Response of Arctic 1000 hPa circulation to changes in horizontal resolution and sea ice forcing in the Community Atmospheric Model
Bibliographic record
Abstract
Arctic winter surface atmospheric circulation biases inherent to the Community Atmospheric Model version 3 (CAM3) are examined with emphasis on the influence of horizontal resolution (T42, ∼2.8° versus T85, ∼1.4°). Using geopotential height at 1000 hPa (Z1000) to represent surface circulation, the self‐organizing map technique is used to better understand these biases from a synoptic climatology perspective. The previously documented low sea level pressure (SLP) bias over the Beaufort Sea, Canadian Archipelago, Greenland Sea, Norwegian Sea, and northern Europe and high SLP bias over the northern Pacific and Atlantic oceans are shown to result from an underprediction of weak Aleutian and Icelandic low patterns and an overprediction of strong Arctic low patterns, strong Icelandic low patterns, and patterns with pronounced ridges spanning the Arctic Ocean. Resolution‐caused differences are shown to be the result of CAM3 at T85 resolution predicting fewer strong Icelandic lows, North Atlantic lows, and strong high‐pressure ridges across the Arctic relative to CAM3 at T42 resolution. In addition, CAM3 at T85 resolution predicts more strong Arctic low patterns relative to CAM3 at T42 resolution. The ability of CAM3 at both resolutions to resolve mesoscale features such as polar lows near the sea ice edge is also explored. Despite these biases, CAM3 can still be used experimentally to examine atmospheric response to changing climatic conditions, particularly if results are viewed in the framework of a sensitivity study. With this in mind, the impact of future projections of sea ice on winter Arctic circulation is examined at both model resolutions. It is found that weak Arctic low patterns and strong Icelandic low patterns increase in frequency in the winter, while Aleutian/Icelandic low patterns and North Atlantic low/Pacific high patterns decrease in frequency in the winter with future projections of sea ice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".