Seasonality and climate modes influence the temporal clustering of unique atmospheric rivers in the Western U.S.

November 27, 2024

A recent study has provided new insights into the temporal clustering of atmospheric rivers (ARs) in the Western United States, highlighting the significant role of climate modes and seasonality in shaping these impactful weather phenomena–ARs clustering and orientation. This research is detailed in the paper titled “Seasonality and Climate Modes Influence the Temporal Clustering of Unique Atmospheric Rivers in the Western U.S.”, recently published in Nature Communications Earth & Environment. The study was conducted by Zhiqi Yang (CW3E), Michael J. DeFlorio (CW3E), Agniv Sengupta (CW3E), Jiabao Wang (CW3E), Christopher M. Castellano (CW3E), Alexander Gershunov (CW3E), Kristen Guirguis (CW3E), Emily Slinskey (CW3E), Bin Guan (UCLA/JPL), Luca Delle Monache (CW3E), and F. Martin Ralph (CW3E). This research was supported by the California Department of Water Resources (CA DWR) Atmospheric River Program and aligns with CW3E’S 2019-2024 Strategic Plan by seeking to improve subseasonal and seasonal predictability of extreme hydroclimate variables over the western U.S. region.

The study was motivated by the occurrence of nine consecutive atmospheric rivers within a mere three-week span in California during winter 2022/2023 (DeFlorio et al. 2024), as well as the significant economic and hydrological impacts of AR temporal clustering, which can triple damages compared to isolated events (Bowers et al. 2024). Temporal clustering, coupled with the orientation of ARs, modulates their interaction with terrain and influences precipitation and flooding. Despite its importance, the drivers behind AR clustering remain unclear, as well as differences in location and magnitude of clustering between early-winter and late-winter.

In this new article, the authors identified unique ARs over the North Pacific and Western U.S. and utilized Cox regression and composite analyses to examine the influence of six major climate modes—the Arctic Oscillation (AO), Quasi-Biennial Oscillation (QBO), El Niño–Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and Pacific-North American pattern (PNA) on temporal clustering and AR orientation during the extended boreal winter (November–March). This study also characterizes differences in location and magnitude of AR clustering in early and late winter periods.

The results reveal that climate modes significantly condition the temporal clustering of ARs. The PNA pattern strongly influences clustering from early to late winter, while the AO dominates early winter clustering. The QBO and PDO emerge as key modulators of late winter clustering (Figure 1). Furthermore, ENSO has a profound effect on AR orientation, particularly influencing the orientation of temporally clustered ARs (Figure 2).

The discovery of relationships between factors with high subseasonal predictability and AR clustering/orientation would lay a foundation to evaluate the subseasonal (2–6 week lead) and seasonal (3–6 month lead) predictability of AR sequences occurring within a short time period. Water resource managers and other applied end users across the Western U.S. stand to reap numerous benefits, such as minimization of flood risks and enhanced planning and decision-making, from improved predictability of hydroclimate phenomena at these extended lead times.

Figure 1. Cox regression coefficients showing climate modes modulations on temporal clustering of unique ARs. Cox regression coefficients between AO, QBO, ENSO, MJO-RMM1, MJO-RMM2, PDO, PNA (removed ENSO signal), and the occurrence of unique ARs based on the ERA-5 AR dataset from 1940/1941 to 2017/2018 (for PDO analysis) and MERRA-2 AR dataset from 1982/1983 to 2020/2021 (for other climate modes analysis) during extended winter (NDJFM, left column), early winter (NDJ, middle column), and late winter (JFM, right column). Note that the coefficients from the regression of AO, QBO, ENSO, MJO, and PNA cannot directly be comparable to those from PDO (see Methods). The results show coefficients that are statistically significant at the 5% level. (Figure 2 in Yang et al., 2024)

Figure 2. Climate modes modulate IVT orientation of temporally clustered unique ARs. Composite analysis in anomalies of the life-cycle IVT orientation of temporally clustered unique ARs during early winter (NDJ) and late winter (JFM). The analysis based on positive (columns 1,3) and negative phases (columns 2,4) of AO, QBO, ENSO, MJO-RMM1, MJO-RMM2, PDO, PNA (removed ENSO signal), using the ERA-5 AR dataset from 1940/1941 to 2017/2018 (for PDO analysis) and MERRA-2 AR dataset from 1982/1983 to 2020/2021 (for other climate modes analysis). The anomalies represent a composite analysis of temporally clustered AR orientation based on the positive and negative phases of climate modes, minus the climatology of temporally clustered AR orientation. Dots indicate statistical significance at the 5% level. Unit: degree. (Figure 10 in Yang et al., 2024)

Bowers, C., Serafin, K. A. and Baker, J. W.(2024). Temporal compounding increases economic impacts of atmospheric rivers in California. Science Advances, 10(3), adi7905. https://doi.org/10.1126/sciadv.adi7905

DeFlorio, M. J., A. Sengupta, C. M. Castellano, J. Wang, Z. Zhang, A. Gershunov, K. Guirguis, R. Luna Niño, R. E. Clemesha, M. Pan, M. Xiao, B. Kawzenuk, P. B. Gibson, W. Scheftic, P. D. Broxton, M. B. Switanek, J. Yuan, M. D. Dettinger, C. W. Hecht, D. R. Cayan, B. D. Cornuelle, A. J. Miller, J. Kalansky, L. Delle Monache, F. M. Ralph, D. E. Waliser, A. W. Robertson, X. Zeng, D. G. DeWitt, J. Jones, and M. L. Anderson. (2024). From California’s extreme drought to major flooding: Evaluating and synthesizing experimental seasonal and subseasonal forecasts of landfalling atmospheric rivers and extreme precipitation during winter 2022/23. Bulletin of the American Meteorological Society, 105(1), E84-E104. https://doi.org/10.1175/BAMS-D-22-0208.1

Yang, Z., DeFlorio, M.J., Sengupta, A., Wang, J., Castellano, C.M., Gershunov, A., Guirguis, K., Slinskey, E., Guan, B., Delle Monache, L. and Ralph, F.M.,. (2024). Seasonality and climate modes influence the temporal clustering of unique atmospheric rivers in the Western US. Nature Communications Earth & Environment, 5(1), p.734. https://doi.org/10.1038/s43247-024-01890-x