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Xiuzhen Li, Wen Zhou, and Yongqin David Chen

1. Introduction The climate in China differs throughout its vast territory because of differences in latitude, elevation, wind direction, and distance to oceans. A good understanding of how the climate varies by region is of great importance in a wide variety of applications. They include not only simply identifying regions with similar climate variability but also forecasting seasonal climate and applying hydrological measures, such as drought evaluation. Many climate classification schemes

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Shannon Mason, Christian Jakob, Alain Protat, and Julien Delanoë

were found to have the greatest effect on the overall SW radiation budget in the area: an optically thick midtopped cloud regime and a very frequent low cloud regime. A similar method based on mean cloud-top properties is frequently used for model evaluation (see Williams and Webb 2009 ). Historically, climate models have tended to overestimate frontal cloud and optically thick low cloud while underrepresenting optically thin low cloud (e.g., trade cumulus) and midtopped clouds ( Webb et al. 2001

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Rosana Nieto Ferreira, Linwood Hall, and Thomas M. Rickenbach

1. Introduction As population increases ( U.S. Census Bureau 2013 ) and climate changes ( Solomon et al. 2007 ), water management and sustainability policymaking in the southeast (SE) United States will be increasingly dependent upon an improved understanding of the spatial and temporal distribution of regional precipitating systems ( Robinson 2006 ). Each year the southeastern United States receives precipitation from a variety of weather systems such as midlatitude cyclones ( Curtis 2006

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Yizhak Feliks, Michael Ghil, and Andrew W. Robertson

irregular oscillations in the presence of colored noise. J. Climate , 9 , 3373 – 3404 . Barnston , A. G. , and R. E. Livezey , 1987 : Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev. , 115 , 1083 – 1126 . Bedrosian , E. , 1963 : A product theorem for Hilbert transforms. Proc. IEEE , 51 , 868 – 869 . Block , P. , and B. Rajagopalan , 2007 : Interannual variability and ensemble forecast of upper Blue Nile basin

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Gregory R. Quetin and Abigail L. S. Swann

in the future: 1) climate-biome classification—treating the current boundaries between biomes as determined by climate ( Peel et al. 2007 ; Kottek et al. 2006 ; Smith et al. 2002 ; Metzger et al. 2013 ); 2) simplified models of climate constraint—based on physiological constraints on net primary productivity ( Churkina and Running 1998 ; Nemani et al. 2003 ; Jolly et al. 2005 ; Running et al. 2004 ); and 3) global process-based models—extending plant- or plot-scale research to global scales

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David Fereday, Robin Chadwick, Jeff Knight, and Adam A. Scaife

.1175/JCLI3775.1 . 10.1175/JCLI3775.1 Barnes , E. , and L. Polvani , 2013 : Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models . J. Climate , 26 , 7117 – 7135 , . 10.1175/JCLI-D-12-00536.1 Beck , C. , A. Philipp , and F. Streicher , 2016 : The effect of domain size on the relationship between circulation type classifications and surface climate . Int. J. Climatol. , 36 , 2692 – 2709

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Katrina E. Bennett, Arelia T. Werner, and Markus Schnorbus

SRTM DEMs for each subbasin. Topographic relief is illustrated using a consistent color ramp to intercompare elevation profiles for each basin. Environment Canada gauges are illustrated with red circles at the outlets of each watershed. Table 1. Statistics from each basin, including the area of basin (km 2 ), elevation range, average elevation, hydrologic regime, climate classification, and temperature and precipitation, rainfall, and snowfall statistics. Results are provided for the 1961

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Clémence Macron, Benjamin Pohl, Yves Richard, and Miloud Bessafi

time ( Vigaud et al. 2012 ). These recurrent regimes are accompanied by deep convection, located northwest of negative ZDEF anomalies. Figure 7 thus gives a picture symmetrical to Fig. 2 . The joint analysis of both classifications will help separating tropical (OLR classes) and temperate (ZDEF regimes) influences on TTT formation. Concomitance between ZDEF regimes and OLR classes is given in Table 1 . While the chi-square test is useful to assess the overall significance between ZDEF regimes

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George Tselioudis, William Rossow, Yuanchong Zhang, and Dimitra Konsta

constrain the number of clusters needed to describe the cloud field and to interpret the resulting cloud structures with respect to the known features of the dynamic regime that creates a particular WS. However, it was obvious from the maps of WS distributions (e.g., Oreopoulos and Rossow 2011 ) that the derived weather states were not confined to the arbitrarily defined boundaries of these climate zones. Furthermore, such boundaries made it harder to examine variability of the weather states with

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Robert Lund and Bo Li

in synoptic climatological classification. J. Climate Appl. Meteor. , 26 , 717 – 730 . Kidson , J. W. , 2000 : An analysis of New Zealand synoptic types and their use in defining weather regimes. Int. J. Climatol. , 20 , 299 – 316 . Lund , R. B. , and I. V. Basawa , 2000 : Recursive prediction and likelihood evaluation for periodic ARMA models. J. Time Ser. Anal. , 21 , 75 – 93 . Lund , R. B. , and J. Reeves , 2002 : Detection of undocumented changepoints: A revision of

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