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| Titel |
Improving land surface models with FLUXNET data |
| VerfasserIn |
M. Williams, A. D. Richardson, M. Reichstein, P. C. Stoy, P. Peylin, H. Verbeeck, N. Carvalhais, M. Jung, D. Y. Hollinger, J. Kattge, R. Leuning, Y. Luo, E. Tomelleri, C. M. Trudinger, Y.-P. Wang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 6, no. 7 ; Nr. 6, no. 7 (2009-07-30), S.1341-1359 |
| Datensatznummer |
250003917
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| Publikation (Nr.) |
 copernicus.org/bg-6-1341-2009.pdf |
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| Zusammenfassung |
| There is a growing consensus that land surface models (LSMs) that simulate
terrestrial biosphere exchanges of matter and energy must be better
constrained with data to quantify and address their uncertainties. FLUXNET,
an international network of sites that measure the land surface exchanges of
carbon, water and energy using the eddy covariance technique, is a prime
source of data for model improvement. Here we outline a multi-stage process
for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models
with quantifiable uncertainty. First, we describe FLUXNET data availability,
and its random and systematic biases. We then introduce methods for
assessing LSM model runs against FLUXNET observations in temporal and
spatial domains. These assessments are a prelude to more formal model-data
fusion (MDF). MDF links model to data, based on error weightings. In theory,
MDF produces optimal analyses of the modelled system, but there are
practical problems. We first discuss how to set model errors and initial
conditions. In both cases incorrect assumptions will affect the outcome of
the MDF. We then review the problem of equifinality, whereby multiple
combinations of parameters can produce similar model output. Fusing multiple
independent and orthogonal data provides a means to limit equifinality. We
then show how parameter probability density functions (PDFs) from MDF can be
used to interpret model validity, and to propagate errors into model
outputs. Posterior parameter distributions are a useful way to assess the
success of MDF, combined with a determination of whether model residuals are
Gaussian. If the MDF scheme provides evidence for temporal variation in
parameters, then that is indicative of a critical missing dynamic process. A
comparison of parameter PDFs generated with the same model from multiple
FLUXNET sites can provide insights into the concept and validity of plant
functional types (PFT) – we would expect similar parameter estimates among
sites sharing a single PFT. We conclude by identifying five major model-data
fusion challenges for the FLUXNET and LSM communities: (1) to determine
appropriate use of current data and to explore the information gained in
using longer time series; (2) to avoid confounding effects of missing process
representation on parameter estimation; (3) to assimilate more data types,
including those from earth observation; (4) to fully quantify uncertainties
arising from data bias, model structure, and initial conditions problems;
and (5) to carefully test current model concepts (e.g. PFTs) and guide
development of new concepts. |
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