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\begin{document}

\title{Disaggregating the carbon exchange of degrading permafrost peatlands
using Bayesian deep learning}



\author[1]{Norbert Pirk}%
\author[2]{Kristoffer Aalstad}%
\author[2]{Erik Schytt Mannerfelt}%
\author[3]{François Clayer}%
\author[3]{Heleen Agnes de Wit}%
\author[4]{Casper Tai Christiansen}%
\author[5]{Inge Althuizen}%
\author[6]{Hanna Lee}%
\author[2]{Sebastian Westermann}%
\affil[1]{Department of Geosciences, University of Oslo}%
\affil[2]{University of Oslo}%
\affil[3]{Norwegian Institute for Water Research}%
\affil[4]{University of Copenhagen}%
\affil[5]{NORCE Norwegian Research Centre}%
\affil[6]{Unknown}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{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.%
\end{abstract}%



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