Comment on tc-2021-109
Bibliographic record
Abstract
Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study North American Regional Reanalysis (NARR) data are used to force a physically-based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess it sensitivity to climate change. A two-year record (2014–2016) from an on-glacier automatic weather station (AWS) and a homogenized historical precipitation record from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the nearest NARR gridpoint to the glacier AWS site. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multi-temporal digital elevation model (DEM) dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance showed a large sensitivity to the biases in NARR precipitation and solar radiation, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these parameters with ancillary data. The difference between the static (1979) and dynamic simulations showed small differences (mean = 0.06 m w.e. a−1 or 1.5 m w.e. over 37 yrs), indicating minor effects of elevation changes on the glacier specific mass balance. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 to 7 °C and precipitation between −20 to +20 %, which comprise the spread of ensemble IPCC representative concentration pathways climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass-balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. °C−1. Increased melting accounted for 90 % of the temperature sensitivity while precipitation phase feedbacks accounted for only 10 %. Roughly half of the melt response to warming was driven by a positive albedo feedback, in which glacier albedo decreases as the snow cover on the glacier thins and recedes earlier in response to warming, increasing net solar radiation fluxes. About one quarter of the melt response to warming was driven by latent heat energy gains (positive humidity feedback). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.033 | 0.020 |
| Insufficient payload (model declined to judge) | 0.035 | 0.030 |
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".