Divergent controls of soil organic carbon between observations and process-based models

Springer Science and Business Media LLC - Tập 156 - Trang 5-17 - 2021
Katerina Georgiou1,2, Avni Malhotra1, William R. Wieder3,4, Jacqueline H. Ennis1, Melannie D. Hartman3,5, Benjamin N. Sulman6, Asmeret Asefaw Berhe7, A. Stuart Grandy8, Emily Kyker-Snowman8, Kate Lajtha9, Jessica A. M. Moore10, Derek Pierson9, Robert B. Jackson1,11
1Department of Earth System Science, Stanford University, Stanford, USA
2Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
3Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA
4Institute of Arctic and Alpine Research, University of Colorado, Boulder, USA
5National Resource Ecology Laboratory, Colorado State University, Fort Collins, USA
6Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, USA
7Department of Life and Environmental Sciences, University of California, Merced, USA
8Department of Natural Resources and the Environment, University of New Hampshire, Durham, USA
9Department of Crop and Soil Sciences, Oregon State University, Corvallis, USA
10Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, USA
11Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, USA

Tóm tắt

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.

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Tập 156

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