the ground surface and installed within a naturally aspirated gill radiation shield. The sensors are designed to measure temperatures from −50°C to +50°C. Ground temperature is measured with CSI 107 temperature probes, which have a measurement range of −35°C to 50°C and accuracy of ±0.2°C. Ground probes were installed with a slide hammer in individual holes at 10-, 20-, and 50-cm depths, with additional sensors at 75 cm or 100 cm at selected stations.

Temperatures are sampled every 60 s and then averaged once per hour. Snow depth is sampled once per hour with a CSI SR50 ultrasonic depth sensor from a crossarm mounted 2.0 to 2.5 m above the ground surface. Hourly values for all climate parameters are stored onsite on a CSI CR1000 datalogger and downloaded annually. These data are also available in near real time by telemetry, but the more complete downloaded data record was used for this analysis. All hourly time series are quality controlled by a combination of automated flagging of threshold exceedances and manual data plots. Additional information on data collection and quality control is available in the standard operating procedures for the Arctic and Central Alaska monitoring networks (Sousanes and Hill 2017; Sousanes 2018. The Hoodoo Hill, Kavet Creek, Kelly, and Noatak stations (Supplementary Table S1) are maintained following similar standards under the National Wildfire Coordinating Group (2019).

Computation of mean annual temperatures and n-factors

Our analysis is based on annual mean temperatures computed for water years, which run from October to September and are named by the later of the two calendar years covered. We chose water years rather than calendar years for several reasons: we had better quality downloaded data (as opposed to data obtained from telemetry) through the summer of 2019 for NPS stations (i.e. most of water year 2019); the climate shift of interest here appears to have occurred in October 2013, coinciding with a new water year; and water years conveniently do not split winters, which tend to show continuity in ground temperatures due to the accumulating seasonal snowpack.

We obtained monthly mean air temperatures for NWS Cooperative Observer Program (COOP) weather stations from the xmACIS data set and summary tools (NOAA Regional Climate Centers 2020). We used the default maximum of five days of missing data allowed in a month to compute the mean. In a few cases where individual months of COOP data were missing during our time period of interest, we computed the annual means by replacing the missing month with means from a nearby station. Just 7 months were missing out of 2,520 months of data: Kotzebue months 2019–06, 2019–07, and 2019–08 (replaced with data from the NPS Mt. Noak (MNOA) station, 33 km north–northwest and 255 m higher in elevation); Eagle months 1985–08, 1985–09, and 2006–07 (replaced by Dawson, Yukon Territory data, 115 km east and 20 m higher in elevation), and Northway month 1985–05 (replaced by Tok, Alaska data, 66 m west and 24 m lower in elevation). At NPS stations we calculated daily means for all days with at least 80 percent of hourly values and monthly means for all months with at least 80 percent of daily values.

We computed annual mean ground and air temperatures from monthly means, weighted by the month length. Inspection of the data suggested a stepwise change in the MAAT in 2014. We used the Mann-Kendall trend test (R package “Kendall”; McLoed 2011) to determine whether indeed there was no long-term trend in the thirty years prior to 2014. We tested for the significance of the possible stepwise change between annual means before and after 2014 using a Welch’s t-test, which accounts for unequal variances between the samples (R Core Team 2017). We also estimated annual sums of thawing degree days and freezing degree days from monthly mean temperatures (Braithwaite 1984).

We computed n-factors using the monthly mean air temperatures and ground temperatures at 10 cm depth. The freezing n-factor is the ratio of the annual sum of negative thawing degree days at the surface to the sum in the air; likewise, the thawing n-factor is the ratio of the annual sum of positive thawing degree days at the surface to the corresponding sum in the air.

Computation of change in mean annual temperatures

We also computed multiyear means for the period before and after the observed 2014 temperature shift. Some of the NPS climate stations had short pre-2014 records (Supplementary Table S1) and sporadic missing months of data due to wildlife damage to the sensors and other issues. To allow us to make use of all available months of data (i.e., even months where the full year was incomplete) when computing multiyear means, we first computed monthly means for the multiyear period (e.g., 2014–2019) using all available months and then computed the overall multiyear mean from these (weighted by month length as before). Confidence intervals for the multiyear monthly means were computed using the t-test critical value multiplied by the standard error. The confidence intervals for annual means derived from the monthly means were computed by adding the monthly uncertainties in quadrature (the square root of the squared sums of each monthly uncertainty).

