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Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical modelshave traditionally been simulated as immeasurable fluxes between conceptuallydefined pools. This greatly limits how empirical data can be used to improvemodel performance and reduce the uncertainty associated with theirpredictions of carbon (C) cycling. Recent advances in our understanding ofthe biogeochemical processes that govern SOM formation and persistence demanda new mathematical model with a structure built around key mechanisms andbiogeochemically relevant pools. Here, we present one approach that aims toaddress this need. Our new model (MEMS v1.0) is developed from the MicrobialEfficiency-Matrix Stabilization framework, which emphasizes the importance oflinking the chemistry of organic matter inputs with efficiency of microbialprocessing and ultimately with the soil mineral matrix, when studying SOMformation and stabilization. Building on this framework, MEMS v1.0 is alsocapable of simulating the concept of C saturation and representsdecomposition processes and mechanisms of physico-chemical stabilization todefine SOM formation into four primary fractions. After describing the modelin detail, we optimize four key parameters identified through avariance-based sensitivity analysis. Optimization employed soil fractionationdata from 154 sites with diverse environmental conditions, directly equatingmineral-associated organic matter and particulate organic matter fractionswith corresponding model pools. Finally, model performance was evaluatedusing total topsoil (0–20 cm) C data from 8192 forest and grassland sitesacross Europe. Despite the relative simplicity of the model, it was able toaccurately capture general trends in soil C stocks across extensive gradientsof temperature, precipitation, annual C inputs and soil texture. The novelapproach that MEMS v1.0 takes to simulate SOM dynamics has the potential toimprove our forecasts of how soils respond to management and environmentalperturbation. Ensuring these forecasts are accurate is key to effectivelyinforming policy that can address the sustainability of ecosystem servicesand help mitigate climate change.
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Emergent temperature sensitivity of soil organic carbon driven by mineral associations
Abstract Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.
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- PAR ID:
- 10491626
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Geoscience
- Volume:
- 17
- Issue:
- 3
- ISSN:
- 1752-0894
- Format(s):
- Medium: X Size: p. 205-212
- Size(s):
- p. 205-212
- Sponsoring Org:
- National Science Foundation
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