ArticlePDF Available

Effects of Stratified Active Layers on the High-Altitude Permafrost Warming: A Case Study on the Qinghai-Tibet Plateau

Authors:
  • Institute of Soil Science Chinese Academy of Sciences

Abstract and Figures

Seasonally variable thermal conductivity in active layers is one important factor that controls the thermal state of permafrost. The common assumption is that this conductivity is considerably smaller in the thawed than in the frozen state, λt/λf < 1. Using a 9-year dataset from the Qinghai-Tibet Plateau (QTP) in conjunction with the GEOtop model, we demonstrate that the ratio λt/λf may approach or even exceed 1. This can happen in thick active layers, with thicknesses larger than some 1.5 m, with strong seasonal liquid water content changes. It is additionally furthered by typical soil architectures that may lead to a dry inter-layer. These findings suggest that, given the increase in air temperature and precipitation, soil hydraulic properties, particularly soil architecture in those thick active layers must be taken into account properly in permafrost models.
Content may be subject to copyright.
1
Effects of Stratified Active Layers on the High-Altitude Permafrost
Warming: A Case Study on the Qinghai-Tibet Plateau
X. Pan1, Y. Li1, Q. Yu2, X. Shi3, D. Yang4, K. Roth5
1Global Institute for Water Security, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK S7N 3H5,
Canada
5
2Laboratory of Frozen Soils Engineering, Cold and Arid Regions Environmental and Engineering Research Institute,
Chinese Academy of Sciences, Donggang West Road 320, Lanzhou, 730000, China
3CSIRO Land and Water, Christian Laboratory, Clunies Ross Street, Black Mountain, Canberra, Australian Capital
Territory, 2601, Australia
4National Hydrology Research Centre, Environment Canada, 11 Innovation Boulevard, Saskatoon, SK S7N 3H5, Canada
10
5Institute of Environmental Physics, Heidelberg University, Im Neuenheimer Feld 229, Heidelberg, 69120, Germany
Correspondence to: Y. Li (yanping.li@usask.ca)
Abstract. Seasonally variable thermal conductivity in active layers is one important factor that controls the thermal state of
permafrost. The common assumption is that this conductivity is considerably smaller in the thawed than in the frozen state,
λtf < 1. Using a 9-year dataset from the Qinghai-Tibet Plateau (QTP) in conjunction with the GEOtop model, we
15
demonstrate that the ratio λtf may approach or even exceed 1. This can happen in thick active layers, with thicknesses
larger than some 1.5 m, with strong seasonal liquid water content changes. It is additionally furthered by typical soil
architectures that may lead to a dry inter-layer. These findings suggest that, given the increase in air temperature and
precipitation, soil hydraulic properties, particularly soil architecture in those thick active layers must be taken into account
properly in permafrost models.
20
1 Introduction
Along with climate warming, permafrost warming has been widely observed in the Arctic and sub-Arctic as well as in mid-
latitude high mountains like the Alps and the Tibetan Plateau (Romanovsky et al., 2013; Harris et al., 2009; Cheng and Wu,
2007). The mean annual ground temperature (MAGT) at a depth of zero annual amplitude is often used to indicate
permafrost warming (e.g., Wu et al., 2012). The warming rate is controlled by a variety of factors such as weather regimes,
25
geography/geology and ecosystems. Generally, the cold permafrost has a higher warming rate than the warm permafrost (Wu
et al., 2012). However, permafrost temperature can differ greatly in the same region due to local factors like topography, soil
properties and vegetation. Their responses to climate change is thus also expected to differ. For instance, the permafrost
along the Qinghai-Xizang (Tibet) Railway experienced a mean warming rate of 0.02˚C yr-1 at a depth of 6.0 m over the
period from 2006 to 2010, and the highest warming rate even reached 0.08˚C yr-1 in the Fenghuo Mts. area (Wu et al., 2012).
30
Given the same change in climate variables, these local factors still cause the underlying permafrost to develop differently.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
2
For instance, one recent study in the central QTP shows that permafrost at 10 sites experienced highly differing warming
rates over the period of 2002-2014 (Wu et al., 2015). Even though there was no extraordinary increase in air temperature
(0.02˚C yr-1), the permafrost temperature at 10-m depth increased at an average rate of 0.01˚C yr-1. Wu et al. (2015)
suggested that this was due to the increasing rainfall and the asymmetrical seasonal changes in subsurface soil temperatures.
Reflecting the high warming rate of the permafrost on the warming of the atmosphere, some 0.02˚C yr-1, we expect a
5
dominating role of subsurface processes in the active layer that amplify the climate warming input.
As a buffer layer, the active layer regulates the energy transfer between atmosphere and permafrost in addition to
vegetation and snow cover. In this study, we focus on the high-altitude permafrost on the QTP, which is characterized by a
thick unsaturated active layer and sparsely vegetated surface. In contrast to the commonly thin active layers in the Arctic, the
active layers on the QTP are usually over 1.5 m thick. A further important difference between the permafrost in the Arctic
10
and on the QTP is that the latter has a strong diurnal forcing with some 180 freeze-thaw days (Yang et al., 2007).
Furthermore, with precipitation concentrated in the summer season as rainfall, the subsurface soil hydraulic properties play a
key role in ground heat transfer, in addition to the soil thermal properties. In contrast to the well-studied permafrost in the
Arctic, the applicability of the analytic models of the climate-permafrost relationship basing on the seasonally variable
thermal conductivity might be challenging. For instance, TTOP (mean annual temperature at the top of the permafrost table)
15
and related concept of thermal offset, namely the TTOP minus the MAGST (mean annual ground surface temperature)
(Smith and Riseborough, 1996; Smith and Riseborough, 2002) are strongly influenced by the seasonally variable thermal
conductivity. Normally, a wet active layer has a larger thermal offset than a dry active layer, which has small or even
vanishing thermal offset (e.g., Romanovsky and Osterkamp, 1995; Hasler et al., 2011). However, a reversed thermal offset,
namely TTOP > MAGST, has been reported by Lin et al. (2015) on the QTP. To evaluate the applicability of this concept,
20
further exploration of the hydraulic and thermal mechanisms in the active layers on the QTP is highly demanded. This might
facilitate us to understand the impact of the active layer on the permafrost warming.
In this study, we use observations over a nine-year period and numerical simulations to investigate a recent permafrost
warming at a site in a warm permafrost region on the QTP and to demonstrate the role of a typical stratified active layer in
permafrost warming. Our goals are (1) to diagnose the quick permafrost warming at the study site, (2) to reveal the unique
25
phenomenon of the reversed seasonally variable thermal conductivity in the active layer that challenges the application of the
analytic models by comparing with observations and physically based modeling, and (3) to emphasize the importance of
incorporating structural soil hydraulic properties in permafrost projections given a rain-dominated weather pattern on the
QTP.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
3
2 Material and methods
2.1 Site descriptions
The Chumaer site is located on a high plain of the Chumaer River catchment in the northeastern QTP with an average
altitude of over 4450 m (Fig. 1). In the catchment area, the land surface is covered by bare soil or sparse vegetation.
