Fine-scale Climate Projections over Minnesota for the 21st Century 
Bibliographic record
Abstract
Global warming has its largest amplitude in the higher latitude regions of the Northern Hemisphere. This is especially the case during winter months when reduced reflectivity from diminished snow cover leads to higher average temperatures. This process has led to warming at twice the rate as the rest of the planet. In addition to accelerated warming from local snow melt, this Arctic warming is contributing to strong warming over Minnesota, especially during winter, when Minnesota is one of the states that is warming the strongest within the contiguous United States. We have previously emphasized this strong warming in our study on high-resolution climate projections over Minnesota with CMIP5, and we are now producing an updated dataset with higher spatial resolution and with input from six CMIP6 global climate models (GCMs), namely BCC-CSM2-MR, CESM2, CMCC-ESM2, CNRM-ESM2-1, IPSL-CM6A-LR, and MIROC-ES2L. We use ensemble climate simulations over Minnesota with the Weather Research and Forecasting (WRF) model to compute downscaled versions of the comprehensive global climate projections for the 20-year periods 2040-2059, 2060-2079, and 2080-2099. We also perform model integrations over the historical period of 1995-2014 in order to assess any systematic model uncertainties. These projections build on our previous results at 10-km resolution, but now we use a higher 4-km horizontal resolution over Minnesota nested in a 20-km grid over the contiguous USA and southern Canada with 38 vertical levels in the atmosphere and a sophisticated representation of the many lakes that exist in Minnesota. Our final results will show a more detailed representation of the ongoing warming for individual counties in Minnesota in all seasons, especially in winter. We expect conditions near the end of the 21st century that are significantly different from current climate. Our results will influence regional decision-making related to agriculture, infrastructure, water resources, and other sectors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".