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Sigalit Berkovic

: 10.1029/JD094iD15p18495 . Alpert , P. , I. Osetinsky , B. Ziv , and H. Shafir , 2004 : Semi-objective classification for daily synoptic systems: Application to the eastern Mediterranean climate change . Int. J. Climatol. , 24 , 1001 – 1011 , doi: 10.1002/joc.1036 . Beaver , S. , and A. Palazoglu , 2006 : Cluster analysis of hourly wind measurements to reveal synoptic regimes affecting air quality . J. Appl. Meteor. Climatol , 45 , 1710 – 1726 , doi: 10.1175/JAM2437

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Debjani Ghatak, Gavin Gong, and Allan Frei

– 392 . Barnston , A. G. , and R. E. Livezey , 1987 : Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev. , 115 , 1083 – 1126 . Blalock , H. , 1961 : Causal Inferences in Nonexperimental Research . The University of North Carolina Press, 193 pp . Bretherton , C. S. , C. Smith , and J. M. Wallace , 1992 : An intercomparison of methods for finding coupled patterns in climate data. J. Climate , 5 , 541 – 560 . Cayan

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Christopher D. Roller, Jian-Hua Qian, Laurie Agel, Mathew Barlow, and Vincent Moron

. The link between the WTs and large-scale circulation regimes is explored by investigating the relationship of each WT to several important climate teleconnections: the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific–North American (PNA) pattern, and El Niño–Southern Oscillation (ENSO). A brief summary of the teleconnections and their links to Northeast weather follows. The NAO pattern ( Barnston and Livezey 1987 ) is based on out-of-phase variations of sea level pressure (SLP

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Xueke Wu, Xiushu Qie, Tie Yuan, and Jinliang Li

versus from the Arabian Sea for forming intense convective systems (ICSs) over the concave indentation region is still not clear. To better understand the physical processes that determine the occurrence of intense convection along the SHF, this study investigates the meteorological regimes for the onset of intense convection along the SHF using 16 years of TRMM observational data and the NCEP Climate Forecast System (CFS) Reanalysis data. Data and methods are described first, and then the temporal

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James E. Favors and John T. Abatzoglou

seasonal window of the NAM in the southwestern United States has been referred to a variety of ways in the literature, we constrain our focus to the core monsoonal season defined as 1 July to 15 September (e.g., Adams and Comrie 1997 ) for the 28-yr period (1980–2007). We employ three classes of data in our analysis: (i) hourly surface observations for Yuma from the Western Regional Climate Center ( ), (ii) synoptic and mesoscale reanalysis from the National Centers for

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Jiarong Shi and Liu Yang

1. Introduction There are many different climate types across the world and they have distinctive influences on several fields such as agriculture and energy usage of most residential and commercial buildings ( Briggs et al. 2003a ). Given sufficient climate data, climate classification will provide valuable guidelines to agriculture and building design. There are many climate classification methods based on different meteorological variables and indices. A proper and reasonable classification

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Jasmine Rémillard and George Tselioudis

of the satellite-derived cloud regimes. The clustering analysis is finally applied to climate model output to reveal deficiencies in model cloud regime simulations and to examine the relevance of the Azores observations in global model cloud evaluation. 2. Datasets and methods a. Cloud regime variability—The weather states method The International Satellite Cloud Climatology Project (ISCCP) provides a cloud-top pressure–cloud optical thickness (PC-TAU) histogram for each 2.5° grid cell on a

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Farhat Abbas, Nida Sarwar, Muhammad Ibrahim, Muhammad Adrees, Shafaqat Ali, Farhan Saleem, and Hafiz Mohkum Hammad

. , and G. Rasul , 2004 : Agro-climatic classification of Pakistan . Sci. Vision , 9 , 59 – 66 , . Choi , G. , and Coauthors , 2009 : Changes in means and extreme events of temperature and precipitation in the Asia-Pacific Network region, 1955–2007 . Int. J. Climatol. , 29 , 1906 – 1925 , . 10.1002/joc.1979 Donat , M. G. , and Coauthors , 2013 : Updated analyses of temperature and

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V. E. Kousky and R. W. Higgins

as “El Niño conditions” or “La Niña conditions” are used. 4. ENSO alert classification system The El Niño and La Niña phenomena, which are extremes in the ENSO cycle, are the most important sources for the predictable portion of interannual climate variability. Since the early 1980s, the CPC has routinely documented the state of the global climate system, with a focus on climate conditions in the tropical Pacific. As mentioned earlier, CPC established the monthly Climate Diagnostics Bulletin

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John T. Abatzoglou, Kelly T. Redmond, and Laura M. Edwards

on a monthly basis to account for seasonally varying relationships among stations. This is crucial, as such interstation relations can vary greatly (sometimes changing sign), depending on season, elevation, coastal proximity, land use, large-scale flow regime, and climate element, therein reflecting a wide variety in physical coupling mechanisms. The top four monthly correlation values across all elements exceed r = 0.65 (median value), with correlations generally higher in regions of high

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