To estimate the change in MAGT associated with the 2014 temperature shift, we used the MAGTs from sensors at 50-cm depth (MAGT₅₀). This was the deepest sensor that was available across all sites. We assume that the change in MAGT₅₀ (from pre- to post-2014) was similar to the change in MAGT at the top of the permafrost. This assumption would only be violated where there was a significant change in thermal offset below 50 cm between the pre- and post-2014 periods. We compared the means at all available depths to determine which sites were potentially affected by such a change in thermal offset. At sites where the 50-cm depth sensor had less than one year of data or many more missing values than the 20-cm depth sensor, we used the 20-cm depth sensor data to compare the pre- and post-2014 means (stations WRST_TANA, DENA_DKLA, CAKR_MNOA, and CAKR_TAHA; see Supplementary Table S1 for station abbreviations).

Results

The 2014 increase in mean annual air temperatures

MAATs increased abruptly beginning in water year 2014 at each of the eight NWS long-term weather stations near the NPS units studied (Table 2). Figure 3 shows Bettles as an example station; plots for all eight NWS stations are provided in Supplementary Figures S1 and S2. A comparison of the mean temperatures for the thirty years prior to 2014 (1984–2013) to those for 2014 to 2019 shows that the magnitude of this stepwise increase was 2.2°C to 3.4°C at the westernmost stations (Kotzebue, Nome, McGrath) and 1.1°C to 1.7°C at the remaining stations. The mean air temperatures at these eight stations for 2006 to 2013, the period of record available for the NPS climate monitoring data, were similar to but slightly colder (by 0.1°C–0.6°C) than the means for the thirty-year period ending in 2013. Thus, the magnitude of the 2014 increase shown by the NPS station data is probably close to or slightly larger than what would have been produced by comparison with a long-term (thirty-year) record. That the increase was stepwise, rather than a gradual upward trend, is indicated by the lack of temperature trends for the thirty-year period prior to 2014: Mann-Kendall trend tests for 1984 to 2013 were not significant at any NWS station (Table 2).

Figure 3. Mean annual air temperatures at Bettles, Alaska. Multiyear means are shown for 1984 to 2013 (brown), 2006 to 2013 (blue), and 2014 to 2019 (red).

Table 2. Change in mean annual temperatures in 2014 at National weather service COOP stations and selected NPS stations.^a

	Mean air temperature (°C)				Change in mean air temperature (°C)^c
Station	1984–2013	Trend sign, p-value for 1984–2013^b	2006–2013	2014–2019	1984–2013 to 2014–2019	2006–2013 to 2014–2019	Elevation (m)
NWS Kotzebue	−5.2	(+) .18	−5.3	−1.8	3.4	3.5	9
NWS McGrath	−2.6	(+) .57	−2.9	−0.0	2.6	2.9	101
NWS Nome	−2.7	(−) .80	−3.3	−0.5	2.2	2.8	4
NOAT_KTZA			−8.3	−5.7		2.6	300
DENA_TKLA			−3.6	−1.0		2.6	925
DENA_EVCA			−2.2	0.2		2.5	1,141
NOAT_KELA			−4.4	−2.1		2.4	94
KOVA_KAVA			−4.7	−2.3		2.4	72
DENA_SMPA			−4.8	−2.4		2.4	571
NWS McKinley Park	−2.4	(−) .59	−2.9	−0.8	1.6	2.2	631
NWS Bettles	−4.8	(−) .89	−5.2	−3.1	1.7	2.1	193
WRST_CREA			−4.3	−2.3		2.0	1,598
WRST_GGLA			−2.3	−0.4		1.9	1,330
YUCH_UPRA			−5.2	−3.3		1.9	1,109
YUCH_CLCA			−4.8	−3.0		1.8	278
NWS Eagle	−3.6	(+) .64	−3.8	−1.9	1.7	1.8	259
NWS Northway	−4.7	(−) .83	−5.0	−3.2	1.5	1.8	522
DENA_DKLA			−3.4	−1.6		1.8	819
WRST_CTUA			−2.9	−1.2		1.7	1,399
WRST_TEBA			−2.2	−0.5		1.7	584
NWS Gulkana	−2.1	(−) .39	−2.5	−1.0	1.1	1.5	476

^aThe NPS stations included here all had at least 6.8 years of data for 2006 to 2013 and 5.2 years for 2014 to 2019. The NPS stations lack data for the full thirty-year preceding time period (1984–2013). Stations are sorted by the amount of change from 2006–2013 to 2014–2019, from largest to smallest.

