DOWNSCALING TROPICAL CYCLONES FROM GLOBAL RE-ANALYSIS AND SCENARIOS: STATISTICS OF MULTI-DECADAL VARIABILITY OF TC ACTIVITY IN E ASIA
Bibliographic record
Abstract
An atmospheric regional climate model was employed for describing weather of E Asia for the last decades as well as for the coming century. Re-analyses provided by Global National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) for the past six decades, as well a scenario generated by the ECHAM5/MPI-OM model were dynamically downscaled to a 50 km grid using a state-of-the-art regional climate model (CCLM). Using an automated tracking system, all tropical cyclones (TCs) are identified in the multi-decadal simulations. The different analysis products of TC-statistics were found to differ strongly, also in recent times when the data base was good, so that in the long-term statistics 1950-2010 inhomogeneities mask real climatic variations. The 1948-2009 time series of the annual numbers of TCs in the NCEP-driven simulation and in the JMA best track data (BT) correlate favourably. The number is almost constant, even if there is a slight tendency in BT to show less storms, whereas CCLM shows somewhat more storms, which became more intense. The ECHAM5/MPI-OM-driven scenario simulation, subject to 1959-2100 observed and projected greenhouse gas concentrations, shows a reduction of the number of storms, which maintains a stationary intensity in terms of maximum sustained winds and minimum pressure. Thus, BT-trends and downscaled trends were found to be inconsistent, but also the downscaled trends 1948-2009 and the trends derived from the A1B-scenario were different.
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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 | 0.001 |
| 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) | 0.001 | 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".