Evaluationand improvementof aglobal landmodel against soil carbon data using a Bayesian Markov chain Monte Carlo method

@inproceedings{Hararuk2014EvaluationandIA,
  title={Evaluationand improvementof aglobal landmodel against soil carbon data using a Bayesian Markov chain Monte Carlo method},
  author={Oleksandra Hararuk and J. Xia and Yiqi Luo},
  year={2014}
}
Long-term land carbon (C) cycle feedback to climate change is largely determined by dynamics of soil organic carbon (SOC). However, most evaluation studies conducted so far indicate that global land models predict SOC poorly. This study was conducted to evaluate predictions of SOC by the Community Land Model with Carnegie-Ames-Stanford Approach biogeochemistry submodel (CLM-CASA’), investigate underlying causes of mismatches between model predictions and observations, and calibrate model… CONTINUE READING
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