^bMann-Kendall trend test sign (positive (+) or negative (–) trend) and p-value for 1984 to 2013.

^ct-Tests comparing pre-2014 and post-2014 mean annual air temperatures for all stations listed here had p-values of less than .05, except Gulkana 1984–2013 vs. 2014–2019 with p = .06.

The regionwide jump in air temperatures in 2014 was apparent at all of the NPS climate monitoring stations that have data across this transition (Figure 4, Table 2). The magnitude of the change (+1.6°C to +2.6°C) was similar to that observed at the nearest NWS stations and the values modeled for the associated regions of Alaska by NCEI (2020; Figure 4). Warming at stations above 1,000 m elevation was slightly greater (up to 0.3°C) than increases at the nearest stations below 1,000 m, but by far the strongest geographic trend in the amount of warming of air temperatures was an increase from east to west across central Alaska. Based on the data in Table 2 and Figure 4, the 2014 increase in air temperature relative to the preceding thirty-year period was probably at least 1°C at all locations, close to 2°C in DENA and most of the Arctic parks, 3°C or more in the far western coastal areas of ARCN.

Figure 4. Change in mean air temperature between 2006–2013 and 2014–2019 at the NPS climate stations in ARCN and CAKN parks. Includes all remote monitoring stations in Arctic (ARCN) and Central Alaska (CAKN) NPS units that have no more than fourteen months of missing data in each of the indicated intervals (2006–2013 and 2014–2019). Temperature changes were also calculated for the Alaska climate divisions (Bieniek et al. 2012) for the same time periods (by water year) from regional data by NCEI (2020). Darker shading indicates a larger temperature increase post-2013 and highlights the greater amount of warming in the west.

The warming affected all seasons but was concentrated in the winter. Both the annual sum of thawing degree days and the annual sum of freezing degree days show that 2014 to 2019 was warmer than 1984 to 2013 at the eight NWS stations, but the change in freezing degree days (i.e., winter) was more than double the change in thawing degree days at all stations except Gulkana (Figure 5).

Figure 5. Average annual sum of freeze and thawing degree days at NWS stations surrounding the study area, during the periods 1984 to 2013 and 2014 to 2019.

Relationship between mean annual air and ground temperatures

The 2014 warming resulted in clear increases in MAGT₅₀ at most locations. Air and ground mean temperatures before and after the 2014 shift are summarized for all stations in Figure 6 (Arctic parks) and Figure 7 (central Alaska parks). Some example stations are presented in detail with annual means in Supplementary Figures S3 and S4.

Figure 6. Summary of air and ground temperatures in the Arctic parks before and after the 2014 temperature shift. Error bars give the t-test 95 percent confidence interval for the mean. Where error bars are missing there was less than two years of data. “Air PRISM” is the modeled mean air temperature for 1981 to 2010 by PRISM Climate Group (2020). Station abbreviations (Supplementary Table S1) are arrayed along the bottom. The blue bar above the station identifier indicates permafrost was present (observed or inferred from temperatures).

Figure 7. Summary of air and ground temperatures in the Central Alaska parks before and after the 2014 temperature shift. Error bars give the t-test 95 percent confidence interval for the mean. Where error bars are missing there was less than two years of data. “Air PRISM” is the modeled mean air temperature for 1981 to 2010 by PRISM Climate Group (2020). Station abbreviations (Supplementary Table S1) are arrayed along the bottom. The blue bar above the station identifier indicates permafrost was present (observed or inferred from temperatures).

