Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS) Journal Articles uri icon

  •  
  • Overview
  •  
  • Research
  •  
  • Identity
  •  
  • Additional Document Info
  •  
  • View All
  •  

abstract

  • AbstractThe Global Carbon Assimilation System that assimilates ground‐based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C ( ), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both and Q10 exhibit pronounced seasonal variations at middle‐high latitudes. The maxima in occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of and Q10 are larger at higher latitudes. Optimized and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in . The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

authors

  • Chen, Zhuoqi
  • Chen, Jing
  • Zhang, Shupeng
  • Zheng, Xiaogu
  • Ju, Weiming
  • Mo, Gang
  • Lu, Xiaoliang

publication date

  • December 2017