Forty-year climatology and variability of atmospheric rivers in the Arctic using MERRA-2 reanalysis from 1980 to 2020
Bibliographic record
Abstract
A significant increase in the atmospheric moisture content over the Arctic region has been recently documented, that might be caused by the enhanced poleward moisture flux which is expected to continuously increase in the future. This change can be attributed to different causes, in which increasing moisture transport intensity is included. In this study we focus on events with anomalous moisture transport confined to long, narrow and transient corridors, known as atmospheric rivers (ARs), which are expected to have a strong influence on Arctic mass and energy budget. This study is based on MERRA-2 reanalysis (Modern-Era Retrospective analysis for Research and Applications, Version 2) extending from an historical period until present (1980-2020). ARs are identified using the tracking algorithms by Gorodetskaya et al. (2020) and Guan et al. (2018). We explored the frequency of ARs focusing on annual, seasonal and monthly values. Spatial patterns were analysed for the Arctic latitudes, covering both Atlantic and Pacific moisture transport pathways, and showing the importance of the Siberian moisture pathway during summer. Furthermore, we include a more detailed analysis performed at different sites north of the Arctic circle. Specific attention is given to the ARs characteristics during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from September 2019 to October 2020, as compared to the forty-year climatology and variability of the ARs in the Arctic. Preliminary results show a higher frequency of ARs over the Norwegian and Barents Sea (Atlantic pathway), mainly during autumn and winter, although during May and June there is a high frequency of ARs over Western Siberia and Barents Sea. In contrast, the Canadian Artic has a lower frequency of ARs regardless the season, which is explained by a steep decrease of ARs frequency in the Gulf of Alaska and Bering Sea that block their progression to further north latitudes. References: Gorodetskaya, I. V., Silva, T., Schmithüsen, H., and Hirasawa, N., 2020: Atmospheric River Signatures in Radiosonde Profiles and Reanalyses at the Dronning Maud Land Coast, East Antarctica. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-020-9221-8. Guan, B., Waliser, D. E. and Ralph, F. M., 2018: An Intercomparison between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers. J. Hydrometeorol., 19, 321–337, https://doi.org/10.1175/JHM-D-17-0114.1. Acknowledgments: This work is supported by FCT PhD Grant SFRH/BD/129154/2017 and developed in collaboration with Transregional Collaborative Research Centre (AC)3, AWI and U. Cologne.
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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 | 0.000 |
| 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) | 0.000 | 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".