Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7197-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-7197-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DReaMIT: a dynamical reanalysis framework for modelling surface-based temperature inversions in cold environments
Victor Pozsgay
CORRESPONDING AUTHOR
Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada
Nick Noad
Department of Geography and Environment, University of Lethbridge, Lethbridge, Alberta, Canada
Philip Bonnaventure
Department of Geography and Environment, University of Lethbridge, Lethbridge, Alberta, Canada
Stephan Gruber
Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada
Related authors
No articles found.
Madeleine C. Garibaldi, Philip P. Bonnaventure, Robert G. Way, Alexandre Bevington, Sharon L. Smith, Scott F. Lamoureux, Jean E. Holloway, Antoni G. Lewkowicz, and Hannah Ackerman
The Cryosphere, 20, 2375–2392, https://doi.org/10.5194/tc-20-2375-2026, https://doi.org/10.5194/tc-20-2375-2026, 2026
Short summary
Short summary
We assessed the sensitivity of a simple permafrost model to changes in parameter values using measured data across northern Canada. We altered the value of one parameter at a time to assess the changes in the resulting temperature. The model was most sensitive to changes in the freezing season parameters and least sensitive to changes in the thawing parameters. However, the importance of specific parameters varied across Canada. The findings of this study can aid in development of future models.
Nicholas Brown and Stephan Gruber
The Cryosphere, 20, 1771–1796, https://doi.org/10.5194/tc-20-1771-2026, https://doi.org/10.5194/tc-20-1771-2026, 2026
Short summary
Short summary
This study improves how we track changes in permafrost by testing new ways to use ground temperature data. A set of five simple but powerful metrics was found to give a clearer picture of thawing than current methods. The results also show that the depth where sensors are placed can strongly affect measured warming rates. These findings help make permafrost monitoring more accurate and support better planning for a changing climate.
Olivia Meier-Legault, Nicholas Brown, Larry Adjun, Michel Allard, Alejandro Alvarez, Maude Auclair, Alex Bevington, Samuel Bilodeau, William Cable, Olivia Carpino, Ariane Castagner, Lin Chen, Alexandre Chiasson, Ryan Connon, Stephanie Coulombe, Jeffrey Crompton, Derek Cronmiller, Gautier Davesne, Mason Dominico, Marc-André Ducharme, Timothy Ensom, Louise Farquharson, Vanessa Foord, Daniel Fortier, Philippe Fortier, Duane Froese, Samuel Gagnon, Francis Gauthier, Marten Geertsema, Etienne Godin, Galina Jonat, Steven V. Kokelj, Michelle Landry, Antoni Lewkowicz, Panya Lipovsky, Emmanual L’Hérault, Hannah Macdonell, Lancelot Massé, Dmitry Nicolsky, Moya Painter, Leesee Papatsie, Victor Pozsgay, William Quinton, Vladimir Romanovsky, Ashley C.A. Rudy, Denis Sarrazin, Emilie Stewart-Jones, Donald Walker, Thomas Wright, Joseph Young, and Stephan Gruber
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-96, https://doi.org/10.5194/essd-2026-96, 2026
Revised manuscript accepted for ESSD
Short summary
Short summary
Ground temperature data is vital for permafrost and climate research yet data is often fragmented. We created a standardized collection of ground temperatures from over 900 sites across Canada. From 42 published and unpublished sources, we manually verified, cleaned and standardized data with a new software tool. This dataset supports permafrost research on a nationwide scale and can help improve models by acting as a reliable benchmark.
Bin Cao and Stephan Gruber
The Cryosphere, 19, 4525–4532, https://doi.org/10.5194/tc-19-4525-2025, https://doi.org/10.5194/tc-19-4525-2025, 2025
Short summary
Short summary
The climate-driven changes in cold regions have an outsized importance for local resilient communities and for global climate through teleconnections. We show that reanalyses are less accurate in cold regions compared to other more populated regions, coincident with the low density of observations. Our findings likely point to similar gaps in our knowledge and capabilities of climate research and services in cold regions.
Niccolò Tubini and Stephan Gruber
EGUsphere, https://doi.org/10.5194/egusphere-2025-2649, https://doi.org/10.5194/egusphere-2025-2649, 2025
Short summary
Short summary
This research introduces a new model for simulating how melting ground ice in permafrost reshapes the land surface over time. It shows that small differences in soil and the depth where ice is found can cause large differences in how the ground sinks or rises. This helps improves our ability to predict future impacts on terrain, ecosystems, and infrastructure as the climate warms.
