Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2011-2023
https://doi.org/10.5194/gmd-16-2011-2023
Model description paper
 | 
13 Apr 2023
Model description paper |  | 13 Apr 2023

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer

Related authors

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023,https://doi.org/10.5194/gmd-16-3407-2023, 2023
Short summary

Related subject area

Biogeosciences
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025,https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025,https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025,https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025,https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025,https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary

Cited articles

Abbott, B. W. and Jones, J. B.: Permafrost collapse alters soil carbon stocks, respiration, CH4, and N2O in upland tundra, Glob.Change Biol., 21, 4570–4587, https://doi.org/10.1111/gcb.13069, 2015. 
Albrich, K., Rammer, W., Turner, M. G., Ratajczak, Z., Braziunas, K. H., Hansen, W. D., and Seidl, R.: Simulating forest resilience: A review, Global Ecol. Biogeogr., 29, 2082–2096, https://doi.org/10.1111/geb.13197, 2020. 
Alexander, H. D. and Mack, M. C.: A canopy shift in interior Alaskan boreal forests: Consequences for above- and belowground carbon and nitrogen pools during post-fire succession, Ecosystems, 19, 98–114, https://doi.org/10.1007/s10021-015-9920-7, 2016. 
Anderegg, W. R. L., Wu, C., Acil, N., Carvalhais, N., Pugh, T. A. M., Sadler, J. P., and Seidl, R.: A climate risk analysis of Earth's forests in the 21st century, Science, 377, 1099–1103, https://doi.org/10.1126/science.abp9723, 2022. 
Anderson, P. M., Edwards, M. E., and Brubaker, L. B.: Results and paleoclimate implications of 35 years of paleoecological research in Alaska, in: Developments in Quaternary Sciences, vol. 1, Elsevier, 427–440, https://doi.org/10.1016/S1571-0866(03)01019-4, 2003. 
Download
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Share