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.
| Original language | English |
|---|---|
| Pages (from-to) | 221-240 |
| Number of pages | 20 |
| Journal | Earth System Science Data |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 18 Feb 2019 |
| Externally published | Yes |
Bibliographical note
Funding Information:Acknowledgements. Without the dedicated work of many scientists, logistics experts and engineers over the years, we would not have been able to present this long-term eddy covariance NEE data set. We want to thank Niko Bornemann, Tim Eckhardt, Mauel Helbig, Lars Heling, Oliver Kaufmann, Zoé Rehder, Norman Rößger, Norman Rüggen, Günter Stoof and Waldemar Schneider for their commitment, diligence and ingenuity. We thank Jakob Sievers for providing us with a starting point for the Matlab implementation of the Kormann and Meixner (2001) footprint model and Norman Rößger for sharing his analysis of the long-term meteorological data from Tiksi with us. This work was supported through the Cluster of Excellence CliSAP (EXC177), Universität Hamburg, funded through the German Science Foundation (DFG), by the European Commission through the project PAGE21 (FP7-ENV-2011, 282700), and by the German Ministry of Education and Research (BMBF) through the projects CarboPerm (03G0836A) and KoPf (03F0764A).
Publisher Copyright:
© Author(s) 2019.
Funding
Acknowledgements. Without the dedicated work of many scientists, logistics experts and engineers over the years, we would not have been able to present this long-term eddy covariance NEE data set. We want to thank Niko Bornemann, Tim Eckhardt, Mauel Helbig, Lars Heling, Oliver Kaufmann, Zoé Rehder, Norman Rößger, Norman Rüggen, Günter Stoof and Waldemar Schneider for their commitment, diligence and ingenuity. We thank Jakob Sievers for providing us with a starting point for the Matlab implementation of the Kormann and Meixner (2001) footprint model and Norman Rößger for sharing his analysis of the long-term meteorological data from Tiksi with us. This work was supported through the Cluster of Excellence CliSAP (EXC177), Universität Hamburg, funded through the German Science Foundation (DFG), by the European Commission through the project PAGE21 (FP7-ENV-2011, 282700), and by the German Ministry of Education and Research (BMBF) through the projects CarboPerm (03G0836A) and KoPf (03F0764A).