Measurements at the study site comprise a monitoring station and several boreholes, with discontinuous ground temperature
5
measurements since 2006 (Pan et al., 2014). The monitoring station has been complemented by soil-weather measurements
since 2006. The weather data from 2006 to 2014 show an average air temperature of about -16.0˚C in January and 7.2˚C in
July, and precipitation is dominated by summer monsoon from June to September, which brings about 350 mm precipitation
annually, falling mostly as rainfall. Irregular thin snow cover occurs in late spring or early winter, lasting usually just a few
weeks. The stratigraphy includes a fine top soil (30 cm) and a middle layer of alluvial sandy and gravelly sediment up to 3 m
10
that lies over deeply weathered mudstone. The borehole data indicate that the permafrost has a thickness about 25 m, and the
temperature at a depth of 10 m is less than 1.0˚C. The active-layer thickness is around 2.5 m.
2.2 Surface-subsurface monitoring scheme Subsection
The surface-subsurface interaction has been investigated since 2006. Regular meteorological variables including air
temperature, precipitation, relative humility, wind speed and direction and net radiation were monitored at an automatic
15
weather station. Subsurface hydraulic and thermal dynamics within the active layer were monitored by measuring soil
temperature and soil water content at a variety of depths (soil temperature: 0.05, 0.10, 0.15, 0.20, 0.30, 0.50, 0.70, 0.90, 1.10,
1.30, 1.50, 1.70, 1.92, 2.08, 2.18, 2.30, 2.50, 2.70, 3.00, 3.30, 3.60 m; Soil water content: 0.10, 0.20, 0.40, 0.65, 0.89, 1.19,
1.54, 1.92, 2.10 m). They were recorded with a time interval of 60 minutes. Soil temperature was measured with thermistors,
which provides an accuracy of 0.05˚C. Liquid soil water content was measured with CS616 sensors (Campbell Scientific
20
Ltd.), and the total water content in frozen soils was deduced from the value measured just before freezing. Here it assumes
negligible soil water redistribution during freezing due to the coarse soils. Thereby, its accuracy is around ±5% (Pan, 2011).
More detailed technical description of the instrumentation can be found in Pan (2011). In addition, permafrost temperature
was investigated with two boreholes. They were about 30 m away in a flat area. The shallow borehole is 15 m in depth and
the deep one is 60 m that penetrates through the permafrost.
25
2.3 GEOtop model
We use the GEOtop (version: 1.45) to simulate fluxes of moisture and energy between the atmosphere, surface and soil, and
the soil freezing and thawing. GEOtop is a process-based energy- and mass-balance model (Rigon et al, 2006; Endrizzi,
2009), which has a number of advantages for a wide range of permafrost applications. It allows to simulate hydrological
fluxes from the energy balance in the complex terrain with snow-covered and snow-free regimes (e.g., Simoni et al., 2008;
30
Endrizzi and Marsh, 2010). For the subsurface, soil temperature and moisture dynamics are simulated using a robust and
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
4
energy-conserving model of freezing in variably-saturated soil (Dall’Amico et al., 2011a). It invokes a relation between the
soil freezing characteristic and the soil water characteristic and assumes a rigid. The model’s versatility allows to investigate
the responses of permafrost degradation on the QTP, where permafrost is characterized as a thick and stratified active layer
with pronounced hydraulic dynamics due to the rainfall-evaporation dominated land surface fluxes.
Various applications of GEOtop can be found in the literature (Dall’Amico et al., 2011b and Endrizzi et al., 2014). In this
5
study, we apply the model for the single site as a one-dimensional (1-D) simulation, where the spatial factors, e.g.,
topography and snow, are not important. However, the detailed representation of the subsurface is essential for our research
questions. They are introduced in the following subsections.
2.4 Model set-up
Considering the features of land surface energy exchange and subsurface hydraulic and thermal dynamics, a 1-D
10
conceptualized model for the study site is setup with GEOtop. Some assumptions for this model are given as: (1) no lateral
flows exist like surface runoff and subsurface groundwater flow; (2) surface features like vegetation and soils, and associated
parameterization are constant in the long term simulations.
The simulated stratigraphic profile constitutes three layers according to field drilling. They are sandy loam (0-0.3 m), sand
(0.3 - 3.0 m) and gradually weathered bedrock (3.0 - 30 m). The profile domain was generated with a high resolution in size
15
of 10 cm for the shallow layer (0-3.0 m) and was gradually reduced to 0.5 m and 1.0 m for the lower layer. There are 63
layers in total. In order solely to diagnose the effect of stratified active layer on permafrost degradation, model simulations
were driven by the same atmospheric forcing.
2.4.1 Input parameters
The required climate forcing for the GEOtop models includes precipitation (snow and rain), air temperature, wind speed,
20
relative humidity, and incoming short and longwave radiation. The bottom boundary conditions for energy and water balance
are set as follows. Considering the weak impact of the bottom mudstone on surface water flux, the bottom drainage rate
through the mudstone was simply set as zero. Whereas, the bottom thermal condition is essential to the permafrost warming
rate, as well as surface energy fluxes. The geothermal flux at the depth of 30 m was determined from the measured ground
temperature gradient (0.07˚C m-1) and calculated thermal conductivity of the mudstone. Given a thermal conductivity of soil
25
matrix 2.0 W m-1 K-1, and a porosity of 0.2 for the mudstone, the geothermal flux at the bottom boundary was set as 0.14 W
m-2. This high geothermal flux is consistent with other observed values in the same Kunlun Mountains area (Wu et al.,
2010).
Taking the analysis of the sensitivities and uncertainties of the GEOtop by Gubler et al. (2013) into account, we set the
following surface and subsurface parameters: The vegetation coverage for the sparsely-vegetated ground surface is set to 0.3
30
and the surface roughness length, which is required for the calculation of the turbulent fluxes, to 120 mm. The latter was
chosen in agreement with studies on the QTP (Ishikawa et al., 1999; Yang et al., 2008; Ma et al., 2009). The surface
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
5
parameters are assumed to be constant during the simulations. The subsurface hydraulic and thermal parameters for the
actual soil profile are listed in Table 1. The required van Genuchten parameters were obtained from soil texture information
using the neural network routine (Schaap and Bouten, 1996). Soil textures for the two sub-layers (I and II) are available from
König (2008). No data are available for the bottom layer. Thus, hydraulic properties for the third layer are assumed to be the
same as the typical clay (Domenico and Schwartz, 1990). Both soil thermal conductivity and heat capacity are functions of
5
the four volumetric components: water, ice, air and soil matrix (Dall’Amico et al., 2011a). The bulk thermal conductivity
b) was estimated with the following equation proposed by Cosenza at al. (2003)
𝜆𝑏= [(1 − 𝛷)𝜆𝑚+ 𝜃𝑤𝜆𝑤+ 𝜃𝑖𝜆𝑖+ 𝜃𝑎𝜆𝑎]2, (1)
where, λm, λw, λi and λa are thermal conductivities of soil matrix, water, ice and air, respectively; Φ is soil porosity; θw, θi,
and θa are the volume fractions of water, ice and air, respectively. Thermal properties of the soil matrix were set as common
10
values for different soil types (Farouki, 1986).
