International attention to climate change phenomena has grown in the last
decade; the active layer and permafrost are of great importance in
understanding processes and future trends due to their role in energy flux
regulation. The objective of this paper is to present active-layer
temperature data for one Circumpolar Active Layer Monitoring South hemisphere (CALM-S) site located on the Fildes Peninsula, King George
Island, maritime Antarctica over an 57-month period (2008–2012).
The monitoring site was installed during the summer of 2008 and consists of
thermistors (accuracy of
International attention to climate change phenomena has grown in the last decade, and intense modeling of climate scenarios was carried out by scientific investigations researching the sources and trends of these changes (Mora et al., 2013; IPCC, 2012; Moss et al., 2010). The cryosphere and its energy flux became the focus of many investigations, being recognized as a key component of climate for the understanding of actual (climate variability) and future trends (Ledley, 1985; Flanner et. al., 2011; van den Broeke et. al., 2008). The active layer and permafrost, part of the terrestrial cryosphere, are of great importance due to their role in energy flux regulation and sensitivity to climate change (Kane et al., 2001; Smith and Brown, 2009). Compared to other regions of the globe, our understanding of Antarctic permafrost is poor, especially in relation to its thermal state and evolution, physical properties, links to pedogenesis, hydrology, geomorphic dynamics, response to atmosphere and, thusly, to the variability and global climate change (Bockheim, 1995, Bockheim et al., 2008). An understanding of the distribution and properties of Antarctic permafrost is essential not only for the cryospheric sciences but also for the life sciences, since it will be a major effect ecosystem modification following climate-induced changes and variability (Vieira et. al., 2010). The scientific interest in King George Island has grown in the last few years due to the intensity of climate change effects such as permafrost degradation (Beyer et al., 1999).
The objective of this paper is to present active-layer temperature data for one CALM-S site located on the Fildes Peninsula, King George Island, maritime Antarctica over a 57-month period (2008–2012).
Location of Fildes Peninsula within Antarctica and the South Shetland Islands.
The archipelago of the South Shetland Islands, extending more than 400 km from southwest to northeast, lies near the northern tip of the Antarctic Peninsula. The archipelago is separated from the Antarctic Peninsula by the Bransfield Strait and from South America by Drake Passage. King George Island is the largest in the archipelago and Fildes Peninsula is at its southwestern end (Fig. 1). This peninsula is about 10 km long and 2–4 km wide. It is washed on three sides by the waters of Drake Passage, Fildes Strait and Maxwell Bay. Most of the Fildes Peninsula is free of ice; glaciers cover only the extreme northeastern part (Simonov, 1975). Fildes Peninsula has a gentle topography dominated by a wide central plain, and several other plains CE5 at different altitudes; it consists mostly of lava with small outcrops of tuffs, volcanic sandstones and agglomerates (Smellie et al., 1984).
The region experiences a subantarctic maritime climate according to the
Köppen climate classification. The South Shetland Islands have an ET (tundra climate)
climate, South Hemispheric Polar Oceanic (Köppen, 1936), characterized by
mean annual air temperatures of
Soils in Fildes Peninsula are well developed for Antarctic standards with large areas of soils with Leptic/Lithic and Skeletic characters. Arenosols/Entisols and Cryosols/Gelisols (frequently turbated) are the most important soil classes, while Leptosols/Entisols, Gleysols/Aquents and Cambisols/Inceptisols also occur with gelic regime. Faunal activity plays a marked role in soil genesis on Fildes Peninsula and is commonly found in the north shore of the peninsula. The dominant soils are Cryosols, related to cryoturbation and active-layer processes and developed on wide areas occupied by stone fields, patterned grounds, moraines and slopes on middle and upper platforms and hills in the northern and southern areas (Michel et al., 2014).
The active-layer monitoring site (
General characteristics of the monitored site.
Soil texture of the studied profile.
The characteristics of the monitored site and the exact depth of the probes are presented in Tables 1 and 2. The depth of the probes was established in respect to pedological differentiation of horizons. Sites affected by cryoturbation frequently form AB horizons; the intense migration of the organic material in depth associated with the limited carbon input (mosses and lichens) makes the formation of A horizons difficult. Air temperature was obtained from the Marsh automatic meteorological station located at the Teniente Rodolfo Marsh Martin Airport.
We calculated the thawing days (days in which all hourly soil temperature
measurements are positive and at least one reading is warmer than
The apparent thermal diffusivity (ATD) was estimated for different seasons
from the equation of McGaw et al. (1978):
Nelson et al. (1985), Outcalt and Hinkel (1989) and Hinkel et al. (1990, 2001) have used this estimative to assess the resistance to energy flux in the profile. Hourly estimations were made for intermediate depths of both profiles, and mean values were calculated and plotted for each day.
A series of statistical analyses were performed to describe the soil temperature time series. The Box–Pierce test and augmented Dickey–Fuller tests where performed to confirm the stationarity and independent distribution of the time series (data not shown). The histogram (frequency distribution of temperature readings) and first difference (the difference between consecutive hourly temperature readings) were plotted, and the time series was decomposed into its seasonal and trend components by locally weighted smoothing (Loess) using a window of 25. A linear fit was applied to the time series in order to identify a global trend. Finally, a series of autoregressive integrated moving average (ARIMA) models were tested until satisfactory results were found. In order to define the best fit for the data, we first examined the ACF (Auto correlation function) and PACF (Partial Auto correlation function) plots to determine the appropriate model and then tested a series of combinations; standardized residuals, autocorrelation plot, Ljung–Box statistics and the Akaike information criterion (AIC) (Burnham and Anderson, 2002) were the major parameters used to judge the suitability of the model. Considering the seasonal nature of the data, a seasonal component was added after the best models were selected.