Hosein Fereydooni, Stephan Gruber, David Stillman, and Derek Cronmiller
EGUsphere, https://doi.org/10.5194/egusphere-2025-1801, https://doi.org/10.5194/egusphere-2025-1801, 2025
Preprint archived
Short summary
Short summary
Detecting ground ice in permafrost is crucial for climate research and infrastructure, but traditional methods often struggle to distinguish it. This study examines the dielectric properties of ground ice as a unique fingerprint. Field measurements were taken at two Yukon permafrost sites: a retrogressive thaw slump and a pingo. Comparing these with electrical resistivity and impedance results, we found relaxation time is a more reliable indicator for ground ice detection.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
Short summary
Short summary
This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
Short summary
Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
Short summary
Short summary
Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Cited articles
Canada, Environment and Climate Change: Historical Data – Climate – Environment and Climate Change Canada, http://climate.weather.gc.ca/historical_data/search_historic_data_e.html (last access: 7 November 2025), 2011. a
Cao, B., Quan, X., Brown, N., Stewart-Jones, E., and Gruber, S.: GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses, Geosci. Model Dev., 12, 4661–4679, https://doi.org/10.5194/gmd-12-4661-2019, 2019. a, b
Crosman, E. T. and Horel, J. D.: Large-eddy simulations of a Salt Lake Valley cold-air pool, Atmos. Res., 193, 10–25, https://doi.org/10.1016/j.atmosres.2017.04.010, 2017. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a
Dice, M. J., Cassano, J. J., and Jozef, G. C.: Forcing for varying boundary layer stability across Antarctica, Weather Clim. Dynam., 5, 369–394, https://doi.org/10.5194/wcd-5-369-2024, 2024. a
Draeger, C., Radić, V., White, R. H., and Tessema, M. A.: Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada, The Cryosphere, 18, 17–42, https://doi.org/10.5194/tc-18-17-2024, 2024. a
Etzelmüller, B.: Recent advances in mountain permafrost research, Permafrost. Periglac., 24, 99–107, https://doi.org/10.1002/ppp.1772, 2013. a
Fiddes, J. and Gruber, S.: TopoSCALE v.1.0: downscaling gridded climate data in complex terrain, Geosci. Model Dev., 7, 387–405, https://doi.org/10.5194/gmd-7-387-2014, 2014. a, b
Fiddes, J., Endrizzi, S., and Gruber, S.: Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost, The Cryosphere, 9, 411–426, https://doi.org/10.5194/tc-9-411-2015, 2015. a
Fiddes, J., Aalstad, K., and Lehning, M.: TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1, Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, 2022. a, b
Fochesatto, G. J., Mayfield, J. A., Starkenburg, D. P., Gruber, M. A., and Conner, J.: Occurrence of shallow cold flows in the winter atmospheric boundary layer of interior of Alaska, Meteorol. Atmos. Phys., 127, 369–382, https://doi.org/10.1007/s00703-013-0274-4, 2015. a
Gallant, J. C. and Dowling, T. I.: A multiresolution index of valley bottom flatness for mapping depositional areas, Water Resour. Res., 39, https://doi.org/10.1029/2002WR001426, 2003. a, b
Garibaldi, M. C., Bonnaventure, P. P., Noad, N. C., and Kochtitzky, W.: Modelling air, ground surface, and permafrost temperature variability across four dissimilar valleys, Yukon, Canada, Arctic Science, 10, 611–629, https://doi.org/10.1139/as-2023-0067, 2024. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b. a, b
Hrebtov, M. and Hanjalić, K.: Numerical study of winter diurnal convection over the city of Krasnoyarsk: effects of non-freezing river, undulating fog and steam devils, Bound.-Lay. Meteorol., 163, 469–495, https://doi.org/10.1007/s10546-016-0231-0, 2017. a
Hughes, J. K., Ross, A. N., Vosper, S. B., Lock, A. P., and Jemmett-Smith, B. C.: Assessment of valley cold pools and clouds in a very high-resolution numerical weather prediction model, Geosci. Model Dev., 8, 3105–3117, https://doi.org/10.5194/gmd-8-3105-2015, 2015. a