2.4.2 Simulation protocol
The investigated stratified active layer is commonly found on the QTP along with a “dry inter-layer” between top soil and
bottom soil. While the soil water distribution is mainly related to the unique soil architecture (A1) that a fine-grained layer
without (or only thin) surface organic horizons overlaying the coarse immature soils, which is characterized as low content
15
of fine-grained materials like silt and clay (Huang et al., 2006). The role of soil architecture in the stratified active layer in
regulating hydraulic and thermal dynamics in active layer, as well as long term permafrost change is investigated with
following numerical simulations. For comparison, another two reference active layer with soil architectures A2 and A3 are
also used. The references A2 and A3 consist of a single layer for the shallow soils (0-3.0 m) but with fine (I) and coarse (II)
minerals, respectively. Since the focus of this study is to investigate the effect of soil architecture on permafrost warming,
20
the thermal conductivity of soil matrix is set as the same value when comparing different soil architectures. Apart from the
actual case of soil matrix with high thermal conductivity (5.0 W m-1 K-1), another one with low thermal conductivity (2.5 W
m-1 K-1) was also investigated. Thus, six simulations with corresponding model settings in Table 2 were projected with a
long period from 1980 to 2100. Besides, similar simulations were also conducted for comparison on hydraulic and thermal
pattern in 2008 by replacing the meteorological forcing with the observed air temperature and precipitation.
25
The atmospheric forcing used in this study is produced from the fifth Coupled Model Inter-comparison Project database of
GCM output (CMIP5). The projected climate scenario of the Representative Concentration Pathway 8.5 (RCP8.5) was
dynamically downscaled using the CanESM2/CGCM4 Model (Verseghy, 1991), which corresponds to a usual warming
scenario with 8.5 W m-2 forcing by 2100. Figure 2 provides the projected changes in mean annual air temperature (MAAT)
and annual total precipitation from 1900 to 2100. Generally, a pronounced increase in air temperature started in 1980s and
30
there is also a noticeable change in precipitation. These features are generally consistent with the regional trend of air
temperature and precipitation obtained from local observations (Guo and Wang, 2013, Hu et al., 2014). Considering a quick
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
6
warming in permafrost during the past few decades (Cheng and Wu, 2007), a reasonable hypothesis is to presume the
climate as steady for the 80 years before 1980s. Accordingly, we assume that the thin permafrost around 1980s was in
pseudo equilibrium. Therefore, the model was spun up with the atmospheric forcing by using a repeated 10-year period from
1970 to 1979 that keep the mean annual soil temperature change less than 0.01˚C in all soil layers. The initial condition for
the spin-up was a constant ground temperature of -0.5˚C and a water pressure in static equilibrium with a water table at 1.0
5
m below ground.
3 Results and Discussion
3.1 Relationship between air temperature and near-surface soil temperature
Figure 3 shows the relationship between daily mean air temperature and near-surface soil temperature (5 cm below ground
surface) over the period of 2006-2014. A few sporadic outliers are related to abrupt cold weather, e.g., summer/autumn
10
freezing. The well fitted linear fit indicates that the freeze-thaw process does not exert significant impact on heat transfer,
which means a small change in seasonal thermal properties. This might be attributed to the seasonal liquid water change. In
addition, the average temperature difference was about 5.0˚C, whereas the mean annual air temperature was even higher than
-5.0˚C. Thus, the mean annual near-surface temperature should fluctuate around 0˚C.
3.2 Surface and thermal offsets
15
Figure 4 shows the thermal profiles measured over the period of 2007-2013. It covers the mean annual temperature data from
1.5 m above ground surface to 2.18 m in subsurface. The interaction between the lower atmosphere and permafrost can be
characterized with surface and thermal offsets (Smith and Riseborough, 2002). Limited by available measurements, surface
offsets were approximately estimated from the difference between mean annual near-surface (10 cm) temperature (MAGST)
and MAAT, and the thermal offsets were calculated from the difference between mean annual temperature (MAT) close to
20
the permafrost table (2.18 m) and MAGT. Here we should mention that these thermal offsets were overestimated slightly due
to the used bottom MAT, which is not exactly at the permafrost table. Calculations show that the surface offsets in 2008,
2009 and 2013 were 4.40˚C, 3.78˚C and 4.30˚C, respectively. These values indicate a weak coupling between the lower
atmosphere and ground surface. While the thermal offsets changed from positive values, i.e. 0.47˚C (2007) and 0.33˚C
(2008) to negative ones, -0.18˚C (2009) and -0.15˚C (2013). Surprisingly, the positive thermal offsets occurred in colder
25
weather conditions. Similar phenomenon has been found in a nearby region by Lin et al. (2015). This result seems to conflict
that permafrost commonly exists a negative thermal offset (Smith and Riseborough, 2002). This might be related to the
unique hydraulic and thermal dynamics in the active layer, which can cause a reversed seasonally variable thermal
conductivity.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
7
3.3 Pattern of hydraulic and thermal regimes in the stratified active layer
Figure 5 shows a typical annual evolution of the active layer to the weather condition (MAAT: -4.40˚C and total rain: 316
mm) in 2008. Figure 5a reflects a typical climate regime with dominant rainfall in the rainy season from June to September
on the northeastern QTP. Note that the precipitation measurements mainly include rainfall, and light snowfall was detected
by an acoustic sensor to measure distance change. Figure 5b shows the active layer during an annual freeze-thaw cycle. The
5
distribution of liquid soil water indicates that a large amount of suprapermafrost groundwater existed during the period from
late May to the end of January with a maximum thickness of this saturated layer in excess of 1 m. The groundwater table
roughly fluctuated around 1.0 m below the ground surface, and was mainly recharged by rainfall infiltration during the
thawing period (late April to late October).
A noteworthy pattern of the water content distribution is the dry inter-layer around 0.7 m. This layer was occasionally
10
wetted by rain infiltration during the rainy reason, but else was rather dry, as was also the case during the freezing period.
This situation results from a fine-textured and less permeable layer overlying a coarse-textured one. We anticipate from the
seasonal contrast in liquid soil water (reduction from the thawing period to the freezing period) in this dry layer will modify
the seasonal thermal properties of the active layer.
3.4 Effect of seasonal liquid water content reduction on the ratio of thermal conductivity
15
For active layers with mineral soils, the ratio of thermal conductivity in the thawed and frozen states tf) is assumed to be
less than or equal to one (Riseborough and Smith, 1998). However, the factor of seasonal liquid water content reduction is
not negligible at our study site. Figure 6a compares the change in total liquid water content between summer and winter in
2008. The maximum seasonal liquid water content reduction occurred around 0.7 m in the active layer. Assuming a thermal
conductivity of 5.0 W m-1 K-1 for the sand with high content of quartz, the thermal conductivities at different depths were
20
calculated with Equation (1) in Fig. 6b. There are two locations with λt > λf at 0.65 and 0.89 m depth. Significant reduction
of the liquid water content also occurred at other depths, all accompanied by corresponding reductions of λf.
