TY - JOUR
T1 - Seasonal predictability of baroclinic wave activity
AU - Zhang, Gan
AU - Murakami, Hiroyuki
AU - Cooke, William F.
AU - Wang, Zhuo
AU - Jia, Liwei
AU - Lu, Feiyu
AU - Yang, Xiaosong
AU - Delworth, Thomas L.
AU - Wittenberg, Andrew T.
AU - Harrison, Matthew J.
AU - Bushuk, Mitchell
AU - McHugh, Colleen
AU - Johnson, Nathaniel C.
AU - Kapnick, Sarah B.
AU - Tseng, Kai Chih
AU - Zhang, Liping
N1 - Funding Information:
This study is supported by Princeton University’s Cooperative Institute for Modeling the Earth system, through the Predictability and Explaining Extremes Initiative. The d4PDF large ensemble simulations are produced with the Earth Simulator jointly by science programs (SOUSEI, TOUGOU, SI-CAT, and DIAS) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan. G.Z. thanks Dr. Isaac Held for stimulating conversations at the early stage of this study and Dr. Gudrun Magnusdottir for providing the original code for RWB identification during his doctoral study.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
AB - Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
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U2 - 10.1038/s41612-021-00209-3
DO - 10.1038/s41612-021-00209-3
M3 - Article
AN - SCOPUS:85118199026
SN - 2397-3722
VL - 4
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
IS - 1
M1 - 50
ER -