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Record W3017000531 · doi:10.5194/tc-14-3249-2020

Seasonal and interannual variability of melt-season albedo at Haig Glacier, Canadian Rocky Mountains

2020· article· en· W3017000531 on OpenAlexafffundabout
Shawn J. Marshall, Kristina Miller

Bibliographic record

Venue˜The œcryosphere · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of CalgaryEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Calgary
KeywordsAlbedo (alchemy)SnowpackSnowGlacierEnvironmental scienceClimatologyPrecipitationIce-albedo feedbackAtmospheric sciencesGlacier mass balanceSnowmeltCryosphereGeologyPhysical geographyMeteorologyAntarctic sea iceGeographySea iceGeomorphology

Abstract

fetched live from OpenAlex

In situ observations of summer (June through August, or JJA) albedo are presented for the period 2002–2017 from Haig Glacier in the Canadian Rocky Mountains. The observations provide insight into the seasonal evolution and interannual variability of snow and ice albedo, including the effects of summer snowfall, the decay of snow albedo through the melt season, and the potential short-term impacts of regional wildfire activity on glacier-albedo reductions. Mean JJA albedo (± 1 σ ) recorded at an automatic weather station in the upper ablation zone of the glacier was α S =0.55 ± 0.07 over this period, with no evidence of long-term trends in surface albedo. Each summer the surface conditions at the weather station undergo a transition from a dry, reflective spring snowpack ( α S ∼0.8) to a wet, homogeneous midsummer snowpack ( α S ∼0.5) to exposed, impurity-rich glacier ice, with a measured albedo of 0.21 ± 0.06 over the study period. The ice albedo drops to ∼ 0.12 during years of intense regional wildfire activity such as 2003 and 2017, but it recovers from this in subsequent years. This seasonal albedo decline is well simulated through a parameterization of snow-albedo decay based on cumulative positive degree days (PDDs), but the parameterization does not capture the impact of summer snowfall events, which cause transient increases in albedo and significantly reduce glacier melt. We introduce this effect through a stochastic parameterization of summer precipitation events within a surface energy balance model. The amount of precipitation and the date of snowfall are randomly selected for each model realization based on a predefined number of summer snow events. This stochastic parameterization provides an improved representation of the mean summer albedo and mass balance at Haig Glacier. We also suggest modifications to conventional degree-day melt factors to better capture the effects of seasonal albedo evolution in temperature-index or positive-degree-day melt models on mountain glaciers. Climate, hydrology, or glacier mass balance models that use these methods typically use a binary rather than continuum approach to prescribing melt factors, with one melt factor for snow and one for ice. As alternatives, monthly melt factors effectively capture the seasonal albedo evolution, or melt factors can be estimated as a function of the albedo where these data are available.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.014
GPT teacher head0.197
Teacher spread0.183 · 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 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

Citations36
Published2020
Admission routes3
Has abstractyes

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