Disaggregating the carbon exchange of degrading permafrost peatlands using Bayesian deep learning

Abstract

Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post-thaw decomposition. We present three years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iskoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble-based Bayesian deep neural network framework. The three-year mean CO2-equivalent flux is estimated to be 106 gCO2 m-2 yr-1 for palsas, 1780 gCO2 m-2 yr-1 for ponds, and -31 gCO2 m-2 yr-1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

Multitemporal UAV LiDAR detects seasonal heave and subsidence on palsas

“…The transition from an elevated, often dry, palsa bog to a lower lying, wet fen, is associated with an increase in CH4 and CO2 emissions (e.g. Łakomiec et al, 2021;Pirk et al, 2023;Swindles et al, 2015;Voigt et al, 2019). Due to the tendency to enhance atmospheric greenhouse gas emissions, degrading palsas therefore not only indicate but can also contribute to climatic change.…”