In order to exceed the ratio of 1, the seasonal liquid water content has to fall below a certain threshold, which depends on
soil thermal conductivity and water content in thawed state. For instance, the soils with high thermal conductivity of soil
matrix will need larger liquid water content reduction than that of the soils with small thermal conductivity of soil matrix.
25
However, given the same amount of liquid water content reduction, the soils with a low soil water content in thawed state
will be prone to reach a ratio over 1. Generally, the soil water content condition in thawed state depends on soil type and soil
structure. Considering the unique precipitation characteristics on the QTP, seasonal liquid water content reduction is
common in this kind of permafrost regions. Unfortunately, the role of soil architecture in thermal conductivity
parameterization is rarely addressed to date.
30
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
8
3.5 Comparison of observed and simulated permafrost warming rates
In this section, the model is validated by comparing with the observations. Figure 7 compares the observed and CMIP5-
projected air temperature over the period of 2006-2014. Generally, the patterns were very similar in Fig. 7a, but there was a
daily averaged up-shift of 0.96˚C of the projected values according to the linear regression of all available values (Fig. 7b).
Since there were several data gaps in the observed air temperature, it is difficult to derive a trend of the measured MAAT
5
change in Fig. 7c. However, a linear-fitted warming rate of 0.07˚C yr-1 of the projected MAAT from 2006 to 2100 can be
derived from Fig. 2a, it is higher than that of the reported warming trend (0.02˚C yr-1) between 2003 and 2012 for a reference
station (Beiluhe) in the same region (Wu et al., 2015).
Figure 8 compares the observed and simulated permafrost temperature changes over the period from 2006 to 2014. The
measured temperature Tobs,06 was taken from a shallow borehole on August 30, 2006 and Tobs,14 was from a nearby deep
10
borehole on February 22, 2014. Here we assume that permafrost ground temperature distribution was roughly uniform within
a small area (30 m×30 m) due to similar surface and subsurface properties. Thus, permafrost warming can be deduced from
ground temperature change from the boreholes. Considering the small annual fluctuation of ground temperature change at
the depth of 10 m, two corresponding measurements irregularly conducted once a year in 2006 and 2014 can roughly provide
a permafrost warming rate of 0.05±0.1˚C yr-1, concerning the uncertainty of annual fluctuation as simulated ones shown in
15
Fig. 8. By assuming a constant warming rate in permafrost temperature at the depth of about 10 m, a value of 0.02˚C yr-1 was
calculated from Tsim,06 and Tsim,14. This is just about half of the observed one. Compared to the projected warming rate of air
temperature at the same period (0.07˚C yr-1), the simulated warming rate is underestimated.
The evident discrepancy between observed and simulated permafrost warming rate is mainly attributed to the following
three factors. First, snow process is not represented reasonably in the model due to the limitation of the meteorological
20
forcing. Permafrost is extremely sensitive to snow cover, which has a much higher albedo (> 0.9) than regular ground
surface (0.1-0.4). Field observations show that snow cover only lasts a few weeks in pre/post winter, and the missing snow
cover is mainly caused by evaporation and sublimation during the diurnal thawing. However, the projected meteorological
data are in daily resolution and the precipitation is also not well accurate in general. Second, the atmospheric forcing was
down-scaled from large scale climate modeling, and it differs from site observations. Particularly, the observed increasing
25
rainfall but less snowfall is not well predicted in the projected meteorological forcing. Third, some simplifications like
constant surface albedo and flat ground without lateral flow, as well as empirical parameters might also influence the
simulations. Nevertheless, the model can reasonably mimic hydraulic and thermal regime and current permafrost thermal
status, and it can help us to investigate the effect of the stratified active layer on permafrost warming.
3.6 Role of the stratified thick active layer in permafrost warming
30
In this section, the effect of the stratified active layer on permafrost warming is validated with modeling, and subsurface
controlling factors in the active layer including soil architecture and thermal conductivity of soil matrix are examined with
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
9
thermal offset and permafrost temperature. Subsection 3.6.1 demonstrates the simulated hydraulic and thermal pattern in the
active layers, and subsections 3.6.2 and 3.6.3 present the evolution of the unique thermal offset and permafrost temperature,
respectively.
3.6.1 Simulated hydraulic and thermal pattern in the active layers
Given a typical observed meteorological forcing in 2008, the simulated hydraulic and thermal patterns of the active layers
5
with different soil architectures (A1, A2 and A3) in 2008 are shown in Fig. 9. Generally, the thermal conductivity of the soil
matrix dominates the active layer thickness, regardless of the soil architecture. However, contrast soil hydraulic patterns are
controlled by hydraulic properties, mainly soil architectures here, and also influence the active layer thickness. Notice that
the order of the maximum thawing depth is A3 > A1 > A2, which is similar in both columns. In addition, the one with
realistic soil architecture A3 in Fig. 9c presents a similar hydraulic and thermal pattern as the one observed (Fig. 9cʹ), an d
10
this shows also that the model can reasonably capture the hydraulic and thermal dynamics in the active layer. However, the
simulated downward thawing in early summer is slower than the observed one. This is mainly related to the underestimated
permafrost temperature in the modeling.
3.6.2 Evolution of the unique thermal offset
Based on the above different hydraulic and thermal patterns we investigated the impact of soil architecture on the warming
15
of the underlying permafrost by using the thermal offset. Figure 10 shows the evolution of the thermal offset with the change
of MAAT, the latter the result of climate warming. The thermal offset is calculated as the annual temperature difference (Ttop
- T0.1m) between the top of the permafrost table and the near-surface (0.1 m), and disappears till talik present, which
disconnects the permafrost from the seasonal frost layer. Generally, all the thermal offsets decreases with increasing MAAT.
However, the thermal offsets of A3 in Fig. 10a and 10b are both positive at the beginning of the simulation, when the
20
permafrost is close to thermal equilibrium. Whereas, the thermal offset of A2 is always negative and the thermal offset of A1
is in between A2 and A3.
