Assessing the role of atmospheric rivers in Arctic precipitation and temperature in present and future climate
Bibliographic record
Abstract
In recent decades, the Arctic has experienced remarkable changes, including enhanced poleward heat and moisture transport. Atmospheric rivers (ARs), defined as long and narrow corridors with high moisture content, are renowned for their significant moisture transport, with implications on temperature and precipitation. Along with the faster warming, precipitation phase (snow vs. rain) plays a major role in the Arctic, as rainfall contributes to sea-ice decline, thereby triggering the ice-albedo feedback. Since previous studies indicate increasing moisture transport towards the Polar Regions, it is crucial to understand the changes in ARs reaching the Arctic in present and future climates and their impacts in a warmer climate. This thesis started by adapting an algorithm used for AR identification, applied to specific case studies. Building on this knowledge, the study was extended to cover the last 43 years, including further improvement of the algorithm, evaluation of the data used, assessment of changes in the AR characteristics, and the analysis of their impacts, with a specific emphasis on precipitation, its phase, and temperature. The final step involved studying ARs in a future climate under various scenarios. After evaluating the available models, the algorithm was applied, followed by the study of AR changes and their impacts in a future climate. This thesis relied on observational and reanalysis datasets, and model simulations. The detailed analysis of the case studies focused on the synoptic-scale evolution, thermodynamic, and precipitation properties during three intense AR events reaching Svalbard in May-June 2017 during the ACLOUD/PASCAL campaign. The results underscore the importance of using data with adequate temporal and spatial resolution and the relevance of employing different AR detection algorithms. After, this study was extended from 1980 to 2022 and the results show a poleward shift of AR frequency and their intensification in the North Atlantic pathway. ARs are responsible for over 20% of precipitation in the Atlantic, concurrently with AR-related snowfall on coastal Greenland and AR-related rainfall in the open ocean. Recent trends show increasing AR-related rainfall and decreasing AR-related snowfall, concurrently with positive temperature trends and increasing AR moisture content. After the comparison of different algorithms, the necessity of using various methods became more evident. Finally, the future AR climatology was analysed using MRI-ESM2.0 CMIP6 model under SSP1-2.6 and SSP5-8.5 (2081-2100), in comparison with results from 1995 to 2014. Results project higher AR frequency and intensity, along with a poleward shift in the North Atlantic pathway and an increasing significance of the Pacific and Canadian Arctic. Finally, in the future AR-related rainfall and temperature are expected to increase, conversely to a decrease in AR-related snowfall. These changes are amplified in the SSP5-8.5 scenario.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".