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@prefix dcterms: <> .
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@prefix gcis: <> .
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@prefix biro: <> .

   dcterms:identifier "key-message-12-2";
   gcis:findingNumber "12.2"^^xsd:string;
   gcis:findingStatement "Most Earth System Models (ESMs) are highly variable in projecting the direction and magnitude of soil carbon change under future scenarios. Predictions of global soil carbon change through this century range from a loss of 72 Pg C to a gain of 253 Pg C with a multimodel mean gain of 65 Pg C. ESMs projecting large gains do so largely by projecting increases in high-latitude soil organic carbon (SOC) that are inconsistent with empirical studies that indicate significant losses of soil carbon with predicted climate change (<em>high confidence</em>)."^^xsd:string;
   gcis:isFindingOf <>;
   gcis:isFindingOf <>;

## Properties of the finding:
   gcis:descriptionOfEvidenceBase "A description of the scientific concerns with current ESMs is presented in He et al. (2016). They analyzed C data from 157 globally distributed soil profiles sampled to a depth of 1 m to demonstrate that ESMs currently overestimate the soil carbon sink potential. Todd-Brown et al. (2014) also pointed out major sources of error in current ESMs and suggested that most ESMs poorly represented permafrost dynamics and omitted potential constraints on SOC storage, such as priming effects, nutrient availability, mineral surface stabilization, and aggregate formation. For example, many ESMs simulated large changes in high-latitude SOC that ranged from losses of 37 Pg C to gains of 146 Pg C. The poor performance of current ESMs can result from biases in model structure, parameterization, initial values of carbon pools, and other variables (Luo et al., 2016).<br><br>>There is currently a great deal of controversy over how to improve the representation of soil carbon in models (Chen et al., 2015); several authors suggest that microbial dynamics, including the priming effect, need better representation (Georgiou et al., 2015; Sulman et al., 2014; Wieder et al., 2014), as does soil carbon response to nitrogen enrichment (Janssens and Luyssaert 2009; Riggs and Hobbie 2016). However, there is no evidence that suggests how much detail is needed to adequately represent future soil carbon dynamics and soil carbon pools.<br><br>Deep carbon (&gt;1 m in depth) generally has been found to be more stable and resistant to management or climate change than carbon in surface soils (Rumpel and Kögel-Knabner 2010; Schrumpf et al., 2013), but, given that subsurface horizons contain more than half the soil carbon (Jobbágy and Jackson 2000), small changes could significantly affect carbon budgets. Although less well studied, deep carbon has been shown to be sensitive to management practices (Alcantara et al., 2016; Ward et al., 2016).<br><br>>Microbial dynamics, including the priming effect, are key controls on soil carbon turnover (Bernal et al., 2016; Guenet et al., 2012). Carbon-use efficiency of different substrates by microbes might be a key factor in soil carbon stabilization (Cotrufo et al., 2013)."^^xsd:string;
   gcis:assessmentOfConfidenceBasedOnEvidence "Models can be tested against empirical data, and they do not perform very well; thus, determining the accuracy of future projections is difficult."^^xsd:string;
   gcis:newInformationAndRemainingUncertainties "How much detailed information on microbial physiology, coupled carbon-nitrogen cycles, or other processes is needed to improve soil carbon models is not well known."^^xsd:string;

   a gcis:Finding .

## This finding cites the following entities:

   prov:wasDerivedFrom <>.