In the Arctic parks (Figure 6, Supplementary Figure S3), pre-2014 MAATs were −4°C to −9°C, a range that would be expected for locations near the warm limit of the continuous permafrost zone (Jorgenson et al. 2008b). All stations had permafrost except for Kavet Creek (KOVA_KAVA). The latter station had young spruce trees growing in dense tall shrubs and cryoturbated horizons in the soil profile, suggesting that there was probably a talik zone over permafrost due to a wildfire (Swanson 1996); the fire was more than fifty years ago and prior to modern records. MAGT₅₀ data prior to 2014 are limited in ARCN, but all were colder than −4°C except BELA_SRTA (which is probably affected by geothermal heating as evidenced by the hot springs located about 400 m from the site; Nordstrom et al. 2015). In the period 2014 to 2019, MAATs had all risen to be −2°C to −5°C (which is typical of the discontinuous permafrost zone) except for NOAT_KTZA (−5.7°C) and GAAR_KLIA (−6.4°C). Warming from 2014 to 2019 raised the six-year MAGT₅₀ at two of the stations with permafrost above 0°C (BELA_SRTA and KOVA_SRWA), and CAKR_MNOA rose to be very near 0°C. MAGT₅₀ was above 0°C in individual years during 2014 to 2019 at six of the twenty stations in the Arctic parks with permafrost. At the coldest stations in NOAT and GAAR, post-2014 MAGT₅₀ remained near or below −2°C.

In the central Alaskan parks (Figure 7, Supplementary Figure S4), pre-2014 MAATs were −1°C to −5°C, which is typical of the discontinuous permafrost zone. Permafrost was present at five of the stations (indicated by the blue bars above the station names in Figure 7). MAGT₅₀ where permafrost was present was −1.0°C to −2.6°C prior to 2014. From 2014 to 2019, MAATs rose to be slightly above 0°C at three stations, and the coldest stations (in YUCH) rose to near −3°C (Figure 7). In the period 2014 to 2019 the six-year mean ground temperatures at all of the stations with permafrost had risen to be between 0°C and −2°C. Three of the five stations on permafrost (DENA_WIGA, WRST_CTUA, and YUCH_UPRA) registered MAGT₅₀ above 0°C in individual years, whereas MAGT₅₀ at the other two stations (WRST_CREA, YUCH_CCLA) rose after 2014 but remained just below freezing. At the three stations in WRST that lack permafrost and have deep snow covers (GGLA, TANA, TEBA), the MAGT₅₀ rose less in 2014 than the MAATs. The median maximum snow depths at GGLA, TANA, and TEBA were 1.59 m, 1.72 m, and 1.44 m respectively, as measured by the snow depth sensors at the site (Supplementary Table S2). At these three stations, ground temperatures remained near freezing all winter under the deep snow and they did not react strongly to the warmer winters that have occurred since 2014.

Estimates of the typical amount of change in ground temperatures that occurred between the long-term pre-2014 period and 2014 to 2019 are of interest because they help us predict the temperature range of permafrost that would have been destabilized by recent warming. For example, if there were a widespread and permanent 1°C increase in ground temperatures, most permafrost with prior temperatures of −1°C to 0°C would be destabilized. We summarized the observed amount of change from pre-2014 to 2014 to 2019 in multiyear average air temperatures and ground temperatures (Figure 8). Figure 8 is based on the same data as Figures 6 and 7, using stations where mean air and ground temperature data from both time periods were available. The data for the central Alaska parks cluster near a change in MAAT (ΔMAAT) of +2°C (range 1.5°C–2.6°C) and change in MAGT (ΔMAGT₅₀) of +1°C (range +0.5°C to +1.7°C). For only stations on permafrost in central Alaska, the ranges are +1.7°C to +2.0°C for air temperature change and +0.7°C to +1.2°C for ground temperature change. In the Arctic parks, the data cluster near +3°C change in air temperature (range 2.3°C–4.3°C) and +3°C change in ground temperature (range +2.1°C to +3.6°C).

Figure 8. Plot of the change (from pre-2014 to 2014–2019) in mean air temperature vs. change in mean ground temperature at NPS climate monitoring stations. Circles (pf+) indicate stations where permafrost was present and triangles (pf−) indicate stations where permafrost was absent. Snowpack types are based on Sturm, Holmgren, and Liston (1995). The sites from the Arctic (ARCN) and Central Alaska (CAKN) parks are enclosed with circles. The solid diagonal line marks equal change in air and ground temperatures (y = x), and the dashed line indicates where the change ground temperatures was half of the change in air temperatures (y = 0.5x).

