Sea level pressure response to the specification of eddy-resolving sea surface temperature in simulations of Australian east coast lows
Bibliographic record
Abstract
Four east coast lows (ECLs) were simulated with the Weather Research and Forecast model to investigate the influence of the sea surface temperature (SST) distribution on the sea level pressure (SLP). Each ECL was simulated with two different SST datasets: the Bluelink SST field and NCEP skin temperature field. The former resolved eddies in the East Australian Current while the latter did not. The simulated SLP fields in the eddy-resolving SST runs were compared with those in the non-eddy-resolving SST runs. On time-scales of about 48 hours, higher SSTs were asso-ciated with lower SLPs. The spatial scale of the SLP response was similar to that of the ocean eddies, indicative of the rapidity and robustness of the response given the rapidly evolving conditions within the storms. On shorter time-scales, the SLP response to SST change can become substantially larger. The largest reductions in SLP in the eddy-resolving SST runs were associated with regions of deep atmospheric convection that warm the tropospheric column. These areas were shown to be related to the SST distribution with the greatest SLP reductions associated with convection over strong SST gradient regions. The landfall of a damaging convective mesoscale low pressure system on 8 June 2007 was also investigated. It was found that a region of strong SST gradients on the southern flank of a large warm ocean eddy was associated with lower pressures at the time of formation of this meso-low. In addition, the only case that simulated the low pressure at the correct time (albeit at not quite the correct location) was the eddy-resolved SST run. It was hy-pothesized that the development of this meso-low that impacted the coast around Newcastle, was enhanced because of the eddy-scale SST distribution at the time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".