Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method

Keyphrases

• Soil Carbon 100%
• Soil Organic Carbon 100%
• Carbon Data 100%
• Global Land Model 100%
• Bayesian Markov Chain Monte Carlo Method 100%
• Climate Change 50%
• Biogeochemistry 37%
• Carnegie-Ames-Stanford Approach 37%
• Community Land Model 37%
• Parameter Calibration 25%
• Soil Organic Carbon Pools 25%
• Global Variation 12%
• Partition Coefficient 12%
• Model Prediction 12%
• Carbon (C) Cycle 12%
• Temperature Sensitivity 12%
• Model Performance 12%
• Global Datasets 12%
• Observational Data 12%
• Data Assimilation 12%
• C Decomposition 12%
• Carbon Residence Time 12%
• Model Observations 12%
• Carbon Prediction 12%
• Gridded Observations 12%
• C Partitioning 12%

Earth and Planetary Sciences

• Soil Carbon 100%
• Soil Organic Carbon 100%
• Markov Chain Monte Carlo 100%
• Biogeochemistry 30%
• Climate Change 20%
• Data Assimilation 10%
• Data Product 10%