The Science

Scientists have spent decades making measurements of soil respiration (R_S), the flow of carbon dioxide from the soil to the atmosphere, but only recently have they started to collect and synthesize this information. A recent review argues that these data offer untapped potential for better understanding the larger carbon cycle and improving the performance of ecosystem- to global-scale computer models.

The Impact

Soil respiration data can bring a range of benefits to model development, particularly with larger databases and improved data-sharing protocols that make R_S data more robust and broadly available to the research community. These efforts can help usher in new global syntheses and spark progress in both measurement and modeling of biogeochemical cycles.

Summary

Model-data synthesis activities are increasingly important to understand the carbon and climate systems, but only rarely have they used R_S data. In an invited review, U.S. Department of Energy researchers at Pacific Northwest National Laboratory and co-authors argue that overlooking R_S data is a mistake and identify three major challenges in interpreting and using R_S data more extensively and creatively. First, when R_S is compared to ecosystem respiration measured from eddy covariance towers, it is not uncommon to find the former to be larger, which is impossible. This finding is most likely because of difficulties in calculating ecosystem respiration, which provides an opportunity to utilize R_S for eddy covariance quality control. Second, R_S integrates belowground heterotrophic and autotrophic activity (i.e., from plant- and animal-derived carbon), and opportunities exist to use the total R_S flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. Finally, R_S is generally measured at a different resolution than that needed for comparison to eddy covariance or ecosystem- to global-scale models. Downscaling these fluxes to match the scale of R_S, and improving R_S upscaling techniques, will improve resolution challenges.

Principal Investigator

Ben Bond-Lamberty
Pacific Northwest National Laboratory
bondlamberty@pnnl.gov

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
daniel.stover@science.doe.gov

Funding

ARD acknowledges support to UW from National Science Foundation (NSF) Advances in Biological Informatics. Funding for AmeriFlux data resources was provided by the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy’s (DOE) Office of Science. RV acknowledges support from the U.S. Department of Agriculture. Ben Bond-Lamberty was supported by the DOE Office of Science as part of the BER Terrestrial Ecosystem Science program. Katherine Todd-Brown was supported by the Linus Pauling Distinguished Postdoctoral Fellowship program, part of the Laboratory-Directed Research and Development Program at Pacific Northwest National Laboratory, a multiprogram national laboratory operated by Battelle for DOE. JT was supported by NSF, the University of Chicago, and the MBL Lillie Research Innovation Award.

References