Sensitivity Study of North Atlantic Summer Cyclone Activity in Dynamical Downscaled Simulations
Bibliographic record
Abstract
Abstract We conduct a sensitivity study of the North Atlantic cyclone climate, with respect to the cumulus parameterization (CP) and planetary boundary layer (PBL) schemes, applied in the dynamical downscaling of HadGEM2‐ES simulations using Polar Weather Research and Forecasting. A set of seven sensitivity experiments is conducted for 1979–2004 using these CP and PBL schemes. CP schemes are Kain‐Fritsch (KF), Zhang‐McFarlane (ZM), and the modified Tiedtke (TZ). PBL schemes are the local Mellor‐Yamada‐Janjić (MYJ), Mellor‐Yamada‐Nakanishi‐Niino Level 2.5 (MYNN2) schemes, and nonlocal Yonsei University (YSU) scheme. In terms of cyclone intensities, our results show that the fine‐resolution Weather Research and Forecasting simulations capture higher intensities of summer cyclones than the HadGEM2‐ES results, in better agreement with the ERA‐Interim reanalysis, and exhibit a weak sensitivity to CP and PBL schemes. However, the frequency of summer cyclones is quite sensitive to CP and PBL schemes. Four KF experiments suggest comparable numbers of cyclone tracks as derived from ERA‐Interim reanalysis data, which is about 21% more than the ZM‐MYJ or TZ experiments and 11% more than HadGEM2‐ES results. Larger tropospheric potential vorticity and more cyclogenesis in the KF simulation than in TZ or ZM simulations are associated with different cyclone triggering functions and different vertical distributions of heating. We also find increased cyclone activities over the North Atlantic in the YSU or MYNN2 schemes compared to MYJ, but results also depend on the KF CP scheme. This can be attributed to stronger vertical mixing in YSU and MYNN schemes, resulting in smaller static stability and enhanced atmospheric moisture.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".