For a time series of data
When the term
Daily temperatures records for air
Thaw days, freeze days, isothermal days and freeze–thaw days for F1, F2, F3 and F4.
Interannual variability of the active-layer temperature shows parallel
behavior despite contrasts between different years; daily temperatures
records are presented in Fig. 2. The temperature at 10.5 cm reaches a
maximum daily average (4.1
ALT was estimated for every season and the results are summarized in Table 1 (2008 and 2012 being incomplete). Maximum ALT ranged between 89 and 106 cm with a mean of 101 cm; the totality of the active layer froze during winter every year over the studied period. During 2010 a curious phenomenon occurred: temperatures at F3 remained negative the whole year, reaching values above zero at the bottom-most layer (F4); that year ATL was estimated slightly below the deepest probe, probably due to the accumulation of water over the permafrost table.
The grouping of days into freezing, thawing, isothermal and freeze–thaw offers
a quick parameter for comparing different periods (Fig. 3). Most of the thawing
days occurred between January and March, more frequently for the upper-most
layers; only 8 days were recorded for F4 in March 2009. Freeze days were
concentrated between April and November and are more evenly distributed in
depth although F1 and F2 were frozen for longer periods in 2008, 2011 and
2012. Isothermal and freeze–thaw days occurred between December and May.
Isothermal periods were long for F3 and F4, which shows a strong zero curtain
effect (buffered temperature change due to freezing and thawing of soil
moisture). Freeze–thaw days were more common in F1; the site experienced
frequent freeze–thaw cycles in the surface, especially during summer. Great
temperature changes in depth, especially on the
The cumulative sum of the daily averages reached a maximum in 2009 and a
minimum in 2011 for all layers; values varied greatly over the years, with
December and January being the hottest months and June always the coldest
(Fig. 4). Over the 57 months, the TDD were 902
Thaw degree days and freeze degree days for F1, F2, F3 and F4.
Histograms and first differences for F1, F2, F3 and F4.
Loess time series decomposition for F1, F2, F3 and F4.
Average season temperatures (
ATD was calculated for F3 and F4 by considering hourly readings and then
averaging
for the climatic seasons; results are shown in Table 3. Thermal diffusivity
can experience considerable seasonal variations when thawing and freezing
processes occur (Hinkel, 1997). The ability of the profile to transmit
energy varies during the year due mostly to water content: moisture, on the one
hand,
enhances energy flux through percolation but, on the other hand, absorbs and
emits energy on freezing and thawing processes. Average ATD for the 57 months
was 4.2
Linear regression for F1, F2, F3 and F4.
ARIMA order, coefficients sigma squared and log of likelihood for F1, F2, F3, and F4.
Histograms for the studied layers show a predominance of temperatures around
0. F3 expresses the largest frequency in this region while the greatest
amplitude is found for F1 (15
Although the studied period was limited to 57 months and any forecast is not
suitable, an ARIMA model was tested at each layer in order to evaluate which
model better fit the data. Autocorrelation and partial autocorrelation plots
were estimated as a guide for the selection of the ARIMA parameters. The
plots (Fig. 8) show strong correlation with data from the previous month.
This correlation is smaller for the subsequent period and, after the fourth
month, starts to show increasing negative correlation, reaching a maximum
at the seventh month. After 3 years, significant correlation can still be
seen. Partial autocorrelations express a “cleaner” picture of serial
dependencies for individual lags. The plot shows a strong correlation for the
previous month, which is negative for the subsequent period and not
significant after the fifth lag. The best ARIMA model that fits the monthly
averages includes a seasonal component;
Monitoring of the CALM-S site on Fildes Peninsula has provided data on the
thermal dynamics and frost conditions on a densely occupied maritime
Antarctic site. The effects of weather on the thermal regime of the active
layer have been identified, providing insights into the influence of
climate change on permafrost and leading to the following
conclusions:
The active-layer thermal regime in the studied period was typical of periglacial environments, with extreme variation in
surface temperature
during summer resulting in frequent freeze and thaw cycles. The ALT over the studied period shows a degree of variability related to different annual weather conditions, reaching a maximum of 117.5 cm in 2009. The calculated ATD suggests strong influence of water content on the soil thermal regime. The ARIMA model can describe the data adequately and is an important tool for more conclusive analysis and predictions when longer data sets are available. Despite the variability when comparing temperature readings and ACT over the studied period, no trend can be identified.
Autocorrelation and partial autocorrelation plots for F1, F2, F3 and F4.
Standardized residuals, autocorrelation plot and Ljung–Box statistics for ARIMA models fitted to F1, F2, F3 and F4.
This study was supported technically and logistically by the Brazilian Navy, MMA, UFV and FEAM-MG; grants were received from CNPq. We also thank the Chilean Antarctic Institute (INACH) for technical and logistical support during field activities. This is a contribution of the Terrantar laboratory, part of the Brazilian National Institute of Cryospheric Science and Technology. Edited by: A. Navas