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Larry W. O’Neill, Tracy Haack, Dudley B. Chelton, and Eric Skyllingstad

). This map was constructed from 10 years of QuikSCAT wind observations spanning the time period November 1999–October 2009, as described in section 2a . Since both raining and rain-free conditions are included in this mean, it is referred to as the all-weather (AW) mean. The band of time-mean convergence—the GSCZ—overlies the approximate position of the Gulf Stream, from the Charleston Bump off the coast of South Carolina, separating from the shelf near Cape Hatteras, extending to the northeast

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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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Hidetaka Hirata, Ryuichi Kawamura, Masaya Kato, and Taro Shinoda

; Tsuboki 2008 ). The basic design of the CReSS model was same as that in H15 and H16 , and the following text is derived from there with minor modifications. A bulk parameterization of cold rain ( Tsuboki and Sakakibara 2007 ) was applied to the cloud microphysical processes in this model. The microphysical scheme used in the present experiments included both the prognostic equations for the mixing ratio of water vapor, cloud water, cloud ice, rain, snow, and graupel and the number concentrations of

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Hidetaka Hirata, Ryuichi Kawamura, Masaya Kato, and Taro Shinoda

heating in the cyclone ( Nuss and Kamikawa 1990 ; Kuo et al. 1991a ; Neiman and Shapiro 1993 ; Reed et al. 1993b ; Takayabu et al. 1996 ; Booth et al. 2012 ). Reed et al. (1993b) highlighted an explosive cyclone developing along the Gulf Stream and suggested that airmass modification by the warm current led to intensification of the cyclone. Nuss and Kamikawa (1990) compared two cyclones—an explosive cyclone and a nonexplosive cyclone developing along the Pacific coast of Japan during March

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Peter Gaube, Dudley B. Chelton, Roger M. Samelson, Michael G. Schlax, and Larry W. O’Neill

Ekman pumping from the vorticity ζ , eddies may induce Ekman pumping through their effect on local surface stress, either through the influence of eddy surface currents on the local relative wind (see below) or through air–sea coupling arising from eddy-induced modifications of local SST. The contribution to the total Ekman pumping that arises from the meridional derivative of the Coriolis parameter ( β ), and is proportional to the zonal stress τ x , has been neglected in (1) . An eddy

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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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Hyodae Seo, Arthur J. Miller, and Joel R. Norris

description We utilize the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007b , 2014 ). SCOAR currently couples one of two weather models, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) or the Regional Spectral Model (RSM; Juang and Kanamitsu 1994 ), to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). This study uses the WRF–ROMS version of SCOAR ( Seo et al. 2014 ). The interacting boundary layer

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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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