Toward the credibility of Northeast United States summer precipitation projections in CMIP5 and NARCCAP simulations
Bibliographic record
Abstract
Abstract Precipitation projections for the northeast United States and nearby Canada (Northeast) are examined for 15 Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) models. A process‐based evaluation of atmospheric circulation features associated with wet Northeast summers is performed to examine whether credibility can be differentiated within the multimodel ensemble. Based on these evaluations, and an analysis of the interannual statistical properties of area‐averaged precipitation, model subsets were formed. Multimodel precipitation projections from each subset were compared to the multimodel projection from all of the models. Higher‐resolution North American Regional Climate Change Assessment Program (NARCCAP) regional climate models (RCMs) were subjected to a similar evaluation, grouping into subsets, and examination of future projections. CMIP5 models adequately simulate most large‐scale circulation features associated with wet Northeast summers, though all have errors in simulating observed sea level pressure and moisture divergence anomalies in the western tropical Atlantic/Gulf of Mexico. Relevant large‐scale processes simulated by the RCMs resemble those of their driving global climate models (GCMs), which are not always realistic. Future RCM studies could benefit from a process analysis of potential driving GCMs prior to dynamical downscaling. No CMIP5 or NARCCAP models were identified as clearly more credible, but six GCMs and four RCMs performed consistently better. Among the “Better” models, there is no consistency in the direction of future summer precipitation change. CMIP5 projections suggest that the Northeast precipitation response depends on the dynamics of the North Atlantic anticyclone and associated circulation and moisture convergence patterns, which vary among “Better” models. Even when model credibility cannot be clearly differentiated, examination of simulated processes provides important insights into their evolution under greenhouse warming.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".