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Record W4214899799 · doi:10.5194/acp-2022-120

Optimizing Four Years of CO <sub>2</sub> Biospheric Fluxes from OCO-2 and in situ data in TM5: Fire Emissions from GFED and Inferred from MOPITT CO data

2022· preprint· en· W4214899799 on OpenAlexfundno aff
Hélène Peiro, Sean Crowell, Berrien Moore

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersEurostarsAustralian Research CouncilCanadian Space AgencyCentre National de la Recherche ScientifiqueMinistry of Business, Innovation and EmploymentUniversité de La RéunionCentre National d’Etudes SpatialesCalifornia Institute of TechnologyEuropean CommissionCanadian Foundation for Climate and Atmospheric SciencesOntario Innovation TrustGovernment of CanadaConseil Régional, Île-de-FranceNational Aeronautics and Space AdministrationSorbonne UniversitéNova Scotia Research Innovation Trust
KeywordsEnvironmental scienceAtmospheric sciencesSCIAMACHYInversion (geology)Carbon monoxideCombustionMeteorologyCarbon fluxClimatologyEcosystemChemistryGeographyGeologyTroposphere

Abstract

fetched live from OpenAlex

Abstract. Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often used as a tracer of CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using MOPITT v8 data is performed to estimate fire emissions which are then converted in CO2 fire emissions through the use of emission ratio. These CO2 fire emissions allow us, then, to estimate adjusted CO2 Net Ecosystem Exchange (NEE) and respiration which are then used as priors for CO2 inversions constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Database Emissions (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 and GFED4.1s, as well as with an inversion using CASA-GFED3 fire and NEE priors. After comparison at the different scales, the inversions are evaluated against TCCON data. Results show that variations in posterior flux are much smaller across different prior mean fluxes when compared with the data assimilated. However, at global scale and for most of the regions, while the net fluxes remain robust, we can observe differences in fire emissions among the priors, resulting in large adjustments in the Net Ecosystem Exchange (NEE) to match the fires and observations. Tropical flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger CO2 net sources are observed when using GFED4.1s and MOPITT CO prior in CO2 OCO-2 inversions than compared with the other priors, particularly during the 2015 El Niño event for most Tropical regions. Larger CO2 net sources with MOPITT CO and GFED4.1s priors are also observed in Tropical Asia in CO2 in situ inversions than compared with the other priors during the 2015–2016 El Niño period and shows large net emissions than compared to OCO-2 inversions. Evaluation with TCCON suggests that the re-balanced posterior simulated give biases and accuracy very close each other where biases have decreased and variability matches better the validation data than with the CASA-GFED3. Further work is needed to improve prior fluxes in Tropical regions where fires are a significant component.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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