Soil CO 2 efflux errors are lognormally distributed – implications and guidance

Wutzler, Thomas; Perez-Priego, Oscar; Morris, Kendalynn; El-Madany, Tarek S.; Migliavacca, Mirco

Soil inline-formulaCO2 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 lognormally distributed random error in observed fluxes by aggregating 1 year of data of four neighboring automatic chambers at a Mediterranean savanna-type site.

With the lognormal assumption, problems with error structure diminished, and more reasonable prediction intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important on short timescales 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 lognormal assumption. Model data integration studies should compare predictions and observations of soil inline-formulaCO2 efflux on a log scale. This study provides methodology and guidance that will improve the analysis of soil inline-formulaCO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.

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Wutzler, Thomas / Perez-Priego, Oscar / Morris, Kendalynn / et al: Soil CO2 efflux errors are lognormally distributed – implications and guidance. 2020. Copernicus Publications.

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