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Record W2147347053 · doi:10.1029/2010jd014243

The vertical distribution of ozone instantaneous radiative forcing from satellite and chemistry climate models

2011· article· en· W2147347053 on OpenAlexaff
A. M. Aghedo, K. W. Bowman, H. M. Worden, S. S. Kulawik, Drew Shindell, Jean‐François Lamarque, G. Faluvegi, Mark Parrington, Dylan B. A. Jones, Sebastian Rast

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTroposphereAtmospheric sciencesRadiative forcingTropospheric ozoneEnvironmental scienceOzoneForcing (mathematics)ClimatologyTotal Ozone Mapping SpectrometerRadiative transferMiddle latitudesLongwaveOzone layerClimate modelStratosphereClimate changeMeteorologyGeologyPhysicsAerosol

Abstract

fetched live from OpenAlex

[1] We evaluate the instantaneous radiative forcing (IRF) of tropospheric ozone predicted by four state-of-the-art global chemistry climate models (AM2-Chem, CAM-Chem, ECHAM5-MOZ, and GISS-PUCCINI) against ozone distribution observed from the NASA Tropospheric Emission Spectrometer (TES) during August 2006. The IRF is computed through the application of an observationally constrained instantaneous radiative forcing kernels (IRFK) to the difference between TES and model-predicted ozone. The IRFK represent the sensitivity of outgoing longwave radiation to the vertical and spatial distribution of ozone under all-sky condition. Through this technique, we find total tropospheric IRF biases from −0.4 to + 0.7 W/m2 over large regions within the tropics and midlatitudes, due to ozone differences over the region in the lower and middle troposphere, enhanced by persistent bias in the upper troposphere-lower stratospheric region. The zonal mean biases also range from −30 to +50 mW/m2 for the models. However, the ensemble mean total tropospheric IRF bias is less than 0.2 W/m2 within the entire troposphere.

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)0.001
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 score0.633
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.038
GPT teacher head0.268
Teacher spread0.230 · 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

Citations98
Published2011
Admission routes1
Has abstractyes

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