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

most relevant to local variability. For example, in the Amazon basin, variability in the timing of the rainy season strongly influences annual precipitation totals, with the onset of the wet season also weakly related to rainy season rainfall rate ( Liebmann and Marengo 2001 ). Similarly, on the Indian subcontinent, interannual variability of the monsoon rainfall has been linked to both a large-scale persistent seasonal component and intraseasonal (i.e., frequency and intensity) components

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Lei Zhou, Raghu Murtugudde, Dake Chen, and Youmin Tang

summer is found. The CIO mode and related processes have been diagnosed in Zhou et al. (2017) at intraseasonal time scales. As can be expected of this multiscale system, the CIO mode also has distinct features at seasonal–interannual time scales. Such low-frequency variability of the CIO mode and the driving mechanism are analyzed in this study. Decadal and multidecadal time scales and trends under global climate change will be diagnosed in a separate study especially in the context of the negative

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Yang Yang and X. San Liang

potential role in the overlying atmosphere ( Qiu and Chen 2005 ; Pierini 2006 ; Jayne et al. 2009 ; Kelly et al. 2010 ; Waterman et al. 2011 ; Nakamura et al. 2015 ; Yang et al. 2017 ). It has been identified that there exist significant seasonal variabilities in the regional circulation and ocean heat content in this region ( Qiu et al. 1991 ; Qiu and Kelly 1993 ; Yasuda et al. 2000 ; Vivier et al. 2002 ; Cronin et al. 2013 ; Lee et al. 2015 ). In cold seasons (winter and spring), the

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Dujuan Kang and Enrique N. Curchitser

; Chen et al. 2014 ; Kang and Curchitser 2015 ; Kang et al. 2016 ). When evaluating the seasonal variability of the ocean KE and its components, different types of KE decomposition have been used besides the orthogonal one as described above. In some previous studies, the research focus was on the seasonal variability of EKE, so the time-mean state for velocity decomposition was often chosen to be either a climatological mean or a yearly mean to ensure a constant MKE within the annual cycle (e

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Jiacan Yuan, Benkui Tan, Steven B. Feldstein, and Sukyoung Lee

-frequency climate variability are based on either monthly or seasonal-mean data. Although this time averaging is sufficient for understanding anomalies that have a time scale longer than 2 months, such averaging can also obscure some of the underlying dynamical processes if the time scale of the anomalies is much shorter than 2 months. The investigation of teleconnections with daily unfiltered data, rather than with monthly mean data, has been used to study the intraseasonal variability of teleconnection

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Arun Kumar, Bhaskar Jha, Qin Zhang, and Lahouari Bounoua

1. Introduction Predictability of seasonal climate anomalies can arise from two possible sources: 1) boundary conditions external to the atmosphere [e.g., sea surface temperatures (SSTs)] and 2) atmospheric initial conditions. Within the paradigm of seasonal atmospheric predictability due to external boundary conditions, the potential for skillful predictions depends on the fraction of the atmospheric seasonal mean variability that is related to the anomalous boundary conditions and the

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Maria J. Molina, John T. Allen, and Vittorio A. Gensini

) contains considerable intraseasonal variability, not all of which is explained by ENSO. Furthermore, the densely populated southeastern region of the United States (SE-US; 25°–37°N, 93°–70°W) is disproportionately impacted by DJF tornadoes. The dense populations of the SE-US and poorly understood DJF tornadic variability raise concerns and serve as motivation to assess ENSO and the GoM as subseasonal and seasonal climate drivers of DJF tornadoes. Modulations in tornadic activity based on ENSO phase are

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Clémentde Boyer Montégut, Jérôme Vialard, S. S. C. Shenoi, D. Shankar, Fabien Durand, Christian Ethé, and Gurvan Madec

, since no two monsoons are alike, in no two years does the NIO behave the same way: there is considerable interannual variability ( Webster et al. 1998 ). This is reflected in the variations of temperature, salinity, and mixed layer processes and in the heat and salt budgets. The interannual variability of the heat budget of the upper ocean (or mixed layer) is of paramount interest for air–sea coupling. Several studies have examined the seasonal cycle of the mixed layer in the Arabian Sea (AS

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Marc P. Marcella and Elfatih A. B. Eltahir

of 1975 sees a WMO rainfall total of 260 mm while CRU’s value is merely 190 mm ( Fig. 4b ). b. Seasonal cycle and variability Rainfall in Kuwait occurs in a relatively short, distinct, cool period during the winter months when synoptic-scale systems originating farther north reach the region. This study aims at describing the rainy period using CRU, GPCP, and WMO data ( Fig. 5 ). The seasonal cycles of the precipitation for all three datasets are remarkably similar. Some of this similarity is

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Xujing Jia Davis, Lewis M. Rothstein, William K. Dewar, and Dimitris Menemenlis

; Qiu et al. 2006 , 2007 ) with emphasis on seasonal time scales, as summarized by Oka (2009) . More recently, Hanawa and Kamada (2001) , Roemmich et al. (2005) , and Qiu and Chen (2006) employed longer data records from the World Ocean Database, XBT surveys, satellite altimeters, and Argo profiling floats to provide the first evidence of interannual and decadal variations in NPSTMW. There have also been several numerical investigations of NPSTMW variability based on general circulation models

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