The stations with tundra-scoured snowpacks (snow mostly removed by wind; Supplementary Table S2) showed positive ΔMAGT₅₀ roughly similar to ΔMAAT (i.e., near the “y = x” line in Figure 8). Stations with slightly deeper tundra snowpacks (median maximum snow depths of 0.2 to 0.5 m) showed a range of ΔMAGT₅₀ from nearly equal to ΔMAAT to less than half ΔMAAT (below the “y = 0.5x” line in Figure 8). The deeper taiga and alpine snowpack types all had ΔMAGT₅₀ smaller than ΔMAAT. Note that the ecosystems of each station generally match their snow type names (Supplementary Table S2), with the exception of DENA_TKLA (the sole CAKN tundra station lacking permafrost; open light green circle in Figure 8). This station is on a windswept floodplain just below treeline.

n-Factors and thermal offset effects

Freezing n-factors were strongly related to snowpack type (Figure 9, Supplementary Table S3). The highest freezing n-factors were associated with tundra-scoured snowplack types, followed by tundra, taiga, and alpine. This is the inverse order of the depth and insulation value of the snowpack types (Sturm, Holmgren, and Liston 1995). The highest freezing n-factors under tundra-scoured snowpacks were near 1, whereas the lowest n-factors under alpine snowpacks were below 0.1. The freezing n-factors in 2014 to 2019 were similar to or lower than the pre-2014 values, except at station WRST_CREA where the n-factor increased. Thawing n-factors were mostly above 0.75 and weakly related to the environment (Figure 9). The two exceptions with thawing n-factors near 0.6 were stations with organic surface soils and active layers less than 1 m thick. We used data from our shallowest sensor at 10-cm depth as the surface temperature to compute n-factors; at these two stations there was probably some damping of summer warming at 10 cm.

Figure 9. Plots of freezing n-factors (upper) and thawing n-factors (lower). The x-axes are n-factors for pre-2014 and the y-axes are n-factors for 2014 to 2019. The diagonal line marks equal n-factors pre- and post-2014.

Average thermal offsets of MAGT₅₀ (TO₅₀) relative to near-surface (10 cm) MAGT₁₀ were within the range of sensor accuracy (±0.2°C) at the majority of stations (Supplementary Table S3). Thus, at these stations TO was minimal and MAGT₅₀ cm is probably close to the MAGT in the upper permafrost. Eight of the stations on permafrost had negative TO₅₀ of more than −0.2°C. Of these eight stations, four had deeper sensors (at 75 or 100 cm) that registered the same TO as the 50-cm sensor, indicating that additional TO below 50 cm was probably negligible, and one had TO₅₀ similar to the TO at 20 cm depth, suggesting little additional TO below 20 cm. The three remaining are BELA_DVLA (TO₅₀ = −0.4°C), GAAR_PAMA (TO₅₀ = −0.3°C), and DENA_WIGA (TO₅₀ = −1.0°C). An additional decline in MAGT of a few tenths of a degree is possible down to the bottom of the active layer at these three sites. Our data are insufficient to test whether the TO₅₀ changed pre- to post-2014 at these three stations.

Discussion

The 2014 temperature increase

All of our monitoring stations showed an abrupt increase in MAAT in 2014 that resulted in an increase in ground temperatures. The amount of increase in MAAT varied regionally, with the greatest increases (up to 3.5°C) in the parks of coastal northwest Alaska and the smallest increases (1°C–2°C) in the parks of southeast Interior Alaska. The 2014 climatic shift in MAATs and MAGTs that we observed affected all of central and northern Alaska: The 2014 shift in MAATs that we observed is also evident in the aggregated time series data for the corresponding Alaska climate divisions (Bieniek et al. 2012; Figure 4) and in the NWS station data. The climate attributes responsible for the 2014 shift are a combination of global climatic warming, regional changes in the atmospheric circulation patterns, and a decline in sea ice (Walsh et al. 2017; Walsh and Brettschneider 2019; Thoman et al. 2020).

Evidence for elevation-dependent warming is weak in our data set, which ranges from near sea level to about 1,600 m elevation. Enhanced warming at high elevations has been noted adjacent to WRST in the St. Elias Range in Yukon, Canada (Williamson et al. 2020), but only at elevations well above the range of our data (over 3,000 m). Any elevation-dependent warming in our data is overwhelmed by the stronger trend of enhanced warming from east to west across Alaska.

