Earth System Science Data (Oct 2020)

A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models

  • X. Morel,
  • B. Hansen,
  • C. Delire,
  • P. Ambus,
  • M. Mastepanov,
  • M. Mastepanov,
  • B. Decharme

DOI
https://doi.org/10.5194/essd-12-2365-2020
Journal volume & issue
Vol. 12
pp. 2365 – 2380

Abstract

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Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon because their high water content limits decomposition rates to levels below their net primary productivity. Their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration. Nuuk fen is a well-instrumented Greenlandic fen with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2) and is of particular interest for testing and validating land-surface models. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To address this issue, we measured, for the first time, soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland (64∘07′51′′ N, 51∘23′10′′ W), colocated with the greenhouse gas measurements. Measurements were made along two transects, 60 and 90 m long and with a horizontal resolution of 5 m and a vertical resolution of 5 to 10 cm, using a 4 cm diameter gouge auger. A total of 135 soil samples were analyzed. Soil carbon density varied between 6.2 and 160.2 kg C m−3 with a mean value of 50.2 kg C m−3. Mean soil nitrogen density was 2.37 kg N m−3. Mean soil carbon and nitrogen stocks are 36.3 kg C m−2 and 1.7 kg N m−2. These new data are in the range of those encountered in other arctic peatlands. This new dataset, one of very few in Greenland, can contribute to further development of joint modeling of greenhouse gas emissions and soil carbon and nitrogen in land-surface models. The dataset is open-access and available at https://doi.org/10.1594/PANGAEA.909899 (Morel et al., 2019b).