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Record W4396598526 · doi:10.5194/egusphere-2024-1252

Upper Stratospheric Temperature Trends: New Results from OSIRIS

2024· preprint· en· W4396598526 on OpenAlexafffund
Kimberlee Dubé, Susann Tegtmeier, Adam Bourassa, Daniel Zawada, Douglas A. Degenstein, William J. Randel, Sean Davis, M. Schwartz, N. J. Livesey, Anne K. Smith

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Space AgencyNational Aeronautics and Space Administration
KeywordsOsirisStratosphereEnvironmental scienceDepth soundingMicrowave Limb SounderAtmospheric sciencesClimatologyGeographyPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract. Temperature trends in the upper stratosphere, particularly above ~45 km are difficult to quantify due to a deficit of long-term observational data in this region. The recent v7.3 upper stratospheric (35–60 km) temperature data product from the Optical Spectrograph and InfraRed Imager System (OSIRIS) includes over 22 years of observations that can be used to estimate temperature trends. The trends in OSIRIS temperatures over 2005–2021 are compared to those from two other satellite limb instruments: SABER and MLS. We find that the upper stratosphere cooled by ~0.5 to 1 K/decade during this period. Results from the three instruments are generally in agreement. By merging the OSIRIS observations with those from channel 3 of the Stratospheric Sounding Unit (SSU), we find that the stratosphere cooled at a rate of approximately -0.6 K/decade between 1979 and 2021 near 45 km, in agreement with earlier results based on SSU and MLS. The similarity between OSIRIS temperature trends and those from other records improves confidence in observed upper stratospheric temperature changes over the last several decades.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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