Statistical Uncertainty of Eddy Covariance CO2 Fluxes Inferred Using Residual Bootstrap Approach


Carbon dioxide (CO2) exchange between terrestrial systems and the atmosphere are an important element of the carbon cycle and greenhouse gas climate forcing. High-frequency eddy-covariance measurements of net ecosystem CO2 exchange (NEE) with the atmosphere are valuable resources for model parameterization, calibration, and validation. However, uncertainties in measured data (i.e., data gaps and inherent random errors) create problems for researchers attempting to quantify uncertainties in model projections of terrestrial ecosystem carbon cycling. In a recent study, researchers demonstrated that a model data fusion method (residual bootstrap) produces defensible annual NEE sums by mimicking the behavior of random errors, filling missing values, and simulating gap-filling biases. Annual NEE sums are estimated for 53 site years based on nine AmeriFlux eddy-covariance tower sites in the United States. In most cases, the annual estimates were comparable in magnitude with those obtained from gap-filled data. Additionally, compared to the AmeriFlux standardized gap filling, this approach provides better NEE estimates for moderate to longer, and more frequent, data gaps. Annual accumulated uncertainties in NEE at the 95% confidence level were ±30 gC m-2 yr-1 for evergreen needleleaf forests, ±60 gC m-2 yr-1 for deciduous broadleaf forests, and ±80 gC m-2 yr-1 for croplands. The residual bootstrap approach performed worst when gap length was greater than one month or data exclusion was greater than 90% during the growing season, common to other gap-filling techniques. However, this study produced robust results for most site years when monthly data coverage during the growing season is not extremely low. These results therefore suggest that the inclusion of NEE uncertainty estimates and better estimation for moderate to longer, and more frequent, data gaps as provided by the residual bootstrap approach can be beneficial for ecosystem model evaluation.


Wang, H.-J., W. J. Riley, and W. D. Collins. 2015. “Statistical Uncertainty of Eddy Covariance CO2 Fluxes Inferred Using a Residual Bootstrap Approach,” Agricultural and Forest Meteorology 206, 163–71. DOI: 10.1016/j.agrformet.2015.03.011.