Revisiting the Role of Mountains in the Northern Hemisphere Winter Atmospheric Circulation
Bibliographic record
Abstract
Abstract The impact of global orography on Northern Hemisphere wintertime climate is revisited using the Whole Atmosphere Community Climate Model, version 6 (WACCM6). A suite of experiments explores the roles of both resolved orography and the parameterized effects of unresolved orographic drag (hereafter parameterized orography), including gravity waves and boundary layer turbulence. Including orography reduces the extratropical tropospheric and stratospheric zonal mean zonal wind by up to 80%; this is substantially greater than previous estimates. Ultimately, parameterized orography accounts for 60%–80% of this reduction; however, away from the surface most of the forcing of by parameterized orography is accomplished by resolved planetary waves. We propose that a catalytic wave–mean-flow positive feedback in the stratosphere makes the stratospheric flow particularly sensitive to parameterized orography. Orography and land–sea contrast contribute approximately equally to the strength of the midlatitude stationary waves in the free troposphere, although orography is the dominant cause of the strength of the Siberian high and Aleutian low at the surface and of the position of the Icelandic low. We argue that precisely quantifying the role of orography on the observed stationary waves is an almost intractable problem, and in particular should not be approached with linear stationary wave models in which is prescribed. We show that orography has less impact on stationary waves, and therefore on , on a backward-rotating Earth. Last, we show that atmospheric meridional heat transport shows remarkable constancy across our simulations, despite vastly different climates and stationary wave strengths.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".