Explicitly Accounting for the Role of Remote Oceans in Regional Climate Modeling of South America
Bibliographic record
Abstract
Abstract The common practice in dynamic downscaling is to nest a higher‐resolution regional climate model (RCM) into a global model that resolves the large‐scale circulation. However, nested RCMs can develop distinct large‐scale features that substantially diverge from those of the driving model. This is especially problematic over regions such as South America (SA), where the climate features strong teleconnection with remote oceans. Here we propose to explicitly resolve the atmospheric processes underlying the teleconnection by expanding the RCM domain to include the influential oceans. Using the coupled RegCM4.3.4‐CLM4.5 model, RCM simulations designed under the new paradigm demonstrate a substantial improvement of model skills over those using the standard CORDEX SA domain. Analysis of the underlying physical mechanisms indicates that the RCM captures the large‐scale dynamics and climate teleconnections substantially better when it includes the influential oceans. The Big Brother experimental protocol is then used to identify sources of uncertainties and skills, and the results suggest that the nesting practice cannot effectively capture the impact of forcings and processes acting outside the RCM domain. This uncertainty introduces substantial systematic bias to RCM simulations yet is not sampled by existing coordinated regional modeling projects (e.g., CORDEX) due to the use of a single domain focusing over land. Explicitly including oceans within the domain substantially reduces the sensitivity of the SA model climate to domain size/location and promises great potential for RCM applicability in studying regional mechanisms and feedback processes of SA's hydroclimate.
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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 | 0.002 | 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".