Improving North American terrestrial CO.sub.2 flux diagnosis using spatial structure in land surface model residuals

We evaluate spatial structure in North American CO.sub.2 flux observations using a simple diagnostic land surface model. The vegetation photosynthesis respiration model (VPRM) calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) alo...

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Veröffentlicht in:Biogeosciences 2013-07, Vol.10 (7), p.4607
Hauptverfasser: Hilton, T. W, Davis, K. J, Keller, K, Urban, N. M
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Sprache:eng
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Zusammenfassung:We evaluate spatial structure in North American CO.sub.2 flux observations using a simple diagnostic land surface model. The vegetation photosynthesis respiration model (VPRM) calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture. We use observed NEE from a group of 65 North American eddy covariance tower sites spanning North America to estimate VPRM parameters for these sites. We investigate spatial coherence in regional CO.sub.2 fluxes at several different time scales by using geostatistical methods to examine the spatial structure of model-data residuals. We find that persistent spatial structure does exist in the model-data residuals at a length scale of approximately 400 km (median 402 km, mean 712 km, standard deviation 931 km). This spatial structure defines a flux-tower-based VPRM residual covariance matrix. The residual covariance matrix is useful in constructing prior fluxes for atmospheric CO.sub.2 concentration inversion calculations, as well as for constructing a VPRM North American CO.sub.2 flux map optimized to eddy covariance observations. Finally (and secondarily), the estimated VPRM parameter values do not separate clearly by plant functional type (PFT). This calls into question whether PFTs can successfully partition ecosystems' fundamental ecological drivers when the viewing lens is a simple model.
ISSN:1726-4170
1726-4189