An analysis of regional climate model performance over the tropical Americas. Part I: simulating seasonal variability of precipitation associated with ENSO forcing
Bibliographic record
Abstract
Sea surface temperature (SST) anomalies associated with El Niño/Southern Oscillation (ENSO) constitute a major source of predictability in the tropics. We evaluate the ability of a regional climate model (the Rossby Centre Atmospheric Model; RCA) to downscale SST and large-scale atmospheric anomalies associated with ENSO. RCA is configured over the tropical east Pacific and tropical Americas and runs for the period 1979–2005, using European Centre for Medium-Range Weather Forecasts (ECMWF) lateral and surface boundary conditions. We study the ability of RCA to represent regional patterns of precipitation, with respect to both the climatology and interannual variability associated with ENSO. The latter is achieved by grouping the simulations into El Niño and La Niña composites and studying the delayed response of precipitation to SST forcing in four regions of Central and South America.In this paper, we concentrate on seasonal mean timescales. We find that RCA accurately simulates the main features of the precipitation climatology over the four regions and also reproduces the majority of the documented regional responses to ENSO forcing. Furthermore, the model captures the variability in precipitation anomalies between different ENSO events. The model exhibits a wet bias over the northern Amazon and slightly overestimates the magnitude of ENSO anomalies over Central America.
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 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 | 0.001 | 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 | 0.001 |
| 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".