On the Inefficiency of Moist Geostrophic Turbulence: A Theory for the Energetic Output under Subsaturated Conditions
Bibliographic record
Abstract
Abstract The equator-to-pole temperature gradient has traditionally been understood as the primary driver of the midlatitude storm tracks, which derive their kinetic energy in the process of transporting sensible heat down the gradient. Latent heat, however, accounts for an estimated 30%–60% of the meridional energy transport, a portion which is likely to increase in a warmer world. The contribution of latent heat to the energetics is complicated in that it is inefficient: Only a fraction of the transported latent heat is converted into kinetic energy. Currently, there is no complete theory to explain the relationship between meridional energy transport and kinetic energy generation by midlatitudes eddies. We use a two-layer moist quasigeostrophic model to develop a theory of how the energetic output of the midlatitude atmosphere depends on the relative humidity structure. By varying the surface evaporation rate, we show that the system only reaches the maximum possible energetic output in the saturated limit, producing substantially less kinetic energy at lower evaporation rates. We quantify this reduction in kinetic energy production in terms of a moist conversion efficiency. Using a moist energetic framework, we identify that precipitation dissipation and the diffusion of moisture in subsaturated regions account for the reduction in energetic output. We then show that the moist conversion efficiency can be diagnosed from the distribution of humidity. Significance Statement The impact of humidity on the strength of midlatitude storms is not well understood. Humidity will increase as the planet warms, but it is unclear whether storms will become stronger or weaker as a result. We use an idealized computer model to learn how humidity will impact the strength of storms. We focus on the effect of evaporation at the planet’s surface, with simulations ranging from a completely dry atmosphere to one with rain everywhere. In between these two limits, it is raining in only part of the atmosphere, and storms are much weaker than in the case with rain everywhere. We discuss how to connect these results to more complex models and real-world data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".