Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling

By Wolfgang Knorr, Jens Kattge. 2005

232 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/41f5d7b2f17af8b36c1835b502bcb6ac9c5f21d3

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In Global and Planetary Change

Citations in the corpus

A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes (2006, methodology): “…of advanced parameterization schemes for model fitting (e.g., maximum likelihood, van Wijk and Bouten, 2002; data assimilation or model-data fusion, Knorr and Kattge, 2005; Raupach et al., 2005; Williams et al., 2005; Gove and Hollinger, in press), to make statistical comparisons between…”
Estimation of parameters in complex 15N tracing models by Monte Carlo sampling (2007, methodology): “In particular, it identifies groups of parameters that tend to be constrained together (Knorr and Kattge, 2005).”
Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals (2008, background): “The standard deviation of the inferred random error, s(d), was 20% smaller for the paired observation approach (2.19 mmol m 2 s 1) than for the best of the model-based approaches (2.73–3.10 mmol m 2 s 1), presumably because uncertainties estimated via the paired observation approach do not incorpora”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “Attachment kinetics are usually not included in applications of the capacitance model, though Koenig (1971) reduced the mass growth rates by a constant factor to account for”
TRY – a global database of plant traits (2011, background): “(3) Trait related parameters r 2011 Blackwell Publishing Ltd, Global Change Biology, 17, 2905–2935 and state variables in vegetation models can be assumed log-normal distributed as well, e.g. Figs 7 and 8 (Knorr & Kattge, 2005).”
Influences of observation errors in eddy flux data on inverse model parameter estimation (2008, methodology): “To get an estimate of the random error we used the gapfilling algorithm of Reichstein et al. 10 (2005). This tool computes the expected value of the flux using data measured under the same meteorological conditions in a time window of ±7 days.”
Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global‐scale terrestrial biosphere models (2009, methodology): “However, results are particularly sensitive to the parameterisation of photosynthetic capacity, which substantially contributes to uncertainties of model predictions (White et al., 2000; Knorr & Heimann, 2001; Knorr & Kattge, 2005; Zaehle et al., 2005).”
‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, background): “Other groups of investigators are inverting flux data to derive stand-scale model parameters (Wang et al. 2001; Reichstein et al. 2003; Aalto et al. 2004; Jarvis et al. 2004; Schulz and Jarvis 2004; Wang et al. 2004; Braswell et al. 2005; Knorr and Kattge 2005; Raupach et al. 2005; Richardson and Ho”
Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes (2007, methodology): “Two scenarios were evaluated; first BETHY model parameters were estimated from all of the observed data, and secondly, the parameters were estimated using only 12 days of observed data, chosen to represent seasonality.”
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data (2009, methodology): “452 Discussion 453 There have been previous attempts to parameterise C models using time series of C fluxes 454 (Braswell et al. 2005, Knorr and Kattge 2005, Wang et al. 2007).”
A global scale mechanistic model of photosynthetic capacity (LUNA V1.0) (2016, background): “Our nitrogen partitioning scheme would help remove the pre- diction bias of future photosynthetic rates, which will also improve considerably related climate processes that are de- pendent on these predictions (Bonan et al., 2011; Knorr and Kattge, 2005; Rogers, 2014).”