The representation of atmospheric processes in northeast Greenland in CMIP6 models
Bibliographic record
Abstract
Since the end of the 20th century, Greenland has been the largest contributor to sea level rise. As temperatures continue to increase, this tendency is projected to continue. This has resulted in numerous studies which evaluate present and future conditions of the Greenland Ice Sheet, many of which use general circulation models (GCMs). The majority of these focus on sea level rise and/or surface mass balance. While some analyses of atmospheric processes have been undertaken, these have typically been over a larger scale (Arctic or Greenland). This has led to a lack of regional studies of atmospheric processes and how they are represented in GCMs, particularly over northeast Greenland, an area of increased interest in both its glaciology and atmosphere. To address this, 67 CMIP6 GCM realizations were subject to the Pickler and Mölg (2021) model selection procedure to determine the most suitable realization over northeast Greenland. The historical simulation of these realizations were evaluated for: (i) their ability to capture the space-time climatic anomalies over 1979-2014 with respect to ERA5 reanalysis data and (ii) their ability to simulate the mean climatic state of northeast Greenland with respect to four automated weather stations over 2009-2020. MPI-ESM1-2-HR r6i1p1f1 was found to rank highest and ACCESS-ESM1-5 r10i1p1f1 lowest. The 67 realizations were then evaluated on their ability to capture two important processes influencing the region: the North Atlantic Oscillation (NAO) and the Greenland blocking (GBI). All realizations were able to simulate the NAO during boreal winter, while all failed to capture the GBI during boreal summer. Furthermore, the ability of the top and bottom ranked realizations to simulate precipitation, katabatic winds, sea ice, and warm-air events were examined. This analysis reveals key differences between the representation of regional climates within the GCMs, which highlights the need for a rigorous selection procedure prior to estimating future changes.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".