Apparently, the active layer A3 here does not conform to the expectation of a negative thermal offset, i.e., TTOP <
MAGST due to λf > λt (Smith and Riseborough, 2002). The positive values close to the initial equilibrium state in Fig. 10
indicate that the permafrost at the simulated condition does not fit the concept of thermal offset. The schematic mean annual
25
ground temperature profile is more close to the one described by Brown (1970) other than the one suggested by Smith and
Riseborough (2002) in Fig. 11. The reversed thermal offset at equilibrium state was mainly led by the high ratio of λtf
around 1 via seasonal liquid water content reduction. Whereas, the subsequent “normal” thermal offsets were not caused by
λf > λt but by non-equilibrium effects. This is corroborated by the observed thermal offsets (Fig. 4) that positive values
occurred in 2007 and 2008 and they decreased to negative in 2009 and 2013. Therefore, the concept of the normal offset is
30
not suitable for the studied case, and the plausible “normal” thermal offset might not necessarily be attributed to λ f > λt, but
to permafrost degradation.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
10
3.6.3 Subsurface factors controlling the permafrost warming rate
Apart from the change rate of climatic forcing, the evolution of thermal regime in active layers is also not negligible for
permafrost warming, and it is related to subsurface factors like thermal conductivity of soil matrix and soil hydraulic
properties. In particular, the thermal regime in the studied thick active layer with unique soil architecture strongly relies on
the hydraulic regime. Given three different soil architectures and two different thermal conductivities of soil matrix, the
5
differences of the permafrost warming are shown in Fig. 12. Generally, the permafrost with a higher thermal conductivity of
the matrix (left column of Fig. 12) degrades faster than that in the right column with a lower thermal conductivity, and the
influence of soil architecture in the left column is negligible. In contrast to, the role of soil architecture emerges in the right
column, and the stratified active layer (A3) leads to a faster permafrost warming rate. This is mainly attributed to the effect
of seasonal liquid water content change on seasonal variation of thermal conductivity. For the active layer with a high λm, the
10
ratio of seasonal thermal conductivity is always close to 1.0, and the impact of seasonal liquid water content reduction is
rather weak. However, for the active layer with a low λm, the ratio of seasonal thermal conductivity depends strongly on the
seasonal liquid water content reduction. Besides, soil architecture with low soil water content will have a high ratio of
seasonal thermal conductivity, given the same amount of seasonal liquid water content reduction.
4 Conclusions
15
In summary, this study presented an interesting case to show the effects of stratified active layers with a high ratio of
seasonal thermal conductivity, λtf ≥ 1.0, on permafrost warming on the QTP. Combining with intensive observations of soil
hydraulic and thermal dynamics in the active layer and weather over a period of almost 9 years, as well as corresponding
numerical simulations, key findings are listed in the following:
(1) An extraordinary permafrost warming rate (> 0.5˚C per decade) was found at the study site with sparsely vegetation and
20
annual precipitation 300-400 mm. Apart from the climate drivers and the unusual high geothermal flux from the bottom, a
high ratio of seasonal thermal conductivity in the stratified active layer is indispensable in regulating the interaction between
climate and permafrost.
(2) Observation and simulation suggest that the concept of the thermal offset proposed by Smith and Riseborough (2002) is
not suitable for the studied permafrost on the QTP. In contrast to the normal thermal offset caused by the seasonally variable
25
thermal conductivity, a reversed thermal offset at equilibrium state is formed due to the remarkable high ratio of seasonal
thermal conductivity, namely close 1.0 or even higher, given such a weather pattern and soil properties.
(3) Furthermore, the specific soil architecture plays a non-negligible role in forming a dry inter-layer while facilitating to
raise the ratio λtf, and resulting in a higher permafrost warming rate than the active layers with uniform soils.
Considering the importance of rainfall in the mechanism of the hydraulic and thermal dynamics in the active layers, there
30
is no doubt that permafrost warming would be influenced by the increasing precipitation in recent years and in future on the
QTP. Consequently, soil hydraulic properties, particularly soil architecture become more and more important for the thermal
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
11
conductivity parameterization in land surface and permafrost modeling. Particularly, the empirical permafrost models using a
ratio of seasonal thermal conductivity smaller than 1.0 might underestimate the effect of climate warming. However, this
study is mainly based on a specific site. More field investigations are required to reveal the regional difference in permafrost
degradation over the QTP.
5
Acknowledgements. We acknowledge Dr. Yanhui You for field data collection and Dr. Liang Chen for processing climate data. This
research was funded in part by the National Natural Science Foundation of China (Grant No. 41171059).
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
12
References
Brown, R.J.E.: Permafrost in Canada: its influence on northern development, Toronto: University of Toronto Press, 1970.
Cosenza, P., Guerin, R., and Tabbagh, A.: Relationship between thermal conductivity and water content of soils using
numerical modelling, Eur. J. Soil Sci., 54, 581587, 2003.
Dall’Amico, M., Endrizzi, S., Gruber, S., and Rigon, R.: A robust and energy-conserving model of freezing variably-
5
saturated soil, The Cryosphere, 5, 469484, doi:10.5194/tc-5-469-2011, 2011a.
Dall’Amico, M., Endrizzi, S., Gruber, S., and Rigon, R.: GEOtop Users Manual, Version 1.0, Technichal report, Mountain-
eering Srl, Siemensstr. 19 Bolzano, Italy, 2011b.
Domenico, P.A., and Schwartz, F.W.: Physical and Chemical Hydrogeology, John Wiley & Sons, New York, 824 p, 1990.
Endrizzi, S., and Marsh, P.: Observations and modeling of turbulent fluxes during melt at the shrub-tundra transition zone 1:
10
point scale variations, Hydrol. Res., 41, 471491, 2010.
Endrizzi, S., Quinton, W. L., and Marsh, P.: Modelling the spatial pattern of ground thaw in a small basin in the arctic
tundra, The Cryosphere Discuss., 5, 367400, doi:10.5194/tcd-5-367-2011, 2011.
Farouki, O. T.: Thermal properties of soils, Series on Rock and Soil Mechanics, 11, 1-136, 1986.
Guo, D., and Wang, H.: Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981-
15
2010, J. Geophys. Res. Atmos., 118, 52165230, doi:10.1002/jgrd.50457, 2013.
Gubler, S., Endrizzi, S., Gruber, S., and Purves, R. S.: Sensitivities and uncertainties of modeled ground temperatures in
mountain environments, Geosci. Model Dev., 6, 13191336, doi:10.5194/gmd-6-1319-2013, 2013.
Hasler, A., Gruber, S., and Haeberli, W.: Temperature variability and offset in steep alpine rock and ice faces, The
Cryosphere, 5, 977988, doi:10.5194/tc-5-977-2011, 2011.
20
Harris, C., Arenson, L.U., Christiansen, H.H., Etzelmüller, B., Frauenfelder, R., Gruber, S., Haeberli, W., Hauck, C., Hölzle,
M., Humlum, O., Isaksen, K., Kääb, A., Kern-Lütschg, M.A., Matsuoka, N., Murton, J.B., Nötzli, J., Phillips, M., Ross,
N., Seppälä, M., Springman, S.M., and Vonder Mühll, D.: Permafrost and climate in Europe: monitoring and modeling
thermal, geomorphological and geotechnical responses, Earth-Sci. Rev, 92, 117171, 2009.
Hu, Q., Jiang, D. and Fan, G.: Evaluation of CMIP5 Models over the Qinghai-Tibetan Plateau. Chinese J. Atmos Sci (in
25
Chinese), 38, 5, 924938, 2014.
Huang, B., Gong, Z., and Gu, G.: Elemental geochemistry of alto-cryic soils of Qinghai-Tibet Plateau in China - An example
from the unpopulated Kekexili region, Geochem. J., 40, 211218, 2006.
Ishikawa, H., Hayashi, T., and Tanaka, T.: Summary and the preliminary results of PBL observation, Proceedings of the 1st
International Workshop on GAME-Tibet. Xi'an, China, 11-13, January 1999, 6972, 1999.
30
König, K.: Thermal dynamics of permafrost sites on the Qinghai-Tibet Plateau, Diploma thesis, University of Heidelberg,
Heidelberg, 2008.
