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Record W2553013167 · doi:10.5194/acp-2016-951

The radiative role of ozone and water vapour in the temperatureannual cycle in the tropical tropopause layer

2016· article· en· W2553013167 on OpenAlexfundno aff
Alison Ming, Amanda C. Maycock, Peter B. Hitchcock, Peter Haynes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaNatural Environment Research CouncilSight Research UK
KeywordsTropopauseAtmospheric sciencesWater vaporOzoneAnnual cycleWater cycleTroposphereDiurnal cycleEnvironmental scienceClimatologyRadiative transferOzone layerChemistryStratospherePhysicsGeology

Abstract

fetched live from OpenAlex

Abstract. The prominent annual cycle in temperatures (with maximum peak to peak amplitude of ~ 8 K around 70 hPa and ~ 6 K at 90 hPa) is a key feature of the tropical tropopause layer (TTL). There is also a strong annual cycle observed in both ozone and water vapour in the TTL, with the latter understood as a consequence of the temperature annual cycle. The radiative contributions of the annual cycle in ozone and water vapour to the temperature annual cycle are studied, first with a seasonally evolving fixed dynamical heating calculation (SEFDH) where the dynamical heating is assumed to be unaffected by the radiative heating. In this framework, the variations in ozone and water vapour derived from satellite data lead to variations in temperature that are respectively in phase and out of phase with the observed annual cycle. The ozone contribution is at the upper range of previous calculations. This difference in phasing can be understood from the fact that an increase in water vapour cools the TTL, predominantly through enhanced local emission, whereas an increase in ozone warms the TTL, mostly through enhanced absorption of upwelling longwave radiation from the troposphere. The relative phasing of the water vapour and ozone effects on temperature is further influenced by the fact that for water vapour there is a strong non-local effect on temperatures from variations in concentrations occurring in lower layers of the TTL. In contrast, for ozone it is the local variations in concentration that have the strongest impact on local temperature variations. The factors that determine the vertical structure of the annual cycle in temperature are also examined. Radiative damping time scales are shown to maximize over a broad layer centred on the cold point. Non-radiative processes in the upper troposphere are inferred to impose a strong constraint on temperature perturbations below 130 hPa. These effects, combined with the annual cycles in dynamical and radiative heating, which both peak above the cold point, result in a maximum amplitude of temperature response that is relatively localized around 70 hPa. Finally, the SEFDH assumption is relaxed by considering the temperature responses to ozone and water vapour variations in a zonally symmetric dynamical model. While the magnitude of the tropical averaged temperature annual cycle in this framework is found to be consistent with the SEFDH results, the effects of the dynamical adjustment act to reduce the strong latitudinal gradients and inter-hemispheric asymmetry in the temperature response. This results in a temperature response that shows a considerably smoother structure than inferred from the SEFDH model. Whilst precise numerical values are likely to be sensitive to changes in the details of radiation code and of ozone and water vapour concentrations, the net contribution to the annual cycle in temperature from both ozone and water vapour averaged between 20° N–S, calculated in this work, is substantial and around 35 % of the observed peak to peak amplitude at both 70 hPa and 90 hPa.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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