Modelling the impacts of historical and future extreme precipitation days on seasonal surface mass balance in the Eastern Canadian Arctic and Greenland
Bibliographic record
Abstract
Land ice in the Arctic is losing mass as temperatures increase, contributing to global sea level rise. While this loss is largely driven by melt induced by atmospheric warming, precipitation can alter the rate at which loss occurs depending on its intensity and phase. Case studies have illustrated varied potential impacts of extreme precipitation events on the surface mass balance (SMB) of land ice, but the importance of extreme precipitation to seasonal SMB has not been investigated. In this study, simulations from the Regional Atmospheric Climate Model (RACMO) and Variable-Resolution Community Earth System Model (VR-CESM) are explored over historical (1980–1998) and future (2080–2098, SSP5-8.5) periods to reconstruct and further project seasonal SMB for the Greenland Ice Sheet and ice caps of the Eastern Canadian Arctic. Historically, extreme precipitation days consistently had higher SMB than non-extreme precipitation days throughout the study area in both the cold season (DJFM) and warm season (JJAS). In future simulations, this relationship persists for the cold season. However, for the warm season, projections indicate a shift towards less positive and more variable SMB responses to extreme precipitation days in the future, accounting for a larger portion of cumulative seasonal positive and negative SMB. Mass loss during extreme precipitation days becomes more common, particularly in SW Greenland and Baffin Island. This likely occurs in part because of a shift toward more rainfall during extreme precipitation events. In other words, in a strong warming scenario, extreme warm season precipitation may no longer reliably yield mass gain for the Greenland Ice Sheet and surrounding ice caps.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".