Modelling growing season carbon fluxes at a low-center polygon ecosystem in the Mackenzie River Delta

Abstract

A temporal upscaling study was conducted to estimate net ecosystem exchange (NEE) of carbon dioxide and net methane exchange (NME) for a low-center polygon (LCP) ecosystem in the Mackenzie River Delta, for each of the 11 growing seasons (2009–2019). We used regression models to create a time series of flux drivers from in situ weather observations (2009–2019) combined with ERA5 reanalysis and satellite data. We then used neural networks that were trained and validated on a single growing season (2017) of eddy covariance data to model NEE and NME over each growing season. The study indicates growing season NEE was negative (net uptake) and NME was positive (net emission) in this LCP ecosystem. Cumulative carbon (C) uptake was estimated to be −46.7 g C m−2 (CI95% ± 45.3) per growing season, with methane emissions offsetting an average 5.6% of carbon dioxide uptake (in g C m−2) per growing season. High air temperatures (>15 °C) reduced daily CO2 uptake and cumulative NEE was positively correlated with mean air growing season temperatures. Cumulative NME was positively correlated with the length of the growing season. Our analysis suggests warmer climate conditions may reduce carbon uptake in this LCP ecosystem.

Keywords

• carbon fluxes
• polygonal tundra
• permafrost
• climate change
• machine learning