A Methodology for the Regional-Scale-Decomposed Atmospheric Water Budget: Application to a Simulation of the Canadian Regional Climate Model Nested by NCEP–NCAR Reanalyses over North America
Bibliographic record
Abstract
Abstract The purpose of this work is to study the added value of a regional climate model with respect to the global analyses used to drive the regional simulation, with a special emphasis on the nonlinear interactions between different spatial scales, focusing on the moisture flux divergence. The atmospheric water budget is used to apply the spatial-scale decomposition approach, as it is a key factor in the energetics of the climate. A Fourier analysis is performed individually for each field on pressure levels. Each field involved in the computation of moisture flux divergence is separated into three components that represent selected scale bands, using the discrete cosine transform. The divergence of the moisture flux is computed from the filtered fields. Instantaneous and monthly mean fields from a simulation performed with the Canadian Regional Climate Model are decomposed and allowed to separate the added value of the model to the total fields. Results show that the added value resides in the nonlinear interactions between large (greater than 1000 km) and small (smaller than 600 km) scales. The main small-scale forcing of the wind is topographic, whereas the humidity tends to show more small scales over the ocean. The time-mean divergence of moisture flux is also decomposed into contributions from stationary eddies and transient eddies. Both stationary and transient eddies are decomposed into different spatial scales and show very different patterns. The time-mean divergence due to transient eddies is dominated by large-scale (synoptic scale) features with little small scales. The divergence due to stationary eddies is a combination of small- and large-scale terms, and the main small-scale contribution occurs over the topography. The same decomposition has been applied to the NCEP–NCAR reanalyses used to drive the regional simulation; the results show that the model best reproduces the time-fluctuation component of the moisture flux divergence, with a correlation between the model simulation and the NCEP–NCAR reanalyses above 0.90.
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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 | 0.001 | 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".