A test for evaluating the downscaling ability of one-way nested regional climate models : the big-brother experiment
Bibliographic record
Abstract
The purpose of this thesis is to evaluate the downscaling ability of one-way nesting regional climate models (RCM). To do this, a rigorous and well-defined experiment for assessing the reliability of the one-way nesting approach is developed. This experiment, baptised the Big-Brother Experiment (BBE), is used for addressing some important one-way nesting issues. The first part of this work is dedicated to the development of a scale decomposition tool employed for the BBE. This tool involves a new spectral analysing technique suitable for two-dimensional fields on limited-area domains, and is based on the discrete cosine transform (DCT). It is used for degrading the spatial resolution of the lateral boundary conditions (LBC) used to drive the Canadian RCM (CRCM), for extracting mesoscale features from the atmospheric fields, and for regional validation, and producing power spectra. The second part of the thesis describes the BBE framework and its first results. The BBE consists in first establishing a reference virtual-reality climate from an RCM simulation using a large and high-resolution domain. This simulation is called the "Big Brother". This big-brother simulation is then degraded toward the resolution of today's global objective analyses (OA) and/or global climate models (GCM) by removing the short scales. The resulting fields are then used as nesting data to drive an RCM (called the "Little Brother") which is integrated at the same high-resolution as the Big Brother, but over a sub-area of the big-brother domain. The climate statistics of the Little Brother are then compared with those of the big-brother simulation over the little-brother domain. Differences between the two climates can thus be unambiguously attributed to errors associated with the dynamical downscaling technique, and not to model errors nor to observation limitations. The results for a February simulation shows that the Canadian RCM, using a factor of 6 between the model and the LBC spatial resolution, and an update interval of 3 hours, is capable to pass the BBE test; thus showing the reliability of the one-way nesting approach. In the third and last part of the thesis, the BBE is used to investigate the sensitivity of an RCM to the spatial resolution and temporal update frequency of the LBC. It is shown that spatial resolution jumps of 12, and an update frequency between twice a day (every 12 hours) and four times a day (every 6 hours), are the limits for which a 45-km RCM yields acceptable results.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".