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Record W2596254280 · doi:10.1175/jhm-d-16-0272.1

Offline Implementation and Evaluation of the Canadian Small Lake Model with the Canadian Land Surface Scheme over Western Canada

2017· article· en· W2596254280 on OpenAlexafffundabout
Diana Verseghy, Murray Mackay

Bibliographic record

VenueJournal of Hydrometeorology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change Canada
FundersCenter for Neuroscience and Regenerative MedicineEnvironment and Climate Change Canada
KeywordsEnvironmental scienceSnowpackShortwave radiationClimatologySnowPrecipitationClimate modelShortwaveForcing (mathematics)Albedo (alchemy)Atmospheric sciencesClimate changeMeteorologyGeologyGeographyRadiative transfer

Abstract

fetched live from OpenAlex

Abstract The Canadian Small Lake Model (CSLM), version 2, was run with the Canadian Land Surface Scheme (CLASS), version 3.6.1, in an offline regional test over western Canada. Forcing data were derived from ERA-Interim and downscaled using the fifth-generation Canadian Regional Climate Model (CRCM5). The forcing precipitation field was adjusted using monthly data from the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) observation-based dataset. The modeled surface air temperature was evaluated against CANGRD data, the modeled albedo against MODIS data, and the modeled snow water equivalent against Canadian Meteorological Centre (CMC) and Global Snow Monitoring for Climate Research (GlobSnow) data. The lake simulation itself was evaluated using the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) dataset. Summer surface lake temperatures and the lake ice formation and breakup periods were well simulated, except for slight warm/cold summer/fall surface temperature biases, early ice breakup, and early ice formation, consistent with warm/cold biases in the climate simulation. Tests were carried out to investigate the sensitivity of the CSLM simulation to the default values assigned to the shortwave extinction coefficient and the average lake depth, and changing the former from 0.5 to 2.0 m−1 and the latter from 10.0 to 50.0 or 5.0 m had minimal effects on the simulation. Comparisons of the average annual variations of the simulated net shortwave radiation, turbulent fluxes, snowpack, and maximum and minimum daily surface temperatures between the land and the lake fractions for tundra, boreal, and southern regions showed patterns consistent with those expected. Finally, a test of the CSLM over the large resolved lakes in the model domain demonstrated a performance comparable to that for subgrid lakes.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.039
GPT teacher head0.259
Teacher spread0.220 · 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
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

Citations20
Published2017
Admission routes3
Has abstractyes

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