The Critical Role of Cloud–Infrared Radiation Feedback in Tropical Cyclone Development
Bibliographic record
Abstract
The deep convective clouds of developing tropical cyclones (TCs) are highly effective at trapping the infrared (or longwave) radiation welling up from the surface. This “cloud greenhouse effect” locally warms the lower–mid-troposphere relative to the TC’s surroundings – an effect that manifests in all stages of the TC lifecycle. While idealized studies suggest the importance of this feedback for TC formation, this issue has remained unexplored for TCs in nature, where non-zero background flow, wind shear, and synoptic-scale variability are known to greatly constrain TC development. To address this gap, we examine the potential role of this cloud–infrared (or longwave) radiation feedback in the context of two archetypal storms: Super Typhoon Haiyan (2013) and Hurricane Maria (2017). We conduct a set of numerical model experiments for both storms with a convection-resolving model (WRF-ARW) from the very early stages of TC development. We examine sensitivity experiments wherein this cloud–radiation feedback is removed at various lead-times prior to TC genesis and the onset of rapid intensification (RI). In both storms, removing this cloud–radiation feedback at a lead-time of ~1 day or less leads to delayed and/or weaker intensification than in the control case. When this feedback is removed with a lead-time of two days or longer, however, the storms altogether fail to development and intensify. This local cloud greenhouse effect strengthens the thermally direct transverse circulation of the incipient storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation via angular momentum convergence. These findings indicate that the cloud greenhouse effect plays a critical role in accelerating and promoting TC development in nature. Progress in the prediction of TC formation and intensification has been very limited in recent decades. Cloud–radiation feedback represents a large source of uncertainty in models, which hence manifests as uncertainty in the prediction of TC development. Our findings highlight the pressing need to better constrain this feedback in models. Doing so holds promise for advancing our ability to forecast TCs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".