MétaCan
Menu
Back to cohort
Record W2042624076 · doi:10.1029/1999gl011333

A model estimate of cooling in the mesosphere and lower thermosphere due to the CO<sub>2</sub> Increase over the last 3–4 decades

2000· article· en· W2042624076 on OpenAlexaff
R. A. Akmaev, V. I. Fomichev

Bibliographic record

VenueGeophysical Research Letters · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsThermosphereMesosphereAtmospheric sciencesEnvironmental scienceMixing ratioAltitude (triangle)Forcing (mathematics)Radiative coolingOzoneClimatologyPhysicsIonosphereMeteorologyStratosphereGeophysicsGeology

Abstract

fetched live from OpenAlex

Long‐term observations indicate a substantial cooling in the mesosphere and lower thermosphere (MLT) over the last 3–4 decades. Available model studies have primarily considered the effects of CO2 doubling expected to occur in the future. We present a benchmark estimate of radiative forcing in the MLT due to the increase of CO2 mixing ratio from 313 ppm to about 360 ppm (or by 15%) observed over the last four decades. The Spectral Mesosphere/Lower Thermosphere Model is employed for “retrocasting” the atmospheric response. As expected, the thermal response is predominantly negative. As a function of altitude, the cooling maximizes in the mesosphere at about 3 K, practically vanishes at 100–120 km, and grows to 10–15 K in the thermosphere. Although this vertical shape is remarkably consistent with various sets of observations, the magnitude of the cooling rate is smaller by about a factor of 2–10. This suggests that other mechanisms, e.g., the ozone depletion, might have contributed substantially to the negative temperature trend.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.283
Teacher spread0.271 · 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

Citations77
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207