U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Statistical Uncertainty of Eddy Covariance CO2 Fluxes Inferred Using Residual Bootstrap Approach
Published: June 15, 2015
Posted: July 24, 2015

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.

Reference: 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. (Reference link)

Contact: Dorothy Koch, SC-23.1, (301) 903-0105
Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science
  • Research Area: Carbon Cycle, Nutrient Cycling

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]

Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]

Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]

Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]

Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]

List all highlights (possible long download time)