The sudden, stepwise nature of our air and ground temperature increases across all sites occurred in the absence of substantial local vegetation change, showing that the phenomenon is regional in scope. Vegetation change in the Arctic, notably the increase in tall shrubs, is well documented and has been shown to increase snow depths and winter ground temperatures (Sturm et al. 2001; Tape, Sturm, and Racine 2006). However, in our study area vegetation changes have affected a relatively small proportion of the land area to date (Swanson 2013, 2015; Brodie et al. 2019). Annual photographs of our climate monitoring sites reveal very stable vegetation since the stations were installed. Over the course of the study we observed little shrub increase and only minor tree growth around two sites where trees had been killed prior to installation by disturbance (at KOVA_KAVA after fire and WRST_TEBA after beetle kill). The consistency of n-factors before and after the 2014 temperature shift (Figure 9) also indicates relatively stable local site conditions.

Change in MAGT vs. change in MAAT

The amount of increase in MAGT₅₀ per unit increase in MAAT showed the expected relationship with n-factors described in the Introduction: The ratio between ΔMAGT₅₀ and ΔMAAT was intermediate between the freezing and thawing n-factors at the site. The n-factors, in turn, were closely tied to vegetation and snowpack types, a fact well established from previous research (Lunardini 1978; Jorgenson et al. 2010; Lewkowicz et al. 2012; Way and Lewkowicz 2016). Thus, the stations in sparsely vegetated, windswept tundra areas with very thin tundra-scoured snowpacks had high n-factors (greater than 0.7) in both summer and winter and an approximately 1:1 ratio of ΔMAGT₅₀ to ΔMAAT. In other words, without the thermal dampening effect of winter snow, ΔMAGT₅₀ tracked ΔMAAT quite closely. The stations with deeper tundra snowpack types (20- to 50-cm median maximum snow depth; Supplementary Table S2) had lower freezing n-factors (down to about 0.5) and a wider range of ΔMAGT₅₀ to ΔMAAT ratios down to about 0.5. The most anomalous permafrost tundra site was WRST_CREA (filled light green circle in Figure 8) with ΔMAGT₅₀ of less than 1°C. This was the station with a positive change in freezing n-factor with post-2014 warming (see the labeled point on Figure 9), presumably due to thinner snowpacks: The four deepest maximum snow depths at WRST_CREA were all pre-2014. An increase in freezing n-factor implies relatively more surface chilling post-2014 than would have occurred with a constant n-factor, resulting in less ground warming than would otherwise have occurred at this station. The stations with highly insulative taiga and alpine snowpack types had low freezing n-factors (0.5 or less; Figure 9) and correspondingly low (less than 1) ratios of ΔMAGT₅₀ to ΔMAAT (Figure 8). Thus, where the snow cover dampens the effect of changing winter temperatures, the response of ΔMAGT₅₀ to ΔMAAT is reduced. The majority of the stations with ratios of ΔMAGT₅₀ to ΔMAAT of less than 0.5 lacked permafrost as a result of both the low freezing n-factors and milder climates overall at these locations.

The central Alaskan stations generally had smaller ΔMAAT from pre- to post-2014 than the Arctic parks, due to the smaller climate shift observed in eastern interior Alaska discussed previously. The central Alaskan stations also generally showed a smaller response of MAGT₅₀ to the change (i.e., the ratio of ΔMAGT₅₀ to ΔMAAT was near 0.5:1 in CAKN vs. near 1:1 in ARCN). The more muted response of the CAKN stations to the temperature shift is probably due to the predominance in our CAKN data set of stations with taiga and alpine snowpack types vs. tundra-scoured and tundra types in ARCN. Thus, it is possible that some windswept tundra areas in the central Alaskan parks could behave like the Arctic parks and show a greater response to increasing MAAT (e.g., 1°C of rise in MAGT per degree of rise in MAAT). In the Arctic parks a weaker response to warming of MAAT (i.e., just 0.5°C rise in MAGT per degree of MAAT) is possible in two environments where we lack direct observations in ARCN: (1) under deep tundra snow drifts, which are present across most of the parks and could have conditions approaching those of “alpine” snowpacks, and (2) in the forested lowlands in the southern parts of KOVA and GAAR, where taiga snowpack types are expected (Sturm, Holmgren, and Liston 1995).

