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Record W1833367443 · doi:10.1002/2015gl065311

Revisiting the evidence of increasing springtime ozone mixing ratios in the free troposphere over western North America

2015· article· en· W1833367443 on OpenAlexaff
Meiyun Lin, Larry W. Horowitz, Owen R. Cooper, D. W. Tarasick, Stephen Conley, Laura T. Iraci, B. J. Johnson, Thierry Leblanc, Irina Petropavlovskikh, E. L. Yates

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric AdministrationAmes Research CenterNational Aeronautics and Space Administration
KeywordsHindcastClimatologyEnvironmental scienceOzoneAtmospheric sciencesTroposphereTropospheric ozoneMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

We present a 20 year time series of in situ free tropospheric ozone observations above western North America during springtime and interpret results using hindcast simulations (1980–2014) conducted with the Geophysical Fluid Dynamics Laboratory global chemistry-climate model (GFDL AM3). Revisiting the analysis of Cooper et al. (2010), we show that sampling biases can substantially influence calculated trends. AM3 cosampled in space and time with observations reproduces the observed ozone trend (0.65 ± 0.32 ppbv yr−1) over 1995–2008 (in simulations either with or without time-varying emissions), whereas AM3 “true median” with continuous temporal and spatial sampling indicates an insignificant trend (0.25 ± 0.32 ppbv yr−1). Extending this analysis to 1995–2014, we find a weaker ozone trend of 0.31 ± 0.21 ppbv yr−1 from observations and 0.36 ± 0.18 ppbv yr−1 from AM3 “true median.” Rising Asian emissions and global methane contribute to this increase. While interannual variability complicates the attribution of ozone trends, multidecadal hindcasts can aid in the estimation of robust confidence limits for trends based on sparse observational records.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.306
Teacher spread0.253 · 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

Citations102
Published2015
Admission routes1
Has abstractyes

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