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Anna-Lena Deppenmeier
,
Frank O. Bryan
,
William S. Kessler
, and
LuAnne Thompson

. 2007 ). Not all studies agree on the contribution of w ci to WWV variability, however. Brown and Federov (2010) and Bosc and Delcroix (2008) argue that w ci varies little interannually. With the current observing array, this important part of the circulation is difficult to constrain. Here, we make use of a high-resolution global ocean model to characterize the diabatic processes and their seasonal and subseasonal variability. In our previous work we examined the modulation of w ci with

Open access
Marybeth C. Arcodia
,
Emily Becker
, and
Ben P. Kirtman

tidal cycles (predominantly a 4.4- and 18.6-yr cycle) can contribute to extreme high water events ( Merrifield et al. 2013 ; Enríquez et al. 2022 ), and decadal to centennial variability has also been detected in sea level extremes ( Marcos et al. 2015 ; Marcos and Woodworth 2017 ). Lacking from these analyses is a focus on the subseasonal (2 weeks–3 months) time scale. Often called the “desert of predictability” ( Vitart et al. 2017 ), subseasonal prediction of extremes can mitigate human loss

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Indrani Ganguly
,
Alex O. Gonzalez
, and
Kristopher B. Karnauskas

splitting events ( Gonzalez et al. 2022 ). In this study, we quantify whether the “fingerprints” of positive LN87 and negative WH89 WES mechanisms coexist in the weeks prior to and following February–April subseasonal ITCZ events over the east Pacific Ocean using reanalysis data. We also determine whether the atmosphere–ocean variables that drive the WES feedbacks are structurally antisymmetric during Northern Hemisphere versus Southern Hemisphere ITCZ events. The paper is organized as follows

Open access
Cheng Zheng
,
Mingfang Ting
,
Yutian Wu
,
Nathan Kurtz
,
Clara Orbe
,
Patrick Alexander
,
Richard Seager
, and
Marco Tedesco

these studies also showed that fluctuations of downward LWR are often associated with moisture intrusions into the Arctic. Sea ice fluctuations can also be driven by other factors including wind-induced sea ice motion ( Fang and Wallace 1994 ; Rigor et al. 2002 ; Rigor and Wallace 2004 ; Kwok 2005 ; Sorteberg and Kvingedal 2006 ; Liptak and Strong 2014 ) and local temperature anomalies ( Deser et al. 2000 ). The sea ice variability on synoptic to subseasonal time scales has not received much

Full access
Y. Peings
,
Y. Lim
, and
G. Magnusdottir

source of predictability at subseasonal time scales is the Madden–Julian oscillation (MJO; Zhang 2005 ), an atmospheric mode of variability that is characterized by large-scale convective anomalies that propagate in the tropics with a period comprised between 30 and 90 days. The MJO influence is identifiable in the subseasonal evolution of temperature and precipitation over North America, and geopotential over the North Pacific ( Jenney et al. 2019 ). It is one of the main sources of predictability

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Yonghong Yao
,
Hai Lin
, and
Qigang Wu

weather hazards are occasional prolonged ice storm events affecting nearly all of southeastern and east-central China ( Zhou et al. 2011 ; Wang et al. 2008 ; Gao et al. 2008 ). The precipitation variability on a subseasonal scale, ranging from a week to a season, is closely related to certain characteristic phenomena. Some previous studies have linked subseasonal wintertime precipitation variability to influences of the Madden–Julian oscillation (MJO; Lin et al. 2010 ; Liu and Yang 2010 ; Jia et

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Yingxia Gao
,
Pang-Chi Hsu
,
Shaojing Che
,
Changwen Yu
, and
Shiru Han

extratropical intraseasonal variability and its impact on local precipitation, compared to extensive studies on the tropical intraseasonal oscillation. For the tropical monsoon precipitation, the effects of the boreal summer intraseasonal oscillation (BSISO) have been widely studied ( Annamalai and Slingo 2001 ; Wang et al. 2006 ; Lau and Waliser 2012 ; Zhang 2013 ). The BSISO signals indeed provide the source of subseasonal predictability for summer monsoon rainfall ( Fu et al. 2013 ; Li and Robertson

Free access
Nedjeljka Žagar
,
Žiga Zaplotnik
, and
Khalil Karami

showed positive temperature anomalies after 2000, and especially after 2010, relative to the 1981–2010 period. The ERA-Interim reanalyses were also shown to contain trends in several aspects of subseasonal variability since the turn of the century; Kretschmer et al. (2018) discussed more frequent states of the weak polar vortex, whereas White et al. (2017) showed that the number of precipitation events in ERA-Interim lasting 1–5 days in the 40°S–40°N spatial average significantly increased since

Free access
Kevin Boyd
and
Zhuo Wang

synoptic time scales due in part to their small spatiotemporal scales and rapid development ( Moreno-Ibáñez et al. 2021 ). In contrast, the variability of PLs on subseasonal and longer time scales has received considerably less attention. Earlier studies focused on large-scale modes of atmospheric circulation ( Claud et al. 2007 ; Mallet et al. 2013 ) over the North Atlantic and illustrated their relationship with PLs primarily through the use of environmental proxies (i.e., static stability, upper

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Simon H. Lee
,
Michael K. Tippett
, and
Lorenzo M. Polvani

-scale climate focus of seasonal prediction. This aspect of “regime thinking” can be contrasted with that of weather “types” ( Sheridan 2002 ; Fereday et al. 2008 ; Neal et al. 2016 ), which typically incorporate a larger number of patterns and thus resolve higher-frequency synoptic variability. As a result, regimes-based methods have gained prominence in subseasonal prediction ( Grams et al. 2020 ; White et al. 2021 ). Owing to their persistence, regimes are well-suited to the weekly time scales typical

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