Multi-decadal CESM2-Large Ensemble direct downscaled reference and mid 21st century hydroclimate simulations Alaska and the Yukon at 4 kilometer (km) resolution
Bibliographic record
Abstract
### Access Files be accessed and downloaded from the directory via: [http://arcticdata.io/data/10.18739/A2DJ58J94](http://arcticdata.io/data/10.18739/A2DJ58J94). ### Overview We present a novel, high-resolution land-atmosphere regional climate model (RCM) configuration for Alaska and Yukon River basin. The Regional Arctic System Model (RASM) was reconfigured from its 50 kilometer (km) grid-spacing to 4 km and coupled to the Community Terrestrial Systems Model (CTSM) with hydrology specific optimization providing improved treatment of orographic precipitation, spatial heterogeneity of the landscape, and improved surface and sub-surface processes particularly tailored to cold regions. The new RASM configuration was used to dynamically downscale four Community Earth System Model version 2-Large Ensemble (CESM2-LE) climate model members for reference (model timestamp water year (WY) 1991-2021) and mid-century (WY2035-2065) time-slices. We performed a simple bias correction to the CESM2-LE specific humidity, with vertical variations, to reduce a high precipitation bias when using the CESM2-LE data directly. The simulations have resulted in a unique high-resolution small ensemble of the hydroclimate over Alaska and the Yukon that can be used for studying atmospheric and hydrologic processes, and assessing impacts related to climate variability and change across the region. Hourly, three-hourly, daily, and monthly outputs are available for surface precipitation, temperature, streamflow, and snowpack, among many other variables. A companion dataset (https://doi.org/10.5065/ZPSB-PS82) is available that provides an ERA5 forced historical (WY1991-2021) and two future pseudo global warming simulations with the same outputs and future time slices. ### Model Scripts and Example Run An example of model inputs, scripts, and outputs are available through the Arctic Data Center at [https://doi.org/10.18739/A2RN3094N](https://doi.org/10.18739/A2RN3094N). ### Citation The corresponding description paper is Newman et al. (2026) and we appreciate it if users cite both this dataset and the description paper when basing outcomes or analysis on this work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".