As a result of TO below 50 cm depth, MAGT₅₀ is not necessarily equal to MAGT at the top of the permafrost. However, as long as the TO below 50 cm changed little with the 2014 temperature shift, our observed ΔMAGT₅₀ should be very close to the change in mean annual temperature at the top of the permafrost. Our results suggested little TO below 50 cm depth at all except three stations. In addition, TO depends on soil composition and wetness (Burn and Smith 1988); the former generally does not change, and the latter is largely controlled by the setting. However, changing TOs are possible at least locally in the future—for example, as a result of thermokarst inundation or drying due to thickening of the active layer—and these could cause local deviations from the simple relationships of ΔMAGT₅₀ and temperature at the top of the permafrost to ΔMAAT described here.

Implications for permafrost

Given the magnitude of the stepwise MAAT increase that occurred in 2014 relative to the preceding thirty years (at least 1°C at all locations, probably closer to 2°C in DENA and the ARCN parks, and potentially 3°C or more in the far western coastal areas of ARCN) and the MAAT–MAGT₅₀ relationships discussed above, we conservatively estimate that the 2014 temperature shift raised MAGT₅₀ throughout the central Alaskan parks by 0.5°C, with 1°C possible in DENA. Likewise, the 2014 temperature shift should have raised MAGT₅₀ by about 2°C across most of the Arctic parks, with 3°C or more possible in some areas, particularly in the coastal west, and as little as 1°C possible in the taiga of southern KOVA and GAAR.

The implication of this temperature shift for the central Alaskan parks is that permafrost in most areas with MAGT at the top of the permafrost of −0.5°C or warmer prior to 2014 should have begun thawing during the past six years, and many areas that had permafrost in the −0.5°C to −1°C range may have also begun to thaw. The area covered by permafrost warmer than −1°C prior to 2014 was estimated to be 34 percent of DENA and 19 percent of WRST (Table 1). Maps derived from Panda, Marchenko, and Romanovsky (2014a, 2014b) data show that the probable thawing permafrost in DENA is located in the Alaska Range foothills and on the plains north of the Alaska Range (Figure 2b), and in WRST the probable thawing permafrost is located in the valley bottoms and lower slopes of the Copper and Chitina River valleys (Figure 2c).

In the Arctic parks, nearly all areas that had permafrost warmer than −1°C prior to 2014 should have begun to thaw during 2014 to 2019, along with most areas with permafrost above −2°C and some areas with temperatures around −3°C, especially in the west. Panda, Marchenko, and Romanovsky’s (2016) model shows that in ARCN the probable thawing permafrost (previously warmer than −1°C or −2°C) is concentrated in the lowlands and valleys of southern KOVA and GAAR. With +3°C of ground warming, large areas of gently sloping tundra in the western Arctic parks would be vulnerable to thaw (Figure 2a). The area covered by permafrost warmer than −3.0°C is estimated to be 33 percent of BELA and 40 percent of KOVA (Table 1). The stations where 2014 to 2019 mean ground temperatures were observed to rise to near 0°C (CAKR_MNOA) or above (BELA_SRTA, KOVA_SRWA) had modeled 2000 to 2009 ground temperatures in Figure 2a of −3°C to −4°C, suggesting that the areas with modeled prior permafrost temperatures of −3°C are indeed vulnerable to thaw.

Thus, the warming-related thaw of permafrost in Alaska predicted by Osterkamp and Lachenbruch (1990) has apparently arrived, after a delay due to flat temperature trends over much of Alaska for several decades following the mid-1970s’ stepwise temperature increase. The relationship between the change in air temperatures and change in ground temperatures developed in our study will be useful for understanding the state of permafrost in response to future warming.

