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Record W1586437318 · doi:10.1002/2013jd020717

Impact of land use change on the diurnal cycle climate of the Canadian Prairies

2013· article· en· W1586437318 on OpenAlexaffabout
Alan K. Betts, R. L. Desjardins, Devon E. Worth, Darrel Cerkowniak

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRelative humidityEnvironmental scienceEvapotranspirationDiurnal cycleCloud coverPrecipitationClimate changeHumidityLand coverTranspirationAtmospheric sciencesClimatologyDiurnal temperature variationGeographyLand useMeteorologyCloud computingEcology

Abstract

fetched live from OpenAlex

Abstract This paper uses hourly observations from 1953 to 2011 of temperature, relative humidity, and opaque cloud cover from 14 climate stations across the Canadian Prairies to analyze the impact of agricultural land use change on the diurnal cycle climate, represented by the mean temperature and relative humidity and their diurnal ranges. We show the difference between the years 1953–1991 and 1992–2011. The land use changes have been largest in Saskatchewan where 15–20% of the land area has been converted in the past four decades from summer fallow (where the land was left bare for 1 year) to annual cropping. During the growing season from 20 May to 28 August, relative humidity has increased by about 7%. During the first 2 months, 20 May to 19 July, maximum temperatures and the diurnal range of temperature have fallen by 1.2°C and 0.6°C, respectively, cloud cover has increased by about 4%, reducing surface net radiation by 6 W m −2 , and precipitation has increased. We use the dry‐downs after precipitation to separate the impact of cloud cover and show the coupling between evapotranspiration and relative humidity. We estimate, using reanalysis data from ERA‐Interim, that increased transpiration from the larger area of cropland has reduced the surface Bowen ratio by 0.14–0.2. For the month on either side of the growing season, cloud cover has fallen slightly; maximum temperatures have increased, increasing the diurnal temperature range and the diurnal range of humidity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.333
Teacher spread0.259 · 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

Citations58
Published2013
Admission routes2
Has abstractyes

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