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Record W2086860108 · doi:10.1029/2010jd014334

Effects of postcondensation exchange on the isotopic composition of water in the atmosphere

2010· article· en· W2086860108 on OpenAlexaff
Robert D. Field, Dylan B. A. Jones, D. P. Brown

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Toronto
FundersInternational Atomic Energy Agency
KeywordsSnowWater vaporEnvironmental sciencePrecipitationAtmospheric sciencesAtmosphere (unit)Rain and snow mixedClimatologyChemistryGeologyMeteorology

Abstract

fetched live from OpenAlex

We conducted experiments with an atmospheric general circulation model to determine the effects of non‐Rayleigh, postcondensation exchange (PCE) on the isotopic composition of water in the atmosphere. PCE was found to universally deplete vapor of heavy isotopes but had differential effects on the isotopic composition of precipitation. At low latitudes, local PCE with fresh vapor at the surface enriches precipitation in heavy isotopes, particularly during light rainfall. When rainfall is heavy, PCE tends to deplete vapor and precipitation of heavy isotopes via atmospheric moisture recycling, supporting recent interpretations of vapor isotope measurements from satellites, particularly over the Asian Monsoon region. In the extratropics, PCE causes local enrichment of precipitation, which is often entirely offset by upstream PCE depletion of the source vapor, resulting in a net depletion in local precipitation. The transition from net enrichment to net depletion is controlled by the transition from rain to snow‐dominated precipitation. Surprisingly, this transition was also found to influence the temperature effect. In regions with a strong seasonal mix of rain and snow, such as Europe, the temperature effect appears to be controlled by PCE rather than Rayleigh depletion.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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 designBench or experimental
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

Citations93
Published2010
Admission routes1
Has abstractyes

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