A long-term (2002 to 2017) record of closed-path and open-path eddy covariance CO<sub>2</sub> net ecosystem exchange fluxes from the Siberian Arctic

Abstract

Abstract. Ground-based observations of land–atmosphere fluxes are necessary to progressively improve global climate models. Observed data can be used for model evaluation and to develop or tune process models. In arctic permafrost regions, climate–carbon feedbacks are amplified. Therefore, increased efforts to better represent these regions in global climate models have been made in recent years. We present a multi-annual time series of land–atmosphere carbon dioxide fluxes measured in situ with the eddy covariance technique in the Siberian Arctic (72∘22′ N, 126∘30′ E). The site is part of the international network of eddy covariance flux observation stations (FLUXNET; site ID: Ru-Sam). The data set includes consistently processed fluxes based on concentration measurements of closed-path and open-path gas analyzers. With parallel records from both sensor types, we were able to apply a site-specific correction to open-path fluxes. This correction is necessary due to a deterioration of data, caused by heat generated by the electronics of open-path gas analyzers. Parameterizing this correction for subperiods of distinct sensor setups yielded good agreement between open- and closed-path fluxes. We compiled a long-term (2002 to 2017) carbon dioxide flux time series that we additionally gap-filled with a standardized approach. The data set was uploaded to the Pangaea database and can be accessed through https://doi.org/10.1594/PANGAEA.892751.

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

“…Previous research based at the research site has focused on greenhouse gas cycling (Abnizova et al, 2012;Knoblauch et al, 2018Knoblauch et al, , 2015Kutzbach et al, , 2007Langer et al, 2015;Runkle et al, 2013;Sachs et al, 2010Sachs et al, , 2008Holl et al, 2019), aquatic biology (Abramova et al, 2017), upscaling of land surface characteristics and parameters from ground-based data to remote-sensing data (Cresto Aleina et al, 2013;Muster et al, , 2012, and hydrology (Boike et al, 2008b;Fedorova et al, 2015;Helbig et al, 2013). Data from a few years have also been used in earth system modelling (Chadburn et al, 2015(Chadburn et al, , 2017Ekici et al, 2014Ekici et al, , 2015 and for modelling land surface, snow, and permafrost processes Langer et al, 2016;Westermann et al, 2016Westermann et al, , 2017Yi et al, 2014;Aas et al, 2019).…”

Partitioning net ecosystem exchange of CO<sub>2</sub> on the pedon scale in the Lena River Delta, Siberia

“…These individual process-based fluxes governing the CO 2 balance respond differently to changing climatic conditions such as temperature and moisture. For instance, it was shown that temperature changes in arctic soils could cause a significant increase in the CO 2 uptake via GPP (Shaver et al, 1998;Oberbauer et al, 2007;Natali et al, 2012;Mauritz et al, 2017), which can be, beside other factors, attributed to shifts in vegetation composition (Elmendorf et al, 2012;Hudson et al, 2011) and increased nutrient availability (Johnson et al, 2000;Salmon et al, 2016;Beermann et al, 2015). Furthermore, the effect of drainage on GPP remains uncertain; some studies found drainage of arctic soils to reduce GPP (Merbold et al, 2009;Chivers et al, 2009;Kwon et al, 2016), while other studies found drainage to lead to a slight increase in GPP (Olivas et al, 2010;Kittler et al, 2016).…”

Cushion bogs are stronger carbon dioxide net sinks than moss-dominated bogs as revealed by eddy covariance measurements on Tierra del Fuego, Argentina

“…A comprehensive description of the EC approach is given, for example, by Aubinet et al (2012). Half-hourly turbulent CO 2 fluxes were computed using the software EddyPro 6.2.0 (LI-COR, USA) and included the following standardized steps; see Holl (2017); Holl et al (2019).…”

“…Correction factors F 1 were calculated with two methods depending on sensible and latent heat fluxes being above (high fluxes) or below (low fluxes) the thresholds of 10 and 5 W m −2 respectively (Fratini et al, 2012). For high fluxes, EddyPro calculated the correction factors as proposed by Hollinger et al (1999). F 1 estimation included the degradation of the unattenuated sensible heat flux cospectrum by multiplying it with H IIR (f n /f c ) for the previously determined f c .…”