Modeling the gap: Scaling soil carbon models from the microbe to the globe
Soil carbon decomposition is a large contributor of natural CO2 emissions that is poorly captured in current Earth system models. Predictions for soil carbon stocks in the Earth system models in the most recent IPCC report vary widely in the 21stcentury and have relatively poor correlation to soil survey data products. This has motivated the development of several new microbial-focused global scale decomposition models. However these new microbial-focused models frequently produce unrealistic decadal scale oscillations and are quite difficult to parameterize at the global scale.
Here we propose combining several key mechanistic models borrowed from other fields with explicit scaling techniques as a potential path forward.
Soil decomposition is the result of both biotic and abiotic processes occurring across a range of spatial and temporal scales. Biologically soil decomposition occurs when microbes consume organic carbon found in their environment, processes that can be modeled using various growth dynamics and evolutionary tradeoffs. The abiotic environment in which this biology operates presents it’s own modeling history and challenges including: 1) chemical reactions using kinetic models from biochemistry, 2) the diffusion of organic carbon and mineral-organic interactions which parallels contaminant transport models in subsurface soils, 3) soil hydrology and rate-limited diffusion which has a long history in soil hydrological modeling, and 4) the formation of soil aggregates and physical changes in pore space which has only one model know to the authors.
The main challenge is to integrate these various components at the appropriate scale and then extend that to the global scale. Decomposition occurs at the microbe scale, is generally measured at the field scale, and is of interest to climate projections at the global scale. Explicit scaling could take two forms: analytical or empirical. Analytically simplifications in the underlying equations could simplify the governing equations either through an order of magnitude analysis or bifurcation analysis. Empirically the model could be aggregated across a reasonable distribution of environmental conditions using statistical methods; this approach has already shown that scaling traditional models from the field to the globe collapses the multi-pool model to a single pool across reasonable parameterization and driving variables.
New models must take into account both driving processes and explicit scaling if we are to advance the field and incorporate new fine resolution biological and chemical measurements.