Bayesian Optimization Of The Community Land Model Simulated Biosphere–Atmosphere Exchange Using CO2 Observations From A Dense Tower Network And Aircraft Campaigns Over Oregon

  • Sites: US-Bsg
  • Publication Type: JOUR
  • Authors: Schmidt, A.; Law, B. E.; Göckede, M.; Hanson, C.; Yang, Z.; Conley, S.

  • The vast forests and natural areas of the Pacific Northwest compose one of the most productive ecosystems in the Northern Hemisphere. The heterogeneous landscape of Oregon poses a particular challenge to ecosystem models. This study presents a framework using a scaling factor Bayesian inversion to improve the modeled atmosphere–biosphere exchange of CO2. Observations from five CO/CO2 towers, eddy covariance towers, and airborne campaigns were used to constrain the Community Land Model, version 4.5 (CLM4.5), simulated terrestrial CO2 exchange at a high spatial and temporal resolution (1/24°; 3 hourly). To balance aggregation errors and the degrees of freedom in the inverse modeling system, the authors applied an unsupervised clustering approach for the spatial structuring of the model domain. Data from flight campaigns were used to quantify the uncertainty introduced by the Lagrangian particle dispersion model that was applied for the inversions. The average annual statewide net ecosystem productivity (NEP) was increased by 32% to 29.7 TgC yr−1 by assimilating the tropospheric mixing ratio data. The associated uncertainty was decreased by 28.4%–29% on average over the entire Oregon model domain with the lowest uncertainties of 11% in western Oregon. The largest differences between posterior and prior CO2 fluxes were found for the Coast Range ecoregion of Oregon that also exhibits the highest availability of atmospheric observations and associated footprints. In this area, covered by highly productive Douglas fir forest, the differences between the prior and posterior estimate of NEP averaged 3.84 TgC yr−1 during the study period from 2012 through 2014.


  • Journal: Earth Interactions
  • Funding Agency: —
  • Citation Information:
  • Volume: 20
  • No: 22
  • Pages: 1-35
  • Publication Year: 2016
  • DOI: 10.1175/EI-D-16-0011.1