Kosaka, Y., Kobayashi, S., Harada, Y., Kobayashi, C., Naoe, H., Yoshimoto, K., Harada, M., Goto, N., Chiba, J., Miyaoka, K., Sekiguchi, R., Deushi, M., Kamahoria, H., Nakaegawa, T., Tanaka, T. Y., Tokuhiro, T., Sato, Y., Matsushita, Y., and Onogi, K.: The JRA-3Q reanalysis, J. Meteorol. Soc. Jpn., 102, 49–109, https://doi.org/10.2151/jmsj.2024-004, 2024. a, b, c
LaZerte, S. E. and Albers, S.: weathercan: download and format weather data from environment and climate change Canada, The Journal of Open Source Software, 3, 571, https://doi.org/10.21105/joss.00571, 2018. a, b
Lewkowicz, A. G. and Bonnaventure, P. P.: Equivalent elevation: a new method to incorporate variable surface lapse rates into mountain permafrost modelling, Permafrost. Periglac., 22, 153–162, https://doi.org/10.1002/ppp.720, 2011. a
Liu, Z., Guo, D., Hua, W., and Chen, Y.: Near-surface permafrost extent and active layer thickness characterized by reanalysis/assimilation data, Atmos. Sci. Lett., 26, https://doi.org/10.1002/asl.1289, 2025. a
Mayfield, J. A. and Fochesatto, G. J.: The layered structure of the winter atmospheric boundary layer in the interior of Alaska, J. Appl. Meteorol., 52, 953–973, https://doi.org/10.1175/JAMC-D-12-01.1, 2013. a, b
Nkiaka, E., Nawaz, N. R., and Lovett, J. C.: Evaluating global reanalysis datasets as input for hydrological modelling in the Sudano-Sahel region, Hydrology, 4, https://doi.org/10.3390/hydrology4010013, 2017. a
Noad, N. C. and Bonnaventure, P. P.: Surface temperature inversion characteristics in dissimilar valleys, Yukon Canada, Arctic Science, 8, 1320–1339, https://doi.org/10.1139/as-2021-0048, 2022. a, b, c
Noad, N. C. and Bonnaventure, P. P.: Examining the influence of microclimate conditions on the breakup of surface-based temperature inversions in two proximal but dissimilar Yukon valleys, Canadian Geographies/Géographies canadiennes, 68, 323–339, https://doi.org/10.1111/cag.12886, 2024. a
Noad, N. C. and Bonnaventure, P. P.: Spatiotemporal variability of surface-based temperature inversions in high-latitude northcentral Yukon valleys utilizing a dense network of elevation transects, Arct. Antarct. Alp. Res., 58, https://doi.org/10.1080/15230430.2026.2614790, 2026. a, b, c, d
Noad, N. C., Bonnaventure, P. P., Gilson, G. F., Jiskoot, H., and Garibaldi, M. C.: Surface-based temperature inversion characteristics and impact on surface air temperatures in northwestern Canada from radiosonde data between 1990 and 2016, Arctic Science, 9, 545–563, https://doi.org/10.1139/as-2022-0031, 2023. a, b, c
Ntagkounakis, G. E., Nastos, P. T., and Kapsomenakis, Y.: Statistical downscaling of ERA5 reanalysis precipitation over the complex terrain of Greece, Environmental Sciences Proceedings, 26, https://doi.org/10.3390/environsciproc2023026081, 2023. a
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H., Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A., Khomutov, A., Kæb, A., Leibman, M. O., Lewkowicz, A. G., Panda, S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316, https://doi.org/10.1016/j.earscirev.2019.04.023, 2019. a
Oyler, J. W., Dobrowski, S. Z., Ballantyne, A. P., Klene, A. E., and Running, S. W.: Artificial amplification of warming trends across the mountains of the western United States, Geophys. Res. Lett., 42, 153–161, https://doi.org/10.1002/2014GL062803, 2015. a
Park, H., Watanabe, E., Kim, Y., Polyakov, I., Oshima, K., Zhang, X., Kimball, J. S., and Yang, D.: Increasing riverine heat influx triggers Arctic sea ice decline and oceanic and atmospheric warming, Science Advances, 6, https://doi.org/10.1126/sciadv.abc4699, 2020. a
Pepin, N. C., Arnone, E., Gobiet, A., Haslinger, K., Kotlarski, S., Notarnicola, C., Palazzi, E., Seibert, P., Serafin, S., Schöner, W., Terzago, S., Thornton, J. M., Vuille, M., and Adler, C.: Climate changes and their elevational patterns in the mountains of the world, Rev. Geophys., 60, https://doi.org/10.1029/2020RG000730, 2022. a
