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James F. Booth, Young-Oh Kwon, Stanley Ko, R. Justin Small, and Rym Msadek

. , and Coauthors , 2013 : The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation . Geosci. Model Dev. , 6 , 687 – 720 , doi: 10.5194/gmdd-5-2843-2012 . 10.5194/gmd-6-687-2013 Blackmon , M. L. , 1976 : A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere . J. Atmos. Sci. , 33 , 1607 – 1623 , doi: 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 . 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 Booth , J. F. , L

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

-level wind forecasts have been evaluated in several modeling studies ( Kindle et al. 2002 ; Haack et al. 2005 ; Pullen et al. 2006 , 2007 ; Hong et al. 2011 ), and the coupling of wind stress curl and divergence to spatial variations in SST has been documented in the U.S. West Coast region by Haack et al. (2008) . These studies have demonstrated that COAMPS is capable of predicting all-weather surface winds with good accuracy and with high spatial and temporal resolution. For this study, the model

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Ryu Saiki and Humio Mitsudera

-gravity, 1.5-layer ocean, where g ′ and denote the reduced-gravity and upper-layer thickness, respectively ( Muench et al. 1983 ). This lee-wave model is equivalent to that for an oceanic response to a moving storm (e.g., Gill 1982 ). In this paper, based on the internal inertia–gravity wave model, we will discuss basic ice-band characteristics such as what determines the ice-band spacing; and is there any favorable wind direction for ice-band formation? We will discuss these ice-band problems from a

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Xiaohui Ma, Ping Chang, R. Saravanan, Raffaele Montuoro, Hisashi Nakamura, Dexing Wu, Xiaopei Lin, and Lixin Wu

ensemble into two randomly selected five-member ensembles; the test results show little change. 3. Model validation Before proceeding to the examination of the effect of mesoscale SSTs on the atmosphere, we first evaluate the fidelity of WRF simulations by comparing model results against observations. a. Winter mean rainfall and mean flow The performance of WRF in simulating the winter mean rainfall and atmospheric circulation is validated by comparing model results against observations and reanalysis

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Hyodae Seo, Young-Oh Kwon, Terrence M. Joyce, and Caroline C. Ummenhofer

anomalies are also too large (e.g., ±6°C is used in Small et al. 2014 ) compared to the observed range of ±1°C inferred from Fig. 1 . Thus, a high-resolution model simulation forced with realistic amplitude and distribution of the SST anomaly is needed to evaluate the relevant dynamical adjustment processes that can be unambiguously attributed to the GS shift. The challenge is to detect a statistically significant response with amplitudes substantially smaller than the level of internal variability in

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Kotaro Katsube and Masaru Inatsu

Technology. The JMA/Meteorological Research Institute’s nonhydrostatic model and JRA-55 data were used with permission of the JMA and with special assistance by Dr. S. Hayashi. The model simulations were performed on the Hokkaido University High Performance Computing System. The LBM was used with the permission of Dr. M. Watanabe. Figures were drawn with Grid Analysis and Display System. APPENDIX Performance of the RAM Here we check the performance of the RAM with a 15-km horizontal mesh size for

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Ryusuke Masunaga, Hisashi Nakamura, Takafumi Miyasaka, Kazuaki Nishii, and Youichi Tanimoto

circulation model (AGCM) suggest that these two mechanisms can be operative comparably over the KE in January (cf. Shimada and Minobe 2011 ). Samelson et al. (2006) argued that the positive correlations between SST and surface wind stress away from the immediate vicinity of oceanic fronts may be attributable to the deeper MABL over the warmer SST. Recent high-resolution satellite observations and numerical experiments have suggested mesoscale influences of SST on clouds and precipitation systems

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Hyodae Seo

northeasterly wind along the coast of Oman, explaining reduced upwelling there. It should be emphasized that our regional model domain is not broad enough to evaluate the full downstream development of the Findlater Jet response. The sensitivity of domain size for regional model simulations has been well recognized (e.g., Seth and Giorgi 1998 ; Ludec and Laprise 2009 ). The scale of the response seen in Fig. 12i reaches that of the model domain (i.e., domain wavenumber 1), indicating that the emerging

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Ayumu Miyamoto, Hisashi Nakamura, and Takafumi Miyasaka

ERA-Interim data (see Figs. S1 – S4 in the supplemental material). In addition, we use the SST data prescribed for the ERA-Interim. Near-surface temperature advection and wind divergence have been evaluated on a daily basis at the lowest level of the forecast model used for the ERA-Interim data, as in Masunaga et al. (2015) . In this study, the estimated inversion strength (EIS) defined by Wood and Bretherton (2006) is used as a measure of the strength of the inversion layer at the top of the

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Yi-Hui Wang and W. Timothy Liu

southward. Satellite data, which maintain high-resolution signals in time and space and are less influenced by the limitations of models such as imperfect parameterizations (e.g., Cintineo et al. 2014 ), can illustrate the air–sea interaction well at small scales. The results here can be compared with other observations and models to help evaluate previous findings. This study begins with a survey of the satellite and reanalysis data in section 2 and is followed by an introduction to the climate

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