Li, S. and Cheng, G.: Map of Frozen Ground on Qinghai -Xizang Plateau, Lanzhou: Gansu Culture Press, 1996.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
13
Lin, Z., Burn, C. R., Niu, F., Luo, J., Liu, M. and Yin, G.: The thermal regime, including a reversed thermal offset, of arid
permafrost sites with variations in vegetation cover density, Wudaoliang basin, Qinghai-Tibet Plateau, Permafrost
Periglac., 26, 142159, doi:10.1002/ppp.1840, 2015.
Ma, Y., Menenti, M., Feddes, R. and Wang, J.: Analysis of the land surface heterogeneity and its impact on atmospheric
variables and the aerodynamic and thermodynamic roughness lengths, J. Geophys. Res., 113, D08113,
5
doi:10.1029/2007JD009124, 2008.
Ma, Y., Wang, Y., Wu, R., Hu, Z., Yang, K., Li, M., Ma, W., Zhong, L., Sun, F., Chen, X., Zhu, Z., Wang, S., and Ishikawa,
H.: Recent advances on the study of atmosphere-land interaction observations on the Tibetan Plateau, Hydrol. Earth Syst.
Sci., 13, 11031111, doi:10.5194/hess-13-1103-2009, 2009.
Pan, X.: Hydraulic and thermal dynamics at various permafrost sites on the Qinghai-Tibet Plateau, Ph. D thesis, University
10
of Heidelberg, Heidelberg, 2011.
Pan, X., You, Y., Roth, K., Guo, L., Wang, X., and Yu, Q.: Mapping permafrost features that influence the hydrological
processes of a thermokarst lake on the Qinghai-Tibet Plateau, China, Permafrost Periglac., 25, 6068,
doi:10.1002/ppp.1797, 2014.
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: a distributed hydrological model with coupled water and energy budgets,
15
J. Hydrometeorol., 7, 371388, 2006.
Riseborough, D.W., and Smith, M.W.: Exploring the limits of permafrost, Permafrost: Seventh International Conference,
Yellowknife, Canada, Proceedings, Lewkowicz AG, Allard M(eds), Nordicana 57, Quebec, 935942, 1998.
Romanovsky, V.E., and Osterkamp, T.E.: Interannual variations of the thermal regime of the active layer and near surface
permafrost in Northern Alaska, Permafrost Periglac., 6, 313335, doi:10.1002/ppp.3430060404, 1995.
20
Romanovsky, V.E.Smith, S.L., Christiansen, H.H., Shiklomanov, N.I., Streletskiy, D.A., Drozdov, D.S., Oberman, N.G.,
Kholodov, A.L., and Marchenko, S.S.: Permafrost (Arctic Report Card 2011), 2013,
http://www.arctic.noaa.gov/report11/.
Schaap, M.G., and Bouten, W.: Modeling water retention curves of sandy soils using neural networks, Water Resour. Res.,
32, 30333040, 1996.
25
Simoni, S., Zanotti, F., Bertoldi, G., and Rigon, R.: Modelling the probability of occurrence of shallow landslides and
channelized debris flows using GEOtop-FS, Hydrol. Process., 22, 532545, 2008.
Smith, M.W., and Riseborough, D.W.: Climate and the limits of permafrost: a zonal analysis, Permafrost Periglac., 13, 115,
2002.
Smith, M. W., and Riseborough, D. W.: Permafrost monitoring and detection of climate change, Permafrost Periglac., 7,
30
301309, 1996.
Verseghy, D. L.: CLASS - a Canadian land surface scheme for GCMs. I. Soil model, Int. J. Climatol., 11, 111133, 1991.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
14
Wu, Q., Hou, Y., Yun, H., and Liu, Y.: Changes in active-layer thickness and near-surface permafrost between 2002 and
2012 in alpine ecosystems, QinghaiXizang (Tibet) Plateau, China, Glob. Planet. Change, 124, 149155,
doi:10.1016/j.gloplacha.2014.09.002, 2015.
Wu, Q., and Zhang, T.: Recent permafrost warming on the Qinghai-Tibet Plateau, J. Geophys. Res., 113, D13108,
doi:10.1029/2007JD009539, 2008.
5
Wu, Q., Zhang, T., and Liu, Y.: Permafrost temperatures and thickness on the Qinghai-Tibet Plateau, Glob. Planet. Change,
72, 3238, 2010.
Wu, Q., Zhang, T., and Liu, Y.: Thermal state of the active layer and permafrost along the Qinghai-Xizang (Tibet) Railway
from 2006 to 2010, The Cryosphere, 6, 607612, doi:10.5194/tc-6-607-2012, 2012.
Yang, K., Koike, T., Ishikawa, H., Kim, J., Li, X., Liu, H., Liu, S., Ma, Y., and Wang, J.: Turbulent flux transfer over bare-
10
soil surfaces: characteristics and parameterizations, J. Appl. Meteorol. Clim., 47, 276290, 2008.
Yang M., Yao, T., Gou, X., Nozomu, H., Yuki, F. H., Hao, L. and Levia D. F.: Diurnal freeze/thaw cycles of the ground
surface on the Tibetan Plateau, Chin. Sci. Bull., 52, 136139, 2007.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
15
Table 1. Soil properties for the actual soil profile. Ks: saturated hydraulic conductivity; α and n: van Genuchten parameters;
θr and θs: residual and saturated soil water content, respectively; λm: thermal conductivity of soil matrix; C: thermal capacity.
5
10
description
layering
/ m
0-3.0
3.0-30
I
Soil texture
%
sand
66.3
-
silt
12.0
-
clay
21.7
-
Hydraulic
properties
Ks / m d-1
0.19
2.2×10-3
α / cm-1
0.03
0.01
n / -
1.33
1.5
θr / m3 m−3
0.06
0.10
θs / m3 m−3
0.38
0.2
Thermal
properties
λm / W m-1 K-1
5.0
2.0
C / J m-3 K-1
2×106
2×106
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
16
Table 2. Combinations of soil architecture and thermal conductivity of soil matrix for the shallow layer (0-3.0 m) for the six
simulations. A1, A2 and A3 stand for three types of soil architecture for the shallow layer.
Simulations
1
2
3
4
5
6
Architecture
A1
A2
A3
A1
A2
A3
λm / W m-1 K-1
5.0
5.0
5.0
2.5
2.5
2.5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
17
Figure 1. Study site location and permafrost distribution on the Qinghai-Tibet Plateau. The background map is the
permafrost classes from Li and Cheng (1996).
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
18
Figure 2. Time series of projected mean annual air temperature (MAAT) (a) and annual total precipitation (b) since 1900 to
2100. The black section presents historical data, and the red section presents projected data. The period in shadow (1970-
1979) was repeatedly used for spin-up.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
19
Figure 3. Relationship between the daily mean air temperature (Ta) and near-surface soil temperature (T5cm, measured at 5
cm below the ground surface) over the period from 2006 to 2014.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
20
Figure 4. All available thermal profiles from 2007 to 2013. The temperatures at 1.5 m are the mean annual air temperatures
(MAAT). Note: missing years were caused by data gaps.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
21
Figure 5. Typical dynamics of the active layer during an annual cycle (data from 2008). (a) Daily mean air temperature and
daily rainfall. (b) Liquid water content (colors) and 0°C-isotherm (black line).
