Divergent data-driven estimates of global soil respiration

Abstract

The release of carbon dioxide from the soil to the atmosphere, known as soil respiration, is the second largest terrestrial carbon flux after photosynthesis, but the convergence of the data-driven estimates is unclear. Here we collate all historical data-driven estimates of global soil respiration to analyze convergence and uncertainty in the estimates. Despite the development of a dataset and advanced scaling techniques in the last two decades, we find that inter-model variability has increased. Reducing inter-model variability of global soil respiration is not an easy task, but when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable.

Inter-model variability of global soil respiration estimates has increased, highlighting the urgency to understand model uncertainty and the need for an accurate estimate of global soil respiration, according to a review of historical data-driven spatiotemporal estimates.