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Record W1670075577 · doi:10.1002/jgrd.50157

The relation between atmospheric humidity and temperature trends for stratospheric water

2013· article· en· W1670075577 on OpenAlexfundno aff
S. Fueglistaler, Y. S. Liu, T. J. Flannaghan, Peter Haynes, Dick Dee, W. Read, Ellis E. Remsberg, L. W. Thomason, D. F. Hurst, John R. Lanzante, P. F. Bernath

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyJet Propulsion LaboratoryGoddard Space Flight CenterNational Oceanic and Atmospheric AdministrationCanon Foundation for Scientific ResearchUniversity of CambridgeLangley Research CenterCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsRadiosondeAtmospheric sciencesStratosphereEnvironmental scienceTroposphereWater vaporNorthern HemisphereClimatologyAtmospheric temperatureHygrometerOccultationHumidityMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

We analyze the relation between atmospheric temperature and water vapor—a fundamental component of the global climate system—for stratospheric water vapor (SWV). We compare measurements of SWV (and methane where available) over the period 1980–2011 from NOAA balloon‐borne frostpoint hygrometer (NOAA‐FPH), SAGE II, Halogen Occultation Experiment (HALOE), Microwave Limb Sounder (MLS)/Aura, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE‐FTS) to model predictions based on troposphere‐to‐stratosphere transport from ERA‐Interim, and temperatures from ERA‐Interim, Modern Era Retrospective‐Analysis (MERRA), Climate Forecast System Reanalysis (CFSR), Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC), HadAT2, and RICHv1.5. All model predictions are dry biased. The interannual anomalies of the model predictions show periods of fairly regular oscillations, alternating with more quiescent periods and a few large‐amplitude oscillations. They all agree well (correlation coefficients 0.9 and larger) with observations for higher‐frequency variations (periods up to 2–3 years). Differences between SWV observations, and temperature data, respectively, render analysis of the model minus observation residual difficult. However, we find fairly well‐defined periods of drifts in the residuals. For the 1980s, model predictions differ most, and only the calculation with ERA‐Interim temperatures is roughly within observational uncertainties. All model predictions show a drying relative to HALOE in the 1990s, followed by a moistening in the early 2000s. Drifts to NOAA‐FPH are similar (but stronger), whereas no drift is present against SAGE II. As a result, the model calculations have a less pronounced drop in SWV in 2000 than HALOE. From the mid‐2000s onward, models and observations agree reasonably, and some differences can be traced to problems in the temperature data. These results indicate that both SWV and temperature data may still suffer from artifacts that need to be resolved in order to answer the question whether the large‐scale flow and temperature field is sufficient to explain water entering the stratosphere.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.291
Teacher spread0.262 · 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

Citations77
Published2013
Admission routes1
Has abstractyes

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