The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
All-Season Climatology and Variability of Atmospheric River Frequencies over the North Pacific
Abstract
Recent work on atmospheric rivers (ARs) has led to a characterization of these impactful features as primarily cold-season phenomena. Here, an all-season analysis of AR incidence in the North Pacific basin is performed for the period spanning 1979–2014 using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis dataset. An occurrence-based detection algorithm is developed and employed to identify and characterize ARs in instantaneous fields of anomalous vertically integrated water vapor transport. The all-season climatology and variability of AR frequencies due to the seasonal cycle, the El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and their interactions are presented based on composites of the detected features. The results highlight that ARs exist throughout the year over the North Pacific, although their preferred locations shift substantially throughout the year. This seasonal cycle manifests itself as northward and westward displacement of ARs during the Northern Hemisphere warm seasons, rather than an absolute change in the number of ARs within the domain. It is also shown that changes to the North Pacific mean-state due to ENSO and the MJO may enhance or completely offset the seasonal cycle of AR activity, but that such influences on AR frequencies vary greatly based on location.
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MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., et al. (2011) · Journal of Climate · doi:10.1175/jcli-d-11-00015.1 · Cited by 4,852
Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006)
Smith, T. M., Reynolds, R. W., Peterson, T. C., & Lawrimore, J. (2008) · Journal of Climate · doi:10.1175/2007jcli2100.1 · Cited by 2,923
Wheeler, M. C., & Hendon, H. H. (2004) · Monthly Weather Review · doi:10.1175/1520-0493(2004)132<1917:aarmmi>2.0.co;2 · Cited by 2,891
The Steady Linear Response of a Spherical Atmosphere to Thermal and Orographic Forcing
Hoskins, B. J., & Karoly, D. J. (1981) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(1981)038<1179:tslroa>2.0.co;2 · Cited by 2,609
Planetary-Scale Atmospheric Phenomena Associated with the Southern Oscillation
Horel, J. D., & Wallace, J. M. (1981) · Monthly Weather Review · doi:10.1175/1520-0493(1981)109<0813:psapaw>2.0.co;2 · Cited by 1,910
Capotondi, A., Wittenberg, A. T., Newman, M., Di Lorenzo, E., Yu, J.-Y., Braconnot, P., Cole, J., Dewitte, B., Giese, B., Guilyardi, E., et al. (2015) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00117.1 · Cited by 1,073
A Proposed Algorithm for Moisture Fluxes from Atmospheric Rivers
Zhu, Y., & Newell, R. E. (1998) · Monthly Weather Review · doi:10.1175/1520-0493(1998)126<0725:apafmf>2.0.co;2 · Cited by 1,011
Atmospheric Rivers, Floods and the Water Resources of California
Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J., & Cayan, D. R. (2011) · Water · doi:10.3390/w3020445 · Cited by 963
Flooding on California's Russian River: Role of atmospheric rivers
Ralph, F. M., Neiman, P. J., Wick, G. A., Gutman, S. I., Dettinger, M. D., Cayan, D. R., & White, A. B. (2006) · Geophysical Research Letters · doi:10.1029/2006gl026689 · Cited by 806
Ralph, F. M., Neiman, P. J., & Wick, G. A. (2004) · Monthly Weather Review · doi:10.1175/1520-0493(2004)132<1721:sacaoo>2.0.co;2 · Cited by 753
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Golaz, J., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., et al. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003156 · Cited by 371
Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins, D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., et al. (2020) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-18-0326.1 · Cited by 312
Self-citationAhmadalipour, A., & Moradkhani, H. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124106 · Cited by 133
Deep learning for bias correction of MJO prediction
Kim, H., Ham, Y. G., Joo, Y. S., & Son, S. W. (2021) · Nature Communications · doi:10.1038/s41467-021-23406-3 · Cited by 92
Metadata
- Authors
- Mundhenk, B. D., Barnes, E. A., & Maloney, E. D.
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2016-06-16
- Volume
- 29
- Issue
- 13
- Pages
- 4885-4903
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2338094677
- Cited by
- 264
- References
- 39
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- closed
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