Skip to main content
U.S. flag

An official website of the United States government

Publication Date
8 April 2019

Comparison with Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model

Subtitle
Soil radiocarbon observations highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.
Print / PDF
Powerpoint Slide
Science

Berkeley Lab and University of California, Irvine researchers used a large database of soil organic bulk carbon (C) and radiocarbon (14C) to evaluate the new global land model ELMv1-ECA. The model provided good estimates of Δ14C values and soil organic carbon (SOC) stocks near the surface but underestimated ages at depth. However, using gridcell-specific rooting and decomposition rates reduced discrepancies between observed and predicted Δ14C values and SOC stocks, indicating that site-specific parameters are important. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.

Impact

Soil organic matter Δ14C measurements can provide an important constraint to land model biogeochemical representations, potentially alleviating the large parameter equifinality common to land models. Using observations from over 500 sites, we highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.

Summary

Quantifying feedbacks between the terrestrial carbon cycle and climate is important for predicting land-atmosphere interactions. Among many factors that control terrestrial carbon cycle responses to climate, soil organic carbon (SOC) dynamics are particularly important, although highly uncertain. In this regard, radiocarbon (14C) is an important observational constraint for land model predictions. We evaluated, against worldwide observations of SOC stocks and 14C, predictions from a land model (ELMv1-ECA) used for climate-change analyses. We analyzed differences between model predictions and observations using a machine-learning method at the ESM gridcell scale (~200 km). The influences of soil albedo index, soil order, and air temperature were primarily important for topsoil. Our sensitivity analysis highlights the role of plant root activity in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical-chemical protection of SOC. Allowing for gridcell-specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted values. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.

Point of Contact
William J. Riley
Institution(s)
Lawrence Berkeley National Laboratory (LBNL)
Funding Program Area(s)
Publication