Western North Pacific Tropical Cyclones in the Met Office Global Seasonal Forecast System: Performance and ENSO Teleconnections
Bibliographic record
Abstract
Abstract The performance of the Met Office Global Seasonal Forecast System (GloSea5-GC2) for tropical cyclone (TC) frequency for the western North Pacific (WNP) in July–October is evaluated, using 23 years of ensemble forecasts (1993–2015). Compared to observations, GloSea5 overpredicts the climatological TC frequency in the eastern WNP and underpredicts it in the western and northern WNP. These biases are associated with an El Niño–type bias in TC-related environmental conditions (e.g., low-level convergence and steering flow), which encourages too many TCs to form throughout the tropical Pacific and slows TC propagation speed. For interannual TC frequency variability, GloSea5 overestimates the observed negative TC–ENSO teleconnection in the western and northern WNP, associated with an eastward shift in the ENSO teleconnection to environmental conditions. Consequently, GloSea5 fails to predict interannual TC variability in the northeast WNP (south of Japan); performance is higher in the southwest WNP (e.g., the South China Sea) where the sign of the TC–ENSO teleconnection is correct. This study suggests the need to reduce biases in environmental conditions and associated ENSO teleconnections in GloSea5 to improve the TC prediction performance in the NWP.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".