Interpreting air mass and precipitation structures from a weather-climate interface perspective: Analyses and projections
Bibliographic record
Abstract
Future Arctic air masses are likely to be altered by Arctic amplification of tropospheric warming and the declining sea ice exposing large regions of open water. These changes are expected to alter mass fields across the Northern Hemisphere and be accompanied by changes climatological storm tracks and precipitation distributions. In order to quantify future changes in precipitation, we must first understand how well precipitation variability is captured both in observations and in global climate models (GCM). An experiment is conducted to quantify the representativeness errors, the errors incurred while upscaling station precipitation measurements to a gridded product that can be employed for GCM validation. Error ranges for both median and extreme precipitation are computed by repeatedly gridding station data with subsequently fewer stations for regions in the United States. The representation of the full distribution of precipitation intensity in the Community Climate System Model (CCSM4) over the contiguous United States and southern Canada, is investigated through comparison to several observational and reanalysis reference datasets. The skewness in the precipitation intensity distributions, relative to the reference datasets, varies regionally. In particular, we found a systematic bias toward lighter precipitation occurring in the Great Plains and eastern United States in the model. The bias is towards heavier precipitation however over the Rocky Mountains and the western United States. We find that model errors in extreme precipitation are approaching the magnitude of the disparity between the reference products, likely both a reflection of both strong model performance and the existence of significant bias in some commonly used reference products.To investigate how Arctic air masses will change across the 21st century, we employ the Community Earth System model large ensemble to explore how patterns in January-February equivalent potential temperature at 850hPa (θe850) will change. To separate change in the mean from internal variability, the large number of ensemble members is leveraged to create an anomaly θe850 field computed as the daily θe850 values minus the yearly January-February ensemble average. A technique of self-organizing maps is applied to the daily equivalent potential temperatures anomalies at 850hPa, producing a set of archetypes of air mass patterns across the 21st century. The frequency of occurrence of each archetype changes through the period of study, where most notably there is a statistically significant decline in a pattern with low θe850 over the central Arctic. This pattern, when compared with a decadal average, has a more zonal circulation at 500hPa and higher sea ice concentrations over the peripheral Arctic seas. There is also a significant increase in the frequency of patterns with both higher and lower θe850 over North America, associated with an enhanced meridional circulation at 500hPa. These changes in the internal variability of air masses and of the general circulation will likely alter the climatological distribution of precipitation amongst other impactful atmospheric phenomena.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".