Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements

• Published in: Biogeosciences
• Main Authors: Pallandt, Martijn A., Kumar, Jitendra, Mauritz, Marguerite, Schuur, Edward G., Virkkala, Anna-Maria, Celis, Gerardo, Hoffman, Forrest M., Göckede, Mathias
• Language: unknown
• Published: 2023
• Subjects: 54 ENVIRONMENTAL SCIENCES; Arctic; Climate change; permafrost
• Online Access: http://www.osti.gov/servlets/purl/1844894; https://www.osti.gov/biblio/1844894; https://doi.org/10.5194/bg-19-559-2022

Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatiotemporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network's representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets and changes therein. This study provides an inventory of Arctic (here >= 60°N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/, last access: 25 January 2022). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain based on 18 bioclimatic and edaphic variables. We find that this allows us to assess a general level of similarity between ecosystem conditions within the domain, while not necessarily reflecting changes in greenhouse gas flux rates directly. We define two metrics based on this representativeness score: one that measures whether a location is represented by an EC tower with similar characteristics (ER1) and a second for which we assess if a minimum level of representation for statistically rigorous extrapolation is met (ER4). We find that while half of the domain is represented by at least one tower, only a third has enough towers in similar locations to allow reliable extrapolation. When we consider methane measurements or year-round (including wintertime) ...