Stephan Gruber

Ground temperature (GT) is an essential variable for monitoring the thermal state of permafrost, developing models, and conducting fundamental research. However, the usability of GT data in Canada is often hampered by inconsistent file formats, a lack of metadata standardization between organizations, and varied temporal resolutions. This fragmentation has prevented the development of a comprehensive benchmark dataset, leaving researchers reliant on small, ad-hoc data subsets for model evaluation. To address this gap, we present a new, standardized collection of GT and ground surface temperature (GST) data from across Canada, designed to serve as a benchmark for synthesis and model evaluation. The collection integrates data from 29 published datasets and 13 unpublished contributions, encompassing 427 GT and 491 GST sites across British Columbia, Labrador, Northwest Territories, Nunavut, Québec, and Yukon. The dataset includes records from 1960 through 2025 and observations range in depth from 0 to 300 m. The longest individual time series comprises 34 consecutive years of daily data with no more than 10 % missing data per year. On average for all sites and depths, this value is 6 years. All included time series feature at least one year of measurements at daily resolution. We performed quality control and standardization using tempcf – a newly-developed python software tool – to document and remove anomalies, outliers, and drilling disturbances. Then we aggregated sub-daily data to daily means. The dataset is standardized into NetCDF files adhering to CF, ACDD, and IOOS metadata standards as well as a compatible permafrost-specific metadata profile. Data are provided as both individual NetCDF files and text files. They are fully accessible at https://doi.org/10.5281/zenodo.18022924 (version 0.9.1) (Meier-Legault et al., 2025) and via an ERDDAP server (https://data.permafrostnet.ca/erddap, last access: 21 July 2026), following FAIR principles. Several limitations remain, including time zone uncertainty in some datasets, and the lack of a standardized methodology for harmonizing site description metadata. Nevertheless, this collection improves the accessibility and usability of Canadian ground temperature data for national-scale permafrost research.

Warming permafrost is destabilizing landscapes, intensifying thaw‐driven mass wasting and giving rise to novel landslide forms. We characterize two recent (2017 and 2018), deep‐seated translational failures that we term Permafrost Detachment Slides (PDSs), which rapidly translocated 4.5 and 18 × 10 ⁶ m ³ of frozen material into downstream environments. The PDS failure mechanism involves bottom‐up thaw, initiating gradual movement of the entire permafrost layer, producing distinct failure styles and deposits. Initial movement results in extensional cracks that increase water infiltration, accelerating thaw at depth and slope failure. We have identified hundreds of similar landslides in relatively thin (−1°C) permafrost of the Mackenzie Valley, NWT. Thermal modeling indicates fire‐induced effects can cause thawing at the permafrost base over decadal timescales. Though PDSs occur rapidly, their effects persist, as thermokarst processes alter ice‐rich scarps and deposits, mobilizing sediments, solutes, and carbon into downstream environments.

Surface-based temperature inversions (SBIs) are critical to high-latitude mountain climatology, shaping permafrost stability and near-surface thermal regimes. This study develops and evaluates a new surface-based inversion model, DReaMIT (Dynamical Reanalysis Model for Inversions of Temperature), that extends the framework of Pozsgay and Gruber (2025) by spatializing the inversion strength parameter ( α ) using hypsometric position rather than absolute elevation. The reformulated approach enables a unified calibration across two contrasting Yukon valleys (WS01 and WS02), improving model transferability and reducing site-specific bias. The model also goes beyond the linear treatment of the hypsometric variable to capture the observed nonlinear decay of lapse rates within SBIs, consistent with in-valley observations where temperature increases of up to 8 °C over 19 m were recorded. Model performance was assessed across the Yukon and Northwest Territories, where it successfully reproduced inversion structure even in areas with weaker or transient SBIs, such as Whitehorse and Haines Junction. Limitations were observed in flat regions with minimal hypsometric relief (e.g. Tulita, Old Crow), highlighting cases where microtopographic indices may better represent cold-air pooling potential. The new hypsometric formulation enhances the physical realism and spatial applicability of SBI modelling, providing an empirically-constrained, transferable tool for predicting near-surface temperature regimes and assessing permafrost sensitivity under northern climate change.

