 |
| Titel |
Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model |
| VerfasserIn |
M. Jung, M. Reichstein, A. Bondeau |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 6, no. 10 ; Nr. 6, no. 10 (2009-10-06), S.2001-2013 |
| Datensatznummer |
250004028
|
| Publikation (Nr.) |
 copernicus.org/bg-6-2001-2009.pdf |
|
|
|
|
|
| Zusammenfassung |
Global, spatially and temporally explicit estimates of carbon and water
fluxes derived from empirical up-scaling eddy covariance measurements would
constitute a new and possibly powerful data stream to study the variability
of the global terrestrial carbon and water cycle. This paper introduces and
validates a machine learning approach dedicated to the upscaling of
observations from the current global network of eddy covariance towers
(FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that
performs hierarchical stratification of the data set into units where
particular multiple regressions for a target variable hold. We propose an
ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base
learning algorithm is perturbed in order to gain a diverse sequence of
different model trees which evolves over time.
We evaluate the efficiency of the model tree ensemble (MTE) approach using
an artificial data set derived from the Lund-Potsdam-Jena managed Land
(LPJmL) biosphere model. We aim at reproducing global monthly gross primary
production as simulated by LPJmL from 1998–2005 using only locations and
months where high quality FLUXNET data exist for the training of the model
trees. The model trees are trained with the LPJmL land cover and
meteorological input data, climate data, and the fraction of absorbed
photosynthetic active radiation simulated by LPJmL. Given that we know the
"true result" in the form of global LPJmL simulations we can effectively
study the performance of the MTE upscaling and associated problems of
extrapolation capacity.
We show that MTE is able to explain 92% of the variability of the global
LPJmL GPP simulations. The mean spatial pattern and the seasonal variability
of GPP that constitute the largest sources of variance are very well
reproduced (96% and 94% of variance explained respectively) while the
monthly interannual anomalies which occupy much less variance are less well
matched (41% of variance explained). We demonstrate the substantially
improved accuracy of MTE over individual model trees in particular for the
monthly anomalies and for situations of extrapolation. We estimate that
roughly one fifth of the domain is subject to extrapolation while MTE is
still able to reproduce 73% of the LPJmL GPP variability here.
This paper presents for the first time a benchmark for a global FLUXNET
upscaling approach that will be employed in future studies. Although the
real world FLUXNET upscaling is more complicated than for a noise free and
reduced complexity biosphere model as presented here, our results show that
an empirical upscaling from the current FLUXNET network with MTE is feasible
and able to extract global patterns of carbon flux variability. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|