A multi-resolution analysis of historical and future precipitation variability across the western United States 
Bibliographic record
Abstract
Evaluating the future of surface water availability in the western United States requires a robust analysis of the projected trends in precipitation variability within the new generation of global climate model (GCM) simulations. To understand the reliability of future projections, we first construct a historical baseline (1950-2014) of the precipitation climatology and contribution of heavy precipitation events to the total annual precipitation from an ensemble of in-situ (Global Historical Climatology Network (GHCN)) and gridded precipitation products (Abatzoglou, 2013; Livneh et al., 2015; Newman et al., 2015). This historical baseline is used to evaluate the representation of precipitation variability during the historical period of GCM simulations from the CMIP6 HighResMIP and ScenarioMIP ensembles as well as the multi-resolution, factual-counterfactual ensemble of CAM5 simulations. We frame our analysis in the context of water resources by using a collection of large basins across the western US to demonstrate that the role of GCM resolution in the representation of precipitation variability is highly dependent on regional differences in topographical controls and dominant climatological drivers of precipitation. In most regions, we find that the highest-resolution GCM simulations (25-50 km) portray realistic occurrences of heavy precipitation events when compared to gridded historical precipitation at the same spatial resolution, whereas coarser GCM simulations (100-200 km) tend to distribute precipitation more evenly throughout the year than expected. When compared to the historical period (1950-2014), future projections (2014-2050) from both HighResMIP and ScenarioMIP ensembles produce more variable precipitation with a higher fraction of the annual precipitation falling in heavy precipitation events. Furthermore, we explore methods for constraining uncertainty in the projection of future precipitation variability across the Western US using a statistical assessment of the historical GCM simulations compared to the historical baseline. References Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413 Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., & Brekke, L. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Scientific Data, 2(1), 1–12. https://doi.org/10.1038/sdata.2015.42 Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., & Duan, Q. (2015). Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19(1), 209–223. https://doi.org/10.5194/hess-19-209-2015
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".