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Hugo Carrão, Andrew Singleton, Gustavo Naumann, Paulo Barbosa, and Jürgen V. Vogt

Monitor product ( Svoboda et al. 2002 ; Goodrich and Ellis 2006 ; Quiring 2009 ). The information from the six indicators is blended with further (local) indicators and expert knowledge from hundreds of climate and water experts across the country to derive the final drought intensity map over the United States ( Svoboda et al. 2002 ). Because droughts are generally slow to emerge and slow to recede, the classification system defined in the Drought Monitor includes two extra SPI categories to the

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Natalie Teale and David A. Robinson

1. Introduction Substantial changes in the precipitation regime, particularly in heavy precipitation, have been observed in the northeastern United States (e.g., Groisman et al. 2005 , 2004 ; Karl et al. 2009 ; Walsh et al. 2014 ; Douglas and Fairbank 2011 ; Matonse and Frei 2013 ; Frei et al. 2015 ). Particularly large increases in the magnitude of extreme precipitation events have also been observed (e.g., Groisman et al. 2005 , 2004 ; Karl et al. 2009 ). The National Climate

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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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Jingyu Wang, Xiquan Dong, Aaron Kennedy, Brooke Hagenhoff, and Baike Xi

the Southern Great Plains (SGP) of the United States, this was offset by a nocturnal, negative bias, whereas biases were predominantly positive over the Northern Great Plains (NGP). The segregation of model performance by meteorological regimes has been commonly used in the climate modeling community where ample data allow for separation of model performance by prevailing conditions or synoptic patterns. In turn, this can provide insight into model behavior (e.g., forcing mechanisms responsible

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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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A. S. Alhumaima and S. M. Abdullaev

climate classifications are the basis to indicating climate change (e.g., Phillips and Bonfils 2015 ; Fernandez et al. 2017 ) and assessing the ecology (e.g., Baker et al. 2010 ). The second question is as follows: Is it possible to find a dataset with time series of precipitation and temperatures that best reflects (is correlated with) the space–time variability of primary biological productivity of landscapes (NDVI)? This question is more complex than finding a measure of similarity between some

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Peter A. Bieniek, Uma S. Bhatt, Richard L. Thoman, Heather Angeloff, James Partain, John Papineau, Frederick Fritsch, Eric Holloway, John E. Walsh, Christopher Daly, Martha Shulski, Gary Hufford, David F. Hill, Stavros Calos, and Rudiger Gens

1. Introduction The climate of a geographic location is strongly linked to its latitude, elevation, and proximity to oceans. There has long been a great need to understand how the climate varies by region for climatic research and forecasting applications. Climate-classification techniques have often been employed to account for regional variability; the most well known being the Koeppen scheme ( Koeppen 1923 ), which broadly classifies regions by their mean temperature and precipitation. The

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Muhammad E. E. Hassim and Bertrand Timbal

meteorological states of the atmosphere that make the climate of a region. The idea of classifying the atmosphere into states stems from the observation that local weather patterns are considerably related to specific structures of the regional-scale flow regime. Each weather state is therefore characterized by distinct mean spatial patterns of wind, moisture, rainfall, and temperature. Classifying the broad-scale circulation through weather regimes also has the advantage of providing information on the

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