Distribution and changes of active layer thickness (ALT) and soil temperature (TTOP) in the source area of the Yellow River using the GIPL model

2014 ◽  
Vol 57 (8) ◽  
pp. 1834-1845 ◽  
Author(s):  
DongLiang Luo ◽  
Jin HuiJun ◽  
Sergei Marchenko ◽  
Vladimir Romanovsky
Keyword(s):  
Soil Temperature ◽  
Layer Thickness ◽  
Active Layer ◽  
Yellow River ◽  
Source Area ◽  
The Yellow River ◽  
Active Layer Thickness

scholarly journals The ERA5-Land Soil-Temperature Bias in Permafrost Regions

2020 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Donghai Zheng ◽  
Xin Li
Keyword(s):  
Soil Temperature ◽  
Layer Thickness ◽  
Active Layer ◽  
Active Layer Thickness ◽  
Near Surface ◽  
Average Bias ◽  
Warm Bias ◽  
Temperature Bias ◽  
Low Latitudes ◽  
Permafrost Regions

Abstract. ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates its soil temperature in permafrost regions based on observations and published permafrost products. Soil in ERA5L is predicted too warm in northern Canada and Alaska, but too cold in mid-low latitudes, leading to an average bias of −0.08 °C. The warm bias of ERA5L soil is stronger in winter than in other seasons. Diagnosed from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (

scholarly journals Linking tundra vegetation, snow, soil temperature, and permafrost

2020 ◽  
Vol 17 (16) ◽  
pp. 4261-4279
Author(s):  
Inge Grünberg ◽  
Evan J. Wilcox ◽  
Simon Zwieback ◽  
Philip Marsh ◽  
Julia Boike
Keyword(s):  
Soil Temperature ◽  
Snow Cover ◽  
Layer Thickness ◽  
Active Layer ◽  
Vegetation Change ◽  
Vegetation Type ◽  
Spatial Scales ◽  
Active Layer Thickness ◽  
Vegetation Types ◽  
Thermal Dynamics

Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.

Modelled (1990-2100) Variations in Active-Layer Thickness and Ice-Wedge Activity Near Salluit, Nunavik (Canada)

2020 ◽  
Author(s):  
Samuel Gagnon ◽  
Michel Allard
Keyword(s):  
Soil Temperature ◽  
Layer Thickness ◽  
Active Layer ◽  
Air Temperature ◽  
Climate Warming ◽  
Active Layer Thickness ◽  
The Past ◽  
Temperature Thresholds ◽  
Ice Wedge ◽  
Cooling Trend

<p>Between 2016 and 2018, Gagnon and Allard (2019) investigated the impact of climate change on winter ice-wedge (IW) cracking frequency and IW morphology. In this study, they revisited 16 sites in the Narsajuaq valley (Canada) that were extensively studied between 1989 and 1991. Climate warming only started around 1993 whence mean annual air temperatures started to rise from -10 °C then to about -6 °C nowadays. This gave the unique opportunity to observe and measure changes by directly comparing field data with data pre-dating a climate warming of known amplitude. They found that based on IW tops, the active layer reached depths that were 1.2 to 3.4 times deeper than in 1991, which led to the widespread degradation of IW in the valley. Whereas 94% of the IWs unearthed in 1991 showed multiple recent growth structures, only 13% of the IWs unearthed in 2017 still had such features.</p><p>However, about half of the IWs in 2017 had ice veins connecting them to the base of the active layer, an indication that the recent cooling trend (2010-now) in the region was enough to reactivate frost cracking and IW growth. This shows that the soil system can respond quickly to short-term climate variations. For this study, we aimed to determine how changes in surface temperatures affected active-layer thickness (ALT) and dynamics over the past 25 years in order to understand the timing and reaching times of ground temperature thresholds for soil cracking and IW degradation. We used TONE, a one-dimensional finite-element thermal model, to simulate ground temperatures over the past 25 years. A monthly mean air temperature from a reanalysis (1948-2016) was combined with data from a weather station about 9 km west of the study area (2002-2018) to simulate the soil temperature profiles of four typical soil types found in the valley: thick sandy peat cover, thick peat cover, thin sandy peat cover, and fluvial sands.</p><p>Our results show that ALT variations were predominantly controlled by changes in thawing season air temperature with regards to the previous year. As soon as 1998, the active layer had already reached the main stages of the IWs, i.e. the largest and oldest part composing the IWs, but it is only from 2006 that the main stages started melt until 2010, an exceptionally warm year. Based on soil temperature thresholds, our results show that IWs remained active until around 2006. This means that as the active layer deepened and caused IW tops degradation, freezing season temperatures were still cold enough to induce soil cracking and IW growth in width. After 2010, the cooling trend was enough to reactivate the IWs from as a soon as 2011. This study shows that prior to advanced degradation, IWs can melt substantively and remain active at the same time as long as freezing season temperatures are cold enough to induce soil contraction cracking. However, it is likely that pulse events such as ground collapse will cause positive feedbacks contributing to rapid IW degradation before the soil completely stops cracking.</p>

