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| Titel |
Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis |
| VerfasserIn |
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, E. Weng |
| 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 ; 10, no. 11 ; Nr. 10, no. 11 (2013-11-04), S.6893-6909 |
| Datensatznummer |
250085390
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| Publikation (Nr.) |
 copernicus.org/bg-10-6893-2013.pdf |
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| Zusammenfassung |
| Earth system processes exhibit complex patterns across time, as do the models
that seek to replicate these processes. Model output may or may not be
significantly related to observations at different times and on different
frequencies. Conventional model diagnostics provide an aggregate view of
model–data agreement, but usually do not identify the time and frequency
patterns of model–data disagreement, leaving unclear the steps required to
improve model response to environmental drivers that vary on characteristic
frequencies. Wavelet coherence can quantify the times and timescales at
which two time series, for example time series of models and measurements,
are significantly different. We applied wavelet coherence to interpret the
predictions of 20 ecosystem models from the North American Carbon Program
(NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured
net ecosystem exchange (NEE) from 10 ecosystems with
multiple years of available data. Models were grouped into classes with
similar approaches for incorporating phenology, the calculation of NEE, the
inclusion of foliar nitrogen (N), and the use of model–data fusion. Models
with prescribed, rather than prognostic, phenology often fit NEE observations
better on annual to interannual timescales in grassland, wetland and
agricultural ecosystems. Models that calculated NEE as net primary
productivity (NPP) minus heterotrophic respiration (HR) rather than gross
ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on
annual timescales in grassland and wetland ecosystems, but models that
calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in
two coniferous forests. Models that incorporated foliar nitrogen (N) data
were successful at capturing NEE variability on interannual (multiple year)
timescales at Howland Forest, Maine. The model that employed a model–data
fusion approach often, but not always, resulted in improved fit to data,
suggesting that improving model parameterization is important but not the
only step for improving model performance. Combined with previous findings,
our results suggest that the mechanisms driving daily and annual NEE
variability tend to be correctly simulated, but the magnitude of these fluxes
is often erroneous, suggesting that model parameterization must be improved.
Few NACP models correctly predicted fluxes on seasonal and interannual
timescales where spectral energy in NEE observations tends to be low, but where
phenological events, multi-year oscillations in climatological drivers, and
ecosystem succession are known to be important for determining ecosystem
function. Mechanistic improvements to models must be made to replicate
observed NEE variability on seasonal and interannual timescales. |
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