Porter, C., Howat, I., Noh, M.-J., Husby, E., Khuvis, S., Danish, E., Tomko, K., Gardiner, J., Negrete, A., Yadav, B., Klassen, J., Kelleher, C., Cloutier, M., Bakker, J., Enos, J., Arnold, G., Bauer, G., and Morin, P.: ArcticDEM – Mosaics, Version 4.1, Dataverse, [data set], https://doi.org/10.7910/DVN/3VDC4W, 2023. a
Pozsgay, V. and Gruber, S.: Modelling the temporal dynamics of subarctic surface temperature inversions from atmospheric reanalysis for producing point-scale multi-decade meteorological time series in mountains, Arctic Science, 11, 1–16, https://doi.org/10.1139/as-2025-0027, 2025. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Pozsgay, V., Noad, N. C., Bonnaventure, P. P., and Gruber, S.: DReaMIT, Zenodo [code], https://doi.org/10.5281/zenodo.17545268, 2025 (code also available at: https://github.com/geocryology/globsim/tree/DReaMIT_gmd-submit, last access: 4 August 2026). a
Prowse, T. D., Bonsal, B. R., Duguay, C. R., and Lacroix, M. P.: River-ice break-up/freeze-up: a review of climatic drivers, historical trends and future predictions, Ann. Glaciol., 46, 443–451, https://doi.org/10.3189/172756407782871431, 2007. a
Riseborough, D., Shiklomanov, N., Etzelmüller, B., Gruber, S., and Marchenko, S.: Recent advances in permafrost modelling, Permafrost. Periglac., 19, 137–156, https://doi.org/10.1002/ppp.615, 2008. a
Roberts, D. R., Wood, W. H., and Marshall, S. J.: Assessments of downscaled climate data with a high-resolution weather station network reveal consistent but predictable bias, Int. J. Climatol., 39, 3091–3103, https://doi.org/10.1002/joc.6005, 2019. a
Sheridan, P. F.: Synoptic-flow interaction with valley cold-air pools and effects on cold-air pool persistence: influence of valley size and atmospheric stability, Q. J. Roy. Meteor. Soc., 145, 1636–1659, https://doi.org/10.1002/qj.3517, 2019. a
Tao, J., Koster, R. D., Reichle, R. H., Forman, B. A., Xue, Y., Chen, R. H., and Moghaddam, M.: Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis, The Cryosphere, 13, 2087–2110, https://doi.org/10.5194/tc-13-2087-2019, 2019. a
Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrol. Earth Syst. Sci., 24, 2527–2544, https://doi.org/10.5194/hess-24-2527-2020, 2020. a
Tikhomirov, A. B., Lesins, G., and Drummond, J. R.: Drone measurements of surface-based winter temperature inversions in the High Arctic at Eureka, Atmos. Meas. Tech., 14, 7123–7145, https://doi.org/10.5194/amt-14-7123-2021, 2021. a
Urban, M., Eberle, J., Hüttich, C., Schmullius, C., and Herold, M.: Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the pan-arctic scale, Remote Sens.-Basel, 5, 2348–2367, https://doi.org/10.3390/rs5052348, 2013. a
Urraca, R. and Gobron, N.: Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends, The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, 2023. a
Wang, T., Hamann, A., Spittlehouse, D., and Carroll, C.: Locally downscaled and spatially customizable climate data for historical and future periods for North America, PLoS One, 11, 1–17, https://doi.org/10.1371/journal.pone.0156720, 2016. a
Way, R. G. and Bonnaventure, P. P.: Testing a reanalysis-based infilling method for areas with sparse discontinuous air temperature data in northeastern Canada, Atmos. Sci. Lett., 16, 398–407, https://doi.org/10.1002/asl2.574, 2015. a
Zhang, Y., Qian, B., and Hong, G.: A long-term, 1-km resolution daily meteorological dataset for modeling and mapping permafrost in Canada, Atmosphere-Basel, 11, https://doi.org/10.3390/atmos11121363, 2020. a
Short summary
Surface-based temperature inversions occur when cold air becomes trapped near the ground beneath a layer of warmer air. This study combines field data, analysis, and modelling to develop DReaMIT, a model that captures the timing and strength of inversions across northern mountain terrain. The model's transferability beyond the valleys where it was developed makes it valuable globally to cold-region researchers for mapping and modelling permafrost and assessing climate change impacts.
Surface-based temperature inversions occur when cold air becomes trapped near the ground beneath...