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Moisture Flux Estimates Derived from EOS Aqua Data in the Arctic

2013· dissertation· en· W13906522 on OpenAlexaboutno aff
Linette Boisvert

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceFlux (metallurgy)ArcticClimatologyMoistureThe arcticAtmospheric sciencesMeteorologyGeographyOceanographyGeologyChemistry

Abstract

fetched live from OpenAlex

The Arctic sea ice acts as a barrier between the ocean and atmosphere inhibiting the exchange of heat, momentum, and moisture. Recently, the ice pack has been decreasing in area and concentration. This diminished sea ice coverage could potentially allow for larger moisture fluxes that affect surface energy budgets, the occurrence of clouds, and the near-surface humidity and temperature. Currently, reanalyses are known to produce large errors and biases in the Arctic, warranting improved moisture flux algorithms and input data. Using the Monin-Obukhov similarity theory, with adjustments made to better suit the conditions of the Arctic, and observations from NASA's EOS Aqua satellite, specifically the AIRS and AMSR-E instruments, the daily moisture flux is calculated from 2003-2011. The moisture flux is studied for a series of North Water polynya events between 2003-2009 to test the accuracy of the Aqua products and our algorithm. Using in situ data we validated moisture flux results, finding an error of 20.3%, improving the moisture flux accuracy compared to other climate models. The moisture flux for the entire Arctic was studied to look for inter-annual variations and was compared to changes in the sea ice. Instead of an expected increase in the moisture flux due to a declining sea ice pack, there has been a 15% decrease. On a regional scale and based on their average moisture flux, the Chukchi/Beaufort Seas, Laptev/E. Siberian Seas, Canadian Archipelago and Central Arctic are increasing, between 2.1 and 4.8 %/yr. Increases are due to the changes in the ice concentration, which allows for the surface temperatures to increase substantially in the fall and winter months when the amount of moisture exchanged is highest. The Kara/Barents Seas, E. Greenland Sea and Baffin Bay are decreasing, between 0.53 and 9.2 %/yr. These regions have areas of open water year round, and their exchanges of moisture are due mostly to smaller differences in surface and 2 m specific humidities. The contribution of the sea ice zone to the total moisture flux (from the open ocean and sea ice zone) has increased by 3.6% because the amount of open water within the sea ice zone has increased by 4.3%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.240
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations0
Published2013
Admission routes1
Has abstractyes

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