Permafrost warming has the potential to amplify global climate change, because when frozen sediments thaw it unlocks soil organic carbon. Yet to date, no globally consistent assessment of permafrost temperature change has been compiled. Here we use a global data set of permafrost temperature time series from the Global Terrestrial Network for Permafrost to evaluate temperature change across permafrost regions for the period since the International Polar Year (2007–2009). During the reference decade between 2007 and 2016, ground temperature near the depth of zero annual amplitude in the continuous permafrost zone increased by 0.39 ± 0.15 °C. Over the same period, discontinuous permafrost warmed by 0.20 ± 0.10 °C. Permafrost in mountains warmed by 0.19 ± 0.05 °C and in Antarctica by 0.37 ± 0.10 °C. Globally, permafrost temperature increased by 0.29 ± 0.12 °C. The observed trend follows the Arctic amplification of air temperature increase in the Northern Hemisphere. In the discontinuous zone, however, ground warming occurred due to increased snow thickness while air temperature remained statistically unchanged.

Results obtained during the International Polar Year (IPY) on the thermal state of permafrost and the active layer in the Antarctic are presented, forming part of ANTPAS (‘Antarctic Permafrost and Soils’), which was one of the key projects developed by the International Permafrost Association and the Scientific Committee for Antarctic Research for the IPY. The number of boreholes for permafrost and active‐layer monitoring was increased from 21 to 73 during the IPY, while CALM‐S sites to monitor the active layer were increased from 18 to 28. Permafrost temperatures during the IPY were slightly below 0°C in the South Shetlands near sea‐level, showing that this area is near the climatic boundary of permafrost and has the highest sensitivity to climate change in the region. Permafrost temperatures were much lower in continental Antarctica: from the coast to the interior and with increasing elevation they ranged between −13.3°C and −18.6°C in Northern Victoria Land, from −17.4°C to −22.5°C in the McMurdo Dry Valleys, and down to −23.6°C at high elevation on Mount Fleming (Ross Island). Other monitored regions in continental Antarctica also showed cold permafrost: Queen Maud Land exhibited values down to −17.8°C on nunataks, while in Novolazarevskaya (Schirmacher Oasis) at 80 m a.s.l. the permafrost temperature was −8.3°C. The coastal stations of Molodeznaya at Enderby Land showed permafrost temperatures of −9.8°C, Larsemann Hills – Progress Station in the Vestfold Hills region – recorded −8.5°C, and Russkaya in Marie Byrd Land, −10.4°C. This snapshot obtained during the IPY shows that the range of ground temperatures in the Antarctic is greater than in the Arctic. Copyright © 2010 John Wiley & Sons, Ltd.

Low-land permafrost areas are subject to intense freeze-thaw cycles and characterized by remarkable surface displacement. We used Sentinel-1 SAR interferometry (InSAR) in order to analyse the summer surface displacement over four spots in the Arctic and Antarctica since 2015. Choosing floodplain or outcrop areas as the reference for the InSAR relative deformation measurements, we found maximum subsidence of about 3 to 10 cm during the thawing season with generally high spatial variability. Sentinel-1 time-series of interferograms with 6–12 day time intervals highlight that subsidence is often occurring rather quickly within roughly one month in early summer. Intercomparison of summer subsidence from Sentinel-1 in 2017 with TerraSAR-X in 2013 over part of the Lena River Delta (Russia) shows a high spatial agreement between both SAR systems. A comparison with in-situ measurements for the summer of 2014 over the Lena River Delta indicates a pronounced downward movement of several centimetres in both cases but does not reveal a spatial correspondence between InSAR and local in-situ measurements. For the reconstruction of longer time-series of deformation, yearly Sentinel-1 interferograms from the end of the summer were considered. However, in order to infer an effective subsidence of the surface through melting of excess ice layers over multi-annual scales with Sentinel-1, a longer observation time period is necessary.

A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal) where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use). Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model) over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i) the definition of thresholds of rainfall (quantity-duration) responsible for past landslide events; (ii) the calculation of the relevant return periods; (iii) the assumption that the same rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area). The landslide hazard is present as the probability of each pixel to be affected by a slope movement, and results from the coupling between the susceptibility map, the prediction-rate curve, and the return periods of critical rainfall events, on a scenario basis. Using this methodology, different hazard scenarios were assessed, corresponding to different rain paths with different return periods.