Preprints
https://doi.org/10.5194/egusphere-2022-1062
https://doi.org/10.5194/egusphere-2022-1062
 
14 Nov 2022
14 Nov 2022
Status: this preprint is open for discussion.

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

Winslow D. Hansen1, Adrianna Foster3, Bejamin Gaglioti4, Rupert Seidl2,5, and Werner Rammer2 Winslow D. Hansen et al.
  • 1Cary Institute of Ecosystem Studies, Millbrook, NY, USA, 12545
  • 2Technical University of Munich, School of Life Sciences, 85354 Freising, Germany
  • 3National Center for Atmospheric Research, Boulder, CO, USA, 80035
  • 4Water and Environmental Research Center, Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, AK USA, 99775
  • 5Berchtesgaden National Park, 83471 Berchtesgaden, Germany

Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) 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 SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model’s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate. 

Winslow D. Hansen et al.

Status: open (until 11 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Winslow D. Hansen et al.

Data sets

Inputs and outputs for iLand simulations Winslow Hansen https://doi.org/10.25390/caryinstitute.21339090

Model code and software

iLand source code Werner Rammer Winslow Hansen https://doi.org/10.25390/caryinstitute.21339090

Winslow D. Hansen et al.

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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.