Surface-based temperature inversions (SBIs) are critical to high-latitude mountain climatology, shaping permafrost stability and near-surface thermal regimes. This study develops and evaluates a new surface-based inversion model, DReaMIT (Dynamical Reanalysis Model for Inversions of Temperature), that extends the framework of (Pozsgay and Gruber, 2025) by spatializing the inversion strength parameter (α) using hypsometric position rather than absolute elevation. The reformulated approach enables a unified calibration across two contrasting Yukon valleys (WS01 and WS02), improving model transferability and reducing site-specific bias. Exponentiation of the elevation variable captures the observed nonlinear decay of lapse rates within SBIs, consistent with in-valley observations where temperature increases of up to 8 °C over 19 m were recorded. Model performance was assessed across the Yukon and Northwest Territories, where it successfully reproduced inversion structure even in areas with weaker or transient SBIs, such as Whitehorse and Haines Junction. Limitations were observed in flat regions with minimal hypsometric relief (e.g., Tulita, Old Crow), highlighting cases where microtopographic indices may better represent cold-air pooling potential. The new hypsometric formulation enhances the physical realism and spatial applicability of SBI modelling, providing an empirically-constrained, transferable tool for predicting near-surface temperature regimes and assessing permafrost sensitivity under northern climate change.

Surface-based temperature inversions (SBIs) are critical to high-latitude mountain climatology, shaping permafrost stability and near-surface thermal regimes. This study develops and evaluates a new surface-based inversion model, DReaMIT (Dynamical Reanalysis Model for Inversions of Temperature), that extends the framework of (Pozsgay and Gruber, 2025) by spatializing the inversion strength parameter (α) using hypsometric position rather than absolute elevation. The reformulated approach enables a unified calibration across two contrasting Yukon valleys (WS01 and WS02), improving model transferability and reducing site-specific bias. Exponentiation of the elevation variable captures the observed nonlinear decay of lapse rates within SBIs, consistent with in-valley observations where temperature increases of up to 8 °C over 19 m were recorded. Model performance was assessed across the Yukon and Northwest Territories, where it successfully reproduced inversion structure even in areas with weaker or transient SBIs, such as Whitehorse and Haines Junction. Limitations were observed in flat regions with minimal hypsometric relief (e.g., Tulita, Old Crow), highlighting cases where microtopographic indices may better represent cold-air pooling potential. The new hypsometric formulation enhances the physical realism and spatial applicability of SBI modelling, providing an empirically-constrained, transferable tool for predicting near-surface temperature regimes and assessing permafrost sensitivity under northern climate change.

Metrics such as the mean annual ground temperature (MAGT) and active layer thickness (ALT) are used to monitor and quantify permafrost change. However, these have limitations including those arising from the effects of latent heat, which reduce their sensitivity. We investigated the behaviour of existing and novel metrics derived from temperature observations (TSP metrics) using an ensemble of more than seventy 120-year simulations. We evaluated which TSP metrics provide new insight into permafrost change and evaluated how reliably each one indicates changes in sensible, latent, and total heat contents for different levels of sensor quality. We also quantified the effect of sensor placement on the magnitude of observed MAGT trends. We observed depth-related differences in decadal MAGT warming rates of more than 0.23 °C decade−1 (50th percentile) for observation depths between 10 and 20 m. The magnitude of these differences is reduced to 0.17 °C decade−1 (50th percentile) when considering the thermal integral(τ‾) – A metric describing a depth-averaged warming trend. The effect of sensor depth on warming trends is greatest in ice-poor soils. In warm permafrost, we find that depth of zero annual amplitude (dza) and mean annual surface temperature (MAGST) exhibit qualitatively different behaviour than MAGT which can help disambiguate low or imperceptible warming rates by the latter metric. Finally, we recommend a parsimonious set of five TSP metrics to provide a better picture of permafrost thaw than MAGT or ALT alone. These are: height of the permafrost table (TOP), dza, τ‾, mean annual ground temperature (MAGT), and MAGST. Our results can be used to inform permafrost monitoring strategies and help contextualize observed trends. Consistent metrics can be produced from observed and simulated thermal data via the ”tspmetrics” library available on the Python Package Index (PyPi).

