توضیحات
ABSTRACT
Soil carbon dynamics are a key process in the terrestrial carbon cycle. Future climate change will dramatically 10 change the carbon balance in 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 produce future climate projections, 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 factors, that govern the SOC stock, remain unclear, and the inclusion of such missing information would improve the agreement between 15 modelled and observational data. In this study, we aimed 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) scheme (boosted regression trees: BRT) to reveal the factors affecting the SOC stock. We applied BRT 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, 20 and land-use history). These analyses revealed the influential variables and their correlations with SOC. Globally, the contributions of mean annual temperature, clay content, CN ratio, wetland ratio, and land cover were high in observational databases, whereas the contribution of mean annual temperature, land cover, and NPP governed the SOC distribution in ESMs. A comparison of the influential factors in observational databases and ESMs, at the global scale, revealed that the CN ratio and clay content were key processes to include in ESMs to reproduce the distribution of SOC in observational 25 databases. The results of this study will help elucidate the nature of both observational SOC databases and ESM outputs and improve the modelling of terrestrial carbon dynamics with ESMs. This study shows that a data-mining algorithm can be used to assess model outputs.
INTRODUCTION
Soil is the largest organic carbon stock in terrestrial ecosystems (Batjes, 1996; IPCC, 2013; Köchy et al., 2015). The soil 30 organic carbon (SOC) stock is the result of the balance between carbon input into soil and decomposition, and the soil carbon influx and efflux are controlled directly and indirectly by environmental conditions (Carvalhais et al., 2014; Schimel et al., 1994). Future climate change will dramatically affect the carbon balance in the soil cycle (Bond-Lamberty and Thomson, 2010; Friedlingstein et al., 2006; Hashimoto et al., 2011, 2015), and this change will affect the terrestrial carbon and, consequently, the climate itself (Cox et al., 2000; Zaehle, 2013). 35 In the last two decades, several global soil databases have been developed, and some are under further improvement (Scharlemann et al., 2014). These databases describe the global distribution of soil physiochemical properties, enabling us to calculate the global distribution of the SOC stock (e.g., Harmonized World Soil Database), and some databases provide the SOC stock by default (e.g., IGBP-DIS). These databases are based on observed data points with global coverage, although there are biases in the spatial distribution or densities of the data points.
Year: 2016
Publishe: FFPRI , P.O
By: Shoji Hashimoto , Kazuki Nanko , Boris Ťupek , Aleksi Lehtonen
File Information: English Language/ 22 Page / size:2,858KB
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سال : 2016
ناشر : FFPRI , P.O
کاری از : Shoji Hashimoto , Kazuki Nanko , Boris Ťupek , Aleksi Lehtonen
اطلاعات فایل : زبان انگلیسی / 22 صفحه / حجم : 2,858KB
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