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Record W4254034444 · doi:10.5194/acpd-15-10813-2015

Sensitivity analysis of the potential impact of discrepancies in stratosphere–troposphere exchange on inferred sources and sinks of CO <sub>2</sub>

2015· preprint· en· W4254034444 on OpenAlexafffund
Feng Deng, Dylan B. A. Jones, Thomas Walker, Martin Keller, K. W. Bowman, Daven K. Henze, Ray Nassar, E. A. Kort, Steven C. Wofsy, Kaley A. Walker, Adam Bourassa, D. A. Degenstein

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGoddard Space Flight CenterTekesCentre National d’Etudes SpatialesNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyCanadian Space AgencyJet Propulsion Laboratory
KeywordsTroposphereStratosphereTropopauseAtmospheric sciencesClimatologyEnvironmental scienceSubtropicsQuasi-biennial oscillationChemical transport modelOzoneSink (geography)ArcticPhysicsMeteorologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. The upper troposphere and lower stratosphere (UTLS) represents a transition region between the more dynamically active troposphere and more stably stratified stratosphere. The region is characterized by strong gradients in the distribution of long-lived tracers, which are sensitive to discrepancies in transport in models. We evaluate the GEOS-Chem model in the UTLS using carbon dioxide (CO2) and ozone (O3) observations from the HIAPER (The High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO) campaign in March 2010. GEOS-Chem CO2 / O3 correlation suggests that there is a discrepancy in mixing across the tropopause in the model, which results in an overestimate of CO2 and an underestimate of O3 in the Arctic lower stratosphere. We assimilate stratospheric O3 data from OSIRIS and used the assimilated O3 fields together with the HIPPO CO2 / O3 correlations to obtain a correction to the modeled CO2 profile in the Arctic UTLS (primarily between the 320 and 360 K isentropic surfaces). The HIPPO-derived correction corresponds to a sink of 0.13 Pg C month−1 in the Arctic. Imposing this sink during March–August 2010 results in a reduction in the CO2 sinks inferred from GOSAT observations for temperate North America, Europe, and tropical Asia of 20, 12, and 50%, respectively. Conversely, the inversion increased the source of CO2 from tropical South America by 20%. We found that the model also underestimated CO2 in the upper tropical and subtropical troposphere, which may be linked by mixing across the subtropical tropopause. Correcting for the bias relative to HIPPO in the tropical upper troposphere, by imposing a source of 0.33 Pg C, led to a reduction in the source from tropical South America by 44%, and produced a flux estimate for tropical Asia that was in agreement with the standard inversion (without the imposed source and sink). However, the seasonal transition from a source to a sink of CO2 for tropical Asia was shifted from April to June. It is unclear whether the discrepancies found in the UTLS are due to errors in mixing associated with the large-scale dynamics or are due to the numerical errors in the advection scheme. However, our results illustrate that discrepancies in the CO2 distribution in the UTLS can affect CO2 flux inversions and suggest the need for more careful evaluation of model transport errors in the UTLS.

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)0.006
metaresearch head score (Gemma)0.017
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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