Lead Authors:
Kate Lajtha, Oregon State University
Vanessa L. Bailey, Pacific Northwest National Laboratory
Contributing Authors:
Karis McFarlane, Lawrence Livermore National Laboratory
Keith Paustian, Colorado State University
Dominique Bachelet, Oregon State University
Rose Abramoff, Lawrence Berkeley National Laboratory
Denis Angers, Agriculture and Agri-Food Canada
Sharon A. Billings, University of Kansas
Darrel Cerkowniak, Agriculture and Agri-Food Canada
Yannis G. Dialynas, University of Cyprus (formerly at Georgia Institute of Technology)
Adrien Finzi, Boston University
Nancy H. F. French, Michigan Technological University
Serita Frey, University of New Hampshire
Noel P. Gurwick, U.S. Agency for International Development
Jennifer Harden, U.S. Geological Survey and Stanford University
Jane M. F. Johnson, USDA Agricultural Research Service
Kristofer Johnson, USDA Forest Service
Johannes Lehmann, Cornell University
Shuguang Liu, Central South University of Forestry and Technology
Brian McConkey, Agriculture and AgriFood Canada
Umakant Mishra, Argonne National Laboratory
Scott Ollinger, University of New Hampshire
David Paré, Natural Resources Canada, Canadian Forest Service
Fernando Paz Pellat, Colegio de Postgraduados Montecillo
Daniel deB. Richter, Duke University
Sean M. Schaeffer, University of Tennessee
Joshua Schimel, University of California, Santa Barbara
Cindy Shaw, Natural Resources Canada, Canadian Forest Service
Jim Tang, Marine Biological Laboratory
Katherine Todd-Brown, Pacific Northwest National Laboratory
Carl Trettin, USDA Forest Service
Mark Waldrop, U.S. Geological Survey
Thea Whitman, University of Wisconsin, Madison
Kimberly Wickland, U.S. Geological Survey
Science Lead:
Melanie A. Mayes, Oak Ridge National Laboratory
Review Editor:
Francesca Cotrufo, Colorado State University
Federal Liaison:
Nancy Cavallaro, USDA National Institute of Food and Agriculture


At the global scale, the response of SOC to the influences of land use, disturbances, and climate change is projected using ESMs, which include simplified versions of soil carbon cycling models (Harmon et al., 2011; Tian et al., 2015). These early soil carbon models (e.g., CENTURY, Bolker et al., 1998; RothC, Gottschalk et al., 2012) largely assume exchanges of carbon between soil carbon pools are first-order exchanges defined by pool turnover times (Todd-Brown et al., 2013), and such assumptions (and model frameworks) continue into contemporary large-scale ESMs such as the Community Land Model (Huang et al., 2018) or the E3SM Land Model (Tang and Riley 2016). However, different models use different strategies to simplify and represent the complex cycling processes that were discussed in Section 12.2; thus, model simulation results tend to diverge. For example, model outputs can vary widely in their projections of global carbon stocks and microbial respiration (Tian et al., 2015) based on nonmodeled outputs such as deep carbon storage and wetland carbon storage. The addition of land use to some models has indicated that soils previously projected to be sinks for CO2 may actually be sources (Eglin et al., 2010). Because SOC stocks are so large compared to other global compartments (e.g., vegetation and atmosphere), the wide variations in projections of SOC stocks contribute a great deal of uncertainty to future carbon cycle projections (Todd-Brown et al., 2013). Wider adoption of global data products including the Harmonized World Soil Database and SoilsGrid (FAO/IIASA/ISRIC/ISSCAS/JRC 2012; Hengl et al., 2014) may facilitate the development of new tools to better integrate both local SOC observations (Dietze et al., 2014; Xia et al., 2013; Xu et al., 2006) and global data products into future models (Hararuk et al., 2014).

At a finer scale, the recognition that small-scale proc­esses, including microbial respiration, nutrient limitation, and soil microclimate (Luo et al., 2016; Tian et al., 2015), affect overall soil carbon fluxes has prompted the emergence of microbially explicit and process-rich models for soil carbon cycling (Manzoni and Porporato 2009; Sulman et al., 2014; Tang and Riley 2014; Wieder et al., 2013). Models that include the size of the microbial biomass, microbial dormancy, and enzyme functions (Wang et al., 2014) are beginning to represent previously ignored processes such as priming (accelerated decomposition of stable carbon), mineral association, and temperature sensitivities, as well as their feedbacks to the Earth’s physical system in the form of altered GHG emissions. The most recent soil-specific models, such as the Millennial Model (Abramoff et al., 2018), further classify SOC into measurable physicochemical categories (e.g., mineral-associated carbon, carbon physically entrapped in aggregates, dissolved carbon, and fragments of plant detritus) and include explicit processes regulating the transfers of carbon between pools, in contrast to the earlier models based on empirical turnover times (Abramoff et al., 2018).

These modeling types reflect very different scales, with ESMs simulating kilometer-scale landscapes and the more process-rich models simulating regional processes at finer scales such as centimeters to meters. Bridging these scales requires further empirical understanding and new mathematical frameworks (e.g., Wang et al., 2017). As models continue to advance, other challenges include determining which new models and approaches can be parameterized with empirical data and used for larger-scale decision making.

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