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Record W2200904543 · doi:10.1073/pnas.1504633113

Influence of sea ice on Arctic precipitation

2015· article· en· W2200904543 on OpenAlexaffabout
Ben Kopec, Xiahong Feng, F. A. Michel, Eric S. Posmentier

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCarleton University
FundersNational Science Foundation
KeywordsEnvironmental sciencePrecipitationSea iceArcticArctic ice packMoistureClimate changeClimatologyWater cycleCryosphereArctic sea ice declineAtmospheric sciencesOceanographyAntarctic sea iceGeologyMeteorologyGeographyEcology

Abstract

fetched live from OpenAlex

Global climate is influenced by the Arctic hydrologic cycle, which is, in part, regulated by sea ice through its control on evaporation and precipitation. However, the quantitative link between precipitation and sea ice extent is poorly constrained. Here we present observational evidence for the response of precipitation to sea ice reduction and assess the sensitivity of the response. Changes in the proportion of moisture sourced from the Arctic with sea ice change in the Canadian Arctic and Greenland Sea regions over the past two decades are inferred from annually averaged deuterium excess (d-excess) measurements from six sites. Other influences on the Arctic hydrologic cycle, such as the strength of meridional transport, are assessed using the North Atlantic Oscillation index. We find that the independent, direct effect of sea ice on the increase of the percentage of Arctic sourced moisture (or Arctic moisture proportion, AMP) is 18.2 ± 4.6% and 10.8 ± 3.6%/100,000 km(2) sea ice lost for each region, respectively, corresponding to increases of 10.9 ± 2.8% and 2.7 ± 1.1%/1 °C of warming in the vapor source regions. The moisture source changes likely result in increases of precipitation and changes in energy balance, creating significant uncertainty for climate predictions.

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)0.001
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 score0.044
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.271
Teacher spread0.235 · 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

Citations191
Published2015
Admission routes2
Has abstractyes

Explore more

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