scholarly journals Analysis of Permafrost Thermal Dynamics and Response to Climate Change in the CMIP5 Earth System Models

2013 ◽  
Vol 26 (6) ◽  
pp. 1877-1900 ◽  
Author(s):  
Charles D. Koven ◽  
William J. Riley ◽  
Alex Stern
Keyword(s):  
Soil Temperature ◽  
Layer Thickness ◽  
Active Layer ◽  
Air Temperature ◽  
High Latitude ◽  
Cmip5 Models ◽  
Active Layer Thickness ◽  
Thermal Dynamics ◽  
Wide Range ◽  
Soil Temperatures

Abstract The authors analyze global climate model predictions of soil temperature [from the Coupled Model Intercomparison Project phase 5 (CMIP5) database] to assess the models’ representation of current-climate soil thermal dynamics and their predictions of permafrost thaw during the twenty-first century. The authors compare the models’ predictions with observations of active layer thickness, air temperature, and soil temperature and with theoretically expected relationships between active layer thickness and air temperature annual mean- and seasonal-cycle amplitude. Models show a wide range of current permafrost areas, active layer statistics (cumulative distributions, correlations with mean annual air temperature, and amplitude of seasonal air temperature cycle), and ability to accurately model the coupling between soil and air temperatures at high latitudes. Many of the between-model differences can be traced to differences in the coupling between either near-surface air and shallow soil temperatures or shallow and deeper (1 m) soil temperatures, which in turn reflect differences in snow physics and soil hydrology. The models are compared with observational datasets to benchmark several aspects of the permafrost-relevant physics of the models. The CMIP5 models following multiple representative concentration pathways (RCP) show a wide range of predictions for permafrost loss: 2%–66% for RCP2.6, 15%–87% for RCP4.5, and 30%–99% for RCP8.5. Normalizing the amount of permafrost loss by the amount of high-latitude warming in the RCP4.5 scenario, the models predict an absolute loss of 1.6 ± 0.7 million km2 permafrost per 1°C high-latitude warming, or a fractional loss of 6%–29% °C−1.

scholarly journals Permafrost temperature and active-layer thickness of Yakutia with 0.5-degree spatial resolution for model evaluation

2013 ◽  
Vol 5 (2) ◽  
pp. 305-310 ◽  
Author(s):  
C. Beer ◽  
A. N. Fedorov ◽  
Y. Torgovkin
Keyword(s):  
Layer Thickness ◽  
Spatial Resolution ◽  
Active Layer ◽  
Land Surface ◽  
Grid Cell ◽  
Active Layer Thickness ◽  
East Siberia ◽  
Permafrost Temperature ◽  
Surface Models ◽  
The Mean

Abstract. Based on the map of landscapes and permafrost conditions in Yakutia (Merzlotno-landshaftnaya karta Yakutskoi0 ASSR, Gosgeodeziya SSSR, 1991), rasterized maps of permafrost temperature and active-layer thickness of Yakutia, East Siberia were derived. The mean and standard deviation at 0.5-degree grid cell size are estimated by assigning a probability density function at 0.001-degree spatial resolution. The gridded datasets can be accessed at the PANGAEA repository (doi:10.1594/PANGAEA.808240). Spatial pattern of both variables are dominated by a climatic gradient from north to south, and by mountains and the soil type distribution. Uncertainties are highest in mountains and in the sporadic permafrost zone in the south. The maps are best suited as a benchmark for land surface models which include a permafrost module.

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