New Techniques and Data for Understanding the Global Soil Respiration Flux

Abstract

Soil respiration (Rs; the soil surface-to-atmosphere CO2 flux) has been measured in the field for decades, but only recently have we begun to assemble and leverage these small-scale but extensive data. Recently, Zhao et al. (2017, https://doi.org/10.1002/2016ef000480) applied a novel artificial neural network model to the problem of estimating the global Rs flux and understanding its variations between regions and biomes. Their results point to a convergence in estimates of global Rs, and the power of leveraging the long record of observed Rs in global ecosystems, but also to uncertainties about soils' response to climate change. It will take a combination of long-term studies, data syntheses, modeling intercomparisons, and probably a new generation of sampling networks and experiments to fully resolve these questions.