The research on Soil Organic Carbon, SOC, cited in the linked article and associated reference will be incorporated into models like E3SMv2; which will allow such advanced climate models to account for topographical and environmental controllers of associated GHG emission/sequestering feedbacks that are currently ignored by climate models (including those in CMIP6). While this may seem esoteric, I am concerned that potential freshwater hosing events in the coming decades (e.g. from the Beaufort Gyre, from the WAIS and from the GIS) many abruptly increase tropical ocean SSTAs (due to a slowing of the MOC); which may warm GMSTA sufficiently to release substantial amounts of GHGs, from the SOC, into the atmosphere; which could (in coming centuries) contribute to
sustained 'Hot House' global conditions (resulting from temporary freshwater hosing events):
Title: "Argonne soil carbon research reduces uncertainty in predicting climate change impacts"
https://www.eurekalert.org/pub_releases/2020-07/dnl-asc070920.phpExtract: "The scaling algorithms that he and his collaborators created as part of the research are important to Earth system models, like the DOE's Energy Exascale Earth System Model, in addition to predicting changes in climate more accurately.
Scaling, Mishra noted, is an issue which has traditionally been ignored in biogeochemical/natural sciences, where it was believed that properties or processes associated with one spatial scale can be applied at both smaller or larger scales. In reality, however this is not the case.
Current Earth system models, which are used to predict the future global carbon climate feedbacks and associated climate changes, operate at coarse spatial scales (50-100 km) and are currently unable to represent environmental controllers and their effect on soil organic carbon in a manner consistent with field observations."
See also:
K.Adhikari aet al. (2020), "Importance and strength of environmental controllers of soil organic carbon changes with scale", Geoderma,
https://doi.org/10.1016/j.geoderma.2020.114472https://www.sciencedirect.com/science/article/pii/S0016706120305139AbstractSpatial heterogeneity in environmental factors on the land surface moderates exchanges of water, energy, and greenhouse gases between the land and the atmosphere. However, appropriately representing this heterogeneity in earth system models remains a critical scientific challenge. We used a large dataset of environmental factors (n = 31) representing soil-forming factors, field observations of soil organic carbon (SOC) (n = 6213), and a machine-learning algorithm (Cubist) to analyze the scaling behavior of SOC across the conterminous United States. We found that various environmental factors are significant predictors of SOC stocks at different spatial scales. Out of the 31 environmental factors we investigated, only 13 were significant predictors of SOC stocks at spatial scales ranging from 100 m to 50 km. Overall, topographic variables had higher influence at finer scales, whereas climatic variables were more important at coarser scales. The model performance worsened with increasing scale or the spatial resolution of prediction (R2 = 0.38–0.65). The strength of environmental controls (median regression coefficient) on SOC weakened with scale, and we represented them using mathematical functions (R2 = 0.38–0.98). Both the mean and variance of SOC stocks decreased linearly with increasing the scale in soils of the conterminous United States. Fitted linear functions accounted for 81% and 82% of the variability in the mean and variance of SOC, respectively. We also found linear relationships among mean and high-order moments of SOC (R2 = 0.51–0.97). Improved understanding of the scaling behavior of SOC stocks and their environmental controllers can improve earth system model benchmarking and may eventually improve representation of the spatial heterogeneity of land surface biogeochemistry.