Characteristics of the Turkana Low-Level Jet Stream and the Associated Rainfall in Cmip6 Models
Bibliographic record
Abstract
Abstract The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in CMIP6 AMIP model simulations with ERA5. Models reveal wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37°E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. Stronger ascent is conducive for rainfall formation, enhancing wet anomalies by up to 8% above the observed climatology at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".