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
22
Figure 6. Seasonal change in soil water content (a) and thermal conductivity (b) in the thick active layer in 2008. θt and θf
are the mean total water content during the summer period and the winter period, respectively, and λt and λf are the
corresponding thermal conductivities.
5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
23
Figure 7. Air temperature change over the period of 2006-2014. (a) Comparison on observed and projected daily averaged
air temperature (
a
T
and
'
a
T
); (b) Relationship between
a
T
and
'
a
T
with a linear regression; (c) Comparison on the observed
and projected annual mean air temperature (MAAT) after correction with (b). A linear-fitted (dashed line) warming rate of
0.07˚C yr-1 is derived from the projected one.
5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
24
Figure 8. Comparison on observed and simulated permafrost temperature changes at the Chumaer site over the period from
2006 to 2014. The measured temperatures Tobs,06 and Tobs,14 were taken from a shallow borehole and a nearby deep borehole
on August 30, 2006 and on February 22, 2014, respectively, while the simulated ones Tsim,06 and Tsim,14 show all values of the
corresponding years.
5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
25
Figure 9. Comparison of the simulated unfrozen soil water content (color) and the 0°C-isotherm (black line) in the active
layer with different soil architectures and thermal conductivities in 2008. The rows correspond to different architectures of
the soil: (a, d) single fine-grained layer (A1), (b, e) single coarse-grained layer (A2), (c, f) two layer structure (A3). The
columns correspond to different thermal conductivities of the soil matrix: 5.0 W m-1 K-1 (left) and 2.5 W m-1 K-1 (right). (cʹ)
5
Observed case (Same as Fig. 5b).
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
26
Figure 10. Thermal offset as a function of mean annual air temperature (MAAT) obtained from the simulations for the
period 1980-2100 of the three soil architectures A1, A2 and A3. The lines connect the means of 1.0˚C-bins, the bars indicate
the corresponding standard deviations. The upper frame is for λm = 5.0 W m-1 K-1, the lower one for λm = 2.5 W m-1 K-1.
5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
27
Figure 11. Schematic mean annual ground temperature for two types of permafrost. Black curve: the studied permafrost
with positive thermal offset; red curve: common permafrost with negative thermal offset.
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
28
Figure 12. Comparison of the influence of soil architecture and thermal conductivity of soil matrix on permafrost
degradation with simulations over the period from 1980 to 2100. (a) - (e) Annual mean ground temperature (MAT) of A1,
A2 and A3 with a high thermal conductivity of soil matrix (5.0 W m-1 K-1) at selected years; (f) - (j) the same as (a) - (e) but
with low thermal conductivity of soil matrix (2.5 W m-1 K-1).
5
The Cryosphere Discuss., doi:10.5194/tc-2015-201, 2016
Manuscript under review for journal The Cryosphere
Published: 18 January 2016
c
Author(s) 2016. CC-BY 3.0 License.
... 19 Thus, thermal offsets are small or even reversed even though the ALT is several meters on the QTP, which correlates to some extent with the dry conditions of permafrost. 19,55,56 Consequently, permafrost degrades completely at QSH-1 in the context of rapidly rising surface tempera- tures. This may partly explain the different degrees of degradation of warm permafrost. ...
Article
To investigate and monitor permafrost in the Bayan Har Mountains (BHM), northeastern Qinghai–Tibet Plateau, south‐west China, 19 boreholes ranging from 20 to 100 m in depth were drilled along an elevational transect (4,221–4,833 m a.s.l.) from July to September 2010. Measurements from these boreholes demonstrate that ground temperatures at the depth of zero annual amplitude (TZAA) are generally higher than −2.0°C. The lapse rate of TZAA is 4–6°C km−1, and the lower limits of permafrost with TZAA < −1°C are approximately 4,650 m on northern slopes (near Yeniugou) and 4,750 m on southern slopes (near Qingshui'he). TZAA changes abruptly within short distances from −0.2 to +1.2°C near the northern lower limits of permafrost and from about +0.5 to +1.5°C near the southern lower limits of permafrost. Thawing and freezing on the ground surface at Qingshui'he (4,413 m a. s. l.) are 13.3 d earlier and 26 d later than that at Chalaping (4,724 m a. s. l.), respectively. The temperature gradient at Qingshui'he is clearly larger than that at Chalaping. The changes of permafrost TZAA ranged from 0.03°C to 0.2°C from 2010 to 2017. A 3.5‐m‐thick permafrost near Qingshui'he was observed to disappear in summer 2013. There is no significant correlation between elevation and permafrost temperature changes in the study area, while the changes of very warm (close to 0°C) permafrost seem to be slow in the intermontane basins.
Article
Full-text available
The thermal dynamics was important for permafrost change processes under climate change. However, little studies were focused on the soil thermal dynamics with long-term observed data in the permafrost region on the Qinghai-Tibetan Plateau (QTP). From 2005 to 2017, we have monitored thermal dynamic of active layer overlying permafrost in the Kunlun Pass (CN06 site) region of the QTP. Results demonstrated that the number of thaw days is lower than the number of freeze days, and the start dates of thawing and freezing were delayed over this period. Moreover, air and soil temperature were all fastest warming in summer at different depths, then in autumn, except in spring and winter which has a cooling trend at some depths. Accordingly, the mean annual soil temperatures exhibited an evident warming trend at different depths. In addition, thawing degree-days (TDD) for air and soil temperature (at 10 cm) showed an increasing trend, whereas the respective freezing degree-days (FDD) had a decreasing trend. The mean freezing and thawing n factor were 1.43 and 0.50 from 2005 to 2017, and the surface offset of the study site ranged from 2.65 to 3.42 °C, which was lower than those in the subarctic and Arctic regions. Meanwhile, there was a linear relationship between the TDDs and active layer thickness, and a power function relationship between the TDDa and active layer thickness. The active layer thickness exhibited a significant increase with the rate of 2.4 cm/year from 2005 to 2017. These results can be used to understand the thermal dynamics response to climate change and indicate related changes and differences in permafrost in different permafrost regions.
Article
Full-text available
In this study, we investigated changes in active layer thickness (ALT) and permafrost temperatures at different depths using data from the permafrost monitoring network along the Qinghai-Xizang (Tibet) Railway (QXR) since 2005. Among these sites, mean ALT is ~3.1 m, with a range of ~1.1 to 5.9 m. From 2006 through 2010, ALT has increased at a rate of ~6.3 cm a<sup>−1</sup>. The mean rate of permafrost temperature rise at the depth of 6.0 m is ~0.02 °C a<sup>−1</sup>, estimated by linear regression using 5 yr of data, and the mean rate of mean annual ground temperature (MAGT) rise at a depth of zero amplitude is ~0.012 °C a<sup>−1</sup>. Changes for colder permafrost (MAGT <−1.0 °C) are greater than changes for relatively warmer permafrost (MAGT >−1.0 °C). This is consistent with results observed in the Arctic and subarctic.
