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Record W2102906774 · doi:10.5194/bg-10-4607-2013

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

2013· article· en· W2102906774 on OpenAlexfundno aff
T. W. Hilton, K. J. Davis, Klaus Keller, Nathan M. Urban

Bibliographic record

VenueBiogeosciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersNatural Resources CanadaBiological and Environmental ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric SciencesPennsylvania State UniversityNational Oceanic and Atmospheric AdministrationUniversity of PennsylvaniaOffice of ScienceNational Aeronautics and Space AdministrationU.S. Department of Energy
KeywordsEddy covarianceEnvironmental scienceAtmospheric sciencesSpatial variabilityFlux (metallurgy)Photosynthetically active radiationSpatial ecologyResidualEcosystem respirationCovarianceClimatologyEcosystemMathematicsStatisticsGeologyEcologyPhotosynthesisChemistry

Abstract

fetched live from OpenAlex

Abstract. We evaluate spatial structure in North American CO2 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 CO2 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 CO2 concentration inversion calculations, as well as for constructing a VPRM North American CO2 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.211
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2013
Admission routes1
Has abstractyes

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