Lajtha, K., V. L. Bailey, K. McFarlane, K. Paustian, D. Bachelet, R. Abramoff, D. Angers, S. A. Billings, D. Cerkowniak, Y. G. Dialynas, A. Finzi, N. H. F. French, S. Frey, N. P. Gurwick, J. Harden, J. M. F. Johnson, K. Johnson, J. Lehmann, S. Liu, B. McConkey, U. Mishra, S. Ollinger, D. Paré, F. Paz Pellat, D. deB. Richter, S. M. Schaeffer, J. Schimel, C. Shaw, J. Tang, K. Todd-Brown, C. Trettin, M. Waldrop, T. Whitman, and K. Wickland, 2018: Chapter 12: Soils. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report [Cavallaro, N., G. Shrestha, R. Birdsey, M. A. Mayes, R. G. Najjar, S. C. Reed, P. Romero-Lankao, and Z. Zhu (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 469-506, https://doi.org/10.7930/SOCCR2.2018.Ch12
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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