Calibration of a surface mass balance model for global-scale applications
Bibliographic record
Abstract
Abstract. Global applications of surface mass balance models have large uncertainties, as a result of poor climate input data and limited availability of mass balance measurements. This study addresses several possible consequences of these limitations for the modelled mass balance. This is done by applying a simple surface mass balance model that only requires air temperature and precipitation as input data, to glaciers in different regions. In contrast to other models used in global applications, this model separately calculates the contributions of net solar radiation and the temperature-dependent fluxes to the energy balance. We derive a relation for these temperature-dependent fluxes using automatic weather station (AWS) measurements from glaciers in different climates. With local, hourly input data, the model is well able to simulate the observed seasonal variations in the surface energy and mass balance at the AWS sites. Replacing the hourly local data by monthly gridded climate data removes summer snowfall and winter melt events and, hence, influences the modelled mass balance most on locations with a small seasonal temperature cycle. Modelled winter mass balance profiles are fitted to observations on 82 glaciers in different regions to determine representative values for the multiplication factor and vertical gradient of precipitation. For 75 of the 82 glaciers, the precipitation provided by the climate dataset has to be multiplied with a factor above unity; the median factor is 2.5. The vertical precipitation gradient ranges from negative to positive values, with more positive values for maritime glaciers and a median value of 1.5 mm a−1 m−1. With calibrated precipitation, the modelled annual mass balance gradient closely resembles the observations on the 82 glaciers, the absolute values are matched by adjusting either the incoming solar radiation, the temperature-dependent flux or the air temperature. The mass balance sensitivity to changes in temperature is particularly sensitive to the chosen calibration method. We additionally calculate the mass balance sensitivity to changes in incoming solar radiation, revealing that widely observed variations in irradiance can affect the mass balance by a magnitude comparable to a 1 °C change in temperature or a 10% change in precipitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".