ESPO-G6-R2 : Ensemble de Simulations Post-traitées d'Ouranos - modèles Globaux CMIP6 - RDRS v2.1 / Ouranos Ensemble of Bias-adjusted Simulations - Global models CMIP6 - RDRS v2.1
Bibliographic record
Abstract
Context The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, Ouranos is working to create a set of operational multipurpose climate scenarios at a high spatial resolution called "Ensemble de Simulations Post-traitées d'Ouranos" (ESPO). Dataset In ESPO-G6-R2 v1.0.0, CMIP6 global climate model simulations are bias-adjusted using the RDRS v2.1 reference dataset. The simulation ensemble covers the period for years 1950-2100 and includes the daily minimum temperature (tasmin), the daily maximum temperature (tasmax) and the daily mean precipitation flux (pr). The dataset has a resolution of 0.1° over a North American domain from 179.9°W to 10.0°W and from 10.0°N to 83.3°N. Though, we recommend caution close to the edge of the domain, especially in the south.The experiments included are SSP2-4.5 and SSP3-7.0. To avoid the "hot model problem", only models with a Transient Climate Response in the likely range (1.4–2.2 °C) were kept in the official ensemble. Extra "hot models" and experiments are available even if they are not in the official ensemble. Reference The ESPO-G6-R2 v1.0.0 dataset uses the RDRS v2.1 (Gasset et al., 2021) as reference dataset. This is a product from Environment and Climate Change Canada (ECCC) created by using the Regional Deterministic Reforecast System (RDRS) to downscale the Global Deterministic Reforecast System (GDRS) initialized by ERA-Interim. The system is also coupled with the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA). Method The code attached to this DOI performs the bias-adjustment on the raw simulations to create the ESPO-G6-R2 v1.0.0 ensemble. First, each simulation is regridded with a bilinear interpolation in cascades onto the RDRS v2.1 reference grid. Then, they are adjusted following the Detrended Quantile Mapping procedure. The adjustment is performed on tasmax, pr and dtr (daily temperature range). The variable tasmin is reconstructed from tasmax and dtr. The three variables are then assembled to create the official timeseries. The code also contains extra tasks. It computes diagnostics, indicators, climatology, deltas and ensemble statistics. Code and data availability At the time of publication, the data is stored on Ouranos THREDDS, a part of the PAVICS project. OPenDAP: https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip6/ouranos/ESPO-G/ESPO-G6-R2v1.0.0/catalog.html NetCDF: https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/ESPO-G/ESPO-G6-R2v1.0.0/catalog.html This version of the code: https://github.com/Ouranosinc/ESPO-G/releases/tag/ESPO-G6-R2v1.0.0The github repository: https://github.com/Ouranosinc/ESPO-G
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".