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Record W6927656782 · doi:10.34734/fzj-2023-04944

Long-term trends and radiative impact in vertically resolved stratospheric water vapour from ESA WV_cci data records

2023· article· en· W6927656782 on OpenAlexaff

Bibliographic record

VenueJuSER Publikationsportal · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStratosphereTroposphereWater vaporRadiative forcingRadiative transferForcing (mathematics)

Abstract

fetched live from OpenAlex

Water vapour in the upper troposphere and stratosphere has a significant impact both on theradiative and chemical properties of the atmosphere. Reliable water vapour climate data records(CDRs) are essential for use in climate research, to assess vertically resolved trends and associatedradiative impacts. Within the ESA Water Vapour Climate Change Initiative (WV_cci), new verticallyresolved water vapour CDRs in the stratosphere and UTLS were merged from a range of satelliteobservations. In this contribution, we provide an overview of these CDRs, highlighting innovationsin the merging methodologies and results from a detailed quality assessment. In particular, thelong-term trends derived from the new water vapour CDRs are compared to other mergeddatasets, reanalyses, and simulations from chemistry-climate models, with the ESA WV_cci CDRsdeemed to be valuable new datasets for climate studies. We conclude that, mostly driven bydynamical variability, the derived water vapour trends vary significantly depending on the datasetused, chosen time period and location in the atmosphere. Using an off-line radiative transfermodel, we estimate the consequence of these differences on the radiative forcing from watervapour changes in the upper troposphere and stratosphere over the past 30+ years.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.032
GPT teacher head0.275
Teacher spread0.243 · 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.

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
Published2023
Admission routes1
Has abstractyes

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