Modelling the Impacts of Summer Extreme Precipitation Events on Surface Mass Balance in Southern Greenland
Bibliographic record
Abstract
The warming Arctic climate drives an increased potential for extreme precipitation events. Here, extreme precipitation is defined as the top 5% of daily accumulations where at least 1 mm occurred. Case studies have shown that these events can have substantial impacts on the regional surface mass balance (SMB) of the Greenland Ice Sheet. Depending on the precipitation phase and timing, mass may be added via the precipitation, or melt may be enhanced from rainfall, driving increased runoff and ice discharge. Southern Greenland is an area undergoing substantial change in terms of both intense precipitation occurrence and SMB, so it is essential to understand their relationship as the climate warms.Observations of extreme precipitation are limited due to its rare nature and sparse observational networks. Modelling studies can shed light on broader changes by filling in data gaps and providing future projections, allowing for a deeper look into physical linkages and changes. Here, historical and future simulations of the Regional Atmospheric Climate Model (RACMO) and Variable-Resolution Community Earth System Model (VR-CESM) are used. Representation of summer extreme precipitation events in southern Greenland in VR-CESM and RACMO is explored and compared through case studies. Key variables, including precipitation phase, runoff, and overall SMB are evaluated to discern potential impacts in each model. Events in the historical and future (following SSP5-8.5) periods are investigated to determine whether the response to events of similar magnitude and seasonal timing differs in a warmer climate.Furthermore, an approximation of how these extreme precipitation events influence seasonal SMB is presented by assessing the ratio of the event-related anomaly to the cumulative seasonal SMB anomalies. Comparisons of event-specific contributions with broader seasonal variations shed light on the connection between short-term meteorological events and longer-term climatic shifts in shaping Greenland's SMB.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".