Article
Full-text available
[1] Permafrost and seasonally frozen ground conditions on the Tibetan Plateau were investigated using the Community Land Model, version 4 (CLM4), forced by a suite of new, high-resolution data. This new data set was highly accurate and had an advantage in the frozen ground simulations for its fine temporal and spatial resolution. The simulated current (1981–2000) near-surface permafrost area was 151.50 × 104 km2, which is close to, but slightly larger than, the range from previous studies (111.80 ~ 150.0 × 104 km2). The simulated current active layer thicknesses ranged from 0 to 4.74 m, with an average of 2.01 m. The other frozen ground parameters, such as the maximum freezing depths for seasonally frozen ground, the date of freeze start, the date of freeze end, and the freeze duration at 1 m depth, were also examined. Considering the issue of scale mismatch, the simulated soil temperature and other frozen ground parameters were reasonable compared to our observations. In response to the Plateau warming of approximately 0.44°C/decade from 1981 to 2010, the near-surface permafrost area decreased at a rate of 9.20 × 104 km2/decade, and the area-mean active layer thickness increased by 0.15 m/decade. The area-mean maximum freezing depth of the seasonally frozen ground decreased by 0.34 m/decade. At a depth of 1 m, the dates of freeze start for permafrost and seasonally frozen ground delayed linearly by 3.8 and 4.0 days/decade, respectively, while the dates of freeze end for them advanced linearly by 5.9 and 4.6 days/decade, respectively. These trends in the dates of freeze start and freeze end resulted in freeze durations that were shortened by 9.7 and 8.6 days/decade for permafrost and seasonally frozen ground, respectively. These results give detailed permafrost and seasonally frozen ground states as well as their changes, which will be useful for studying frozen ground's response to climate change and frozen ground engineering stabilization.
Article
Heat transfer mechanisms governing the permafrost-atmosphere interaction are essential to understand present permafrost degradation. Hydraulic and thermal dynamics of various active layers at four di erent permafrost sites on the QTP were investigated with various geophysical methods and soil-weather monitoring stations. Complex eld data were detected and processed with appropriate methods. The principal physical processes controlling the active-layer thermal regime were characterized with a surface energy balance method at Chumaer, Qumahe and Tianshuihai. As a geophysical tool for characterizing soil properties, multi-channel GPR was further explored. Through Monte Carlo uncertainty analysis and eld tests, the accuracy of the multi-channel GPR method and its capability of quantifying eld-scale hydraulic properties and processes was validated at a non-permafrost sandy site. Based on precise soil temperature and soil water content data, heat transfer in various active layers were characterized with a transfer function method. Given the characteristics of ground heat transfer at the study sites, an inverse method for seasonal thermal conductivity parameterization was tested. Combining the transfer function method and the multi-channel GPR, a eld-scale thermal conductivity parameterization was proposed at the end.
Article
A new GCM land surface scheme is introduced, incorporating three soil layers with physically based calculations of heat and moisture transfers at the surface and across the layer boundaries. Snow-covered and snow-free areas are treated separately.. The energy balance equation is solved iteratively for the surface temperature; the surface infiltration rate is calculated using a simplified theoretical analysis allowing for surface ponding. Snow cover is modelled as a discrete "soil' layer. -from Author
Article
Vegetation has a significant influence on snow accumulation and energy availability for snowmelt. This is particularly true in the vicinity of the arctic treeline, characterized by the alternation of shrub-tundra and open-tundra, with the former expected to spread more and more. This work considers the time variation in turbulent fluxes over two open-tundra and shrub-tundra sites, where measurements of sensible and latent heat fluxes over the canopy are available. An improved version of the GEOtop hydrological model with a dual-layer surface scheme has been used to interpret and reproduce the measurements. The model allows us to separate the contribution of the vegetation and the surface to the turbulent fluxes measured above the canopy and, despite some issues related to the parameterization of the turbulence in the canopy, is able to reasonably reproduce the turbulent fluxes measured above the vegetation and the snowmelt acceleration observed in the shrub-tundra. The maximum energy contribution to the surface during snowmelt is found to occur for values of the leaf and stem area index around 1.0. The model proves to be a valuable platform to be applied in a distributed model to predict the spatial variability of snowmelt and surface energy balance.
Article
Commonly, permafrost conditions are characterised by one or two temperature profiles within each unit of a study area. Intra-unit variation is therefore unknown, and the extent to which the data represent unit conditions cannot be estimated. We have collected ground temperatures from 55 boreholes to 5 m depth drilled in four 200 m2 plots next to the Qinghai-Tibet Railway near the town of Wudaoliang. The sites are in a dry area of active aeolian erosion and straddle an alpine meadow ecotone between well- and sparsely vegetated ground cover. The annual mean air temperature at Wudaoliang is currently about -4°C. There is pronounced asymmetry in the seasonal distribution of precipitation, with very little falling between October and April, so that there is almost no snow cover in winter. At every site, air and ground surface temperatures were similar in winter, but the ground surface was considerably warmer than the air in summer in the radiation-rich environment of the Qinghai-Tibet Plateau. Thawing season n-factors were between 2 and 3 as a result. Ground conditions in the well-vegetated meadows were in thermal equilibrium with surface conditions, with the annual mean temperature near the permafrost table (Tps) between -1.2 and -1.9°C. Within plot spatial variability for Tps, estimated from the standard error of the replicate measurements, was ∼ 0.05°C at each site. Thermal conditions in the eroded, sparsely vegetated area appeared to be transient and warming, with current Tps of -0.5°C. The driest site showed a reversed thermal offset in the uppermost active layer. We hypothesise that the reversed offset may occur due to extremely dry conditions in winter, so that at the ground surface, the thermal conductivity is lower in winter than under the moist conditions of summer. Copyright © 2015 John Wiley & Sons, Ltd.
Article
Between 2002 and 2012, daily soil temperature measurements were made at 10 sites within five alpine ecosystems in the Beiluhe area of the central Qinghai-Tibet Plateau. Changes in freeze-thaw occurrence, active- layer thickness and near-surface permafrost temperature in barren, desert grassland, alpine steppe and alpine meadow ecosystems indicate that alpine ecosystems are sensitive to climate variability. During this time, the average onset of spring thawing at 50-cm depth advanced by at least 16 days in all but the barren alpine settings, and the duration of thaw increased by at least 14 days for all but the desert grassland and barren ecosystems. All sites showed an increase in active-layer thickness (ALT) and near-surface permafrost temperature: the average increase of ALT was ~ 4.26 cm/a and the average increase in permafrost temperatures at 6 m and 10 m depths were, respectively, ~ 0.13 °C and ~ 0.14 °C. No apparent trend in mean annual air temperature was detected at the Beiluhe weather station. However, an increasing trend in precipitation was measured. This suggests that the primary control on the ALT increase was an increase in summer rainfall and the primary control on increasing permafrost temperature was probably the combined effects of increasing rainfall and the asymmetrical seasonal changes in subsurface soil temperatures.