A model for regional‐scale estimation of temporal and spatial variability of active layer thickness and mean annual ground temperatures

Abstract

High-latitude ecosystems where the mean annual ground surface temperature is around or below 0°C are highly sensitive to global warming. This is largely because these regions contain vast areas of permafrost, which begins to thaw when the mean annual temperature rises above freezing. The Geophysical Institute Permafrost Lab has developed a new interactive geographical information systems (GIS) model to estimate the long-term response of permafrost to changes in climate. An analytical approach is used for calculating both active layer thickness (ALT) and mean annual ground temperatures (MAGTs). When applied to long-term (decadal or longer time scale) averages, this approach shows an accuracy of š0.2-0.4°C for MAGTs and š0.1-0.3 m for ALT calculations. The relative errors do not exceed 32% for ALT calculations, but typically they are between 10 and 25%. A spatial statistical analysis of the data from 32 sites in Siberia indicated a confidence level of 75% to have a deviation between measured and calculated MAGTs of 0.2-0.4°C. A detailed analysis has been performed for two regional transects in Alaska and eastern Siberia that has validated the use of the model. The results obtained from this analysis show that a more economical (in terms of computational time) analytical approach could be successfully used instead of a full-scale numerical model in the regional and global scale analysis of permafrost spatial and temporal dynamics. This project has been a successful contribution to the Arctic Climate Impact Assessment project.

Permafrost, Infrastructure, and Climate Change: A GIS-Based Landscape Approach to Geotechnical Modeling

“…It is based on the modified Kudryavtsev solution (Kudryavtsev et al, 1974) to the general Stefan problem of heat conduction with a moving phase change boundary, and effectively accounts for the effects of snow cover, vegetation, soil moisture, ground thermal properties, and regional climate variations. This approach lends itself to the Russian tradition of landscape mapping and analysis (e.g., Shaw and Oldfield, 2007), and was also adopted by Shiklomanov and Nelson (1999), Sazonova and Romanovsky (2003), and Anisimov and Reneva (2006). Computational details are available in Anisimov et al (1997) and Shiklomanov and Nelson (1999).…”

Spatial distribution of pingos in northern Asia

“…Only a very small number of pingos (204; 3.4%) were identified in areas within the extent of the Last Glacial Maximum ice sheet and mountain glaciations in the region after Ehlers and Gibbard (2003). On the contrary, more than 50% of all pingo locations coincide with a region identified by Romanovskii (1993) as accumulation zone for Yedoma, a thick ice-rich permafrost deposit typical for non-glaciated regions (Schirrmeister et al, 2010), during the LGM. A large number of pingos (2525; 41.7%) are found in lowland coastal plains in North East Siberia with limnic deposits, encompassing sequences of sandy to silty limnic and alluvial sediments.…”

“…A majority of pingos in our database is located in drained thermokarst basins in lake-rich lowlands massively reshaped by degradation of ice-rich Yedoma permafrost (Romanovskii, 1993;Schirrmeister et al, 2010) since the Late Glacial and early Holocene (Romanovskii et al, 2004;Grosse et al, 2007). Thermokarst lakes were major agents in reshaping these landscapes and resulted in deep thaw of permafrost beneath lakes in taliks.…”

“…Assuming all ASTER identified features (Fig. 4) are true pingos, ratios for pingo numbers between ASTER: topo map range from 1.3:1 to 16.3:1 ( Table 1), highlighting that our current dataset is an underestimation of pingos and Derived from Global Lake and Wetland Database (Lehner and Döll, 2004) corrected by excluding lagoons and rivers; scale 1:1 to 1:3 Mio; includes lakes ≥0.1 km 2 ; lake density ranges from 0-100% with resolution of 1% Maximum ALT in 2000 and 2100 UAF Geophysical Institute Permafrost Model (GIPL 1) output Sazonova and Romanovsky, 2003 (Brown et al, 1998) Permafrost extent Brown et al (1998); scale 1:10 Mio; four distinguished permafrost classes include: isolated (0-10%), sporadic (10-50%), discontinuous (50-90%), and continuous (90-100%) permafrost Ground ice content Brown et al (1998); scale 1:10 Mio; Ground ice contents by volume are distinguished as low (0-10%), moderate (10-20%) and high (>20% in lowlands; >10% in mountains) Sediment thickness Brown et al (1998); scale 1:10 Mio; Sediment cover thickness is distinguished as low (<5-10 m) and high (>5-10 m)…”

PIC: Comprehensive R package for permafrost indices computing with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau

“…Accordingly, successfully summarizing and categorizing a variety of frozen soil indices require permafrost modeling that concerns analytical, numerical, and empirical methodologies to compute the past and present condition of permafrost. The Stefan solution (Nelson et al, 1997), Kudryavtsev's approach (Kudryavtsev et al, 1977), TTOP 10 model (Smith and Riseborough, 1996), and Geophysical Institute Permafrost Lab model (Romanovsky and Osterkamp, 1997;Sazonova and Romanovsky, 2003) are several important developments for permafrost modeling in recent years. Permafrost is a subsurface feature that is difficult to directly observe and map.…”