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Stephen P. Good, Kaiyu Guan, and Kelly K. Caylor

1. Introduction Higher levels of atmospheric carbon dioxide and associated elevated global temperatures have led to an acceleration of the global hydrologic cycle ( Held and Soden 2006 ) that has resulted in changes in the occurrence of both extreme climate events and interannual variability in precipitation ( O’Gorman 2012 ; Allan and Soden 2008 ; Sun et al. 2012 ; Polade et al. 2014 ; Portmann et al. 2009 ). Accompanying these climatic shifts are observed changes in the intensity of

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Kevin Schwarzwald, Andrew Poppick, Maria Rugenstein, Jonah Bloch-Johnson, Jiali Wang, David McInerney, and Elisabeth J. Moyer

1. Introduction Changes in climate variability may be as important for societal impacts as are changes in climate means, and potential increases in precipitation variability are of particular concern for human welfare. Precipitation variability matters for societal risks and impacts on all time scales: months or longer for droughts affecting crop yields, weeks for large-scale flooding, and hours or shorter for severe storms that produce field-level crop damage or loss of life. Studies suggest

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Verónica Martín-Gómez, Emilio Hernández-Garcia, Marcelo Barreiro, and Cristóbal López

South America. Precipitation along with its variability is very important because it plays a key role in the generation of hydroelectric energy and in the economy, which over LPB is mainly based on harvesting and ranching ( Berbery and Barros 2002 ). Moisture that could lead to future precipitation over the region can come from two different sources: (i) water vapor advection from other regions and (ii) local recycling. While the advection of water vapor depends on the atmospheric circulation and

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Xiaofan Li, Zeng-Zhen Hu, and Bohua Huang

1. Introduction Multidecadal variation of sea surface temperature (SST) in the North Atlantic is substantial with a dominant time scale of 60–80 years ( Fig. 1a ) and was thus named originally as the Atlantic multidecadal oscillation (AMO; Schlesinger 1994 ; Kerr 2000 ). Recently, it is also referred to as the Atlantic multidecadal variability (AMV; Ting et al. 2011 ) to reflect its broad frequency range of fluctuations on the multidecadal time scale. Spatially, the AMV is characterized as

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Rong Zhang, Thomas L. Delworth, Rowan Sutton, Daniel L. R. Hodson, Keith W. Dixon, Isaac M. Held, Yochanan Kushnir, John Marshall, Yi Ming, Rym Msadek, Jon Robson, Anthony J. Rosati, MingFang Ting, and Gabriel A. Vecchi

and Atlantic hurricane activity ( Goldenberg et al. 2001 ; Knight et al. 2006 ; Zhang and Delworth 2006 ). In particular, tropical North Atlantic surface warming coincided with above-normal Atlantic hurricane activity during the 1950s, 1960s, and the recent decade. These multidecadal NASST variations are often thought to be associated with Atlantic meridional overturning circulation (AMOC) variability ( Delworth and Mann 2000 ; Latif et al. 2004 ; Knight et al. 2005 ). On the other hand, some

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Pablo Zurita-Gotor and Pablo Álvarez-Zapatero

studies have addressed the related question of the impact of eddy driving on Hadley cell strength variability ( Caballero 2007 , hereafter C07 ). As is well known, the mean Ferrel cell is forced by transient and planetary eddy stresses associated with the meridional propagation of Rossby waves in the extratropical upper troposphere ( Chang 1996 ). Although the Hadley cell has been traditionally regarded as the response to localized convection over the ITCZ, meridionally propagating Rossby waves also

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Fisseha Berhane, Benjamin Zaitchik, and Amin Dezfuli

, and interdecadal variability (e.g., Conway and Hulme 1993 ; Camberlin 1995 ; Seleshi and Demaree 1995 ; Conway 1997 , 2000 ; Segele and Lamb 2005 ; Abtew et al. 2009 ; Jury 2010 ; among many others). Improved understanding of the drivers of precipitation variability is required in order to enhance subseasonal and seasonal precipitation forecasts under current climate conditions and to provide a sound basis for projecting potential shifts in precipitation under climate change. a

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Ville Lindgren, Joseph H. A. Guillaume, Timo A. Räsänen, Juho Jakkila, Noora Veijalainen, and Matti Kummu

for a region over a longer period of time. Measures such as mean state, natural variability, and extreme events can be included when climate is studied. The focus of this study is on climate-induced changes in hydroclimatic variability, rather than trends in hydroclimatic mean conditions. Climate variability refers to variations around the mean state of the climate or other climate statistics, that is, standard deviations or the occurrence of extremes, on different temporal and spatial scales

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Brant Liebmann, Martin P. Hoerling, Chris Funk, Ileana Bladé, Randall M. Dole, Dave Allured, Xiaowei Quan, Philip Pegion, and Jon K. Eischeid

2000 ; Indeje et al. 2000 ; Mason and Goddard 2001 ). The Indian Ocean also plays a role in short rain variability, arguably more important than that of ENSO ( Behera et al. 2005 ). Anomalously warm sea surface temperatures (SSTs) in the western equatorial Indian Ocean, together with cold SSTs in the east, centered at about 5°S—a pattern known as the Indian Ocean dipole (IOD)—force anomalous southeasterly trade winds that increase moisture supply into the coastal plain, enhancing rainfall there

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Zeyuan Hu, Aixue Hu, Yongyun Hu, and Nan Rosenbloom

reproduce the slowdown in the early twenty-first century. Meehl et al. (2014) attribute the skill of this subset of models to improvements in the simulated interdecadal Pacific oscillation (IPO) and conclude that to close the gap between observations and model simulations of trends in GMST, we need a better understanding of observational errors ( Karl et al. 2015 ; Cowtan and Way 2014 ), external forcing ( Santer et al. 2014 ), and internal variability (e.g., Meehl et al. 2013a ; Dai et al. 2015

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