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Discussion papers
https://doi.org/10.5194/gi-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Jun 2019

Submitted as: research article | 04 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Instrumentation, Methods and Data Systems (GI).

Soil CO2 efflux errors are log normally distributed – Implications and guidance

Thomas Wutzler, Oscar Perez-Priego, Kendalynn Morris, Tarek El-Madany, and Mirco Migliavacca Thomas Wutzler et al.
  • Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany

Abstract. Soil CO2 efflux is the second largest carbon flux in terrestrial ecosystems. Its feedback to climate determines model predictions of the land carbon sink, which is crucial to understanding the future of the earth system. For understanding and quantification, however, observations by the most widely applied chamber measurement method need to be aggregated to larger temporal and spatial scales. The aggregation is hampered by random error that is characterized by occasionally large fluxes and variance heterogeneity that is not properly accounted for under the typical assumption of normally distributed fluxes.

Therefore, we explored the effect of different distributional assumptions on the aggregated fluxes. We tested the alternative assumption of log-normally distributed random error in observed fluxes by aggregating one year of data of four neighbouring automatic chambers at a Mediterranean savanna-type site.

With the lognormal assumption, problems with error structure diminished and more reasonable confidence intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important at short time scales and were especially pronounced when aggregating across chambers to plot level.

Hence we recommend as a good practice that researchers report plot-level fluxes with uncertainties based on the log-normal assumption. Model-data integration studies should compare predictions and observations of soil CO2 efflux at log scale. This study provides methodology and guidance that will improve the analysis of soil CO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.

Thomas Wutzler et al.
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Thomas Wutzler et al.
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Latest update: 19 Aug 2019
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Short summary
Continuous data of soils CO2 efflux can improve model predictions of climate warming and automated data is becoming increasingly available. However, aggregating chamber-based data to plot scale poses challenges. Therefore, we showed using one year of half-hourly data how using the lognormal assumption tackles several challenges. We propose that plot-scale SO2 efflux observations should be reported together with lognormally based uncertainties and enter model constraining frameworks at log-scale.
Continuous data of soils CO2 efflux can improve model predictions of climate warming and...
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