Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 1321-1337, 2017
https://doi.org/10.5194/gmd-10-1321-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
28 Mar 2017
Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models
Shoji Hashimoto1, Kazuki Nanko1, Boris Ťupek2, and Aleksi Lehtonen2 1Forestry and Forest Products Research Institute (FFPRI), Tsukuba, Japan
2Natural Resources Institute Finland, Latokartanonkaari 9, Helsinki, Finland
Abstract. Future climate change will dramatically change the carbon balance in the soil, and this change will affect the terrestrial carbon stock and the climate itself. Earth system models (ESMs) are used to understand the current climate and to project future climate conditions, but the soil organic carbon (SOC) stock simulated by ESMs and those of observational databases are not well correlated when the two are compared at fine grid scales. However, the specific key processes and factors, as well as the relationships among these factors that govern the SOC stock, remain unclear; the inclusion of such missing information would improve the agreement between modeled and observational data. In this study, we sought to identify the influential factors that govern global SOC distribution in observational databases, as well as those simulated by ESMs. We used a data-mining (machine-learning) (boosted regression trees – BRT) scheme to identify the factors affecting the SOC stock. We applied BRT scheme to three observational databases and 15 ESM outputs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and examined the effects of 13 variables/factors categorized into five groups (climate, soil property, topography, vegetation, and land-use history). Globally, the contributions of mean annual temperature, clay content, carbon-to-nitrogen (CN) ratio, wetland ratio, and land cover were high in observational databases, whereas the contributions of the mean annual temperature, land cover, and net primary productivity (NPP) were predominant in the SOC distribution in ESMs. A comparison of the influential factors at a global scale revealed that the most distinct differences between the SOCs from the observational databases and ESMs were the low clay content and CN ratio contributions, and the high NPP contribution in the ESMs. The results of this study will aid in identifying the causes of the current mismatches between observational SOC databases and ESM outputs and improve the modeling of terrestrial carbon dynamics in ESMs. This study also reveals how a data-mining algorithm can be used to assess model outputs.

Citation: Hashimoto, S., Nanko, K., Ťupek, B., and Lehtonen, A.: Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models, Geosci. Model Dev., 10, 1321-1337, https://doi.org/10.5194/gmd-10-1321-2017, 2017.
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Short summary
Soil organic carbon (SOC) stock simulated by Earth system models (ESMs) and those of observational databases are not well correlated when the two are compared at fine grid scales. To identify the key factors that govern global SOC distribution, we applied a data-mining scheme to observational databases and outputs from ESMs. This study not only identifies key factors but it also presents a new approach that compares the observational databases with ESM outputs.
Soil organic carbon (SOC) stock simulated by Earth system models (ESMs) and those of...
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