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Record W2113519787 · doi:10.5194/tc-5-1011-2011

A preliminary assessment of glacier melt-model parameter sensitivity and transferability in a dry subarctic environment

2011· article· en· W2113519787 on OpenAlexafffundabout
Andrew H. MacDougall, B. A. Wheler, Gwenn E. Flowers

Bibliographic record

Venue˜The œcryosphere · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSimon Fraser UniversityParks Canada
KeywordsSnowMean squared errorGlacierGlacier mass balanceAlbedo (alchemy)Lapse rateEnergy balanceEnvironmental scienceSensitivity (control systems)Coefficient of determinationElevation (ballistics)ClimatologyAtmospheric sciencesGeologyStatisticsMathematicsGeomorphologyGeometryPhysics

Abstract

fetched live from OpenAlex

Abstract. Efforts to project the long-term melt of mountain glaciers and ice-caps require that melt models developed and calibrated for well studied locations be transferable over large regions. Here we assess the sensitivity and transferability of parameters within several commonly used melt models for two proximal sites in a dry subarctic environment of northwestern Canada. The models range in complexity from a classical degree-day model to a simplified energy-balance model. Parameter sensitivity is first evaluated by tuning the melt models to the output of an energy balance model forced with idealized inputs. This exercise allows us to explore parameter sensitivity both to glacier geometric attributes and surface characteristics, as well as to meteorological conditions. We then investigate the effect of model tuning with different statistics, including a weighted coefficient of determination (wR2), the Nash-Sutcliffe efficiency criterion (E), mean absolute error (MAE) and root mean squared error (RMSE). Finally we examine model parameter transferability between two neighbouring glaciers over two melt seasons using mass balance data collected in the St. Elias Mountains of the southwest Yukon. The temperature-index model parameters appear generally sensitive to glacier aspect, mean surface elevation, albedo, wind speed, mean annual temperature and temperature lapse rate. The simplified energy balance model parameters are sensitive primarily to snow albedo. Model tuning with E, MAE and RMSE produces similar, or in some cases identical, parameter values. In twelve tests of spatial and/or temporal parameter transferability, the results with the lowest RMSE values with respect to ablation stake measurements were achieved twice with a classical temperature-index (degree-day) model, three times with a temperature-index model in which the melt parameter is a function of potential radiation, and seven times with a simplified energy-balance model. A full energy-balance model produced better results than the other models in nine of twelve cases, though the tuning of this model differs from that of the others.

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)0.000
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 score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.031
GPT teacher head0.222
Teacher spread0.192 · 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

Citations30
Published2011
Admission routes3
Has abstractyes

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