Microbial ecology motivates statistical approaches to soil carbon dynamics

Terrestrial ecosystems absorb ≈30% of anthropogenic CO2 emissions in a process termed the land sink. This process thus mitigates a large fraction of current and future climate change, and Earth’s future climate depends greatly on whether or not the land sink continues. Accumulation of soil organic carbon (SOC), is responsible for a large fraction of carbon absorbed, yet the computational models that we rely on to simulate SOC dynamics are too complex to effectively use the limited available data, leading to very large uncertainty in their projections. To help address these challenges, we develop simple-yet-powerful statistical models of soil organic carbon degradation that use observations of carbon turnover time and radiocarbon content to constrain the ~10-100 year dynamics of SOC, the time scale over which societies plan for climate change. In this talk, I will motivate a statistical approach to SOC dynamics from a simple picture of collective microbial metabolism in three dimensions. I will then show that these “disordered” statistical models can be independently parameterized from available data, and have predictive performance on par with or exceeding state-of-the-art models, while having many fewer parameters.