Evaluation of dynamically downscaled near-surface mass and energy fluxes for three mountain glaciers, British Columbia, Canada
Bibliographic record
Abstract
Abstract. Most models of glacier melt are forced by observed meteorological data in the vicinity of the glacier in question. In the absence of these observations, the forcing is commonly derived from statistical or dynamical downscaling of low resolution reanalysis datasets. Here we perform dynamical downscaling via the Weather Research and Forecasting (WRF) model in order to evaluate its performance against the observations from automatic weather stations (AWSs ) for three mountain glaciers in the interior of British Columbia, Canada over several summer seasons. The WRF model, nested within the ERA-Interim global reanalysis, produced output fields at 7.5 km and 2.5 km spatial resolution for all glaciers, as well as 1 km resolution for one of the glaciers. We find that the surface energy balance (SEB) model, forced by WRF at 2.5 km, adequately simulates the AWS-derived seasonal melt rates despite large biases in the individual SEB components. Overestimation of the number of clear sky days in WRF at 2.5 km explains the positive bias in the seasonal incoming shortwave radiation. This positive bias, however, is compensated by a negative bias in the seasonal incoming longwave radiation, and by an underestimation of sensible and latent heat fluxes. The underestimation of sensible heat fluxes down to −80 % of AWS-derived fluxes, as calculated by the bulk aerodynamic method, is due to the underestimated near-surface wind speeds. An increase of WRF spatial resolution from 7.5 to 1 km does not improve the simulation of downscaled variables other than near-surface air temperature. For relatively small glaciers (
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".