Surface water and energy budgets over the Mississippi and Columbia River basins as simulated by two generations of the Canadian regional climate model
Bibliographic record
Abstract
This paper aims to compare and evaluate the surface energy and water budgets of simulations with the operational version of the Canadian Regional Climate Model (CRCM op) and the developmental version (CRCM dev). The CRCM op and CRCM dev differ in their use of second‐ and third‐generation physical parametrization packages of the Canadian General Circulation Model (CGCM) II and III, respectively. The improvements to the physics of CGCM III include the use of the Canadian LAnd Surface Scheme (CLASS), a three‐layer soil model with explicit treatment of snow and canopy layers; it replaces the so‐called Bucket hydrological scheme and one‐layer force‐restore surface energy budget in the CGCM II. The common experimental configuration for this comparison is taken from the Project to Intercompare Regional Climate Simulations (PIRCS‐1c) over the continental United States between 1987 and 1994. The analysis focuses on two major river basins with substantial differences in atmospheric forcings, vegetation and topography: the Mississippi and the Columbia river basins. The evaluation is made using observation‐based data for monthly means of screen temperature, diurnal temperature range, precipitation, run‐off estimated from streamflow, and snow depth. Some surface fluxes are also compared with the reanalyses from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the European Centre for Medium‐range Weather Forecasts (ECMWF). Results show that CRCM dev constitutes an improvement over CRCM op, particularly for summer evapotranspiration, precipitation and diurnal temperature range; a remaining cold bias in screen temperature, however, is associated with an excessive amount of snow in winter and a high run‐off peak in spring. CRCM op underestimates the snow cover at the expense of the frozen water in the soil.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".