TY - JOUR
T1 - Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter
AU - Zhang, Wei
AU - Xiang, Baoqiang
AU - Tseng, Kai Chih
AU - Johnson, Nathaniel C.
AU - Harris, Lucas
AU - Delworth, Tom
AU - Kirtman, Ben
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.
AB - Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.
UR - https://www.scopus.com/pages/publications/85208745788
U2 - 10.1038/s41612-024-00827-7
DO - 10.1038/s41612-024-00827-7
M3 - Article
AN - SCOPUS:85208745788
SN - 2397-3722
VL - 7
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
IS - 1
M1 - 275
ER -