Abstract
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 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 CO2 efflux on a 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.
Highlights
Instantaneous measurements of soil CO2 efflux, such as those made with automated respiration chambers, have gained importance for understanding ecosystem carbon dynamics in recent years (Phillips et al, 2016)
The objectives of this study are, first, to demonstrate that using the lognormal assumption leads to improved analysis of soil CO2 efflux and, second, to help readers to apply the lognormal assumption to their own data
The log transformation avoided the problematic scaling of random error with flux magnitude
Summary
Instantaneous measurements of soil CO2 efflux, such as those made with automated respiration chambers, have gained importance for understanding ecosystem carbon dynamics in recent years (Phillips et al, 2016). Derivation of ecosystem-scale CO2 efflux, involves aggregating data across several chambers and across time. This aggregation poses problems in data analysis. Flux measurements from several chambers, which are typically representative of an area below 1 m2, need to be aggregated to the plot level of hectares in order to compare them with ecosystem respiration inferred from eddy-covariance-based net land–atmosphere carbon fluxes (net ecosystem exchange, NEE) (Laville et al, 1999; Christensen et al, 1996; Held et al, 1990; Reth et al, 2005). Upscaled soil respiration should always be smaller than ecosystem respiration and NEE, because soil respiration is only a part of ecosystem respiration, and NEE is always smaller than or equal to ecosystem respiration (and see Janssens et al, 2001)
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