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Record W2336959538 · doi:10.1175/jcli-d-15-0559.1

Atmospheric Moisture Transport to the Arctic: Assessment of Reanalyses and Analysis of Transport Components

2016· article· en· W2336959538 on OpenAlexaboutno aff
Ambroise Dufour, Olga Zolina, Sergey Gulev

Bibliographic record

VenueJournal of Climate · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsEnvironmental sciencePrecipitable waterRadiosondeClimatologyExtratropical cycloneMoisturePrecipitationArcticMiddle latitudesAtmospheric sciencesAdvectionStorm trackStormMeteorologyGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract The atmospheric water cycle of the Arctic is evaluated via seven global reanalyses and in radiosonde observations covering the 1979–2013 period. In the regional moisture budget, evaporation and precipitation are the least consistent terms among different datasets. Despite the assimilation of radiosoundings, the reanalyses present a tendency to overestimate the moisture transport. Aside from this overestimation, the reanalyses exhibit a remarkable agreement with the radiosondes in terms of spatial and temporal patterns. The northern North Atlantic, subpolar North Pacific, and Labrador Sea stand out as the main gateways for moisture to the Arctic in all reanalyses. Because these regions correspond to the end of the storm tracks, the link between moisture transports and extratropical cyclones is further investigated by decomposing the moisture fluxes in the mean flow and transient eddy parts. In all reanalyses, the former term tends to cancel out when averaged over a latitude circle, leaving the latter to provide the bulk of the midlatitude moisture imports (89%–94% at 70°N). Although the Arctic warms faster than the rest of the world, the impact of these changes on its water cycle remains ambiguous. In most datasets, evaporation, precipitation, and precipitable water increase in line with what is expected from a warming signal. At the same time, the moisture transports have decreased in all the reanalyses but not in the radiosonde observations, though none of these trends is statistically significant. The fluxes do not scale with the Clausius–Clapeyron relation because the increasing humidity is not correlated with the meridional wind, particularly near the surface.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.425

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 head0.012
GPT teacher head0.254
Teacher spread0.241 · 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 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

Citations147
Published2016
Admission routes1
Has abstractyes

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