{"type": "FeatureCollection", "features": [{"id": "10.1029/2018GB005950", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:18:29Z", "type": "Journal Article", "created": "2018-10-12", "title": "Soil Organic Matter Persistence as a Stochastic Process: Age and Transit Time Distributions of Carbon in Soils", "description": "<p>The question of why some types of organic matter are more persistent while others decompose quickly in soils has motivated a large amount of research in recent years. Persistence is commonly characterized as turnover or mean residence time of soil organic matter (SOM). However, turnover and residence times are ambiguous measures of persistence, because they could represent the concept of either age or transit time. To disambiguate these concepts and propose a metric to assess SOM persistence, we calculated age and transit time distributions for a wide range of soil organic carbon models. 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(2) The difference between mean ages and mean transit times is large, with estimates of soil organic carbon persistence on the order of centuries or millennia when assessed using ages and on the order of decades when using transit or turnover times. (3) The age distribution is an appropriate metric to characterize persistence of SOM. An important implication of our analysis is that random chance is a factor that helps to explain why some organic matter persists for millennia in soil.</p></article>", "keywords": ["2. Zero hunger", "Aging", "time scales", "04 agricultural and veterinary sciences", "carbon storage", "15. Life on land", "Oceanography", "01 natural sciences", "soil models", "Atmospheric Sciences", "Geochemistry", "Climate change impacts and adaptation", "13. 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(2) The difference between mean ages and mean transit times is large, with estimates of soil organic carbon persistence on the order of centuries or millennia when assessed using ages and on the order of decades when using transit or turnover times. (3) The age distribution is an appropriate metric to characterize persistence of SOM. An important implication of our analysis is that random chance is a factor that helps to explain why some organic matter persists for millennia in soil.</p></article>", "keywords": ["2. Zero hunger", "Aging", "time scales", "04 agricultural and veterinary sciences", "carbon storage", "15. Life on land", "Oceanography", "01 natural sciences", "soil models", "Atmospheric Sciences", "Geochemistry", "Climate change impacts and adaptation", "13. 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Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time&amp;amp;#8208;dependent linear compartmental system is computed with its respective state&amp;amp;#8208;transition matrix. Using transient atmospheric &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C values as inputs, the state&amp;amp;#8208;transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. We demonstrate the approach with ELMv1&amp;amp;#8208;ECA, the land component of an ESM model that explicitly represents &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C, and &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C in 7 soil pools and 10 vertical layers. Results from our proposed method are highly accurate (relative error &amp;lt;0.01%) compared with the ELMv1&amp;amp;#8208;ECA &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C and &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C predictions, demonstrating the potential to use this approach in CMIP6 and other model simulations that do not explicitly represent &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C.&amp;lt;/p&amp;gt;         </p></article>", "keywords": ["Physical geography", "Earth system models", "GC1-1581", "dynamical systems", "15. Life on land", "Oceanography", "compartmental systems", "01 natural sciences", "GB3-5030", "13. 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Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed 12C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time\uffe2\uff80\uff90dependent linear compartmental system is computed with its respective state\uffe2\uff80\uff90transition matrix. Using transient atmospheric 14C values as inputs, the state\uffe2\uff80\uff90transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. 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