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Record W2992141643 · doi:10.1029/2019gl085725

Mean European Carbon Sink Over 2010–2015 Estimated by Simultaneous Assimilation of Atmospheric CO<sub>2</sub>, Soil Moisture, and Vegetation Optical Depth

2019· article· en· W2992141643 on OpenAlexaff
Marko Scholze, T. Kaminski, Wolfgang Knorr, Michael Voßbeck, Minchao Wu, P. Ferrazzoli, Yann H. Kerr, Arnaud Mialon, Philippe Richaume, Nemesio Rodríguez-Fernández, Cristina Vittucci, Jean‐Pierre Wigneron, Susanne Mecklenburg, Matthias Drusch

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsInversa Systems (Canada)
FundersSwedish National Space AgencyEuropean CommissionEuropean Space Agency
KeywordsSCIAMACHYEnvironmental scienceBiosphereSink (geography)Carbon sinkAtmospheric sciencesData assimilationAtmospheric chemistryCarbon dioxideClimatologyMeteorologyClimate changeGeologyTroposphereOzoneChemistryGeography

Abstract

fetched live from OpenAlex

Abstract The northern land biosphere is believed to be the main global sink of CO 2 , but the contribution of Europe is uncertain. While bottom‐up estimates and inverse atmospheric transport studies based on atmospheric CO 2 observed in situ or from space by OCO‐2 point to a moderate rate of uptake, some other inversions based on remotely sensed atmospheric CO 2 from GOSAT/SCIAMACHY and biomass estimates from passive microwave satellite data point to a large sink of around 1 Gt C/yr. We present results from combining both approaches in a data assimilation framework, inverting a biosphere model against in situ atmospheric CO 2 and passive microwave measurements. When assimilating all observations, we estimate a European carbon sink of 0.303 ± 0.083 Gt C/yr for 2010–2015. The result agrees with other bottom‐up studies and atmospheric inversions using in situ CO 2 or OCO‐2 observations pointing to potential data problems when using observations from GOSAT or SCIAMACHY to estimate the European CO 2 sink.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.828

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 communication0.0000.000
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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Citations66
Published2019
Admission routes1
Has abstractyes

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