Effects and rate of permafrost thaw

The effects of a sudden increase in surface temperatures on underlying permafrost are well known from research on the effects of fire (Dyrness 1982; Burn 1998; Yoshikawa et al. 2002; Viereck et al. 2008; Jafarov et al. 2013; Nossov et al. 2013) and mechanical vegetation removal and surface disturbance (Péwé 1954; Linell 1973). These disturbance events result in a sudden ground surface temperature increase, followed by an increase in the thaw depth within a year. If the magnitude of warming is sufficient to raise the MAGT at the base of the seasonally freezing layer above 0°C, a continuously thawed layer (talik) will develop. If thaw encounters ice-rich material, then a variety of dramatic effects may result, including subsidence and formation of pits or troughs and mass movements on slopes (e.g., Péwé 1954; Jorgenson et al. 2001; Lewkowicz and Harris 2005; Jorgenson, Shur, and Pullman 2006; Liljedahl et al. 2016). Even in the absence of massive ice, permafrost thaw alters groundwater hydrology by increasing shallow sediment water storage capacity and opening new potential water flow paths (Yoshikawa and Hinzman 2003; Hinzman et al. 2006). Surface disturbance by wildfire combined with climatic warming may enhance the effects of either driver alone (Jafarov et al. 2013). Where warming causes permafrost temperatures to rise but remain slightly below freezing, permafrost that formed under colder climates in the past may persist as “ecosystem-protected” (Shur and Jorgenson 2007, p. 10) but will be unable to re-form after any kind of disturbance.

Rapid thaw of permafrost occurs where erosion by flowing water or slope failure removes the thawed material or where ponding of water reduces winter freeze-back. Elsewhere the rate of thaw slows as the newly thawed layer increases in thickness, and the long-term rate of thaw can be quite slow. In a detailed case study by Burn (1998), a fire in spruce forest near Whitehorse, Yukon Territory, Canada, in 1958 produced a sudden increase in MAGT at 50-cm depth from about −0.6°C to 2.3°C (based on a comparison with neighboring unburned areas). This resulted in the degradation of permafrost from a depth of 1.4 m (the estimated bottom of the original active layer) to 3.75 m by 1996 (thirty-eight years post-fire), though the entire thawed layer was still freezing back entirely in 1996. Burn estimated that over a thousand years would be required to thaw the entire 14-m-thick layer of ice-rich permafrost at the site. More rapid permafrost degradation occurred in an agricultural field in Fairbanks, Alaska (Nicolsky and Romanovsky 2018; Permafrost Laboratory 2020; where a 12.5-m-thick talik zone had formed by 2016 after clearing of the field in the 1930s. Borehole temperatures suggest that the original permafrost temperature was about −0.2°C and clearing increased MAGTs below the depth of thermal offset to about +1°C. These two case studies illustrate the range in possible talik development that could result if our observed stepwise temperature shift persists.

Conclusions

Observations at a network of climate monitoring stations in the national parks of central and northern Alaska and at nearby long-term weather stations show that a stepwise increase in MAAT and MAGT occurred in 2014. MAATs for the period 2014 to 2019 were 1°C to 3.5°C higher than in the preceding thirty-year period, with the greatest increases in coastal northwestern parks. The associated increases in MAGT from the 2014 temperature shift were similar in magnitude to the increases in MAAT in areas with thin, wind-scoured snowpacks and high freezing n-factors (near 1.0). In permafrost environments with deeper snow and associated lower freezing n-factors (near 0.5), the magnitude of MAGT temperature increase was lower, roughly half the change in MAAT. Thus, the resulting increase in permafrost temperatures for the period 2014 to 2019 relative to the preceding thirty-year period probably ranged from 0.5°C to about 3.0°C, with the greatest changes in the western Arctic parks. Our study area has extensive regions of permafrost warmer than −3.0°C that are likely to have begun to thawing as a result of warming.

Whether conditions as warm as 2014 to 2019 will persist in the near future is unknown, though climate models suggest that conditions as warm as those observed in recent years could become the norm by the 2050s (Walsh et al. 2017). If MAGTs in lands with permafrost rise above freezing as we observed and stay there, an ever-thickening thawed surface layer will develop and a centuries-long process of transition to a very different landscape without permafrost will have begun. This transition would have widespread implications for natural resources and infrastructure in America’s northernmost national parks and neighboring lands.

Data availability statement

The data analyzed in this report are openly available in the National Park Service Data Store at:

https://irma.nps.gov/DataStore/Reference/Profile/2266884

https://irma.nps.gov/DataStore/Reference/Profile/2266885

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental material for this article can be accessed on the publisher’s website.

Additional information

Funding

This study was funded by the US Department of the Interior, National Park Service, Inventory and Monitoring Program through the Central Alaska and Arctic Inventory and Monitoring Networks.