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Record W2076599072 · doi:10.1029/2009gl040880

Sensitivity of stable water isotopic values to convective parameterization schemes

2009· article· en· W2076599072 on OpenAlexfundno aff
Jung‐Eun Lee, Raymond T. Pierrehumbert, Abigail L. S. Swann, Benjamin R. Lintner

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Energy Research Scientific Computing CenterCanadian Institute for Advanced ResearchNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsConvective available potential energyConvectionEnvironmental scienceWater vaporAtmospheric sciencesTropospherePrecipitationFree convective layerCondensationClimatologyMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

Convective parameterization has been argued as a principal generator of inter‐model differences in climate sensitivity, but it is difficult in practice to constrain simulated convective processes. Here we show how stable water vapor isotopes, which are sensitive to the convective condensation rates, may be useful for evaluating convective parameterizations. By varying one of the least constrained convection parameters in the NCAR Community Atmosphere Model (CAM), namely the timescale for consumption of convective available potential energy (CAPE), τ , the simulated precipitation experiences substantial changes in response to changes in both the deep and shallow convection schemes—increasing τ from the standard 2 hours to 8 hours increases the contribution from shallow convection. The lowest order effect of increasing τ is a decrease (increase) in lower (upper) tropospheric condensation rates, with approximately the opposite vertical structure for the change in simulated isotopic signature. Increasing τ from the standard 2 hours to 8 hours also provides a better match to satellite‐observed water vapor isotope ratios, albeit with some uncertainty related to the quality of currently‐available satellite measurements. Thus, the incorporation of water vapor isotopes into GCMs provides additional constraints on convective parameterizations, especially as more and better quality water vapor isotope measurements become available.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.313
Teacher spread0.276 · 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

Citations68
Published2009
Admission routes1
Has abstractyes

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