Ground temperature is an essential variable for monitoring the thermal state of permafrost, developing models, and conducting fundamental research. However, the usability of GT data in Canada is often hampered by inconsistent file formats, a lack of metadata standardization between organizations, and varied temporal resolutions. This fragmentation has prevented the development of a comprehensive benchmark dataset, leaving researchers reliant on small, ad-hoc data subsets for model evaluation. To address this gap, we present a new, standardized collection of ground temperature (GT) and ground surface temperature (GST) data from across Canada, designed to serve as a benchmark for synthesis and model evaluation. The collection integrates data from 29 published datasets and 13 unpublished contributions, encompassing 427 GT and 491 GST sites across British Columbia, Labrador, Northwest Territories, Nunavut, Québec, and Yukon. All included time series feature at least one year of measurements at daily resolution. We performed quality control and standardization using tempcf — a newly-developed python software tool — to document and remove anomalies, outliers, and drilling disturbances. Then we aggregated sub-daily data to daily means. The dataset is standardized into NetCDF files adhering to CF, ACDD, and IOOS metadata standards as well as a compatible permafrost-specific metadata profile. Data are provided as both individual NetCDF files and text files. They are fully accessible at https://doi.org/10.5281/zenodo.18022925 (Meier-Legault et al., 2025) and via an ERDDAP server (data.permafrostnet.ca/erddap), following FAIR principles. Several limitations remain, including time zone uncertainty in some datasets, and the lack of a standardized methodology for harmonizing site description metadata. Nevertheless, this collection improves the accessibility and usability of Canadian ground temperature data for national-scale permafrost research.

Permafrost and seasonally frozen ground are critical components of the Arctic cryosphere, playing a fundamental role in regulating hydrological processes. Permafrost underlies approximately 13-18% of the Northern Hemisphere’s exposed land surface and stores vast quantities of organic carbon. Its thermal stability has profound implications for infrastructure, carbon cycle, and hydrological processes under changing climate. Modeling permafrost dynamics is critical, yet standard cryo-hydrological models often struggle to capture the complex phase changes in fine-grained soils, particularly in clayey soils where liquid water persists at temperatures well below the classical freezing point.To overcome this challenge, we propose an advanced thermodynamic framework that moves beyond the classical soil freezing characteristic curves (SFCC). By incorporating adsorption potential (μads), this framework accounts for the exponential energy decay near mineral surfaces, physically explaining the persistence of liquid water in clay nanopores down to −80℃. While the theoretical foundation for this approach is established in the enthalpy-based solver WHETGEO-1D (Tubini & Rigon, 2022), we evaluate its significance through a comparative study using field forcing and hypothetical experiments.We conducted this analysis in two stages. First, the 1D GEOtop model was applied to two sites along the Inuvik-Tuktoyaktuk Highway (Canada) using ERA5 and JRA-3QG reanalysis data (1950–2023). At this stage, the model performance was evaluated against ground temperature observations (2017–2022) to ensure realistic surface fluxes and ground temperatures. Second, using these validated forcing conditions, we performed hypothetical experiments to compare the classical and adsorption-aware frameworks across different soil types.Our initial analysis indicates that the classical and adsorption-aware frameworks yield divergent results in the timing of latent heat exchange and moisture redistribution. By bridging the gap between pore-scale thermodynamics and Darcy-scale modeling, this study provides a robust roadmap for implementing next-generation physics into permafrost models.

Surface-based temperature inversions (SBIs) are critical to high-latitude mountain climatology, shaping permafrost stability and near-surface thermal regimes. This study develops and evaluates a new surface-based inversion model, DReaMIT (Dynamical Reanalysis Model for Inversions of Temperature), that extends the framework of (Pozsgay and Gruber, 2025) by spatializing the inversion strength parameter (α) using hypsometric position rather than absolute elevation. The reformulated approach enables a unified calibration across two contrasting Yukon valleys (WS01 and WS02), improving model transferability and reducing site-specific bias. Exponentiation of the elevation variable captures the observed nonlinear decay of lapse rates within SBIs, consistent with in-valley observations where temperature increases of up to 8 °C over 19 m were recorded. Model performance was assessed across the Yukon and Northwest Territories, where it successfully reproduced inversion structure even in areas with weaker or transient SBIs, such as Whitehorse and Haines Junction. Limitations were observed in flat regions with minimal hypsometric relief (e.g., Tulita, Old Crow), highlighting cases where microtopographic indices may better represent cold-air pooling potential. The new hypsometric formulation enhances the physical realism and spatial applicability of SBI modelling, providing an empirically-constrained, transferable tool for predicting near-surface temperature regimes and assessing permafrost sensitivity under northern climate change.