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A. Foussard, G. Lapeyre, and R. Plougonven

currents and the variability of the storm tracks ( Smirnov et al. 2015 ; Révelard et al. 2016 ). In particular, deep convection intensifies above the warm flank of the front ( Minobe et al. 2008 ; Tokinaga et al. 2009 ) with a locally stronger storm track at low levels ( Small et al. 2014 ) along with more explosive cyclogenesis ( Kuwano-Yoshida and Minobe 2017 ). In addition to these local effects, a large-scale downstream response in terms of eddy-driven jet position or weather regimes develops in

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Larry W. O’Neill, Tracy Haack, and Theodore Durland

measurements and then time averaged (e.g., at monthly, annual, or decadal periods). We refer to this method as derivatives first, averages second (DFAS). We also use tags to indicate whether this method has been applied to rain-free (RF) or all-weather 1 (AW) winds. If any one of the four required wind measurements is rain flagged, the divergence and curl calculations are not executed. When applied to rain-free winds, the DFAS_RF method would appear to be the correct way to estimate the time

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Masayo Ogi, Bunmei Taguchi, Meiji Honda, David G. Barber, and Søren Rysgaard

, both in raw and detrended data ( Ogi and Wallace 2007 ). The recent very low Arctic sea-ice extent reflects increasing areas of open ocean. Therefore, these marginal seas are the most important regions to understand climate change over the Arctic Ocean. The recent low Arctic sea-ice extent is not limited to climate change over the Arctic Ocean, but has also impacted weather and climate in midlatitudes with atmospheric variability ( Honda et al. 2009 ; Orsolini et al. 2011 ). Arctic warming is

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Bunmei Taguchi and Niklas Schneider

1. Introduction Pacific decadal variability (PDV) is a crucial low-frequency variability that regulates, together with a global warming trend due to anthropogenic forcing, near-term (10–30 yr) climate and weather in Pacific rim countries, as well as ecosystems in the Pacific Ocean (e.g., Mantua et al. 1997 ; Nakamura et al. 1997 ; Minobe 1997 ; Schneider and Cornuelle 2005 ; Di Lorenzo et al. 2008 ; Solomon et al. 2011 ; Liu 2012 ). Because of the societal impact of PDV (and the

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Ryusuke Masunaga, Hisashi Nakamura, Bunmei Taguchi, and Takafumi Miyasaka

(typically, 100–200 km) and intensive weather noise in the midlatitudes (e.g., Xie 2004 ). Recent implementation of high-resolution satellite measurements and numerical modeling has substantially advanced our understanding of the influence of SST and its variability around the WBCs on frontal-scale atmospheric features in addition to the basin-scale atmospheric features. Today, it has been well established that distinct surface wind convergence forms along the axes of the WBCs and strong divergence

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Dimitry Smirnov, Matthew Newman, Michael A. Alexander, Young-Oh Kwon, and Claude Frankignoul

domain marked by the box in Fig. 1b is shown in Fig. 1c . The OEI captures the northward shifted SST front [~(3°–4°) farther north in the warm phase compared to the cold phase] and suggests that the front is significantly broader in meridional extent during the warm phase. However, note that the maximum SST front strength is essentially unchanged [~3.6°C (100 km −1 )] between the warm and cold phases. We determine how significant these modifications of the original OEI pattern are by projecting

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R. Justin Small, Frank O. Bryan, Stuart P. Bishop, and Robert A. Tomas

example, Frankignoul and Hasselmann (1977) , Barsugli and Battisti (1998) , von Storch (2000) , and Wu et al. (2006) . As reviewed in Bishop et al. (2017) , when these models include a substantial amount of atmosphere noise (i.e., the weather systems), the SST tendency responds to the approximated air–sea heat flux term, giving a negative simultaneous correlation between heat flux and SST tendency, and also near-zero simultaneous correlation between SST and heat flux. In contrast, when the

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Atsuhiko Isobe, Shin’ichiro Kako, and Shinsuke Iwasaki

, doi: 10.1029/2002GL015884 . Yamada , K. , J. Ishizaka , S. Yoo , H. C. Kim , and S. Chiba , 2004 : Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES) . Prog. Oceanogr. , 61 , 193 – 211 , doi: 10.1016/j.pocean.2004.06.001 . Yamamoto , M. , and N. Hirose , 2007 : Impact of SST reanalyzed using OGCM on weather simulation: A case of a developing cyclone in the Japan Sea area . Geophys. Res. Lett. , 34 , L05808 , doi

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Ryusuke Masunaga, Hisashi Nakamura, Takafumi Miyasaka, Kazuaki Nishii, and Bo Qiu

atmospheric reanalysis outputs of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay et al. 1996 ; Kanamitsu et al. 2002 ) and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005 ). Daily mean SST, surface air temperature (SAT), surface specific humidity, and surface wind speed are also used, which are available from 1985. b. ERA-Interim Since some atmospheric variables (e.g., SLP

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