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.