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@ARTICLE{Post:843536,
      author       = {Post, Hanna and Hendricks-Franssen, Harrie-Jan and Han,
                      Xujun and Baatz, Roland and Montzka, Carsten and Schmidt,
                      Marius and Vereecken, Harry},
      title        = {{E}valuation and uncertainty analysis of regional-scale
                      {CLM}4.5 net carbon flux estimates},
      journal      = {Biogeosciences},
      volume       = {15},
      number       = {1},
      issn         = {1726-4189},
      address      = {Katlenburg-Lindau [u.a.]},
      publisher    = {Copernicus},
      reportid     = {FZJ-2018-01122},
      pages        = {187 - 208},
      year         = {2018},
      abstract     = {Modeling net ecosystem exchange (NEE) at the regional scale
                      with land surface models (LSMs) is relevant for the
                      estimation of regional carbon balances, but studies on it
                      are very limited. Furthermore, it is essential to better
                      understand and quantify the uncertainty of LSMs in order to
                      improve them. An important key variable in this respect is
                      the prognostic leaf area index (LAI), which is very
                      sensitive to forcing data and strongly affects the modeled
                      NEE. We applied the Community Land Model (CLM4.5-BGC) to the
                      Rur catchment in western Germany and compared estimated and
                      default ecological key parameters for modeling carbon fluxes
                      and LAI. The parameter estimates were previously estimated
                      with the Markov chain Monte Carlo (MCMC) approach DREAM(zs)
                      for four of the most widespread plant functional types in
                      the catchment. It was found that the catchment-scale annual
                      NEE was strongly positive with default parameter values but
                      negative (and closer to observations) with the estimated
                      values. Thus, the estimation of CLM parameters with local
                      NEE observations can be highly relevant when determining
                      regional carbon balances. To obtain a more comprehensive
                      picture of model uncertainty, CLM ensembles were set up with
                      perturbed meteorological input and uncertain initial states
                      in addition to uncertain parameters. C3 grass and C3 crops
                      were particularly sensitive to the perturbed meteorological
                      input, which resulted in a strong increase in the standard
                      deviation of the annual NEE sum (σ ∑ NEE) for the
                      different ensemble members from  ∼ 2 to
                      3 g C m−2 yr−1 (with uncertain parameters) to
                       ∼ 45 g C m−2 yr−1 (C3 grass) and
                       ∼ 75 g C m−2 yr−1 (C3 crops) with
                      perturbed forcings. This increase in uncertainty is related
                      to the impact of the meteorological forcings on leaf onset
                      and senescence, and enhanced/reduced drought stress related
                      to perturbation of precipitation. The NEE uncertainty for
                      the forest plant functional type (PFT) was considerably
                      lower
                      (σ ∑ NEE ∼ 4.0–13.5 g C m−2 yr−1
                      with perturbed parameters, meteorological forcings and
                      initial states). We conclude that LAI and NEE uncertainty
                      with CLM is clearly underestimated if uncertain
                      meteorological forcings and initial states are not taken
                      into account.},
      cin          = {IBG-3 / JARA-HPC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / $I:(DE-82)080012_20140620$},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / Better predictions with environmental
                      simulation models: optimally integrating new data sources
                      $(jicg41_20100501)$},
      pid          = {G:(DE-HGF)POF3-255 / $G:(DE-Juel1)jicg41_20100501$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000419815000003},
      doi          = {10.5194/bg-15-187-2018},
      url          = {https://juser.fz-juelich.de/record/843536},
}