2020 ESA Annual Meeting (August 3 - 6)

OOS 8 Abstract - Evaluating soil and ecosystem carbon dynamics at the continental scale by leveraging data available from the National Ecological Observatory Network

Monday, August 3, 2020: 3:15 PM
Debjani Sihi, Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD
Background/Question/Methods

The terrestrial biosphere is currently serving as a large sink for atmospheric carbon dioxide (CO2), absorbing approximately one-third of the anthropogenic CO2 emissions. However, the fate of this land-atmosphere exchange of CO2 remains uncertain under current and future climate, partly due to our limited understanding of belowground processes and the belowground-aboveground linkages. Land-atmosphere exchange of CO2 is likely the results of the dynamic interplay between biological, physical, and geochemical processes. Earth system models generally ignore these complex feedbacks and often fail to reproduce the soil and ecosystem response to global change drivers. To that end, standardized measurements available across 47 NEON terrestrial field sites, covering 20 ecoclimatic domains, across the United States provide an unprecedented opportunity to evaluate soil and ecosystem carbon (C) dynamics at the continental scale. In this presentation, I will focus on the influence of biotic and abiotic drivers, identify the variables of interest out of a given set of drivers, explore the shape of the response-driver relations by leveraging terrestrial biogeochemistry, microbial ecology, eddy covariance, and airborne data products.

Results/Conclusions

Preliminary findings indicated that a comprehensive understanding of biotic and abiotic controls on terrestrial C cycle processes requires multi-scale and multi-disciplinary approaches. The C-climate feedbacks in the terrestrial systems is contingent on our capacity to deconvolve when belowground processes are coupled with or decoupled from ecosystem C fluxes across different vegetation types, climates, and timescale. The product of this work will provide emerging constraints for improving the predictive capability of land surface models.