A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data

Abstract

Abstract. Permafrost is a key element of the terrestrial cryosphere which makes mapping and monitoring of its state variables an imperative task. We present a modeling scheme based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be derived at a spatial resolution of 1 km at continental scales. The approach explicitly accounts for the uncertainty due to unknown input parameters and their spatial variability at subgrid scale by delivering a range of MAGTs for each grid cell. This is achieved by a simple equilibrium model with only few input parameters which for each grid cell allows scanning the range of possible results by running many realizations with different parameters. The approach is applied to the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2 ranging from the Ural Mountains in the east to the Canadian Archipelago in the west. A comparison to in situ temperature measurements in 143 boreholes suggests a model accuracy better than 2.5 °C, with 139 considered boreholes within this margin. The statistical approach with a large number of realizations facilitates estimating the probability of permafrost occurrence within a grid cell so that each grid cell can be classified as continuous, discontinuous and sporadic permafrost. At its southern margin in Scandinavia and Russia, the transition zone between permafrost and permafrost-free areas extends over several hundred km width with gradually decreasing permafrost probabilities. The study exemplifies the unexploited potential of remotely sensed data sets in permafrost mapping if they are employed in multi-sensor multi-source data fusion approaches.

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

“…During cloudy periods, reanalysisderived air temperatures may indeed facilitate an adequate representation of surface temperatures, as the near-surface temperature gradient is smaller compared to clear-sky conditions (e.g., Hudson and Brandt, 2005;Gallo et al, 2011;Westermann et al, 2012). As demonstrated by Westermann et al (2015) for the N Atlantic region, the composite product features a considerably reduced bias and is significantly better suited as input for permafrost modeling than the original MODIS LST record. However, a small, but consistent, cold bias of about 0.8 • C remains.…”

“…Studies with equilibrium models have demonstrated that the latter can to a certain degree be captured by statistical approaches that employ an (estimated) distribution of snow depths to obtain distributions of ground temperatures for each grid cell (Gisnås et al, , 2016Westermann et al, 2015). However, with the transient modeling scheme employed in this study, new issues arise that strongly complicate the application of a statistical representation of snow cover.…”

“…For this purpose, the daily MODIS level 3 LST products MOD11A1 and MYD11A1 in the version 005 (NASA LP DAAC, 2014) were employed, which deliver four LST values per day (Terra and Aqua satellites, day-and nighttime LST each). The merging procedure is similar to that described in Westermann et al (2015), in which spatially distributed data sets of freezing and thawing degree days were generated. In essence, gaps in the MODIS LST record due to cloud cover are filled by the reanalysis data, which creates a data record with homogeneous data density and has the potential to moderate the cold bias of temporal averages of surface temperatures computed from clear-sky MODIS LST .…”

“…As permafrost is a subsurface temperature phenomenon, it is not possible to observe it directly from satellite-borne sensors. However, remotely sensed data sets can be used as input for the abovementioned permafrost models (Hachem et al, 2009;Westermann et al, 2015). Langer et al (2013) demonstrated and evaluated a transient ground temperature modeling scheme forced by remote-sensing data for a point in the Lena River delta (LRD).…”

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

“…For example, the subgrid variability of permafrost temperatures is closely tied to that of SWE depth (e.g., Gisnås et al, 2016), which has major implications for permafrost mapping (e.g., Westermann et al, 2015b. Similarly, snow cover information is an important component of many ecological models (e.g., Kohler and Aanes, 2004), and peak SWE is intimately linked to streamflow, which is crucial for hydrology and water resource management (Andreadis and Lettenmaier, 2006;Barnett et al, 2005).…”